EPA/600/R-13/074
United States ARS/294076
Environmental Protection June 2013
Agency www.epa.gov/research
Assessing Hydrologic
Impacts of Future Land
Cover Change Scenarios
in the San Pedro River
(U.S./Mexico)
RESEARCH AND DEVELOPMENT
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Assessing Hydrologic Impacts
of Future Land Cover Change
Scenarios in the San Pedro
River (U.S./Mexico)
I.S. Burns1, W.G. Kepner2, G.S. Sidman1, D.C. Goodrich3, DP. Guertin1,
L.R. Levick1, W.W.S. Yee4, M.M.A. Scianni4, C.S. Meek4, and J.B. Vollmer4
1University of Arizona, School of Natural Resources, Tucson, AZ
2U.S. Environmental Protection Agency, Office of Research and Development, Las Vegas, NV
3USDA-Agricultural Research Service, Southwest Watershed Research Center, Tucson, AZ
4U.S. Environmental Protection Agency, Region 9, San Francisco, CA
U.S. Environmental Protection Agency
Office of Research and Development
Washington, DC 20460
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Acknowledgements
This project was funded through the U.S. Environmental Protection Agency (EPA) Regional
Applied Research Effort (RARE) Program, which is administered by the Office of Research and
Development's (ORD) Regional Science Program.
We would like to acknowledge the key reviewers of this report for their helpful suggestions.
Specifically, our thanks in particular go to Dr. W. Paul Miller, Senior Hydrologist, National
Oceanic and Atmospheric Administration (NOAA), Colorado Basin River Forecast Center, Salt
Lake City, UT; Dr. Britta G. Bierwagen, Physical Scientist, EPA/ORD, Global Change Research
Program, Washington, D.C.; and Timothy Keefer, Hydrologist, U.S. Department of Agriculture
(USDA)/Agricultural Research Service (ARS), Southwest Watershed Research Center, Tucson,
AZ.
This report has been subjected to both the EPA/ORD and USD A/ARS peer and administrative
review processes and has been approved for publication. The Automated Geospatial Watershed
Assessment (AGWA) tool was jointly developed by EPA/ORD, USD A/ARS, and the University
of Arizona. The Integrated Climate and Land Use Scenarios (ICLUS) database was developed
by EPA/ORD. AGWA and ICLUS are endorsed and recommended by each of the respective
agencies, especially in regard to their integrated use.
in
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IV
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Table of Contents
Acknowledgements iii
List of Figures vii
List of Tables ix
List of Acronyms and Abbreviations xi
Abstract 1
Introduction 1
Methods 4
Project/Watershed Extent 4
Land Cover 4
Soils 8
Precipitation 8
AGWA-SWAT Modeling 9
Results 9
Discussion 21
Conclusions 22
Appendix A 25
Appendix B 26
Appendix C 28
References 33
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VI
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List of Figures
Figure 1. Location Map of the Study Area Contrasting the Extent of the ICLUS
Data Used in the Future Scenarios to the San Pedro Watershed 3
Figure 2. Population Projections for ICLUS Scenarios by Decade 7
Figure 3. Watershed Average Human Use Index (HUI) for All Scenarios 10
Figure 4. Watershed Average Percent Change in Surface Runoff for All Scenarios 10
Figure 5. Watershed Average Percent Change in Sediment Yield for All Scenarios 11
Figure 6. Subwatershed #340 Average Human Use Index (HUI) for All Scenarios 11
Figure 7. Subwatershed #340 Average Percent Change in Surface Runoff
for all Scenarios 12
Figure 8. Subwatershed #340 Average Percent Change in Sediment Yield
for all Scenarios 12
Figure 9. Subwatersheds #340 and #341 for Scenarios Al and A2 from 2010 to
2100 Depict How a Larger Absolute Change in One Scenario Can
Undergo a Smaller Explicit Percent Change (Average Subwatershed
Percent Change Divided by the Ratio of Changed Land Cover Area to
Entire Subwatershed Area) 14
Figure 10. Change in Human Use Index (HUI), Sediment Yield, and Surface Runoff
(Both Average and Explicit) in Percent from 2010 to 2100 for Scenario Al 15
Figure 11. Change in Human Use Index (HUI), Sediment Yield, and Surface Runoff
(Both Average and Explicit) in Percent from 2010 to 2100 for Scenario A2 16
Figure 12. Change in Human Use Index (HUI), Sediment Yield, and Surface Runoff
(Both Average and Explicit) in Percent from 2010 to 2100 for Scenario Bl 17
Figure 13. Change in Human Use Index (HUI), Sediment Yield, and Surface Runoff
(Both Average and Explicit) in Percent from 2010 to 2100 for Scenario B2 18
Figure 14. Change in Human Use Index (HUI), Sediment Yield, and Surface Runoff
(Both Average and Explicit) in Percent from 2010 to 2100 for Scenario BC 19
Figure 15. ArcMap Geoprocessing Model that Clipped, Projected, and Reclassified
the ICLUS Data into Classified Land Cover for use in AGWA 25
vn
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Vlll
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List of Tables
Table 1. Summary of the Types of Changes of the Different ICLUS Scenarios 5
Table 2. Reclassification Table for 1992 NALC in Mexico to 2006 NLCD
Land Cover Types 6
Table 3. Explanation of ICLUS Housing Density Categories 6
Table 4. Reclassification Table for ICLUS Housing Density Classes to 2006
NLCD Land Cover Types 8
TableS. Climate Stations Used from the NCDC 9
Table 6. Change in Human Use Index Over Time 26
Table 7. Change in Surface Runoff Over Time 26
Table 8. Change in Sediment Yield Over Time 27
Table 9. Land Cover Change for Scenario Al from Baseline 2010 to 2100.
(Note: Largest Positive/Negative Changes are Highlighted Red/Orange;
values in parenthesis are the percent change in cover type from the 2010
base case) 28
Table 10. Land Cover Change for Scenario A2 from Baseline 2010 to 2100.
(Note: Largest Positive/Negative Changes are Highlighted Red/Orange;
values in parenthesis are the percent change in cover type from the 2010
base case) 29
Table 11. Land Cover Change for Scenario Bl from Baseline 2010 to 2100.
(Note: Largest Positive/Negative Changes are Highlighted Red/Orange;
values in parenthesis are the percent change in cover type from the 2010
base case) 30
Table 12. Land Cover Change for Scenario Bl from Baseline 2010 to 2100.
(Note: Largest Positive/Negative Changes are Highlighted Red/Orange;
values in parenthesis are the percent change in cover type from the 2010
base case) 31
Table 13. Land Cover Change for Scenario BC from Baseline 2010 to 2100.
(Note: Largest Positive/Negative Changes are Highlighted Red/Orange;
values in parenthesis are the percent change in cover type from the 2010
base case) 32
IX
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Acronyms and Abbreviations
ACOE
AGWA
ARS
BC
BLM
CWA
DEM
DOD
DST
EPA
FWS
CIS
HD
HUI
ICLUS
IPCC
NALC
NCDC
NCGC
NED
NLCD
NOAA
NFS
NRCS
Army Corps of Engineers
Automated Geospatial Watershed Assessment
Agricultural Research Service
Base Case
U.S. Bureau of Land Management
Clean Water Act
Digital Elevation Model
Department of Defense
Decision Support Tools
U.S. Environmental Protection Agency
U.S. Fish and Wildlife Service
Geographic Information System
Housing Density
Human Use Index
Integrated Climate and Land-Use Scenarios
Intergovernmental Panel on Climate Change
North American Landscape Characterization
National Climatic Data Center
National Cartography and Geospatial Center
National Elevation Dataset
National Land Cover Database
National Oceanic and Atmospheric Administration
National Park Service
Natural Resources Conservation Service
XI
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RARE Regional Applied Research Effort
SRES Special Report on Emissions Scenarios
SPRNCA San Pedro Riparian National Conservation Area
STATSGO State Soil Geographic database
SWAT Soil and Water Assessment Tool
USDA U.S. Department of Agriculture
USFS U.S. Forest Service
USGS U.S. Geological Survey
xn
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Abstract
Long-term land-use and land cover change and their associated impacts pose critical
challenges to sustaining vital hydrological ecosystem services for future generations. In this
study, a methodology was developed to characterize hydrologic impacts from future urban
growth through time. Future growth is represented by housing density maps generated in
decadal intervals from 2010 to 2100, produced by the U.S. Environmental Protection Agency
(EPA) Integrated Climate and Land-Use Scenarios (ICLUS) project. ICLUS developed future
housing density maps by adapting the Intergovernmental Panel on Climate Change (IPCC)
Special Report on Emissions Scenarios (SRES) social, economic, and demographic storylines to
the conterminous United States. To characterize hydrologic impacts from future growth, the
housing density maps were reclassified to National Land Cover Database (NLCD) 2006 land
cover classes and used to parameterize the Soil and Water Assessment Tool (SWAT) using the
Automated Geospatial Watershed Assessment (AGWA) tool. The objectives of this project were
to 1) develop and describe a methodology for adapting the ICLUS data for use in AGWA as an
approach to evaluate basin-wide impacts of development on water-quantity and -quality, 2)
present initial results from the application of the methodology to evaluate water scenario
analyses related to a baseline condition and forecasted changes, and 3) discuss implications of
the analysis for the San Pedro River Basin, an arid international watershed on the U.S./Mexico
border.
