Direct-Delayed Response Project: Future Effects of Long-Term Sulfur Deposition on Surface Water Chemistry in the Northeast and Southern Blue Ridge Province: Volume IV (Volumes A-D Sold as Set)

                                            EP A/600/3-89/061 d
                                            July 1989
          Direct/Delayed  Response Project:
   Future Effects of Long-Term Sulfur Deposition
             on Surface Water Chemistry
in the Northeast and Southern Blue Ridge Province
               Volume IV: Appendices
                            by

   M. R. Church, K. W. Thornton, P. W. Shaffer, D. L. Stevens, B. P. Rochelle
      G. R. Holdren, M. G. Johnson, J. J. Lee, R. S. Turner, D. L. Cassell
      D. A. Lammers, W. G. Campbell, C. I. Liff, C. C. Brandt, L H. Liegel,
       G. D. Bishop, D. C. Mortenson, S. M. Pierson, D. D. Schmoyer
                      A Contribution to the
            National Acid Precipitation Assessment Program
             U.S. Environmental Protection Agency
    Office of Research and Development, Washington, DC 20460
    Environmental Research Laboratory, Corvallis, Oregon 97333

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                                        NOTICE

The information in this document has been funded  wholly (or in part) by the U.S.  Environmental
Protection Agency.  It has been subjected to the Agency's peer and administrative review, and it has
been approved for publication as an EPA document.  Mention of trade names or commercial products
does not constitute endorsement or recommendation for use.

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                                      CONTENTS
SECTION
Notice	  jj
Tables   	'.'.'.'.'.'.   xii
Figures	;	   xx
Plates	    xxix
Contributors	    xxxi
Acknowledgments  	  xxxiii

1    EXECUTIVE SUMMARY	                      1
     1.1  INTRODUCTION	  1
          1.1.1  Project Background	  1
          1.1.2 Primary Objectives	  2
          1.1.3 Study Regions  	  2
          1.1.4 Time Frames of Concern  	  2
     1.2 PROCESSES OF ACIDIFICATION   	  4
          1.2.1  Sulfur Retention	  4
          1.2.2 Base Cation Supply	  4
     1.3 GENERAL APPROACH  	  5
          1.3.1  Soil Survey	  5
          1.3.2 Other Regional Datasets	  7
          1.3.3 Scenarios of Atmospheric Deposition 	  7
          1.3.4 Data Analysis  	                     7
     1.4 RESULTS 	  8
          1.4.1  Retention of Atmospherically Deposited Sulfur	  8
               1.4.1.1 Current Retention  	  8
               1.4.1.2 Projected Retention	  8
          1.4.2 Base Cation Supply	10
               1.4.2.1 Current Control	10
               1.4.2.2 Future Effects	10
          1.4.3 Integrated Effects on Surface Water ANC  	12
               1.4.3.1 Northeast Lakes 	12
               1.4.3.2 Southern Blue Ridge Province  	    15
     1.5  SUMMARY DISCUSSION	                            18
     1.6  REFERENCES	'..'.'.'.'.'.'.'.'.'.'.'. 18

2    INTRODUCTION TO THE DIRECT/DELAYED RESPONSE PROJECT	                23
     2.1  PROJECT BACKGROUND	                          23
     2.2  PRIMARY OBJECTIVES  	                          24
     2.3  STUDY REGIONS   	'.'.'.'.'.'.'.'.'. 24
     2.4  TIME FRAMES OF CONCERN  	                        27
     2.5  PROJECT PARTICIPANTS	                    	27
     2.6  REPORTING  	[ 27

3    PROCESSES  OF ACIDIFICATION  	                              29
     3.1  INTRODUCTION	'"'''' 29
     3.2  FOCUS OF THE DIRECT/DELAYED RESPONSE PROJECT  . . '. '.	30
     3.3  SULFUR  RETENTION PROCESSES	30
          3.3.1  Introduction  	30
          3.3.2  Inputs  	! ! !  ! ! 31
                                         in

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                                   CONTENTS (Continued)
          3.3.3 Controls on Sulfate Mobility within Forest/Soil Systems	32
                3.3.3.1  Precipitation/Dissolution of Secondary Sulfate Minerals	32
                3.3.3.2  Sulfate Reduction in Soils and Sediments	32
                3.3.3.3  Plant Uptake	34
                3.3.3.4  Retention as Soil Organic Sulfur 	34
                3.3.3.5  Sulfate Adsorption by Soils	35
          3.3.4 Models of Sulfur Retention  	37
          3.3.5 Summary	38
     3.4  BASE CATION SUPPLY PROCESSES	39
          3.4.1 Introduction	39
          3.4.2 Factors Affecting Base Cation Availability  	42
                3.4.2.1  Mineral Weathering  	42
                3.4.2.2  Cation Exchange Processes	45
          3.4.3 Modelling Cation Supply Processes  	47
                3.4.3.1  Modelling Weathering	47
                3.4.3.2  Modelling Cation Exchange Processes	48

4    PROJECT APPROACH	49
     4.1  INTRODUCTION	49
     4.2  SOIL SURVEY	49
          4.2.1 Watershed Selection	49
          4.2.2 Watershed Mapping  	49
          4.2.3 Sample Class Definition	51
          4.2.4 Soil Sampling	51
          4.2.5 Sample Analysis	51
          4.2.6 Database Management  	51
     4.3  OTHER REGIONAL DATASETS      	51
          4.3.1 Atmospheric Deposition	52
          4.3.2 Runoff Depth	52
     4.4  DATA ANALYSIS  	52
          4.4.1 Level I Analyses	53
          4.4.2 Level II  Analyses	53
          4.4.3 Level III Analyses 	53
          4.4.4 Integration of Results  	54
          4.4.5 Use of a Geographic Information  System	54

5    DATA SOURCES AND DESCRIPTIONS  	55
     5.1  INTRODUCTION	55
     5.2  STUDY SITE SELECTION  	55
          5.2.1 Site Selection Procedures	55
          5.2.2 Eastern Lake Survey Phase I Design  	55
          5.2.3 Pilot Stream Survey Design	58
          5.2.4 DDRP Target Population	58
                5.2.4.1  Northeast Lake Selection  	58
                5.2.4.2  Southern Blue Ridge Province Stream Selection   	60
                5.2.4.3  Final DDRP Target Populations  	   82
     5.3  NSWS LAKE AND STREAM DATA  	   82
          5.3.1  Lakes in the Northeast Region	:	   82
                5.3.1.1  Lake Hydrologic Type  	   82
                5.3.1.2  Fall Index Sampling	   82
                5.3.1.3  Chemistry of DDRP Lakes	   89
                                             iv

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                              CONTENTS (Continued)
      5.3.2  Streams in the Southern Blue Ridge Province Region	  91
           5.3.2.1  Spring Baseflow Index Sampling  	  91
           5.3.2.2 Chemistry of DDRP Stream Reaches  	  93
5.4  MAPPING PROCEDURES AND DATABASES  	  93
      5.4.1 Northeast Mapping	  95
           5.4.1.1  Soils	  95
           5.4.1.2 Depth to Bedrock  	  99
           5.4.1.3 Forest Cover Type	101
           5.4.1.4 Bedrock Geology	101
           5.4.1.5 Quality Assurance   	101
           5.4.1.6 Land Use/Wetlands   	105
           5.4.1.7 Geographic  Information Systems Data Entry  	118
      5.4.2 Southern Blue Ridge Province Mapping	132
           5.4.2.1  Soils	134
           5.4.2.2 Depth to Bedrock  	137
           5.4.2.3 Forest Cover Type/Land Use  	137
           5.4.2.4 Bedrock Geology	137
           5.4.2.5 Drainage  	139
           5.4.2.6 Quality Assurance    	139
           5.4.2.7 Land Use/Wetlands	142
           5.4.2.8 Geographic  Information Systems Data Entry  	143
5.5  SOIL SAMPLING PROCEDURES AND DATABASES	146
      5.5.1 Development/Description of Sampling Classes	147
           5.5.1.1  Rationale/Need  for Sampling  Classes   	147
           5.5.1.2  Approach Used for Sampling Class Development  	147
           5.5.1.3  Description of Sampling Classes   	148
      5.5.2 Selection  of Sampling Sites  	150
           5.5.2.1  Routine Samples  	150
           5.5.2.2  Samples on  Special Interest Watersheds	155
      5.5.3 Soil Sampling  	155
           5.5.3.1  Soil Sampling Procedures  	156
           5.5.3.2  Quality Assurance/Quality Control of Sampling   	156
      5.5.4 Physical and Chemical  Analyses  	157
           5.5.4.1  Preparation Laboratories	157
           5.5.4.2  Analytical Laboratories	159
      5.5.5 Database Management	 157
           5.5.5.1  Database Structure  	172
           5.5.5.2  Database Operations	174
      5.5.6 Data Summary  	178
           5.5.6.1  Summary of  Sampling Class Data	178
           5.5.6.2  Cumulative Distribution Functions   	178
5.6  DEPOSITION DATA  	178
      5.6.1  Time Horizons of Interest  	190
           5.6.1.1  Current Deposition	190
           5.6.1.2  Future Deposition  	190
      5.6.2 Temporal Resolution 	190
           5.6.2.1  Level I Analyses  	190
           5.6.2.2  Level II Analyses	190
           5.6.2.3  Level III Analyses	190

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                                   CONTENTS (Continued)
          5.6.3  Data Acquisition/Generation	192
                5.6.3.1  Level III Analyses - Typical Year Deposition Dataset	192
                5.6.3.2  Level I  and II Analyses - Long-Term Annual Average
                        Deposition Dataset	208
          5.6.4  Deposition Datasets Used in DDRP Analyses	224
     5.7 HYDROLOGIC DATA	224
          5.7.1  Runoff	224
                5.7.1.1  Data Sources	224
                5.7.1.2  Runoff  Interpolation Methods	224
                5.7.1.3  Uncertainty Estimates	227
          5.7.2  Derived Hydrologic Parameters  	227
                5.7.2.1  TOPMODEL 	228
                5.7.2.2  Soil Contact (Darcy's Law)    	231
                5.7.2.3  Mapped Hydrologic Indices      	234

6    REGIONAL POPULATION ESTIMATION  	242
     6.1 INTRODUCTION	242
     6.2 PROCEDURE	242
          6.2.1  Use of Variable Probability Samples	242
          6.2.2  Estimation Procedures for Population Means	243
          6.2.3  Estimators of Variance	244
          6.2.4  Estimator of Cumulative Distribution Function	245
     6.3 UNCERTAINTY ESTIMATES  	245
     6.4 APPLICABILITY  	246

7    WATERSHED SULFUR RETENTION	247
     7.1 INTRODUCTION	247
     7.2 RETENTION IN LAKES AND WETLANDS	248
     7.2.1  Introduction   	248
          7.2.2  Approach	249
          7.2.3  Results	251
     7.3 WATERSHED SULFUR RETENTION  	253
          7.3.1  Methods	253
                7.3.1.1  Input/Output Calculation  	253
                7.3.1.2  Data Sources   	255
          7.3.2  Uncertainty Estimates	255
                7.3.2.1  Introduction 	255
                7.3.2.2  Individual Variable Uncertainties  	255
                7.3.2.3  Uncertainty Calculation  - Monte Carlo Analysis  	260
          7.3.3  Internal Sources of Sulfur	262
                7.3.3.1  Introduction/Approach  	262
                7.3.3.2  Bedrock Geology   	662
                7.3.3.3  Upper  Limit Steady-State Sulfate Concentration   	265
          7.3.4  Results and Discussion  	268
                7.3.4.1  Northeast  	271
                7.3.4.2  Mid-Appalachians  	279
                7.3.4.3  Southern Blue Ridge Province	280
                7.3.4.4  Conclusions	280
                                             vi

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                            CONTENTS (Continued)
 LEVEL I STATISTICAL ANALYSES	                          285
 8.1  INTRODUCTION	'.'.'.'.'.'.'.'.'.'. 285
     8.1.1  Approach	'.'.'.'. 285
     8.1.2  Statistical Methods	   	:    286
 8.2  RELATIONSHIPS BETWEEN ATMOSPHERIC DEPOSITION  AND  SURFACE	
     WATER CHEMISTRY	291
     8.2.1  Introduction   	! ! !  ! 291
     8.2.2  Approach	 ... 291
     8.2.3  Results and  Discussion  	292
          8.2.3.1  Northeast	'.'.'.'.'.'.'.'. 292
          8.2.3.2  Southern Blue Ridge Province	292
          8.2.3.3  Summary	                       292
 8.3  DERIVED HYDROLOGIC PARAMETERS	'.'.'.'.'.'.'.'.'.'. 295
   •  8.3.1  Soil Contact (Darcv's Law)  	295
          8.3.1.1  Introduction 	295
          8.3.1.2  Results and Discussion 	299
     8.3.2  Geomorphic/Hvdroloaic Parameters	.....'.'.'.'.'. 302
          8.3.2.1  Introduction	302
          8.3.2.2  Results and Discussion 	310
     8.3.3  TOPMODEL  Parameters  	[[ 316
          8.3.3.1  Introduction	! 317
          8.3.3.2  Results and Discussion 	317
          8.3.3.3  Summary	                 326
 8.4  MAPPED BEDROCK GEOLOGY	'.'.'.'.'.'.'.'.'.'.'.'.'. 326
     8.4.1  DDRP Bedrock Sensitivity Scale ....'.	'.'.'.'.'.'.'.'.'.'. 327
     8.4.2  Results	'.'.'.'. 328
          8.4.2.1  Sulfate and Percent Retention	332
          8.4.2.2  Sum  of Base Cations, ANC, and pH  	335
     8.4.3  Summary	                     336
 8.5  MAPPED LAND USE/VEGETATION	'.'.'.'.'.'.'.'.'.'.'.'.'.'.'.'.'.'.'.'.'. 337
     8.5.1  Introduction   	'.'.'.'.'.'. 337
     8.5.2  Data Sources 	    337
     8.5.3  Statistical Methods	'.'.'.'.'.'.'.'.'.'.'.'.'.'.'.'.'.'.'.'.'.*.'. 338
     8.5.4  Sulfate and Percent Sulfur Retention  	338
          8.5.4.1  Northeast  	333
          8.5.4.2  Southern Blue  Ridge Province	347
          8.5.4.3  Regional Comparisons	347
     8.5.5 ANC. Ca plus Ma.  and pH   	'.'.'. 347
          8.5.5.1  Northeast	'.'.'.'.'.'. 347
          8.5.5.2  Southern Blue  Ridge Province	349
          8.5.5.3  Regional Comparisons	349
     8.5.6 Summary and Conclusions	                         351
8.6 MAPPED SOILS	'.'.'.'.'.'.'.'.'. 351
     8.6.1 Introduction  	351
     8.6.2 Approach	352
     8.6.3 Sulfate and Sulfur Retention	354
          8.6.3.1  Northeast	'.'.'.'.'.'.'.'.'.'. 360
          8.6.3.2 Southern Blue Ridge Province	362
          8.6.3.3 Regional Comparisons	355
                                     vii

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8.7
8.8
                            CONTENTS (Continued)
     8.6.4  ANC. Ca Plus Ma. and pH  	367
          8.6.4.1  Northeast	'.'.'.'.'.'.'.'.'.'.'. 367
          8.6.4.2 Southern Blue Ridge Province	'.'.'.'.'.'.'.'.'.'.'. 369
          8.6.4.3 Regional Comparisons	377
     8.6.5  Summary and Conclusions	      	378
    ANALYSES OF DEPTH TO BEDROCK	'.'.'.'.'.'.'.'.'.'.'.'.'.'.'.'.'.'.'.'.379
     8.7.1  Introduction  	_'  '	379
     8.7.2  Approach	'.'.'.'.'.'.'.'.'.'. 379
     8.7.3  Sulfate and Percent Sulfur Retention  	','.',	331
          8.7.3.1  Northeast	'.'.'.'.'.'.'.'.'.'.'. 381
          8.7.3.2  Southern Blue Ridge Province	'.'.'.'.'.'.'.'.'.'. 381
          8.7.3.3  Comparison of Regions  	   33-)
     8.7.4  ANC. Ca plus Mq and pH	'.'.'.'.'.'.'.'.'.'. 385
          8.7.4.1  Southern Blue Ridge Province	335
          8.7.4.2  Comparison of Regions  	'.'.'.'.'.'. 386
     8.7.5  Summary and Conclusions	                        386
    INTEGRATED ANALYSIS OF  ALL MAPPED VARIABLES ....................'. 388
     8.8.1  Introduction  	     388
     8.8.2  Approach	'.'.'.' 388
     8.8.3  Sulfate and Sulfur Retention 	'.'.'.'.'' 388
          8.8.3.1  Northeast	;	'.'.'.'.'.'.'.'.'.'.'.'.'.'.'.'.'.'.'. 388
          8.8.3.2  Southern Blue Ridge Province	390
          8.8.3.3  Regional Comparisons	     392
     8.8.4 ANC. Ca plus Ma. and pH   	'.'.'.'.'.'.'.'.'. 393
          8.8.4.1  Northeast	'.'.'.'.'.'.'.'.'.'.'.'. 393
          8.8.4.2  Southern Blue Ridge Province	395
          8.8.4.3  Regional Comparisons	'.'.'.'.'. 398
     8.8.5 Summary and Conclusions	                          398
8.9  SOIL PHYSICAL AND CHEMICAL CHARACTERISTICS	.............. 3Q9
     8.9.1 Introduction  	    399
     8.9.2 Approach	'.'.'.'. 399
          8.9.2.1  Statistical Methods  	'.'.'.','.'.'.'.'.'.'.'.'.'.'.'.'. 400
     8.9.3 Aggregation of Soil Data	'.'.'.'.'.'.'.'.'.'.'.'.'.'.'.'. 402
          8.9.3.1  Introduction  	402
          8.9.3.2  Aggregation of Soil Data  	403
          8.9.3.3  Assessment of the DDRP Aggregation Approach	404
          8.9.3.4  Estimation of Watershed Effect  	406
          8.9.3.5  Evaluation of Watershed Effect  	407
     8.9.4 Regional Soil Characterization	'.'.'.'.'.'.'. 407
     8.9.5 Sulfate and Sulfur Retention	 . ' 413
          8.9.5.1   Northeast	'.'.'.'.'.'.'.'.'.'.'.'.'.'.'.'.'. 418
          8.9.5.2 Southern Blue Ridge  Province	        421
     8.9.6 Ca plus Ma (SOBC). ANC. and pH  	'.'.'.'.'.'.'.'.'. 421
         8.9.6.1  Northeast	!  . ! ! ! 421
         8.9.6.2 Southern Blue Ridge  Province	425
    8.9.7 Evaluation of  Alternative Aggregation Schemes  	426
    8.9.8 Summary and Conclusions	426
         8.9.8.1  Alternative Aggregation Schemes	426
         8.9.8.2 Sulfate and Sulfur Retention	429
         8.9.8.3 Ca plus Mg (SOBC), ANC, and pH	 429
    8.9.9 Summary Conclusions 	430
                                    viii

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                                  CONTENTS (Continued)
     8.10 EVALUATION OF  ASSOCIATIONS  BETWEEN WATERSHED  ATTRIBUTES  AND
          SURFACE WATER CHEMISTRY	 430
          8.10.1  Introduction	430
          8.10.2  Approach  . .	431
          8.10.3  Regional Characterization of Watershed Attributes	431
               8.10.3.1 Northeast Subregions	431
               8.10.3.2 Northeast and Southern Blue Ridge Providence	435
          8.10.4  Sulfate and Sulfur Retention  	436
               8.10.4.1 Northeast  	436
               8.10.4.2 Southern Blue Ridge Province	436
          8.10.5  Ca plus Mq (SOBC). ANC. and pH	437
               8.10.5.1 Northeast  	437
               8.10.5.2 Southern Blue Ridge Province  	437
          8.10.6  Summary and Conclusions  	450
               8.10.6.1 Sulfate and Sulfur Retention  	450
               8.10.6.2 Ca plus Mg (SOBC), ANC, and pH  	450
          8.10.7  Summary Conclusions	450

9    LEVEL II ANALYSES - SINGLE  FACTOR RESPONSE TIME ESTIMATES  	452
     9.1  INTRODUCTION	452
     9.2  EFFECTS OF SULFATE ADSORPTION ON WATERSHED SULFUR RESPONSE TIME  . 453
          9.2.1  Introduction  	453
          9.2.2  Section Objectives	454
          9.2.3  Approach	455
               9.2.3.1   Model Description   	455
               9.2.3.2  Data Sources	456
               9.2.3.3  Model Assumptions and Limitations	456
               9.2.3.4  Adsorption  Data  	458
               9.2.3.5  Evaluation of Aggregated Data and Model Outputs   	461
               9.2.3.6  Target Populations for Model Projections  	462
          9.2.4  Results	464
               9.2.4.1   Comparison of Northeast and Southern Blue Ridge Province Isotherm
                       Variables  	454
               9.2.4.2  Model Results - Northeastern United States  	466
               9.2.4.3  Model Results - Southern Blue Ridge Province  	479
               9.2.4.4  Uncertainty Analyses and Alternative Aggregation Approaches	493
               9.2.4.5  Summary of Results from the Southern Blue Ridge Province  	501
          9.2.5  Summary Comments on Level II Sulfate Analyses	502
          9.2.6  Conclusions  	504
     9.3  EFFECT OF CATION EXCHANGE AND WEATHERING ON SYSTEM  RESPONSE .... 506
          9.3.1  Introduction   	506
               9.3.1.1   Level  II Hypotheses	506
               9.3.1.2 Approach .	509
          9.3.2  Descriptions of Models	512
               9.3.2.1  Reuss Model  	512
               9.3.2.2 Bloom-Grigal Model  	527
          9.3.3  Model Forecasts 	533
               9.3.3.1  Reuss Model  	535
               9.3.3.2  Bloom-Grigal Model	577
          9.3.4 Comparison of the  Bloom-Grigal and Reuss  Model Projections	605
          9.3.5 Summary and Conclusions	612
                                           ix

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                                 CONTENTS (Continued)
10   LEVEL 111 ANALYSES - DYNAMIC WATERSHED MODELLING  ...                      618
     10.1  INTRODUCTION	                        ''"'"' 618
     10.2  DYNAMIC WATERSHED MODELS	'.'.'.'.'.'.'.'.'.'.'.'.'.'.'.'.'.'.'.'.'.'.'.620
          10.2.1  Enhanced Trickle Down (ETD) Model .  . .	['. 622
          10.2.2 Integrated Lake-Watershed Acidification Study (ILWAS) Model ........... 627
          10.2.3 Model of Acidification of Groundwater in Catchments (MAGIC)               628
     10.3  OPERATIONAL ASSUMPTIONS  	                             '  629
     10.4  WATERSHED PRIORITIZATION	'.'.'.'.'.'.'.'.'.'.'.'. 629
          10.4.1  Northeast	'.'.'.'.'.'.'.'.'.'.'.'.'.'. 629
          10.4.2 Southern Blue Ridge Province	632
          10.4.3 Effects of Prioritization on Inclusion Probabilities  	              632
     10.5  MODELLING DATASETS	634
          10.5.1  Meteorological/Deposition Data	634
          10.5.2 DDRP Runoff Estimation	'.'.'.'.'.'. 634
               10.5.2.1 Annual Runoff	634
               10.5.2.2 Monthly Runoff  	][][ 635
          10.5.3 Morphometrv 	636
          10.5.4 Soils	'.'.'.'.'.'.'.'.'.'.'.'.'.'.'.'.'. 636
          10.5.5  Surface Water Chemistry 	637
          10.5.6  Other Data	'.'.'.'.'.'.'.'.'.'.'.'.'. 637
          10.5.7  Chloride Imbalance	                    637
     10.6  GENERAL APPROACH 	  [	639
     10.7  MODEL CALIBRATION	'.'.'.'.'.'.'.'.'. 642
          10.7.1  Special Interest Watersheds  	642
              10.7.1.1 Northeast	'.'.'.'.'.'.'.'. 643
              10.7.1.2 Southern Blue Ridge Province	643
          10.7.2  General Calibration Approach	644
          10.7.3  Calibration of the Enhanced Trickle Down Model	644
          10.7.4 Calibration of the Integrated Lake-Watershed Acidification Model   	647
          10.7.5 Calibration of the Model of Acidification  of Groundwater in Catchments	650
          10.7.6 Calibration/Confirmation Results	           652
     10.8  MODEL SENSITIVITY ANALYSES  	! ! ! 656
          10.8.1 General Approach	657
          10.8.2 Sensitivity Results	                    667
     10.9  REGIONAL PROJECTIONS REFINEMENT	'.'.'.'.'.'.'. 658
          10.9.1 Enhanced Trickle Down	658
          10.9.2 Integrated Lake-Watershed Acidification Study	659
          10.9.3 Model of Acidification of Groundwater in Catchments	659
          10.9.4 DDRP Watershed Calibrations	! 661
              10.9.4.1  Integrated  Lake-Watershed Acidification Study	     661
              10.9.4.2 MAGIC	664
              10.9.4.3 Southern Blue Ridge Province	         664
    10.10  MODEL PROJECTIONS	668
          10.10.1 General Approach	668
          10.10.2 Forecast Uncertainty  	672
              10.10.2.1  Watershed Selection	672
              10.10.2.2  Uncertainty Estimation Approaches	673
              10.10.2.3  Relationship Among Approaches  	674
              10.10.2.4  Confidence Intervals	678

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                                  CONTENTS (continued)
                                                                                    Page
      10.11  POPULATION ESTIMATION AND REGIONAL FORECASTS  	678
           10.11.1 Northeast Regional Projections  	678
                10.11.1.1  Target Population Projections Using MAGIC	678
                10.11.1.2  Target Population Projections Using MAGIC and ETD	687
                10.11.1.3  Restricted Target Population Projections Using All Three Models ... 796
           10.11.2 Southern Blue Ridge Province	723
                10.11.2.1  Target Population Projections Using MAGIC	723
                10.11.2.2  Restricted Target Population Projections Using ILWAS and
                         MAGIC	749
           10.11.3  Regional Comparisons  	765
                10.11.3.1  Northeastern Projections of Sulfate Steady State  	765
                10.11.3.2  Southern Blue Ridge Province Projections of Sulfate
                         Steady State	771
                10.11.3.3  ANC and Base Cation Dynamics 	771
      10.12  DISCUSSION   	790
           10.12.1  Future Projections  of Changes in Acid-Base Surface Water Chemistry  .... 790
           10.12.2  Rate of Future Change	790
                10.12.2.1  Northeast	790
                10.12.2.2  Southern Blue Ridge Province	792
           10.12.3  Uncertainties and Implications for Future Changes in Surface  Water
                  Acid-Base Chemistry	795
                10.12.3.1  Deposition  Inputs	795
                10.12.3.2  Watershed  Processes	797
      10.13  CONCLUSIONS FROM LEVEL III ANALYSES  	,	799

11    SUMMARY  OF RESULTS	801
      11.1   RETENTION OF ATMOSPHERICALLY DEPOSITED SULFUR  	801
           11.1.1  Current Retention	801
           11.1.2 Projected Retention  	801
      11.2  BASE CATION SUPPLY  	805
           11.2.1  Current Control  	805
           11.2.2 Future Effects	805
      11.3 INTEGRATED EFFECTS ON  SURFACE WATER ANC 	806
           11.3.1  Northeast Lakes	807
           11.3.2 Southern Blue Ridge Province    	814
      11.4  SUMMARY DISCUSSION	820

12    REFERENCES	823

13   GLOSSARY	856
      13.1   ABBREVIATIONS AND SYMBOLS 	856
           13.1.1 Abbreviations	 856
           13.1.2 Symbols	         858
     13.2  DEFINITIONS	862

APPENDICES	888
                                           XI

-------
TABLES
1-1. Lakes in the NE Projected to Have ANC Values <0 and <50 ^/eq L1
for Constant and Decreased Sulfur Deposition 14
1-2. SBRP Stream Reaches Projected to Have ANC Values <0 and <50 /ueq L'1
for Constant and Increased Sulfur Deposition 17

3-1. Major Rock Forming Minerals and Their Relative Reactivities 44

5-1. Sampling Structure for Phase I, Region 1 (Northeast), Eastern Lake Survey 57
5-2. Sample Structure for the Direct/Delayed Response Project -Northeastern Sample 61
5-3. ANC Group, Lake Identification, ELS-I Phase I ANC, Weight and Inclusion
Probabilities for the Direct/Delayed Response Project Northeast Sample Watersheds ... 62
5-4. Lake Identification and Name, and State and Latitudinal/Longitudinal Location
of the Northeast Sample Watersheds 66
5-5. Lake Identification and Name, Sorted by State » Northeast Sample Watersheds 69
5-6. Stream Identification, Weight, and Inclusion Probabilities for the Southern
Blue Ridge Province Direct/Delayed Response Project Sample Watersheds 78
5-7. Stream Identification and Name, and State and Latitudinal/Longitudinal Location
of the Southern Blue Ridge Province Sample Watersheds 79
5-8. Stream Identification and Name, Sorted by State - Southern Blue Ridge Province
Sample Watersheds 80
5-9. DDRP Reclassification of Northeastern Lakes Classified as "Seepage" or "Closed"
by the NSWS 83
5-10. Depth-to-Bedrock Classes and Corresponding Level of Confidence 100
5-11. Interpretation Codes for Northeast Map Overlays - Land Use/Land Cover,
Wetlands, and Beaver Activity 106
5-12. Northeast Watersheds Studied for Independent Field Check of Land Use and
Wetland Photointerpretations 109
5-13. Chi-Square Test for General Land Use Categories 110
5-14. Comparison of Field Check (Matched) General Land Use Determinations with
Office Photointerpretations 111
5-15. Chi-Square Test for Detailed Wetland Categories 113
5-16. Comparison of Field Check (Matched) Detailed Wetland Determinations with
Office Photointerpretations 114
5-17. Comparison of Beaver Dam Number, Breached and Unbreached Status,
and Lodges, Identified via Field Check and Office Photointerpretation Methods 115
5-18. Aggregated Land Use Data for Northeast Watersheds . 117
5-19. Watershed No. 1E1062 Soil Mapping Units 130
5-20. Land Use Codes Used as Map Symbols 138
5-21. Percent Land Use Data for Southern Blue Ridge Province Watersheds 144
5-22. Laboratory Analysis of DDRP Soil Samples 158
5-23. Analytical Variables Measured in the DDRP Soil Survey 160
5-24. Data Quality Objectives for Detectability and Analytical Within-Batch Precision 163
5-25. Detection Limits for Contract Requirements, Instrument Readings,
and System-Wide Measurement in the Northeast 165
5-26. Detection Limits for the Contract Requirements, Instrument Readings,
and System-wide Measurement in the Southern Blue Ridge Province 166
5-27. Attainment of Data Quality Objectives by the analytical laboratories as
determined from blind audit samples for the Northeast 168
5-28. Attainment of Data Quality Objectives by the Analytical Laboratories as Determined
from Blind Audit Samples for the Southern Blue Ridge Province 170
XII

-------
TABLES (Continued)
5-29. Quality Assurance and Quality Control Checks Applied to Each Data Batch 176
5-30. Medians of Pedon-Aggregated Values of Soil Variables for the DDRP
Regions and Subregions 189
5-31. Monthly Values of Leaf Area Index Used to Apportion Annual Dry Deposition to
Monthly Values 202
5-32. Ratios of Coarse-to-Fine Particle Dry Deposition 205
5-33. Ratios of Dry Deposition to Wet Deposition for DDRP Study Sites for the
Typical Year Deposition Dataset 207
5-34. Deposition Datasets Used in DDRP Analyses '.'.'.'.'.'.'.'. 225
5-35. DDRP texture classes and saturated hydraulic conductivity (K) for the NE
study systems 229
5-36. SCS slope classifications '.'.'.'.'.'.'. 233
5-37. Mapped and calculated geomorphic parameters collected for the NE study sites 236
5-38. Mapped and calculated geomorphic parameters collected for the SBRP study sites. . . 240

7-1. Summary of Computed Sulfur Retention by In-lake Reduction for Lake
Systems in the Eastern United States 250
7-2. Intensively Studied Sites Used in Surface Water Chemistry Uncertainty Analysis 257
7-3. Summary Statistics on Percent Differences Between Flow-weighted Average
Annual Sulfate Concentration and the Fall/Spring Flow-weighted Averages 261
7-4. Bedrock Geology Maps Used in the DDRP Internal Sources of Sulfur
Bedrock Geology Analyses 263
7-5. Potential for Sulfur Contribution by Geologic Type 264
7-6. Summary of Watersheds (by ELS and NSS Subregion) Dropped Due
to Suspected Internal Sources of Sulfur Identified by Steady-State Analysis 270
7-7. Percent Sulfur Retention - Summary Statistics by Region 273
7-8. Summary of Sulfur Retention Status and of Watershed Variables
Contributing to Sulfur Retention for 42 Watersheds in the Northeastern United States . 278

8-1. Surface Water Chemistry and Percent Sulfur Retention Summary Statistics
for the Northeastern DDRP Sample of 145 Lake Watersheds 287
8-2. Surface Water Chemistry and Percent Sulfur Retention Summary Statistics
for the DDRP Sample of 35 SBRP Stream Watersheds 288
8-3. Summary Statistics for Wet and Dry Deposition on the DDRP Sample
of 145 Northeastern Lake Watersheds 289
8-4. Summary Statistics for Wet and Dry Deposition on the DDRP Sample of 35
SBRP Stream Watersheds 290
8-5. Results of Regressions Relating Surface Water Chemistry to Atmospheric Deposition
in the Northeast Region 293
8-6. Results of Regressions Relating Surface Water Chemistry to Atmospheric Deposition
in the Southern Blue Ridge Province 294
8-7. Estimated Population-Weighted Summary Statistics on the Darcy's Law Estimates
of Flow Rate and the Index of Flow Relative to Runoff 296
8-8. Estimated Population-Weighted Summary Statistics for Northeastern Geomorphic/
Hydrologic Parameters 303
8-9. Estimated Population-Weighted Summary Statistics for Southern Blue Ridge
Province Hydrologic/Geomorphic Parameters 304
8-10. Mapped and Calculated Geomorphic Parameters Collected for
' the Northeastern Study Sites 305
8-11. Mapped and Calculated Geomorphic Parameters Collected for the SBRP Study Sites . 308
8-12. Stratification Based on Sulfur Deposition 311
XIII

-------
TABLES (Continued)
8-13. Results of Stepwise Regression Relating Surface Water Chemistry
versus Geomorphic/Hydrologic Parameters for the Entire NE 312
8-14. Stepwise Regression Equations for Surface Water Chemistry and Hydrologic/
Geomorphic Parameters Based on Sulfur Deposition Stratification 313
8-15. Results of Stepwise Regression Relating Surface Water Chemistry and
Geomorphic/Hydrologic Parameters for the SBRP 314
8-16. Population-Weighted Summary Statistics for ln(a/Kt>TanB) for the Northeast 318
8-17. Population-Weighted Summary Statistics for ln(a/TanB) for the Southern Blue
Ridge Province 319
8-18. Spearman's Correlation Coefficients Between ln(a/KbTanB) and Surface Water
Chemistry 320
8-19. Pearson's Correlation Coefficients Between ln(a/TanB) and NSS Pilot Chemistry 325
8-20. Tabulation of the Generic Bedrock Types Used to Classify the Mapped Units
Identified on State Map Legends 329
8-21. Tabulation of the Generic Bedrock Types Used to Classify the Mapped Units
Identified on State Map Legends 330
8-22. Regional and Subregional Statistics for the Bedrock Sensitivity Code Variables 331
8-23. Results of Regressions of Surface Water Chemistry on Bedrock Sensitivity
Code Statistics and Deposition Estimates for Northeast 333
8-24. Results for SBRP of Regressions of Surface Water Chemistry on Bedrock
Sensitivity Code Statistics and Deposition Estimates 334
8-25. Land Use and Other Environmental Variables Related to Surface Water
Chemistry of Northeastern Lakes 339
8-26. Factor Loadings for First 13 Principal Components after Varimax Rotation of
the Correlation Matrix of Land Use and other Environmental Variables for
Northeastern Lakes 340
8-27. Interpretation of the First 13 Principal Components After Varimax Rotation of the
Correlation Matrix of Land Use and Other Environmental Variables for Northeastern
Lakes 342
8-28. Land Use and Other Environmental Variables Related to Surface Water Chemistry of
Southern Blue Ridge Province Streams 343
8-29. Composition of First 11 Principal Component Analysis (PGA) Factors Land
Use and Other Environmental Variables Related to Surface Water Chemistry
of Southern Blue Ridge Province Streams 344
8-30. Interpretation of the First 11 Principal Components of Land Use and Other
Environmental Variables for Southern Blue Ridge Province Streams 345
8-31. Results of Regressions Relating Surface Water Chemistry of Northeastern Lakes
to Land Use and Other Environmental Data ; 345
8-32. Results of Regressions Relating Sulfate and Percent Sulfur Retention of
Southern Blue Ridge Province Streams to Land Use Data 348
8-33. Results of Regressions Relating ANC, Ca plus Mg, and pH of Southern Blue
Ridge Province Streams to Land Use Data 350
8-34. Summary Statistics for Percent Area Distribution of the 38 Soil Sampling
Classes and the 4 Miscellaneous Land Areas on the DDRP Sample of 145
NE Lake Watersheds 355
8-35. Summary Statistics for the Percent Area Distribution of the 38 Soil Sampling
Classes and the 4 Miscellaneous Land Areas in the GIS 40-ft Contour on the
DDRP Sample of 145 NE Lake Watersheds 356
8-36. Summary Statistics for the Percent Area Distribution of the 38 Soil Sampling
Classes and the 4 Miscellaneous Land Areas in the Combined GIS Buffers on the
DDRP Sample of 145 NE Lake Watersheds 357
xiv

-------
TABLES (Continued)
Page

8-37. Summary Statistics for the Percent Area Distribution of the 12 Soil Sampling
Classes and the 2 Miscellaneous Land Areas on the DDRP Sample of 35 SBRP
Stream Watersheds 353
8-38. Summary Statistics for the Percent Area Distribution of the 12 Soil Sampling
Classes and the 2 Miscellaneous Land Areas in the 100-Meter Linear GIS Buffer
on the DDRP Sample of 35 SBRP Stream Watersheds 359
8-39. Lake Sulfate and Percent S Retention Regression Models Developed for NE Lakes
Using Deposition, Mapped Soils and Miscellaneous Land Areas as Candidate
Independent Variables 361
8-40. Regression Models of Sulfate in SBRP Streams, Developed Using Deposition,
Mapped Soils and Miscellaneous Land Areas as Candidate
Independent Variables 363
8-41. Regression Models of Percent Sulfur Retention In SBRP Stream Watersheds
Developed Using Deposition, Mapped Soils, and Miscellaneous Land Areas as
Candidate Independent Variables 366
8-42. Lake ANC and the Sum of Lake Calcium and Magnesium Regression Models
Developed for NE Lakes Using Deposition, Mapped Soils, and Miscellaneous Land
Areas as Candidate Independent Variables 368
8-43. Lake pH Regression Models Developed for NE Lakes Using Deposition,
Mapped Soils, and Miscellaneous Land Areas as Candidate
Independent Variables 370
8-44. Regression Models of ANC in SBRP Stream Watersheds, Developed Using
Deposition, Mapped Soils, and Miscellaneous Land Areas as Candidate
Independent Variables 372
8-45. Regression Models of Calcium Plus Magnesium in SBRP Streams, Developed
Using Deposition, Mapped Soils, and Miscellaneous Land Areas as a Candidate
Independent Variables 373
8-46. Regression Models of SOBC in SBRP Streams, Developed Using Deposition,
Mapped Soils, and Miscellaneous Land Areas as Candidate
Independent Variables 375
8-47. Regression Models of Stream pH in SBRP Streams, Developed Using Deposition,
Mapped Soils, and Miscellaneous Land Areas as Candidate
Independent Variables 376
8-48. Depth-to-Bedrock Classes for the Northeast and the Southern Blue Ridge Province . . 380
8-49. Regional and Subregional Statistics for Percentage of Watershed Coverage of the
Depth-to-Bedrock Classes 382
8-50. Results for NE of Regressions of Surface Water Chemistry on Depth-to-Bedrock
Classes and Deposition Estimates 384
8-51. Results for SBRP of Regressions of Surface Water Chemistry on Depth-to-Bedrock
Classes and Deposition Estimates 387
8-52. Regression Models of Surface Water Sulfate and Sulfur Retention in the
NE Lake Watersheds 339
8-53. Regression Models of Surface Water Sulfate and Sulfur Retention in the SBRP
Stream Watersheds 392
8-54. Regression Models of Surface Water ANC, Ca plus Mg, and pH in the NE Lake
Watersheds 394
8-55. Regression Models of Surface Water ANC, Ca plus Mg, and pH in the SBRP
Stream Watersheds 397
8-56. Standard Deviations Within and Among Northeast Sampling Classes Estimated
from B Master Horizon Data 405
8-57. Means and Standard Deviations of Soil Characteristics by Aggregation
Method and Region 408
xv

-------
TABLES (Continued)
Page
8-58. Population Means and Standard Errors for Selected Variables, by Subregion/Region
and Aggregation (Watershed Adjusted Data) 411
8-59. Non-parametric Correlations Between Lake Chemistry Variables and Selected Soil
Properties for the NE DDRP Watersheds 414
8-60. Non-parametric Correlations Between Stream Chemistry Variables and Selected
Soil Properties for the SBRP DDRP Watersheds 416
8-61. Results of Stepwise Multiple Regressions for DDRP Lake and Stream Sulfate
Concentrations Versus Soil Physical and Chemical Properties 419
8-62. Results of Stepwise Multiple Regressions for DDRP Watershed Sulfur Retention
Versus Soil Physical and Chemical Properties 420
8-63. Results of Stepwise Multiple Regressions for DDRP Lake Calcium plus Magnesium
Concentrations and Stream Sum of Base Cation Concentrations Versus Soil Physical
and Chemical Properties 422
8-64. Results of Stepwise Multiple Regressions for DDRP Lake and Stream ANC
Versus Soil Physical and Chemical Properties 423
8-65. Results of Stepwise Multiple Regressions for DDRP Lake and Stream pH
Versus Soil Physical and Chemical Properties 424
8-66. Results of Stepwise Multiple Regressions for DDRP Lake and Stream ANC
Versus Unadjusted and Watershed Adjusted Soil Physical and Chemical Properties . . 427
8-67. Results of Stepwise Multiple Regressions for DDRP Lake and Stream Sulfate
Versus Unadjusted and Watershed Adjusted Soil Physical and Chemical Properties . . 428
8-68. Population Means and Standard Errors for Selected Variables, by Subregion/
Region and Aggregation 432
8-69. Non-parametric Correlations Between Lake Chemistry Variables and Selected
Watershed Attributes for the NE DDRP Watersheds 438
8-70. Non-parametric Correlations Between Stream Chemistry Variables and Selected
Watershed Attributes for the SBRP DDRP Watersheds 442
8-71. Results of Stepwise Multiple Regressions for DDRP Lake and Stream Sulfate
Concentration Versus Watershed Attributes 445
8-72. Results of Stepwise Multiple Regressions for DDRP Watershed Sulfur Retention
Versus Watershed Attributes 446
8-73. Results of Stepwise Multiple Regressions for DDRP Lake Calcium Plus Magnesium
Concentrations and Stream Sum of Base Cations Versus Watershed Attributes 447
8-74. Results of Stepwise Multiple Regressions for DDRP Lake and Stream ANC Versus
Watershed Attributes 448
8-75. Results of Stepwise Multiple Regressions for DDRP Lake and Stream Air Equilibrated
pH Versus Watershed Attributes 449

9-1. Comparison of Summary Data for Sulfate Adsorption Isotherm Data for Soils in the
Northeastern United States and Southern Blue Ridge Province 465
9-2. Summary Statistics for Modelled Changes in Sulfate Concentration, Percent Sulfur
Retention, and Delta Sulfate for Northeast Watersheds Using Long-Term Average
Deposition Data 470
9-3. Summary Statistics for Modelled Changes in Sulfate Concentration, Percent Sulfur
Retention, and Delta Sulfate for Northeast Watersheds Using Typical Year
Deposition Data 471
9-4. Comparison of Measured and Modelled Base Year (1985) Sulfate Data for SBRP
Watersheds, Using Long-Term Average Deposition Data 482
9-5. Comparison of Modelled Rates of Increase for [SO42~] in DDRP Watersheds in the
SBRP with Measured Rates of Increase in Watersheds in the Blue Ridge and
Adjoining Appalachians 484
XVI

