4\	United States
Environmental Protection
^1 *m Agency
EPA/600/R-19/185 j December 2019
www.epa.gov/ord
WMOST
Watershed Manageme^E^"
WMOST v3 Case Study: Cabin John Creek, Maryland

Legend
Final HRU
H Impervious
~	MCo Tg AB
MCo Tg CD
nn Natural
| Natural forested
| Oth Tg AB
| Oth Tg CD
~	Rk Tg AB
| Rk Tg CD
| SHATgAB
H SHATg CD
 Water
0	0.75 1.5	3 Kilometers
	1	I	I	i	I	i	I	i	I
Cabin John Creek Hydrologic Response Units
Office of Research and Development
Center for Environmental Measurement and Modeling

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S'ER'V
United States
Environmental Protection
Agency
EPA/600/R-19/039 | December 2019
www.epa.gov/ord
WMOST v3 Case Study: Cabin John Creek,
Maryland
1 U.S. Environmental Protection Agency, Office of Research and Development,
Center for Environmental Measurement and Modeling,
Atlantic Coastal Environmental Sciences Division, Narragansett, Rl
2 Former ORISE participant at US EPA,
Atlantic Coastal Environmental Sciences Division, Narragansett, Rl
3Maryland Department of the Environment, Baltimore, MD
6 Northeast Water Solutions, Exeter, Rl, formerly Federal post-doc at U.S. EPA,
Atlantic Coastal Environmental Sciences Division, Narragansett, Rl
Center for Environmental Measurement and Modeling
Office of Research and Development
U.S. Environmental Protection Agency
Atlantic Coastal Environmental Sciences Division
Narragansett, Rhode Island 02882
Detenbeck, N.E1., T. Stagnitta2, J. White3, S. McKenrick3
A. Le4-5, A. Brown5, A. Piscopo6, and M. ten Brink1
4ICF International, Cambridge, MA
formerly at Abt Associates, Inc., Cambridge, MA

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Notice and Disclaimer
This work was supported by the US EPA Safe and Sustainable Waters Research Program,
including contract support (Contract No. EP-C-13-039) to Abt Associates and
interagency agreement (92429801) support to Oak Ridge Institute for Science and
Education, Department of Energy. This document has been reviewed by the U.S.
Environmental Protection Agency, Office of Research and Development, and approved
for publication. Any mention of trade names, products, or services does not imply an
endorsement by the U.S. Government or the U.S. Environmental Protection Agency
(EPA). The EPA does not endorse any commercial products, services, or enterprises.
11

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Table of Contents
Notice & Disclaimer	ii
Abstract	v
Acknowledgements	vi
List of Figures	vii
List of Tables	viii
Acronyms	x
Introduction	1
Problem Definition	1
Watershed Management Optimization Tool	2
Objectives	3
Study Area Characteristics	6
Methods	6
Optimization Problem: Objective Function, Constraints, Decision Variables	7
Conceptual Model for System	9
HRU Definitions	9
Baseline Time Series	11
Calibration and Validation Approach	13
Data Sources for Management Action Implementation Areas, Costs & Effectiveness. 15
Riparian Buffer	15
Stormwater Control Measures, Alternative, and Nonstructural BMPs	16
Sediment and Flow Loading Targets	18
Optimization Runs	19
Results	21
Calibration	21
Flow	21
TN	22
TP	24
TSS	26
Optimization Runs	26
Overall Rankings of Management Actions to Achieve Annual and Maximum
Daily Load Targets	27
Management Actions Required to Achieve Flow Targets Consistent with 21%
Sediment TMDL Target	30
TN and TP Scenarios with TSS Optimal Management	30
in

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Comparison of Stormwater BMP and Riparian Implementation by HRU	35
Alternative and Nonstructural BMPs	39
Distribution of Proposed Riparian Zone Restoration Among
Relative Load Groups	39
Discussion	41
Model Fits	41
Relative Cost of BMPs Per Unit Load Reduction	42
Resolution of Differences Among Solutions by Parameter	42
Robustness of Solutions Across Weather Regimes	42
Ancillary Benefits to Flow Regime	44
Transferability of CJC Results to Other MD Watersheds	44
Sources of Uncertainty, Quality and Use of Data	44
Comparison with Similar Optimization Studies	48
Future Improvements	49
Conclusions	49
References	50
Appendices	56
Appendix A. Full-size maps of HRU component distributions	56
Appendix B. Summary of data sources	60
Appendix C. Original land-use classes for Phase 6 Chesapeake Bay Watershed Model ..61
Appendix D. Selected screenshots from WMOST calibration and optimization runs	65
Appendix El. Summary of costs of best management practices for structural & non-
structural (alternative) applications by regulated entity in Montgomery County, MD.76
Appendix E2. Raw data used in calculating summary of costs of best management
practices for structural and nonstructural (alternative) applications by regulated
entity in Montgomery County, MD	76
Appendix F. 2014 HRU areas and history of implementation of existing stormwater
BMPs in Cabin John Creek	76
Appendix G. MDE Estimates of pollutant removal efficiencies for structural, alternative,
and nonstructural stormwater BMPs	76
Appendix H. Maryland watersheds with Total Maximum Daily Loads for total
suspended solids and similarities to Cabin John Creek	76
Appendix I. Summary of urban stormwater BMP scenario and optimization studies	76
Appendix J. Example WMOST v3.01 run set-ups	76
Calibration for TN (2006)
Calibration for TP (2006)
Optimization for TP (2003) including stormwater BMPs and riparian buffer
Optimization for TP (2014) including stormwater BMPs and riparian buffer
iv