Introduction
Changes in land-use and land cover are critical in the determination of water availability,
quality, and demand. The consequences of human modification to the Earth's surface for
extraction of natural resources, agricultural production, and urbanization may rival those that are
anticipated via climate change (Vitousek 1994, Vorosmarty et al. 2000, Chapin et al. 2002,
DeFries and Eshleman 2004, Brauman et al. 2007, Whitehead et al. 2009, Triantakonstantis and
Mountrakis 2012). Responding to change requires improvements in our ability to understand
vulnerabilities and to develop processes and metrics to better understand the consequences of
choice. It also requires an ability to communicate highly technical information to risk managers
and decision makers.
Scenario analysis provides the capability to explore pathways of change that diverge from
baseline conditions and lead to plausible future states or events. Scenario analysis has been used
extensively in studies related to environmental decision support (USDI 2012). Although a
number of scenario frameworks are available to assist in evaluating policy or management
options, most are designed to analyze alternative futures related to decision options, potential
impacts and benefits, long-term risks, and management opportunities (Steinitz et al. 2003,
Kepner et al. 2012, March et al. 2012). They frequently are combined with process modelling
and are intended to bridge the gap between science and decision making and are effective across
a range of spatial and temporal scales (Liu et al. 2008a and 2008b, Mahmoud et al. 2009).
This report describes a methodology to integrate a widely used watershed modeling tool and
a consistent national database with alternative future scenarios which can then be scaled to
regional applications. This report further describes the cumulative impacts of housing densities
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parsed out at decadal intervals to the year 2100 on a hydrological ecosystem consisting primarily
of ephemeral and intermittent waters.
Ephemeral waters are extremely important in the arid west and Arizona as a key source of
groundwater recharge (Goodrich et al. 2004) and providing important near channel alluvial
aquifer recharge to support aquatic ecosystems in downstream perennial and intermittent streams
(Bailie et al. 2007). They also provide critical ecosystem services supporting numerous species
(Levick et al. 2008). In addition, the beneficial uses of main-stem rivers cannot be meaningfully
protected if their supporting watersheds are degraded through significant hydrological and
ecological modifications (Brooks et al. 2007a and 2007b). The U.S. Environmental Protection
Agency (EPA) supports a watershed approach to resource restoration and protection, exemplified
by the San Pedro River watershed, a globally-important watershed described in the case study
presented here.
At present, issuance of U.S. Army Corps of Engineers (ACOE) Clean Water Act (CWA)
Section 404 permits are carried out in a project-by-project fashion with little consideration of
how multiple projects might collectively impact hydrology and biodiversity. However, the
cumulative impact of multiple projects on watershed function is a concern. From Part 1 l(g) of
Part 230 - Section 404(B) (1) Guidelines for Specification of Disposal Sites for Dredged or Fill
Material (Guidelines), ".. .cumulative impacts are the changes in an aquatic ecosystem that are
attributable to the collective effect of a number of individual discharges of dredged or fill
material." Although the impact of a particular discharge may constitute a minor change in itself,
the cumulative effect of numerous such changes can result in degradation and impairment of the
water resources, interfering with the productivity and overall integrity of biological, chemical,
and physical processes of aquatic ecosystems. Section 230.11 of the Guidelines describes
special conditions for evaluation of proposed permits to be issued, which includes the evaluation
of potential individual and cumulative impacts of the category of activities to be regulated under
general permit. The Guidelines constitute the substantive environmental criteria used in
evaluating activities regulated under Section 404. Section 404 requires a permit before dredged
or fill material may be discharged into the waters of the United States. The Guidelines state the
terms aquatic environment and aquatic ecosystem mean waters of the United States, including
wetlands, that serve as habitat for interrelated and interacting communities and populations of
plants and animals (part 230.3 [c]), and that "waters of the United States" includes tributaries
(part 230.3 [s]).
In an effort to build an improved capability for environmental decision makers and managers
to plan and respond to potential change, the EPA, U.S. Department of Agriculture (USDA)
Agricultural Research Service, and the University of Arizona have recently initiated two projects
under the Regional Applied Research Effort (RARE) Program. The two case studies selected for
this project are the San Pedro River (U.S./Mexico) in EPA Region 9 and the South Platte River
Basin (CO, WY, and ME) in EPA Region 8.
For the purpose of this report, the results are restricted to the San Pedro River. The intent is
to quantitatively evaluate hydrologic impacts of future developments at the basin scale, which
intrinsically addresses the cumulative impact of multiple housing development projects. The
study area encompasses the entire San Pedro Watershed (~11500 km2 or -4440 mi2) from
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Sonora, Mexico to the stream gage (USGS 09473500) in Winkelman, AZ (Figure 1). The San
Pedro River flows 230 km from its headwaters in Sonora, Mexico to its confluence with the Gila
River in central Arizona. It is nationally known as one of the last free-flowing rivers in the
Southwest. It has significant ecological value, supporting one of the highest numbers of
mammal species in the world and providing crucial habitat and a migration corridor to several
hundred bird species. Vegetation ranges from primarily semi-desert grassland and Chihuahuan
desert scrub in the Upper San Pedro to primarily Sonoran desert scrub and semi-desert grassland
in the Lower San Pedro. The Upper San Pedro is home to the San Pedro Riparian National
Conservation Area (SPRNCA). It was designated as the first National Conservation Area for
riparian protection by Congress in 1988. The SPRNCA protects approximately 64 kilometers
(-40 miles) of river and is administered by the U.S. Department of the Interior, Bureau of Land
Management (Kepner et al. 2004, Bagstad et al. 2012).
FORT THOMAS
_ Climate Stations
^\ Extent of ICLUS data
SPRNCA
0 10 20
40
60
Miles
A
Figure 1: Location Map of the Study Area Contrasting the Extent of the ICLUS Data Used in the Future Scenarios to
the San Pedro Watershed.
An underlying premise of this project is that watershed assessments can be significantly
improved if environmental resource managers have Decision Support Tools (DSTs) that are
easy-to-use, access readily available data, and are designed to address hydrologic and water
quality processes that are influenced by development at both the project-and basin-wide scale.
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The Automated Geospatial Watershed Assessment (AGWA; Miller et al. 2007;
http://www.epa.gov/esd/land-sci/agwa/index.htm and http: //www. tuc son. ar s. ag. gov/agwa) tool,
i.e. the DST used in this project, will assist the EPA and other agencies with permitting and
enforcement responsibilities under CWA Sections 401, 404 (FWS, NOAA, and ACOE), 402,
311 (US Coast Guard and states), and CWA 319 grant recipients (states, tribes, and local
organizations). It is designed to identify areas that are most sensitive to environmental
degradation as well as areas of potential mitigation or enhancement opportunities, and thus
inform restoration, permitting, and enforcement strategies. AGWA is recognized as one of the
world's primary watershed modeling systems (Daniel et al. 2011) providing the utility to
generate hydrologic responses at the subwatershed scale and spatially visualize results for
qualitative comparisons (also see
http://cfpub.epa.gov/crem/knowledge base/crem report.cfm?deid=75821).
Methods
The methodology developed to ascertain local vulnerabilities and cumulative impacts
associated with basin-wide development is a multi-step process. First, the project/watershed
extent must be defined to ensure that data are obtained for the entire study area. The various
land cover data must then be converted to a format compatible with AGWA. Next, soils and
precipitation data for the study area must be located and extracted. Finally, AGWA is used to
parameterize and run the Soil and Water Assessment Tool (SWAT; Neitsch et al. 2002;
Srinivasan and Arnold 1994) for the baseline condition and future land cover/use scenarios.
Project/Watershed Extent
Defining an accurate project and watershed extent is a critical first step that will minimize
difficulties later because this extent is used to locate other required data, including land cover,
soils, precipitation, and climate data. To define the project extent, the project watershed is
delineated in AGWA and given a buffer distance of 500 meters. The watershed is delineated
using a 10-meter digital elevation model (DEM) that has been hydrologically corrected to ensure
proper surface water drainage. In the United States (and for basins extending into Mexico), the
U.S. Geological Survey (USGS) The National Map Viewer and Download Platform
(http://nationalmap.gov/viewer.html) maintains the National Elevation Dataset (NED;
http://ned.usgs.gov/), which is a recommended source for DEM data. The delineated watershed
is buffered 500 meters to establish the project extent and ensure there are no gaps in coverage for
the land cover and soils data.
Land Cover
The land cover data used in this report comes from an array of sources. Because the project
extent includes Mexico, a land cover dataset with coverage in Mexico must be used. The
National Land Cover Database 2006 (NLCD; Fry et al. 2011), available nationally in the United
States, is used as the base land cover for the United States. It does not include the Mexico
portion of the watershed however, so the North American Landscape Characterization Project
(NALC; EPA, 1993), which has national coverage of both Mexico and the United States up to
1992, was used as source imagery for the derived land cover for Mexico (Kepner et al. 2000,
Kepner et al. 2003, Figure 1). The Integrated Climate and Land-Use Scenarios (ICLUS;
Bierwagen et al. 2010; EPA, 2009; EPA, 2010) project data were identified as an ideal dataset
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for projecting basin-wide development into the future because its national-scale housing-density
(HD) scenarios are consistent with the Intergovernmental Panel on Climate Change (IPCC 2001)
Special Report on Emissions Scenarios (SRES; Nakicenovic and Swart 2000) greenhouse gas
emissions storylines (Table 1, Figure 2). Though the NALC data has coverage for the entire
watershed the NLCD is used for the United States because it is the most current dataset available
and because others have utilized NLCD (from 2001 instead of 2006) with ICLUS data to project
future growth (Johnson et al. 2012).