-------
TABLES (Continued)
Page
9-6. Summary Statistics for Modelled Changes in Sulfate Concentration, Percent Sulfur
Retention, and Delta Sulfate for Watersheds in the Southern Blue Ridge Province, Using
Long-Term Average Deposition Data 488
9-7. Summary Statistics for Modelled Changes in Sulfate Concentration, Percent Sulfur
Retention, and Delta Sulfate for Watersheds in the Southern Blue Ridge Province,
Using Typical Year Deposition Data 489
9-8. Summary Comparison of Watershed Sulfur Status and Model Forecasts in the
Northeastern United States and Southern Blue Ridge Province 503
9-9. List of the Chemical Species and Reactions Considered Within the Reuss
Model Framework 515
9-10. Effect of pCO2 on Changes Projected to Occur in Surface Water ANC Values at
50 and 100 Years Using the Reuss Model. Deposition Used in the Model is LTA . . . 524
9-11. List of Input Data for the Bloom-Grigal Soil Acidification Model 534
9-12. Summary Statistics for the Population Estimates of Current ANC Conditions for
Lakes in the NE Region for Five Different Deposition or Soils Aggregation
Schemes 541
9-13. Descriptive Statistics of the Population Estimates for Changes
in Lake Water ANC for Systems in the NE 546
9-14. Summary Statistics Comparing the Projections Regarding Changes in Surface
Water ANC Values Obtained Using Different Soils Aggregation Schemes 549
9-15. Summary Statistics of the Differences Between the Population Estimates for
Future ANC Projections Made Using the Constant Level and Ramped
Deposition Scenarios 550
9-16. Summary Statistics for the Population Estimates of Current ANC Conditions for Stream
Reaches in the SBRP for Four Different Deposition Scenarios 552
9-17. Descriptive Statistics of the Population Estimates for Changes in Stream Reach
ANC Values for Systems in the SBRP 555
9-18. Summary Statistics of the Differences Between the Population Estimates for Future
ANC Projections Made Using the Constant Level and Ramped Deposition Scenarios for
Stream Reaches in the SBRP 559
9-19. Summary Statistics of the Projected Changes in Soil Base Saturations in the NE
Region, Obtained Using the Different Deposition Scenarios or Soil
Aggregation Schemes 562
9-20. Summary Statistics of the Projected Changes in Soil pH in the NE Region, Obtained
Using the Different Deposition Scenarios or Soil Aggregation Schemes 563
9-21. Summary Statistics of the Projected Changes in Soil Base Saturations in the SBRP,
Obtained Using the Different Deposition Scenarios 571
9-22. Summary Statistics of the Projected Changes in Soil pH in the SBRP, Obtained
Using the Different Deposition Scenarios 572
9-23. Comparison of the Changes in Soil Base Saturation and Soil pH that Are Projected to
Occur in the NE and SBRP 576
9-24. Regionally Weighted Median Values of Initial Annual Deposition Inputs to the Bloom-
Grigal Model for the Northeastern Region and the Southern Blue Ridge Province .... 579
9-25. Regionally Weighted Median Values of Annual Initial Soil Chemical Values Input
Into the Bloom-Grigal Model for the Northeastern Region and the Southern Blue
Ridge Province 581
9-26. Bloom-Grigal Model Regional Projections for the Change in Soil pH in the Northeastern
United States. Organic Soil Horizons Included 585
xvii

-------
TABLES (Continued)
9-27. Bloom-Grigal Model Regional Projections of the Change in Percent Base Saturation in
the Northeastern United States. Organic Soil Horizons Included 587
9-28. Bloom-Grigal Model Regional Projections of the Change in Soil pH in the Northeastern
United States. Organic Soil Horizons Included 592
9-29. Bloom-Griga! Model Regional Projections for the Change in Percent Base Saturation in
the Northeastern United States. Organic Soil Horizons Included 594
9-30. Bloom-Grigal Model Regional Projections for the Change in Soil pH in the Southern
Blue Ridge Province. Organic Soil Horizons Included 598
9-31. Bloom-Grigal Model Regional Projections for the Change in Percent Base Saturation
in the Southern Blue Ridge Province. Organic Soil Horizons Included 600
9-32. Summary of the Bloom-Grigal Projected Changes in Soil pH and Percent Base
Saturation in the NE and SBRP Under Constant LTA Deposition 603
9-33. Comparison of the Results from the Reuss and Bloom-Grigal
Models with Regard to the Magnitude of Changes in Soil pH and Base Saturation
Projected in Soils of the NE 607
9-34. Comparison of the Results from the Reuss and Bloom-Grigal Models with Regard to
the Magnitude of Changes in Soil pH and Base Saturation Projected in Soils of the
SBRP 613

10-1. Major Processes Incorporated in the Dynamic Model Codes 621
10-2. Meteorological Data Required by the Dynamics Model Codes 623
10-3. Chemical Constituents in Wet and Dry Deposition Considered by the MAGIC, ETD, and
ILWAS Codes 624
10-4. Chemical Constituents Included in Soil Solutions
and Surface Water for the MAGIC, ETD, and ILWAS Codes 625
10-5. Definitions of Acid Neutralizing Capacity (ANC) Used by the MAGIC, ETD,
and ILWAS Codes (Brackets indicate concentration in molar or molal units, and R',
R", and R'" represent mono-, di-, and triprotic organic acids, respectively.) ANC
Simulated by All Three Models is Equivalent to the Modified Gran ANC 626
10-6. Level III Operational Assumptions 630
10-7. Comparison of Calibration/Confirmation RMSE for Woods Lake Among ETD, ILWAS, and
MAGIC Models, with the Standard Error of the Observations 653
10-8. Comparison of Calibration/Confirmation RMSE for Panther Lake Among ETD,
ILWAS, and MAGIC Models, with the Standard Error of the Observations 654
10-9. Comparison of Calibration RMSE for Clear Pond Among ETD, ILWAS, and MAGIC
Models, with the Standard Error of the Observations 655
10-10. Percent Change in RMSE for MAGIC and ETD for a Ten Percent Change in Parameter
Values 658
10-11. Watersheds, by Priority Class, for Which Calibration Criteria Were Not Achieved 671
10-12. Deposition Variations Used in Input Uncertainty Analyses 675
10-13. Target Populations for Modelling Comparisons and Population Attributes 679
10-14. Descriptive Statistics of Projected ANC, Sulfate, pH, Calcium Plus Magnesium, and
Percent Sulfur Retention for NE Lakes in Priority Classes A - I Using MAGIC for Both
Current and Decreased Deposition 682
10-15. Change in Median ANC and Sulfate Concentrations Over a 40-Year Period as a
Function of the Initial ELS-Phase I or NSS Pilot Survey ANC Groups 690
10-16. Descriptive Statistics of Projected ANC, Sulfate, and Percent Sulfur Retention for NE
Lakes in Priority Classes A - E Using MAGIC and ETD for Both Current and Decreased
Deposition 797
XVIII

-------
                                    TABLES (Continued)
10-17.  Descriptive Statistics for Projected ANC, Sulfate, Percent Sulfur Retention,
       and Calcium Plus Magnesium for NE Lakes in Priority Classes A and B Using
       ETD, ILWAS, and MAGIC for Both Current and Decreased Deposition	
10-18.  Descriptive Statistics of Projected ANC,  Sulfate, and Percent Sulfur Retention, and
       Calcium and Magnesium for SBRP Streams in Priority Classes A -E Using MAGIC for
       Both Current and Increased Deposition	
10-19.  Descriptive Statistics of Projected ANC, Sulfate, Percent Sulfur Retention,
       and Calcium Plus Magnesium for SBRP Streams in Priority Classes A and B Using
       ILWAS and MAGIC for Both Current and Increased Deposition	
10-20.  Effects of Critical Assumptions on Projected Rates of Change	
11-1.   Weighted  Median Projected Change  in ANC at 50 Years for Northeastern DDRP
       Lakes	
11-2.   Lakes in the  NE Projected to Have ANC Values <0 and <50 /jeq L1 for
       Constant and Decreased Sulfur Deposition	
11-3.   Weighted Median Projected Change in ANC at 50 Years for DDRP SBRP
       Stream Reaches  	
11-4.   SBRP Stream Reaches Projected to  Have ANC Values <0 and <50 jueq L1  for
       Constant and Increased Sulfur Deposition	
716


744


756
896

809

812

816

819
                                           XIX

-------
FIGURES
1-1. Steps of the Direct/Delayed Response Project (DDRP) approach 6

2-1. Activities of the Aquatic Effects Research Program within the National Acid
Precipitation Assessment Program 25

3-1. Diagram of sulfur cycle in forest ecosystems 33
3-2. Diagram of terrestrial base cation cycle 41

4-1. Steps of the Direct/Delayed Response Project (DDRP) approach 50

5-1. Representation of the point frame sampling procedure for selecting NSS
Stage I reaches 59
5-2. DDRP site locations for Subregion 1A 72
5-3. DDRP site locations for Subregion 1B 73
5-4. DDRP site locations for Subregion 1C 74
5-5. DDRP site locations for Subregion 1D 75
5-6. DDRP site locations for Subregion 1E 76
5-7. The pH-ANC relationship for (A) lakes of the ELS Phase I sampling in the Northeast
and (B) DDRP study lakes in the Northeast 90
5-8. The pH-ANC relationship for samples with ANC <400 /L/eq L taken at the downstream
nodes of stream reaches sampled in the NSS 94
5-9. Location of Northeast field check sites and other DDRP watersheds 108
5-10. Example of digitization log sheet 125
5-11. Example of attribute entry log sheet 126
5-12. Definition of soil sampling classes for the DDRP Soil Survey in the Northeast 149
5-13. Definition of soil sampling classes for the DDRP Soil Survey in the Southern
Blue Ridge Province 151
5-14. Selection of watersheds for sampling 152
5-15. Selection of starting points for sampling 153
5-16. Field selection of a sampling point for sampling class on a watershed 154
5-17. Major steps and datasets from the DDRP database 173
5-18. Calculation percentage of regional or subregional area in each soil sampling 179
5-19. Relative areas of sampling classes in the Northeast subregions 180
5-20. Relative areas of sampling classes in the entire Northeast and Southern
Blue Region Province 181
5-21. Aggregated soil variables for individual pedons in the Northeast 182
5-22. Aggregated soil variables for individual pedons in the Southern Blue Ridge Province. . 184
5-23. Calculation of cumulative distribution function for a soil variable in a region
or subregion 186
5-24. Cumulative distribution functions for pedon aggregated soil variables for the
Northeast and the Southern Blue Ridge Province 187
5-25. Sulfur deposition scenarios for the NE and SBRP for Level II and III Analyses 191
5-26. Example of average annual runoff map for 1951-80 226
5-27. Flow chart of Darcy's Law soil contact calculation as applied to the DDRP
study sites 235

7-1. Estimated percent sulfur retention by in-lake processes in drainage lakes
in ELS Region 1 (northeastern United States) 252
7-2. Percent sulfur retention for intensively studied sites in the United States and
Canada relative to the southern extent of the Wisconsinan glaciation 254
xx

-------
FIGURES (Continued)
Page
7-3. Model of flow-weighted average concentration calculations for Biscuit Brook 259
7-4. Flow chart for the determination of internal sources of sulfur using the
steady-state sulfate concentration 267
7-5. Scatter plot of the Monte Carlo calculated standard deviation versus the
calculated mean [SO42']SS 269
7-6. Comparison of percent sulfur retention calculated using (A) modified-LTA
deposition and (B) modified-LTA deposition adjusted with a 20 percent increase
in dry deposition 272
7-7. Population-weighted distribution of projected percent sulfur retention
(upper and lower bounds for 90 percent confidence interval): (A) Northeast;
(B) Mid-Appalachians, and (C) Southern Blue Ridge Province 274
7-8. Supplemental watersheds mapped for special evaluation of sulfur retention 276
7-9. Population-weighted distributions of projected percent sulfur retention, with
upper and lower bounds for 90 percent confidence intervals, for additional NSS
subregions: (A) Southern Appalachian Plateau, (B) Mid-Atlantic Coastal Plain,
(C) Catskills/Poconos, and (D) Piedmont 281
7-10. Combination regional population-weighted distributions of projected percent
sulfur retention, with upper and lower bounds for 90 percent confidence intervals,
for the Northeast, Mid-Appalachians, and Southern Blue Ridge Province 282

8-1. Distribution of estimated contact rate using Darcy's Law calculation 297
8-2. Distribution of index of contact using Darcy's Law calculation 298
8-3. Scatter plot of ANC versus contact rate calculated using Darcy's Law 300
8-4. Scatter plot of ANC versus index of soil contact calculated using Darcy's Law 301
8-5. Scatter plot of ANC versus ln(a/KbTanB) 321
8-6. Scatter plot of Ca plus Mg versus ln(a/KbTanB) 322
8-7. Scatter plot of pH versus In(a/KbTanB). 323
8-8. Data and regression model development flow diagrams 353
8-9. Model development procedure 401
8-10. Histograms of unadjusted and adjusted watershed means for selected SBRP soils
variables 409
8-11. The mean pH ± 2 standard errors for the SBRP watersheds estimated using the
common aggregation and the watershed effects adjusted aggregation the lack of
variation among the common aggregation values 410

9-1. Schematic diagram of extended Langmuir isotherm fitted to data points from
laboratory soil analysis 459
9-2. Comparison of measured lake (NE) or stream (SBRP) sulfate concentration with
computed soil solution concentration 462
9-3. Historic deposition inputs and modelled output for soils in a representative
watershed in the northeastern United States 466
9-4. Schematic of surface water response to changes in sulfur inputs 467
9-5. Comparison of measured, modelled and steady-state sulfate for Northeast lake
systems in 1984 472
9-6. Projected changes in percent sulfur retention and sulfate concentration for
soils in northeastern lake systems at 10, 20, 50 and 100 years 474
9-7. Box-and-whisker plots showing changes in sulfate concentration, percent sulfur
retention, and change in sulfate concentration for soils in northeastern lake
watersheds, using long-term average deposition data 475
XXI

-------
FIGURES (Continued)
9-8. Box-and-whisker plots showing changes in sulfate concentration, percent sulfur
retention, and change in sulfate concentration for soils in northeastern lake
watersheds, using Typical Years deposition data 476
9-9. Projected time to steady-state concentration for^ sulfate in northeastern lakes (A)
at current deposition and (B) after end of decreasing input in ramp scenario 478
9-10. Historic deposition inputs and modelled output for soils in stream systems in the
Southern Blue Ridge Province 480
9-11. Comparison of measured, modelled, and steady-state sulfate for stream systems in
the Southern Blue Ridge Province in 1985 483
9-12. Comparison of forecasts based on two sulfur deposition datasets for soils in SBRP
watersheds 485
9-13. Projected changes in percent sulfur retention and in sulfate concentration for stream
systems in the Southern Blue Ridge Province at 0, 20, 50, 100 and 140 years 487
9-14. Box and whisker plots showing changes in sulfate concentration, percent sulfur
retention, and change in sulfate concentration for soils in watersheds of the Southern
Blue Ridge Province. Data are shown for Typical Year deposition data 490
9-15. Box and whisker plots showing changes in sulfate concentration, percent sulfur
retention, and change in sulfate concentration for soils in watersheds of the Southern
Blue Ridge Province. Data are shown for Typical Year deposition data 491
9-16. Projected time to 95 percent of steady-state sulfur concentration of Southern
Blue Ridge Province stream systems 492
9-17. Comparison of model simulation results for DDRP Southern Blue Ridge
watersheds 495
9-18. Projected base year sulfate concentration with upper and lower bounds for 90
percent confidence intervals for Southern Blue Ridge Province watersheds 496
9-19. Projected time to sulfur steady state with upper and lower bounds for 90
percent confidence intervals in Southern Blue Ridge Province watersheds 497
9-20. Effects of data aggregation on simulated watershed sulfur response for soils
in DDRP watersheds of the Southern Blue Ridge Province 499
9-21. Evaluation of alternate soil aggregation procedures for soils in SBRP watersheds. . . . 500
9-22. Schematic diagram of the principal process involved in the cycling of base
cations in surficial environments 513
9-23. Plot of the log of the activity of AI3+ vs. soil solution pH for individual soil
samples collected for DDRP 518
9-24. Plot of the log of the selectivity coefficient for the calcium-aluminum exchange
reaction vs. the measured base saturation in A/E horizons in the NE 520
9-25. Histograms of the (unweighted for the population estimates) projected
present-day ANC values for lakes in the NE 521
9-26. Histograms of the (unweighted for the population estimates) projected, present-day
ANC values for lakes in the NE 523
9-27. Flow diagram for the one-box Bloom-Grigal soil simulation model 529
9-28. Cumulative distribution of projected, present-day ANC values for lakes in the study
population in the NE as projected using Reuss cation exchange model 538
9-29. Scatter plot of the projected, present-day ANC values for lakes in the NE, obtained
using the Reuss model vs. observed (ELS) values 539
9-30. Scatter plot of the present-day lake ANC values projected using the Reuss model in
conjunction with the Watershed-Based Aggregation (WBA) soils data vs. observed (ELS)
ANC values 542
9-31. Cumulative distribution of the projected surface water ANC values projected for the
study population of lakes in 50 years in the NE 544
9-32. Cumulative distribution of the projected surface water ANC values projected for the
study population of lakes in 100 years in the NE. 545

xxii

-------
FIGURES (Continued)
9-33. Schematic illustration of the titration-like behavior displayed by soils in response to
constant loadings of acidic deposition 547
9-34. Cumulative distribution of projected present-day ANC values for stream reaches
in the study population in the SBRP, as projections using Reuss's cation exchange
model 551
9-35. Scatter plot of the projected present-day ANC values for stream reaches in the SBRP,
obtained using the Reuss model, vs. observed (NSS) values 553
9-36. Cumulative distribution of projected changes (at 50 years) in surface water ANC
obtained using the Reuss model for stream reaches in the SBRP 556
9-37. Cumulative distribution of projected changes (at 100 years) in surface water ANC
obtained using the Reuss model for stream reaches in the SBRP 557
9-38. Comparison of measured vs. calculated soil pH values for the 580 aggregated master
horizons in the NE 561
9-39. Cumulative distribution of projected (a) base saturations and (b) soil pH values for soils
in NE. Projections made using the Reuss model 564
9-40. Cumulative distribution of projected (a) base saturations and (b) soil pH values for soils
in the NE. Projections were made using the Reuss model 565
9-41. Plot of the measured (ELS) ANC values for lakes in the NE vs. the estimated,
watershed-level base saturations for mineral horizons in those watersheds 566
9-42. Plot of the changes in surface water ANC values at (a) 20, (b) 50, and (c) 100 years
as projected by the Reuss model vs. the estimated, present-day, watershed-level base "
saturations for mineral horizons in those watersheds 567
9-43. Plot of the projected changes in soil base saturations vs. he observed, present-day,
aggregated base saturations for mineral horizons in the NE. The projections were made
with the Reuss model 568
9-44. Cumulative frequencies of changes in (a) soil base saturation and (b) soil pH for the
population of soils in the SBRP 573
9-45. Cumulative frequencies of changes in (a) soil base saturation and (b) soil pH for the
population of soils in the SBRP 574
9-46. Cumulative distributions of aggregate initial soil pH and percent base saturation in
the NE and SBRP, with and without organic horizons 582
9-47. Regional CDFs of the projected change in the pH of soils on NE lake watersheds under
constant and ramp down (30 percent 4) deposition scenarios after 20, 50, and 100
years of LTA, LTA-rbc, and LTA-zbc deposition. Organic horizons included 583
9-48. Regional CDFs of the projected change in the percent base saturation of soils
on NE lake watersheds under constant and ramp down (30 percent 4) deposition
scenarios after 20, 50, and 100 years of LTA, LTA-rbc, and LTA-zbc deposition.
Organic horizons included 534
9-49. Regional CDFs of the projected change in the pH of soils on NE lake watersheds under
constant and ramp down (30% 4.) deposition scenarios after 20, 50, and 100 years of
LTA, LTA-rbc, and LTA-zbc deposition. Organic horizons are excluded 590
9-50. Regional CDFs of the projected change in the percent base saturation of soils on NE
lake watersheds under constant and ramp down (30% 4) deposition scenarios after 20,
50, and 100 years of LTA, LTA-rbc, and LTA-zbc deposition. Organic horizons
excluded 59-j
9-51. Regional CDFs of the projected change in the pH of soils on SBRP stream watersheds
under constant and ramp up (20% t) deposition scenarios after 20, 50, 100, and 200
years of LTA, LTA-rbc, and LTA-zbc deposition. Organic horizons included 597
9-52. Regional CDFs of the projected change in the percent base saturation of soils on SBRP
stream watersheds under constant and ramp up (20% t) deposition scenarios after 20,
50, 100, and 200 years of LTA, LTA-rbc, and LTA-zbc deposition. Organic horizons
included 598

xxiii

-------
FIGURES (Continued)
9-53. Cumulative distributions of changes in soil base saturation for the population of
watersheds in the NE 608
9-54. Cumulative distributions of changes in soil pH for the population of watersheds
in the NE 609
9-55. Scatter diagrams of the projected changes in base saturation for individual
systems (not population weighted) in the NE obtained from the Reuss and
Bloom-Grigal models 610
9-56. Scatter diagrams of the projected changes in soil pH for individual systems (not
population weighted) in the NE obtained from the Reuss and Bloom-Grigai models. . . 611
9-57. Cumulative distributions of changes in soil base saturation for the population of
watersheds in the SBRP 614
9-58. Cumulative distributions of changes in soil pH for the population of watersheds
in the SBRP. 615

10-1. Modelling priority decision tree: Northeast 631
10-2. Modelling priority decision tree: Southern Blue Ridge Province 633
10-3. Decision tree used to identify watersheds with net chloride export and procedures for
determining chloride imbalance 638
10-4. Approach used in performing long-term projections of future changes in surface water
chemistry .- 640
10-5. Schematic of modelling approach for making long-term projections 641
10-6. Representation of horizontal segmentation of Woods Lake, NY, watershed for MAGIC
and ETD 645
10-7. Representation of vertical layers of Woods Lake Basin for ETD 646
10-8. Representation of horizontal segmentation of Woods Lake Basin for ILWAS 648
10-9. Representation of vertical layers of Woods Lake Basin for ILWAS 649
10-10. Representation of vertical layers of Woods Lake, NY, watershed for MAGIC 651
10-11. Comparison of population histograms for simulated versus observed (Eastern Lake
Survey Phase I 1984 values) ANC for ILWAS and MAGIC 662
10-12. Comparison of population histograms for simulated versus observed (Eastern Lake
Survey - Phase I 1984 values) sulfate concentrations for ILWAS and MAGIC, Priority
Classes A and B 663
10-13. Comparison of population histograms for simulated versus observed (Eastern Lake
Survey Phase I 1984 values) ANC and sulfate concentrations for MAGIC, Priority
Classes A - E 665
10-14. Comparison of population histograms for simulated versus observed (Eastern Lake
Survey Phase I 1984 values ) ANC and sulfate concentrations for MAGIC, Priority
Classes A - I ; 666
10-15. Comparison of population histograms for simulated versus observed (NSS Pilot Survey
values) ANC, Priority Classes A and B using ILWAS and MAGIC 667
10-16. Comparison of population histograms for simulated versus observed (NSS Pilot Survey
values) sulfate concentrations, Priority Classes A and B using ILWAS and MAGIC. . . . 677
10-17. Comparison of population histograms for simulated versus observed (NSS Pilot Survey
values) ANC and sulfate concentrations, Priority Classes A - E using MAGIC 678
10-18. Comparison of projection standard errors as a function of ANC (top figure) and sulfate
(bottom figure) concentrations for the NE uncertainty analysis watersheds using ETD
and MAGIC 685
10-19. Projections of ANC and sulfate concentrations for NE lakes, Priority Classes
A - I, using MAGIC for 20, 50, and 100 years, under current deposition and a
30 percent decrease in deposition 689
10-20. pH projections for NE lakes, Priority Classes A - I, using MAGIC for 20, 50,
and 100 years, under current deposition and a 30 percent decrease in deposition. . . 692

xxiv

-------
FIGURES (Continued)
10-21. Box and whisker plots of ANC distributions at 10-year intervals for NE
Priority Classes A - I using MAGIC 686
10-22. Box and whisker plots of sulfate distributions at 10-year intervals for NE
Priority Classes A - I using MAGIC 687
10-23. Box and whisker plots of pH distributions at 10-year intervals for NE
Priority Classes A - I using MAGIC 688
10-24. Comparison of population histograms for ANC under current levels of deposition
and a 30 percent decrease in deposition for NE lakes, Priority Classes A - I,
using MAGIC 691
10-25. Comparison of population histograms for sulfate concentrations at current
levels of deposition and a 30 percent decrease for NE lakes, Priority Classes
A - I, using MAGIC 692
10-26. Comparison of MAGIC and ETD projections of ANC for NE lakes, Priority
Classes A - E, under current and decreased deposition 693
10-27. Comparison of MAGIC and ETD projections of sulfate concentrations for NE lakes,
Priority Classes A - E, under current and decreased deposition 694
10-28. Comparison of MAGIC and ETD projections of pH for NE lakes, Priority
Classes A -E, under current and decreased deposition 695
10-29. Comparisons of projected change in ANC under current and decreased
deposition for NE Priority Classes A - E, using ETD and MAGIC . 699
10-30. Comparisons of projected change in sulfate concentrations under current and
decreased deposition for NE Priority Classes A - E, using ETD and MAGIC 700
10-31. Comparisons of projected change in pH under current and decreased
deposition for NE Priority Classes A - E, using ETD and MAGIC 701
10-32. Box and whisker plots of ANC distributions projected using ETD in 10-year
intervals for NE lakes, Priority Classes A - E 702
10-33. Box and whisker plots of sulfate distributions projected using ETD in
10-year intervals for NE lakes, Priority Classes A - E 703
10-34. Box and whisker plots of pH projected using ETD in 10-year intervals for
NE lakes, Priority Classes A - E 704
10-35. Box and whisker plots of ANC distributions in 10-year intervals using MAGIC
for NE lakes, Priority Classes A - E 705
10-36. Box and whisker plots of sulfate distributions in 10-year intervals using
MAGIC for NE lakes, Priority Classes A - E 706
10-37. Box and whisker plots of pH in 10-year intervals using MAGIC for NE lakes,
Priority Classes A - E 707
10-38. ETD ANC distributions at year 10 and year 50 for NE lakes, Priority
Classes A - E, under current and decreased deposition 708
10-39. MAGIC ANC distribution at year 10 and year 50 for NE lakes, Priority
Classes A - E, under current and decreased deposition 709
10-40. ETD sulfate distributions at year 10 and year 50 for NE lakes, Priority
Classes A - E, under current and decreased deposition 710
10-41. MAGIC sulfate distributions at year 10 and year 50 for NE lakes, Priority
Classes A - E, under current and decreased deposition 711
10-42. Comparison of ANC projections using ETD, ILWAS, and MAGIC for NE lakes,
Priority Classes A and B, under current and decreased deposition 713
10-43. Comparison of sulfate projections using ETD, ILWAS, and MAGIC for NE lakes,
Priority Classes A and B, under current and decreased deposition 714
10-44. Comparison of pH projections using ETD, ILWAS, and MAGIC for NE lakes,
Priority Classes A and B, under current and decreased deposition 715
xxv

-------
FIGURES (Continued)
Page
10-45. Comparison of ANC projections under current and decreased deposition for
NE lakes, Priority Classes A and B, at year 20 and year 50 using ETD, ILWAS,
and MAGIC 720
10-46. Comparison of sulfate projections under current and decreased deposition for
NE lakes, Priority Classes A and B, at year 20 and year 50 using ETD, ILWAS,
and MAGIC 721
10-47. Comparison of pH projections under current and decreased deposition for NE
lakes, Priority Classes A and B, at year 20 and year 50 using ETD, ILWAS,
and MAGIC 722
10-48. Box and whisker plots of ANC distributions in 10-year intervals projected
using ETD for NE lakes, Priority Classes A and B 724
10-49. Box and whisker plots of ANC distributions in 10-year intervals projected
using ILWAS for NE lakes, Priority Classes A and B 725
10-50. Box and whisker plots of ANC distributions in 10-year intervals projected
using MAGIC for NE lakes, Priority Classes A and B 726
10-51. Box and whisker plots of sulfate distributions in 10-year intervals projected
using ETD for NE lakes, Priority Classes A and B 727
10-52. Box and whisker plots of sulfate distributions in 10-year intervals projected
using ILWAS for NE lakes, Priority Classes A and B 728
10-53. Box and whisker plots of sulfate distributions in 10-year intervals projected
using MAGIC for NE lakes, Priority Classes A and B 729
10-54. Box and whisker plots of pH distributions in 10-year intervals projected
using ETD for NE lakes, Priority Classes A and B. . . . -. 730
10-55. Box and whisker plots of pH distributions in 10-year intervals projected
using ILWAS for NE lakes, Priority Classes A and B 731
10-56. Box and whisker plots of pH distributions in 10-year intervals projected
using MAGIC for NE lakes, Priority Classes A and B 732
10-57. ETD ANC population distributions at year 10 and year 50 for current and
decreased deposition 733
10-58. ILWAS ANC population distributions at year 10 and year 50 for current and
decreased deposition 734
10-59. MAGIC ANC population distributions at year 10 and year 50 for current and
decreased deposition , 735
10-60. ETD sulfate population distributions at year 10 and year 50 for current and
decreased deposition 736
10-61. ILWAS sulfate population distributions at year 10 and year 50 for current and
decreased deposition 737
10-62. MAGIC sulfate population distributions at year 10'and year 50 for current and
decreased deposition 738
10-63. MAGIC ANC and sulfate projections for SBRP streams, Priority Classes A - E,
at year 20, year 50, year 100, and year 200 under current and increased
deposition 740
10-64. MAGIC pH projections for SBRP streams, Priority Classes A - E, at year 20,
year 50, year 100, and year 200 under current and increased deposition 742
10-65. Box and whisker plots of ANC distributions in 10-year intervals projected
using MAGIC for SBRP streams, Priority Classes A - E, for current and
increased deposition 746
10-66. Box and whisker plots of sulfate distributions in 10-year intervals projected
using MAGIC for SBRP streams, Priority Classes A - E, for current and
increased deposition. 747
xxvi

-------
FIGURES (Continued)
Page
10-67. Box and whisker plots of pH distributions in 10-year intervals projected
using MAGIC for SBRP streams, Priority Classes A - E, for current and
increased deposition 748
10-68. MAGIC ANC population distributions at year 10 and year 50 for current and
increased deposition, SBRP streams, Priority Classes A - E 750
10-69. MAGIC sulfate population distributions at year 10 and year 50 for current
and increased deposition, SBRP streams, Priority Classes A - E 751
10-70. Comparison of ILWAS and MAGIC projections for ANC at years 0, 20, and 50
for SBRP streams, Priority Classes A and B, under current and increased deposition. . 753
10-71. Comparison of ILWAS and MAGIC projections for sulfate concentration at years
0, 20, and 50 for SBRP streams, Priority Classes A and B, under current
and increased deposition 754
10-72. Comparison of ILWAS and MAGIC projections for pH at years 0, 20, and 50 for
SBRP streams, Priority Classes A and B, under current and increased deposition. . . . 755
10-73. Box and whisker plots for ANC distributions in 10-year intervals projected
using ILWAS for SBRP streams, Priority Classes A and B, for current and
increased deposition 759
10-74. Box and whisker plots for ANC distributions in 10-year intervals projected
using MAGIC for SBRP streams, Priority Classes A and B, for current and
increased deposition 760
10-75. Box and whisker plots for sulfate distributions in 10-year intervals
projected using ILWAS for SBRP streams, Priority Classes A and B, for current
and increased deposition , 761
10-76. Box and whisker plots for sulfate distributions in 10-year intervals projected
using MAGIC for SBRP streams, Priority Classes A and B, for current and
increased deposition 752
10-77. Box and whisker plots for pH distributions in 10-year intervals projected
using ILWAS for SBRP streams, Priority Classes A and B, for current and
increased deposition 753
10-78. Box and whisker plots for pH distributions in 10-year intervals projected
using MAGIC for SBRP streams, Priority Classes A and B, for current and
increased deposition 764
10-79. ILWAS ANC population distributions at year 10 and year 50 for current and
increased deposition, SBRP Priority Class A and B streams 766
10-80. MAGIC ANC population distributions at year 10 and year 50 for current and
increased deposition, SBRP Priority Class A and B streams 767
10-81. ILWAS sulfate population distributions at year 10 and year 50 for current and
increased deposition, SBRP Priority Class A and B streams 768
10-82. MAGIC sulfate population distributions at year 10 and year 50 for current and
increased deposition, SBRP Priority Class A and B streams 769
10-83. Comparison of projected sulfate versus sulfate steady-state concentrations
using ETD, ILWAS, and MAGIC for NE lakes 770
10-84. Comparison of projected sulfate concentrations under decreased deposition
with the current sulfate steady-state concentrations using ETD, ILWAS, and
MAGIC for NE lakes 772
10-85. Comparison of projected sulfate concentrations between models for NE lakes
after 50 years under current and decreased deposition 773
10-86. Comparison of projected sulfate versus sulfate steady-state concentrations
for SBRP streams using ILWAS and MAGIC under both current and increased
deposition 774
10-87. Comparison of projected ANC between models in NE lakes after 50 years
under current and decreased deposition 775

xxvii

-------
                                   FIGURES (Continued)
10-88.  Projected changes in ANC as a function of changes in sulfate for NE lakes
       using ETD, ILWAS, and MAGIC for current and decreased deposition	  776
10-89.  Comparison of pH - ANC relationship for each of the models	  777
10-90.  Comparison of projected pH values between models for NE lakes after 50 years
       under current and decreased deposition	  779
10-91.  Comparison of projected changes in calcium and magnesium versus  changes in
       sulfate using ILWAS and MAGIC for NE lakes	  780
10-92.  Change in median ANC, calcium and magnesium, and sulfate concentrations
       projected for NE lakes  using MAGIC under current and decreased deposition	  781
10-93.  Comparison of the change in pH after 50 years as a function of the initial
       calibrated pH for MAGIC, ETD and ILWAS on northeastern lakes	  782
10-94.  Comparisons  of projected ANC and sulfate concentrations and pH between
       ILWAS and MAGIC after 50 years for SBRP streams.  .	  793
10-95.  Comparison of projected AANC and Asulfate relationships in SBRP Priority
       Class A and B streams using ILWAS and MAGIC	  785
10-96.  Comparison of projected AANC and Asulfate relationships and A(calcium  and
       magnesium) and Asulfate relationships for SBRP Priority Class A - E streams
       using MAGIC	  786
10-97.  Comparison of projected A(calcium and magnesium) and Asulfate relationships
       for SBRP Priority Class A and B streams using ILWAS and MAGIC	  787
10-98.  Change in median ANC, calcium and magnesium, and sulfate concentrations
       projected for SBRP streams under current and increased deposition  using MAGIC.  . .  788
10-99.  Comparison of the change in pH after 200 years  as a function of the initial
       calibrated pH for MAGIC on SBRP streams, Priority Classes A -  E	  789
10-100. Comparison of projected MAGIC change in pH versus derived pH after 50 years
       for NE lakes	  793
                                          xxviii

-------
PLATES
1-1. Direct/Delayed Response Project study regions and sites 3
1-2. Sulfur retention and wet sulfate deposition for National Surface Water Survey
subregions in the eastern United States 9
1-3. Changes in sulfur retention in the Southern Blue Ridge Province as projected
by MAGIC for constant sulfur deposition 11
1-4. Change in median ANC of northeastern lakes at 50 years as projected by MAGIC .... 13
1-5. Change in median ANC of Southern Blue Ridge Province stream reaches at 50 years
as projected by MAGIC 16

2-1. Direct/Delayed Response Project study regions and sites 26

5-1. Northeastern subregions and ANC map classes, Eastern Lake Survey Phase I 56
5-2. ANC of DDRP lakes by ANC group 77
5-3. DDRP stream reach study sites in the Southern Blue Ridge Province 81
5-4. Final DDRP classification of lake hydrologic type - Subregion 1A 84
5-5. Final DDRP classification of lake hydrologic type - Subregion 1B 85
5-6. Final DDRP classification of lake hydrologic type - Subregion 1C 86
5-7. Final DDRP classification of lake hydrologic type - Subregion 1D 87
5-8. Final DDRP classification of lake hydrologic type - Subregion 1E 88
5-9. Example of watershed soil map 119
5-10. Example of watershed vegetation map 120
5-11. Example of depth-to-bedrock map 121
5-12. Example of watershed land use map 122
5-13. Example of watershed geology map 123
5-14. Example of 40-ft contour delineations on a 15' topographic map 131
5-15. Example of combination buffer: (A) stream and 30-m linear buffer for streams, (B)
wetlands and 30-m linear buffer for wetlands, (C) elevational buffer for lake, and (D)
combination of all preceding buffers 133
5-16. ADS and NCDC sites linked with DDRP study sites for NE Subregion 1A 194
5-17. ADS and NCDC sites linked with DDRP study sites for NE Subregion 1B 195
5-18. ADS and NCDC sites linked with DDRP study sites for NE Subregion 1C 196
5-19. ADS and NCDC sites linked with DDRP study sites for NE Subregion 1D 197
5-20. ADS and NCDC sites linked with DDRP study sites for NE Subregion 1E 198
5-21. ADS and NCDC sites linked with DDRP study sites for the SBRP 199
5-22. DDRP study sites relative to distance from Atlantic Coast 204
5-23. Pattern of typical year sulfate deposition for the DDRP NE study sites. 209
5-24. Pattern of typical year sulfate deposition for the DDRP s'tudy sites in Subregion 1A. . . 210
5-25. Pattern of typical year sulfate deposition for the DDRP study sites in Subregion 1 B. . . 211
5-26. Pattern of typical year sulfate deposition for the DDRP study sites in Subregion 1C. . . 212
5-27. Pattern of typical year sulfate deposition for the DDRP study sites in Subregion 1D. . . 213
5-28. Pattern of typical year sulfate deposition for the DDRP study sites in Subregion 1E. .. 214
5-29. Pattern of typical year sulfate deposition for the DDRP SBRP study sites 215
5-30. Pattern of LTA sulfate deposition for the DDRP NE study sites 217
5-31. Pattern of LTA sulfate deposition for the DDRP study sites in Subregion 1A 218
5-32. Pattern of LTA sulfate deposition for the DDRP study sites in Subregion 1B 219
5-33. Pattern of LTA sulfate deposition for the DDRP study sites in Subregion 1C 220
5-34. . Pattern of LTA sulfate deposition for the DDRP study sites in Subregion 1D 221
5-35. Pattern of LTA sulfate deposition for the DDRP study sites in Subregion 1E 222
5-36. Pattern of LTA sulfate deposition for the DDRP SBRP study sites 223
xxix

-------
PLATES (Continued)
7-1. Sulfur retention and wet sulfate deposition for National Surface Water Survey
subregions in the eastern United States 275
7-2. Regional percent sulfur retention by major land resource area (MLRA) based
on target populations (ELS and NSS sites) 283

11-1. Sulfur retention and wet sulfate deposition for National Surface Water Survey
subregions in the eastern United States 802
11-2. Changes in sulfur retention in the Southern Blue Ridge Province as projected by
MAGIC for constant sulfur deposition 804
11-3. Change in median ANC of northeastern lakes at 50 years as projected by MAGIC . . . 808
11-4. ANCs of northeastern lakes versus time, as projected by MAGIC for constant sulfur
deposition 810
11-5. ANCs of northeastern lakes versus time, as projected by MAGIC for decreased sulfur
deposition 811
11-6. Changes in median pH of northeastern lakes at 50 years as projected by MAGIC . . . 813
11 -7. Change in median ANC of Southern Blue Ridge Province stream reaches at 50 years
as projected by MAGIC 815
11-8. ANCs of Southern Blue Ridge Province stream reaches versus time, as projected by
MAGIC for constant sulfur deposition 817
11-9. ANCs of Southern Blue Ridge Province stream reaches versus time, as projected by
MAGIC for increased sulfur deposition 818
11-10. Changes in pH of SBRP stream reaches as projected by MAGIC 821
11-11. Changes in pH of SBRP stream reaches as projected by ILWAS 822
xxx

-------
PRIMARY CONTRIBUTORS TO THE DDRP REPORT

tDil?cyDelayed Response Project and this Review Draft Report represent the efforts of many
, technical and support staff. The primary contributors to this report are noted here

Section 1: Executive Summary
M. R. Church, U.S. Environmental Protection Agency

Section 2: Introduction
M. R. Church, U.S. Environmental Protection Agency

Section 3: Processes of Acidification
P. W. Shaffer, NSI Technology Services Corp.
G. R. Holdren, NSI Technology Services Corp.
M. R. Church, U.S. Environmental Protection Agency

Section 4: Project Approach
M. R. Church, U.S. Environmental Protection Agency

Section 5: Data Sources and Descriptions1
L. J. Blume, U.S. Environmental Protection Agency
G. E. Byers, Lockheed Engineering and Sciences Co
W. G. Campbell, NSI Technology Services Corp.
M. R. Church, U.S. Environmental Protection Agency
D. A. Lammers, U.S.D.A. Forest Service
J. J. Lee, U.S. Environmental Protection Agency
L H. Liegel, U.S.D.A. Forest Service
D. C. Mortenson, NSI Technology Services Corp.
G. J. Palmer, NSI Technology Services Corp.
M. L Papp, Lockheed Engineering and Sciences Co
B. P. Rochelle, NSi Technology Services Corp.
D. D. Schmoyer, Martin Marietta Energy Systems Inc
K. W. Thornton, FTN & Associates, Ltd.
R. S. Turner, Oak Ridge National Laboratory
R. D. Van Remortel, Lockheed Engineering and Sciences Co.

Section 6: Regionalization of Analytical Results
D. L. Stevens, Eastern Oregon State University
K. W. Thornton, FTN & Associates, Ltd.

Section 7: Watershed Sulfur Retention
B. P. Rochelle, NSI Technology Services Corp.
M. R. Church, U.S. Environmental Protection Agency
P. W. Shaffer, NSI Technology Services Corp.
G. R. Holdren, NSI Technology Services Corp.

Section 8: Level I Statistical Analyses
M. G. Johnson, NSI Technology Services Corp
R. S. Turner, Oak Ridge National Laboratory
D. L Cassell, NSI Technology Services Corp.
D. L. Stevens, Eastern Oregon State University
M. B. Adams, Automated Systems Group, Inc 2
C. C. Brandt, Oak Ridge National Laboratory
W. G. Campbell, NSI Technology Services Corp.
M. R. Church, U.S. Environmental Protection Agency
G. R. Holdren, NSI Technology Services Corp.
L. H. Liegel, U.S.D.A. Forest Service
xxx.

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Section 8: Level I Statistical Analyses (continued):
      B. P. Rochelle, NSI Technology Services Corp.
      P. F. Ryan, University of Tennessee
      D. D. Schmoyer, Martin Marietta Energy Systems,  Inc.
      P. W.  Shaffer, NSI Technology Services Corp.
      D. A. Wolf, Martin  Marietta Energy Systems, Inc.

Section 9:  Level II Single-Factor Time Estimates1
      G. R. Holdren, NSI Technology Services Corp.
      M. G.  Johnson, NSI Technology Services  Corp.
      C. I. L'rff, Utah State University
      P. W.  Shaffer, NSI Technology Services Corp.

Section 10:  Level III Dynamic Watershed Models
      K. W. Thornton, FTN &  Associates, Ltd.
      D. L. Stevens, Eastern Oregon State University
      M. R. Church, U.S. Environmental  Protection Agency
      C. I. Liff, Utah State University
           Extramural Cooperators Providing Modelling Expertise and Support:
                 C. C. Brandt,  Oak Ridge National  Laboratory
                 B. J. Cosby, University of Virginia
                 S. A. Gherini, Tetra-Tech, Inc.
                 G. M.  Hornberger, University of Virginia
                 M. Lang, Tetra-Tech, Inc.
                 S. Lee, University of Iowa
                 R. K.  Munson, Tetra-Tech,  Inc.
                 R. M. Newton, Smith College
                 N. P.  Nikolaidis, University of  Connecticut
                 P. F. Ryan,  University  of Tennessee
                 J. L. Schnoor, University of Iowa
                 R. S. Turner, Oak Ridge National Laboratory
                 D. M. Wolock, U.S. Geological Survey

Section 11:  Integration and Summary
      M. R. Church, U.S. Environmental  Protection Agency
      P. W. Shaffer, NSI  Technology Services Corp.
  Contributors to this section listed alphabetically
  Beginning on this line, remaining contributors listed alphabetically
                                              XXXII

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ACKNOWLEDGMENTS

The performance of this portion of the Direct/Delayed Response Project (DDRP) and the
preparation of this report have required the efforts of hundreds of scientists and support personnel. We
acknowledge here a few of those persons who made particularly outstanding contributions. To all the
others who helped us, but who are not named here, we also extend our sincere thanks.

William Ruckleshaus led the way in calling for the initiation of the DDRP and Lee Thomas showed
a continued and very patient interest in seeing that it was completed properly. We thank them for their
foresight and leadership.

Courtney Riordan and Gary Foley of the EPA Office of Research and Development (ORD) provided
much encouragement and support for the Project throughout its development and implementation. We
thank them for their appreciation of the technical complexity of the task.

Rick Linthurst, the first Director of the Aquatic Effects Research Program (AERP), played an
absolutely critical role in the development and nurturing of the Project during its early years. We greatly
appreciate his early and continuing commitment to the DDRP. Dan McKenzie, as Director of the AERP,
provided important continuing support for the Project and we thank him for his efforts in helping guide
this phase of the Project to its conclusion.

Tom Murphy, Laboratory Director for EPA's Environmental Research Laboratory-Corvallis (ERL-C),
and Ray Wilhour, Bob Lackey and Spence Peterson, Branch Chiefs for ERL-C, have all supported the
Project and its staff from the first to the last. We thank them for their support.