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Abstract
A case study application of EPA's Watershed Management Optimization Support Tool
(WMOST v3) was carried out to inform management options for the heavily urbanized
watershed of Cabin John Creek (CJC), MD, a tributary to the Potomac River, for the 10-
year period 2014 - 2025. CJC was chosen as a representative case study of a watershed
required to meet loading targets for both the Chesapeake Bay Total Maximum Daily
Load (TMDL) as well as a Maryland nontidal stream TMDL for total suspended solids
(TSS), but without water supply constraints. WMOST is an application designed to
facilitate cost-effective integrated water resource management at the scale of 12- to 10-
digit Hydrologic Units (HUC12 - HUC10; 10,000 - 250,000 acres). Optimizations were
performed to meet the single least-cost objective, while meeting one of the constraints
imposed by total nitrogen and total phosphorus load reduction targets (4% TN, 5% TP)
associated with the Chesapeake Bay TMDL or the TSS loading reduction targets (21%)
for a CJC non-tidal sediment TMDL, implemented either as a TSS load constraint or as
an associated flow target. Alternative approaches to deriving TSS loading targets were
explored: reduction of 1) maximum modelled daily load for a given year, 2) confidence
limits for maximum modelled daily load based on historic temporal variability, 3) peak
flow targets based on simulation of pre-development conditions (1-year 24-hour event),
and 4) peak flow targets associated with predicted TSS load based on sediment rating
curves. Decision variables included the type and implementation level of upland
management (seven structural stormwater control measures [SCMs], nonstructural urban
tree canopy planting, riparian buffer restoration), instream measures (outfall
enhancement, stream restoration), and programmatic measures (street sweeping).
Baseline unit runoff, recharge, and loading time series were derived from the Beta 3
version of the Phase 6 Chesapeake Bay Watershed Model, adjusted to distribute loads
between two hydrologic soil groups (A + B, C + D) for all developed hydrologic
response units. Costs included annualized capital costs for design and construction of
SCMs or other BMPs (based on actual cost data from Montgomery County, MD, the city
of Rockville, and the MD State Highway Administration) plus operation and maintenance
costs based on default values from EPA's System for Urban Stormwater Treatment and
Analysis IntegratioN (SUSTAIN) tool. Robustness of solutions was tested by comparing
results for a year with above-average annual precipitation (2003) with results for 2014, a
year with 100-year average annual precipitation but one large event. The flow target that
was set based on estimates of pre-development 1-year 24-hour events (11 - 13 cfs), was
not achievable with any level of SCM or BMP implementation. Peak flow targets based
on application of sediment rating curves (499 cfs in 2014, 535 cfs in 2003) were also not
achievable. The minimum achievable targets based on incremental scenarios of steadily
decreasing peak flow targets were 700 cfs for the 2014 weather regime and 525 cfs for
the 2003 weather regime. These flow targets required implementation of a combination
of sand filters, infiltration basins, and porous pavement for both 2003 and 2014 weather
v