Table 1: Summary of the Types of Changes of the Different ICLUS Scenarios.
National Scenario
Al
Bl
A2
B2
Base
Case
(2000)
medium population
growth; fast economic
development; high
global integration
medium population
growth; low domestic
migration resulting in
compact urban
development
high population
growth; greatest land
conversion; high
domestic migration
resulting in new
population centers
moderate economic
development; medium
population growth;
medium international
migration
U.S. Census medium
scenario
Demographic Model
Fertility
low
low
high
medium
medium
Domestic
Migration
high
low
high
low
medium
Net
International
Migration
high
high
low
low
medium
Spatial Allocation Model
Household
Size
smaller
(-15%)
smaller
(-15%)
larger
(+15%)
no change
no change
Urban
Form
no change
slight
compaction
no change
slight
compaction
no change
Because the 2006 NLCD and 1992 NALC datasets have different classifications, the NALC
land cover is reclassified to match the NLCD land cover (Table 2). The reclassified NALC
dataset of Mexico is then combined with the 2006 NLCD dataset of the U.S. resulting in a
derived NLCD dataset that covers the entire project extent. Note that the "Grasslands" class in
the NALC dataset was reclassified to "Scrub/Shrub" to be consistent with the observed
classification methodology of the NLCD. For applications entirely within the United States, the
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NLCD land cover will not need to be combined with other datasets, simplifying the process and
application of this methodology.
The ICLUS HD data is combined with the NLCD/NALC data to project future development
by decade to 2100. The ICLUS data has five categories of housing density representing rural,
exurban, suburban, urban, and commercial/industrial (Table 3).
Table 2: Reclassiflcation Table for 1992 NALC in Mexico to 2006 NLCD Land Cover Types.
1992 NALC (Mexico)
Code
1
2
3
4
5
6
7
8
9
10
Land Cover Type
Forest
Oak Woodlands
Mesquite Woodlands
Grasslands
Desert Scrub
Riparian
Agricultural
Urban
Water
Barren
2006 NLCD
Code
42
41
52
52
52
90
82
22
11
31
Land Cover Type
Evergreen Forest
Deciduous Forest
Scrub/Shrub
Scrub/Shrub
Scrub/Shrub
Woody Wetlands
Cultivated Crops
Developed, Medium Intensity
Open Water
Barren Land
Table 3: Explanation of ICLUS Housing Density Categories.
Class
99
4
3
2
1
Acres Per
Housing Unit
NA
<0.25
0.25-2
2-40
>40
Housing Units
Per Acre
NA
>4
0.5-4
0.025-0.5
<0.025
Hectares Per
Housing Unit
NA
<0.1
0.1-0.81
0.81-16.19
>16.19
Housing Units
Per Hectare
NA
>10
1.23-10
0.06-1.23
<0.06
Density Category
Commercial/Industrial
Urban
Suburban
Exurban
Rural
The ICLUS database produced 5 seamless, national-scale change scenarios for urban and
residential development (Table 1). The A2 Scenario is characterized by high fertility and low net
international migration; it represents the highest population scenario gain (690 million people by
2100). The Base Case (BC) and Scenario B2 are the middle scenarios, with medium fertility and
medium to low international migration. Differences between BC and B2, as well as Al and Bl,
reflect how housing is allocated - sprawl vs. compact growth patterns. As a result of this
distinction, the county populations in urban and suburban areas generally grow faster than in
rural areas in the base case, but the experiences of individual counties vary. Al and Bl, with
low fertility and high international migration are the lowest of the population scenarios. The
primary difference between these scenarios occurs at the domestic migration level, with an
assumption of high domestic migration under Al and low domestic migration under Bl. The
effect of different migration assumptions becomes evident in the spatial model when the
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population is allocated into housing units across the landscape. The Baseline forecast for 2100 is
450M people and Bl could be lower at 380M people. The A2 scenario results in the largest
changes in urban and suburban housing density classes and greater conversion of natural land-
cover classes into new population centers, or urban sprawl. The largest shift from suburban
densities to urban occurs in 2050 - 2100 for the A-family scenarios (Bierwagen et al. 2010,
Figure 2). The ICLUS scenarios were developed using a demographic model to estimate future
populations through the year 2100 and then allocated to 1-hectare pixels by county for the
conterminous U.S. (EPA 2009, EPA 2010). The final data sets provide decadal projections of
both housing density and impervious surface cover from the 2000 baseline year projected out to
the year 2100.
800,000,000
Conterminous US Population Projections, 2005-2100
700,000,000 -
600,000,000 -
c
O
5 500,000,000 -
400,000,000 -
300,000,000 -
200,000,000
a.
o
a.
* Base Case
--A1
--A2
B1
Figure 2: Population Projections for ICLUS Scenarios by Decade.
The NLCD data has different land cover classes, a different projection, and is at a different
resolution (30m) than the ICLUS data (100m); therefore the ICLUS data were pre-processed for
use in this project. Preprocessing includes clipping the ICLUS data to the boundary of Arizona,
projecting the ICLUS data to UTM Zone 12 NAD83, reclassifying the ICLUS data to NLCD
classes (Table 4) and resampling the ICLUS data from 100m to 30m. The resulting dataset was
then merged with the NLCD dataset so the ICLUS data replaced the NLCD data if there was a
change in land cover. The reclassification scheme was determined based on housing density
definitions, which were different between the two datasets. As a result the "Rural" land cover
type in the ICLUS data was defaulted to the NLCD class present at that location. This
methodology was incorporated into a tool in ArcToolbox in ArcGIS for easy conversion of the
ICLUS datasets (Appendix A, Figure 15).
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Table 4: Reclassiflcation Table for ICLUS Housing Density Classes to 2006 NLCD Land Cover Types.
ICLUS Data
Code
1
2
3
4
99
Land Cover Type
Rural
Exurban
Suburban
Urban
Commercial/Industrial
2006 NLCD
Code
-
22
23
24
24
Land Cover Type
Default to NLCD cover type
Developed, Low Intensity
Developed, Medium Intensity
Developed, High Intensity
Developed, High Intensity
For the purposes of developing the methodology, only scenario Al (corresponding to
storyline Al in the SRES) of the ICLUS data was used in an interim report (Burns et al. 2012),
however all five ICLUS scenarios (Al, A2, Bl, B2, and BC) were used in this final report.
Ten land cover datasets per scenario (50 total) are produced from the combination of the
NLCD/NALC datasets and the ICLUS datasets, representing the change in landscape attributed
to population and development changes by decade from 2010 to 2100. Tables 9 through 13 in
Appendix C contain the changes in land cover/use by decade for each of the ICLUS national
scenarios. For each scenario, the dataset from 2010 is used as the project baseline to which the
successive decadal datasets are compared.
Soils
Soils data for the U.S. were obtained from the Natural Resources Conservation Service
(NRCS) - National Cartography and Geospatial Center's (NCGC) State Soil Geographic
(STATSGO; USDA-NRCS 1994) database. Soils data for Mexico were obtained from the San
Pedro Data Browser (Kepner et al. 2003, Boykin et al. 2012). STATSGO and the Mexico soils
have different soil definitions and the Mexico soils are not supported directly in AGWA, so the
Mexico soil types were matched and redefined to equivalent STATSGO soil types. Because
neither dataset covered the entire project extent, the redefined Mexico soils were merged with
the STATSGO dataset to create a seamless coverage of the entire project extent. The mapping
scale of the two datasets is somewhat generalized with a mapping scale of 1:250,000, but
nonetheless they are suitable for this application given the watershed size and focus on
hydrologic response due to land cover change. For applications entirely within the United States,
the STATSGO dataset will not need modification or merging with other soil layers, simplifying
the process and application of this methodology.
Precipitation
Precipitation data obtained from the National Climatic Data Center (NCDC;
http://www.ncdc.noaa.gov/) were used to drive the SWAT model in AGWA. Climate stations in
the vicinity of the San Pedro Watershed were reviewed for periods of record and completeness of
the dataset. The review produced a total of seven climate stations in Arizona with the recorded
precipitation needed for the SWAT model (Table 5, Figure 1). Values of "-99" were used in
place of missing data in the period of record to flag SWAT to use its built-in stochastic weather
generator to determine how much precipitation to supply for the missing records. The period of
record is from 1971-2001.
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Table 5: Climate Stations Used from the NCDC.
Cooperative Station ID
21330
22139
23150
26119
27530
28619
29562
Station Name
Cascabel
Coronado National Monument Headquarters
Fort Thomas
Oracle 2 SE
San Manuel
Tombstone
Y Lightning Ranch
AGWA-SWAT Modeling
The AGWA tool was used to model the San Pedro Watershed with the SWAT model. The
AGWA tool is a user interface and framework that couples two watershed-scale hydrologic
models, the KINematic Runoff and EROSion model (KINEROS2; Semmens et al. 2008) and the
Soil and Water Assessment Tool (SWAT; Arnold et al. 1994), within a geographic information
system (GIS). The coupling of hydrologic models and GIS within the AGWA tool performs
model parameterization, execution, and watershed assessment at multiple temporal and spatial
scales, and visualization of model simulation results (Daniel et al. 2011). Current outputs
generated through use of the AGWA tool are runoff (volumes and peaks) and sediment yield,
plus nitrogen and phosphorus with the SWAT model. Simulations were parameterized using a
10m DEM and derived flow direction and accumulation, the modified STATSGO soils, the
seven precipitation stations in Table 5, and the ten land cover datasets produced by combining
the NLCD/NALC dataset (Table 2) with the decadal ICLUS datasets. AGWA facilitates the
identification of areas more susceptible/sensitive to environmental degradation and also areas for
potential mitigation or enhancement by mapping spatially distributed modeling results back onto
the watershed.