Dwain Winters and Brian McLean from the Office of Air and Radiation at EPA-Headquarters provided
insight and suggestions for analyses of particular relevance to questions of Agency policy. We thank
them for their interest and assistance.

Dixon Landers, Technical Director of the National Surface Water Survey, Jay Messer, Technical
Director of the Pilot Stream Survey, and Phil Kaufmann, Technical Director of the National Stream Survey
and their staffs all provided valuable help in interpreting and correctly using their surface water chemistry
data. We thank especially Tim Sullivan, Joe Eilers, Jim Blick, Mark DeHaan, Alan Herlihy and Mark
Mitch.

Jim Omernik (EPA), Andy Kinney (NSI) and Andy Herstrom (NSI) provided many interesting hours
of instruction and discussion on the topics of physical geography and the proper use and application
of Geographic Information Systems. Our efforts in these technical areas have certainly profited from
their valuable advice and counsel.

Bill Fallon (ORD), Chuck Frank (EPA) and his staff, Linda Looney (EPA), and Cindy Burgeson (NSI
Technology Services Corp.) all have provided much administrative assistance to help keep the Project
moving in the right direction and at the pace required. We thank them all for their efforts and assistance.

Many landowners and state and government agencies allowed us to map and sample soils on
their properties. We thank them for permission to do so.
XXXIII

-------
      The cooperation of the U.S. Department of Agriculture (USDA) Soil Conservation Service (SCS)
was essential to the completion of the DDRP Soil Survey. People in the SCS state offices who were
responsible for mapping of DDRP watersheds and obtaining the soil descriptions  and samples included
Ed  Sautter, Roy Shook (Connecticut and Rhode Island); Gene Grice, Steve Hundley (Massachusetts);
Dick Babcock, Bob Joslin, Kenny LaFlamme (Maine); Sid Pilgrim,  Henry Mount (New Hampshire); Fred
Gilbert, Keith Wheeler, Will  Hanna (New York);  Garland Lipscomb, George Martin (Pennsylvania); Dave
Van Houten (Vermont); Talbert Gerald, Bob Wilkes  (Georgia); Horace Smith, Andy Goodwin  (North
Carolina); Darwin Newton,  David Lewis (Tennessee); Niles McLoda (Virginia). In addition, more than 100
soil scientists were involved in mapping and sampling.

      Regional consistency and comparability was greatly assisted by the efforts of people at the SCS
National Technical  Centers, especially Oliver Rice, Ted Miller (Northeast) and Larry Ratliff (South). The
continuing support of DDRP activities by Milt Meyer, Ken Hinkley,  and Dick Arnold  of the SCS National
Office was extremely helpful.

      John  Warner,  former  SCS Assistant State  Soil Scientist for  New  York  was the  Regional
Correlator/Coordinator of the  Soil Survey for both the Northeast and Mid-Appalachian Regions. Hubert
Byrd, former State  Soil Scientist for North Carolina, served as RCC for the SBRP Soil Survey.

      Elissa Levine and Harvey Luce (University of Connecticut),  Bill Waltman and Ray Bryant (Cornell
University), Cheryl Spencer and Ivan Fernandez (University of Maine), Steve Bodine and  Peter Veneman
(University of Massachusetts), Bill Smith and Lee Norfleet  (Ciemson University),  and Dave Litzke and
Marilew Bartling  (University of Tennessee) supervised the operation of the soils preparation laboratories
for  the DDRP Soil Survey.

      A large and dedicated staff at EPA's Environmental Monitoring and Systems Laboratory-Las Vegas
(EMSL-LV) played an  absolutely crucial role in support of the DDRP  Soil Survey.  Gareth Pearson and
Bob Schonbrod  provided supervisory guidance for the DDRP Soil Survey activities at EMSL-LV.  Lou
Blume (EPA)  served as Technical Monitor for the  program  and was  responsible for delivery of verified
field, soil preparation laboratory, and analytical databases.  Lou Blume was responsible for contracting
and management of  soil preparation laboratories  and analytical  laboratories  and for  the delivery  of
operations reports,  quality assurance reports, methods manuals and field sampling manuals for the Soil
Survey.   Mike Papp of Lockheed Engineering and Sciences Corporation (LESC) was  responsible for
delivery of verified field, soil preparation and analytical databases for the Soil Survey.  Rick Van  Remortel
(LESC) assisted  in  the verification of the SBRP analytical database and in the preparation of laboratory
operations and quality assurance reports.  Bill Cole (LESC) was the Task Lead for the verification of the
analytical  database for the NE and  assisted in  the  preparation  of  the methods  manual and quality
assurance report for the NE Soil Survey.  Gerry  Byers (LESC) assisted in the preparation of  methods
manuals and  quality assurance reports for the NE and SBRP.  Marilew Bartling  (LESC) served as the
Task Lead for the verification of Soil Survey data for the SBRP, served  as a manager of a soil preparation
laboratory for the SBRP Soil Survey and contributed to the operations and quality assurance reports for
the  SBRP.  Rod  Slagle (LESC) served as the DDRP soils database manager at EMSL-LV.  Steve Simon
and Dan  Hillman (LESC) assisted in methods development and project implementation early in the
Project.  Craig  Palmer of the Environmental Research  Center of the University  of Nevada-Las Vegas
provided invaluable  technical assistance on  quality assurance of soils analytical data.
                                             xxxiv

-------
Deborah Coffey (NSI) played a critical role in ensuring the quality of the watershed and soils data
gathered for the Project. She either had a major responsibility for, or assisted in, the development of
data quality objectives, field sampling manuals, laboratory methods manuals, field operations reports,
field quality assurance reports and numerous other facets of the Soil Survey. We thank her for her
unswerving attention to detail. Jeff Kern (NSI) has also assisted in helping to assure the quality of field
and laboratory data.

Other scientists who made major contributions to the design of the soil survey activities included
Stan Buol (North Carolina State University), John Ferwerda (University of Maine-Orono), Maurice
Mausbach (Soil Conservation Service), Ben Hajek (Auburn University), John Reuss (Colorado State
University), Mark David (University of Illinois), and Fred Kaisaki (Soil Conservation Service).

Phil Arberg (EPA) and Dave Williams (LESC) of EMSL-LV were responsible for acquisition and
interpretation of aerial photography of the DDRP watersheds.

Numerous extramural cooperators assisted in this Project. Jack Cosby, George Hornberger, Pat
Ryan and David Wolock (University of Virginia), Jerry Schnoor, Tom Lee, Nikolaos Nikolaidis, Konstantine
Georgakakos and Harihar Rajaram (University of Iowa), Steve Gherini, Ron Munson and Margaret Lang
(Tetra-Tech, Inc.), Carl Chen and Louis Gomez (Systech, Inc.) all assisted in watershed modelling
analyses. Bob Newton of Smith College assisted in gathering supplementary watershed data for use in
calibrating the models to the Special Interest lake/watersheds in the Adirondacks. John Reuss and Mark
Walthall of Colorado State University and Tom Voice of Michigan State University performed investigations
of processes of base cation supply and sulfate adsorption, respectively, that assisted us in interpreting
our Soil Survey data and in modelling soil responses. Warren Gebert, Bill Krug, David Graczyk and Greg
Allord of the U.S. Geological Survey (Madison, Wisconsin) supplied runoff data and maps that were
crucial to the Project. Wayne Swank and Jack Waide of the USDA Forest Service cooperated with the
Project in allowing us to use data gathered by the Coweeta Hydrologic Laboratory. Jack Waide also
provided many insights into the workings of watersheds in the Southern Blue Ridge and in the application
of watershed simulation models. Tony Olsen, Sally Wampler and Jeanne Simpson of Battelle Pacific
Northwest Laboratories provided a great deal of information on estimates of wet deposition to sites of
interest in the Eastern United States. Tony Olsen also assisted in editing text describing analyses of the
wet deposition data. Robin Dennis and Terry Clark of the EPA's Atmospheric and Exposure Assessment
Laboratory-Research Triangle Park and Steve Seilkop of Analytical Services, Incorporated, provided key
information on estimates of atmospheric dry deposition. Steve Lindberg of Oak Ridge National laboratory
and Bruce Hicks and Tilden Myers of the National Oceanographic and Atmospheric Administration
provided considerable assistance in the form of discussions and preliminary data on rates of atmospheric
dry deposition. We thank all of these cooperators for their assistance.

No project of the magnitude of the DDRP can be successfully completed without the assistance
of peer reviewers. The DDRP benefitted immensely from peer review comments all the way from its
inception to the completion of this report.

The following scientists served as reviewers of the initial Review Draft Report: David Grigal of the
University of Minnesota, Peter Chester, R. Skeffington and D. Brown of the Central Electricity Generating
Board (London), Jerry Elwood of Oak Ridge National Laboratory, John Melack of the University of
California - Santa Barbara, Phil Kaufmann of Utah State University, Bruce Hicks of the National
Oceanographic and Atmospheric Administration, Gary Stensland of the Illinois State Water Survey, Jack

xxxv

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Waide of the USDA Forest Service, David Lam of the National Water Research Institute (Burlington,
Ontario), Nils Christophersen of the Institute of Hydrology (Wallingford Oxon, Great Britain), Bill McFee
of Purdue University, Steve Norton of the University of Maine, Scott Overton of Oregon State University,
Ken Reckhow of Duke University, Dale Johnson of the Desert Research Institute (Reno, Nevada), and
Gray Henderson of the University of Missouri. We thank these scientists for their efforts in reviewing a
long and complex document. We especially thank Dave Grigal (Chairman), Jerry Elwood, John Melack
and Phil Kaufmann who served on the Overview Committee of reviewers. This report benefitted greatly
from the comments and constructive criticisms of all of these reviewers.

Numerous other scientists also served as reviewers over the years of individual aspects of the
Project or of the Project as a whole. We thank them also for helping us to improve the quality of the
work that we performed.

Dave Marmorek, Mike Jones, Tim Webb and Dave Barnard of ESSA, Ltd. provided much valuable
assistance in the planning of various phases of the DDRP. Their assistance in this planning was
invaluable.

John Berglund of InstaGraphics, Inc. prepared many of the figures that appear in this report. We
thank him for the fine job that he did.

A majority of the word processing throughout the DDRP and, especially, for this report was done
by Carol Roberts of NSI. We thank Carol for her many, many hours of diligent work and for her
forbearance in helping us in our attempts to get everything "exactly right". Significant word processing
support was also provided by Laurie Ippoliti (NSI), Amy Vickland (NSI), Lana McDonald, Rose Mary Hall
and Deborah Pettiford of Oak Ridge National Laboratory, and Eva Bushman and Suzanne Labbe of Action
Business Services.

Penelope Kellar and Perry Suk of Kilkelly Environmental Associates performed truly amazing tasks
in editing both the Review Draft and Final Draft of this report. The job could not have been completed
on time without their efforts. Ann Hairston (NSI), Amy Vickland (NSI), Susan Christie (NSI) and Linda
Allison (ORNL) also provided important editorial assistance.

The DDRP Technical Director sincerely thanks all of the Project staff and extramural cooperators
for their unquenchable enthusiasm and dedication to seeing that this very tough job was done correctly.
Good work gang...thank you.
xxxvi

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                             APPENDICES



             MODEL CALIBRATION/CONFIRMATION, WATERSHEDS



                 SIMULATED AND UNCERTAINTY ANALYSES
A.1:      MODEL CALIBRATION/CONFIRMATION REPORTS



A.2:      WATERSHEDS STIMULATED BY ETD, ILWAS, AND MAGIC



A.3:      UNCERTAINTY ESTIMATES CONFIDENCE BOUNDS FOR MODEL PROJECTIONS

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              APPENDIX A.1 MODEL CALIBRATION/CONFIRMATION REPORTS







A.1-1 ENHANCED TRICKLE-DOWN (ETD)



A.1-2 INTEGRATED LAKE-WATERSHED ACJDIFICATION STUDY (ILWAS)



A. 1-3 MODEL OF ACIDIFICATION OF GROUNDWATER IN CATCHMENTS (MAGIC)

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       APPENDIX A.1-1



ENHANCED TRICKLE-DOWN (ETD)

-------
Technical Report No. CEE-ARRG-86.03
Modeling Short and Long Term Impacts
    of Acid Precipitation Using the
    Enhanced Trickle-Down Model:
       Lake Woods Case Study
                by:
        Nikolaos P. Nikolaidis
     Konstantine P. Georgakakos
          Jerald L Schnoor
  Civil and Environmental Engineering
       The University of Iowa
       Iowa City, Iowa 52242
          November 1986

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MODELING SHORT AND LONG TERM IMPACTS OF ACID PRECIPITATION USING THE
ENHANCED TRICKLE-DOWN MODEL: LAKE WOODS CASE STUDY

INTRODUCTION

Public awareness of the effect of acid deposition on the terrestrial and aquatic environment has
imposed pressure on the governments of European and North American countries to control the
emissions of sulfur and nitrogen oxides. However, before any threshold limit be established a critical
question concerning the response of aquatic ecosystems to acidification should be answered. The case
is whether further decreases in alkalinity of surface waters will occur if present emission loadings remain
constant. It is the purpose of this research to aid in answering the above question by application of a
mathematical model to lakes in the Adirondack Mountains of New York, where most of the effects of acid
rain in the United States have been documented.

In order to examine the behavior of aquatic responses to acid rain, let us define those waters
whose alkalinity changes further with time as "direct response" systems. Figure 1 is from a 1984 National
Research Council Panel on "Processes of Lake Acidification", which discussed direct response and
delayed response systems. A direct response system is expected to respond to changes in acid
deposition over a relatively short period of time, depending on the hydraulic detention time of the lake.
In this case, the rate of neutralization in the watershed is a kinetically-controlled process, and changes
in acid deposition may modify the rate of acid neutralization but would not exhaust the capacity of the
watershed for neutralization. A delayed response watershed, on the other hand, would gradually lose its
capacity for neutralizing acid deposition, and the alkalinity of its waters would gradually decrease over
time (decades to centuries). There are two possible causes of a delayed response: (1) a gradual
depletion of mineral bases of base cations from soil exchange sites, and/or (2) a gradual exhaustion of
sulfate adsorption sites in the soil. The two processes are related to the ordinate and abscissa of Figure
1. If a watershed has a low ability to supply bases and a low ability to retain sulfate (SO/2 from H^SO.)
then it is a direct (quick) response system. 24"

Figure 2 gives a more temporal picture of the difference between direct and delayed response
systems. If watersheds are out of the "protected" type of Figure 1, there will be no response to a step
function increase in acid deposition (panel B., Figure 2). These watersheds are capable of neutralizing
any observed amount of acid deposition. Moderately sensitive lakes may respond with significant
decreases in alkalinity. However, they will retain some acid neutralizing capacity (ANC). These lakes are
shown in panel C of Figure 2 and are the "direct" response lakes. Depending on their watershed
characteristics (water flows only through shallow acidic soil zones, etc.), these lakes may become acidic
after 1000 years (delayed-1) or some may have a much faster response (delayed-2), depending on when
their soil ANC or sulfate-sorption capacity become exhausted. Extremely sensitive lakes (panel D) have
already become acidified due to acid deposition.

Mathematical models can be used to .estimate the alkalinity response of lakes over time. The
Trickle-Down model has been used to assess resources-at-risk to acid deposition in the Northeast and
Upper Midwest. A steady state version of the model was first reported by Stumm and Schnoor (1983),
and a time variable version of the model was given by Schnoor, Palmer and Glass (1984) for Omaday
Lake and Filson Creek, Minnesota. The time variable model has been applied to two seepage lakes in
Wisconsin (Lakes Clara and Vandercook) by Lin (1985). The steady state version has been used to
estimate the number of lakes that would become acidic under various acid loading scenarios for
approximately 1400 lakes across the depositional gradient (Schnoor, Lee, Nikolaidis and Nair, 1986).
Being simple and yet based on the principle of continuity for alkalinity, the model has proven to be useful
in acid precipitation assessments. The model, as it is used by Lin (1985) is suitable for seepage lakes
only. Several modifications were required to generalize the model for every surface water hvdrolooic
system.

The Enhanced Trickle-Down model (ETD) is a combination of four submodels (Nikolaidis et al.,
1986). It simulates the hydrology, alkalinity, sulfate and chloride of a catchment simultaneously. The
construction of the hydrologic submodel equations was done in such a way that each flow component
A. 1-1-2

-------
 is multiplied by a correction factor that reflects the lumped nature of the model.  The alkalinity and sulfate
 submodels are sets of mass balance equations on each compartment. The mass balance equations for
 alkalinity and sulfate include the predominant processes of ion exchange, chemical weathering, sulfate
 sorption, sulfate reduction and iron oxidation as  reaction term.

      The scope of this report is to calibrate, verify, and perform a sensitivity analysis on the parameters
 and examine the long term response to various acid loadings of Lake Woods, using the ETD model.
 Lake Woods is a 2.1 km forested watershed located in the Adirondack Park region of New York State.
 Lake Woods is considered  acidic, with a mean outlet pH of 4.7 and mean outlet alkalinity of -10 ^eq/L.
 The lake receives an annual precipitation of 1/2 m and the  mean annual temperature is 5°  C.   The
 vegetation  of the watershed is dominated by Sugar Maple, Beech, Yellow Birch and Red Spruce.  The
 watershed  is underlain  by  granitic bedrock. The surficial geology is comprised by thin but variable
 thickness glacial till. The soils are predominately Becket Spodosols.

 MODEL CALIBRATION

 PROCEDURE

      The calibration of ETD for Lake Woods was achieved by decoupling the hydrologic, sulfate  and
 alkalinity submodels.   Since there is  a coupling between hydrology and  chemistry, the hydrologic
 parameters were calibrated first. When  a good fit between the simulated and the observe outflow was
 achieved, then the hydrologic parameters were fixed  and the sulfate reaction parameters were calibrated.
 Finally,  with fixed hydrologic and sulfate parameters the weathering rates were adjusted so a  good fit
 would  be  established for  alkalinity.  The calibration of each submodel was achieved  by  using  a
 standardized optimization package, (DESIGN (Arora et. al, 1985) and a trial and error procedure.  The
 ranges of parameters for calibration were input to IDESIGN, which was connected with the ETD model.
 At each iteration  (complete two year simulation)  the cost function was evaluated.  The cost function is
 of the form:

                           m       <>
                   COST = S (St  - Ot)2
                           t=1
where:    St  = simulation value of model output variable at time, t;
          Ot  = observed value of model output variable at time, t;
          m  = total number of time steps of observed data.
      The model output variables that participated in the cost function were outflow, alkalinity and sulfate.
IDESIGN perform minimization of the cost function using Fletcher-Reeves algorithm (gradient method).
Bounds known a  priori  on physical quantities  and  parameters were included  as constraints  in  the
optimization process.  However,  in all gradient  methods when the objective function  is of the  least-
squares type, assume that the residuals are homoscedastic, independent and sufficiently small to assume
their normality.  When this is  the case, the maximum likelihood method justifies the use of the least
squares objective functions (Isabel et. al,  1986).  In our case the residuals are not homoscedastic, thus
the results of the optimization by IDESIGN tend  to bias the results towards the extreme values of  the
residuals.  On the other hand,  IDESIGN does bring the parameters within a reasonable range from their
optimal value.  After this point, optimal calibration  was based on trial  and error method. There were three
main guidelines used to establish the optimum value:

      1)    obtain closure of cumulative flow or  mass during the whole period,
      2)    capture the seasonal  variability of the state variables, and
      3)    capture the peaks and valleys of the daily flows and concentrations.

      The calibration of the hydrologic parameters was established in six steps. Model variables symbols
were borrowed from Nikolaidis et. al, 1986.
                                            A. 1-1-3

-------
Figure 9 and 10 present the wet and dry daily sulfate and acidity loading time series. An analysis
of wet and dry deposition is presented in Table 1. Here, one should notice that dry deposition of acidity
is about 32% of the total acidity and dry deposition of sulfate is about 42% of the total sulfate loading
On the average, 12 metric tons of sulfate are deposited on Lake Woods' watershed per year which
roughly corresponds to 4 metric tons of sulfur.

RESULTS

A list of the hydrologic and chemical watershed descriptors that were input to the model are
presented in Table 2. The optimum values of the calibrated parameters after following the procedure
described previously are presented in Table 3. Time-series input for precipitation, evaporation and
temperature for the calibration period (9/78 through 8/80) are shown in Figures 3, 4, and 5. Time series
plots for wet and dry sulfate and acidity loadings for both calibration and verification periods are
presented in Figures 9 and 10, respectively. Comparisons between lake outflow, chloride, sulfate and
alkalinity simulations and field data are presented in Figures 11 through 16.

A complete hydrologic budget for Lake Woods is shown in Table 4. 64% of the total inflow to
the lake is from the soil compartment. Direct precipitation and snow contributions are 12% each
Finally, 4% of the total inflow to the lake is from the unsaturated zone. The I/Q ratio (total precipitation
input to outflow ratio) for Lake Woods is 1.65. As a final comment, one could say that the watershed
of Lake Woods is very flashy, since 75% of the total inflow is due either to direct precipitation or due to
flow through soil.

An alkalinity budget of Lake Woods is presented in Table 5. The majority of the acidity input to
the lake is coming through soil and total, direct to the lake deposition with 69.2 and 18%, respectively
Wet precipitation contributes 11.5% and snowmelt contributes 12.5% of the total acidity input. According
to the simulation results, 63.3% of the neutralization of the total acidity input to the watershed is due to
the in-lake processes (weathering, sulfate reduction and iron oxidation).

A sulfate budget is shown in Table 6. The majority of sulfate input is through the soil and total,
direct to the lake deposition with 66% and 12.9%, respectively. Wet precipitation and snowmelt contribute
about 7.1% each. The net sulfate reduction in the lake sediments is 11.2% of the total input to the lake.

Table 7 presents the MSE evaluation for the calibration period for discharge, cumulative discharge
chloride, alkalinity, and sulfate.

VERIFICATION

The verification simulations of Lake Woods were performed by using one more year of input field
data time series not included in the calibration period. The time-series input for precipitation, evaporation
and temperature for the verification period (9/80-8/81) are shown in Figures 6, 7, and 8. The wet and
dry deposition time-series of sulfate and acidity were part of Figures 9 and 10, respectively. The
simulation results between lake outflow, chloride, sulfate and alkalinity for the verification period and field
data are presented in Figures 17 through 22. The results are comparable with the calibration results
except for the fact that the model underestimates the total cumulative outflow. This is due to the fact
that a 25 mm/day flow was measured for a few days the first week of March 1981 where the temperature
was about 10°C and there was snowcover. Examining the amount of evaporation during the same period,
one can see that it is rather high (-5-7 mm/day). This indicates that instead of melting this water, the
model evaporates it. It is obvious that such a condition is not taken under consideration. Since it was
observed during the verification period, no changes were made in the model formulation. Table 8
presents the MSE evaluation for the verification period.
A. 1-1-5

-------
SENSITIVITY ANALYSIS

The sensitivity of lake outflow to each of the hydrologic parameters was checked by varying the
parameters 10% above and 10% below their optimum values using the calibration period record. The
percent change of the mean square error (MSE) of the outflow is presented in Table 9. The most
sensitive parameters were: the snow parameters BETA, KAPPA and KPAN2, the lake evaporation
correction factor, KPAN5 and the soil lateral and vertical permeability correction coefficients.

The sensitivity of outflow alkalinity to each of the chemical parameters was similarly checked.
Table 10 contains the percent change in MSE. The most sensitive parameters were: the lake
compartment weathering constants KH5 and KO5 and the lake sulfate reduction reaction rate k. The
alkalinity MSE sensitivity results were expected to be low because Lake Woods has a flashy hydrology
so the detention time of the water in the watershed is small and there is not enough time for weathering
to occur.

LONG TERM SIMULATION

In order to perform long term simulations, the existing 3 year record was input repeatedly for 17
times, which extended the simulation period to 51 years. Three simulation runs were made: one for
present loading, one for half loading and one for double loading. In order to establish the half and
double loadings both wet and dry sulfate and alkalinity inputs were halved or doubled, respectively.
Figures 23 and 24 show the projection of lake alkalinity and sulfate for the next 50 years under three
different loading scenarios. The prediction indicates that Lake Woods is a direct response system and
that it would only take a few years for the lake to respond to a decrease in acid deposition loading.
The lake currently has reached steady state with a mean alkalinity of about -10 [ieq/L and a mean
sulfate of 130 A«eq/L. If the loading was to be halved, the mean alkalinity would be 25 ^eq/L and the
mean sulfate concentration 65 neq/L On the other hand, if the loading was to be doubled, the mean
alkalinity would be -40 neq/L and the mean sulfate concentration 260
A.1-1-6

-------
                                         REFERENCES

Arora, J.S., Thanadar, P.B. and Tseng, C.J.  (1985).  User's manual for program IDESIGN, version 3.4
for PRIME computers.  Optimal Design Laboratory, College of Engineering, University of Iowa, Technical
Report No. ODL 85.10.

Isabel, B. and  Villeneuve,  J.P.   (1986).   Importance of the convergence criterion  in the  automatic
calibration of hydrological models. Water Resources Research.  Vol. 22, No. 10,  pp. 1367-1370.

Lin, J.C.  (1985). Modeling aluminum and alkalinity concentrations in watershed receiving acid deposition.
Ph.D. Thesis, University of Iowa, Iowa City, 235 pp.

Nikolaidis, N.P., Rajaram, H.,  Schnoor, J.L and Georgakakos, K.P. (1986).   Enhanced  Trickle-Down
model description.  Civil and Environmental Engineering, University of Iowa, Technical Report No. CEE-
ARRG-86.01.

Schnoor, J.L, Lee, S. J.,  Nikolaidis,  N.P. and Nair, D.R.   (1985).  Lake resources at risk to acidic
deposition in the eastern United States submitted to Water,  Air and Soil Pollution.

Schnoor, J.L., Palmer, W.D. Jr., and Glass, G.E. (1984).  In "Modeling of total acid precipitation impacts";
Schnoor, J.L. (ed.), Butterworth Publishers, Woburn, MA.

Stumm, W., Furrer, G and Kunz, B.   (1983).  The  role of surface  coordination in  precipitation and
dissolution of mineral  phases.  Croat.  Chem. Acta. 56, 4, 593-611.

Stumm, W. and Schnoor, J.L.  (1983).  Naturwissenschaften, 70, 126 (in German).
                                            A. 1-1-7

-------
Table 1.  Lake Woods analysis of wet and dry deposition
Average
                    ACIDITY (eq/ha-yr)
788.7
352.3
Total average Acidity
      Dry Deposition

Total average Sulfate
      Dry Deposition
                               SULFATE (eq/ha-yr)
Period:
9/78-8-79
8/79-B/80
9/80-8/81
Wet
865.6
719.4
781.0
Dry
382.4
342.3
332.3
Wet
820.0
617.0
671.0
Dry
547.6
457.4
487.0
     1141  eq/ha-yr
     30.9% total Deposition

     1200  eq/ha-yr
     41.5% total Deposition
                              702.7
497.3
Sulfate Loading (Average)

         Wet  = 33.73 kg/ha-yr
         Dry  = 23.87 kg/ha-yr
         Total = 57.60 kg/ha-yr


      On the average,  12 metric tons of sulfate are deposited on Lake Woods' watershed (or 4 metric
tons of Sulfur).
                                            A. 1-1-8

-------
Table 2.  List of watershed descriptors used for model calibration
GENERAL WATERSHED CHARACTERISTICS
AQUATIC AREA =
TERRESTRIAL AREA =
CHARACTERISTIC DISTANCE =
DEPTH TO BEDROCK =
PARTIAL PRESSURE OF ATM CO2 =
SURFACE WATER PCO2 IS 1.50 TIMES SATURATED PCO2
0.2300E+06 SQUARE METERS
0.1840E+07 SQUARE METERS
0.2625E+03 METERS
2.3000 METERS
0.0003 ATMOSPHERES
SOIL COMPARTMENT CHARACTERISTICS:
POROSITY =
DEPTH OF SOIL LAYER =
SUM OF BASES =
SOIL DENSITY  =
.2700
0.3684 METERS
106.1000 EQUIVALENTS/KILOGRAM
1009.0000 KILOGRAMS/CU. METER
UNSATURATED ZONE COMPARTMENT CHARACTERISTICS:
POROSITY =
TRANSPIRATION COEFFICIENT =
BARE-GROUND FROST COEFFICIENT =
REDUCTION IN FROST COEFFICIENT =
DAILY THAW RATE =
INITIAL FROST INDEX =
LIMITING FROST INDEX =
THAW COEFFICIENT =
BULK DENSITY =
0.2000
0.0010 INCHES/DAY
0.1000
0.0800
0.1200 DEGREES C
0.0000 DEGREES C
-3.0000 DEGREES C
0.2000
1620.0000 KILOGRAMS/CU. METER
SURFACE WATER BODIES CHARACTERISTICS:
STREAM  BED ELEVATION AT OUTFLOW =  11.9000 METERS

GROUNDWATER COMPARTMENT CHARACTERISTICS:
POROSITY =                         0.2000
                                    A. 1-1-9

-------
Table 3.  List of optimum values of the calibrated parameters
a)  Hydrologic Parameters
   -Snow:
   BETA = 0.6795
   KAPPA =1.1423 in/day/ °C
   KPAN2 =1.0066

   -Evaporation:
   KPAN3 = 1.5471
   KPAN4 = 1.6381

   -Lateral and Vertical Flows:
   KLAT3 = 242.810
   KLAT4 = 14.791
   KPERC3 = 1.3117E-2
   KPERC4 = 1.1741E-2
b) Alkalinity Parameters
   RES = 6.4E-8 m3/eq/day
   KH4 = 9.0E-1 meq/m j'day
   KH5 = 8.5E-2 meq/m j'day
   KH6 = 1.0E-2 meq/m 2/day
   K04 = 1.1E-2 meq/m ^day
   K05 = 7.9E-2 meq/m j'day
   K06 = 1.1 E-3 meq/m 2/day
c)  Sulfate Parameters
   CF = 2.0247
   KP3  = 6.0E-5 eq/kg/eq/m  3
   KP4  = 4.90E-6 eq/kg/eq/m 3
   K =  2.033E-3 1/day
   KP6  = 4.90E-7 eq/kg/eq/m 3
   -Groundwater:

   D1  = 0.5963
   FRAX = 0.4804
   ALF1 = 6.2375E-4
   ALF2 = 4.4043E-1
                                        A.1-1-10

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-------
Table 7.  MSE Evaluation for the calibration period
Variable
Units
              MSE
                      RMSE
Discharge
Cum. Discharge
Chloride
Alkalinity
Sulfate
m3/s
m/yr
peq/L
peq/L
730
730
 77
 77
 77
  0.00225
  0.00133
  3.47
319.9
129.75
 0.0475
 0.0364
 1.86
17.88
11.4
                                           A.1-1-14
-------
Table 8.  MSE evaluation for the verification period
Variable
Units
MSE
                                                                           RMSE
Discharge
Cum. Discharge
Chloride
Alkalinity
Sulfate
m3/s
m/yr
^eq/L
^eq/L
/jeq/L
365
365
45
45
45
0.00468
0.00144
3.57
216.25
111.13
0.0684
0.1202
1.89
14.70
10.54
                                           A.1-1-15
-------
Table 9. Sensitivity analysis of hydrological parameters for outflow
    PERCENT MSE  OF OUTFLOW  CHANGES
Parameter
x + 10% x
                                        x - 10% x
BETA
KAPPA
KPAN2
KPAN3
KPAN5
KLAT3
KLAT4
KPERC3
KPERC4
D1
FRAX
ALF1
ALF2
-0.466
2.236
6.584
0.435
0.745
-2.050
-0.186
0.683
0.124
0.093
0.217
0.000
-0.093
-6.335
-1.180
-1.863
-0.497
-0.839
-1.739
0.124
-0.745
-0.186
-0.124
0.062
0.000
0.062
* %MSE Change =  , MSE
                       NEW
                       "MSE
                           •opt
                                       A.1-1-16
-------
Table 10. Sensitivity analysis of chemical parameters for alkalinity

           PERCENT MSE OF OUTFLOW CHANGES
Parameter
x + 10% x
* %MSE Change =  , MSE
                       NEW
                       ~MSE
                                        x - 10% x
CF
RE3
KH4
KH5
KH6
K04
K05
K06
KP3
KP4
K
KP6
0.310
-0.035
-0.070
2.178
0.000
0.000
1.660
0.000
0.207
0.000
-1.660
0.000
-0.207
0.0000
0.070
-1.522
0.000
0.000
-0.830
0.000
-0.207
0.000
2.144
0.000
                           •opt
                                       A.1-1-17
-------
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                                      High.
                                        	^- Low
            8
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-------
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                Depositior
                Input
                Alkalinit;
                Concentr.
                (ANC)
                                                 100
                                                       —I—
                                                        1000
                                                100
                                                                  A.   Input
                •*•
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                   B.  Not Sensitive
                       (no response)
                                                                0000
                 Allealinit
                 Concentr.
                 (ANC)
                 Alk&lini
                 Concentr
                 (ANC)
                    C.   Moderately
                        Sensitive
                    D.   Extremely
                         Sensitive
100
                                                        1000   10000
                                        Time  in years
Figure 2.  Direct and delayed responses of surface waters to a step-function increase in acid deposition
(showing watershed sensitivity classes).
                                            A.1-1-19
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                                   A. 1-1-41
-------
 Technical Report No. CEE-ARRG-86.04
Modeling Short and Long Term Impacts
    of Acid Precipitation Using the
    Enhanced Trickle-Down Model:
       Lake Panther Case Study
                    by:
            Nikolaos P. Nikolaidis
             Jerald L. Schnoor
         Konstantine P. Georgakakos
     Civil and Environmental Engineering
           The University of Iowa
           Iowa City, Iowa  52242
              December 1986
-------
           MODELING SHORT AND LONG TERM IMPACTS OF ACID PRECIPITATION
        USING THE ENHANCED TRICKLE-DOWN MODEL:  LAKE PANTHER CASE STUDY


INTRODUCTION

        The Enhanced Trickle-Down (ETD) model (Nikolaidis et al. 1986a) is being used to assess the
impacts of acid precipitation of Lake Panther's watershed.  Lake Panther is a 1.24 km forested watershed
located in the Adirondack Park region of New York State.   Lake Panther is considered neutral, with a
typical outlet pH of 7.  The  lake receives an annual precipitation  of 1.16 m and the mean annual air
temperature is 5°C.  The vegetation of the watershed is dominated by Sugar Maple, Beech,  Yellow  Birch
and  Red Spruce.  The watershed is underlain by hornblende granitic gneiss  bedrock.  The  depth to
bedrock is 24.5 m. The basin is covered by thick glacial till.  The soils are predominantly spodosols that
have been developed on  the till and average less than 1  m  in  depth.

        Lake Panther is located 10 km south of Lake Woods (Nikolaidis et al. 1986b).  Both watersheds
receive the same quality loading, and on the average both have similar vegetation, soils and  bedrock type
(with the exception of the depth of the till - Panther has 10 times deeper till than Woods), but they have
different responses.   This  was the  topic  of research   that  was  undertaken  by  the Integrated
Lake-Watershed Acidification Study (ILWAS).  The ILWAS project investigated why three lakes (Woods
and  Panther Lakes were included) that receive similar input  of  acidity respond  differently in neutralizing
this acidity.  The thesis of the ILWAS project as it was presented by Peters et al.  1985, was that water
moves through the acidic upper soil horizons rather than the  lower ones and that's why it is more acidic.

        The scope of this research is to  calibrate,  verify,  and perform a sensitivity analysis on  the
parameters and examine the  long term response to various acid loadings of Lake Panther, using the ETD
model.


MODEL CALIBRATION

PROCEDURE

        The calibration of ETD for Lake Panther was obtained by decoupling the hydrologic, sulfate and
alkalinity submodels,  similar to the calibration of Lake Woods (Nikolaidis etal. 1986b). However, in this
case it was not necessary to use the standardized optimization  package, IDESIGN (Arora et al. 1985),
since adequate initial estimates for evaporation and snowmelt  had already been  established  after the
calibration of Lake Woods. Keeping the same  guideline for establishing the optimum value  of discharge,
by trial and error the lateral and vertical percolation flow parameters were adjusted. These adjustments
helped in capturing the seasonal variability as well as the peaks and valleys of the daily discharge. In
order to obtain closure of the cumulative flow during the calibration period, the evaporation and snowmelt
parameters were adjusted as well.  Continuous refinements  of the hydrologic parameters  were applied
until  agreement between  the simulation  and field data was achieved.  Trial and  error adjustments to
sulfate and alkalinity parameters were made starting with the initial values of Lake Woods in  a similar
manner.


INPUT DATA

        The time series data  of surface precipitation, evaporation and temperature for the calibration and
verification  periods are presented in Figures  1, 2  and  3.   Precipitation and temperature data were
collected at  the LMI  station of the ILWAS project.  Evaporation measurements were obtained  by using
van Bavel's combination method (Nikolaidis 1986c).

        Figures 4 and 5 present the wet and dry daily sulfate and acidity loading time series. An analysis
of wet and  dry deposition is  presented in Table 7.   Approximately 37% of the total acidity is  dry
                                           A. 1-1-42
-------
deposition and 47% of the total sulfate loading is dry sulfate.  The total acidity loading is 1205 eg/ha/yr
and the total sulfate loading is 1343 eg/ha/yr.

RESULTS

       Table 2 presents a partial list of the hydrologic and chemical watershed descriptors that were
input to  the model.   The optimum values of the  calibrated  parameters are  presented  in Table  3.
Comparison  between  lake discharge, chloride, sulfate and alkalinity simulations  and field data are
presented in Figures 6 through 11.   Figures 12 through 15 contain the percent saturation, alkalinity, pH
and sulfate plots of the terrestrial compartments.

       Table 4 contains a complete hydrologic budget for Lake Panther.  Direct precipitation accounts
for 14.5% of the total inflow to the lake.  The majority of  the water is coming through the  unsaturated
zone (40.5%).  Finally, 23% is due to snowmelt and 22% is from the soil compartment.

       An alkalinity budget for Lake panther is presented in Table 5. The majority of the acidity input
to the lake is coming  through soil and unsaturated zone with 23 and 42% respectively. Wet deposition
contributes 13% and dry 9.5% of the total acidity input.  According to the simulation results, 58% of the
neutralization of the total acidity input to the watershed occurs in the terrestrial compartments and the
rest is due to the in-lake processes (weathering and  sulfate reduction).

       A sulfate budget is shown in Table 6. The majority of sulfate input to the lake is coming through
the soil and  unsaturated zone with  25 and 48.2% respectively.   Wet and dry deposition contribute 9%
each.  The net sulfate reduction  in the lake sediments is 9% of the total sulfate deposition.  About 25%
of the total sulfate deposition is  sorbed or stored  in the terrestrial compartments.

       Table 7 presents the MSE evaluation for the calibration period for discharge, cumulative discharge,
chloride,  alkalinity and sulfate.


VERIFICATION

       The verification simulations of Lake Panther were performed by using one more year of input field
data time series not included in the calibration period.  The  simulation results  between lake outflow,
chloride,  sulfate and  alkalinity for the verification period  (9/80-8/81) and field  data  are presented  in
Figures 12 through 17.  The results are comparable with the calibration ones.  Table 8 presents the MSE
evaluation for the verification  period.


SENSITIVITY ANALYSIS

       The sensitivity of lake outflow to each of the hydrologic parameters was checked by varying the
parameters 10% above  and 10%  below their optimum values.  The percent change of the mean square
error (MSE)  of the outflow is presented in Table 9.  The most sensitive parameters were:  the  snow
parameter BETA, and  the soil lateral and vertical permeability correction coefficients.

       The sensitivity of outflow alkalinity to each of the chemical parameters was similarly checked.
Table  10  contains  the percent  change in MSE.   The  most sensitive parameters  were:   the  lake
compartment weathering constants KH5 and K05, the lake  sulfate reduction reaction rate K, and the
unsaturated zone ligand attach rate, K04.

LONG TERM SIMULATION

       In order to perform long  term simulations,  the existing 3 year record was  input repeatedly for 17
times,  which  extended the simulation period to 51 years.  Three simulation  runs were made: one for
present loading, one for half loading and one for double loading.  In order to  establish the half and
                                            A.1-1-43
-------
double loadings,  both wet and  dry  sulfate and alkalinity inputs were halved  or doubled,  respectively.
Figure 18 and  19 show the projection of lake alkalinity and sulfate for the next 50 years under three
different loading scenarios.  The prediction indicates that Lake Panther is a direct response system and
that it would only take a few years for the lake to respond to a decrease in acid deposition loading. The
lake  currently has reached steady state with a mean alkalinity of about 170 neq/L and a mean sulfate
of 140 fj,eq/L.   If the loading were to be  halved, the mean alkalinity would be 210 A«eq/L and the mean
sulfate concentration 75 Ateq/L On the other hand,  if the loading were to be doubled, the mean alkalinity
would be 60 A«eq/L and the mean sulfate concentration 310 peq/L.

DISCUSSION

       An issue that  has to be addressed is the importance of the in-lake processes with respect to the
neutralization of acidic deposition.  One  could raise the question why the selection of reducing sulfate
in the lake  sediments  instead of adsorpting it in the  terrestrial compartments was made, or,  by the same
token,  why such an amount of alkalinity had to be  neutralized in the lake instead of in  the soil and
unsaturated zone.

       In order to answer these questions, the calibration exercise of the terrestrial compartments has
to be validated.  Figures 20 through 23 present the simulation results of percent saturation, alkalinity, pH
and  sulfate of  the terrestrial compartments.   Peters et al.  1985, evaluated the sulfate and alkalinity
concentration for summer  and winter periods using tension  lysimeter for the  soil compartment.  They
found that  sulfate concentrations range between 142 to 180 /zeq/L (the low values  occurred during the
winter  periods)  and alkalinity from  -77  to -90 peq/L   The simulation results give a mean  sulfate
concentration of 130 /aeq/L and  a mean alkalinity of -60 fieg/L. Comparing the simulated and observed
values, one could notice that the simulated mean sulfate concentration is less than the observed, which
indicates that adequate sulfate  is being adsorbed in the calibration exercise.  Similarly, simulated mean
alkalinity is higher than the measured values, which  indicates that sufficient weathering has been applied
in the calibration of soil chemistry.

     Since the calibration  of the terrestrial compartments is  in accordance with  the limited  field data,
it is obvious that the validity of the importance of the in-lake processes can be justified.
                                             A. 1-1-44
-------
                                   REFERENCES
Arora, J. S., Thanedar, P. B. and Tseng, C. H. (1985). Users manual for program IDESIGN,
version  3.4 for  PRIME computers. Optimal  Design Laboratory,  College  of  Engineering,
University of Iowa, Technical Report No. ODL  85.10.

Cronan, C. S. (1985).  Biochemical influence of vegetation and soils in the ILWAS watersheds.
Water Air and Soil Pollution, Nol. 26, No. 4.

Nikolaidis,  N. P., Rajaram,  H.,  Schnoor, J. L and  Georgakakos,  K.  P.  (1986). Enhanced
Trickle-Down model  description. Civil  and Environmental Engineering, University of Iowa,
Technical Report No. CEE-ARRG-86.01.

Nikolaidis, N. P., Georgakakos,  K. P., and Schnoor, J. L, (1986b).  Modeling short and long
term impacts of acid precipitation using the Enhanced Trickle-Down model: Lake Woods case
study.  Civil and Environmental Engineering,  University of Iowa, Technical Report  No.
CEE-ARRG-86.03.

Nikolaidis,  N. P. (1986c). Estimation of daily potential evaporation.  Civil and Environmental
Engineering, University of Iowa,  Technical Report No. CEE-ARRG-86.02.