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regimes. Ancillary benefits of this approach included significant increases in predicted
baseflows. The flow-optimized solutions would increase baseflow the greatest amount
during the growing season (456 - 517%), and to a lesser extent during fall and winter
months. The least-cost solutions chosen to achieve a 21% reduction in annual and
maximum daily TSS loads included implementation of infiltration basins, dry pond to
wet pond conversions, and sand filters for both 2003 and 2014 weather regimes.
Additional SCMs and level of implementation were required to achieve maximum daily
TSS loads adjusted for a 95% confidence interval - a combination of infiltration basins,
dry pond to wet pond conversions, sand filters, bioretention basins, and riparian buffer
restoration. Solutions based on the maximum daily load with a 95% confidence interval
yielded similar but slightly higher peak flows than minimum achievable levels.
Implementation of least-cost solutions to meet TSS loading targets would yield load
reductions for total N (17.4 - 17.9%) and total P (16.6 - 19.9%) that far exceed load
targets for those nutrients (5% TN, 4% TP). Meeting sediment load reduction targets will
require greater implementation of green infrastructure stormwater control measures,
particularly those with enhanced infiltration. Modelling results show limited
effectiveness for gray infrastructure (extended detention basins) in meeting water quality
goals.
Acknowledgements
We would like to acknowledge the assistance of Montgomery County, the city of
Rockville, and MD State Highway Administration in compiling actual costs associated
with design and construction of stormwater control measures and other best management
practices in the study region.
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List of Figures
1.	Flow of data and information into and out of WMOST.
*EDM = EPA Estuary Data Mapper	3
2.	Location of Cabin John Creek watershed within Potomac River watershed,
Chesapeake Bay watershed, and Montgomery County, MD boundaries	6
3.	a) Soil Hydrologic Groups, b) Aggregate Land Use Classes, c) Stormwater
Regulation Entity, and d) Final Hydrologic Response Units (HRUs)	8
4.	a) Water and pollutant sources and components and flows among components in
WMOST, including both natural and manmade features, b) Simplified components
and flow routing in WMOST for Cabin John Creek watershed	10
5.	Annual (solid line) and average (dashed line) precipitation for Montgomery County ..14
6.	Location of water quality sampling station CJB0005 near mouth of
Cabin John Creek	15
7.	Relative load groups for contribution of upland HRUs to riparian buffer restoration
areas for a) total N, b) total P, and c) total SS	16
8.	Results of WMOST 2006 model calibration for stream discharge (cfs)	21
9.	Results of WMOST validation procedure for modelled discharge for 2014 (pre-
optimization)	22
10.	Results of 2006 calibration for total N, comparing WMOST measured (circles)
and modeled values	23
11.	Comparison of measured vs modeled instantaneous total N load from WMOST
for 2006 calibration period	23
12.	Comparison of WMOST validation: measured (circles) versus modeled concentration
of I N in 2014	24
13.	Calibration for TP in 2006, measured (circles) versus modeled TP	25
14.	Validation of TP measured (circles) versus modeled concentrations in 2014	25
15.	Time series of measured and modeled concentrations of total suspended sediment
(g/L) for the calibration year, 2006	26
16.	Flow time series associated with baseline discharge, optimization for 21% TSS load
reduction, and minimum peak flow solutions for a) 2014 and b) 2003 conditions.... 31
17.	Daily loads for total N (TN) under optimal management regime for meeting 21%
sediment load reduction under a) 2014 weather conditions and b) 2003 conditions.. 32
18.	Daily loads for total phosphorus (TP) under optimal management regime for
meeting 21% sediment load reduction under a) 2014 weather conditions and
b) 2003 conditions	33
19.	WMOST baseflow component (cfs) for baseline conditions, TSS load reduction
optimization, flow reduction optimization, and pre-development (100% forested)
conditions for a) 2014 and b) 2003 starting conditions	45
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20. Maryland watersheds with Total Maximum Daily Loads for total suspended
solids (TSS) shaded to indicate similarity to Cabin John Creek watershed	46
List of Tables
1.	Summary of WMOST runs needed to accomplish multiple sub-objectives
related to Chesapeake Bay and Cabin John Creek TMDLs	5
2.	Translation of original Chesapeake Bay Watershed Model (CBWM) Hydrologic
Response Units (HRUs) into WMOST HRUs based on aggregations within
pervious and impervious classes, combination of pervious and impervious units,
disaggregation by MS4 Permittee and hydrologic soil group	12
3.	Siting criteria for stormwater control measures in MD and suitable acres for
SCYls by I IRl	17
4.	Best management practices evaluated for Cabin John Creek watershed	19
5.	Fit statistics for WMOST calibration (2006) and validation (2014)	21
6.	Least-cost optimization selection of BMP management options for Cabin John Creek
to meet maximum daily load and annual load targets for total N, total P, or total
suspended solids under 2014 climate (average precipitation year with one large
event) or 2003 (above average annual precipitation year)	27
7.	Effect of applying optimal strategies for reducing TSS loads by 21% on
achievement of daily maximum and annual loading goals for total N and total P	30
8.	Optimal treatment of HRU acres by stormwater control measures for 2014 vs 2003
input conditions to meet 21% TSS load reduction	34
9.	Optimal treatment of HRU acres to meet 21% annual and maximum daily load
reductions for TSS, including MDE factor for temporal variability	35
10.	Optimal treated acres of HRUs by BMPs for TN load reductions considering
1) all stormwater control measures (SCM), 2) all SCMs plus retrofits, or
3) riparian buffer restoration	36
11.	Optimal treated acres of HRUs by BMPs for TP load reductions considering
1) all stormwater control measures (SCM), 2) all SCMs plus retrofits, or
3) riparian buffer restoration	37
12.	Least-cost optimization selection of alternative and nonstructural BMP management
options for Cabin John Creek to meet maximum daily load and annual load targets
for total N, total P, or total suspended solids under 2014 climate (average precipitation
year with one large event) or 2003 (above average annual
precipitation year)	39
13.	Required conversion of HRUs to forested riparian buffer by relative load group
for TN or TP load reductions to meet Chesapeake Bay TMDL targets at least-cost... 40
viii