Results
All scenarios resulted in an increase to the Human Use Index (HUI) metric averaged over the
entire watershed. HUI (adapted from Ebert and Wade, 2004) is the percent area in use by
humans. It includes NLCD land cover classes "Developed, Open Space"; "Developed, Low
Intensity"; "Developed, Medium Intensity"; "Developed, High Intensity"; "Pasture/Hay"; and
"Cultivated Crops". The ICLUS A2 scenario resulted in the largest increase of the HUI, 2.21%
in year 2100 for the entire watershed (see Figure 3 and Appendix B - Table 6).
Similarly to the increases in HUI over the entire watershed, both simulated runoff and
sediment yield increased at the watershed outlet over time for all scenarios; scenario A2
experienced the largest percent change in surface runoff and sediment yield, 1.04% and 1.19%,
respectively (see Figure 4, Figure 5, and Appendix B - Table 7 and Table 8). Percent change was
calculated using the following equation:
(I decade] - I base])
1 'J L -i±-xlOO
[baset]
9
-------
where [decadet] represents simulation results for a decade from 2020 through 2100 for a given
scenario (/') and [baset] represents the baseline 2010 decade for the same scenario.
HIM Change 2010-2100 (Entire Watershed)
2.50%
ScenarioAl
Scenario A2
Scenario Bl
Scenario B2
Basel ineBC
0.00%
2010 2020 2030 2040 2050 2060 2070 2080 2090 2100
Figure 3: Watershed Average Human Use Index (HUI) for All Scenarios.
Change in Surface Runoff 2010-2100 {Entire
Watershed)
1.20%
ScenarioAl
Scenario A2
Scenario Bl
Scenario B2
Basel ineBC
2010 2020 2030 2040 2050 2060 2070 2080 2090 2100
Figure 4: Watershed Average Percent Change in Surface Runoff for All Scenarios.
10
-------
Change in Sediment Yield 2010-2100 (Watershed Outlet)
1.40%
o 1.20%
-------
Change in Surface Runoff 2010-2100 (Subwatershed #340)
6.00%
o 5.00%
o
g 4.00%
o
* 3.00%
OJ
on
ro 2.00%
.c
u
S 1.00%
0.00%
ScenarioAl
Scenario A2
Scenario Bl
Scenario B2
Basel ineBC
2010 2020 2030 2040 2050 2060 2070 2080 2090 2100
Figure 7: Subwatershed #340 Average Percent Change in Surface Runoff for All Scenarios.
Change in Sediment Yield 2010-2100 (Subwatershed #340)
ScenarioAl
^ Scenario A2
A Scenario Bl
)( Scenario B2
*Basel ineBC
2010 2020 2030 2040 2050 2060 2070 2080 2090 2100
Figure 8: Subwatershed #340 Average Percent Change in Sediment Yield for All Scenarios.
Figure 9 highlights subwatersheds #340 and #341 and the percent change in surface runoff
between 2010 and 2100 for scenarios Al and A2. Subwatersheds #340 and #341 represent the
lower (#340) and upper (#341) divisions of Walnut Gulch, a long-term experimental watershed
operated by the USDA Agricultural Service near Tombstone, AZ. Scenarios Al and A2 have
different growth characteristics, and though scenario A2 has a much larger population than Al in
2100, the percent change in surface runoff depicted in the figure is unexpected because scenario
Al has a higher percent change than scenario A2. Specifically, though the absolute change in
surface runoff for scenario A2 is larger than the absolute change in surface runoff for scenario
Al (bottom of Figure 9), the change occurs on a larger area resulting in an explicit percent
change that is smaller for scenario A2 than scenario Al (top of Figure 9). The explicit percent
change is calculated by dividing the effective percent change, i.e. the average percent change
over the entire Subwatershed, by the ratio of changed land cover area to entire Subwatershed area.
Explicit percent change emphasizes that local change may be much greater than average
watershed or even average Subwatershed percent change can describe.
12
-------
Figure 10 through Figure 14 (and Tables 9 through 13 in Appendix C) depict the percent
change of FUJI, channel sediment yield, and subwatershed surface runoff from 2010 to 2100 for
each of the 5 ICLUS scenarios. The changes in FUJI relate well to the changes in sediment yield
and surface runoff. The figures show the impact of growth locally on one level with the
subwatersheds and in greater detail with the explicit percent change in the growth areas in
contrast to averaging the impacts over the entire watershed as presented in Table 7 and Table 8.
13
-------
Figure 9: Subwatersheds #340 and #341 for Scenarios Al and A2 from 2010 to 2100 depict how a larger Absolute Change in one scenario can undergo a smaller
Explicit Percent Change (Average Subwatershed Percent Change divided by the Ratio of Changed Land Cover Area to Entire Subwatershed Area).
0 2345
Tombstone Watershed
Change in Surface Runoff 2010-2100
^Scenario A1
Percent Change
Scenario A2
Percent Change
Percent Change (%)
0-11.73
11.74-39.09
39.1 - 50.32
50.33-65.96
65.97-38.43
44-124.59
Absolute Change (mm)
0-0.16
0.17- 0.38
0.39- 0.69
0.7-1.17
1.18- 1.52
1.53- 3.2
Absolute Change
Scenario A1
Absolute Change
Scenario A2
14
-------
Figure 10: Change in Human Use Index (HUI), Sediment Yield, and Surface Runoff (Both Average and Explicit) in Percent from 2010 to 2100 for Scenario Al.
0510 20 30 40 50
I Kilometers
30 40 50
0 5 10
20
I Miles
San Pedro Watershed
Change between 2010 and 2100
N
A
Scenario A1
Subwatersheds
Human Use Index (%)
| | 0 - 0.44
| | 0.45-1.81
| | 1.82-4.93
j^H 4.94-7.89
^B 7.9-13.96
Streams
Sediment (% Change}
-0.15-0.53
0.54-1.30
1.31-216
^^2.17-3.72
^^3.73-7.39
Subwatersheds Growth Areas
Runoff (% Change) Runoff (% Change)
| 0.00-0.27
] 0.28-0.97
I 0.98-£39
| 240 - 6.45
I 6.46-10.01
0-11.73
11.74-42.51
42.52-57.65
57.66-70.84
70.85-12459
15
-------
Figure 11: Change in Human Use Index (HUI), Sediment Yield, and Surface Runoff (Both Average and Explicit) in Percent from 2010 to 2100 for Scenario A2.
0 5 10 20 30 40 50
i Kilometers
30 40 50
0 5 10
20
San Pedro Watershed
Change between 2010 and 2100
Subwatersheds
Human Use Index (%)
| | 0-0.44
| | 0.45-1.81
| | 1.82-4.93
^B 4.94-7.89
7.9-13.96
Streams
Sediment (% Change)
-0.15-0.53
0.54-1.30
1.31 -216
^^217-3.72
^^3.73-7.39
N
A
Scenario A2
Subwatersheds Growth Areas
Runoff (% Change) Runoff (% Change)
| [0.00-0.27 | [0-11.73
| 0.28-0.97 | [11.74-42.51
^H 0.98-2.39 ^| 42.52 - 57.65
^B 240 -6.45 ^B 57.66 - 70.84
'3.46-10.01 ^70.85-124.59
16
-------
Figure 12: Change in Human Use Index (HUI), Sediment Yield, and Surface Runoff (Both Average and Explicit) in Percent from 2010 to 2100 for Scenario Bl.
0 5 10 20 30 40 50
i Kilometers
30 40 50
0 5 10
20
San Pedro Watershed
Change between 2010 and 2100
Subwatersheds
Human Use Index (%)
| | 0-0.44
| | 0.45-1.81
| | 1.82-4.93
^B 4.94-7.89
7.9-13.96
Streams
Sediment (% Change)
-0.15-0.53
0.54-1.30
1.31 -Z16
^^217-3.72
^^3.73-7.39
N
A
Scenario B1
Subwatersheds Growth Areas
Runoff (% Change) Runoff (% Change)
^| 0-11.73
^| 11.74-42.51
^ 42.52-57.65
B 57.66-70.84
70.85-124.59
| | 0.00-0.27
| | 0.28-0.97
^B 0.98-239
^H 2-40-6-45
^M 6.46-10.01
17
-------
Figure 13: Change in Human Use Index (HUI), Sediment Yield, and Surface Runoff (Both Average and Explicit) in Percent from 2010 to 2100 for Scenario B2.
0510 20 30 40 50
i Kilometers
30 40 50
0 5 10
20
San Pedro Watershed
Change between 2010 and 2100
Subwatersheds
Human Use Index (%)
| | 0 - 0.44
| | 0.45-1.81
| | 1.82-493
^H 4.94-7.89
^H 7.9-13.96
Stream s
Sediment (% Change)
-0.15-0.53
0.54-1.30
1.31-216
^217-3.72
^^3.73-7.39
N
A
Scenario B2
Subwatersheds Growth Areas
Runoff (% Change) Runoff (% Change)
^ 0-11.73
^| 11.74-42.51
^ 42.52-57.65
B 57.66-70.84
70.85-124.59
| | 0.00-0.27
| | 0.28-0.97
^B 0.98-239
^^ 2.40-6.45
^H 6.46-10.01
18
-------
Figure 14: Change in Human Use Index (HUI), Sediment Yield, and Surface Runoff (Both Average and Explicit) in Percent from 2010 to 2100 for Baseline BC.