Peters, N. E., and Murdoch, P. S. (1985). Hydrologic  comparison of an acidic-lake basin with
a neutral-lake basin in the west-central  Adirondack Mountains, New York. Water, Air and Soil
Pollution, Vol. 26, No. 4.
                                            A. 1-1-45
-------
Table 1.  Lake Panther analysis of wet and dry deposition.
              ACIDITY (eg/ha-yr)
SULFATE  (eq/ha-yr)
Period:
9/78-8/79
9/79-8/80
9/80-8/81
Wet
807.4
702.6
746.0
Dry
446.0
476.3
437.7
Wet
791.4
697.6
647.0
Dry
666.3
648.7
579.0
Total average  Acidity =  1205.3  eq/ha .yr
      Dry  Deposition =  37.6% total  Deposition

Total average  Sulfate =  1343.3  eg/ha .yr
      Dry  Deposition =  47.O%  total  Deposition

Sulfate Loading  (Average)
               Wet = 36.1  kg/ha/yr
               Dry = 30.3  kg/ha/yr
             Total = 66.4 kg/ha/yr
    On the average, 8.25 metric tons of sulfate are deposited on Lake Panther watershed (or
2.7 metric tons of sulfur)
                                            A. 1-1-46
-------
Table 2.  List of watershed descriptors used for model calibration.
GENERAL WATERSHED CHARACTERISTICS:
AQUATIC AREA =
TERRESTRIAL AREA =
CHARACTERISTIC DISTANCE =
DEPTH TO BEDROCK  =
PARTIAL PRESSURE OF ATM C02
0.1800E+06  SQUARE METERS
0.1060E+07  SQUARE METERS
0.65003+03  METERS
24.5000 METERS
0.0003 ATMOSPHERES
SURFACE WATER PCO2 IS 1.50 TIMES SATURATED PCO2
SOIL COMPARTMENT CHARACTERISTICS:
POROSITY =
DEPTH OF SOIL LAYER =
SUM OF BASES =
SOIL DENSITY  =
0.2700
0.6300 METERS
113.1000  EQUIVALENTS/KILOGRAM
1283.0000  KILOGRAMS/CU. METER
UNSATURATED ZONE COMPARTMENT CHARACTERISTICS:
POROSITY =
TRANSPIRATION COEFFICIENT =
BARE-GROUND FROST
   COEFFICIENT =
REDUCTION IN FROST
   COEFFICIENT =
DAILY THAW RATE =
INITIAL FROST INDEX =
LIMITING FROST INDEX =
THAW COEFFICIENT =
BULK DENSITY =
0.2000
0.0010 INCHES/DAY

0.1000

0.0800
0.1200 DEGREES C
0.0000 DEGREES C
-3.0000 DEGREES C
0.2000
1549.0000 KILOGRAMS/CU. METER
SURFACE WATER BODIES CHARACTERISTICS:

STREAM BED ELEVATION AT OUTFLOW = 23.5530 METERS


GROUNDWATER COMPARTMENT CHARACTERISTICS:

POROSITY =                    0.2000
                                    A. 1-1-47
-------
Table 3.  List of optimum values of the calibrated parameters.
a)  Hydrologic Parameters
       -Snow:
BETA =0.8095
KAPPA  = 1.1423
KPAN2 = 0.1766

-Evaporation:
KPAN3  = 1.5471
KPAN5  = 1.5381
b) Alkalinity Parameters
RES = 8.1  E-8 m3/eq/day
KH4 = 2.0E-2 meq/m ^day
KH5 = 6.5E-1 meq/m ^day
KH6 = 1.0E-2 meq/m 2/day
K04 = 1.IE-3  meq/m 2/day
K05 = 3.9E-1  meq/m 2/day
K06 = 1.IE-3  meq/m 2/day
-Lateral and Vertical Flows:
KLAT3 = 4.5281
KLAT4 = 4.9791
KPERC3 = 4.3117E-3
KPERC4 = 4.1741E-3
-Groundwater
Dl = 0.5963
FRAX = 0.4804
ALFI = 6.2375E - 4
ALF2 = 3.4045E  -  2
c) Sulfate  Parameters
CF = 2.0247
KP3 = 4.3E-5
KP4 = 8.5E-6
K = 2.033E-3
KP6 = 4.7E-7
eq/kg/eq/m 3
eq/kg/eq/m 3
1/day
eq/kg/eg/m 3
                                         A. 1-1-48
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Table 7.  MSE Evaluation for the Calibration Period.
Variable
Units
MSE
RMSE
Discharge
Cum. Discharge
Chloride
Alkalinity
Sulfate
m7s
m/yr
jueq/L
/^eq/L
jueq/L
730
730
102
102
102
0.0012
0.003
26.3
6828.0
128
0.034
0.056
5.13
82.6
11.3
                                           A.1-1-52
-------
Table 8.  MSE Evaluation for the Verification Period.
Variable
Units
              MSE
                RMSE
Discharge
Cum. Discharge
Chloride
Alkalinity
Sulfate
m3/s
m/yr
/jeq/L
365
365
 52
 52
 52
   0.0014
   0.0026
   5.85
4619.2
 136.2
 0.038
 0.052
 2.42
70.0
11.7
                                           A. 1-1-53
-------
Table 9. Sensitivity Analysis of Hydrological Parameters for Outflow.



       PERCENT  MSE OF OUTFLOW CHANGE*
Parameter
X + 10% x x - 10% x
BETA
KAPPA
KPAN2
KPAN3
KPAN5
KLAT3
KLAT4
KPERC3
KPERC4
D1
FRAX
ALF1
ALF2
-0.8
13.5
-0.2
-0.4
-0.1
-10.1
-10.1
-0.3
-0.1
0.0
0.0
0.0
0.0
-1.7
11.3
0.0
0.3
0.0
-0.4
-0.4
-7.4
-7.5
0.0
0.0
0.0
0.0
* %MSE Change = (  MSENEW
                       MSE,
                           •opt
                                       A. 1-1-54
-------
Table 10.  Sensitivity Analysis of Chemical Parameters for Alkalinity

                  PERCENT MSE OF ALKALINITY CHANGE*
Parameter
   + 10% if
 x - 10% x
   CF
   RES
   KH4
   KH5
   KH6
   K04
   K05
   K06
   KP3
   KP4
   K
   KP6
 0.4
 0.2
 0.0
12.8
 0.0
 0.0
 9.0
 0.0
-0.2
-0.1
-1.0
 0.0
 0.9
-0.2
 0.0
-1.6
 0.0
12.8
-4.9
 0.0
 0.2
 0.1
 1.0
 0.0
  %MSE Change =  (  MSENEW  - MSEopt)
                         MSE,
                             'opt
                                          A. 1-1-55
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                                                                         -------
                                                           Ill
                                                              Ul
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                                                     4)  
-------
       Technical Report No. CEE-ARRG-86.05
       Modeling Short and Long Term Impacts
of Acid Precipitation Using the Enhanced Trickle-Down Model
              Clear Pond  Case Study
                       by:
               Nikolaos P. Nikolaidis
                Jerald L. Schnoor
            Konstantine P. Georgakakos
        Civil and Environmental Engineering
               The University of Iowa
               Iowa City, Iowa 52242
                 December 1986
-------
           MODELING SHORT AND LONG TERM IMPACTS OF ACID PRECIPITATION
         USING THE  ENHANCED TRICKLE-DOWN MODEL: CLEAR POND CASE STUDY
INTRODUCTION

      The Enhanced Trickle-Down (ETD) model (Nikolaidis et al.  1986a)  was used to calibrate a two
year record of Clear Pond watershed.   Clear Pond  is 5.21 km2 forested watershed  located  in the
Adirondack Park region of New York State. The pond is considered neutral with a mean ANC of 100±19
^eg/L It  receives on the average of 1  m of annual precipitation. The vegetation of the watershed is
dominated by Northern white  Cedar,  Paper  and Yellow Birch, Red Spruce, Sugar maple,  Beech, and
Balsam Fir. The watershed is underlain by metanorthosite and anorthositic gneiss bedrock. The depth
to bedrock is 6.3 m. The predominant soil series is Becket fine sandy loam.  Clear Pond is located about
60 miles  northeast of Lake Woods and Panther.

MODEL CALIBRATION

PROCEDURE

      The calibration of ETD for Lake Panther was obtained by decoupling the hydrologic, sulfate and
alkalinity  submodels, similar to the calibration  of Lake  Panther  (Nikolaidis et al. 1986b).  Keeping the
same guideline for establishing the optimum value of discharge, by trial and error the lateral  and vertical
percolation flow parameters were adjusted. These adjustments helped in capturing the seasonal variability
as well as the peaks and valleys of the daily discharge. In order to  obtain closure of the cumulative flow
during the calibration period, the evaporation and snowmelt parameters were adjusted as well. Continuous
refinements of the hydrologic parameters were  applied until agreement  between the simulation and field
data was achieved. Trial and  error adjustments to sulfate and alkalinity parameters were made starting
with the initial values of Lake Panther in a similar manner.

INPUT DATA

      The time series data of surface precipitation,  evaporation and temperature for the calibration and
verification periods are  presented in  Figures  1, 2 and 3.   Precipitation  and temperature  data were
collected as part of the RILWAS project.  Evaporation measurements were obtained  by using van Bavel's
combination method (Nikolaidis 1986c).

      Figures 4 and 5 present the wet and dry daily sulfate and acidity loading time series.  An analysis
of wet and dry  deposition is  presented in Table 1.   Approximately 29.5% of the total acidity  is dry
deposition  and 40% of the total sulfate loading is dry sulfate.  The total acidity loading is 849 eg/ha/yr
and the total sulfate loading is 790.2  eg/ha/yr.

RESULTS

      Table 2 presents a partial  list  of  the hydrologic and chemical  watershed descriptors that were
input to  the model. The optimum values of the calibrated parameters are  presented  in Table 3.
Comparison between lake discharge and field data are presented in figures 6, 7 and  8.  Figure 6 contains
the cumulative discharge of simulation and field data only for the 443 days of measured discharge. The
missing data days were eliminated from the simulation  results in order to  construct this plot. Figure 8
shows the daily  error between simulation and  field data.

     Figures  9  through  11 present  the chloride,  sulfate  and alkalinity simulations  and  field data
comparison.   In order to obtain these simulations the  sulfate loading was increased 1.5 times, and the
alkalinity and chloride loading was increased 1.3 times the loading of the field  data presented  in Table
1 and Figures 4 and 5.  The  rationale of increasing the measured loading of Clear Pond  is  partially
presented in Figure 12.  Figure 12 presents a simulation of sulfate  under actual measured data.   In this
simulation, there is a definite trend of sulfate decline in the  lake.  When a calibration of the terrestrial
                                            A.1-1-79
-------
compartments was attempted, it was found that the sorption partitioning coefficients had to be at least
2 orders of magnitude less than the optimum coefficients of Woods and Panther lakes and still there was
a definite trend of decline. This prompted the fact that a definite increase in loading is necessary.

      Table 4  contains a complete hydrologic budget for Lake Panther.  Direct precipitation accounts
for 11.2% of the total inflow to the lake.  The majority of the water is coming through the unsaturated
zone (36.0%).   Finally,  26.1%  is due to snowmelt and 26.7% is from the soil  compartment.

      An  alkalinity budget for Lake Panther is presented in Table 5.  The majority of the acidity input
to the lake is  coming through  soil  and unsaturated zone, with 41.6 and 22.6%, respectively.   Wet
deposition contributes  13.8% and dry 7.8% of the total acidity  input.

      A sulfate budget is  shown in Table 6.  The majority of sulfate input to the lake is coming through
the soil and unsaturated zone with 40 and 46.6%,  respectively.  Wet and dry deposition  contribute 5%
each.  The net sulfate reduction in the lake sediments is 13.6% of the total sulfate deposition.

      Table 7 presents the  MSB evaluation for the calibration period for discharge, chloride, alkalinity
and sulfate.
                                            A. 1-1-80
-------
                                        REFERENCES

Nikolaidis, N. P., Rajaram, H., Schnoor, J. L. and Georgakakos, K. P.  (1986a) Enhanced Trickle-Down
model  description. Civil  and Environmental Engineering, University of Iowa, Technical Report No.
CEE-ARRG-86.01.

Nikolaidis, N. P.,  Georgakakos, K. P., and Schnoor, J. L. (1986b) . Modeling Short and long term impacts
of acid  precipitation  using the  Enhanced Trickle- Down model: Lake  Panther case  study. Civil and
Environmental Engineering, University of Iowa, Technical Report No. CEE-ARRG-86.04.

Nikolaidis, N. P.  (1986c). Estimation of daily  potential evaporation. Civil and Environmental Engineering,
University of Iowa, Technical  Report No. CEE- ARRG-86.02.
                                           A. 1-1-81
-------
Table 1. Clear Pond analysis of wet and dry deposition.
Period
ACIDITY (eg/ha-yr)
Wet        Dry
SULFATE (eg/ha-yr)
Wet         Dry
8/82-7/83
7/83-8/84
Average
507.0
690.0
598.5
259.8
241.3
250.5
447.2
503.4
475.3
320.3
309.5
314.9
Total average Acidity = 849 eg/ha-yr
      Dry deposition = 29.5% of total deposition

Total average Sylfate = 790.2 eg/ha-yr
      Dry deposition = 40.0% of total deposition

Sulfate Loading (Average)

     Wet  = 22.82 kg/ha-yr
     Dry  =  15.12 kg/ha-yr
     Total  = 37.94 kg/ha-yr

     On the average, 19.4 metric tons of sulfate are deposited on Clear Pond watershed (or 6.5 metric
tons of sulfur).
                                             A. 1-1-82
-------
Table 2.  List of watershed desciptors used for model calibration.
GENERAL WATERSHED CHARACTERISTICS:

AQUATIC AREA =
TERRESTRIAL AREA =
CHARACTERISTIC DISTANCE =
DEPTH TO BEDROCK =
PARTIAL PRESSURE OF ATM CO2 =
SURFACE WATER PCO2 IS 1.50 TIMES SATURATED PCO2
0.7300E+06  SQUARE METERS
0.4480E+07  SQUARE METERS
0.1100E+4   METERS
6.3000 METERS
0.0003 ATMOSPHERES
SOIL COMPARTMENT CHARACTERISTICS:

POROSITY =
DEPTH OF SOIL LAYER =
SUM OF BASES =
SOIL DENSITY  =
0.2700
0.5500 METERS
33.8000 EQUIVALENTS/KILOGRAM
1140.0000 KILOGRAMS/CU. METER
UNSATURATED ZONE COMPARTMENT CHARACTERISTICS:
POROSITY =
TRANSPORTATION COEFFICIENT =
BARE=GROUND FROST COEFFICIENT =
REDUCTION IN FROST COEFFICIENT =
DAILY THAW RATE =
INITIAL FROST INDEX =
LIMITING FROST INDEX =
THAW COEFFICIENT =
BULK DENSITY =

SURFACE WATER BODIES CHARACTERISTICS:
0.2000
0.0010 INCHES/DAY
0.1000
0.0800
0.1200 DEGREES C
0.0000 DEGREES C
-3.0000 DEGREES C
0.2000
1590.0000 KILOGRAMS/CU. METER
STREAM BED ELEVATION AT OUTFLOW =      18.0000 METERS

GROUNDWATER COMPARTMENT CHARACTERISTICS:
POROSITY =
                                       0.2000
                                    A. 1-1-83
-------
Table 3. List of optimum values of the calibrated parameters.
a)   Hydrologic Parameters
     -Snow:
BETA = 0.8095
KAPPA = 1.1423
KPAN2 = 0.1766

-Evaporation
KPAN3= 1.3171
KPAN5= 1.0381

-Lateral and Vertical Flows:

KLAT3 = 54.781
KLAT4 = 55.791
KPERC3 = 2.3117E-2
b)    Alkalinity Parameters
RES = 5.5E-7 m3/eq/day
KH4 = 9.0E-2 meq/m 2/day
KH5 = 3.1E-1 meq/m 2/day
KH6 = 2.0E-3 meq/m 2/day
K04 = 4.1E-2 meq/m 2/day
K05 = 7.0E-2 meq/m 2/day
K06 = 0.1 E-3 meq/m 2/day
c) Sulfate Parameters

CF = 2.0247
KP3 = 5.0E-4 eg/kg/eq/rr?
KP4 = 4.0E-4 eg/kg/eq/rrt3
K= 1.033E-31/day
KP6 = 9.0E-6 eg/kg/eq/m3
-Groundwater
D1 = 0.6963
FRAX = 0.6804
ALF1 = 6.2375E-4
ALF2 = 3.4045E-2
                                         A. 1-1-84
-------
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                                                  A. 1-1-87
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Table 7.  MSE Evaluation for the Calibration Period.
Variable
 Units
              MSE
             RMSE
Discharge
Chloride
Alkalinity
Sulfate
m3/s
fj.eq/L
443
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 24
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   0.026
   5.61
8294.0
  79.7
 0.16
 2.37
18.6
 8.9
                                                   A. 1-1-88
-------
                                CLEAR POND
                           PERIOD : 7/82  - 7/84
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     40-
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                                         800
Figure 1. Precipitation data.
                                    A. 1-1-89
-------
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                           CLEAR POND

                       PERIOD : 7/82 - 7/84
                                                     700
800
Figure 2. Evaporation data.
                           A.1-1-90
-------
                          CLEAR POND
                      PERIOD : 7/82 - 7/84
  -20
           100
                                                           800
3. Temperature data.
                          A. 1-1-91
-------
                          CLEAR POND
            TIME SERIES OF SULFATE DEPOSITION
            SIMULATION PERIOD: 7/27/82 - 7/24/84
                                     500
600
700
            800
                              DAYS
                                                Legend
                                               D FIELD DATA
Figure 4. Wet and dry sulfate loading.
                           A. 1-1-92
-------
                         CLEAR POND
             TIME SERIES OF ACID DEPOSITION
            SIMULATION PERIOD: 7/27/82 - 7/24/84
                                                      800
                                                 Legend
                                                m WET
                                                D DRY
Figure 5. Wet and dry acidity loading.
                         A. 1-1-93
-------
                         CLEAR POND
 COMPARISON OF SIMULATED VERSUS FIELD OUTFLOWS
          CALIBRATION PERIOD  : 7/27/82 - 7/24/84
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                             400
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                             DAYS
                                                Legend
                                               D FIELD DATA
Figure 6. Calibration results of cumulative outflow.
                             A. 1-1-94
-------
                        CLEAR POND
 COMPARISON OF SIMULATED VERSUS FIELD OUTFLOWS
         CALIBRATION PERIOD : 7/27/82 - 7/24/84
2
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   10-
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          100
200
300
 400
DAYS
500
600
700
800
                                               Legend
                                                FIELD DATA
Figure 7.  Calibration results of daily outflow.
                            A. 1-1-95
-------
                           CLEAR POND
    COMPARISON OF SIMULATED VERSUS FIELD OUTFLOWS

            CALIBRATION PERIOD : 7/27/82 - 7/24/84
   £
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—i—
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                                              350
—I—
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                                        450
Figure 8.  Calibration results of daily outflow error.
                            A. 1-1-96
-------
                          CLEAR POND
     COMPARISON OF SIMULATION AND FIELD CHLORIDE
            CALIBRATION PERIOD: 7/27/82 - 7/24/84
     200
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                                                Legend
                                                800
                                               D FIELD DATA
Figure 9.  Calibration results of lake chloride
                            A. 1-1-97
-------
                          CLEAR POND
      COMPARISON OF SIMULATION AND FIELD SULFATE
            CALIBRATION PERIOD: 7/27/82 -7/24/84
     300
            100
                                                       800
                                               Legend
                                                FIELD DATA
Figure 10. Calibration results of lake sulfate.
                           A. 1-1-98
-------
                            CLEAR POND

    COMPARISON OF SIMULATION AND FIELD ALKALINITY

             CALIBRATION PERIOD: 7/27/82 - 7/24/84
  cr
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     300
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     200
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100     200     300
                                 400

                                DAYS
Figure 11.  Calibration results of lake alkalinity.
                           500     600     700     800
                                                   Legend
                                                  D FIELD DATA
                              A. 1-1-99
-------
                             CLEAR POND

       COMPARISON OF SIMULATION AND FIELD SULFATE

              CALIBRATION PERIOD: 7/27/82 -7/24/84
      300
      250
    cr
    
-------
                APPENDIX A. 1-2





INTEGRATED LAKE-WATERSHED ACIDIFICATION (ILWAS)
-------
           Direct/Delayed Response Project
ILWAS MODEL CALIBRATION AND SENSITIVITY ANALYSIS
 FOR WOODS LAKE, PANTHER LAKE AND CLEAR POND
                  Ronald K. Munson
                  Steven A. Gherini
                  Margaret M. Lang
                  Robert M. Newton
            Smith College/Tetra Tech, Inc.
         3746 Mt. Diablo Boulevard, Suite 300
              Lafayette, California  94549
                   (415) 283-3771
-------
INTRODUCTION

The  ultimate  goal  of the  Direct/Delayed   Response  Project  is to  provide  Congress  with  a
scientifically-based estimate of the degree to which surface waters in the northeast and southeast regions
(as defined by U.S. EPA) will be affected by acidic deposition over the next fifty years. This estimate will
be based, in part, on the results obtained  from dynamic computer simulation  models. The models will
be applied to several lake and stream watershed systems in both  regions. Classification rules will then
be developed based  on the model simulations and used to predict surface water behavior for the entire
regions. Clearly, the  application of these models to many watersheds dictates that the data record for
calibration will, in  most cases, be very short. Therefore, before the models can be applied regionally, their
ability to simulate the behavior of single lakes or streams having relatively long data records must be
demonstrated. In  addition,  the  sensitivity  of the models  to various model parameters must also be
demonstrated.

The ability of the Integrated Lake-Watershed Acidification Study (ILWAS)  model to simulate northeast
lakes  with relatively  long data records  has been demonstrated with the calibration  of Woods Lake,
Panther Lake, and Clear Pond. These calibrations were performed using  data generated  as a  part of
the field components of ILWAS and RILWAS (the regionalization of the ILWAS project). The results of
the calibrations will be presented below as will  a short description of the ILWAS model, a discussion of
the physical  and chemical characteristics of the three watersheds, and the results of the model sensitivity
analyses.


ILWAS MODEL OVERVIEW

ILWAS Model Description

The ILWAS model  conceptualization, development, application, and theory have been described elsewhere
(Chen, Gherini, and Goldstein,  1979; Goldstein,  Gherini, and Chen, 1984; Gherini, Mok, Hudson,  Davis,
Chen, and Goldstein, 1985; Davis, Whipple, Gherini, Chen, Goldstein, Chan, and Munson, 1986). What
follows below is a brief summary of the  model.

The ILWAS  model was developed to simulate the chemical response of surface  waters in forested
ecosystems  to changes in deposition acidity. To do this the model  simulates the maJor physical and
biogeochemical processes occurring in the tributary watersheds as well as in  the surface waters. This
is achieved in the model by routing incident precipitation through the forest canopy,  soil strata, streams
and lakes  (See Figure 1). Concurrently the model simulates the major processes which add  acid or base
to the throughflowing water.

The ILWAS  project  demonstrated  that  for drainage basin systems, surface  water quality is  largely
determined by where precipitation flows en route to becoming surface water (Goldstein,  Gherini, and
Chen, 1984; Peters,  1985; Schofield, 1985). To calculate the distribution of water between flow paths
the ILWAS model  uses various forms of the continuity equation, Darcy's law for flow in unsaturated and
saturated permeable  media, and Manning's  equation, Muskingum routing, and stage-flow relationships for
surface waters. Snow and ice formation and melting are simulated by the model using multiple-term heat
budgets.

In simulating the  acid-base chemistry of the water, the ILWAS model calculates the concentrations of
16 chemical  constituents (See Table 1) in throughfall, surface runoff, soil solution, and in surface waters.
This is achieved using mass-balance techniques and both kinetic and equilibrium formulations to represent
the major  processes  which add acid or base to the water (Table 2). Concentrations of chemical species
which are not readily mass balanced are computed from  the concentrations of other constituents.  For
example,  the  model calculates H+-ion concentration from alkalinity, total  inorganic carbon,  total
monomeric aluminum, and total organic  acid analogue.

The model is applied by dividing a lake or stream-watershed system into catchments, each of which
                                            A. 1-2-2
-------
discharge water directly to either a stream or lake. Each catchment is divided vertically into compartments
with homogeneous characteristics: forest canopy, vegetation, and separate soil layers. Streams are divided
into longitudinal segments and  lakes are divided vertically into well-mixed layers.

Input  to the model includes time invariant parameters which characterize each compartment  (e.g., for
a soil layer - bulk density, organic and mineral composition, cation exchange capacity, etc.) and  the
initial  solution and solid phase concentrations (initial conditions). Time variant model input consists of
meteorological data (e.g.,  daily air temperature and precipitation amounts)  and chemical data  (monthly
dry deposition and precipitation solute concentrations).  As output the model  calculates the  aqueous
concentrations of the base cations (Ca   , Mg  ,  K+, Na+  ,  NH4+),  strong  acid  anions (SO  4, NO3-,
Cr),alkalinity, silicic acid, organic acid analogue, total monomeric aluminum (AIT), organically complexed
monomeric aluminum, and pH  - in throughfall and in soil and  surface waters.
BASIN CHARACTERISTICS

Woods Lake, Panther Lake and Clear Pond are all located in the Adirondack Park region of New York
State (Figure 2). Woods and Panther Lakes are both in the west central region of the Adirondack Park
and are located less than 20 miles from each other. Clear Pond  is located  near the eastern boundary
of the park and is slightly farther  north than Woods Lake.

All three sites are forested watersheds with largely deciduous cover. The  basins  range from 1.2 to 5.7
km, in surface area and lie at approximately the same elevation. Clear Pond has 3 to 4 times the relief
of the other basins and also has  a wider variety of till depths with thin till (less than 1  m) near the top
of the watershed and thick till (as deep as 55  m) near the lake (average depth  = 6.5 m).  Panther Lake
soils are more uniformly deep (average depth = 24.5 m) while Woods Lake soils are thin (average depth
= 2.3 m). The  physical characteristics of all three basins are summarized  in Table 3.

Although Woods and  Panther Lakes each  receive  about the same amount of precipitation of nearly
identical  quality, the  alkaiinities (ANC) of the lake outlet waters are significantly different.  The  alkalinity
at Woods Lake is about -10 A*eq/L while at  Panther Lake the average alkalinity is nearly 150 peq/L
Clear Pond receives  less total precipitation than Woods and Panther and the acidity of the precipitation
is slightly lower as well. The lake  outlet  alkalinity at Clear Pond averages 100 neq/L. The chemical
characteristics of both the wet and dry deposition at all three lakes is summarized in Table 4.
CALIBRATION

Application of the ILWAS model begins with calibration. Basin data are used to quantitatively characterize
the system  to be simulated. Initial  conditions  (e.g., lake stage, soil and  surface water quality)  are
established  as a simulation starting  point. The model is then run using actual meteorological and air
quality data  as input. The model output, the quantity and quality of water at various points in the system,
is made to coincide with observed values by adjustment of calibration parameters (e.g., evapotranspiration
coefficients). The simulated results are  typically compared  to the  observed  data  using graphical
procedures.  Simultaneous  calibration against observed data for several  points within a watershed is
recommended if data  are  available.  For example, throughfall  quantity, snowpack depth, and flows at
various points in any streams, should be calibrated together with lake discharge. Since the ILWAS model
simulates many  processes, calibration of  these processes must follow a logical order  to proceed with
minimal effort. The general rules for calibration are: 1) calibrate the system's hydrologic behavior before
calibrating the chemical behavior: 2) calibrate in the  same order as water flows through the basin; and
3) calibrate  on an annual  basis first, then seasonally, and finally calibrate to  the instantaneous (daily)
behavior.

Hydrologic  Calibration

The first step in  the hydrologic calibration is the adjustment  of  the evapotranspiration parameters so
                                             A. 1-2-3
-------
that the predicted cumulative lake outflow matches the observed data. Observed lake outflow is checked
against the observed rainfall to ensure that all major storm events cause either increases in lake outflow
or at least retard recessions. The seasonal patterns in  the predicted cumulative outflow are controlled by
the length of the snow cover period and by seasonal variations in evapotranspiration. The snow formation
temperature and the snowmelt rate coefficients, along with the sublimation  rate, are adjusted to match
observed snowpack depth and lake outflow during the  snowmelt period. A seasonal calibration parameter
is used to adjust the variation in potential  evapotranspiration. Canopy interception storage, monthly leaf
area  indices,  and  root  distribution determine  the allocation  of  evapotranspiration  to  the different
compartments  of  the system. Depending  on the  depth of the water table,  the distribution of potential
evapotranspiration will influence the actual amount of water lost (i.e., a large distribution factor for the
upper  soil layer together with a deep water  table will  give a low total evapotranspiration  rate despite a
high potential).

The peak flow  and  recession curve characteristics are influenced  by the  snowpack, soil  field capacity,
soil-specific yield, and soil hydraulic conductivity. The fine adjustment of the instantaneous lake discharge
rate can be performed in conjunction with the chemical calibration. However,  the ratio of base flow to
the sum of shallow  and surface flow should be reasonably estimated using flow separation  techniques
before proceeding with chemical calibration.


Chemical Calibration

Lake chemical characteristics are primarily determined by the initial lake-water quality, in-lake processes,
soil solution quality and the routing of water through the different soil layers. The concentrations of several
soil solution constituents is  fairly constant  with time because of equilibration of the aqueous  phase with
the solid phase by cation exchange and anion adsorption reactions. Hence, a reasonable calibration of
lake outflow chemistry can be obtained by establishing the initial solute concentrations in the soil and lake
waters, the soil cation exchange selectivity coefficients and anion adsorption coefficients, and the in-lake
rate process coefficients. The initial  solute levels in the soil solution  must be such that a volumetric
flow-weighted average of the soil solution concentrations, plus direct precipitation onto the lake surface,
will give the average lake concentrations for  species which do not undergo reaction in the lake (e.g., CI"
,). The volumetric flows used in the above averaging are the soil  layer lateral flows obtained from the
hydrologic calibration. The cation  exchange  selectivity coefficients and anion absorption coefficients are
adjusted so that the initial soil solution concentrations are at equilibrium with the adsorbed concentrations.
The soil  nitrification rates, lake nitrification rates, and monthly  algal production rates are adjusted to
calibrate the ammonium and nitrate concentrations in the lake outflow.

Observed lysimeter  and ground water chemical data  can be used  to check the hydrologic  calibration.
The concentrations of solutes which do not equilibrate with the solid phase may drift quickly away from
their initial levels if the rate processes in the soil  are  not calibrated properly. The  aluminum  dissolution
rate is adjusted to maintain a fairly constant level of aluminum in the applicable soil layers from year to
year (aluminum concentration may fluctuate seasonally with the nitrate cycle). The nitrification  rate is
adjusted to  prevent buildup of nitrate in deep soil layers. The observed silicic acid  concentrations are
used in setting the mineral weathering rates.

If observed  data are available,  the throughfall chemistry can be  calibrated by adjusting the gas and
paniculate deposition velocities, canopy nitrification rates, and foliar exudation  rates. This also provides
a means of quantifying total  atmospheric deposition (wet and dry). The snowpack ion levels are calibrated
by adjusting a  solute leaching coefficient.
                                              A. 1-2-4
-------
The dynamic behavior of the  lake outflow water quality is influenced by the lake thermal profile, the
fraction of solute retained in the lake ice, and the fraction of flow that  comes  in as surface runoff or
ground water seepage.

The calibrated and observed cumulative lake outflow, instantaneous lake outflow,  pH, and concentrations
of ANC, NH4, A1T, Ca, Mg, K,  Na, SO., NO,, Cl, and Silicic Acid are shown in Figures 3 through 16 for
Panther Lake, Figures 17 through 29 for Woods Lake and Figures 30 through 42 for Clear Pond.  The
figures indicate good agreement between simulated and observed dynamic concentrations. The calculated
mean square errors  between the simulated and observed concentrations are presented in Tables  5, 6,
and 7.


Discussion of Results

The results indicate that the primary  reason for the difference in the acidity  of Woods and  Panther lake
waters is the depth of till in the watersheds. Panther basin has thick till and thus a larger reservoir of
exchangeable bases and weatherable minerals to neutralize acid deposition.  Sensitivity analysis indicates
that if the depth of till at Panther Lake were reduced, it too would be more acidic (see next  section). For
both Woods and Panther Lakes, the majority of the base supplied to throughflowing waters comes  from
cation exchange (70%).

The simulation of Clear Pond led to some interesting comparisons of sources of alkalinity.  For example,
although the average till thickness at Clear Pond is only 6.5 m, the thick till in the immediate vicinity of
the lake provides significant buffering capacity. Another  factor in the alkalinity supply at Clear  Pond is
the presence of anorthosite minerals. These are similar to the minerals at Woods and Panther but  have
a higher ratio of calcium  to sodium. The calcic  minerals at  Clear Pond weather faster than the sodic
minerals at Woods and Panther and thus provide a much larger fraction  of the base  supply. Mineral
weathering contributes is over  50 percent of the base supply at Clear compared  to less  than 30 percent
at Woods and Panther.
                                             A. 1-2-5
-------
Table 1.  Solution-Phase Chemical Constituents.

Solutes
tracked
by mass
balance

Solutes
which
can be
calculated
from
those
above







Cations
CaT
MgT
KT
NaT
NH4T
AI3+
AI(OH)2+
AI(OH)2
AIF2+
AI2
AI(S04) +
AIR22+
A1(R2)2


Note: Subscripted "T"
Anions
S04T
N03T
CIT
P04T
F~
AI(OH)4
HC03
C03
R13'
HR12'
H2R1'
R2"
AIF4
AIF_
O
AIF6
AI(S04)2
indicates total
Neutral
Species
H4Si04
C02 (aq)
AlFg
AIR1
AI(R2)3
AI(OH)3
H3R1
HR2







Analytical Gases
Totals (Input Only)
Alk(ANC) (SOX)
Org Acid Lig 1 (NO )
(triprotic, R1)
Org Acid Lig 2
(monoprotic, R2)
AIT
FT








analytical solution phase concentration.
                                             A. 1-2-6
-------
Table 2.  Chemical and Physical Processes Simulated by the ILWAS model.
Canopy Processes

Dry Deposition
Foliar Exudation
Nitrification
Solution Phase  Equilibration
Washoff

Snowpack Processes

Accumulation
Sublimation
Leaching
Nitrification

Soil Processes
Heat Transfer
Biomass Loop
   Litter Accumulation
   Litter Decay
   Organic Acid Decay
   Nitrification
   Nutrient Uptake
   Root Respiration
Abiotic Processes
   Mineral Weathering
   Competitive Cation Exchange (Al, Ca, Mg, K, Na, NHr, H)
   Anion Adsorption (SO4, PO4, organic acid ligand)
   COo Exchange
   AI(OH)3 (am) Dissolution-Precipitation
   Solid-Liquid-Gas Phase Equilibration
Surface Water Processes

Gas Transfer
Mixing (Advection & Dispersion)
Heat Exchange
Ice Formation & Melting
Algal Nutrient Uptake
Nitrification
Reductive Loss of Strong Acid Anions
Solution Phase Equilibration
                                            A. 1-2-7
-------
Table 3.  Characteristics of Panther, Woods, and Clear Lakes.

Basin area (km2)
Lake surface elevation8 (m)
Reliefb (m)
Forest cover (%)
Coniferous (%)
Deciduous (%)
Mean till depth (m)
Lake
Area (km )
Mean depth (m)
Maximum depth (m)
Lake Outlet
Alkalinity (/ieq/L)
pH
Panther
1.2
557
170
98
3
95
24.5
0.18
3.5
7

-35 to 240
4.5-7.2
Woods
2.1
606
122
95
5
90
2.3
0.26
4.0
12

-60 to 30
4.4-5.9
Clear
5.7
580
518
95
0
95
6.5
0.74
6.0
24.4

80 - 170
6.1-7.4
  Relative to mean sea level.
  Difference between highest and lowest elevation in the watershed.
                                             A. 1-2-8
-------
Table 4a.  Mean annual wet deposition loading (equivalents/hectare-year).
Constituent
  Clear3
Pantherb
Woods0
Sulfate
Nitrate
Chloride
Ammonium
Calcium
Magnesium
Sodium
Potassium
470
250
46
130
65
23
38
14
670
370
52
200
150
40
49
22
560
320
44
180
110
36
44
17
Table 4t>.  Mean annual dry deposition loading (equivalents/hectare-year).
Constituent
Clear8
Pantherb
Woodsc
Sulfate
Nitrate
Chloride
Ammonium
Calcium
Magnesium
Sodium
Potassium
83
39
15
37
37
11
14
12
120
67
15
36
89
18
17
12
100
50
15
29
62
16
17
12
a Sampling period was for a two year period from August 1982 through July 1984.
b Sampling period was from March 1978 through December 1981.
                                            A. 1-2-9
-------
Table 5.  Panther Lake Outlet.
Parameter
Discharge
Cl
NO,
o
S04
Ca
Mg
Na
K
NH4
pH
Alk
AIT
Number of
Data Points, n
547
250
247
250
250
250
250
250
222
159
205
65
Mean Square Standard Estimate
Error (MSE)* of Error (SEE)**
0.0002 (m3/s)2
20.7 fceq/l)2
279
312
1350
76.7
66.3
4.0
8.3
.28 (S.U.)2
2220 (/^eq/l)
10.5 famol/l)2
0.013
4.6
16.8
17.7
36.9
8.8
8.2
2.0
2.9
.53
47.3
3.3
m3/sec
jueq/l







S.U.
ueq/l
(pmol/l)
              MSE - 2(XOBS-Xs|M)2/n
SEE =  (S(XOBS-XSIM)2/(n-2))
0.5
                                           A. 1-2-10
-------
Table 6.  Woods Lake Outlet.
Parameter
Discharge
Cl
N03
so;
Ca
Mg
Na
K
NH4
PH
Alk
«T
Number of
Data Points, n
1095
263
261
263
263
263
262
262
263
187
205
136
Mean Square Standard Estimate
Error (MSE)* of Error (SEE)**
0.0065 (m3/s)2
11.1 (jjeq/1)
129
301
277
44.7
42.0
10.5
16.8
0.10 (S.U.)2
983 Gueq/l)
12.3 (Aimol/l)2
0.013
3.3
11.4
17.4
16.7
6.7
6.5
3.3
4.1
0.32
31.5
3.5
m3/sec
jueq/l







S.U.
/ieq/l
Oiimol/l)
              MSE = 2(XOBS-XslM)2/n
SEE = (2(XOBS-Xs|M)2/(n-2))a5
                                           A. 1-2-11
-------
Table 7.  Clear Pond Outlet.

Parameter
Discharge
Cl
N03
S04
Ca
Mg
Na
K
NH4
PH
Alk
AIT
* MSE = S(
Number of
Data Points, n
102
23
23
23
23
23
23
23
11
23
23
21
XOBS'XSIM)
Mean Square
Error (MSE)*
0.0011 (m3/s)2
2.1 Gueq/l)2
43.3
114.0
558.0
25.4
39.9
1.0
4.5
0.05 (S.U.)2
320 (A*eq/l)
1.1 (jtmol/l)2
** SEE =
Standard Estimate
of Error (SEE)**
0.034 m3/sec
1.5 /^eq/l
6.9
11.2
24.7
5.3
6.6
1.1
2.3
0.24 S.U.
18.7 /zeq/l
1.1 (^mol/l)
(S(XOBS-Xs|M)2/(n-2))a5
                                              A. 1-2-12
-------
                                          deposition

                                          wet I  I dry
          Transpiration
 LITTER

  ORGANIC LAYErV.J
                    -(«
  "INORGANIC" "• -
or MINERAL LAYER-
              Interlayer Adveclion
              and Dispersion
                                                    LAKE SEDIMENTS
                                                                      T
                                                                                        Stream
                                                                                        Flow
  Figure 1.  Lake-watershed system showing hydrologic setting.
                                           A. 1-2-13
-------
                                      Adirondack
                                          Park
                                                                N.Y. state border
                                                                         N
                                                                       20 km
Figure 2. The DDRP Intensively Studied Lakes in the Adirondack Park of New York.
                                       A. 1-2-14
-------
               aa   a  QQ   g  a
                                                       •a
                                                                &

                                                                5

                                                                fc
                                                                S.
                                                                o

                                                                CD
                                                                I



                                                                1
                                                                
-------
                                      
-------
Q_
             %  ?*  &%''
             0 /°
                      D
                               -1	1	1	1	1	1	i	\	1	1	1	1	1	1	1	1	••	!
 -I	1	1	1	I	1	1	1	1	1	1	1	1	1	1	1	1	T

SDWDJFMRMJJflSDWDJFMRMJJRSO K,' D J F M R N

187B   1979              19S0             1961
                                                    JR
      -  CRLCULRTED

     D  OBSERVED DfiTfl
                  PRNTHER  OUTLET    -  PH
     Figure 5. Simulated and observed pH for Panther Lake.
                         A. 1-2-17
-------
CM
   S£w D-'PMRMJJflSDWDJFrlRMJJflSDMDJFNflrlJJ R
   1978   1979               J9B0               1961
      CflLCULflTED
      OBSERVED DRTfi
                 PflNTHER  OUTLET    -  RLK
   Figure 6. Simulated and observed alkalinity for Panther Lake.
                      A. 1-2-18
-------
    S3
    SS
    If
    E)
UJ
    IS)
    S)
    U*
    C\J
   is
   Si
   if}
       SDMDJFMflMJJRSDWDJFMflMJJflSOMDJFM'RMJJP.
       197B   1979                I960                1BB1
          CRLCULflTED
          OBSERVED DRTfl
                     PflNTHER  OUTLET    -  NH4
     Figure 7. Simulated and observed ammonium concentration for Panther Lake.
                            A. 1-2-19
-------
LU
    S)
    tss
    12
    LTS
    S3
    ro
    si
    in -
    CU
    K:
    »cv
    ru
   Si
   BB 	!
   Si
   ID
                  CD
                   D
       SQNDJFMRrlJJflSQWDJFMRMJJRSDNDJFrlRMJJri
       1976   1979                I960                 1961
          CRLCULRTED
          OBSERVED DflTfl
                      PRNTHER  OUTLET     -  flLT
     Figure 8. Simulated and observed total monomeric aluminum concentration for Panther Lake.
                             A. 1-2-20
-------
si
CO
Si
IS
   S D W D J F M fl M J J fl S D Kl D J F M R M J J R S 0 N D J F M fl M J J R
   197B   1873                I960               i96i
      CRLCULRTEO
      OBSERVtD DflTfl
                 PRNTHER  OUTLET    -  Cfl
  Figure 9. Simulated and observed calcium concentration for Panther Lake.
                         A. 1-2-21
-------
    EC
    52
    if!
    CO
    Si
    ru
LU
   S)
   Si
   IS)
   Si
       SDMDJFMflMJJRSDWDJFMflMJJflSDKOJFMRMJJR
       197B   1979                 I960                1961
         CflLCULRTED
         OBSERVED DflTfi
                      PflNTHER  OUTLET     -  MG
    Figure 10. Simulated and observed magnesium concentration for Panther Lake.
                            AA-2-22
-------
 Si
 Kb
 =r
 (5)
 LTJ
Si
r\j
OJ
S,
isa
Si
   SOKJDJFMflMJJflSQNDJFMRMJJRSONDJFKRMJJR
   1976   1979                 J9B0                 1961
      CRLCULRTED
      OBSERVED DRTR
                  PflNTHER   OUTLET     -   K
  Figure 11.  Simulated and observed potassium concentration for Panther Lake.
                          A. 1-2-23
-------
    Si
    ES
CD
LU
                                                        D
       BONDJFKRMJJflSDNDJFMRMJJRSONDJFMRNJJR
      1876    1979               I960                i9Si
         CRLCULRTED
         OBSERVED DfiTfl
                     PflNTHER  OUTLET    -  NR
    Figure 12. Simulated and observed sodium concentration for Panther Lake.
                             A. 1-2-24
-------
IS:
Si
ta
in
tM
         1  I I  I I  I  I—I—I—I—I—I—I—I—I—I—I—I—I—I—I—I—I—[—I—I—I—I—I—I—I—I—1

   SOWDJFMRMJJflSQMDJFMRMJJflSDNDJFMRrlJJR
   1976   1979                i960                1961
      CRLCULflTED

      OBSERVED DRTR
                 PRNTHER   OUTLET     -   S04
 Figure 13. Simulated and observed sulfate concentration for Panther Lake.
                         A. 1-2-25
-------
S:
ts:
rr
S)
IS;
   SOKDJFKflflJJRSDNDJFMRMJJflSO N D J F N fl M J J R
   1978   1979                I960                1981
      CflLCULRTED

      OBSERVED DRTR
                 PflNTHER   OUTLET     -  N03
  Figure 14. Simulated and observed nitrate concentration for Panther Lake.
                         A. 1-2-26
-------
    Si
    Iffi
    Si
    Si
    i
    CM
- -,  S3
(	J  Lft 	
   (3
   IS:
   Si
   if}
       S D' W D J F M RNJJRSOWDJF M fl M J J fl S D U D J F M R N J J R
       1978    J979                19B0                 i9Si
          CflLCULRTED
          OBSERVED DfiTfi
                      PRNTHER   OUTLET     -   CL
      Figure 15. Simulated and observed chloride concentration for Panther Lake.
                             A. 1-2-27
-------
    S;
    IS]
    Si
    Ln
    CO
    (S3
O
LU
   Si
                      D
                                 i—i—i—i—i—i—i—i—i—i—i—i—i—i T i—i—i—i—i

      SQKBJFKfiKJJRSQNDJFMRMJJflSCNDJFMRKJJR
      197B   J979               J9S0                1961
         CRLCULflTED
         OBSERVED DflTfl
                    PRNTHER  OUTLET     -  SI04
     Figure 16. Simulated and observed silicic acid concentration for Panther Lake.
                            A. 1-2-28
-------
                                                        0)
                                                       J£
                                                       5
                                                        U)
                                                       E
                                                       I
                                                        o
                                                        CO
                                                        I
                                                       1
                                                       co
                                                       3

                                                       CO

  s-  at- a?.
Nldd
ai
         zi
         A. 1-2-29
-------
 SDWDJFMflflJJflSDNDJFMflMJJflSONDJFMflNJJR
 197B    1979                     I960                      1981
 -   CflLCULRTED
0   C-ERVED.™

 Figure 18. Simulated and observed pH for Woods Lake.
                               A. 1-2-30
-------
UJ
    in
    in
    CM
    cu
    in
    N.
    in
    SM
   in
   in
   ru
   in
   K.
    I
   if)
   ru
    1979