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14.	Estimated load or volume reductions from HRU1 (Montgomery County turfgrass
+ impervious on A+B soils) and cost per unit reduction, with capital costs
annualized over 10-year period at 5% interest rate	43
15.	Comparison of stormwater BMP O&M costs estimated by WMOST (based on
SUSTAIN defaults) as compared to generic MD county-level O&M costs per
unit acre of HRU1 (30.87% impervious) based on King and Hagan (2011)	48
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Acronyms
A+B - Soil hydrologic groups A plus B
AMPL - algebraic model programming language
ASR - aquifer storage and recovery
AWQMS - Ambient Water Quality Monitoring System
BMP - best management practice
C+D - Soil hydrologic groups C plus D
CB - Chesapeake Bay
CBWM - Chesapeake Bay Watershed Model
Cfs - cubic feet per second
CJC - Cabin John Creek
CN - Curve Number
CV - Coefficient of variation
DEM - digital elevation model
DQgwsw - WMOST decision variable for flow from groundwater to surface water
EIA - effective impervious area
EPA - Environmental Protection Agency
GIS - geographic information system
GW - groundwater
HRU - hydrologic response unit
HUC - hydrologic unit
HUC10 - 10-digit hydrologic unit
HUC 12 - 12-digit hydrologic unit
Inf- infeasible result
INFIL - infiltration rate
Lb - pound
LTA - Long term average annual load
MCo - Montgomery County
MD - Maryland
MDE - Maryland Department of Environment
MDL - maximum daily load
MG - million gallons
MGD - million gallons per day
MS4 - municipal separate storm sewer systems
Mt - metric ton
Mtg - turfgrass HRU
NEOS - Network-Enabled Optimization System
NHD - National Hydrography Dataset
NSE - Nash Sutcliffe Efficiency coefficient
O&M - operations and maintenance cost
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ORISE - Oak Ridge Institute for Science Education
Oth - Other regulated parties
P - precipitation
PET - potential evapotranspiration
RI - Rhode Island
RO - runoff
Rv - city of Rockville
SCM - stormwater control measure
SHA - State Highway Administration
STST - streambank stabilization
STSW - street sweeping
SUSTAIN - System for Urban Stormwater Treatment and Analysis IntegratioN
SW - southwest
SWMM - stormwater management model
TC - tree canopy
Tg - turfgrass
TMDL - total maximum daily load
TN - total nitrogen
TP - total phosphorus
TR-55 - Technical Release 55 Urban Hydrology for Small Watersheds Model
TSS - total suspended solids
UAW - unaccounted water
UD - underdrain
US EPA - United States Environmental Protection Agency
USGS - United States Geological Survey
WIP - Watershed implementation plan
WMOST - Watershed Management Optimization Support Tool
WWTP - wastewater treatment plant
Z -z score
XI