0510 20 30 40 50
i Kilometers
30 40 50
0 5 10
20
San Pedro Watershed
Change between 2010 and 2100
Subwatersheds
Human Use Index (%)
| | 0 - 0.44
| | 0.45-1.81
| | 1.82-493
^H 4.94-7.89
^H 7.9-13.96
Stream s
Sediment (% Change)
-0.15-0.53
0.54-1.30
1.31-216
^217-3.72
^^3.73-7.39
N
A
Scenario BC
Subwatersheds Growth Areas
Runoff (% Change) Runoff (% Change)
^ 0-11.73
^| 11.74-42.51
^ 42.52-57.65
B 57.66-70.84
70.85-124.59
| | 0.00-0.27
| | 0.28-0.97
^B 0.98-239
^^ 2.40-6.45
^H 6.46-10.01
19
-------
20
-------
Discussion
The results produced by the AGWA-SWAT modeling represent a qualitative assessment of
anticipated hydrologic change resulting from the ICLUS Al, A2, Bl, B2, and BC scenarios.
Historical rainfall and climate data are used to drive the SWAT model, so anticipated climate
change is not accounted for in the results, although climate change may amplify or reduce the
results presented here. Quantitative assessments of anticipated hydrologic impacts resulting
from the ICLUS scenarios would require calibration for the baseline (2010) for each scenario and
additional information to parameterize future decades, including but not limited to the design and
placement of flood mitigation measures (detention basins, riparian buffers, water harvesting,
recharge wells, open space infiltration galleries, etc.) that would be a required component of any
future development.
The methodology presented herein uses HUI as an easily quantifiable metric for land cover
change resulting from urban growth; however it does not distinguish between different types of
human use. Different types of human use, ranging from "Developed, Open Space" to
"Developed, High Intensity" to "Cultivated Crops" have different hydrologic properties associated
with them, so despite the observed relationship between increasing HUI and increasing surface
runoff and sediment yield in the results, HUI cannot be used as a surrogate for actual hydrologic
modeling, which more closely captures the actual land cover properties and the complex
interactions and feedbacks that occur across a watershed.
All the ICLUS scenarios show limited impact to the landscape at the watershed scale which
is also reflected by limited hydrologic impacts at the same scale. Impacts are more pronounced
at the subwatershed level where the effects of growth are not averaged out by the large
percentage of undevelopable lands (i.e. BLM, Forest Service, National Monuments, etc.) in the
watershed. Impacts are the highest when mapped below the subwatershed level, explicitly onto
the areas that experienced change. The greatest changes in surface runoff occur in
subwatersheds where the change in HUI was also greatest; accordingly, the smallest changes in
surface runoff occur in areas where the change in HUI was smallest. Sediment yield in the
channels is largely driven by surface runoff, so channels immediately downstream of
subwatersheds with high changes in HUI and surface runoff experience the largest changes in
sediment yield. The results emphasize the importance of investigating localized impacts to
natural resources at appropriate scales as the impacts at the subwatershed scale and below can be
much greater than at the basin scale. They also highlight the effective modulation of local
changes by large undevelopable areas. Because the San Pedro Watershed is large compared to
the area of developable land within it, the changes occurring on developable subwatersheds need
to be examined at a larger scale (i.e. smaller drainage area). At the subwatershed scale,
unacceptable hydrologic impacts may be observed that would otherwise be captured at the basin
scale if development was occurring basin-wide. Instead, basin-wide impacts are effectively
averaged out by undevelopable lands. Thus any interests in cumulative effect should be
addressed at the subwatershed versus basin scale for this western watershed or others like it
which contain large tracts of land in the public domain, and are therefore not subject to direct
urbanization impacts.
21
-------
Conclusions
Hydrologic impacts of future growth through time were evaluated by using reclassified
ICLUS housing density data by decade from 2010 to 2100 to represent land cover in AGWA.
AGWA is a GIS tool initially developed to investigate the impacts of land cover change to
hydrologic response at the watershed scale to help identify vulnerable regions and evaluate the
impacts of management. AGWA allows for assessment of basin-wide changes and cumulative
effects at the watershed outlet as well as more localized changes at the subwatershed level and
below (explicit change mapped onto growth areas).
ICLUS datasets were used for a number of reasons, including but not limited to their
availability (http://cfpub.epa.gov/ncea/global/recordisplay.cfm?deid=205305): their use in a
similar EPA research effort (Johnson et al. 2012); the relative simplicity of their reclassification
to a product supported by AGWA; and the significant science behind the product (IPCC and
SRES consistent storylines). Reclassification was necessary to convert from housing density
classes to "developed" type classes in the 2006 National Land Cover Database. All land cover
classes of the NLCD are supported in AGWA via look-up tables which allow for translation of
land cover classes into hydrologic parameters necessary to parameterize the hydrologic models.
Changes in land cover/use under the A2 scenario result in the greatest hydrologic impacts
due to a higher population growth rate and a larger natural land cover conversion rate. The
results of the analyses for all scenarios over the 2010 - 2100 year period (Tables 7 and 8)
indicate changes in the range of 0.2% (Bl scenario) to 1.04% (A2 scenario) on average surface
runoff across the watershed, and changes in the range of 0.2% (Bl scenario) to 1.19% (A2
scenario) on sediment yield at the watershed outlet. Investigating the results at the subwatershed
scale (smaller drainage areas for subwatershed #340), the changes in sediment yield are greater,
ranging from 0.56% (Bl scenario) to 7.39% (A2 scenario) and the change in surface runoff
ranges from 0.43% (Bl scenario) to 4.91% (A2 scenario).
Local changes to hydrology and sediment delivery at the subwatershed level and below are
relevant because at those scales the impacts tend to be much more significant. Additionally,
since the hydrologic impacts are tied to changes in land cover, and because the San Pedro
Watershed has large amounts of land that cannot be developed, the hydrologic impacts at a
watershed scale are expected to be limited. The localized impact of development found in this
study may be representative for much of the western arid and semi-arid U.S., where 47.3% of the
11 coterminous western states (AZ, CA, CO, ID, MT, NV, NM, OR, UT, WA, and WY) is
managed as federal public lands by BLM, FWS, NFS, USFS, and DOD (Gorte et al. 2012).
Despite the constraints that limit developable areas, hydrologic changes at the watershed scale
are still expected to occur.
Simulated increases in percent change of surface runoff and sediment yield closely tracked
increases in the HUI metric; consequently growth and development should be moderated to
prevent large increases in surface runoff and sediment yield, which could degrade water quality
from sediment and pollutant transport, erode and alter the stream channel, degrade or destroy
habitat, decrease biological diversity, and increase flooding. The effects of growth may be
magnified or mitigated by climate change, though this is not accounted for in this analysis.
22
-------
Scenario analysis is an important framework to help understand and predict potential
impacts caused by decisions regarding conservation and development. For the EPA and other
stakeholders, hydrologic modeling systems (e.g. AGWA) integrated with internally-consistent
national scenario spatial data (i.e. ICLUS) provide an important set of tools that can help inform
land use planning and permitting, mitigation, restoration, and enforcement strategies.
23
-------
24
-------
Appendix A
Resam^e from
ICLUS 100m
Resolution to
NLCD2006 30m
Figure 15. ArcMap Geoprocessing Model that Clipped, Projected, and Reclassifled the ICLUS Data into Classified Land
Cover for use in AGW.
25
-------
Table 6: Change in Human Use Index over Time.
Appendix B
HUI
Base
2010
Change in Human Use Index from base
2020
2030
2040
2050
2060
2070
2080
2090
2100
Subwatershed #340
Scenario Al
Scenario A2
Scenario Bl
Scenario B2
Baseline BC
14.69%
14.69%
14.69%
14.69%
14.69%
3.32%
3.23%
0.48%
0.40%
1.44%
3.66%
3.72%
0.49%
1.44%
3.56%
4.00%
4.98%
1.44%
3.28%
3.72%
4.31%
5.97%
1.44%
5.19%
4.72%
4.31%
6.67%
1.44%
5.87%
5.56%
4.31%
8.07%
1.44%
6.76%
6.28%
4.31%
10.22%
1.44%
7.38%
6.74%
4.31%
11.92%
1.44%
8.70%
7.77%
4.31%
13.96%
1.44%
9.12%
8.84%
Watershed
Scenario Al
Scenario A2
Scenario Bl
Scenario B2
Baseline BC
5.23%
5.09%
5.15%
5.09%
5.12%
0.36%
0.41%
0.22%
0.23%
0.34%
0.57%
0.66%
0.33%
0.37%
0.57%
0.69%
0.88%
0.39%
0.47%
0.74%
0.76%
1.10%
0.41%
0.52%
0.89%
0.79%
1.33%
0.42%
0.55%
1.04%
0.81%
1.54%
0.43%
0.58%
1.19%
0.83%
1.73%
0.43%
0.61%
1.33%
0.84%
1.95%
0.43%
0.66%
1.44%
0.85%
2.21%
0.43%
0.73%
1.54%
Table 7: Change in Surface Runoff over Time.