CflLCULflTED
OBSERVED DflTfl
                                JFMRMJJflSO
                                i960

                     WOODS  OUTLET     -   flLK

     Figure 19. Simulated and observed alkalinity for Woods Lake.
                            A. 1-2-31
-------
SJ
Si
SI
IS)

ffi
Si
Si •—,
C\J
Si
in —i
Si
    SDNDJFMflMJJRSONDJFMRMJJflSDNDJFMRMJJR
    197B   1979                 i960                1961
      CRLCULRTED
      OBSERVED DflTfl
                  WOODS   OUTLET    -   NH4
 Figure 20. Simulated and observed ammonium concentration for Woods Lake.
                          A. 1-2-32
-------
     El
     to
     CO
_J
               i—I—i—i—i—i—i—i—r-i—i—i—i—i—i—i—i—i—i—i—r"f"T~"i—i—|—r—r—i—i—i—i—i—i
         SOWDJFMflMJJRSQWDJFrlflMJJflSQWDJFMflrlJJfl
         197B     1979                      1980                      1981
                                                                               DBTP
         -   CflLCULRTED                                                         " "~

        o   ™-WOODS   OUTLET      _   RLT
       Figure 21. Simulated and observed total monomeric aluminum concentration for Woods Lake.
                                        A. 1-2-33
-------
    12
    IS:
    a
    ir»
    ro
    ES
    IB
    Si
UJ  g
    C\J
    S)

    S3
    Si
    LD
    Kl 	1	1	,	1	1	1	1	1	1	1	1	1	1—I	1	1	1	1	1	1	1	1	1	T—t	1	1	1	1	1—I  I  I I  I  i

       SOMDJFMflrlJJflSONDJFMRMJJRSDNDJFrlRMJJR

       197B   1979                 1980                 1981
           CflLCULRTED
           OBSERVED DRTfl
                       WOODS   OUTLET     -  Cfl
      Figure 22. Simulated and observed calcium concentration for Woods Lake.
                               A. 1-2-34
-------
ID
i—>


LD
     Si
     la
     in
     CO
     Si
     IS)
     CO
     si
     in
     c\J
     S
     Lft
     IS)
        1
     IS)
     U5
-r—i—I—T—|   i  i  i  i
 SQNDJFMflM
 197B    1979
T—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—r

JJRSONDJFMRMJJRSON
                J9B0
DJFM
  1981

flfl
                                                                                     JJfl
        o
             CflLCULflTED
        Figure 23.  Simulated and observed magnesium concentration for Woods Lake.
                                           A. 1-2-35
-------
Si
S3
3-
Sl
If?
CO
IS)
BCi
Si
in
a
Lft
Si
SI
SJ 	
in  '
    SDNDJFMflMJJRSDNDJFMRMJJflSONDJFMRMJJR
    197B   1979                 19B0                 i9Bi
       CRLGULflTED
       OBSERVED DflTfl
                                                          DRTE
                   WOODS  OUTLET     -   K
   Figure 24. Simulated and observed potassium concentration for Woods Lake.
                           A. 1-2-36
-------
Si
IS
BZi
IS
in
       T	1	1	1	1	1	1	1	1	1—I	T	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1—I	1	'	1	'	'—

     SDNDJFMRMJJflSDWDJFMRMJJflSOWDJFMRMJJR

     1978     J979                       I960                      1981
         CflLCULRTED

     Figure 25. Simulated and observed sodium concentration for Woods Lake.
                                      A.1-2-37
-------
IE)
   6 D M D J F M R M J J R S D W D J F M R M J J R S D W D J F M R M J J R
                            19B0               1DDX
                                                     DRTE
197B   1979
   -  CRLCULflTED
   D  OBSERVED DRTfl
              WOODS  OUTLET    -  S04
  Figure 26. Simulated and observed sulfate concentration for Woods Lake.
                         A. 1-2-38
-------
     IS
UJ
ro
O
     Si
     63
     SI
     Ift
     IM
     Si
     55
     Si
          SDWDJFMflMJJflSDWDJFMflMJJflSDNDJFMRrtJJR
         197B    1979                      I960                      1981
                                                                               DRTE
          -    CflLCULRTED
         a    a™™
        Figure 27.  Simulated and observed nitrate concentration for Woods Lake.
                                        A. 1-2-39
-------
    IS
    Si
    05
    IS!
    LTl
LU
    C\J
r -,  Si
L_J  in —
    El
    If}
       SDMDJFMRMJJflSDWDJFMRMJJflSDWDJFMRMJJn
       1978    1979                 1980                 1981
          CflLCULflTED
          OBSERVED ORTfl
                       WOODS  OUTLET    -   CL
      Figure 28. Simulated and observed chloride concentration for Woods Lake.
                               A. 1-2-40
-------
o
LU
    S3
    LO
    IM
    If)
    (V
    K)
    Kl
    IM
    LO
    K.
    Si
    LO
    LO
    CU
=d-
O
IT)
   in _
                       O
                                                   i  \  i i   i  i  i i  »
      1978   1979


      -   CflLCULflTED
      O   OBSERVED  DRTfl
                      WOODS  OUTLET     -  SI04
     Figure 29. Simulated and observed silicic acid concentration for Woods Lake.
                             A. 1-2-41
-------
  «>-   e-orai   ag-ei   as-ai   airai   «E-ST    a*'ei   BI-BT  war
                                                                    •a
                                                                                •a

                                                                                I
                                                                                o

                                                                                £
                                                                                0)
                                                                                O)
                                                                                I

                                                                                1
                                                                                s
                                                                                •§
                                                                                1
                                                                                CO
                                                                                "8
                                                                                 E
                                                                                eo
                                                                                il
NlUd
                              A. 1-2-42
-------
     CD
     Si
     in
     Sl
     in
     CO
     El
     in
D_  ^
     Kl
     E3
     in
     zr
     in 	
      •
     CO
            I   I    I   f
                                         I   III
 RSDWDJFMR
 1982            1983


 -    SIMULRTED
D    06SER.EO
                                      MJJRSDNDJFMflMJJ
                                                                19B4
                                                                             DRTE
         Figure 31. Simulated and observed pH for Clear Pond.
                                       A. 1-2-43
-------
     S3
     LD
     Kl
     1/5
     if}
LU
     ID
     Si
     cu
      I
     EJ
     in
      i
 	1—T	1	1	1	1	1	r	1	1	1	1	1	1	1	1	1   i   r'r  '   '
 RSDNDJFMflMJJRSDWDJFMRMJJ

 1982           1983                                   1984

                                                                       DRTE

 -   SIMULRTED
D   OBSERVED
         Figure 32. Simulated and observed alkalinity for Clear Pond.
                                       A. 1-2-44
-------
   ru
   in
   cu



   ss
   CM
_J fi-
   IS-I
UJ
   in
       RSDNDJFMRMJJRSDNDJFMRMJJ
       1982        1983                        19B4
                                                      DRTE
       -  SIMULRTED


         resERVED  CLERR  POND  OUTLET   CCU
      Figure 33. Simulated and observed ammonium concentration for Clear Pond.
                            A. 1-2-45
-------
    S3
    in
    CM
    tft
    £\J
    
-------
    SI
    \f)
    C\J
    t\l
    B-
en g _j
LJ -
   in
                                                          O
 0
19B3
                                JflSDNDJFMflMJ
                                             1984
      -  SIMULflTED

         CB8EIWED
                 CLEflR  POND  OUTLET   [CD
      Figure 35. Simulated and observed calcium concentration for Clear Pond.
                            A. 1-2-47
-------
   EJ
   cu
   g
   cu
   Si
   IT*
r f*i £>
L_J KI —
         I  I  I  I   I  I  I  I   I  I
       flSDNDJFMRMJJRSDNDJFMRMJJ
       1982        19B3                        1984
                                                       DRTE
       -  SIMULATED

         CBBHWED CLERR  POND  OUTLET   CCL)
      Figure 36. Simulated and observed magnesium concentration for Clear Pond.
                           A. 1-2-48
-------
     IT)
     CVJ
     OJ
     CM
     S3
     El
     CV)
_J

0
     K)
     If)
     in
     Si
     IS  	
     in   '
                                                                      -&•
          —T=—i	1—i	1—i ~i~  i ~ i	1—i	1	1—i	1   i   I   i    '   r  "    i   i
          RSDNDJFMRMJJRSDNDJFMRMJJ
          1982           1983                                   19B4
                                                                                DRTE
          -    SIMULRTED
         D    OBSERVED
        Figure 37. Simulated and observed potassium concentration for Clear Pond.
                                        A. 1-2-49
-------
Si
Lft
-------
(Q
                                                                           I   I   I
     RSDNDJFMRMJJflSDNDJFMRMJJ
     1982            1983                                  1984

                                                                        DRTE
     -   SIMULflTED
    D   OBSERVED                                                [C|J
   Figure 39. Simulated and observed sulfate concentration for Clear Pond.
                                   A.1-2-51
-------
LU
CO

O
     in
     IM
     in
     ru
     CM
     §
     C\J
     in
     K.
     53
     in
     ru
     in
     s.
     Si  	
     in   '
     in 	
          '
                                             n    —
             (D O Oi'-Oi-^-r
           RSDNDJFMRMJJRSDWDJFrlRMJJ

          1982            1983                                     1964

                                                                                 DRTE

          -   SIMULRTED

         D   OBSERVED
         Figure 40.  Simulated and observed nitrate concentration for Clear Pond.
                                         A. 1-2-52
-------
    Si
    in
    cu
   C\J
   in
   K.
3 g
   U5
   S3
   ID
        "Cr
                      nU  tJ- U  °  D—D Q D 'd"
•S,—Q-S-
           1	1	1
                              1	1	1	r
       RSDWDJFMRMJJRSDWDJFNRdJJ

       1982         19B3                         19B4
       -  SIMULRTED

      D  OBSERVED
                                                        DfiTE
                  CLERR  POND  OUTLET   [CD
      Figure 41. Simulated and observed chloride concentration for Clear Pond.
                            A. 1-2-53
-------
    CU
    CxJ
    BEl
    r\j
    Kl
LU
=t-
O
   LD
   Si
   U5
       RSONDJFMflMJJfiSDNDJFMRMJJ
       1982         1983                         1984
         SIMULRTED
         C8SERVED
                                                         DRTE
                  CLEflR  POND   OUTLET   [CD
      Figure 42. Simulated and observed silicic acid concentration for Clear Pond.
                             A. 1-2-54
-------
                       APPENDIX A.1-3
MODEL OF ACIDIFICATION OF GROUNDWATER IN CATCHMENTS (MAGIC)
-------
PROTOCOL FOR CALIBRATION PHASE OF
           DDRP MODELING
 B.J. Cosby, G.M. Hornberger, P.F. Ryan
           and D .M. Wolock

  Department of Environmental Sciences
          University of Virginia
-------
INTRODUCTION

      Four tasks must be completed in the calibration phase of our DDRP modeling project. 1) First, all
inputs must be specified.  Also, all "fixed" parameters  must be  set and an (objective)  procedure for
selecting these values must be specified. By "fixed" parameters we mean those that are not adjusted in
calibration. 2) Second, the remaining ('adjustable') parameters are  selected so that a loss function, in this
case  an unweighted sum of squared errors (SSE), is minimized.  3) Third, an  analysis of how well the
adjustable  parameters have been estimated must be accomplished. This is, the sensitivity of the SSE to
changes in the optimal values of the  adjustable parameters must be determined. 4)  Fourth, with the
adjustable  parameters fixed at their optimal values, a conventional, univariate sensitivity analysis for the
'fixed' parameters must be done. Again this will  be a sensitivity with respect to the SSE.

      The  first task is relatively straightforward. Inputs will, in general, be specified using measured data.
If data are  unavailable,  suitable extrapolations will be  specified by objective procedures.  In a similar vein,
some model parameters will be fixed (i.e., have values specified) using observations in  conjunction with
well-defined objective procedures. These parameters will not be adjusted in the calibration.

      The  second task requires the minimization of  the SSE between  model predictions  and observed
data.  In general terms the model can be written.
                   Y =  I (x, 0)
                    (1)
where Y is  a vector  of 'outputs' ,  x is a vector  of 'inputs' (and fixed parameters), © is a vector of
adjustable parameters of order k, and f  is a vector function relating the other quantities. We then seek
to minimize
                                  m
                   $ (©)  =
                    (2)
                                  ,.th
where eua Presents the error in the u  component of the vector at the a  temporal observation:

                   eua = tYua (°bs) - Yua (model)].

The order of the output vector is m and the number of observations is n.
                    (3)
      There are any number of techniques  for determining a value of 0 that minimizes $ (©).  (For
example, see Bard, 1974). We propose to use the Rosenbrock (1960) method to optimize, although we
may experiment with other methods (e.g., the Marquardt (1963) algorithm).

      Once a parameter vector © has been chosen such that the objective function is (approximately)
a minimum for these values, questions  about the  reliability and  precision of  our estimates must be
answered. This is the third task.  Essentially we would like to know something  of the sensitivity of the
objective function to parameter changes,  of the parameter estimation error covariance matrix, and of the
goodness-of-fit of the model.

      Useful approximations  for nonlinear models with  normally distributed  errors using maximum
likelihood estimation are given below.

      1. The e - indifference region is defined by the set  of values of © for which

                   $(©)-$*< e ,

      where $* is the estimated minimum  value  of the objective  function.   This region is  defined
      approximately by
                   (a ©r) H   (a ©) < 2
(4)
                                             A. 1-3-2
-------
where  5 0  =0 - ©* and where H* is an estimate of the Hessian matrix with elements H ag (©) = 3
$/© ,.,,30,3 (a and 6 are indices of the k x k matrix).   This equation defines ellipsoids about the estimated
minimum in which the objective function does not vary greatly.

      2. The covariance matrix of the estimates is defined by

                        V = E (5  © * 6 ©  *T)

where  S ©   is the shift in the parameter estimates that would  be caused by an error in the measured
variables of Sy_.  (That is, 50  the estimation error in parameters that arises from measurement  error in
the variables.) For a wide class of maximum  likelihood estimates with normally distributed errors,

                        V « HM.

      Generally speaking, the V computed from any given data sample an only be regarded as a rough
estimate, correct to within an order of magnitude. Approximate  confidence ellipsoids can be computed
by
                        5 © H 5© < c
(5)
where c must be determined based on the sampling distribution. Thus confidence regions coincide with
e-indifference regions (compare equations 4 and 5). For a given confidence level, we choose c such that:

                   Prob   [ S ©T V "1   5© 
-------
                                         REFERENCES

Bard, Y. 1974. Nonlinear Parameter Estimation, Academic Press,  N.Y. , 2341  PP.

Beven,  K.J. and M.J. Kirkby. 1979.   A physically based  variable contributing  area model  of  basin
hydrology.  Hvdro. Sci. Bull., 24: 24:43-69.

Cosby,  B.J., R.F. Wright, G.M. Hornberger and  J.N. Galloway.  1985a.   Modeling the effects of acid
deposition:  assessment of a lumped parameter  model of soil  water and  streamwater chemistry.   Wat.
Resour. Res., 21: 51-63.

Cosby,  B.J., R.F.  Wright, G.M.  Hornberger and J.N.Galloway 1985b.   Modeling  the  effects of acid
deposition:  estimation of long-term water quality  responses in a small forested catchment.  Wat. Resour.
Res., 21, 1591-1601.

Cosby,  B.J., G.M.  Hornberger, J.N. Galloway, and R.F.  Wright.  1985c.   Freshwater acidification  from
atmospheric deposition of sulfuric acid: a quantitative model.  Environ.  Sci. Tec., 19: 1144-1149.

Cosby,  B.J., G.M. Hornberger,  R.F.  Wright, E.B. Rastetter and  J.N. Galloway.  1986a.   Estimating
catchment water quality response to acid deposition using mathematical models of soil ion exchange
processes.  Geoderma (in press).

Cosby,  B.J, P.G. Whitehead and R. Neale. 1986b.  A preliminary  model of long-term changes in stream
acidity in southwest Scotland.  J.  Hydrol., (in press).

Chow, V.T.  1964.  Handbook of Applied Hydrology.  McGraw  Hill,  New York.

Conway, G.R.,  N.R.  Glass and J.C. Wilcox.  1970.  Fitting nonlinear  models to biological  data by
Marquardt's  algorithm. Ecology, 51: 503-507.

Corps of Engineers, U.S. Army, North Pacific Division. 1956. Summary Report of the Snow Investigation,
Snow Hydrology.

Hamon, W.R. 1961. Estimating potential evapotranspiration. J. Hydraul. Div. Am. Soc. Civ. Ens. 87:107-118.

Hornberger, G.M. , K.J. Beven, B.J.  Cosby, and D.E.  Sappington. 1985. Shenandoah watershed study:
Calibration of a  topography-based,  variable contributing area  hydrological  model to a small  forested
catchment. WRR 21:  1841-1850.

Marquardt, D.W.  1963. An algorithm  for least-squares estimation of nonlinear parameters. J. Soc. Indust.
Appl. Math. .11: 431-441.

Neal, C. , P.G. Whitehead, R. Neale and B.J. Cosby.  1986. The effects of acidic deposition and conifer
afforestation on stream acidity in the British uplands. J. Hydrol., (in  press).

Rosenbrock, H.H.  1960. An automatic method for finding the greatest or least value of a  function
Comput. J. 3: 175-184.

Whitehead, P. , Hornberger,  G. , and R. Black. 1979. Effects of parameter uncertainty in a flow routing
model. Hydrol. Sci. Bull. , 24: 445-464.

Wright,  R.F. , B.J. Cosby, G.M. Hornberger and J.N. Galloway. 1986. Interpretation of  paleolimnological
reconstructions using the MAGIC model of soil and water acidification. J. Wat. Air Soil Pollut., (in press).
                                            A. 1-3-4
-------
Table A.1



Table A.2



Table A.3



Figure A.1

Figure A.2

Figure A.3
       SECTION A: SUMMARY

Values of MSB, RMSE and RMSE expressed as a percentage
of the observed mean value  of  the  DDRP variables  for
Woods Lake (calibration and corroboration  periods)

 Values of MSE, RMSE and RMSE expressed as a  percentage
of the observed mean  value of the DDRP variables for  Panther
Lake (calibration and corroboration  periods)

Values of MSE,  RMSE  and RMSE as expressed a  percentage of
the observed mean value  of the DDRP variables for  Clear Pond
(entire period of record)

Output of calibrated model: long-term hindcast  for Woods Lake

Output of calibrated model: long-term  hindcast  for Panther Lake

Output of calibrated model: long-term hindcast  for Clear Pond
OTHER ASSORTED PRELIMINARIES:

Figure A.4             Conceptual basis of the hydrological model (TOPMODEL)

Figure A.5             Conceptual flux routing in the chemical flux model (MAGIC)
Figure A.6
Conceptual linking  of  TOPMODEL state  variables  to  flow
routing parameters in MAGIC
                                       A. 1-3-5
-------
Table A.1.  MSE summary for Woods Lake (based on DDRP variables of interest). MSE = Mean Squared
Deviation, RMSE = Square Root of MSE, %MEAN  = 100.*RMSE/(Mean of observed data).
Variable
             Calibration period (1)
Units      MSE       RMSE    %MEAN
   Corrobration period (2)
MSE      RMSE    %MEAN
Discharge
Cum Disc.
Chloride
Sulfate
Alk(grn)
Calcium
Magnesium
Sodium
Potassium
Alum(tot)
H +
m3/s
m/yr
meq/m3
meq/m3
meq/m3
meq/m3
meq/m3
meq/m3
meq/m3
moi/m3
meq/m3
.0029
-
22.8
368.2
617.0
43.7
6.0
17.6
3.6
41.4
49.5
.05
-
4.8
19.2
24.8
6.6
2.4
4.2
1.9
6.4
7.0

-
50.5
14.7
-183.7
9.1
13.1
21.5
29.7
61.7
36.5
.0046
-
19.2
173.0
266.2
47.1
5.9
8.9
2.8
46.2
64.8
.07
-
4.4
13.2
16.3
6.9
2.4
3.0
1.7
6.8
8.0

-
48.4
10.7
-429.0
9.6
13.1
14.5
26.6
102.0
52.6
(1) 1 September 1978 - 31 May 1980
(2) 1 June 1980 -- 31 August 1981
                                         A. 1-3-6
-------
Table A.2.  MSE summary for Panther Lake (based  on DDRP variables of interest).  MSE = Mean
Squared Deviation, RMSE = Square Root of MSE, %MEAN =  100.*RMSE/(Mean of observed data).
Variable
             Calibration period (1)
Units      MSE       RMSE    %MEAN
   Corrobration period (2)
MSE      RMSE    %MEAN
Discharge
Cum Disc.
Chloride
Sulfate
Alk(grn)
Calcium
Magnesium
Sodium
Potassium
Alum(tot)
H +
m3/s
m/yr
meq/m3
meq/m3
meq/m3
meq/m3
meq/m3
meq/m3
meq/m3
mol/m3
meq/m3
.0012
-
23.6
254.7
6194.2
1630.2
83.6
92.0
4.0
13.8
10.2
.03
-
4.9
16.0
78.6
40.4
9.1
9.6
2.0
3.7
3.2

-
39.5
12.7
61.9
20.1
18.0
24.0
17.0
133.7
290.9
.0025
-
5.2
215.7
3553.4
1597.6
69.5
108.4
7.8
3.5
2.4
.05
-
2.3
14.7
59.6
40.0
8.3
10.4
2.8
1.9
1.6

-
18.6
12.4
39.5
19.2
15.9
23.7
21.9
560.0
400.0
(1) 1 September 1978 - 31 May 1980
(2) 1 June 1980 - 31 August 1981
                                         A. 1-3-7
-------
Table A.3.  MSE summary for Clear Pond (based on DDRP variables of interest). MSE = Mean Squared
Deviation, RMSE = Square Root of MSE, %MEAN = 100.*RMSE/(Mean of observed data).
Variable
              Period of Record (1)
Units      MSE      RMSE     %MEAN
Discharge
Cum Disc.
Chloride
Sulfate
Alk(grn)
Calcium
Magnesium
Sodium
Potassium
Alum(tot)
H +
m3/s
m/yr
meq/m3
meq/m3
meq/m3
meq/m3
meq/m3
meq/m3
meq/m3
mol/rnS
meq/m3
.0219
-
21.7
90.2
347.4
444.0
22.0
25.0
.5
1.4
.0
.15
.
4.7
9.5
18.6
21.1
4.7
5.0
.7
1.2
.2

.
63.5
7.5
17.9
12.7
14.7
12.8
18.4
514.3
28.6
(1) 27 July 1982 -- 24 May 1984
                                         A. 1-3-8
-------
  SECTION B:  OPTIMIZATION PROTOCOL FOR TOPMODEL (HYDROLOGICAL MODEL)
Table B.1

Table B.2



Table B.3


Table B.4
Description of optimization protocol

Values and sources of  fixed parameter values

Ranges and optimal values  of adjustable parameters (optimized
using both the calibration period and the entire  period of record
for Woods and Panther)

Results of Hessian analysis  on optimized  parameters   (Woods
Lake, calibration period)

Results of Hessian analysis  on optimized parameters  (Panther
Lake, calibration period)
Table B.5


Table B.6


Table B.7
Results of Hessian analysis on optimized parameters  (Clear Pond,
entire period  of record)

 Results of Hessian analysis  on optimized parameters  (Woods
Lake, entire period  of record)

Results of Hessian  analysis   on optimized parameter  (Panther
Lake, entire period  of record)
                                         A. 1-3-9
-------
Hydrologicai  Model

     The hydrological model, TOPMODEL (Beven and Kirkby,  1979), will be used to determine routing
parameters for use in the chemistry model. TOPMODEL is a topography-based variable contributing area
catchment  model.  The  model has an upper and a lower storage zone,  with precipitation input and
evapotranspiration loss occurring only within the upper store.  Flow paths within the model include
overland flow directly into the stream, drainage from the upper zone to the lower zone, and baseflow from
the lower zone into the stream.  Precipitation can also bypass the upper zone and flow directly into the
lower store.

     A detailed description of TOPMODEL is given in Hornberger et. al. (1985).  They also  present the
results of a sensitivity analysis which suggest a reduced TOPMODEL structure that captures the critical
elements of catchment hydrological  behavior.  Based on their findings,  a simplified version of the model
is  used for the DDRP. The inputs and parameters listed below correspond to those in Hornberger et.
al.  (1985).

     A simple snow accumulation and melt model was added to the  "front end" of TOPMODEL to be
used for model applications where winter snowpack are significant. The optional TOPMODEL parameters
are determined during the warm months when  possible errors  from a snow model need not be
considered.  The optimal TOPMODEL  parameters  are then taken as fixed  and the optimal snow model
parameters are determined using the entire period of record.

     Since TOPMODEL  simulates the  hydrology  of only the terrestrial portion  of watersheds, it was
necessary to include a  simple  reservoir routing routine on the "back-end" of TOPMODEL for  those
catchments with lakes.  This routine utilizes hypsographic information  to delay water entering the lake
before it flows out through the outlet.


Inputs

    PPT - Measured precipitation [mm/day].  Snow accumulation and melt calculated using  empirical
          formulas given in Chow (1964), and in Corps of Engineers (1956). The cutoff temperature for
          snow accumulation and the temperature-induced snowmelt parameter were optimized whereas
          the rain-induced snowmelt parameter was fixed to the value given in Chow (1964).
    PET - Potential  evapotranspiration  [mm/day].  Daily values calculated  from mean temperature and
          daylength data using  the equation of Hamon (1961).

TOPOGRAPHICAL INFORMATION. Includes A/TANB distribution, total  blue line stream  length and total
catchment area.  The constants A and TANB are the upslope area and the local  slope respectively. All
the information is derived from topographic maps.

    DISCH - Measured  flow [mm/day] .


Fixed Parameters

    SUBV -  Kinematic  streamflow velocity [km/day]. Estimated from  typical values given   in the
             literature.
    RIP -     Riparian area [fraction].   Calculated  as:
             RIP = WID * BLUE / CATCHAREA
             where WID is the  average stream width, BLUE  is the total  catchment blue  line stream
             length and CATCHAREA  is the total  catchment area.
    ARLAK -  Lake area  [fraction].
    UO -     Saturated hydraulic conductivity [mm/day].  Estimated  from available data or literature
             sources.
                                          A. 1-3-10
-------
   SZQ -    Maximum baseflow rate [mm/day]. Calculated as:
            SZQ = KMAX * DEPTH * (2 * BLUE + PERIMETER)
            TERRAREA where KMAX is the maximum saturated hydraulic conductivity,
            DEPTH is the average till depth, BLUE is the blue line stream length, PERIMETER is the He
            perimeter and TERRAREA is the terrestrial area of the catchment.
  SRMAX -   Maximum storage in upper layer [mm]. Calculated as SRMAX =  ROOTDEPTH * FIELDCAP
            where ROOTDEPTH is the rooting zone depth and FIELDCAP  is the volumetric moisture
            proportion at field capacity.

Adjustable  parameters:
   PMAC-
   SZM-

Outputs

   QTOT-
   QOF-
  SMDEF -
Macropore flow parameter [fraction].  Optimum value determined from the range of 0 - 0.75.
Baseflow recession parameter [mml.  Optimum value determined from the range 4-180.
Total Streamflow per day [mm/day].
Total Overland flow per day [mm/day].
Average soil moisture deficit [mm].
                                         A. 1-3-11
-------
Table B.1.  Fixed parameter values.
Parameter
Woods
Panther
Clear Pond
RIP
ARLAK
SRMAX (mm)
SUBV12 (km/day)
DMAX3 (km)
UO1'4 (mm/day)
SZQ (mm/day)
RAINPRO8
total blue3 (m)
width blue9 (m)
total area2'6 (km2)
lake area1'2'6 (km2)
rooting depth (rrj)
average till depth |7 (m)
max permeability (mm/day)
field capacity4'10
lake perimeter (km)
1. 326X1 0"4
0.118
134.
5.
0.9
3.1 97X1 02
32.5
0.007
296.
0.9
2.12
0.25
1.0
2.3
0.134
1.76
1. 645X1 0'4
0.143
206.
5.
0.91
4.579X1 03
828.4
0.007
200.
0.9
1.12
0.17
1.0
24.5
1.12X104
0.206
1.45
1.51 9X1 0"3
0.134
150.
5.
4.21
4.493X1 02
212.3
0.007
8214.
0.9
5.21
0.7
1.0
55-° .
1.9X1049.5X102
0.150
2.95
RIP     = total blue * width blue/total area
ARLAK  = lake area/total area
SRMAX = rooting depth * field capacity
SYBV   = approximate channel flow celerity
DMAX   = maximum channel distance
UO     = average permeability
SZQ    = maximum baseflow rate
        = max permeability * ave till depth
         * (2 * total blue +  lake perimeter)/terrestrial area
RAINPRO = empirical constant for snowmelt induced by rainfall
   Peters and Murdoch, 1975. WASP 26:387-402.
   Chen et al., 1982.  ASCE J. Env. Eng. 108:455-472.
   Digitization of 1:24000 scale topographic maps.
   Smith College geology file.
   Hank Shugart, U.Va., personal communication.
   Robbins Church, personal  communication.
   Ron  Munson, personal communication.
   Chow, 1964.  Handbook of Applied Hydrology.
   Assumption.
 0 April and Newton, 1985,  WASP 26:373-386.
   Assumed a circular lake.
   Dunne and Leopold, 1978.  Water in  Environmental Planning.
                                          A. 1-3-12
-------
Table B.2.  Adjustable parameters.
Range for Optimization
Parameter
Minimum
Maximum
SZM (mm)
PMAC
TOUT (°F)
SNOPROP
4.0
0.0
10.0
0.0
180.0
0.75
50.0
0.5
Optimized Values

Period of

Catchment Optimization SZM (rnm)
Woods
Woods
Panther
Panther
Clear Pond
1 4/14/78 to 5/31/80
2 4/14/78 to 12/19/81
3 8/1/82 to 7/31/84
Calibration1
All Data2
Calibration
All data2
All data3



7.43
10.64
11.22
14.82
12.19




PMAC
0.36
0.51
0.49
0.53
0.30




TCUT(°F)
26.24
25.81
26.89
26.46
32.62




SNOPROP
0.03
0.03
0.06
0.04
0.04



                                          A. 1-3-13
-------
Table B.3.  Hessian analysis.
Woods - Calibration Period
Topmodel Parameters
Parameter
SZM
PMAC
Estimated
Optimum
7.426
.359
Std Dev
Estimate
2.742
.095
% Std Err
Estimate
36.92
26.32
Correlation matrix (R2*100) of parameter estimates

                    SZM        PMAC
SZM
PMAC
100.0
 50.7
 50.7
100.0
Epsilon indifference region is:  .19073E-01
(Based on Hessian Matrix with 2 parameters)
Snow Parameters
Parameter
TCUT
SNOPROP
Estimated
Optimum
26.244
.030
Std Dev
Estimate
1.406
.005
% Std Err
Estimate
5.36
16.60
Correlation matrix (R2*100) of parameter estimates

                   TCUT     SNOPROP
TCUT
SNOPROP
100.0
-11.0
-11.0
100.0
Epsilon indifference region is:  .51813
(Based on Hessian Matrix with 2 parameters)
                                          A. 1-3-14
-------
Table B.4.  Hessian analysis.
Panther - Calibration Period
Topmodel Parameters
Parameter
SZM
PMAC
Estimated
Optimum
11.223
.492
Std Dev
Estimate
19.625
.457
% Std Err
Estimate
174.87
92.79
Correlation matrix (R2*100) of parameter estimates

                    SZM       PMAC
SZM
PMAC
100.0
 96.4
 96.4
100.0
Epsilon indifference region is:  .53167E-02
(Based on Hessian Matrix with 2 parameters)
Snow Parameters
Parameter
TCUT
SNOPROP
Estimated
Optimum
26.888
.060
Std Dev
Estimate
.356
.003
% Std Err
Estimate
1.32
5.80
Correlation matrix (R2*100) of parameter estimates

                   TCUT     SNOPROP
TCUT
SNOPROP
100.0
 37.8
 37.8
100.0
Epsilon indifference region is:  7.3171
(Based on Hessian Matrix with 2 parameters)
                                          A. 1-3-15
-------
Table B.5.  Hessian analysis.
Clear - All Data
Topmodel Parameters
Parameter
SZM
PMAC
Estimated
Optimum
12.193
.304
Std Dev
Estimate
9.402
.393
% Std Err
Estimate
77.11
129.15
Correlation matrix (R2*100) of parameter estimates

                    SZM       PMAC
SZM
PMAC
100.0
   .5
   .5
100.0
Epsilon indifference region is:  .28393E-02
(Based on Hessian Matrix with 2 parameters)
Snow Parameters
Parameter
TCUT
SNOPROP
Estimated
Optimum
32.619
.043
Std Dev
Estimate
.751
.005
% Std Err
Estimate
2.30
11.16
Correlation matrix (R2*100) of parameter estimates

                   TCUT     SNOPROP
TCUT
SNOPROP
100.0
 -1.2
 -1.2
100.0
Epsilon indifference region is:  2.4680
(Based on Hessian Matrix with 2 parameters)
                                          A. 1-3-16
-------
Table B.6. Hessian analysis.
Woods - All Data
Topmodel Parameters
Parameter
SZM
PMAC
Estimated
Optimum
10.645
.505
Std Dev
Estimate
5.021
.064
% Std Err
Estimate
47.17
12.75
Correlation matrix (R2*100) of parameter estimates

                    SZM       PMAC
SZM
PMAC
100.0
-38.2
-38.2
100.0
Epsilon indifference region is:  .11522
(Based on Hessian Matrix with 2 parameters)
Snow Parameters
Parameter
TCUT
SNOPROP
Estimated
Optimum
25.810
.030
Std Dev
Estimate
1.426
.006
% Std Err
Estimate
5.52
19.75
Correlation matrix (R2*100) of parameter estimates

                   TCUT     SNOPROP
TCUT
SNOPROP
100.0
  1.4
  1.4
100.0
 Epsilon indifference region is:  .43858
 (Based on Hessian Matrix with 2 parameters)
                                           A. 1-3-17
-------
Table B.7.  Hessian analysis.
Panther - All Data
Topmodel Parameters
Parameter
SZM
PMAC
Estimated
Optimum
14.818
.533
Std Dev
Estimate
***
***
% Std Err
Estimate
***
***
Correlation matrix (R2*100) of parameter estimates

                    SZM        PMAC
SZM
PMAC
   ***
   ***
   ***
   ***
Epsilon indifference region is:  1.0508
(Based on Hessian Matrix with 2 parameters)
Snow Parameters
Parameter
TCUT
SNOPROP
Estimated
Optimum
26.463
.044
Std Dev
Estimate
.970
.005
% Std Err
Estimate
3.67
10.39
Correlation matrix (R2*100) of parameter estimates

                   TCUT     SNOPROP
TCUT
SNOPROP
100.0
  3.7
  3.7
100.0
Epsilon indifference region is:  1.0230
(Based on Hessian Matrix with 2 parameters)
                                          A. 1-3-18
-------
                     SECTION C: EVALUATION OF MSE FOR THE HYDROLOGICAL MODEL

                     Table C.1                 MSE of cumulative flow

                     Table C.2                 MSE of daily flow (in units of m3/s)

                     Table C.3                 MSE of daily flow and efficiency (in units of mm/day)
                     Table C.4
In each table, entries are made for:
MSE of average monthly discharge and efficiency
(in units of mm/day)
                     a) Snowmodel vs. TOPMODEL contributions to MSE
                     b) Calibration vs. corroboration period MSE values (for Woods and Panther)
                     c) MSE values for the models calibrated to all available data (for Woods and Panther)
                                         A. 1-3-19
-------
Table C.1.  MSB of Cumulative Flow.

Watershed
Woods
Woods
Panther
Panther
Clear Pond
Period of
Optimization
Calibration1
All data2
Calibration1
Ail data2
All data3
Number of
Time steps
1287
1287
1287
1287
372

MSE ([mm]2)
5986.
11975.
103788.
98726.
35664.