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Introduction
Problem Definition
Governments at all levels are faced with the challenge of finding cost-effective solutions
to meet water quality objectives, whether to meet water quality criteria or to meet loading
targets such as Total Maximum Daily Loads (TMDLs) established under Section 303(d)
of the Clean Water Act to bring impaired water bodies back into compliance. The U.S.
Environmental Protection Agency (US EPA) established the Chesapeake Bay (CB)
TMDL on December 29, 2010, to restore conditions in the Bay as well as upstream
waters (US EPA 2010). The TMDL identifies the necessary pollution reductions from
major sources of total nitrogen (TN), total phosphorus (TP) and total suspended sediment
(TSS) across the Bay jurisdictions and sets pollution limits necessary to meet water
quality standards. Bay jurisdictions include Delaware, Maryland, New York,
Pennsylvania, Virginia, West Virginia and the District of Columbia. The TMDL is
designed to ensure that all pollution control measures needed to fully restore the Bay and
its tidal rivers are in place by 2025. The TMDL also calls for practices to be in place by
2017 to meet 60 percent of the overall nitrogen, phosphorus and sediment reductions.
Many of the planned reductions in TN and TP in the TMDL Phase 1 Watershed
Implementation Plans (WIPs) were achieved through point source controls on discharges
from wastewater treatment plants (WWTP) and, to a lesser extent, through agricultural
best management practices (BMPs). Planned reductions in the Phase II and Phase III
WIPs rely much more heavily on reduction of urban runoff through stormwater control
measures. The runoff and septic leaching from urban and suburban land comprise about
20% of the total nitrogen load in Maryland (CSN 2011). To achieve the target load, more
than 2.5 million pounds of nitrogen need to be reduced from the urban sector, equivalent to a
37% reduction of nitrogen coming from existing development in the state. Stormwater runoff
from urban and suburban land also comprises about 20% of the total phosphorus load in
Maryland. To achieve the target load for phosphorus, more than a quarter million pounds
will need to be reduced, equivalent to a 36% reduction in phosphorus load from existing
development in the state (CSN 2011).
The state of Maryland is responsible for meeting not only targets for the Chesapeake Bay
TMDL, which were set to achieve applicable water quality standards in the Chesapeake Bay,
its tidal rivers and embayments, but also TMDLs for impaired non-tidal waters in the state.
For example, the Maryland Department of Environment (MDE) has identified the waters
of the Cabin John Creek watershed within the State's 2010 Integrated Report as impaired
by sediments (1996), phosphorus (1996), bacteria (2002), chlorides (2010), sulfates
(2010), habitat alteration, and impacts to biological communities (2006) (MDE 2010).
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Phosphorus is now listed as meeting criteria. The sediment TMDL established for Cabin
John Creek (CJC), based on a total baseline sediment load of 5,537.7 tons/year, is
4,391.4 tons/year of sediment (total suspended solids) (MDE 2011). Meeting the CJC
sediment TMDL target requires implementation of stormwater control measures by
municipal (Rockville), county (Montgomery County, MD) and state (MD State Highway
Administration) agencies, among others.
Watershed Management Optimization Tool
The U.S. EPA has created the Watershed Management Optimization Support Tool
(WMOST) to facilitate cost-effective integrated water resources management at the local
community and watershed scales, typically a 12- or 10-digit Hydrologic Unit Code
(HUC; 10,000 - 250,000 acres; USGS and USDA NRCS 2013). WMOST version 3.01
allows users to find the least-cost alternative among a suite of potential management
practices spanning stormwater, wastewater, drinking water, and land conservation
options, to meet both water quantity and water quality goals (Detenbeck et al. 2018a,b,c,
Detenbeck and Weaver 2019). During development of WMOST v3, US EPA Office of
Research and Development sought partners to help in design, evaluation and testing to
ensure that the final tool would meet users' needs. MDE was identified as a partner
applying WMOST v3.01 within Cabin John Creek, Montgomery County, MD, as a case
study for testing and development.
WMOST uses baseline time series for unit area runoff, recharge and pollutant loadings
based on outputs from an externally-run watershed model. The effect of stormwater
control measures is simulated through connections to the nonGIS version of EPA's
System for Urban Stormwater Treatment and Analysis IntegratioN (SUSTAIN; US EPA
2009) tool which uses algorithms from EPA's Storm Water Management Model
(SWMM; Rossman 2015) to estimate reductions in unit area runoff and pollutant loads
and corresponding increases in infiltration. WMOST defines the optimization problem
using the algebraic model programming language (AMPL). In formulating the
optimization problem, the objective is defined as the (minimization of) the sum of
annualized capital costs and annual operations and maintenance costs over the planning
period while meeting mass-balance constraints as well as user-defined constraints such as
daily or annual loading targets. (See WMOST v3 Theoretical Documentation for more
detail.) (Detenbeck et al. 2018c) The optimization problem is then submitted to the
online Network-Enabled Optimization System (NEOS) server (https://neos-guide.org/)
for a solution using a mixed integer nonlinear programming method (Bonami et al. 2008).
Results files, including least-cost management options, are imported back into WMOST
and displayed for the user in graphical and tabular form (Figure 1).
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	Formats watershed model output
	Output available on server, EDM", or
processor generated by user
Time Series
Runoff, recharge, loads
By HRU (Land-use + soil type)

Bmmm* * (Optional) FEMa
*	TW'kS  Creates flood
 cost/risk table
Stormwater module
Adjusts time series
for urban BMP
processes
SUSTAIN

Online optimization
programs
Table
Graphs
Figure 1. Flow of data and information into and out of WMOST. *EDM = EPA Estuary
Data Mapper.
Objectives
The main objective of this case study is to apply WMOST v3 to find the most cost-
effective suite of best management practices to meet both the Chesapeake Bay TN, TP,
and TSS loading targets for the Bay TMDL as well as non-tidal sediment load targets
based on the CJC TMDL. Sub-objectives include 1) an evaluation of the robustness of
optimal management strategies under varying weather regimes, 2) a comparison of
optimal management practices under different approaches for establishing sediment
TMDL targets, and 3) an evaluation of the implications of applying optimal management
approaches for suspended sediment goals for meeting the Bay TMDL goals for TN and
TP (Table 1). This study is designed as the first in a series of MD case studies with
WMOST for different use cases, to inform not only cost-effective management of a
particular watershed, but also to provide guidance for communities and agencies dealing
with similar goals and environmental settings. This initial use case represents a
watershed needing to meet both non-tidal and downstream loading targets through
stormwater controls, but without the added complications of significant point sources or
water quantity constraints. Water quantity is not as constrained within the CJC watershed
because communities in the watershed meet their drinking water supply needs and
wastewater treatment needs through interbasin transfers.
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Table 1. Summary of WMOST runs needed to accomplish multiple sub-objectives related
to Chesapeake Bay and Cabin John Creek TMDLs
WMOST