Surface
Runoff Base
2010
Percent Change in Surface Runoff from Base
2020
2030
2040
2050
2060
2070
2080
2090
2100
Subwatershed #340 Outlet
Scenario Al
Scenario A2
Scenario Bl
Scenario B2
Baseline BC
19.4 mm
19.4mm
19.4mm
19.4 mm
19.4mm
1.18%
1.13%
0.17%
0.13%
0.43%
1.32%
1.36%
0.17%
0.43%
1.22%
1.45%
1.67%
0.43%
1.18%
1.36%
1.53%
1.94%
0.43%
1.61%
1.62%
1.53%
2.25%
0.43%
1.94%
1.85%
1.53%
2.61%
0.43%
2.38%
2.08%
1.53%
3.25%
0.43%
2.74%
2.25%
1.53%
3.92%
0.43%
3.49%
2.47%
1.53%
4.91%
0.43%
4.30%
2.93%
Watershed Average
Scenario Al
Scenario A2
Scenario Bl
Scenario B2
Baseline BC
42.98 mm
42.95 mm
42.96 mm
42.96 mm
42.96 mm
0.15%
0.17%
0.08%
0.08%
0.13%
0.23%
0.29%
0.13%
0.14%
0.24%
0.29%
0.38%
0.16%
0.19%
0.32%
0.33%
0.47%
0.18%
0.21%
0.38%
0.34%
0.59%
0.19%
0.24%
0.45%
0.36%
0.70%
0.19%
0.26%
0.52%
0.37%
0.80%
0.20%
0.29%
0.59%
0.38%
0.91%
0.20%
0.34%
0.65%
0.39%
1.04%
0.20%
0.38%
0.71%
26
-------
Table 8: Change in Channel Sediment Yield over Time.
Sediment
Yield Base
2010
Percent Change in Sediment Yield from Base
2020
2030
2040
2050
2060
2070
2080
2090
2100
Subwatershed #340 Outlet
Scenario Al
Scenario A2
Scenario Bl
Scenario B2
Baseline BC
28.55 t
28.55 t
28.55 t
28.55 t
28.55 t
2%
1.93%
0.21%
0.18%
0.56%
2.28%
2.31%
0.21%
0.56%
2.07%
2.45%
2.73%
0.56%
2%
2.31%
2.56%
3.08%
0.56%
2.56%
2.66%
2.56%
3.64%
0.56%
3.08%
3.15%
2.56%
4.13%
0.56%
3.68%
3.43%
2.56%
5.04%
0.56%
4.31%
3.64%
2.56%
5.95%
0.56%
5.57%
3.96%
2.56%
7.39%
0.56%
7.15%
4.66%
Watershed Outlet
Scenario Al
Scenario A2
Scenario Bl
Scenario B2
Baseline BC
25220 1
25200 t
25210 t
25200 t
25200 t
0.16%
0.24%
0.12%
0.12%
0.16%
0.24%
0.32%
0.12%
0.20%
0.24%
0.36%
0.44%
0.16%
0.20%
0.36%
0.40%
0.56%
0.20%
0.24%
0.44%
0.40%
0.60%
0.20%
0.24%
0.52%
0.44%
0.75%
0.20%
0.28%
0.60%
0.48%
0.91%
0.20%
0.32%
0.60%
0.48%
0.95%
0.20%
0.36%
0.67%
0.52%
1.19%
0.20%
0.44%
0.79%
27
-------
Appendix C
Table 9:
Land Cover Change for Scenario Al from Baseline 2010 to 2100. (Note: Largest Positive/Negative Changes are Highlighted Red/Orange; values in parenthesis
are the percent change in cover type from the 2010 base case).
Scenario Al
Land Cover Type
Open Water
Developed, Open Space
Developed, Low Intensity
Developed, Medium Intensity
Developed, High Intensity
Barren Land
Deciduous Forest
Evergreen Forest
Mixed Forest
Scrub/Shrub
Grasslands/Herbaceous
Pasture/Hay
Cultivated Crops
Woody Wetlands
Emergent Herbaceous Wetlands
Base
(km2)
2010
3.70
66.66
384.80
45.80
20.57
46.78
369.00
767.11
9.46
9523.18
104.83
12.33
70.38
57.91
3.90
Change from Base (km2)
2020
-0.05
(-1.24%)
-2.4
(-3.61%)
41.69
(10.84%)
4.17
(9.11%)
0.2
(0.95%)
-0.01
(-0.02%)
0
(0%)
-0.59
(-0.08%)
-0.01
(-0.07%)
-38.54
(-0.4%)
-1.22
(-1.17%)
-0.17
(-1.39%)
-2.11
(-3%)
-0.91
(-1.57%)
-0.06
(-1.48%)
2030
-0.08
(-2.04%)
-3.38
(-5.08%)
64.09
(16.66%)
7.41
(16.19%)
0.35
(1.72%)
-0.07
(-0.15%)
0
(0%)
-1.11
(-0.14%)
-0.02
(-0.19%)
-60.98
(-0.64%)
-1.86
(-1.78%)
-0.29
(-2.36%)
-2.63
(-3.74%)
-1.35
(-2.33%)
-0.09
(-2.42%)
2040
-0.08
(-2.09%)
-3.77
(-5.65%)
77.2
(20.06%)
8.6
(18.78%)
0.41
(2%)
-0.1
(-0.21%)
0
(0%)
-1.37
(-0.18%)
-0.02
(-0.19%)
-73.64
(-0.77%)
-2.25
(-2.15%)
-0.37
(-3.01%)
-2.96
(-4.21%)
-1.57
(-2.71%)
-0.1
(-2.47%)
2050
-0.09
(-2.31%)
-4.01
(-6.01%)
85.21
(22.14%)
9.5
(20.74%)
0.41
(2%)
-0.1
(-0.22%)
0
(0%)
-1.43
(-0.19%)
-0.02
(-0.19%)
-81.71
(-0.86%)
-2.42
(-2.31%)
-0.4
(-3.23%)
-3.1
(-4.41%)
-1.74
(-3%)
-0.11
(-2.88%)
2060
-0.09
(-2.31%)
-4.07
(-6.11%)
85.74
(22.28%)
11.77
(25.7%)
0.41
(2%)
-0.13
(-0.28%)
0
(0%)
-1.45
(-0.19%)
-0.02
(-0.19%)
-84.33
(-0.89%)
-2.42
(-2.31%)
-0.4
(-3.23%)
-3.11
(-4.42%)
-1.81
(-3.12%)
-0.11
(-2.88%)
2070
-0.14
(-3.67%)
-4.16
(-6.24%)
87.07
(22.63%)
13.41
(29.28%)
0.41
(2%)
-0.32
(-0.68%)
0
(0%)
-1.45
(-0.19%)
-0.02
(-0.19%)
-86.78
(-0.91%)
-2.42
(-2.31%)
-0.43
(-3.51%)
-3.11
(-4.42%)
-1.96
(-3.38%)
-0.11
(-2.88%)
2080
-0.14
(-3.89%)
-4.17
(-6.26%)
88.4
(22.97%)
13.95
(30.46%)
0.4
(1.96%)
-0.63
(-1.34%)
0
(0%)
-1.45
(-0.19%)
-0.02
(-0.19%)
-88.3
(-0.93%)
-2.42
(-2.31%)
-0.45
(-3.63%)
-3.11
(-4.42%)
-1.96
(-3.38%)
-0.11
(-2.88%)
2090
-0.16
(-4.33%)
-4.18
(-6.27%)
85.55
(22.23%)
18.43
(40.25%)
0.4
(1.93%)
-0.95
(-2.02%)
0
(0%)
-1.45
(-0.19%)
-0.02
(-0.19%)
-89.59
(-0.94%)
-2.42
(-2.31%)
-0.45
(-3.63%)
-3.11
(-4.42%)
-1.96
(-3.38%)
-0.11
(-2.88%)
2100
-0.18
(-4.75%)
-4.18
(-6.27%)
83.74
(21.76%)
21.31
(46.52%)
0.4
(1.93%)
-1.09
(-2.33%)
0
(0%)
-1.45
(-0.19%)
-0.02
(-0.19%)
-90.48
(-0.95%)
-2.42
(-2.31%)
-0.45
(-3.63%)
-3.11
(-4.42%)
-1.96
(-3.38%)
-0.11
(-2.88%)
28
-------
Table 10:
Land Cover Change for Scenario A2 from Baseline 2010 to 2100. (Note: Largest Positive/Negative Changes are Highlighted Red/Orange; values in parenthesis
are the percent change in cover type from the 2010 base case).