MSE ([m3]2)
2.69X1 010
5.38X1 010
1.52X1011
1.44X1011
9.67X1 011
   4/14/78 to 5/31/80
   4/14/78 to 12/19/81
   8/01/82 to 7/31/84
                                          A. 1-3-20
-------
Table C.2.  MSE ([m3/s]2) of daily flow.
Watershed
Woods
Woods
Woods
Woods
Woods
Woods
Panther
Panther
Panther
Panther
Panther
Panther
Clear Pond
Clear Pond
Period
Calibration1
Calibration
Corroboration2
Corroboration
All data3
All data3
Calibration
Calibration
Corroboration
Corroboration
All Data3
All Data3
All Data4
All Data4
Model
TOPMQDEL5
SNOW6
TOPMODEL
SNOW
TOPMODEL
SNOW
TOPMODEL
SNOW
TOPMODEL
SNOW
TOPMODEL
SNOW
TOPMODEL
SNOW
MSE ([m3/s]2)
69.X10'5
288.X10"5
188.X10'5
455.X10"5
101.X10"5
252.X10'5
7.X10'5
124X10"5
214.X10"5
429.X10"5
7.X10"5
45.X10'5
120X10"5
2190.X10"5
I 4/14/78 to 5/31/80
2 6/1/80 to 12/19/81
3 4/14/78 to 12/19/81
4 8/1/82 to 7/31/84
  Optimizing TOPMODEL parameters from June to October.
  Optimizing SNOW parameters during entire year.
                                          A. 1-3-21
-------
Table C.3.  MSB ([mm/day] )  and efficiency of daily flow.
Watershed
Woods
Woods
Woods
Woods
Woods
Woods
Panther
Panther
Panther
Panther
Panther
Panther
Clear Pond
Clear Pond
Period
Calibration1
Calibration
Corroboration2
Corroboration
All data3
All data3
Calibration
Calibration
Corroboration
Corroboration
All Data3
All Data3
All Data4
All Data4
Model
TOPMODEL5
SNOW6
TOPMODEL
SNOW
TOPMODEL
SNOW
TOPMODEL
SNOW
TOPMODEL
SNOW
TOPMODEL
SNOW
TOPMODEL
SNOW
MoSE o
([m3/s]2)
1.15
4.78
3.12
7.56
1.67
4.19
0.38
6.31
3.12
7.56
0.38
2.29
0.33
6.02
Number of
time steps
296
721
306
567
602
1288
295
720
306
567
601
1287
186
372
Eff
0.54
0.01
0.50
0.20
0.63
0.39
0.63
-0.75
-6.73
-5.38
0.70
0.21
0.76
0.38
I 4/14/78to 5/31/80
6/1/80 to 12/19/81
3 4/14/78 to 12/19/81
8/1/82 to 7/31/84
5 Optimizing TOPMODEL parameters from June to October.
Optimizing SNOW parameters during entire year.
                                             A. 1-3-22
-------
Table C.4.  ([mm/day] ) and efficiency of monthly-averaged flow.
Watershed
Woods
Woods
Woods
Woods
Woods
Woods
Panther
Panther
Panther
Panther
Panther
Panther
Clear Pond
Clear Pond
Period
Calibration1
Calibration
x Corroboration2
Corroboration
All data3
All data3
Calibration
Calibration
Corroboration
Corroboration
All Data3
All Data3
All Data4
All Data4
Model
TOPMQDEL5
SNOW6
TOPMODEL
SNOW
TOPMODEL
SNOW
TOPMODEL
SNOW
TOPMODEL
SNOW
TOPMODEL
SNOW
TOPMODEL
SNOW
MSE ([mm/day]
0.18
0.30
0.31
1.32
0.30
1.32
0.17
0.76
0.25
0.72
0.16
0.63
0.22
3.08
Number of
)time steps
9
23
10
18
19
18
9
23
10
18
19
41
6
11
Eff
0.78
0.86
0.76
0.43
0.76
0.64
0.69
0.69
0.56
0.24
0.72
0.64
0.70
0.48
I 4/14/78to5/31/80
, 6/1/80 to 12/19/81
3 4/14/78 to 12/19/81
4 8/1/82 to 7/31/84
   >*rf^l.ll I ll«_ll iy I V^l t VI V>« L-f 1—I— ^Jf^.1 dl I IGLtslO II Wl II WLJI 1C i\J
   Optimizing SNOW parameters during entire year.
                                              A. 1-3-23
-------
Table D.1. Woods calibration period (units of MSE are (m /s) ).
Gradients
Parameter        Del(MSE)/Del(Par+)
            Unnormalized     Normalized
                                    Del(MSE)/Del(Par-)
                              Unnormalized     Normalized
SZQ
SZM
UO
SRMAX
SUBV
RIP
PMAC
TOUT
SNOPROP
RAINPRO
ARLAK
.1541E-04
-.4130E-03
0.
-.6622E-06
0.
.5015E-03
.6110E-04
.5460E-04
.2866E-01
.3336E-01
.5507E-03
.4999E-03
-.3066E-02
0.
-.8687E-04
0.
.6020E-07
.2197E-04
.1433E-02
.8619E-03
.2335E-03
.6502E-04
.1192E-04
-.4865E-03
0.
.1806E-06
0.
.5013E-03
-.4405E-03
-.3499E-03
-.8121E-02
.2192E-01
.4122E-03
.3867E-03
-.3613E-02
0.
.2516E-04
0.
.6020E-07
-.1583E-03
-.9182E-02
-.2442E-03
.1534E-03
.4864E-04
Gradient Inverses
Parameter        Del(Par+)/Del(MSE)
            Unnormalized     Normalized
                                    Del(Par-)/DeI(MSE)
                              Unnormalized     Normalized
SZQ
SZM
SRMAX
RIP
PMAC
TCUT
SNOPROP
RAINPRO
ARLAK
.6501E+05
-2422.
-.1542E+07
1994.
.1637E+05
.1831E+05
34.88
29.90
1816.
2000.
-326.1
-.1151E+05
.1504E+08
.4553E + 05
697.7
1160.
4282.
.1539E+05
.8404E + 05
-2055.
.5331E +07
1995.
-2270.
-2858.
-123.1
45.68
2426.
2586.
-276.7
.3978E + 05
.1504E + 08
-6316.
-109.0
-4094.
6517.
.2056E + 05
                                         A. 1-3-24
-------
Table D.2.  Woods corroboration period (units of MSB are (m /s) ).
Gradients
Parameter         Del(MSE)/Del(Par+)
             Unnormalized      Normalized
      Del(MSE)/Del(Par-)
Unnormalized     Normalized
SZQ
SZM
UO
SRMAX
SUBV
RIP
PMAC
TOUT
SNOPROP
RAINPRO
ARLAK
.2101 E-04
-.3347E-03
0.
0.
0.
-.4687E-03
-.4250E-02
.4006E-03
-.3483E-01
-.2228E-01
-.3918E-03
.6836E-03
-.2485E-02
0.
0.
0.
-.6020E-07
-.1528E-02
.1051E-01
-.1047E-02
-.1559E-03
-.4623E-04
.1944E-04
-.3770E-03
0.
0.
0.
-.4688E-03
-.2237E-02
.2275E-03
.4298E-01
.1232E-01
-.4543E-03
.6335E-03
-.2799E-02
0.
0.
0.
-.6020E-07
-.8042E-03
.5971 E-02
.1293E-02
.8621 E-04
-.5358E-04
Gradient Inverses
Parameter         Del(Par+)/Del(MSE)
             Unnormalized     Normalized
      Del(Par-)/Del(MSE)
Unnormalized     Normalized
SZQ
SZM
RIP
PMAC
TOUT
SNOPROP
RAINPRO
ARLAK
.4754E+05
-2988.
-2134.
-235.4
2496.
-28.74
-44.85
-2552.
1463.
-402.3
-.1609E+08
-654.7
95.18
-954.7
-6413.
-.2163E+05
.5139E + 05
-2653.
-2133.
-447.0
4395.
23.26
81.23
-2201.
1581.
-357.3
.1608E+08
-1244.
167.4
773.6
.1160E + 05
-.1866E + 05
                                           A. 1-3-25
-------
Table D.3.  Panther calibration period (units of MSB are (m3/s)2).
Gradients
Parameter         Del(MSE)/Del(Par+)
             Unnormalized      Normalized
      Del(MSE)/Del(Par-)
Unnormalized     Normalized
SZQ
S2M
UO
SRMAX
SUBV
RIP
PMAC
TCUT
SNOPROP
RAINPRO
ARLAK
.2548E-06
-.1232E-03
0.
-.3373E-04
0.
-.1213E-02
-.1393E-01
.5391 E-03
.1336E-01
.1345
-.1161E-02
.2046E-03
.1383E-02
0.
-.6951 E-02
0.
-.1960E-06
-.6862E-02
.1450E-01
.1450E-01
.9418E-03
-.1660E-03
.2548E-06
-.3489E-03
0.
-.1023E-03
0.
-.1214E-02
-.4611E-01
-.1886E-02
-.4333E-01
.1227
-.1260E-02
.2104E-03
-.3915E-02
0.
-.2107E-01
0.
-.1960E-06
-.2271 E-01
-.5071 E-01
-.2600E-02
.8588E-03
-.1801 E-03
Gradient Inverses
Parameter         Del(Par+)/Del(MSE)
            Unnormalized      Normalized
      Del(Par-)/Del(MSE)
Unnormalized     Normalized
SZQ
SZM
SRMAX
RIP
PMAC
TCUT
SNOPROP
RAINPRO
ARLAK
.4049E+07
8114.
-.2964E+05
-824.0
-71.94
1855.
75.00
7.653
-861.7
4888.
723.0
-143.9
-.5004E+07
-145.9
68.88
1248.
1062.
-6025.
.3938E+07
-2866.
-9778.
-824.0
-21.94
-530.1
-22.96
8.163
-793.9
4754.
-255.6
-47.45
.5003E+07
-43.88
-19.90
-384.7
1164.
-5552.
                                          A. 1-3-26
-------
Table D.4.  Panther corroboration period (units of MSE are (m3/s)2).
Gradients
Parameter
SZQ
SZM
UO
SRMAX
SUBV
RIP
PMAC
TCUT
SNOPROP
RAINPRO
ARLAK
Del(MSE)/Del(Par+)
Unnormalized Normalized
.8036E-06
-.4813E-03
0.
0.
0.
-.5364E-02
-.2861 E-01
-.4085E-03
.3067
.7861 E-01
-.5277E-02
.6605E-03
-.5402E-02
0.
0.
0.
-.8820E-06
-.1409E-01
-.1099E-01
.1840E-01
.5502E-03
-.7546E-03
Del(MSE)/Del(Par-)
Unnormalized Normalized
.8428E-06
-.4926E-03
0.
0.
0.
-.5364E-02
-.1304E-01
-.3724E-03
.2110
.7896E-01
-.5451 E-02
.6908E-03
-.5529E-02
0.
0.
0.
-.8820E-06
-.6423E-02
-.1001 E-01
.1266E-01
.5527E-03
-.7795E-03
Gradient Inverses
Parameter
SZQ
SZM
RIP
PMAC
TCUT
SNOPROP
RAINPRO
ARLAK
Del(Par+)/Del(MSE)
Unnormalized Normalized
.1254E+07
-2078.
-186.2
-34.69
-2447.
3.061
12.76
-189.3
1514.
-185.2
-.1132E + 07
-70.92
-90.82
54.59
1817.
-1325.
Del(Par-)/Del(MSE)
Unnormalized Normalized
.1199E+07
-2030.
-186.2
-76.53
-2685.
4.592
12.76
-183.7
1447.
-181.1
-.1132E + 07
-155.6
-100.0
79.08
1809.
-1283.
                                             A. 1-3-27
-------
Table D.5.  Clear Pond all data (units of MSE are (m3/s)2).
Gradients
Parameter
SZQ
SZM
UO
SRMAX
SUBV
RIP
PMAC
TOUT
SNOPROP
RA1NPRO
ARLAK
DeI(MSE)/Del(Par+)
Unnormalized Normalized
.2184E-05
-.2876E-03
0.
0.
0.
-.9724E-02
.1373E-01
.8605E-02
.1092
-.2618
-.9713E-02
.4575E-03
-.3506E-02
0.
0.
0.
-.1492E-04
.4178E-02
.2807
.4739E-02
-.1833E-02
-.1301E-02
Del(MSE)/DeI(Par-)
Unnormalized Normalized
.2912E-05
-.4142E-03
0.
0.
0.
-.9726E-02
.1384E-01
-.1908E-02
-.2277
-.2837
-.9835E-02
.6166E-03
-.5050E-02
0.
0.
0.
-.1492E-04
-.4210E-02
-.6225E-01
.9883E-02
-.1986E-02
-.1318E-02
Gradient Inverses
Parameter
SZQ
SZM
RIP
PMAC
TCUT
SNOPROP
RAINPRO
ARLAK
Del(Par+)/Del(MSE)
Unnormalized Normalized
.4640E+06
-3478.
-102.9
72.83
116.2
9.148
-3.819
-103.0
2185.
-285.2
-.6770E+05
239.3
3.571
211.0
-545.7
-768.4
Del(Par-)/Del(MSE)
Unnormalized Normalized
.3442E+06
-2415.
-102.8
72.28
-524.0
-4.396
-3.516
-101.7
1621.
-198.0
-.6768E+05
237.5
-16.07
-101.2
-503.6
-758.8
                                             A. 1-3-28
-------
Table D.6.  Woods all data (units of MSB are (m3/s)2).
Gradients
Parameter         Del(MSE)/Del(Par+)
             Unnormalized     Normalized
                                    Del(MSE)/Del(Par-)
                               Unnormalized     Normalized
SZQ
SZM
UO
SRMAX
SUBV
RIP
PMAC
TOUT
SNOPROP
RAINPRO
ARLAK
.1385E-05
-.9734E-04
0.
-.3853E-05
0.
.1052E-02
-.2262E-03
.2349E-03
.1380E-01
-.4933E-02
.1151E-02
.4461 E-04
-.1036E-02
0.
-.5171E-03
0.
.1204E-04
-.1143E-03
.6062E-02
.4150E-03
-.3455E-04
.1359E-03
-.3612E-06
-.1286E-03
0.
-.5900E-05
0.
.1051E-02
-.3277E-02
-.1474E-03
-.1186E-01
-.1630E-01
.9971 E-03
-.1156E-04
-.1369E-02
0.
-.7921 E-03
0.
.1204E-06
-.1656E-02
-.3805E-02
-.3567E-03
-.11 41 E-03
.1153E-03
Gradient Inverses
Parameter        Del(Par+)/DeI(MSE)
            Unnormalized     Normalized
                                    DeI(Par-)/Del(MSE)
                              Unnormalized     Normalized
SZQ
SZM
SRMAX
RIP
PMAC
TCUT
SNOPROP
RAINPRO
ARLAK
.7282E+06
-.1027E+05
-.2592E+06
951.0
-4421.
4258.
72.43
-202.7
868.4
.2241E +05
-965.3
-1934.
.7172E+07
-8745.
165.0
2410.
-.2896E + 05
7360.
-.2814E+07
-7774.
-.1692E + 06
951.2
-305.1
-6783.
-84.22
-61.30
1023.
-.8659E + 05
-730.2
-1262.
.7173E + 07
-603.7
-262.8
-2804.
-8765.
8673.
                                         A. 1-3-29
-------
Table D.7.  Panther all data (units of MSE are (m /s) ).
Gradients
Parameter         Del(MSE)/Del(Par+)
             Unnormalized      Normalized
      DelI (MSE) /Del (Par-)
Unnormalized     Normalized
SZQ
SZM
UO
SRMAX
SUBV
RIP
PMAC
TOUT
SNOPROP
RAINPRO
ARIAK
.1960E-07
-.8052E-04
0.
.2744E-06
0.
-.9157E-04
-.1767E-02
.7648E-04
.1145E-01
.8317E-02
-.7619E-04
.2207E-04
-.1193E-02
0.
.5517E-04
0.
-.1960E-07
-.9421 E-03
.2024E-02
.5033E-03
.5821 E-04
-.1090E-04
.3920E-07
-.1246E-03
0.
-.3332E-06
0.
-.91 61 E-04
-.5374E-02
-.1994E-03
-.9211E-02
-.1063E-02
-.1037E-03
.2558E-04
-.1847E-02
0.
-.6819E-04
0.
-.1960E-07
-.2865E-02
-.5277E-02
-.4050E-03
-.7448E-05
-.1482E-04
Gradient Inverses
Parameter         Del (Par+)/Del (MSE)
             Unnormalized      Normalized
      Del(Par-)/Del(MSE)
Unnormalized     Normalized
SZQ
SZM
SRMAX
PMAC
TOUT
SNOPROP
RAINPRO
ARLAK
.3753E+08
-.1242E+05
.3734E+07
-565.8
.1308E+05
87.24
120.4
-.1313E+05
.4531 E +05
-838.3
.1813E + 05
-1062.
493.9
1987.
.1718E+05
-.9180E + 05
.3239E + 08
-8024.
-.3021 E+07
-186.2
-5015.
-108.7
-940.3
-9644.
.3910E + 05
-541.3
-.1466E + 05
-349.0
-189.3
-2469.
-.1343E+06
-.6744E+05
                                          A. 1-3-30
-------
Table D.8.  Woods calibration period (units of MSB are (mm/day)2).
Gradients
Parameter
SZQ
SZM
UO
SRMAX
SUBV
RIP
PMAC
TCUT
SNOPROP
RA1NPRO
ARLAK
Del(MSE)/Del(Par+)
Unnormalized Normalized
.0256
-.6860
.0000
-.0011
.0000
.8330
.1015
.0907
47.6024
55.4152
.9148
.8304
-5.0937
.0000
-.1443
.0000
.0001
.0365
2.3808
1.4317
.3879
.1080
Del(MSE)/Del(Par-)
Unnormalized Normalized
.0198
-.8082
.0000
.0003
.0000
.8327
-.7317
-.5812
-13.4899
36.4135
.6847
.6424
-6.0017
.0000
.0418
.0000
.0001
-.2630
-15.2533
-.4057
.2549
.0808
Gradient Inverses
Parameter         Del(Par+)/Del(MSE)
             Unnormalized     Normalized
      Del(Par-)/Del(MSE)
Unnormalized   .  Normalized
SZQ
SZM
UO
SRMAX
SUBV
RIP
PMAC
TCUT
SNOPROP
RAINPRO
ARLAK
39.1370
-1.4578
***
-928.3128
***
1.2005
9.8520
11.0231
.0210
.0180
1.0931
1.2042
-.1963
***
-6.9277
***
9053.7399
27.4102
.4200
.6985
2.5779
9.2635
50.5895
- -1.2373
***
3209.0907
***
1.2010
-1.3667
-1.7206
-.0741
.0275
1.4604
1.5566
-.1666
***
23.9484
***
9056.9471
-3.8024
-.0656
-2.4647
3.9232
12.3765
                                          A.1-3-31
-------
Table D.9.  Woods corroboration period (units of MSB are (mm/day) ).
Gradients
Parameter         Del(MSE)/Del(Par+)
             Unnormalized      Normalized
      Del(MSE)/Del(Par-)
Unnormalized     Normalized
SZQ
SZM
UO
SRMAX
SUBV
RIP
PMAC
TCUT
SNOPROP
RAINPRO
ARLAK
.0349
-.5560
.0000
.0000
.0000
-.7785
-7.0594
.6654
-57.8502
-37.0032
-,6509
1.1356
-4.1286
.0000
.0000
.0000
-.0001
-2.5374
17.4641
-1.7400
-.2590
-.0768
.0323
-.6262 '
.0000
.0000
.0000
-.7788
-3.7165
.3779
71.3914
20.4611
-.7546
1.0506
-4.6498
.0000
.0000
.0000
-.0001
-1.3358
9.9186
2.1472
.1432
-.0890
Gradient Inverses
Parameter
SZQ
SZM
UO
SRMAX
SUBV
RIP
PMAC
TCUT
SNOPROP
RAINPRO
ARLAK
Del(Par+)/Del(MSE)
Unnormalized Normalized
28.6199
-1.7986
***
***
***
-1.2845
-.1417
1.5028
-.0173
-.0270
-1.5364
.8806
-.2422
***
***
***
-9686.6915
-.3941
.0573
-.5747
-3.8607
-13.0207
Del(Par-)/Del(MSE)
Unnormalized Normalized
30.9360
-1.5970
***
***
***
-1.2840
-.2691
2.6460
.0140
.0489
-1.3253
.9519
-.2151
***
***
***
-9683.0026
-.7486
.1008
.4657
6.9819
-11.2312
                                           A. 1-3-32
-------
Table D.10.  Panther calibration period (units of MSB are (mm/day)2).
Gradients
Parameter
SZQ
SZM
UO
SRMAX
SUBV
RIP
PMAC
TOUT
SNOPROP
RAINPRO
ARLAK
Del(MSE)/Del(Par+)
Unnormalized Normalized
.0013
.6288
.0000
-.1721
.0000
-6.1907
-71.0944
2.7505
68.1417
686.4570
-5.9217
1.0437
7.0565
.0000
-35.4626
.0000
-.0010
-35.0101
73.9547
4.0885
4.8052
-.8468
Del(MSE)/Del(Par-)
Unnormalized Normalized
.0013
-1.7799
.0000
-.5218
.0000
-6.1914
-235.2789
-9.6232
-221.0526
625.9177
-6.4268
1.0733
-19.9757
.0000
-107.4959
.0000
-.0010
-115.8622
-258.7487
-13.2632
4.3814
-.9190
Gradient Inverses
Parameter
SZQ
SZM
UO
SRMAX
SUBV
RIP
PMAC
TOUT
SNOPROP
RAINPRO
ARLAK
Del(Par+)/DeI(MSE)
Unnormalized Normalized
793.6812
1.5904
***
-5.8089
***
-.1615
-.0141
.3636
.0147
.0015
-.1689
.9581
.1417
***
-.0282
***
-980.7610
-.0286
.0135
.2446
.2081
-1.1809
Del(Par-)/Del(MSE)
Unnormalized Normalized
771.7963
-.5618
***
-1.9164
***
-.1615
-.0043
-.1039
-.0045
.0016
-.1556
.9317
-.0501
***
-.0093
***
-980.6621
-.0086
-.0039
-.0754
.2282
-1.0881
                                             A. 1-3-33
-------
Table D.11.  Panther corroboration period (units of MSE are (mm/day) ).
Gradients
Parameter         Del(MSE)/Del(Par+)
             Unnormalized     Normalized
      Del(MSE)/Del(Par-)
Unnormalized     Normalized
SZQ
S2M
UO
SRMAX
SUBV
RIP
PMAC
TCUT
SNOPROP
RAINPRO
ARLAK
.0041
-2.4558
.0000
.0000
.0000
-27.3687
-145.9920
-2.0844
1564.8390
401.0485
-26.9225
3.3699
-27.5609
.0000
.0000
.0000
-.0045
-71.8932
-56.0466
93.8903
2.8073
-3.8499
.0043
-2.5134
.0000
.0000
.0000
-27.3697
-66.5425
-1.9002
1076.3864
402.8714
-27.8124
3.5247
-28.2079
.0000
.0000
.0000
-.0045
-32.7686
-51.0923
64.5832
2.8201
-3.9772
Gradient Inverses
Parameter
SZQ
SZM
UO
SRMAX
SUBV
RIP
PMAC
TCUT
SNOPROP
RAINPRO
ARLAK
Del (Par +) /Del (MSE)
Unnormalized Normalized
245.8209
-.4072
***
***
***
-.0365
-.0068
-.4797
.0006
.0025
-.0371
.2967
-.0363
***
***
***
-221.8466
-.0139
-.0178
.0107
.3562
-.2597
Del(Par-)/Del(MSE)
Unnormalized Normalized
235.0261
-.3979
***
***
***
-.0365
-.0150
-.5263
.0009
.0025
-.0360
.2837
-.0355
***
***
***
-221.8383
-.0305
-.0196
.0155
.3546
-.2514
                                           A. 1-3-34
-------
Table D.12. Clear Pond all data (units of MSE are (mm/day)2).
Gradients
Parameter        Del(MSE)/Del(Par+)
            Unnormalized     Normalized
                                    Del(MSE)/Del(Par-)
                              Unnormalized     Normalized
SZQ
SZM
UO
SRMAX
SUBV
RIP
PMAC
TOUT
SNOPROP
RAINPRO
ARLAK
.0006
-.0790
.0000
.0000
.0000
-2.6713
3.7721
2.3641
29.9893
-71.9266
-2.6683
.1257
-.9631
.0000
.0000
.0000
-.0041
1.1478
77.1153
1.3018
-.5035
-.3575
.0008
-.1138
.0000
.0000
.0000
-2.6721
3.801 1
-.5243
-62.5493
-77.9372
-2.7018
.1694
-1.3874
.0000
.0000
.0000
-.0041
1.1567
-17.1014
-2.7152
-.5456
-.3620
Gradient Inverses
Parameter         Del(Par+)/Del(MSE)
            Unnormalized     Normalized
                                    Del(Par-)/Del(MSE)
                              Unnormalized     Normalized
SZQ
SZM
UO
SRMAX
SUBV
RIP
PMAC
TCUT
SNOPROP
RAINPRO
ARLAK
1688.8323
-12.6600
  ***
  ***
  ***
-.3744
.2651
.4230
.0333
-.0139
-.3748
7.9549
-1.0383
  ***
  ***
  ***
-246.4454
.8712
.0130
.7682
-1.9862
-2.7968
1252.9200
-8.7888
  ***
  ***
  ***
-.3742
.2631
-1.9074
-.0160
-.0128
-.3701
5.9016
-.7208
  ***
  ***
  ***
-246.3694
.8646
-.0585
-.3683
-1.8330
-2.7621
                                          A. 1-3-35
-------
Table D.13.  Panther all data (units of MSE are (mm/day)2).
Gradients
Parameter         Del(MSE)/Del(Par+)
             Unnormalized      Normalized
      Del(MSE)/Del(Par-)
Unnormalized     Normalized
SZQ
SZM
UO
SRMAX
SUBV
RIP
PMAC
TOUT
SNOPROP
RAINPRO
ARU\K
.0001
-.4108
.0000
.0014
.0000
-.4672
-9.0147
.3902
58.4028
42.4317
-.3887
.1126
-6.0868
.0000
.2815
.0000
-.0001
-4.8065
10.3257
2.5680
.2970
-.0556
.0002
-.6358
.0000
-.0017
.0000
-.4674
-27.4182
-1.0174
-46.9972
-5.4257
-.5290
.1305
-9.4217
.0000
-.3479
.0000
-.0001
-14.6189
-26.9230
-2.0665
-.0380
-.0756
Gradient Inverses
Parameter        Del(Par+)/Del(MSE)
            Unnormalized     Normalized
      Del(Par-)/Del(MSE)
Unnormalized     Normalized
SZQ
SZM
UO
SRMAX
SUBV
RIP
PMAC
TCUT
SNOPROP
RAINPRO
ARLAK
7356.7523
-2.4344
***
731.8991
***
-2.1404
-.1109
2.5629
.0171
.0236
-2.5729
8.8807
-.1643
***
3.5529
***
***
-.2081
.0968
.3894
3.3668
-17.9922
6348.0179
-1.5728
***
-592.0814
***
-2.1396
-.0365
-.9829
-.0213
-.1843
-1.8903
7.6630
-.1061
***
-2.8742
***
***
-.0684
-.0371
-.4839
-26.3297
-13.2191
                                          A. 1-3-36
-------
Table D.14.  Woods all data (units of MSE are (mm/day) ).
Gradients
Parameter         Del(MSE)/Del(Par+)
             Unnormalized      Normalized
      Del(MSE)/De!(Par-)
Unnormalized     Normalized
SZQ
SZM
UO
SRMAX
SUBV
RIP
PMAC
TOUT
SNOPROP
RAINPRO
ARLAK
.0023
-.1617
.0000
-.0064
.0000
1.7468
-.3758
.3902
22.9272
-8.1943
1.9127
.0741
-1.7210
.0000
-.8589
.0000
.0002
-.1899
10.0697
.6893
-.0574
.2257
-.0006
-.2137
.0000
-.0098
.0000
1.7464
-5.4433
-.2449
-19.7071
-27.0751
1.6231
-.0192
-2.2746
.0000
-1.3157
.0000
.0002
-2.7515
-6.3207
-.5925
-.1895
.1915
Gradient Inverses
Parameter
SZQ
SZM
UO
SRMAX
SUBV
RIP
PMAC
TOUT
SNOPROP
RAINPRO
ARLAK
Del(Par+)/Del(MSE)
Unnormalized Normalized
438.3686
-6.1853
***
-156.0121
***
.5725
-2.6612
2.5631
.0436
-.1220
.5228
13.4883
-.5811
***
-1.1643
***
4317.3967
-5.2646
.0993
1.4508
-17.4338
4.4307
Del(Par-)/Del(MSE)
Unnormalized Normalized
-1694.0493
-4.6798
***
-101.8441
***
.5726
-.1837
-4.0833
-.0507
-.0369
.6161
-52.1246
-.4396
***
-.7600
***
4318.1945
-.3634
-.1582
-1.6879
-5.2763
5.2214
                                           A. 1-3-37
-------
                     SECTION E: OPTIMIZATION PROTOCOL FOR MAGIC
                                  (CHEMICAL FLUX MODEL)
                              Description of optimization protocol
Table E.1      Values and sources of fixed parameter values
Table E.2      Ranges adjustable  parameters for optimization
Table E.3      Optimal values of adjustable parameters (Woods Lake, calibration period and entire record)
Table E.4      Optimal values of  adjustable parameters (Panther Lake,  calibration period  and entire
              record)
Table E.5      Optimal values of adjustable parameters  (Clear Pond, entire record)
Table E.6      Results of Hessian  analysis on optimized parameters (Woods Lake, calibration period)
Table E.7      Results of Hessian  analysis on optimized parameters (Panther Lake, calibration period)
Table E.8      Results of Hessian analysis on optimized parameters (Clear Pond, entire period of record)
                                            A. 1-3-38
-------
Table E.1.  Fixed parameter values for MAGIC - DDRP northeast special interest watersheds.
Parameter
Woods
Panther
Clear
Soil Depth (m)1
  A + B                   0.62          0.68
  C                      1.68         23.82

Bulk Density (kg/m3)2
  A+B                1009.         1283.
  C                   1620.         1549.

Porosity (frac)3
  A + B                   0.62          0.52
  C                      0.39          0.42

CEC (meq/kg)4
  A+B                 121.1          94.7
  C                     20.9          11.4

KAL(OH)35
  A+B                   7.9           7.9
  C                      9.6           9.6
  Lake                   8.9           8.9

SO4 Halfsat. (meq/m3)6
  A + B                 150           150
  C                    150           150

Lake Area/Basin Area7     0.12          0.14

Lake Residence Time(y)8   0.54          0.68
                             0.55
                            54.45
                          1140.
                          1590.
                             0.57
                             0.40
                            33.8
                             9.0
                             7.9
                             9.6
                             8.9
                           150
                           150

                             0.14

                             1.98
1 The average start depth of the "C" horizon for all available samples in the ILWAS file SMIGEOL on card
85 was used as the depth of the A+B horizons.  The sample sizes and standard deviations were 12 and
0.14 m for Woods and 17 and 0.16 m for Panther. The Clear Pond catchment value was based on one
sample in RILWAS file COLX.  The C horizon thickness was the difference between the average depth
to bedrock and the A+B depth. The average depths to bedrock were those cited by Peters and Murdoch
(1985) for Woods and Panther and a value of 55 m for Clear Pond was a preliminary value for the deep
till region determined by Robert Newton (Ron Munson, personal communication).

2  Bulk density =  (1-porosity)*2.65*1000, where 2.65 g/cc  is an assumed specific gravity of the soil
solids.

3  Porosity was assumed to be equal to the percent soil moisture by volume at saturation.  The values
used are the depth-weighted averages of one profile each from the Woods and Panther catchments found
on  card 93  of the SMIGEOL file.  Since no data  were available for the  Clear Pond catchment, the
averages of Woods and Panther were used.

4  CEC data for Woods and  Panther were obtained from the ILWAS file UMACEC.   Depth-weighted
averages were used.  For Woods 7 profiles were averaged for the A+B value and 6 for the C.  The
                                           A. 1-3-39
-------
Panther averages were based on 9 profiles of the A+B and 6 of the C. The values for Clear Pond were
based on one profile from the COLX RILWAS file.

                                    Table E.1.  Continued.

5 KALOH3 values were based on concentrations of total monomeric aluminum and hydrogen  ion found
in the ILWAS file CORAL and from the analysis by Schofield (1983).

6 SO4 half-saturation constant.  Assumed.

7 ILWAS values are from Peters and Murdoch (1985). Clear Pond lake and catchment areas supplied
by EPA (M. R. Church, pers. com.).
Q
  Lake residence time was calculated as lake volume divided by mean annual discharge.  Lake volumes
for Woods and Panther are from Peters and  Murdoch (1985).  Mean annual discharges were calculated
from the ILWAS file USGFLOW.  Clear Pond volume was estimated from the hypsographic curve supplied
by Tetra Tech and discharge from RILWAS file USGSTAGE and the observed yield.
                                          A. 1-3-40
-------
Table E.2.  Adjustable parameters for MAGIC.
Parameter
    	Ranges for Optimization	

   Units          Minimum        Maximum
UP(NH4L) %
UP(N03L) %
UP(NH4A+B) %
UP(NO3A+B) %
0.0
0.0
0.0
0.0
100.0
100.0
100.0
100.0
EMX

WE(Ca)
WE(Mg)
WE(Na)
WE(K)

log(SAlCa)
log(SAIMg)
log(SAINa)
log(SA1K)

COZ(A+B)
COZ(C)
  meq/kg

meq/m2/yr
meq/m2/yr
meq/m2/yr
meq/m2/yr
 0.0

 1.0
 1.0
 1.0
 1.0

-5.00
-5.00
-5.00
-5.00

  .30
  .30
 15.0

150.0
150.0
100.0
100.0

  5.00
  5.00
  5.00
  5.00

 10.00
 10.00
                                         A. 1-3-41
-------
Table E.3. Adjustable parameters for MAGIC.
                           	Optimized Values	
Parameter
UP(NH4L)
UP(NO3L)
UP(NH4A+B)
UP(NO3A+B)
Woods Lake
calibration
Units period
% 86.3
% 59.8
% 92.2
% 73.9
Woods Lake
entire period
of record
87.3
57.1
91.8
68.2
EMX

WE(Ca)
WE(Mg)
WE(Na)
WE(K)

log(SAICa)
log(SAIMg)
log(SAINa)
log(SA1K)

COZ(A+B)
COZ(C)
  meq/kg

meq/m2/yr
meq/m2/yr
meq/m2/yr
meq/m2/yr
 8.30

13.4
 1.3
 1.0
 1.2

 2.62
 1.70
-3.67
-4.99

10.00
 1.55
 7.14

13.5
 1.5
 1.0
 1.0

 3.45
  .21
-2.96
-4.99

 5.35
 1.32
                                        A. 1-3-42
-------
Table E.4.  Adjustable parameters for MAGIC.
                           	Optimized Values	

Parameter
UP(NH4L)
UP(NO3L)
UP(NH4A+B)
UP(NO3A+B)
Panther Lake
calibration
Units period
% 91.2
% 55.0
% 93.8
% 60.8
Panther Lake
entire period
of record
93.0
56.5
94.9
60.9
EMX

WE(Ca)
WE(Mg)
WE(Na)
WE(K)

log(SAICa)
log(SAIMg)
log(SA1Na)
log(SA1K)

COZ(A+B)
COZ(C)
  meq/kg

meq/m2/yr
meq/m2/yr
meq/m2/yr
meq/m2/yr
   .67

112.7
 22.8
 24.5
  7.6

  3.63
  4.44
  3.76
 -4.01

   .33
  4.68
   .67

118.2
 28.3
 20.0
  1.0

  4.50
  2.78
 -2.46
  2.22

  2.94
  4.36
                                         A. 1-3-43
-------
Table E.5.  Adjustable parameters for MAGIC.
           	Optimized Values	
Parameter
   Units
 Clear Pond
entire period
 of record
UP(NH4L)
UP(NO3L)
UP(NH4A+B)
UP(NO3A+B)

EMX

WE(Ca)
WE(Mg)
WE(Na)
WE(K)

log(SAlCa)
log(SAIMg)
log(SAINa)
log(SA1K)

COZ(A+B)
COZ(C)
 meq/kg

meq/m2/yr
meq/m2/yr
meq/m2/yr
meq/m2/yr
   93.1
   90.9
   96.6
   95.4

     .05

   74.3
   12.0
   16.6
    1.0

    1.69
    1.18
    -2.30
    -1.72

     .30
    3.39
                                        A. 1-3-44
-------
Table E.6. Hessian Analysis:  Woods Lake.
Parameter
Estimated
Optimum
Std Dev
Estimate
%Std Error
 Estimate
UNH%L
UNO%L
UNH%B
UNO%B
EMX
  86.260
  59.770
  92.220
  73.880
   8.302
   2.955
   1.163
   1.790
   0.742
   1.113
    3.43
    1.95
    1.94
    1.00
    1.36
Correlation Matrix (R2*100) of parameter estimates.
                    UNH%L     UNO%L     UNH%B
                                  UNO%B
                               EMX
UNH%L
UNO%L
UNH%B
UNO%B
EMX
100.0
0.0
-6.6
0.0
-2.4
0.0
100.0
0.0
-18.8
3.1
-6.6
0.0
100.0
0.0
1.2
0.0
-18.8
0.0
.100.0
-0.9
-2.4
3.1
1.2
-0.9
100.0
The epsilon indifference region is:  119.74
(Based on Hessian Matrix with 5 parameters)
                                          A. 1-3-45
-------
Table E.7.  Hessian Analysis:  Panther Lake.
Parameter
UNH%L
UNO%L
UNH%B
UNO%B
EMX
Estimated
Optimum
91.200
54.970
98.800
60.790
0.674
Std Dev
Estimate
3.032
1.062
2.440
0.901
0.006
%Std Error
Estimate
3.32
1.93
2.60
1.48
0.89
Correlation Matrix (R2*100) of parameter estimates.
                    UNH%L
UNO%L
UNH%B
UNO%B
EMX
UNH%L
UNO%L
UNH%B
UNO%B
EMX
100.0
0.0
-4.9
0.0
-2.7
0.0
100.0
0.0
-23.5
2.8
-4.9
0.0
100.0
0.0
0.7
0.0
-23.5
0.0
100.0
-1.1
-2.7
2.8
0.7
-1.1
100.0
The epsilon indifference region is:  121.95
(Based on Hessian Matrix with 5 parameters)
                                          A. 1-3-46
-------
Table E.8.  Hessian Analysis: Clear Pond.

Parameter
UNH%L
UNO%L
UNH%B
UNO%B
EMX
Estimated
Optimum
93.060
90.880
96.630
95.430
0.052
Std Dev
Estimate
6.969
2.488
4.066
1.593
0.002
%Std Error
Estimate
7.49
2.74
4.21
1.67
4.03
Correlation Matrix (R2*100) of parameter estimates.
                    UNH%L     UNO%L     UNH%B
UNO%B
EMX
UNH%L
UNO%L
UNH%B
UNO%B
EMX
100.0
-0.1
-6.2
0.1
-3.9
-0.1
100.0
0.0
-6.2
2.9
-6.2
0.0
100.0
0.0
1.4
0.1
-6.2
0.0
100.0
-1.1
-3.9
2.9
1.4
-1.1
100.0
The epsilon indifference region is:  32.928
(Based on Hessian Matrix with 5 parameters)
                                          A. 1-3-47
-------
Chemical  flux  model
     The chemical flux model is MAGIC (Cosby et al., 1985 a,b,c).  Details of MAGIC and examples of
its use have been given elsewhere  (Cosby et al., 1986b, Wright et. al., 1986, Neal  et al.,  1986), including
a sensitivity analysis (Cosby et al.,  1986a). MAGIC has been modified for this work in the following ways:
the model now contains two soil  layers; atmospheric inputs  can be measured values when available
(rather than inferred annual means); atmospheric inputs can bypass the upper soil layer (macropore flow)-
atmospheric inputs can  be accumulated  and  released  from a  snowpack.   Several  outputs  from
TOPMODEL are used to set routing parameters and to control  the snow accumulation and  melt in
MAGIC. The basic chemical reactions modeled in MAGIC are  unchanged.  The inputs and parameters
listed  below correspond to those in Cosby et al., (1985b) with the exception of the routing parameters
(identified with a*) which were introduced for this two layer version of MAGIC.

Inputs

   Q-M
    XX
   W..
   W.
     XX
         measured  streamflow  [mm/dayl.
         (xx  =  Ca, Mg,  Na,  K,  S04,  Cl,  N03, F)  -  Measured  atmospheric  deposition of  ions
         [meq/m2/day].  The rate at which the measured deposition is added to the soil is  controlled
         in the case of snow accumulation and melt  by the outputs  of TOPMODEL.
         (xx = Ca,  Mg, Na, K)  - Weathering inputs  of base cations [meq/nr/yr]. Measurements of
         these rates are not available.  These inputs will be treated as adjustable parameters that must
         be optimized (see below).
         (xx = S04, Cl, N03, F) - Net Uptake rates  of anions [meq/m2/yr].  These net uptake rates
         simulate biological utilization of the  anions.  The annual rates of  these  net uptakes will be
         calculated from measured net fluxes of the anions.
   PCO2 Partial pressure of CCL  in the two soils [atm].  These partial are linearly scaled to temperature
         (a measured input). The scaling factor (St)  is an adjustable parameter that is optimized  (see
         below).                                                                            v
   T     Temperature of the two soil  layers  and the surface water [deg. C].  Taken from measurements
         or literature.

Fixed parameters:
   CEC
   D
   BD

   P

   Ka,s
  PMAC
  IF*
           Cation exchange capacities for the two soil layers  [meg/kg].   Taken from appropriately
           weighted measurements.
           Sulfate adsorption parameters (C =  half-saturation  constant [meq/m3]), for the two soil
           layers. Taken from appropriately weighted measurements.
           Average depth of the two soil layers fm].  Taken from  appropriately weighted measurements.
           Bulk density of the two soils [kg/md]. Taken from appropriately weighted measurements or
           the literature.
           Porosity of the two soils [fractionl.  Taken from measurements or literature.
           Equilibrium constant for AI(OH)3 solubility in the stream.  Calculated from observed pH-AI
           relationships.
           Lumped equilibrium constant for the solubility of AI(OH)3 in each soil layer.
           Fraction of deposition (or snow melt) that bypasses the soils and enters surface waters
           directly [fraction].  Derived from the calibration of TOPMODEL.
           Fraction of deposition (or snowmelt) that bypasses the upper soil layer and enters the lower
           soil layer directly [fraction].  Derived from the calibration of TOPMODEL.
           Fraction of water leaving the upper soil layer and flowing directly to the surface waters
           [fraction]. Derived from the  calibration of TOPMODEL.
           Thermodynamic equilibrium  constants for the yy aqueous chemical yy reactions that occur
           in the soil and surface waters (see Cosby et al.,  1985b). Derived from the literature.
                                          A. 1-3-48
-------
Adjustable parameters

     St*   Scaling parameter that relates PCO in the two soil layers and the surface waters to the
           temperature [atm/deg] .
     W^   (xx = Ca, Mg, Na, K) - Mineral  weathering  inputs of base cations for  the  soil  layers
           [meq/m /yr].
     SA11.xx(xx = Ca, Mg,  Na, K) - Selectivity coefficients for exchange of AI and base cations on the
           two soil types.
     Emx  Maximum sulfate adsorption capacity for each soil (meq/kg).


Outputs:

     The output variables of MAGIC are summarized in Cosby et al. (1985b).
                                           A. 1-3-49
-------
               SECTION F: EVALUATION OF MSE FOR CHEMICAL FLUX MODEL

Model driven by observed inputs

   Table F.1
   Table F.2


   Table F.3
MSE values of the DDRP variables for Woods Lake (calibration and
corroboration periods)

MSE values of the DDRP variables for Panther Lake (calibration and
corroboration periods)

MSE values of the DDRP variables for Clear Pond (entire period of
record)
Model driven by average hydrological and chemical fluxes

   Table F.4
   Table F.5


   Table F.6
MSE values of the DDRP variables for Woods Lake (calibration and
corroboration periods)

MSE values of the DDRP variables for Panther Lake (calibration and
corroboration periods)

MSE values of the DDRP variables for Clear Pond (entire period of
record)
                                           A. 1-3-50
-------
Table F.1.  MSE values of the DDRP variables for Woods Lake (calibration and corroboration periods)
Model driven by observed inputs.







Calibration Period
Variable
CA
MG
NA
K
NH4
SO4
CL
NO3
TOT F
ALK
H
TOTAL


Variable
CA
MG
NA
K
NH4
SO4
CL
N03
TOT F
ALK
H
TOTAL
N
109
109
108
108
109
109
109
109
0
88
106
61


N
66
66
66
66
66
66
66
66
7
62
65
75
MSE
43.7
6.0
17.6
3.6
4.2
368.2
22.8
210.9
0.0
617.0
49.5
372.8


MSE
47.1
5.9
8.9
2.8
3.5
183.0
19.2
451.8
0.5
266.2
64.8
415.4
VAR
33.8
3.1
17.4
0.9
3.7
152.0
18.3
191.8
0.0
604.0
38.9
250.3


VAR
24.7
1.3
3.5
0.9
3.5
86.9
3.7
411.9
0.1
113.6
57.2
271.2
EFF
-0.3
-0.9
0.0
-2.9
-0.2
-1.4
-0.2
-0.1
0.0
0.0
-0.3
-0.5

Corroboration
EFF
-0.9
-3.7
-1.5
-2.1
0.0
-1.0
-4.3
-0.1
-3.1
-1.3
-0.1
-0.5
MEAN
72.6
18.3
19.5
6.4
3.6
130.7
9.5
21.3
0.0
-13.5
19.2
31.3

Period
MEAN
72.2
18.3
20.7
6.4
2.4
123.5
9.1
23.5
2.7
-3.8
15.2
20.0
RMSE
6.6
2.4
4.2
1.9
2.1
19.2
4.8
14.5
0.0
24.8
7.0
19.3


RMSE
6.9
2.4
3.0
1.7
1.9
13.2
4.4
21.3
0.7
16.3
8.0
20.4

                                           A. 1-3-51
-------
Table F.2.  MSE values of the DDRP variables for Panther Lake (calibration and corroboration periods).
Model driven by observed inputs.







Calibration Period
Variable
CA
MG
NA
K
NH4
S04
CL
NO3
TOT F
ALK
H
TOT AL


Variable
CA
MG
NA
K
NH4
SO4
CL
NO3
TOT F
ALK
H
TOTAL
N
112
112
112
112
112
112
112
112
0
96
113
59


N
66
66
66
66
66
66
66
66
5
66
71
82
MSE
1630.2
83.6
92.0
4.0
1.8
254.7
23.6
546.8
0.0
6194.2
10.2
124.3


MSE
1597.6
69.5
108.4
6.8
2.2
215.7
5.2
671.7
3.0
3553.3
2.4
31.1
VAR
1621.0
83.8
90.5
3.3
1.4
156.6
21.9
535.0
0.0
5984.1
9.3
120.4


VAR
1492.2
63.7
79.2
4.7
1.3
203.2
4.4
638.1
1.9
2980.7
2.4
22.2
EFF
0.0
0.0
0.0
-0.2
-0.3
-0.6
-0.1
0.0
0.0
0.0
-0.1
0.0

Corroboration
EFF
-0.1
-0.1
-0.4
-0.7
-0.7
-0.1
-0.2
-0.1
-0.6
-0.2
0.0
-0.4
MEAN
201.4
50.6
40.0
11.8
1.4
125.9
12.4
29.2
0.0
127.2
1.1
8.3

Period
MEAN
208.7
52.1
43.8
12.8
1.0
118.4
12.4
24.5
7.1
150.9
0.4
1.0
RMSE
40.4
9.1
9.6
2.0
1.4
16.0
4.9
23.4
0.0
78.7
3.2
11.1


RMSE
40.0
8.3
10.4
2.8
1.5
14.7
2.3
25.9
1.7
59.6
1.6
5.6

                                            A. 1-3-52
-------
Table F.3.  MSE values of the DDRP variables for Clear Pond (entire period of record).  Model driven by
observed inputs.
Calibration Period
Variable
CA
MG
NA
K
NH4
SO4
CL
N03
TOT F
ALK
H
TOTAL
N
24
24
24
24
12
24
24
24
14
24
24
22
MSE
444.0
22.0
25.0
0.5
4.5
90.2
21.7
35.0
0.1
347.4
0.0
12.5
VAR
466.3
22.5
24.6
0.5
3.9
95.2
2.6
35.4
0.1
345.1
0.0
0.3
EFF
0.0
0.0
0.0
0.0
-0.2
0.1
-7.4
0.0
0.1
0.0
-0.1
-35.0
MEAN
165.7
32.0
39.0
3.8
1.8
126.6
7.4
3.6
0.7
104.0
0.2
0.7
RMSE
21.1
4.7
5.0
0.7
2.1
9.5
4.7
5.9
0.3
18.6
0.2
3.5
                                           A. 1-3-53
-------
Table F.4.  MSB values of the DDRP variables for Woods Lake (calibration and corroboration periods).
Model driven by average hydrological and chemical fluxes.







Calibration Period
Variable
CA
MG
NA
K
NH4
S04
CL
NO3
TOT F
ALK
H
TOTAL


Variable
CA
MG
NA
K
NH4
SO4
CL
NO3
TOT F
ALK
H
TOTAL
N
109
109
108
108
109
109
109
109
0
88
106
61


N
66
66
66
66
66
66
66
66
7
62
65
75
MSE
35.2
4.2
17.7
2.8
3.7
202.3
23.7
213.7
0.0
617.8
42.5
290.9


MSE
29.8
2.5
7.1
2.7
4.1
92.1
11.0
417.6
0.5
293.7
63.0
455.9
VAR
33.8
3.1
17.4
0.9
3.7
152.0
18.3
191.8
0.0
604.0
38.9
250.3


VAR
24.7
1.3
3.5
0.9
3.5
86.9
3.7
411.9
0.1
113.6
57.2
271.2
EFF
0.0
-0.4
0.0
-2.1
0.0
-0.3
-0.3
-0.1
0.0
0.0
-0.1
-0.2

Corroboration
EFF
-0.2
-1.0
-1.0
-2.0
-0.2
-0.1
-2.0
0.0
-3.1
-1.6
-0.1
-0.7
MEAN
72.6
18.3
19.5
6.4
3.6
130.7
9.5
21.3
0.0
-13.5
19.2
31.3

Period
MEAN
72.2
18.3
20.7
6.4
2.4
123.5
9.1
23.5
2.7
-3.8
15.2
20.0
RMSE
5.9
2.1
4.2
1.7
1.9
14.2
4.9
14.6
0.0
24.9
6.5
17.1


RMSE
5.5
1.6
2.7
1.6
2.0
9.6
3.3
20.4
0.7
17.1
7.9
21.4

                                           A. 1-3-54
-------
Table F.5.  MSE values of the DDRP variables for Woods Lake (calibration and corroboration periods)
Model driven by average  hydrological and chemical fluxes.