Run
Year
Type of Setup
Parameter
SubObjective
la
2006
Calibration
TSS
Calibrate WMOST for year with average
precipitation
lb
2006
Calibration
TP
Calibrate WMOST for year with average
precipitation
lc
2006
Calibration
TN
Calibrate WMOST for year with average
precipitation
2a
2014
Validation
TSS
Establish baseline for optimizations
2b
2014
Validation
TP
Establish baseline for optimizations
2c
2014
Validation
TN
Establish baseline for optimizations
3a
2014
Optimization
TP
Evaluate optimal management strategies to
meet Bay TMDL maximum daily loading
target for TP, including potential stormwater
control measures and riparian buffer
management for baseline year with average
precipitation
3b
2014
Optimization
TN
Evaluate optimal management strategies to
meet Bay TMDL maximum daily loading
target for TN, including potential stormwater
control measures and riparian buffer
management for baseline year with average
precipitation
3c
2014
Optimization
TSS
Evaluate optimal management strategies to
meet Bay TMDL maximum daily loading
target for TSS, including potential
stormwater control measures and riparian
buffer management for baseline year with
average precipitation
3d
2014
Optimization
TSS
Evaluate optimal management strategies to
meet Bay TMDL maximum daily loading
target for TSS, including alternative
nonstructural BMPs for baseline year with
average precipitation
4a
2003
Optimization
TP
Evaluate robustness of optimal management
strategies to meet Bay TMDL maximum
daily loading target for TP, including
potential stormwater control measures and
riparian buffer management for relatively
wet year to compare with baseline year
(average precipitation)
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Table 1. (continued)
WMOST
Run
Year
Type of Setup
Parameter
SubObjective
4b
2003
Optimization
TN
Evaluate robustness of optimal management
strategies to meet Bay TMDL maximum
daily loading target for TN, including
potential stormwater control measures and
riparian buffer management for relatively
wet year to compare with baseline year
(average precipitation)
4c
2003
Optimization
TSS
Evaluate robustness of optimal management,
strategies to meet Bay TMDL maximum
daily loading target for TSS, including
potential stormwater control measures for
relatively wet year to compare with baseline
year (average precipitation)
4d
2003
Optimization
TSS
Evaluate robustness of optimal management
strategies to meet Bay TMDL maximum
daily loading target for TSS, including
potential riparian management and
streambank stabilization measures for
relatively wet year to compare with baseline
year (average precipitation)
5a
2014
Optimization
TSS (via
flow
target)
Compare optimal management, strategies to
meet non-tidal CJC TMDL via flow-based
target with optimal management strategy via
load-based target for baseline year (average
precipitation)
5b
2003
Optimization
TSS (via
flow
target)
Evaluate robustness of optimal management,
strategies to meet Bay TMDL maximum
daily loading target for TSS (flow-based),
including potential riparian management and
streambank stabilization measures for
relatively wet year to compare with baseline
year (average precipitation)
6a
2014
Simulation
TP
Evaluate whether optimal management,
strategies to meet non-tidal sediment TMDL
target for CJC would meet Bay TMDL
maximum daily loading target for TP for
baseline year with average precipitation
6b
2014
Simulation
TN
Evaluate whether optimal management,
strategies to meet non-tidal sediment TMDL
target for CJC would meet Bay TMDL
maximum daily loading target for TN for
baseline year with average precipitation
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Study Area Characteristics
Cabin John Creek, located in Montgomery County, MD, is a tributary to the Potomac
River, which, in turn, is one of the main tributaries to the Chesapeake Bay (Figure 2).
The CJC watershed covers approximately 67 square kilometers, comprising a single
12-digit Hydrologic Unit (HUC), with a population of approximately 75,170 (MDE
2011). The watershed is contained within the Piedmont geologic province, with gentle to
steep rolling topography. The watershed is comprised of mainly hydrologic soil group B
and C type soils with lesser coverage of type A and D soils (Figure 3a). Based on the CB
Watershed Model 5.2 Land Use categories, the CJC watershed is covered mainly by
urban land uses with less than 10% forest cover, and minimal amounts of agriculture
(0.6%; MDE 2011; Figure 3b). Some stormwater control measures already have been
implemented in the watershed, primarily wet ponds (treating 432.6 ha [1069 acres] or
7.1% of developed land in the watershed) and extended dry detention basins (treating
401 ha [991 acres] or 6.6% of developed land) (Jeff White, MDE, pers. comm.).
Legend
	 Major Rivers
Cabin John Creek
	j Montgomery County
Potomac River Watershed
~ Chesapeake Bay watershed
Maryland
0	25 50	100 Kilometers
	1	I	I	I	I	I	I	I	I
Figure 2. Location of Cabin John Creek watershed within Potomac River watershed,
Chesapeake Bay watershed, and Montgomery County, MD boundaries. The Potomac
River watershed is shown in green, with the Cabin John Creek subwatershed shown in
light green.
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Methods
Data sources required for setting up WMOST model runs are summarized in Appendix B.
Optimization Problem! Objective Function, Constraints, Decision Variables
WMOST v3 was used to define the optimization problem for the CJC case study.
WMOST defines a single-objective optimization problem, with cost minimization over
the planning period as the goal. Costs include both initial capital costs, annualized over
the 10-year planning period (2015 - 2025) based on a given interest rate (5% in our
example), as well as annual operations and maintenance costs. Multiple mass-balance
constraints are defined within WMOST to describe hydrologic flows and pollutant
loadings (Detenbeck et al. 2018b). In addition, user-defined constraints can be set to
represent water quality and quantity targets. In this case, the Bay TMDL TN, TP, and
TSS targets (5%, 4%, and 4% reductions) and non-tidal CJC sediment TMDL targets
(21% reductions in TSS load) were used to define loading constraints. Percent reductions
were applied to WMOST-calibrated 2015 TN, TP and TSS loads for CJC to determine
target loads (Method 1). Outputs from the CBWM for 2015 for the Montgomery County
land-water segment were used as the basis for WMOST 2015 calibrations, with some
adjustments made to better match local hydrology measurements. For comparison, an
alternative approach for establishing sediment targets calculated a peak flow target
corresponding to a sediment daily loading target generated using MDE methods (based
on a correction factor for daily loads designed to account for temporal variability) and a
sediment rating curve established for the nearby Anacostia River (see below for details;
Method 2a-c).
7