Scenario A2
Land Cover Type
Open Water
Developed, Open Space
Developed, Low Intensity
Developed, Medium
Intensity
Developed, High Intensity
Barren Land
Deciduous Forest
Evergreen Forest
Mixed Forest
Scrub/Shrub
Grasslands/Herbaceous
Pasture/Hay
Cultivated Crops
Woody Wetlands
Emergent Herbaceous
Wetlands
Base(km2)
2010
3.74
67.46
368.85
44.59
20.59
46.83
369.00
767.34
9.46
9538.10
105.03
12.35
70.90
58.23
3.94
Change from Base (km2)
2020
-0.09
(-2.34%)
-2.78
(-4.17%)
48.74
(12.67%)
3.58
(7.82%)
0.14
(0.66%)
-0.06
(-0.13%)
0
(0%)
-0.61
(-0.08%)
0
(-0.02%)
-43.96
(-0.46%)
-1.2
(-1.15%)
-0.16
(-1.29%)
-2.4
(-3.42%)
-1.08
(-1.86%)
-0.11
(-2.75%)
2030
-0.12
(-3.14%)
-4.16
(-6.23%)
76.08
(19.77%)
7.46
(16.28%)
0.31
(1.48%)
-0.12
(-0.25%)
0
(0%)
-1.25
(-0.16%)
-0.02
(-0.19%)
-70.78
(-0.74%)
-2.06
(-1.96%)
-0.31
(-2.47%)
-3.31
(-4.7%)
-1.6
(-2.77%)
-0.13
(-3.3%)
2040
-0.12
(-3.16%)
-5
(-7.51%)
99.76
(25.92%)
10.39
(22.7%)
0.37
(1.8%)
-0.16
(-0.35%)
0
(0%)
-1.65
(-0.22%)
-0.02
(-0.19%)
-94.06
(-0.99%)
-2.93
(-2.8%)
-0.42
(-3.38%)
-4.02
(-5.71%)
-1.98
(-3.42%)
-0.16
(-4.01%)
2050
-0.12
(-3.21%)
-5.54
(-8.32%)
123.66
(32.13%)
13.9
(30.34%)
0.4
(1.95%)
-0.17
(-0.37%)
0
(0%)
-1.99
(-0.26%)
-0.05
(-0.49%)
-118
(-1.24%)
-3.53
(-3.37%)
-0.77
(-6.2%)
-5.06
(-7.19%)
-2.53
(-4.36%)
-0.2
(-5.03%)
2060
-0.15
(-3.99%)
-6.12
(-9.17%)
143.26
(37.23%)
22
(48.04%)
0.41
(1.99%)
-0.17
(-0.37%)
0
(0%)
-2.74
(-0.36%)
-0.05
(-0.51%)
-142.37
(-1.5%)
-4.12
(-3.93%)
-1.35
(-10.96%)
-5.63
(-8%)
-2.79
(-4.81%)
-0.2
(-5.05%)
2070
-0.15
(-4.09%)
-6.63
(-9.95%)
157.66
(40.97%)
33.38
(72.89%)
0.43
(2.1%)
-0.2
(-0.43%)
0
(0%)
-3.56
(-0.46%)
-0.06
(-0.62%)
-165.29
(-1.74%)
-4.54
(-4.33%)
-1.78
(-14.4%)
-6.24
(-8.86%)
-2.81
(-4.85%)
-0.21
(-5.47%)
2080
-0.2
(-5.45%)
-7.02
(-10.53%)
164.3
(42.7%)
50.73
(110.77%)
0.52
(2.53%)
-0.44
(-0.94%)
0
(0%)
-4.03
(-0.53%)
-0.11
(-1.13%)
-186.31
(-1.96%)
-4.86
(-4.63%)
-2.53
(-20.53%)
-7.03
(-9.99%)
-2.81
(-4.86%)
-0.21
(-5.47%)
2090
-0.23
(-6.21%)
-7.66
(-11.49%)
166.67
(43.31%)
75.05
(163.88%)
0.57
(2.77%)
-1
(-2.15%)
0
(0%)
-4.5
(-0.59%)
-0.13
(-1.32%)
-209.77
(-2.2%)
-5.14
(-4.91%)
-3.44
(-27.88%)
-7.38
(-10.49%)
-2.82
(-4.87%)
-0.21
(-5.47%)
2100
-0.36
(-9.73%)
-8.1
(-12.15%)
161.1
(41.87%)
112.34
(245.29%)
0.78
(3.81%)
-1.73
(-3.69%)
0
(0%)
-4.7
(-0.61%)
-0.13
(-1.34%)
-238.25
(-2.5%)
-5.65
(-5.39%)
-4.04
(-32.73%)
-8.22
(-11.68%)
-2.84
(-4.91%)
-0.21
(-5.47%)
29
-------
Table 11:
Land Cover Change for Scenario Bl from Baseline 2010 to 2100. (Note: Largest Positive/Negative Changes are Highlighted Red/Orange; values in parenthesis
are the percent change in cover type from the 2010 base case).
Scenario Bl
Land Cover Type
Open Water
Developed, Open Space
Developed, Low Intensity
Developed, Medium
Intensity
Developed, High Intensity
Barren Land
Deciduous Forest
Evergreen Forest
Mixed Forest
Scrub/Shrub
Grasslands/Herbaceous
Pasture/Hay
Cultivated Crops
Woody Wetlands
Emergent Herbaceous
Wetlands
Base
(km2)
2010
3.70
67.09
376.05
45.25
20.55
46.78
369.00
767.27
9.46
9531.24
105.00
12.34
70.69
58.06
3.93
Change from Base (km2)
2020
0
(0%)
-1.41
(-2.12%)
24.24
(6.3%)
2.94
(6.43%)
0.15
(0.71%)
0
(0%)
0
(0%)
-0.51
(-0.07%)
-0.01
(-0.07%)
-22.94
(-0.24%)
-0.72
(-0.69%)
-0.04
(-0.36%)
-1.11
(-1.58%)
-0.52
(-0.89%)
-0.07
(-1.73%)
2030
-0.01
(-0.39%)
-1.97
(-2.96%)
36.5
(9.49%)
5.17
(11.28%)
0.22
(1.06%)
-0.01
(-0.01%)
0
(0%)
-0.61
(-0.08%)
-0.01
(-0.07%)
-34.96
(-0.37%)
-1.34
(-1.28%)
-0.12
(-0.95%)
-1.94
(-2.76%)
-0.82
(-1.41%)
-0.1
(-2.65%)
2040
-0.01
(-0.39%)
-2.3
(-3.45%)
41.35
(10.75%)
7.59
(16.58%)
0.36
(1.75%)
-0.03
(-0.06%)
0
(0%)
-0.94
(-0.12%)
-0.01
(-0.07%)
-41.44
(-0.44%)
-1.42
(-1.35%)
-0.12
(-0.99%)
-2.03
(-2.89%)
-0.9
(-1.55%)
-0.11
(-2.75%)
2050
-0.01
(-0.39%)
-2.37
(-3.56%)
42.38
(11.01%)
9.54
(20.82%)
0.37
(1.79%)
-0.04
(-0.08%)
0
(0%)
-1.35
(-0.18%)
-0.01
(-0.07%)
-43.48
(-0.46%)
-1.45
(-1.38%)
-0.12
(-0.99%)
-2.24
(-3.18%)
-1.09
(-1.89%)
-0.12
(-3.09%)
2060
-0.01
(-0.39%)
-2.37
(-3.56%)
42.56
(11.06%)
10.27
(22.42%)
0.39
(1.91%)
-0.04
(-0.09%)
0
(0%)
-1.52
(-0.2%)
-0.01
(-0.07%)
-44.16
(-0.46%)
-1.46
(-1.39%)
-0.12
(-0.99%)
-2.27
(-3.22%)
-1.13
(-1.95%)
-0.13
(-3.3%)
2070
-0.01
(-0.39%)
-2.39
(-3.58%)
42.38
(11.01%)
10.92
(23.85%)
0.39
(1.91%)
-0.04
(-0.09%)
0
(0%)
-1.55
(-0.2%)
-0.01
(-0.07%)
-44.6
(-0.47%)
-1.46
(-1.39%)
-0.12
(-0.99%)
-2.27
(-3.22%)
-1.13
(-1.95%)
-0.13
(-3.3%)
2080
-0.01
(-0.39%)
-2.39
(-3.58%)
42.1
(10.94%)
11.34
(24.76%)
0.39
(1.91%)
-0.04
(-0.09%)
0
(0%)
-1.55
(-0.2%)
-0.01
(-0.07%)
-44.73
(-0.47%)
-1.46
(-1.39%)
-0.12
(-0.99%)
-2.27
(-3.22%)
-1.13
(-1.95%)
-0.13
(-3.3%)
2090
-0.01
(-0.39%)
-2.4
(-3.61%)
41.28
(10.73%)
12.47
(27.24%)
0.39
(1.91%)
-0.04
(-0.09%)
0
(0%)
-1.55
(-0.2%)
-0.01
(-0.07%)
-45.03
(-0.47%)
-1.46
(-1.39%)
-0.12
(-0.99%)
-2.27
(-3.22%)
-1.13
(-1.95%)
-0.13
(-3.3%)
2100
-0.01
(-0.39%)
-2.4
(-3.61%)
40.88
(10.62%)
12.88
(28.12%)
0.39
(1.91%)
-0.04
(-0.09%)
0
(0%)
-1.55
(-0.2%)
-0.01
(-0.07%)
-45.03
(-0.47%)
-1.46
(-1.39%)
-0.12
(-0.99%)
-2.27
(-3.22%)
-1.13
(-1.95%)
-0.13
(-3.3%)
30
-------
Table 12:
Land Cover Change for Scenario B2 from Baseline 2010 to 2100. (Note: Largest Positive/Negative Changes are Highlighted Red/Orange; values in parenthesis
are the percent change in cover type from the 2010 base case).