Calibration Period
Variable
CA
MG
NA
K
NH4
SO4
CL
NO3
TOT F
ALK
H
TOTAL
N
112
112
112
112
112
112
112
112
0
96
113
59
MSE
1634.8
84.2
91.8
4.1
1.9
211.5
23.3
545.9
0.0
6049.8
10.2
124.9
VAR
1621.0
83.8
90.5
3.3
1.4
156.6
21.9
535.0
0.0
5984.1
9.3
120.4
EFF
0.0
0.0
0.0
-0.2
-0.4
-0.4
-0.1
0.0
0.0
0.0
-0.1
0.0
MEAN
201.4
50.6
40.0
11.8
1.4
125.9
12.4
29.2
0.0
127.2
1.1
8.3
RMSE
40.4
9.2
9.6
2.0
1.4
14.5
4.8
23.4
0.0
77.8
3.2
11.2


Variable
CA
MG
NA
K
NH4
SO4
CL
NO3
TOT F
ALK
H
TOTAL

N
66
66
66
66
66
66
66
66
5
66
71
82

MSE
1629.4
70.2
109.9
8.0
2.5
189.8
6.1
676.5
3.0
3511.6
2.4
30.7

VAR
1492.2
63.7
79.2
4.7
1.3
203.2
4.4
638.1
1.9
2980.7
2.4
22.2
Corroboration
EFF
-0.1
-0.1
-0.4
-0.7
-0.9
0.1
-0.4
-0.1
-0.6
-0.2
0.0
-0.4
Period
MEAN
208.7
52.1
43.8
12.8
1.0
118.4
12.4
24.5
7.1
150.9
0.4
1.0

RMSE
40.4
8.4
10.5
2.8
1.6
13.8
2.5
26.0
1.7
59.3
1.6
5.5

                                           A. 1-3-55
-------
Table F.6.  MSE values of the DDRP variables for Clear Pond (entire period of record).  Model driven by
average hydrological and chemical fluxes.
Calibration Period
Variable
CA
MG
NA
K
NH4
SO4
CL
NO3
TOT F
ALK
H
TOTAL
N
24
24
24
24
12
24
24
24
14
24
24
22
MSE
444.8
22.3
24.9
0.5
4.5
98.4
21.2
35.0
0.1
331.1
0.0
12.5
VAR
466.3
22.5
24.6
0.5
3.9
95.2
2.6
35.4
0.1
345.1
0.0
0.3
EFF
0.0
0.0
0.0
0.1
-0.2
0.0
-7.2
0.0
0.1
0.0
-0.1
-35.0
MEAN
165.7
32.0
39.0
3.8
1.8
126.6
7.4
3.6
0.7
104.0
0.2
0.7
RMSE
21.1
4.7
5.0
0.7
2.1
9.9
4.6
5.9
0.3
18.2
0.2
3.5
                                           A. 1-3-56
-------
Table G.1.  Sensitivity Analysis:  Woods Lake.
DDRP Evaluations of MSE (Weathering at optimal values)
Calibration Period
Variable
CA
MG
NA
K
NH4
SO4
CL
NO3
TOTF
ALK
H
TOTAL
N
109
109
108
108
109
109
109
109
0
88
106
61
MSE
43.7
6.0
17.6
3.6
4.2
368.2
22.8
210.9
0.0
617.0
49.5
372.8
VAR
33.8
3.1
17.4
0.9
3.7
152.0
18.3
191.8
0.0
604.0
38.9
250.3
EFF
-0.3
-0.9
0.0
-2.9
-0.2
-1.4
-0.2
-0.1
0.0
0.0
-0.3
-0.5
MEAN
72.6
18.3
19.5
6.4
3.6
130.7
90.5
21.3
0.0
-13.5
19.2
31.3
RMSE
6.6
2.4
4.2
1.9
2.1
19.2
4.8
14.5
0.0
24.8
7.0
19.3


Variable
CA
MG
NA
K
NH4
SO4
CL
NO3
TOT F
ALK
H
TOTAL

N
66
66
66
66
66
66
66
66
7
62
65
75

MSE
47.1
5.9
8.9
2.8
.3-5
173.0
19.2
451.8
0.5
266.2
64.8
415.4

VAR
24.7
1.3
3.5
0.9
3.5
86.9
3.7
411.9
0.1
133.6
57.2
271.2
Corroboration
EFF
-0.9
-3.7
-1.5
-2.1
0.0
-1.0
-4.3
-0.1
-3.1
-1.3
-0.1
-0.5
Period
MEAN
72.2
18.3
20.7
6.4
2.4
123.5
9.1
23.5
2.7
-3.8
15.2
20.0

RMSE
6.9
2.4
3.0
1.7
1.9
13.2
4.4
21.3
0.7
16.3
8.0
20.4

                                          A. 1-3-57
-------
                                  Table G.1. Continued.
DDRP Evaluations of MSE (Weathering increased by 10%)
Variable
Variable
N
 N
MSE
Calibration Period



VAR         EFF
MEAN    RMSE
CA
MG
NA
K
NH4
SO4
CL
NO3
TOT F
ALK
H
TOTAL
109
109
108
108
109
109
109
109
0
88
106
61
39.0
5.1
17.4
2.9
4.2
366.9
22.8
210.9
0.0
612.9
54.1
394.2
33.8
3.1
17.4
0.9
3.7
152.0
18.3
191.8
0.0
604.0
38.9
250.3
-0.2
-0.7
0.0
-2.2
-0.2
-1.4
-0.2
-0.1
0.0
0.0
-0.4
-0.6
72.6
18.3
19.5
6.4
3.6
130.7
9.5
21.3
0.0
-13.5
19.2
31.3
6.2
2.3
4.2
1.7
2.1
19.2
4.8
14.5
0.0
24.8
7.4
19.9

MSE
     Corroboration Period



VAR          EFF        MEAN
         RMSE
CA
MG
NA
K
NH4
SO4
CL
NO3
TOT F
ALK
H
TOTAL
66
66
66
66
66
66
66
66
7
62
65
75
43.3
4.8
8.4
2.2
3.5
172.6
19.2
451.8
0.5
246.3
64.4
376.0
24.7
1.3
3.5
0.9
3.5
786.9
3.7
411.9
0.1
113.6
57.2
271.2
-0.8
-2.9
-1.4
-1.5
0.0
-1.0
-4.3
-0.1
-3.1
-1.2
-0.1
-0.4
72.2
18.3
20.7
6.4
2.4
123.5
9.1
23.5
2.7
-3.8
15.2
20,0
6.6
2.2
2.9
1.5
1.9
13.1
4.4
21.3
0.7
15.7
8.0
19.4

                                         A. 1-3-58
-------
                                   Table G.1.  Continued.
DDRP Evaluations of MSE (Weathering decreased by 10%)
Calibration Period
Variable
CA
MG
NA
K
NH4
SO4
CL
N03
TOT F
ALK
H
TOTAL
N
109
109
108
108
109
109
109
109
0
88
106
61
MSE
63.7
7.1
17.8
4.4
4.2
369.3
22.8
210.9
0.0
620.4
46.4
370.2
VAR
33.8
3.1
17.4
0.9
3.7
152.0
18.3
191.8
0.0
604.0
38.9
250.3
EFF
-0.9
-1.3
0.0
-3.8
-0.2
-1.4
-0.2
-0.1
0.0
0.0
-0.2
-0.5
MEAN
72.6
18.3
19.5
6.4
3.6
130.7
9.5
21.3
0.0
-13.5
19.2
31.3
RMSE
8.0
2.7
4.2
2.1
2.1
19.2
4.8
14.5
0.0
24.9
6.8
19.2


Variable
CA
MG
NA
K
NH4
SO4
CL
N03
TOT F
ALK
H
TOTAL

N
66
66
66
66
66
66
66
66
7
62
65
75

MSE
63.7
7.0
9.4
3.4
3.5
173.4
19.2
451.8
0.5
287.1
67.7
473.2

VAR
24.7
1.3
3.5
0.9
3.5
86.9
3.7
411.9
0.1
113.6
57.2
271.2
Corroboration
EFF
-1.6
-4.5
-1.7
-2.9
0.0
-1.0
-4.3
-0.1
-3.1
-1.5
-0.2
-0.7
Period
MEAN
72.2
18.3
20.7
6.4
2.4
123.5
9.1
23.5
2.7
-3.8
15.2
20.0

RMSE
8.0
2.6
3.1
1.9
1.9
13.2
4.4
21.3
0.7
16.9
8.2
21.8
-
                                         A. 1-3-59
-------
Table G.2.  Sensitivity Analysis:  Panther Lake.
DDRP Evaluations of MSE (Weathering at optimal values)
Calibration Period
Variable
CA
MG
NA
K
NH4
SO4
CL
N03
TOT F"
ALK
H
TOTAL


Variable
CA
MG
NA
K
NH4
SO4
CL
NO3
TOT F
ALK
H
TOTAL
N
112
112
112
112
112
112
112
112
0
96
113
59


N
66
66
66
66
66
66
66
66
5
66
71
82
MSE
1630.2
83.6
92.0
4.0
1.8
254.7
23.6
546.8
0.0
6194.2
10.2
124.3


MSE
1597.6
69.5
108.4
7.8
2.2
215.7
5.2
671.7
3.0
3553.3
2.4
31.1
VAR
1621.0
83.8
90.5
3.3
1.4
156.6
21.9
535.0
0.0
5984.1
9.3
120.4


VAR
1492.2
63.7
79.2
4.7
1.3
EFF
0.0
0.0
0.0
-0.2
-0.3
-0.6
-0.1
0.0
0.0
0.0
-0.1
0.0

Corroboration
EFF
-0.1
-0.1
-0.4
-0.7
-0.7
203.2 -0.1
4.4
638.1
1.9
2980.7
2.4
22.2
-0.2
-0.1
-0.6
-0.2
0.0
-0.4
MEAN
201.4
50.6
40.0
11.8
1.4
125.9
12.4
29.2
0.0
127.2
1.1
8.3

Period
MEAN
208.7
52.1
43.8
12.8
1.0
118.4
12.4
24.5
7.1
150.9
0.4
1.0
RMSE
40.4
9.1
9.6
2.0
1.4
16.0
4.9
23.4
0.0
78.7
3.2
11.1


RMSE
40.0
8.3
10.4
2.8
1.5
14.7
2.3
25.9
1.7
59.6
1.6
5.6

                                           A. 1-3-60
-------
                                    Table G.2. Continued.
DDRP Evaluations of MSE (Weathering increased by 10%)
Calibration Period
Variable
CA
MG
NA
NH4
S04
CL
NO3
TOTF
ALK
TOTAL
N
112
112
112
112
112
112
112
112
0
96
113
59
MSE
1641.2
82.7
88.4
3.1
1.8
254.8
23.6
546.8
0.0
6607.7
10.3
120.2
VAR
1621.0
83.8
90.5
3.3
1.4
156.6
21.9
535.0
0.0
5984.1
9.3
120.4
EFF
0.0
0.0
0.0
0.0
-0.3
-0.6
-0.1
0.0
0.0
-0.1
-0.1
0.0
MEAN
201.4
50.6
40.0
11.8
1.4
125.9
12.4
29.2
0.0
127.2
1.1
8.3
RMSE
40.5
9.1
9.4
1.8
1.4
16.0
4.9
23.4
0.0
81.3
3.2
11.0


Variable
CA
MG
NA
K
NH4
S04
CL
NO3
TOTF
ALK
H
TOTAL

N
66
66
66
66
66
66
66
66
5
66
71
82

MSE
1454.0
62.6
86.90
5.6
2.2
215.7
5.2
671.7
3.0
3083.7
2.5
35.5

VAR
1492.2
63.7
79.2
4.7
1.3
203.2
4.4
638.1
1.9
2980.7
2.4
22.2
Corroboration
EFF
0.0
0.0
-0.1
-0.2
-0.7
-0.1
-0.2
-0.1
-0.6
0.0
0.0
-0.6
Period
MEAN
208.7
52.1
43.8
12.8
1.0
118.4
12.4
24.5
7.1
150.9
0.4
1.0

RMSE
38.1
7.9
9.3
2.4
1.5
14.7
2.3
25.9
1 7
55.5
1.6
6.0

                                        A. 1-3-61
-------
                                   Table G.2.  Continued.
DDRP Evaluations of MSE (Weathering decreased by 10%)
Calibration Period
Variable
CA
MG
NA
K
NH4
SO4
CL
NO3
TOT F
ALK
H
TOTAL
N
112
112
112
112
112
112
112
112
0
96
113
59
MSE
1924.3
96.4
107.8
6.2
1.8
254.5
23.6
546.8
0.0
6450.5
10.2
129.2
VAR
1621.0
83.8
90.5
3.3
1.4
156.6
21.9
535.0
0.0
5984.1
9.3
120.4
EFF
-0.2
-0.2
-0.2
-0.9
-0.3
-0.6
-0.1
0.0
0.0
-0.1
-0.1
-0.1
MEAN
201.4
50.6
40.0
11.8
1.4
125.9
12.4
29.2
0.0
127.2
1.1
8.3
RMSE
43.9
9.8
10.4
2.5
1.4
16.0
4.9
23.4
0.0
80.3
3.2
11.4


Variable
CA
MG
NA
K
NH4
SO4
CL
NO3
TOT F
ALK
H
TOTAL

N
66
66
66
66
66
66
66
66
5
66
71
82

MSE
2051.5
88.6
142.3
11.3
2.2
215.7
5.2
671.7
3.0
4667.0
2.4
27.5

VAR
1492.2
63.7
79.2
4.7
1.3
203.2
4.4
638.1
1.9
2980.7
2.4
22.2
Corroboration
EFF
-0.4
-0.4
-0.8
-1.4
-0.7
-0.1
-0.2
-0.1
-0.6
-0.6
0.0
-0.2
Period
MEAN
208.7
52.1
43.8
12.8
1.0
118.4
12.4
24.5
7.1
150.90
0.4
1.0

RMSE
45.3
9.4
11.9
3.4
1.5
14.7
2.3
25.9
1.7
68.3
1 6
1 «*J
5.2

                                        A. 1-3-62
-------
Table G.3.  Sensitivity Analysis:  Clear Pond.
and 10% decrease (bottom) of weathering.
Modified values of MSB resulting from 10% increase (top)
DDRP Evaluations of MSE
Calibration Period
Variable
CA
MG
NA
K
NH4
S04
CL
NO3
TOT F
ALK
H
TOTAL


Variable
CA
MG
NA
K
NH4
SO4
CL
NO3
TOT F
ALK
H
TOTAL
N
24
24
24
24
12
24
24
24
14
24
24
22


N
24
24
24
24
12
24
24
24
14
24
24
22
MSE
593.0
21.4
36.0
0.5
4.5
90.2
21.7
35.0
0.1
533.6
0.0
16.5


MSE
522.3
27.2
23.9
0.5
4.5
50.1
21.7
35.0
0.1
564.7
0.0
9.0
VAR
466.3
22.5
24.6
0.5
3.9
95.2
2.6
35.4
0.1
345.1
0.0
0.3


VAR
466.3
22.5
24.6
0.5
3.9
95.2
2.6
35.4
0.1
345.1
0.0
0.3
EFF
-0.3
0.0
-0.5
0.0
-0.2
0.1
-7.4
0.0
0.1
-0.5
-0.2
-46.6

Corroboration
EFF
-0.1
-0.2
0.0
0.0
-0.2
0.1
-7.4
0.0
0.1
-0.6
0.0
-25.0
MEAN
165.7
32.0
39.0
3.8
1.8
126.6
7.4
3.6
0.7
104.0
0.2
0.7

Period
MEAN
165.7
32.0
39.0
3.8
1.8
126.6
7.4
3.6
0.7
104.0
0.2
0.7
RMSE
24.4
4.6
6.0
0.7
2.1
9.5
4.7
5.9
0.3
23.1
0.2
4.1


RMSE
22.9
5.2
4.9
0.7
2.1
9.5
4.7
5.9
0.3
23.8
0.2
3.0

                                          A. 1-3-63
-------
                                   Table G.2.  Continued.
DDRP Evaluations of MSE (Weathering decreased by 10%)
Calibration Period
Variable
CA
MG
NA
K
NH4
S04
CL
NO3
TOT F
ALK
H
TOTAL
N
112
112
112
112
112
112
112
112
0
96
113
59
MSE
1924.3
96.4
107.8
6.2
1.8
254.5
23.6
546.8
0.0
6450.5
10.2
129.2
VAR
1621.0
83.8
90.5
3.3
1.4
156.6
21.9
535.0
0.0
5984.1
9.3
120.4
EFF
-0.2
-0.2
-0.2
-0.9
-0.3
-0.6
-0.1
0.0
0.0
-0.1
-0.1
-0.1
MEAN
201.4
50.6
40.0
11.8
1.4
125.9
12.4
29.2
0.0
127.2
1.1
8.3
RMSE
43.9
9.8
10.4
2.5
1.4
16.0
4.9
23.4
0.0
80.3
3.2
11.4


Variable
CA
MG
NA
K
NH4
SO4
CL
NO3
TOT F
ALK
H
TOTAL

N
66
66
66
66
66
66
66
66
5
66
71
82

MSE
2051.5
88.6
142.3
11.3
2.2
215.7
5.2
671.7
3.0
4667.0
2.4
27.5

VAR
1492.2
63.7
79.2
4.7
1.3
203.2
4.4
638.1
1.9
2980.7
2.4
22.2
Corroboration
EFF
-0.4
-0.4
-0.8
-1.4
-0.7
-0.1
-0.2
-0.1
-0.6
-0.6
0.0
-0.2
Period
MEAN
208.7
52.1
43.8
12.8
1.0
118.4
12.4
24.5
7.1
150.90
0.4
1.0

RMSE
45.3
9.4
11.9
3.4
1.5
14.7
2.3
25.9
1.7
68.3
1.6
5.2

                                         A. 1-3-64
-------
                                           INDEX



Section A: Summary



Section B: Optimization Protocol for TOPMODEL (Hydrological Model)



Section C: Evaluation of MSE for the Hydrological Model



Section E: Optimization Protocol for MAGIC (Chemical Flux Model)



Section F: Evaluation of MSE for the Chemical  Flux Model



Section G: Predicted vs. Observed Plots
                                           A. 1-3-65
-------
Section 6: Predicted vs. Observed Plots

Plots of simulated and observed ANC, S04, Cl and discharge for the data intensive calibration of Woods
Panther, and Clear.

In comparing the simulated to observed for the intensively studied sites several main points need to be
made.

1)   TOPMODEL was applied using a daily time step, therefore,  the hydrology simulations show rapid
response and simulate the discharges faithfully (including snowmelt).  The mean monthly flow routings
from TOPMODEL were input to  MAGIC.

2)  MAGIC was applied using a  monthly time step.  MAGIC is formulated as a long-term model and thus
has (at best) a monthly (i.e., seasonal) variability. The mean observed  monthly depositions were used
as inputs to MAGIC.  MAGIC will not be able a priori to match   episodic response.  The model was
calibrated to match the volume weighted annual average concentrations over the period. For conservative
ions (i.e., chloride) which are not episodic, the match is very good. For ions which can be episodic (S04
and ANC) due to snowmelt, etc., the daily fit  is not as good,  but the average response is correct as
expected.

3)   MAGIC does not have a detailed lake mixing component.  The lake thus acts as a stirred reactor
which serves to further damp the short-term response of ionic concentrations.  MAGIC is basically a soils
model  and the soils reactions have longer time constants than  the lake  reactions or mixing.

4)  Although MAGIC damps the  episodic response, the MSE's from MAGIC are comparable to the other
models, suggesting that the best any of the models can do (in  an MSE  sense) is to faithfully model the
mean chemistry.  That is, the short term  models show more high-frequency variation, but the  fit of that
variable output is equivalent to the damped output from MAGIC.  The  other models show episodic
response, but that response is well constrained.

5) Note that the plotted output from MAGIC appears as a piecewise linear graph.  MAGIC only gives one
value for each month, so the model output is plotted as a series  of flat  (monthly) values.
                                           A. 1-3-66
-------
                           Fixed Parameter Values for Maoic fconfd)

 1.  The average start depth of the "C" horizon for all available samples in the ILWAS file SMIGEOL on
 card 85 was used as the depth of the A+B horizons. The sample sizes and standard deviations were
 12 and 0.14 m for Woods and 17 and 0.16 m  for Panther.  The Clear Pond catchment value was based
 on one sample in RILWAS file COLX. The C horizon thickness was the difference between the average
 MJ   u °edrock and the A+B depth.  The average depths to bedrock were those cited by Peters  and
 Murdoch (1985) for Woods and Panther and a value of 55m for Clear Pond was a preliminary value for
 the deep till region determined by Robert Newton (Ron Munson, personal communication).

 2.  Bulk density = (1-porosity)*2.65*1000, where 2.65 g/cc is an  assumed specific gravity of the  soil
 SOI IQS.

 3.  Porosity was assumed to be equal to the percent soil moisture  by volume at saturation  The values
 used are the depth=weighted averages of one profile each from the Woods and Panther catchments
 tound on card 93 of the SMIGEOL file.  Since  no data were available for the Clear Pond catchment  the
 averages of Woods and Panther were used.

 4.  CEC data for Woods and  Panther were obtained from  the ILWAS file UMACEC.  Depth-weighted
 averages were used. For  Woods, 7 profiles were averaged for the A+B value and 6 for the  C  The
 Panther averages were based on 9 profiles of the A+B and 6 of the C. The values for Clear Pond were
 based on one profile from the COLX RILWAS file.

 5. KALOH3 values were based on concentrations of total monomeric aluminum and hydrogen ion found
 in the ILWAS file CORAL and from the analysis by Schofield (1983).

 6. S04 half-saturation constant. Assumed.

 u  ^ALVSluStare from Peters and Murdoch 0985).  Clear Pond lake and catchment areas supplied
 by EPA (M.R. Church, pers. com.).

8. Lake residence time was calculated as lake volume divided by mean annual discharge  Lake volumes
for Woods and Panther are from Peters  and Murdoch (1985).  Mean annual discharges were calculated
trom the ILWAS file USGFLOW. Clear Pond volume was estimated from the hypsographic curve supplied
by Tetra Tech and discharge from RILWAS file USGSTAGE and the observed yield.
                                         A. 1-3-67
-------
                APPENDIX A.2





WATERSHED SIMULATED BY ETD, ILWAS, AND MAGIC
-------
Table A.2-1.  Watersheds Simulated by MAGIC in the Northeast (Lakes) and
Southern Blue Ridge Province (Streams)
Lake ID
1A1-003 HAWK POND
1A1-012 WHITNEY LAKE
1A1-014 WILMURT LAKE
1A1-017 CONSTABLE POND
1A1-020 FOURTH LAKE (BISBY LAKES)
1A1-028 DRY CHANNEL POND
1A1-029 MIDDLE POND
1A1-033 KIWASSA LAKE
1A1-038 NICKS POND
1A1-039 JOHN POND .
1A1-046 PARTLOW LAKE
1A1-049 MIDDLE SOUTH POND
1A1-057 HITCHCOCK LAKE
1A1-061 WOLF LAKE
1A1-064 MT ARAB LAKE
1A1-066 WOODHULL LAKE
1A1-073 GULL LAKES (SOUTH)
1A2-002 ST. JOHN LAKE
1A2-006 LAKE FRANCES
1A2-037 FISH PONDS (NORTHEAST)
1A2-039 OXBOW LAKE
1A2-041 MUD LAKE
1A2-042 NORTH BRANCH LAKE
1A2-045 WOODS LAKE
1A2-046 NINE CORNER LAKE
1A2-048 (NO NAME)
1A2-052 CHUB LAKE
1A2-054 TROUT LAKE
1A3-001 NATE POND
1 A3 -040 ZACK POND
1A3-042 CHENEY POND
1A3-043 UNKNOWN POND
1A3-046 LONG POND
1 A3- 048 GRASS POND
1A3-065 SOUTH LAKE (EAST BRANCH)
1B1-010 GANOGA LAKE
1B1-023 TWIN LAKES (BRINK P)
1B1-029 NO NAME(WILSON CREEK DAM)
1B1-055 ROCK HILL POND
1B2-028 MILL CREEK RESERVOIR
1B3-004 GUILFORD LAKE
1B3-012 LITTLE BUTLER LAKE
1B3-019 HARTLEY POND
1B3-021 CORD POND
1B3-025 TROUT LAKE
1B3-032 WIXON POND
1B3-041 EAST STROUDSBURG RESERV.
1B3-043 TROUT LAKE
1B3-051 BARRETT POND
1B3-052 (NO NAME)
1B3-053 NO NAME(SNOWFLAKE LAKE)
1B3-059 ISLAND POND
1B3-060 SLY LAKE
1B3-062 BASSETT POND
State
NY
NY
NY
NY
NY
NY
NY
NY
NY
NY
NY
NY
NY
NY
NY
NY
NY
NY
NY
NY
NY
NY
NY
NY
NY
NY
NY
NY
NY
NY
NY
NY
NY
NY
NY
PA
PA
PA
PA
PA
NY
PA
PA
PA
NY
NY
PA
PA
NY
NY
PA
NY
PA
PA
Latitude Longitude
43.9569 74.9583
43.5875 74.5625
43.4292 74.7250
43.8333 74.7958
43.5708 74.9708
44.3528 74.4375
44.3389 74.3792
44.2958 74.1583
44.1431 74.9680
44.1125 74.7639
44.0042 74.8333
43.9894 75.0183
43.8500 75.0417
43.6292 74.6542
44.1883 74.6008
43.5917 74.9869
43.8561 74.8208
43.4417 74.0611
44.6958 74.3250
43.5472 74.0611
43.4417 74.4833
43.3405 74.4539
43.3125 74.7944
43.2528 74.3167
43.1958 74.5500
43.1275 74.5889
43.2583 74.5305
43.3467 74.7139
43.8583 74.0917
43.9333 74.1833
43.8778 74.1625
43.8194 74.2833
43.6375 74.2889
43.6930 75.0611
43.5105 74.8922
41.3583 76.3208
41.3833 74.9042
41.2917 75.2389
41.3136 75.0161
41.2625 75.7500
42.4125 75.5000
41.8625 75.6278
41.6583 75.7083
41.6528 75.8500
41.5861 74.6805
41.3958 73.7347
41.0667 75.1667
41.0042 75.3417
41.4344 73.7403
41.4897 74.5389
41.9050 75.4103
41.2572 74.1403
41.8236 75.3372
41.5925 75.7111
ANC (peq L"1)
-21.7
11.4
30.0
-7.4
6.0
1.8
111.9
183.2
97.2
-1.7
56.3
-30.3
-18.0
-53.0
82.9
1.8
-28.1
1.8
33.5
161.2
140.9
22.4
6.2
7.8
12.9
-5.3
1.1
-14.7
76.9
69.2
30.0
238.4
18.2
7.3
0.5
-23.9
33.3
166.0
52.9
14.6
342.7
342.2
218.5
380.8
30.4
332.5
89.7
143.0
275.3
16.4
245.8
-4.4
190.9
376.4
                                                                                                  Continued
                                                  A.2-1
-------
Table A.2-1 (Continued)
Lake ID
1C1-009 UPPER BAKER POND
1C1-017 WELHERN POND
1C1-018 DECKER PONDS (EASTERN)
1C1-021 CLEAR POND
1C1-031 HUNT POND
1C1-050 BILLINGS POND
1C1-084 UPPER BEECH POND
1C1-086 STAR LAKE
1C2-002 IRON POND
1C2-012 BLACK POND
1C2-016 TRAFTON POND
1C2-028 SUNSET LAKE
1C2-033 LONG POND
1C2-035 SMITH POND
1C2-037 HENDUMS POND
1C2-041 JUGGERNAUT POND
1C2-048 CRANBERRY POND
1C2-050 MOORES POND
1C2-056 DRURY POND
1C2-057 BABBIDGE RESERVOIR
1C2-062 PEMIGEWASSET LAKE
1C2-064 HANCOCK POND
1C2-066 TURTLE POND
1C2-068 QUIMBY POND
1C3-030 PEL HAM LAKE
1C3-031 SADAWGA LAKE
1C3-063 MARTIN MEADOW POND
10 1-034 ROCKY POND
1D1-037 EZEKIEL POND
1D1-046 ROBBINS POND
1D1-054 UPPER MILLPOND
1D1-056 LITTLE WEST POND
1D2-025 LITTLE QUITTACAS POND
1D2-074 STETSON POND
102-084 GOOSE POND
1D3-020 LITTLE ALUM POND
1D3-025 LONG POND
1D3-033 (NO NAME)
1D3-044 MIDDLE FARMS POND
1E1-009 PEEP LAKE
1E1-011 FOURTH DAVIS POND
1E1-025 BEAN PONDS (MIDDLE)
1E1-040 LT. GREENWOOD POND (WEST)
1E1-050 LOWER OXBROOK LAKE
1E1-054 DUCK LAKE
1E1-061 LITTLE SEAVEY LAKE
1E1-062 LONG POND
1E1-073 GEORGES POND
1E1-074 CRAIG POND
1E1-077 PARKER POND
1E1-082 STEVENS POND
1E1-092 GREAT POND
1E1-111 LONG POND
State
NH
ME
ME
ME
ME
NH
NH
NH
ME
ME
ME
NH
NH
NH
NH
NH
NY
MA
ME
NH
NH
ME
NH
ME
MA
VT
NH
MA
MA
MA
MA
MA
MA
MA
MA
MA
CT
CT
NY
ME
ME
ME
ME
ME
ME
ME
ME
ME
ME
ME
ME
ME
ME
Latitude Longitude
43.9083 71.9917
45.2125 70.4944
45.1958 69.9375
45.1083 69.9875
44.0833 71.0000
43.2833 71.9417
43.6483 71.2042
43.4619 72.0555
45.4583 70.3750
44.1458 70.8000
43.8458 70.8917
43.4708 71.3000
43.2039 71.8119
43.1542 72.0292
43.1750 71.0667
42.9597 72.0125
42.7444 73.4333
42.6555 72.3472
44.7042 70.2417
42.9347 72.2167
43.6153 71.5958
44.9556 69.9861
43.2542 71.5167
44.9908 70.7419
42.7000 72.8917
42.7833 72.8750
44.4417 71.6083
41.8861 70.6958
41.8042 70.6125
41.7056 70.1111
41.7308 70.1167
41.9214 70.7067
41.7917 70.9167
42.0278 70.8275
41.6939 70.0078
42.1292 72.1542
42.0208 71.8167
41.6583 73.1917
41.2750 71.9778
44.9083 67.8917
45.2583 69.3944
45.8125 69.1917
45.3667 69.4083
45.2833 67.8417
45.1500 68.1000
44.9375 67.6333
44.9167 68.2697
44.6167 68.2417
44.5833 68.6667
44.3722 68.7083
44.3667 69.3000
44.6008 68.2833
44.5339 68.1703
ANC (jteq L"1)
105.7
325.9
173.3
122.5
62.9
63.6
41.7
25.7
69.2
71.5
128.8
51.7
97.3
•64.7
5.5
2.2
11.5
45.0
213.0
19.6
36.4
86.2
67.7
285.5
86.8
122.4
325.9
9.8
5.3
13.1
63.4
3.5
71.8
80.3
142.5
104.0
162.1
368.5
41.5
11.1
19.5
98.0
36.8
43.8
33.4
66.0
86.2
52.3
70.2
81.0
89.0
77.5
6.3
                                                                                                  Continued
                                                  A.2-2
-------
Table A.2-1 (Continued)
Lake ID
1E2-002
1E2-007
1E2-030
1E2-038
1E2-049
1E2-054
1E2-056
1E2-063
1E2-069
1E3-022
1E3-040
1E3-041
1E3-042
1E3-045
1E3-055
1E3-062
Stream
2A07701
2A07703
2A07802
2A07805
2A07806
2A07812
2A07813
2A07817
2A07821
2A07823
2A07826
2A07827
2A07828
2A07829
2A07830
2A07833
2A07834
2A07835
2A07882
2A08802
2A08803
2A08804
2A08805
2A08806
2A08808
2A08810
2A08811
2A08901
2A08904
2A08906

(NO NAME)
FAIRBANKS POND
ROUND LAKE
NELSON POND
GROSS POND
BRETTUNS POND
PEABODY POND
KALERS POND
(NO NAME)
NUMBER NINE L.AKE
NOKOMIS POND
ROUND POND
SAND POND
MCCLURE POND
TOGUE POND
CAIN POND
ID
SUGAR COVE BRANCH OF N. RIVER
HALL CREEK
PUNCHEON FORK
COSBY CREEK
ROARING FORK
CORRELL BRANCH
LITTLE SANDYMUSH CREEK
FORNEY CREEK
GRASSY CREEK
BRUSH CREEK
HENDERSON CREEK
WELCH HILL CREEK
WHITEOAK CREEK
CATHEYS CREEK
MUD CREEK
ALLISON CREEK
BRUSH CREEK
MIDDLE SALUDA RIVER
LITTLE BRANCH CREEK
DUNN MILL CREEK
OWENBY CREEK
BEAR CREEK
WEAVER CREEK
UNNAMED TRIB.TO KIUTUESTIA CR
WHITE PATH CREEK
BRYANT CREEK
HINTON CREEK
PERSIMMON CREEK
SHE CREEK
DEEP CREEK
State
ME
ME
ME
ME
ME
ME
ME
ME
ME
HE-
HE
ME
ME
ME
ME
ME
State
TN
TN
NC
TN
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
SC
NC
GA
GA
GA
GA
. GA
GA
GA
GA
GA
GA
GA
Latitude
45.9944
44.3891
45.0167
44.4153
44.0583
44.3917
43.9422
44 .'1 080
46.1242
46.4167
44.8708
44.7389
44.5694
44.4833
46.9339
44.4922
Latitude
35.3222
35.0956
35.9100
35.7936
35.8214
35.6758
35.7033
35.5133
35.4642
35.3189
35.3783
35.1850
35.2258
35.2133
35.2547
35.1214
35.1139
35.1206
35.4497
34.9492
34.9869
34.8244
34.8711
34.8589
34.7375
34.6097
34.4853
34.9131
34.8350
34.6769
Longitude
69.7833
69.8311
67.2667
70.2625
69.3931
70.2500
70.6869
69.4228
68.7792
68.0500
69.3000
69.2250
70.1194
68.9639
68.8919
68.9675
Longitude
84.1003
84.3256
82.5489
83.2394
82,8925
83.0886
82.7606
83.5578
82.2819
83.5167
82.3847
83.8939
83.6186
82.7858
82.5006
83.4744
83.2578
82.5386
83.0639
84.4383
84.1464
84.5661
84.3000
84.0236
84.4331
83.9992
84.4214
83.5019
83.3450
83.4561
ANC (/teq L"1)
256.7
75
174
9
-3
228
58
25
238
222
229
349
162
141
299
153
.4
.3
.4
.7
.9
.8
.6
.1
.1
.1
.4
.5
.7
.5
.7
ANC (/teq L"1)
89
145
219
98
104
102
371
30
126
102
347
234
48
64
217
211
43
96
106
87
171
58
118
164
202
138
121
120
186
72
.3
.2
.5
.8
.4
.7
.7
.4
.5
.5
.7
.7
.2
.8
.2
.8
.2
.3
.5
.8
.1
.6
.2
.3
.8
.0
.3
.5
.5
.7
                                                   A.2-3
-------
Table A.2-2.  Watersheds Simulated by ETD in the Northeast (Lakes)
Lake ID
1A1-003 HAWK POND
1A1-012 WHITNEY LAKE
1A1-014 WILMURT LAKE
1A1-017 CONSTABLE POND
1A1-020 FOURTH LAKE (BISBY LAKES)
1A1-028 DRY CHANNEL POND
1A1-029 MIDDLE POND
1A1-038 NICKS POND
1A1-039 JOHN POND
1A1-046 PART LOW LAKE
1A1-049 MIDDLE SOUTH POND
1A1-057 HITCHCOCK LAKE
1A1-061 WOLF LAKE
1A1-064 MT ARAB LAKE
1A1-066 WOODHULL LAKE
1A1-073 GULL LAKES (SOUTH)
1A2-002 ST. JOHN LAKE
1A2-004 DUCK LAKE
1A2-037 FISH PONDS (NORTHEAST)
1A2-041 MUD LAKE
1A2-042 NORTH BRANCH LAKE
1A2-045 WOODS LAKE
1A2-046 NINE CORNER LAKE
1A2-048 (NO NAME)
1A2-052 CHUB LAKE
1A2-054 TROUT LAKE
1A2-058 TROUT LAKE
1A3-001 NATE POND
1A3-040 ZACK POND
1A3-042 CHENEY POND
1 A3 -046 LONG POND
1A3-048 GRASS POND
1A3-065 SOUTH LAKE (EAST BRANCH)
1B1-010 GANOGA LAKE
1B1-055 ROCK HILL POND
1B2-028 MILL CREEK RESERVOIR
1B3-019 HARTLEY POND
1B3-025 TROUT LAKE
1B3-041 EAST STROUDSBURG RESERV.
1B3-053 NO NAME(SNOWFLAKE LAKE)
1B3-056 RIGA LAKE
1B3-059 ISLAND POND
1B3-060 SLY LAKE
1C1-009 UPPER BAKER POND
1C1-017 WELHERN POND
1C1-018 DECKER PONDS (EASTERN)
1C1-021 CLEAR POND
1C1-031 HUNT POND
1C1-068 LINCOLN POND
1C1-084 UPPER BEECH POND
State
NY
NY
NY
NY
NY
NY
NY
NY
NY
NY
NY
NY
NY
NY
NY
NY
NY
NY
NY
NY
NY
NY
NY
NY
NY
NY
NY
NY
NY
NY
NY
NY
NY
PA
PA
PA
PA
NY
PA
PA
CT
NY
PA
NH
ME
ME
ME
ME
MA
NH
Latitude
43.9569
43.5875
43.4292
43.8333
43.5708
44.3528
44.3389
44.1431
44.1125
44.0042
43.9894
43.8500
43. 6292
44.1883
43.5917
43.8561
43.4417
43.2355
43.5472
43.3405
43.3125
43.2528
43.1958
43.1275
43.2583
43.3467
44.3631
43.8583
43.9333
43.8778
43.6375
43.6930
43.5105
41 .3583
41.3136
41.2625
41 .6583
41.5861
41.0667
41 .9050
42.0217
41.2572
41 .8236
43.9083
45.2125
45.1958
45.1083
44.0833
42.6694
43.6483
Longitude
74.9583
74.5625
74.7250
74.7958
74.9708
74.4375
74.3792
74.9680
74.7639
74.8333
75.0183
75.0417
74.6542
74.6008
74.9869
74.8208
74.0611
74.4525
74.0611
74.4539
74.7944
74.3167
74.5500
74.5889
74.5305
74.7139
75.2689
74.0917
74.1833
74.1625
74.2889
75.0611
74.8922
76.3208
75.0161
75.7500
75.7083
74.6805
75.1667
75.4103
73.4833
74.1403
75.3372
71.9917
70.4944
69.9375
69.9875
71.0000
71.9125
71.2042
ANC (/teq L"1)
-21.7
11.4
30.0
-7.4
6.0
1.8
111.9
97.2
-1.7
56.3
-30.3
-18.0
-53.0
82.9
1.8
-28.1
1.8
-32.0
161.2
22.4
6.2
7.8
12.9
-5.3
1.1
-14.7
391.6
76.9
69.2
30.0
18.2
7.3
0.5
-23.9
52.9
14.6
218.5
30.4
89.7
245.8
-6.0
-4.4
190.9
105.7
325.9
173.3
122.5
62.9
-43.1
41.7
                                                                                                  Continued
                                                  A.2-4
-------
Table A.2-2. Continued)
Lake ID
1C2-002 IRON POND
1C2-012 BLACK POND
1C2-028 SUNSET LAKE
1C2-033 LONG POND
1C2-035 SMITH POND
1C2-041 JUGGERNAUT POND
1C2-048 CRANBERRY POND
1C2-056 DRURY POND
1C2-057 BABBIDGE RESERVOIR
1C2-064 HANCOCK POND
1C2-068 QUIHBY POND
1D2-027 SANDY POND
1D3-002 DYKES POND
1D3-025 LONG POND
1E1-011 FOURTH DAVIS POND
1E1-025 BEAN PONDS (MIDDLE)
1E1-040 LT. GREENWOOD POND (WEST)
1E1-050 LOWER OXBROOK LAKE
1E1-054 DUCK LAKE
1E1-062 LONG POND
1E1-106 GREENWOOD POND
1E1-111 LONG POND
1E2-002 (NO NAME)
1E2-038 NELSON POND
1E2-049 GROSS POND
1E2-056 PEABODY POND
1E2-063 KALERS POND
1E3-022 NUMBER NINE LAKE
1E3-040 NOKOMIS POND
1E3-041 ROUND POND
1E3-055 TOGUE POND
State
ME
ME
NH
NH
NH
NH
NY
ME
NH
ME
ME
MA
MA
CT
ME
ME
ME
ME
ME
ME
ME
ME
ME
ME
ME
ME
ME
ME
ME
ME
ME
Latitude
45.4583
44.1458
43.4708
43.2039
43.1542
42.9597
42.7444
44.7042
42.9347
44.9556
44.9908
41.7722
42.6042
42.0208
45.2583
45.8125
45.3667
45.2833
45.1500
44.9167
45.5353
44.5339
45.9944
44.4153
44.0583
43.9422
44.1080
46.4167
44.8708
44.7389
46.9339
Longitude
70.3750
70.8000
71 .3000
71.8119
72.0292
72.0125
73.4333
70.2417
72.2167
69.9861
70.7419
70.6542
70.7294
71.8167
69.3944
69.1917
69.4083
67.8417
68.1000
68.2697
69.2328
68.1703
69.7833
70.2625
69.3931
70.6869
69.4228
68.0500
69.3000
69.2250
68.8919
ANC (Meq L"1)
69.2
71.5
51.7
97.3
64.7
2.2
11.5
213.0
19.6
86.2
285.5
-6.0
1.6
162.1
19.5
98.0
36.8
43.8
33.4
86.2
22.7
6.3
256.7
9.4
-3.7
58.8
25.6
222.1
229.1
349.4
299.5
                                                 A.2-5
-------
Table A.2-3.  Watersheds Simulated by ILWAS in the Northeast  (Lakes)  and
Southern Blue Ridge Province (Streams)
Lake ID
1A1-003
1A1-064
1A2-002
1A2-042
1A2-045
1A2-048
1A2-052
1A3-048
1B1-010
1B1-055
1 82- 028
1B3-025
1B3-056
1C1-031
1C1-068
1C1-084
1C2-012
1C2-028
1C2-035
1C2-048
1C2-057
1D2-027
1E1-011
1E1-050
1E1-062
1E1-106
1E1-111
1E2-056
1E2-063

HAWK POND
HT ARAB LAKE
ST. JOHN LAKE
NORTH BRANCH LAKE
WOODS LAKE
(NO NAME)
CHUB LAKE
GRASS POND
GANOGA LAKE
ROCK HILL POND
MILL CREEK RESERVOIR
TROUT LAKE
RIGA LAKE
HUNT POND
LINCOLN POND
UPPER BEECH POND
BLACK POND
SUNSET LAKE
SMITH POND
CRANBERRY POND
BABBIDGE RESERVOIR
SANDY POND
FOURTH DAVIS POND
LOWER OXBROOK LAKE
LONG POND
GREENWOOD POND
LONG POND
PEABODY POND
KALERS POND
Stream ID
2A07701
2A07703
2A07805
2A07806
2A07811
2A07812
2A07823
2A07828
2A07829
2A07834
2A07835
2A08802
2A08805
2A08806
2A08810
2A08811
2A08901
2A08904
SUGAR COVE BRANCH OF N.
HALL CREEK
COSBY CREEK
ROARING FORK
FALSE GAP
CORRELL BRANCH
BRUSH" CREEK
WHITEOAK CREEK
CATHEYS CREEK
BRUSH CREEK
MIDDLE SALUDA RIVER
DUNN MILL CREEK
WEAVER CREEK
KIUTUESTIA CREEK
BRYANT CREEK
HINTON CREEK
PERSIMMON CREEK
SHE CREEK
State
NY
NY
NY
NY
NY
NY
NY
NY
PA
PA
PA
NY
CT
ME
MA
NH
ME
NH
NH
NY
NH
MA
ME
ME
ME
ME
ME
ME
ME
State
RIVER TN
TN
TN
NC
NC
NC
NC
NC
NC
NC
SC
GA
GA
GA
GA
GA
GA
GA
Latitude
43.9569
44.1883
43.4417
43.3125
43.2528
43.1275
43.2583
43.6930
41 .3583
41.3136
41 .2625
41.5861
42.0217
44.0833
42.6694
43.6483
44.1458
43.4708
43.1542
42.7444
42.9347
41.7722
45.2583
45.2833
44.9167
45.5353
44.5339
43.9422
44.1080
Lat i tude
35.3222
35.0956
35.7936
35.8214
35.6997
35.6758
35.3189
35.2258
35.2133
35.1139
35.1206
34.9492
34.8711
34.8589
34.6097
34.4853
34.9131
34.8350
Longitude
74.9583
74.6008
74.0611
74.7944
74.3167
74.5889
74.5305
75.0611
76.3208
75.0161
75.7500
74.6805
73.4833
71.0000
71.9125
71.2042
70.8000
71 .3000
72.0292
73.4333
72.2167
70.6542
69.3944
67.8417
68.2697
69.2328
68.1703
70.6869
69.4228
Longitude
84.1003
84.3256
83.2394
82.8925
83.3839
83.0886
83.5167
83.6186
82.7858
83.2578
82.5386
84.4383
84.3000
84.0236
83.9992
84.4214
83.5019
83.3450
ANC (Meq L'1)
-21.7
82.9
1.8
6.2
7.8
-5.3
1.1
7.3
-23.9
52.9
14.6
30.4
-6.0
62.9
-43.1
41.7
71.5
51.7
64.7
11.5
19.6
-6.0
19.5
43.8
86.2
22.7
6.3
58.8
25.6
ANC (/ieq L'b
89.3
145.2
98.8
104.4
16.2
102.7
102.5
48.2
64.8
43.2
96.3
87.8
118.2
164.2
138.0
121.3
120.5
186.5
                                                  A.2-6
-------
                          APPENDIX A.3





UNCERTAINTY ESTIMATES AND CONFIDENCE BOUNDS FOR MODEL PROJECTIONS
-------
                       NE Lakes
                 Priority  Class = A  -  I
                    Model =  MAGIC
                 Deposition =  Constant
          1.0 r

       o
       Q.
       2  0.8
       *=  0.4
       JO
       3
       E
       O  0.2
         0.0
                  Upper Bound
                  Predicted
                  Lower Bound
          -100    0     100    200   300    400
              ANC  (jxeq l_-i)  at 20  Yr.
                                                            NE Lakes
                                                      Priority  Class = A  -  I
                                                         Model  = MAGIC
                                                Deposition  = Ramp  30%  Decrease
                                               1.0r                           	
                                                          O  0.8
                                            O
                                            Q.
                                            O
                                                             0.6
                                            0.

                                            CD

                                            ••C 0.4
                                                    E
                                                    Q  0.2
                                                            0.0
                                                                                  Upper Bound
                                                                                  Predicted
                                                                                  Lower Bound
                                                      0     100    200   300    400
                                                   ANC  (jieq L-1)  at 20  Yr.
         1.0 r
      o 0.8
 O
 Q.
 8 0.6
0_

 
-------
          1.0r
       O  0.8
       O
       Q.
       O

       CL
0.8
       09
       3= 0.4
       J5
       :a
       E
       O °-2
         0.0
                       NE Lakes
                Priority  Class = A  - \
                    Model = MAGIC
                Deposition =  Constant
                      Upper Bound
                      Predicted
                      Lower Bound
           0         100         200        300
             [SO42-]  (jieq  L-1) at 20 Yr.
                                                                 NE  Lakes
                                                           Priority  Class  = A - I
                                                              Model =  MAGIC
                                                     Deposition  =  Ramp  30% Decrease
                                                    1.0r
                                                           O 0.8
                                                 O
                                                 Q.
                                                 O
                                                    0.6
                                                 0>

                                                 •-C 0.4
 E
O
                                                             0.0
                                                                                    Upper Bound
                                                                                    Predicted
                                                                                    Lower Bound
                                                      0         100        200        300
                                                        [SO42-]  (jaeq  L-I) at  20  Yr.
         1.0 r
       O 0.8
       o
       Q.
       O
         0.6
       CD

      •-^ 0.4
      .55
       3
       E
      O 0.2
         0.0
                Priority Class =  A  -  I
                   Model  = MAGIC
                Deposition  = Constant
                      Upper Bound
                      Predicted
                      Lower Bound
 0         100
   [SO42-]
                               200        300
                          L-1)  at 50  Yr.
                                                           Priority  Class = A  - I
                                                              Model  = MAGIC
                                                    Deposition  = Ramp  30%  Decrease
                                                             1-0r
                                                           O 0.8
o
Q.
S 0.6
Q.