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Legend
So4l HydroJoflic Group

i
Legend
Aggregate Land Use
frr*p#tvotja
t j Natural
I Nalura?- forested
H Tree Canopy ewer Turfgraw
	] TurfgffliS
I Waw
Legend
Slormwator Regulation
	 I ManlgcrviMy County
H ouw
ftotAvile
H Slate Highway AcJmrwtniLion
Legend
Fmai HRU
J MCo Tg AS
MCo TgCD

OlhTgAS
Ou* g CO
	:R*TflAB
RfcTgCC
SHA Tg
SHATgC
m
0 0.5 1
Figure 3. a) Soil Hydrologic Groups, b) Aggregate Land LJse Classes,
c) Stormwater Regulation Entity, and d) Final Hydrologic Response Units (HRUs).
MCo = Montgomery County, Rv = Rockville, SHA = State Highway Administration,
Oth = Other, AB = Type A + B hydrologic soil groups, CD = C + D hydrologic soil
groups, Tg = Turfgrass. See Appendices la-d for full size maps.
8

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Decision variables allowed to vary during optimization runs included areas of developed
land subjected to treatment by individual BMPs, riparian buffer restoration area,
implementation of alternative BMPs (stream bank restoration, outfall enhancements), or
nonstructural BMPs (nutrient management plan, street sweeping, tree canopy over turf).
Conceptual Model for System
Figure 4a illustrates the WMOST conceptual model describing the interaction of natural
features in a watershed with manmade infrastructure when determining water and
pollutant flows. Figure 4b illustrates the simplified system for CJC, with various
components not present in the CJC watershed WMOST model removed. Most of the
infrastructure components could be omitted from the WMOST model because drinking
water sources and wastewater treatment were located outside of the watershed and thus,
did not affect the water or pollutant load balances.
HRU Definitions
WMOST is a semi-lumped parameter model in which runoff, recharge, and pollutant
loads are tracked for aggregate land areas within a watershed. These lumped land areas
are known as Hydrologic Response Units (HRUs). In watershed models, HRUs are
typically defined by combinations of land-use, soils, and sometimes slope classes as they
are areas with similar runoff/recharge/loading characteristics. Appendix C lists the
original land-use classes for the Montgomery County Land/Water segment defined to
support the Chesapeake Bay Watershed Model (CBWM) v6
(http://cast.chesapeakebaY.net/). The WMOST application is more likely to reach an
optimal solution within convergence time limits established by the NEOS optimization
server with fewer HRUs (e.g., 10 - 12), so we first collapsed classes from the original
CBWM set into Impervious, Turfgrass, Natural, and Water categories (Table 2,
Figure 3b). Second, we disaggregated the developed land classes based on permit
areas. The permit areas are based on a regulated stormwater shapefile developed by
MDE in conjunction with their MS4 program and the regulated jurisdictions/entities
(Figure 3c: City of Rockville, Montgomery County, MD State Highway Administration,
Other regulated) to allow consideration of different site constraints and BMP costs by
land owner/Municipal Separate Storm Sewer (MS4) permit. Third, because WMOST
is designed to work with either pervious or blended pervious + impervious HRUs,
we created combined pervious + impervious classes for the developed HRUs. Finally,
the CBWM does not distinguish between soil hydrologic groups in defining HRUs.
However, soil hydrologic groups are important in defining siting constraints for
stormwater control measures in Maryland (CWP and MDE 2009), and soil infiltration
rates strongly influence the effectiveness of SCMs in reducing runoff and pollutant loads
(TetraTech 2010). Therefore, we further disaggregated HRUs by soil hydrologic group
from the Soil Survey Geographic Database (SSURGO) soil layers (USDA NRCS 2013):
A/B vs C/D to create the final set of 11 HRU classes (Table 2, Figure 3d). In some
analyses, natural forested and nonforested HRUs were combined, but when assessing
9