Scenario B2
Land Cover Type
Open Water
Developed, Open Space
Developed, Low Intensity
Developed, Medium
Intensity
Developed, High Intensity
Barren Land
Deciduous Forest
Evergreen Forest
Mixed Forest
Scrub/Shrub
Grasslands/Herbaceous
Pasture/Hay
Cultivated Crops
Woody Wetlands
Emergent Herbaceous
Wetlands
Base
(km2)
2010
3.72
67.57
368.84
44.43
20.55
46.78
369.00
767.37
9.46
9537.61
105.14
12.37
71.41
58.22
3.95
Change from Base (km2)
2020
-0.02
(-0.49%)
-1.6
(-2.4%)
26.48
(6.88%)
3.14
(6.86%)
0.12
(0.58%)
0
(0%)
0
(0%)
-0.52
(-0.07%)
-0.01
(-0.07%)
-24.48
(-0.26%)
-0.71
(-0.68%)
-0.06
(-0.51%)
-1.67
(-2.38%)
-0.58
(-1.01%)
-0.09
(-2.26%)
2030
-0.03
(-0.88%)
-2.42
(-3.63%)
42.18
(10.96%)
5.65
(12.33%)
0.2
(0.98%)
-0.01
(-0.01%)
0
(0%)
-0.7
(-0.09%)
-0.01
(-0.07%)
-39.46
(-0.41%)
-1.48
(-1.41%)
-0.15
(-1.22%)
-2.68
(-3.81%)
-0.98
(-1.68%)
-0.12
(-3.07%)
2040
-0.04
(-0.97%)
-2.98
(-4.46%)
50.07
(13.01%)
9.64
(21.06%)
0.44
(2.15%)
-0.03
(-0.07%)
0
(0%)
-0.92
(-0.12%)
-0.01
(-0.07%)
-50.14
(-0.53%)
-1.72
(-1.64%)
-0.18
(-1.44%)
-2.93
(-4.16%)
-1.1
(-1.9%)
-0.13
(-3.35%)
2050
-0.04
(-1.02%)
-3.14
(-4.71%)
52.95
(13.76%)
12.75
(27.83%)
0.51
(2.49%)
-0.04
(-0.09%)
0
(0%)
-1.37
(-0.18%)
-0.01
(-0.07%)
-54.95
(-0.58%)
-1.76
(-1.68%)
-0.18
(-1.47%)
-3.21
(-4.56%)
-1.37
(-2.36%)
-0.15
(-3.83%)
2060
-0.04
(-1.02%)
-3.27
(-4.9%)
50.28
(13.07%)
19.07
(41.64%)
0.73
(3.54%)
-0.08
(-0.16%)
0
(0%)
-1.69
(-0.22%)
-0.01
(-0.07%)
-58.25
(-0.61%)
-1.83
(-1.74%)
-0.18
(-1.47%)
-3.22
(-4.58%)
-1.37
(-2.36%)
-0.15
(-3.83%)
2070
-0.09
(-2.31%)
-3.32
(-4.98%)
41.4
(10.76%)
31.1
(67.9%)
0.85
(4.11%)
-0.14
(-0.3%)
0
(0%)
-1.71
(-0.22%)
-0.01
(-0.07%)
-61.27
(-0.64%)
-1.88
(-1.8%)
-0.18
(-1.47%)
-3.22
(-4.58%)
-1.37
(-2.36%)
-0.15
(-3.83%)
2080
-0.09
(-2.31%)
-3.46
(-5.19%)
23.97
(6.23%)
52.38
(114.38%)
0.94
(4.57%)
-0.18
(-0.39%)
0
(0%)
-1.73
(-0.23%)
-0.01
(-0.07%)
-64.97
(-0.68%)
-1.91
(-1.83%)
-0.18
(-1.47%)
-3.23
(-4.6%)
-1.38
(-2.39%)
-0.15
(-3.83%)
2090
-0.23
(-6.08%)
-3.64
(-5.47%)
-0.28
(-0.07%)
82.56
(180.26%)
0.96
(4.65%)
-0.18
(-0.39%)
0
(0%)
-1.75
(-0.23%)
-0.01
(-0.07%)
-70.43
(-0.74%)
-1.98
(-1.89%)
-0.22
(-1.81%)
-3.26
(-4.63%)
-1.38
(-2.39%)
-0.15
(-3.83%)
2100
-0.25
(-6.77%)
-3.78
(-5.67%)
-17.79
(-4.62%)
107.91
(235.63%)
1.04
(5.07%)
-0.24
(-0.52%)
0
(0%)
-1.81
(-0.24%)
-0.01
(-0.07%)
-77.87
(-0.82%)
-1.99
(-1.9%)
-0.22
(-1.81%)
-3.41
(-4.85%)
-1.43
(-2.47%)
-0.15
(-3.83%)
31
-------
Table 13: Land Cover Change for Baseline BC from Baseline 2010 to 2100. (Note: Largest Positive/Negative Changes are Highlighted Red/Orange; values in parenthesis
are the percent change in cover type from the 2010 base case).
Scenario BC
Land Cover Type
Open Water
Developed, Open Space
Developed, Low Intensity
Developed, Medium
Intensity
Developed, High Intensity
Barren Land
Deciduous Forest
Evergreen Forest
Mixed Forest
Scrub/Shrub
Grasslands/Herbaceous
Pasture/Hay
Cultivated Crops
Woody Wetlands
Emergent Herbaceous
Wetlands
Base
(km2)
2010
3.74
67.28
372.25
44.77
20.56
46.83
369.00
767.33
9.46
9534.95
104.96
12.35
70.83
58.14
3.94
Change from Base (km2)
2020
-0.07
(-2%)
-2.28
(-3.43%)
40.45
(10.51%)
3.04
(6.63%)
0.16
(0.76%)
-0.06
(-0.13%)
0
(0%)
-0.53
(-0.07%)
0
(-0.02%)
-36.53
(-0.38%)
-1.19
(-1.13%)
-0.14
(-1.11%)
-2.02
(-2.87%)
-0.71
(-1.23%)
-0.09
(-2.35%)
2030
-0.1
(-2.82%)
-3.44
(-5.16%)
65.79
(17.1%)
5.81
(12.68%)
0.21
(1.03%)
-0.09
(-0.2%)
0
(0%)
-1.03
(-0.13%)
-0.02
(-0.19%)
-60.7
(-0.64%)
-1.78
(-1.7%)
-0.26
(-2.1%)
-2.91
(-4.13%)
-1.36
(-2.35%)
-0.12
(-3.11%)
2040
-0.12
(-3.16%)
-4.29
(-6.44%)
84.62
(21.99%)
8.37
(18.27%)
0.34
(1.67%)
-0.13
(-0.27%)
0
(0%)
-1.32
(-0.17%)
-0.02
(-0.19%)
-79.33
(-0.83%)
-2.4
(-2.29%)
-0.34
(-2.76%)
-3.56
(-5.06%)
-1.67
(-2.88%)
-0.15
(-3.92%)
2050
-0.12
(-3.16%)
-4.83
(-7.25%)
100.71
(26.17%)
10.28
(22.45%)
0.39
(1.91%)
-0.16
(-0.35%)
0
(0%)
-1.74
(-0.23%)
-0.04
(-0.37%)
-95.08
(-1%)
-2.8
(-2.67%)
-0.49
(-3.94%)
-4.07
(-5.78%)
-1.91
(-3.29%)
-0.16
(-4.15%)
2060
-0.13
(-3.43%)
-5.24
(-7.87%)
117.86
(30.63%)
12.07
(26.36%)
0.4
(1.96%)
-0.17
(-0.36%)
0
(0%)
-1.98
(-0.26%)
-0.05
(-0.49%)
-111.86
(-1.17%)
-3.31
(-3.15%)
-0.65
(-5.28%)
-4.73
(-6.72%)
-2.04
(-3.52%)
-0.19
(-4.75%)
2070
-0.15
(-3.99%)
-5.52
(-8.28%)
133.25
(34.63%)
14.3
(31.22%)
0.42
(2.06%)
-0.17
(-0.36%)
0
(0%)
-2.52
(-0.33%)
-0.05
(-0.51%)
-127.87
(-1.34%)
-3.5
(-3.34%)
-0.92
(-7.46%)
-4.98
(-7.08%)
-2.1
(-3.63%)
-0.19
(-4.75%)
2080
-0.15
(-3.99%)
-5.87
(-8.8%)
144.52
(37.56%)
20.08
(43.85%)
0.44
(2.13%)
-0.23
(-0.49%)
0
(0%)
-2.84
(-0.37%)
-0.05
(-0.51%)
-143.4
(-1.51%)
-3.85
(-3.67%)
-1.15
(-9.31%)
-5.21
(-7.4%)
-2.12
(-3.65%)
-0.19
(-4.75%)
2090
-0.21
(-5.57%)
-6.2
(-9.29%)
152.91
(39.74%)
25.51
(55.69%)
0.43
(2.11%)
-0.48
(-1.03%)
0
(0%)
-3.09
(-0.4%)
-0.05
(-0.51%)
-155.34
(-1.63%)
-4.18
(-3.98%)
-1.44
(-11.65%)
-5.56
(-7.9%)
-2.13
(-3.68%)
-0.19
(-4.75%)
2100
-0.23
(-6.11%)
-6.39
(-9.59%)
153.97
(40.01%)
36.23
(79.1%)
0.43
(2.08%)
-1.02
(-2.18%)
0
(0%)
-3.54
(-0.46%)
-0.06
(-0.62%)
-165.19
(-1.73%)
-4.32
(-4.12%)
-1.73
(-14.03%)
-5.83
(-8.28%)
-2.13
(-3.69%)
-0.19
(-4.75%)
32
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