<3>

•ft 0.4
JO

i
O 0.2
                                                             0.0
                                                                                   Upper Bound
                                                                                   Predicted
                                                                                   Lower Bound
     0         100        200         300
       [SO42-]  (jj.eq  L-1) at  50  Yr.
Figure A.3-2.  Sulfate projections with upper and lower bounds for a 90 percent confidence interval
for NE Priority Class A - I lakes using MAGIC.
                                              A.3-2
-------
          1-Or
       O 0.8
       O
       Q.
       O
         0.6
      JO
       3
       E

      O
         0.4
         0.0
                       NE Lakes
                Priority  Class = A  - I
                    Model =  MAGIC
                Deposition =  Constant
Upper Bound
Predicted
Lower Bound
           0     100    200    300    400    500
              [Ca2+] (jxeq  L-1) at  20  Yr.
                                           NE Lakes
                                     Priority  Class  = A - I
                                        Model =  MAGIC
                               Deposition  =  Ramp 30% Decrease
                              1.0r
                                                           O 0.8
                           O
                           Q.
                           O
                                                             0.6
                                                     JO
                                                     n
                                                     E

                                                     O
                                                             0.4
                                                             0.0
                                                                                   Upper Bound
                                                                                   Predicted
                                                                                   Lower Bound
                               0     100    200   300    400    500
                                  [Ca2*]  (p.eq L-1)  at 20  Yr.
      .0

      o
      Q.
      O
         1.0r
         0.8
         0.6
= 0.4
CO

I
O 0.2
        0.0
                Priority Class  = A - I
                   Model =  MAGIC
                Deposition = Constant
                               Upper  Bound
                               Predicted
                               Lower  Bound
          0     100    200    300    400    500
             [Ca2*]  (jieq L-1)  at  50  Yr.
                                    Priority Class =  A  -  I
                                       Model = MAGIC
                              Deposition =  Ramp  30%  Decrease
                             1.0 r
                          _o 0.8
                          £3
                          O
                          Q.
                          E 0.6
                          Q.

                          CD
                          >
                          ~ 0.4
                          JS
                          3
                          E
                          O 0.2
                                                            0.0
                                                   Upper Bound
                                                   Predicted
                                                   Lower Bound
                               0     100   200    300    400    500
                                 [Ca2*] (jo.eq  L-I) at 50 Yr.
Figure A.3-3.  Calcium projections with  upper and lower bounds for  a 90  percent confidence
interval for NE Priority Class A - I lakes using MAGIC.
                                              A.3-3
-------
        1.0
      O 0.8
      O
      Q.
      O
        0.6
     ~ 0.4
     jcg
     r:
     E
     O 0.2
        0.0
                      NE  Lakes
               Priority Class  = A - I
                  Model  =  MAGIC
               Deposition = Constant
                         Upper Bound
                         Predicted
                         Lower Bound
                50     100    150    200    250
                *]  (u.eq  L-1) at  20 Yr.
                                                                    NE  Lakes
                                                              Priority  Class  = A - I
                                                                 Model  =  MAGIC
                                                        Deposition  =  Ramp  30% Decrease
                                                            1-0r
                                                         O  0.8
                                                                                  Upper  Bound
                                                                                  Predicted
                                                                                  Lower  Bound
                                                         0      50     100    150    200   250
                                                           [Mg2*]  (jxeq  L-I) at 20 Yr.
        1.0
     O  0.8
        0.6
O
0.
O

CL

0)
     ~  0.4
     £2
     3
     E
     O  0.2
       0.0
               Priority  Class  = A - I
                  Model =  MAGIC
               Deposition =  Constant
                         Upper Bound
                         Predicted
                         Lower Bound
               50    100    150    200    250
                *]  (jieq L-1)  at 50  Yr,
                                                              Priority  Class =  A  -  I
                                                                 Model  =  MAGIC
                                                       Deposition  = Ramp 30%  Decrease
                                                            1.0r
                                                         O 0.8
                                                         O
                                                         Q.
                                                           0.6
                                                        QL
*3 0.4
to
3
E
O 0.2
                                                           0.0
                                                                                  Upper Bound
                                                                                  Predicted
                                                                                  Lower Bound
                                                        0     50    100    150    200    250
                                                           [Mg2+]  (jieq L-1)  at  50  Yr.
Figure A.3-4.  Magnesium  projections with upper and lower bounds for a 90 percent confidence
interval for NE Priority Class A - I lakes  using MAGIC.
                                              A.3-4
-------
        1.0
      O 0.8
     U-»
      O
      CL
      8 0.6
      CD

     *5 0.4
      E
     O
        o.o
                      NE  Lakes
               Priority  Class = A  -  I
                   Model  = MAGIC
               Deposition =  Constant
         4.0  4.5 5.0 5.5  6.0  6.5  7.0  7.5  8.0
                    pH at  20  Yr.
                                 NE  Lakes
                          Priority  Class = A  -  I
                              Model  =  MAGIC
                    Deposition  = Ramp 30%  Decrease
                                                             1.0
                 O 0.8

                 O
                 Q.
                 S. 0.6
                 
-------
        1.0 r
      O 0.8
      o
      Q.
      S 0.8
      
                          •^ 0.4
                          eg
                          =>
                          E
                          O °-2
                                                                             Upper Bound
                                                                             Predicted
                                                                             Lower Bound
                             o.o
                              -100    0     100    200   300    400
                                  ANC  ([ieq L-I)  at 50  Yr.
Figure A.3-6.  ANC  projections with upper and lower bounds for a 90 percent confidence interval
for NE Priority Class A - E lakes using ETD.
                                              A.3-6
-------
         1.0r

      O
      Q.

      S  0.6
      0

      "~ 0.4
      E
      O
        o.o
                      NE Lakes
               Priority Class =  A -  E
                    Model =  ETD
                Deposition =  Constant
Upper Bound
Predicted
Lower Bound
          o         100         200        300
            [SO42-] (jieq  L-1) at 20 Yr.
                                                         O
                                           NE  Lakes
                                    Priority Class  = A - E
                                         Model  =  ETD
                              Deposition = Ramp  30% Decrease
                              1.0 r
                                                            0.8
                           O
                           Q.
                           e 0.6
•~ 0.4
«
3
E

d
                                                            0.0
                                                                                  Upper Bound
                                                                                  Predicted
                                                                                  Lower Bound
                               0         100        200        300
                                 [SO42-]  (|J.eq  L-1)  at 20  Yr.
         1.0 r
      O
      Q.
      8 0.6
      ra
      3
      E
      o
        0.4
        0.0
               Priority Class  = A - E
                    Model  =  ETD
               Deposition  =  Constant
Upper  Bound
Predicted
Lower  Bound
          0         100        200        300
            [SO42-]  (p.eq  L-1)  at 50  Yr.
                                    Priority  Class  = A  - E
                                         Model  = ETD
                              Deposition =  Ramp  30%  Decrease
                             1.0r
 o
 Q.
 £ 0.6
Q.

 O

*= 0.4
 CO



I «
                                                           0.0
                                                                                  Upper  Bound
                                                                                  Predicted
                                                                                  Lower  Bound
                               0         100        200        300
                                 [SO42-]  (jaeq L-1)  at  50  Yr.
Figure A.3-7.  Sulfate projections with upper and lower bounds for a 90 percent confidence interval
for NE Priority Class A - E lakes using MAGIC.
                                             A.3-7
-------
         1.0
c
_g
£3
l_
O
Q.
O

IX

CD
        0.8
        0.6
        0.4
E
O °-2
        0.0
                      NE Lakes
               Priority Class =  A  - E
                    Model = ETD
                Deposition = Constant
          4.0  4.5  5.0  5.5  6.0  3.5  7.0  7.5  8.0
                    pH  at 20 Yr.
                                                                     NE Lakes
                                                              Priority Class =  A  -  E
                                                                   Model = ETD
                                                        Deposition  = Ramp 30%  Decrease
                                                        to
                                                    .2 °-8
                                                    *3
                                                     o
                                                     Q.
                                                     2 0.6
                                                    0.
                                                     O
                                                    'JS 0.4
                                                          E
                                                          O
                                                             0.0
                                                         4.0  4.5  5.0  5.5  6.0  6.5  7.0  7.5  8.0
                                                                   pH  at 20 Yr.
         1.0
      O 0.8
      O
      a.
CD
«=
J5
n
E
o
        0.4
        0.0
               Priority Class =  A  -  E
                    Model = ETD
                Deposition = Constant
          4.0  4.5  5.0  5.5  6.0  6.5  7.0  7.5  8.0
                    pH  at 50 Yr.
                                                              Priority Class =  A  -  E
                                                                   Model =  ETD
                                                        Deposition  = Ramp 30%  Decrease
                                                        1.0
                                                          O 0.8
                                                     o
                                                     Q.
                                                     2 0.6
                                                          ~  0.4
                                                          E
                                                          O
                                                             0.0
                                                         4.0  4.5  5.0  5.5  6.0  6.5  7.0  7.5  8.0
                                                                   pH  at 50 Yr.
Figure A.3-8.  pH  projections with upper and  lower bounds for a 90 percent confidence  interval
for NE Priority Class A -  E lakes using ETD.
                                              A.3-8
-------
         1.0
      O  0.8
      O
      Q.
      O
         0.6
      CD

      *S  0.4
      s
      o
         0.0
                      NE Lakes
               Priority Class  =  A -  E
                   Model = MAGIC
                Deposition =  Constant
                     Upper Bound
                     Predicted
                     Lower Bound
-100    0     100
    ANC  (p.eq l
                            200    300    400
                             at  20 Yr.
                                                                NE  Lakes
                                                         Priority Class  = A  - E
                                                            Model  =  MAGIC
                                                   Deposition = Ramp  30%  Decrease
                                                            1.0r
                                                         O 0.8
                                                o
                                                Q.
                                                O
                                                            0.6
 0>

 OB
 3
 E

O
                                                            0.4
                                                            0.0
                                                                                  Upper Bound
                                                                                  Predicted
                                                                                  Lower Bound
    -100    0     100    200    300   400
        ANC  (jieq L-i)  at  20  Yr.
         1.0r
      g  0.8
      *-«
      O
      a.
      S.
      QL
      3= 0.4
      CO
      3
      E
      O 0.2
               Priority Class  = A - E
                   Model  = MAGIC
                Deposition =  Constant
                     Upper Bound
                     Predicted
                     Lower Bound
        0.0
         -100     0     100    200    300    400
             ANC  (|ieq L-i)  at 50 Yr.
                                                         Priority  Class =  A  -  E
                                                            Model =  MAGIC
                                                   Deposition  =  Ramp 30%  Decrease
                                                  tor
 O 0.8

 O
 Q.
 2 0.6
Q.

 O

•S 0.4
JO



I'
                                                  o.o
                                                                                  Upper Bound
                                                                                  Predicted
                                                                                  Lower Bound
                                                   -100    0     100    200    300   400
                                                       ANC (p,eq L-I) at  50 Yr.
Figure A.3-9.  ANC projections with upper and lower bounds for a 90 percent confidence interval
for NE Priority Class A - E lakes using MAGIC.
                                             A.3-9
-------
        1.0 r
      o 0.8
      o
      o.
      2 0.8
      CD

      ~ 0.4
      E
     O 0-2
        0.0
                     NE  Lakes
               Priority  Class = A  - E
                  Model  =  MAGIC
               Deposition = Constant
                    Upper Bound
                    Predicted
                    Lower Bound
          0         100        200        300
            [SO42-]  (|J.eq  L-1)  at 20 Yr.
                                                               NE Lakes
                                                        Priority Class  =  A -  E
                                                            Model = MAGIC
                                                  Deposition  = Ramp  30% Decrease
                                                  1.0r
                                                         O 0.8
                                               O
                                               Q.
                                               £  0.6
0)
S=  0.4
.55
:D
E
O  °-2
                                                            0.0
Upper  Bound
Predicted
Lower  Bound
                                                    0         100        200        300
                                                      [SO42-] (u.eq  L-i) at 20 Yr.
        1.0 r
      o o.s

      o
      Q.
      S. 0.6
      CD

      £ 0.4
      15
      3
      E
      O 0-2
        0.0
               Priority  Class  = A - E
                  Model = MAGIC
               Deposition  =  Constant
                    Upper Bound
                    Predicted
                    Lower Bound
0         100
  [SO42-]
                              200    -    300
                          L-1)  at 50 Yr.
                                                        Priority Class =  A -  E
                                                            Model  =  MAGIC
                                                  Deposition  = Ramp 30% Decrease
                                                  1.0 r
                                                         o
                                                            0.8
                                               o
                                               a.
                                                            0.6
JO
3
E

O
                                                  0.4
                                                            0.0
Upper Bound
Predicted
Lower Bound
     0         100        200        300
       [SO,2-]  (jxeq L-i}  at  50  Yr.
Figure A.3-10.  Sulfate projections with upper and  lower bounds for a  90 percent  confidence
interval for NE Priority Class A - E lakes using MAGIC.
                                             A.3-10
-------
        1.0 r
      O 0.8
O
Q.
O
dl

CD
        0.6
        0.4
      E
     O
        o.o
                     NE Lakes
               Priority  Class =  A  -  E
                  Model =  MAGIC
               Deposition = Constant
                         Upper Bound
                         Predicted
                         Lower Bound
     0    100
       [Ca2+]
                      200    300   400    500
                         l_"i) at  20 Yr.
                                                                    NE  Lakes
                                                             Priority Class  = A - E
                                                                 Model  = MAGIC
                                                       Deposition = Ramp  30% Decrease
                                                       1.0r
                                                         O  0.8
O
Q.
£  0.6
0.
   0.4
.55
=}
E
O °-2
                                                            0.0
Upper Bound
Predicted
Lower Bound
     0    100    200    300   400    500
       [Ca2+] (\ieq  L-1) at  20 Yr.
        1.0 r
      O 0.8
      O
      Q.
      O
        0.6
      0>

      •~ 0.4
      E
     O
        o.o
               Priority  Class = A  - E
                  Model =  MAGIC
               Deposition = Constant
                         Upper Bound
                         Predicted
                         Lower Bound
          0     100    200    300    400    500
             [Ca2+] (\ieq  L-1) at  50 Yr.
                                                             Priority Class  = A - E
                                                                 Model  = MAGIC
                                                       Deposition  = Ramp  30% Decrease
                                                            1.0 r
                                                         O  0.8
                                                    O
                                                    Q.
                                                    O
                                                    ol

                                                    0>
                                                            0.6
                                                            0.4
                                                    3
                                                    E
                                                            0.0
                         Upper Bound
                         Predicted
                         Lower Bound
                                                         0     100    200    300    400   500
                                                           [Ca2*]  (p,eq L-1)  at 50  Yr.
Figure A.3-11.  Calcium projections with upper  and lower  bounds for  a 90 percent  confidence
interval for NE Priority Class A - E lakes using MAGIC.
                                             A.3-11
-------
    1.0 r
 o 0.8
 O
 Q.
 O
    0.6
 0)

 ~  0.4
 jg
 :D
 E

 O
   0.0
                  NE  Lakes
           Priority Class  =  A -  E
               Model  = MAGIC
           Deposition =  Constant
Upper Bound
Predicted
Lower Bound
      0  ,   50     100    150    200    250
        [Mg2+]  (ij.eq  L-1) at  20 Yr.
                                            NE Lakes
                                     Priority  Class =  A  - E
                                         Model =  MAGIC
                               Deposition  =  Ramp 30%  Decrease
                               1.0r
                           .2
                           '+2

                            o
                            a.
                            8 0.6
 ffi

+3 0.4
CO
3
E

O
                                                        0.0
                                                                               Upper Bound
                                                                               Predicted
                                                                               Lower Bound
                                  r.. 50, ,  10°    15°    20°    250
                                  [Mg2+] (j4.eq L-1)  at  20  Yr.
 O 0.8
O
Q.
O
   0.6
'•(= 0.4
   0.2
   0.0
          Priority Class =  A  -  E
              Model  =  MAGIC
           Deposition =  Constant
                         Upper Bound
                         Predicted
                         Lower Bound
     0      50     100    150    200    250
       [Mg2*]  (^eq  L-1) at  50 Yr.
                                    Priority  Class = A  - E
                                        Model =  MAGIC
                              Deposition  =  Ramp 30%  Decrease
                                                        1.0r
                                                     O  0.8
                           o
                           Q.
                           20.6

                           0)

                           *= 0.4
                           E
                          O
                                                       0.0
                                                    Upper  Bound
                                                    Predicted
                                                    Lower  Bound
                               0 r   50    100    150    200    250
                                 [Mg2+] (p,eq L-1)  at  50  Yr.

                                                        bounds for a 90
                                        A.3-12
-------
                      NE  Lakes
               Priority Class  = A - E
                   Model  = MAGIC
                Deposition =  Constant
        o.o
         "4.0  4.5  5.0 5.5  6.0  6.5  7.0  75  80
                    pH at  20 Yr.
             NE  Lakes
      Priority Class  = A - E
          Model  = MAGIC
Deposition  = Ramp  30% Decrease
                                                           o.o
 4.0  4.5 5.0  5.5  6.0  6.5  7.0  7.5  80
          pH at  20 Yr.
         1.0
      O 0.8
      O
      CL

      2 0.6
     Q.
     S= 0.4
     JO
     3
     E
     O 0.2
        0.0
               Priority  Class =  A  -  E
                  Model =  MAGIC
               Deposition = Constant
         4.0  4.5  5.0  5.5  6.0  6.5  7.0  7.5  8.0
                   pH at 50 Yr.
      Priority  Class =  A  -  E
         Model =  MAGIC
Deposition  =  Ramp 30%  Decrease
                                                           0.0
4.0  4.5  5.0  5.5  6.0  6.5  7.0  7.5  80
          pH at 50 Yr.
<,  n.   J?H Pr°iections with upper and lower bounds for a 90 percent confidence interval
for NE Priority Class A - E lakes using MAGIC.
                                            A.3-13
-------
         1.0r
      O 0.8
         0.6
o
Q.
O
IX

ffi
      '*-• 0.4
      ra
      n
      E
      O 0.2
         0.0
                       NE  Lakes
               Priority  Class =  A  &  B
                     Model  = ETD
                Deposition  = Constant
                         Upper Bound
                         Predicted
                         Lower Bound
          -100    0     100    200   300   400
              ANC (jj.eq L-1)  at 20  Yr.
                                                                    NE Lakes
                                                             Priority Class  = A & B
                                                                  Model =  ETD
                                                       Deposition  = Ramp 30%  Decrease
                                                             1.0r
                                                          O  0.8
                                                          O
                                                          Q.
                                                          O
                                                             0.6
                                                    CL

                                                    
-------
          1.0
 o
 Q.
 E 0.8
 0.

 03

 3= 0.4
 JO



 l<
         0.0
                       NE Lakes
                Priority Class  = A & B
                     Model =  ETD
                 Deposition =  Constant
                                Upper Bound
                                Predicted
                                Lower Bound
           0         100         200        300
             [SO,2']  (jxeq  L-1) at 20 Yr.
                 NE Lakes
          Priority Class =  A  & B
               Model = ETD
    Deposition  = Ramp 30% Decrease
    1.0r
                                                           O 0.8
                                                           O
                                                           Q.
                                                           O
                                                             0.6
 JO
 3
 E

 O
                                                             0.4
                                                             0.0
                                                                              Upper Bound
                                                                              Predicted
                                                                              Lower Bound
     0         100        200        300
       [SO42-] (n.eq  L-i) at 20 Yr.
         1.0r
      O 0.8
      O
      Q.
         0.6
JO

1
3
O
         0.4
         0.0
               Priority  Class =  A  &  B
                     Model  = ETD
                Deposition  = Constant
                               Upper Bound
                               Predicted
                               Lower Bound
           0          100        200        300
             [SO42-]  (jieq L-1)  at  50  Yr.
         Priority  Class = A  &  B
               Model  = ETD
    Deposition =  Ramp  30% Decrease
                                                             1.0 r
                                                          O  0.8
 o
 a.
 E i
a.
 a>
ss 0.4
E
O 0.2
                                                            0.0
                                                                             Upper Bound
                                                                             Predicted
                                                                             Lower Bound
     0         100        200        300
       [SO42-]  (jaeq L-1)  at 50  Yr.
Figure A.3-15.  Sulfate projections with upper and  lower bounds  for a  90 percent  confidence
interval for NE Priority Class A - B lakes using ETD.
                                             A.3-15
-------
    1.0
 o  0.8
    0.8
o
a.
o
oZ
05
 *3 0.4
 19
 3
 E
 O
   0.0
                  NE  Lakes
          Priority  Class  = A &  B
                Model  =  ETD
           Deposition  =  Constant
     4.0  4.5  5.0  5.5  6.0  6.5  7.0  7.5  80
               pH at  20 Yr.
                  NE  Lakes
          Priority  Class  = A &  B
                Model  =  ETD
     Deposition =  Ramp  30%  Decrease
                                                         to
                                                      o  0.8
 O
 Q.
 E  0.6
 0

 ~  0.4
 ro
 3
 E
 Q  0-2
                                                        0.0
     4.0  4.5  5.0  5.5  6.0  6.5  7.0  7.5  80
               pH  at 20 Yr.
   1.0
 O 0.8
 O
 Q.
 2. 0.6
•~ 0.4
CO
3
E

O
   0.0
          Priority Class  =  A  & B
               Model =  ETD
           Deposition =  Constant
    4.0  4.5  5.0  5.5  6.0  6.5  7.0  75  80
              pH at  50  Yr.
          Priority  Class  = A & B
               Model =  ETD
    Deposition =  Ramp  30%  Decrease
                                                        to
                                                     O 0.8
 o
 Q.
                                                        0.6
*5 0.4
JO

1
O 0.2
                                                        0.0
    4.0  4.5  5.0  5.5  6.0  6.5  7.0  75  80
              pH at  50  Yr.
  Pr?or«y
                                        A.3-16
-------
         1.0r
       O 0.8
       O
       Q.
       O
         0.6
      ffl
      «= 0.4
      a
      13
      E
      O 0-2
         0.0
                      NE  Lakes
               Priority Class =  A  & B
                   Model  =  MAGIC
                Deposition = Constant
                         Upper Bound
                         Predicted
                         Lower Bound
          -100    0     100    200    300   400
              ANC (neq I_-1)  at  20  Yr.
                                                                    NE  Lakes
                                                             Priority  Class  = A  &  B
                                                                 Model  = MAGIC
                                                       Deposition  =  Ramp  30%  Decrease
                                                       1-Or
                                                          O 0.8
 o
 Q.
 S 0.6
 Q.

 O

 *s 0.4 -
 OJ
 3


 Jc
                                                            0.0
                                                                                  Upper Bound
                                                                                  Predicted
                                                                                  Lower Bound
                                                       -100    0     100    200    300    400
                                                           ANC  (p.eq L-I)  at 20 Yr.
         1.0
      O  0.8
      O
      o.
         0.6
-------
    1.0r
 O  0.8
    0.8
       O
       Q.
       O

      CL

       CD
 ~ 0.4
 ca
 3
 E
 O °-2
   0.0
                 NE Lakes
          Priority Class  = A &  B
              Model = MAGIC
           Deposition =  Constant
                               Upper Bound
                               Predicted
                               Lower Bound
     0         100        200        300
       [SO42-]  (jieq  L-1)  at 20  Yr.
                                                                          NE Lakes
                                                                   Priority  Class  = A  &  B
                                                                       Model  = MAGIC
                                                             Deposition  =  Ramp  30% Decrease
                                                             1.0r
                                                    O 0.8
                                                       0.6
 O
 Q.
 O
 IX

 o>
 *=  0.4
 CO
 3
 E

 O
                                                       0.0
                                                                             Upper  Bound
                                                                             Predicted
                                                                             Lower  Bound
                                                                        100        200        300
                                                                [SO42-]  (jxeq L-1)  at  20  Yr.
   1.0 r
Jj 0.8
k_
O
a.
a>
>
   0.4
E
O
   0.0
         Priority Class =  A  & B
             Model =  MAGIC
          Deposition = Constant
                               Upper Bound
                               Predicted
                               Lower Bound
     0         100        200        300
       [SO42-] (jxeq  L-1) at 50 Yr.
                                                                  Priority Class  = A &  B
                                                                      Model = MAGIC
                                                             Deposition  = Ramp  30%  Decrease
                                                            1-0r
                                                         O  0.8
                                                         O
                                                         Q.
                                                      0.6
15
^
E
O
                                                      0.4
                                                      0.0
                                                                            Upper Bound
                                                                            Predicted
                                                                            Lower Bound
                                                             0         100        200        300
                                                               fSO42-]  (|j.eq  L-1)  at 50  Yr.
 nt!
interval for
         _s.ul*fe  Projections with upper and  lower bounds for a 90 percent confidence
         Priority Class A - B lakes using  MAGIC.
                                       A.3-18
-------
         1.0r
       O 0.8
       O
       Q.
       CD

      •~ 0.4
       E
      O
         0.0
                       NE  Lakes
               Priority  Class =  A  &  B
                   Model  =  MAGIC
                Deposition = Constant
                         Upper Bound
                         Predicted
                         Lower Bound
           0     100    200    300   400    500
             [Ca2+] (jaeq  L-I) at  20 Yr.
                                                                    NE Lakes
                                                             Priority Class  = A & B
                                                                 Model =  MAGIC
                                                        Deposition  = Ramp 30%  Decrease
                                                             1.0 r
                                                          O 0.8
                                                    O
                                                    0.
                                                    O
                                                             0.6
 ffl
 *= 0.4
 a
 13
 E

 O
                                                             0.0
                                                                                   Upper  Bound
                                                                                   Predicted
                                                                                   Lower  Bound
                                                         0     100   200    300    400   500
                                                           [Ca2*] (|4.eq  L-1) at 20  Yr.
         1.0 r
      O  0.8
         0.6
O
Q.
O
ol

05
      3=  0.4
      J2
      =J
      E
      O
         0.0
               Priority Class  = A & B
                   Model = MAGIC
                Deposition =  Constant
                         Upper Bound
                         Predicted
                         Lower Bound
           0     100    200    300    400   500
             [Ca2+]  (ueq L-1) at 50  Yr.
                                                             Priority  Class  = A  &  B
                                                                 Model  = MAGIC
                                                       Deposition  =  Ramp  30%  Decrease
                                                             1.0r
                                                    C
                                                    O
                                                          O
                                                          a.
                                                             0.8
0>
>
%-•
JS

I
O
                                                      0.4
                                                            0.0
                                                                                   Upper Bound
                                                                                   Predicted
                                                                                   Lower Bound
                                                        0     100    200    300    400    500
                                                           [Ca2*] (|4,eq L-1)  at  50 Yr.
Figure A.3-19.  Calcium projections with upper and lower bounds for a  90 percent confidence
interval for NE Priority Class A - B  lakes using MAGIC.
                                             A.3-19
-------
         1.0 r
       O 0.8
       O
       a.
       o
         0.6
      *= 0.4
      E
      Q 0.2
         0.0
                       NE  Lakes
               Priority  Class =  A  &  B
                   Model  = MAGIC
                Deposition  = Constant
 Upper Bound
 Predicted
 Lower Bound
           0      50     100    150    200   250
             [Mg2+]  (u.eq  L-1) at 20 Yr.
                                            NE Lakes
                                     Priority Class  = A &  B
                                         Model = MAGIC
                               Deposition  = Ramp  30%  Decrease
                                                             1.0 r
                            O 0.8
                            +3
                            L_
                            O
                            a.
                            8 0.6
 ffl

 33
 JO
 3
 E

 O
                                                             0.4
                                                            o.o
                                                                                   Upper Bound
                                                                                   Predicted
                                                                                   Lower Bound
        ,   50
        [Mg2+]
                                            100    150    200    250
                                               L-1) at  20 Yr.
         1.0 r
      O 0.8
      *—
      L_
      O
      Q.

      8 0.6
      CL
     *3 0.4
     CO
     3
     E
     O 0.2
        0.0
               Priority  Class  = A &  B
                   Model  = MAGIC
                Deposition =  Constant
Upper Bound
Predicted
Lower Bound
          0     50     100    150    200   250
            [Mg2*]  (|xeq  L"0 at  50 Yr.
                                    Priority Class  = A &  B
                                        Model = MAGIC
                              Deposition  = Ramp  30%  Decrease
                                                            1.0r
                           O  0.8
                           o
                           Q.
                           8 0.6
                           a.

                           

                           33 0.4
                           <0
E
O
                                                            0.0
                                                                                  Upper  Bound
                                                                                  Predicted
                                                                                  Lower  Bound
                               0     50    100    150   200    250
                                  [Mg2+] (jj.eq L-1) at  50 Yr.
Figure A 3-20.  Magnesium projections with upper and lower bounds for a 90 percent confidence
interval for NE Priority Class A - B lakes using MAGIC.
                                             A.3-20
-------
    1.0
 O  0.8
    0.6
 o
 Q.
 O
CL

 

                                                     *=  0.4
                                                     co
                                                     3
                                                     E

                                                     O  °-2
                                                       0.0
                                                        4.0  4.5  5.0  5.5  6.0  6.5  7.0  75  80
                                                                  pH at  20  Yr.
   1.0
 O
   0.8
 O
 Q.
 O
CL
  0.6
-------
 O
 O
 Q.
 O
    1.0 r
    0.8
    0.6
 •^3  0.4
 co
 3
 E

 O
   o.o
           SBRP Stream  Reaches
           Priority  Class =  A -  E
              Model =  MAGIC
           Deposition =  Constant
 Upper Bound
 Projected
 Lower Bound
    -100     0    100    200   300    400
        ANC (|ieq L-1)  at 20  Yr.
                                   SBRP  Stream Reaches
                                   Priority Class  = A  - E
                                       Model  = MAGIC
                             Deposition = Ramp  30%  Decrease
                                                       1.0r
                                                                              Upper Bound
                                                                              Projected
                                                                              Lower Bound
                            0.0
                             -100    0     100    200    300    400
                                 ANC  (jieq L-i) at  20 Yr.
          Priority  Class =  A  -  E
             Model =  MAGIC
          Deposition = Constant
    1.0r
 O 0.8
 O
 0.
 O
   0.6
*= 0.4
E
o
Upper  Bound
Projected
Lower  Bound
   o.o
    -100    0    100    200    300    400
        ANC (p.eq  L-1) at 50 Yr.
                                  Priority Class  = A - E
                                      Model  = MAGIC
                            Deposition  = Ramp  30% Decrease
                            1.0 r
                                                   O 0.8
                         o
                         a.
                         o
                                                      0.6
                                                   *=  0.4
E
O
                           0.0
                                                                             Upper  Bound
                                                                             Projected
                                                                             Lower  Bound
                            -100    0     100    200    300   400
                                ANC  (jieq L-1)  at  50  Yr.
                                               *°unaa for"90 percent con(Idence
                                      A.3-22
-------
    1.0r
 O 0.8
 O
 Q.
 O
    0.8
 CD
 JO
 3
 E

 O
    0.4
    0.0
           SBRP  Stream Reaches
           Priority Class  = A - E
              Model = MAGIC
           Deposition  = Constant
Upper Bound
Projected
Lower Bound
      0         100        200        300
        [SO,2-] (n.eq  L-1) at 20 Yr.
                                   SBRP  Stream Reaches
                                   Priority Class  = A - E
                                      Model = MAGIC
                             Deposition  = Ramp  30% Decrease
                            1.0 r
                         .2 °-8
                         '•&
                         o
                         Q.
*= 0.4
-------
    1.0r
  O 0.8
o
Q.
O
ol

Ol
    0.6
    0.4
 £
 O  0.2
    0.0
           SBRP  Stream Reaches
           Priority Class  =  A - E
              Model  = MAGIC
           Deposition =  Constant
                            Upper Bound
                            Projected
                            Lower Bound
      0     100   200    300   400    500
        [Ca2+] (jieq  L-1) at 20 Yr.
                                                               SBRP Stream  Reaches
                                                               Priority  Class =  A  - E
                                                                  Model =  MAGIC
                                                        Deposition  =  Ramp 30%  Decrease
                                                     O
                                                        1.0r
                                                        0.8
                                                     O
                                                     a.
                                                     £ 0.6
                                                     E

                                                    O
                                                       0.0
                                                                               Upper Bound
                                                                               Projected
                                                                               Lower Bound
                                                              100
                                                           [Ca2+]
                                                                     200   300    400    500
                                                                        I_-1)  at  20  Yr.
    1.0
 O 0.8
o
a.
2 0.6
Q.
•-i= 0.4
E
O
   0.0
          Priority Class  =  A -  E
              Model  = MAGIC
           Deposition =  Constant
                           Upper  Bound
                           Projected
                           Lower  Bound
          100    200    300   400    500
            ] (|aeq  l_-i) at  50 Yr.
                                                    o
                                                    Q.
                                                    O
                                                             Priority  Class = A  - E
                                                                 Model =  MAGIC
                                                        Deposition  =  Ramp 30%  Decrease
                                                       1.0r
                                                     O 0.8
                                                       0.6
                                                    JO
                                                    3
                                                    I
                                                    o
                                                       0.4
                                                      0.0
                                                                              Upper Bound
                                                                              Projected
                                                                              Lower Bound
                                                        0     100    200   300    400    500
                                                           [Ca2+] (p,eq L-I)  at  50  Yr.
                                       A.3-24
-------
         1.0r
      O 0.8
      O
      QL
      O
         0.6
CD
U3
JO
3
E
6
         Q.4
         0.0
                SBRP Stream  Reaches
                Priority  Class =  A  -  E
                   Model =  MAGIC
                Deposition =  Constant
                                Upper Bound
                                Projected
                                Lower Bound
           0     50    100    150    200    250
             [Mg2*]  (jj.eq  L-i)  at 20  Yr.
                                                             SBRP Stream  Reaches
                                                             Priority  Class =  A  -  E
                                                                Model =  MAGIC
                                                       Deposition  =  Ramp 30%  Decrease
                                                      1.0 r
                                                    O 0.8
                                                   +3
                                                    O
                                                    a.
                                                    2 0.6
                                                   a.
 CD
 +3
 to
 rj
 E

 O
                                                            0.4
                                                            0.0
                                                                             Upper Bound
                                                                             Projected
                                                                             Lower Bound
                                                              50     100    150    200   250
                                                              2*]  (n.eq  L-1) at 20  Yr.
         1.0 r
      O  0.8
      o
      Q.
      2  0.6
      a.

      CD

      *=  0.4
      E
      O
        0.0
               Priority Class  = A - E
                   Model  - MAGIC
                Deposition =  Constant
                          Upper  Bound
                          Projected
                          Lower  Bound
          0     50    100    150    200    250
             [Mg2*] (jj.eq L-1)  at  50  Yr.
                                                            Priority Class  = A - E
                                                                Model  = MAGIC
                                                      Deposition  = Ramp  30% Decrease
                                                      1.0r
 O 0.8

 O
 Q.
 2 0.6
a.

 CD

+5 0.4
 ra

 E
 —J
O °-2
                                                           0.0
                                                                                   Upper  Bound
                                                                                   Projected
                                                                                   Lower  Bound
                                                       0     50    100    150    200    250
                                                          [Mg2*]  (jo.eq L-1)  at  50  Yr.
Figure A.3-25.  Magnesium projections with upper and lower bounds for a 90 percent confidence
interval for SBRP Priority Class A - E  streams using MAGIC.
                                            A.3-25
-------
         1.0
      O 0.8
o
a.
o
t_
Q.

0>

1o
         0.6
         0.4
      E
      O  0.2
         0.0
               SBRP Stream  Reaches
               Priority Class =  A  -  E
                   Model  =  MAGIC
                Deposition =  Constant
                   Upper
                   Projected
                   Lower
          4.0  4.5  5.0  5.5  6.0  6.5 7.0 7.5  8.0
                    pH  at 20  Yr.
          SBRP  Stream  Reaches
          Priority Class  = A  - E
              Model =  MAGIC
    Deposition = Ramp  30%  Decrease
                                                            1.0

 O
 Q.

 2 0.6
 ffl

'•P 0.4
 E
O
                                                            0.0
              Upper
              Projected
              Lower
    4.0  4.5  5.0  5.5  6.0  6.5  7.0  75  80
              pH at 20 Yr.
        0.0
               Priority Class  = A - E
                   Model  = MAGIC
                Deposition =  Constant
         Priority Class =  A  -  E
             Model  =  MAGIC
    Deposition  = Ramp 30%  Decrease
   to
                                                         O  0.8
                                                         o
                                                         Q.
                                                         O
                                                            0.6
05
>
*=
-------
    1.0 r
 o 0.8
 O
 a.
 O
   0.6
S= 0.4 -
 a
 Z3
 E

O °
   0.0
          SBRP  Stream Reaches
          Priority  Class =  A  & B
              Model = MAGIC
          Deposition  = Constant
                                 Upper Bound
                                 Projected
                                 Lower Bound
    -100    0     100    200   300    400
        ANC  (p.eq L.-1)  at 20  Yr.
                                                                   SBRP  Stream Reaches
                                                                   Priority  Class  =  A  &  B
                                                                    '   Model  = MAGIC
                                                             Deposition =  Ramp  30% Decrease
                                                             1.0 r
                                                          O  0.8
                                                          *3
                                                          u.
                                                          O
                                                          Q.

                                                          £  0.6
                                                          QL

                                                          
-------
    1.0 r
  o 0.8
 o
 Q.
 O
    0.6
 Q>
    0.4
 E
 O  0.2
    0.0
           SBRP  Stream Reaches
          Priority  Class  =  A  &  B
               Model  = MAGIC
           Deposition  =  Constant
                       Upper Bound
                       Projected
                       Lower Bound
      0          100        200        300
        [SO,2-]  (p.eq Li)  at  20  Yr.
                                                           SBRP Stream  Reaches
                                                          Priority Class =  A  & B
                                                              Model =  MAGIC
                                                    Deposition  = Ramp 30% Decrease
                                                         1.0 r
                                                      0~0.8
                                                 O
                                                 Q.
                                                 O
                                                        0.6
sa
3
E
O
                                                        0.4
                                                        0.0
                                                                                Upper Bound
                                                                                Projected
                                                                                Lower Bound
                                                     0 ,       100        200        300
                                                       [SO42-]  (jaeq  L-i)  at 20 Yr.
    1.0r
 O 0.8
 O
 Q.
 O
   0.6
03
>
~ 0.4
_ro
13
E

O °-2
   0.0
          Priority  Class  = A &  B
              Model  = MAGIC
           Deposition =  Constant
                           Upper  Bound
                           Projected
                           Lower  Bound
0 .        100
  [SO42-]
                         200        300
                    L-1)  at 50  Yr.
                                                         Priority Class =  A  & B
                                                             Model =  MAGIC
                                                    Deposition  = Ramp 30% Decrease
                                                   1.0 r
                                                     O  0.8
                                                o
                                                Q.
                                                S.  0.6
                                                ~  0.4
                                                ca
                                                3
                                                E
                                                O  0-2
                                                       0.0
                                                                          Upper Bound
                                                                          Projected
                                                                          Lower Bound
    0 rcv/^  .  100        200        soo
      [S042-]  (ij.eq L-1)  at 50  Yr.
                                                              for
                                        A.3-28
-------
         1.0r
      O  0.8
      O
      Q.

      2  0.8
      O

      z:  0.4
      ro
      3
      E

      O  Q-z
        o.o
               SBRP Stream Reaches
               Priority Class =  A  &  B
                   Model  = MAGIC
                Deposition = Constant
Upper Bound
Projected
Lower Bound
          0     100    200   300   400    500
             [Ca2+]  (jieq L-1) at  20 Yr.
                                   SBRP Stream  Reaches
                                  Priority Class  = A &  B
                                      Model = MAGIC
                             Deposition  = Ramp  30%  Decrease
                                                            1-0r
                                                         o  o.a
                          o
                          0.
                          2 o.e
                         o.
                          0>
si*
 3
 E
O
                            0.4
                                                           0.0
                                                                                   Upper Bound
                                                                                   Projected
                                                                                   Lower Bound
           100
        [Ca2t]
                                         200    300    400    500
                                            L-1)  at 20  Yr.
         1.0r
      O 0.8
      O
      Q.
        0.6
      *= 0.4
      CB
      3
      E
      O 0.2
        0.0
               Priority  Class =  A  & B
                   Model = MAGIC
                Deposition  = Constant
Upper  Bound
Projected
Lower  Bound
          0     100    200    300   400    500
             [Caz+] (|ieq  L-1) at  50 Yr.
                                  Priority  Class  = A  &  B
                                      Model  = MAGIC
                            Deposition  =  Ramp  30% Decrease

                            1-0r
                                                         O 0.8
                         o
                         Q.
                         2  0.6
                         a.

                         o

                         *=  0.4
                         CO
E
o
                                                           0.0
                                                                                   Upper Bound
                                                                                   Projected
                                                                                   Lower Bound
     0    100
       [Ca2*]
                                         200   300   400    500
                                            l_-i) at  50 Yr.
 nt0r,fcnB^nC-iUI?  P™JectiA°ns wlth "PPe"" and  lower bounds for  a 90 percent confidence
interval for SBRP Priority Class A - B streams using MAGIC.
                                            A.3-29
-------
    1.0
 o 0.8
 O
 a.
 o
   0.6
*= 0.4
19
rj
E
O
   0.0
          SBRP  Stream Reaches
          Priority  Class  =  A  &  B
              Model  = MAGIC
          Deposition =  Constant
Upper Bound
Projected
Lower Bound
           50     100    150    200    250
           2*]  (p.eq  L-i) at  20 Yr.
                                   SBRP  Stream Reaches
                                   Priority  Class  = A &  B
                                       Model  = MAGIC
                             Deposition =  Ramp  30% Decrease
                                                       1.0r
                                                    O 0.8
                          O
                          Q.
                          O
                                                       0.6
                                                     JO
                                                     3
                                                     E

                                                     O
                            0.4
                                                       0.0
                                                                               Upper Bound
                                                                               Projected
                                                                               Lower Bound
                                                         0     50
                                                            [Mg2+]
                                          100    150    200    250
                                             L.-1) at  20 Yr.
   1.0 r
 O 0.8

 O
 Q.
 S 0.6
O.

 ffl

= 0.4
JO

 I
O 0.2
  0.0
         Priority Class  = A &  B
             Model = MAGIC
          Deposition =  Constant
                          Upper Bound
                          Projected
                          Lower Bound
    0     50    100    150    200    250
       [Mg2*] (jieq L"0  at  50  Yr.
                                  Priority Class  = A &  B
                                      Model  = MAGIC
                            Deposition  = Ramp  30%  Decrease
                                                       1-0r
                                                    o 0.8
                                                    O
                                                    a.
                                                    o
                                                      0.6
                                                    O

                                                   "
                           0.4
                         (0
                         u
                         E
                        O 0.2
                                                      0.0
                                                   Upper Bound
                                                   Projected
                                                   Lower Bound
                             0     50    100    150    200    250
                               [Mg2+] (jieq  L-1) at  50 Yr.
                                                      bounds for
                                      A.3-30
-------
    1.0
  o 0.8
 O
 Q.
 
 *5 0.4
 JO
 3
 E

 O
    0.0
           SBRP  Stream Reaches
          Priority  Class =  A  & B
              Model =  MAGIC
           Deposition = Constant
               Upper
               Projected
               Lower
     4.0  4.5  5.0  5.5  6.0  6.5  7.0 75  80
               pH  at 20 Yr.
                                                        1.0
                                                     O 0.8
        SBRP Stream Reaches
        Priority  Class  = A &  B
            Model  = MAGIC
  Deposition  =  Ramp  30%  Decrease
                                                     o
                                                     o.
                                                     o
                                                       0.6
o>
_>
4-»
JO

1

O
 0.4
                                                       o.o
            Upper
            Projected
            Lower
  4.0  4.5  5.0  5.5  6.0  6.5  7.0  7.5  80
            pH at 20 Yr.
    to
 O 0.8
 O
 O.
 O
   0.6
~ 0.4
JO
=J
E
O 0.2
   0.0
          Priority  Class =  A  & B
              Model  = MAGIC
          Deposition  = Constant
    4.0  4.5  5.0  5.5  6.0  6.5  7.0  7.5 80
              pH  at 50 Yr.
       Priority Class  = A &  B
           Model  = MAGIC
 Deposition  = Ramp  30%  Decrease
                                                      0.0
 "4.0  4.5  5.0  5.5  6.0  6.5  7.0  7.5  80
           pH at  50 Yr.
«" • "
                                                                       confidence interva,
                                      A.3-31
-------