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a)
Source Water
Treated Water Water Use Wastewater Water Reuse
External SW;
from .VWIP
Private SW
vVithdra- -a I? S>
\Discharges
to ASR
Interbasin
Tranter
Potable
Water
Interbasin
Transfer: I
Wastewater
Surfa ce Water
(SW)
Water Reuse
Facility
Land Use Areas.
Runoff &
Recharge Rates
Non-Potable
Use
to ASR
Baseline HRUs
to SW
6
from
Reservoir
Consumptive
Stormwat
PotableWTP
'vvv I-
Managed
from SW
to ExternalSW
0
from
GW Infiltration
AquiferStorage
and Recovery
(ASR)
Potable Use
to External GW
I Recharge
Out of
Groundwater
(GW)
Septic Systems
O Infiltration to WWTP
[ External GW,
Private GW
I Withdraws Is &
Discharges
O Component
vh storage
Flow in or
out of the jyittm
Component
writhout storage
Flowjump between
i Private GW and SW withdrawal 5 and discharges are water Raws only; water qua&ty in not modeled
a Up CDao flormwater management options may be modeled representing traditional, green infnmtnjavre or lowanpact development praaicasorcombinaoonof pracoott.
b)
External SW;
Managed
(SW)
(GW)
Interbasin
Transfer:
Potable
Water
Interbasin
Transfer:
Wastewater
Figure 4. a) Water and pollutant sources and components and flows among components
in WMOST, including both natural and manmade features, b) Simplified components and
flow routing in WMOST for Cabin John Creek watershed.
10

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riparian buffer restoration, these were kept separate. Likewise, tree canopy over turfgrass
was kept separate from turf grass when considering tree canopy as an alternative BMP.
Baseline Time Series
We derived baseline time series of weather, hydrology, and pollutant loads from datasets
used or generated by the hybrid CBWM v6 (beta 3). Time series were provided by the
Chesapeake Bay Program Office (Ghopal Bhatt, pers. comm.). (The final version 6
model is now available, but the beta 3 model was the most recent version available at the
time this project was initiated.) We used the forested land-use class time series (for) and
non-forested category (osp) time series, combined in proportion to their respective areas,
to represent the Natural HRU. For the pervious component of developed lands, we used
the time series for turfgrass (mtg). We calculated expected unit annual runoff from Type
A/B soils and Type C/D soils using the curve number approach based on Technical
Release Model 55 (TR-55) (USDA NRCS 2009), assuming turf grass is equivalent to
open space in good condition (A/B CN = 59, C/D CN = 79) and adjusting for antecedent
moisture condition. We then partitioned the CBWM unit runoff time series to Type A/B
or Type C/D soil HRUs based on an adjustment ratio that considered both annual runoff
(RO) and ratio of area of Type A/B (Aab) to Type C/D (Acd) soils in the Turfgrass class
(mtg):
ROmtg * Aabcd = (ROab * Aab) + (ROcd * Acd)
where RO = runoff per unit area (mgd/acre)
A = area (acres)
mtg = turfgrass HRU
abed = all soil hydrologic groups
ab = soil hydrologic groups A + B
cd = soil hydrologic groups C + D
ROab * Aab = (ROmtg * Aabcd)  (ROcd * Acd)
The ratio of unit runoff from soil hydrologic groups C + D to unit runoff from all soil
groups was calculated based on estimated runoff from C + D using TR-55 and the unit
runoff for turfgrass from the CBWM.
ROcd/ROabcd = ROcd,TR55/ROmtg
This was substituted into the previous equation to yield an estimate of remaining unit
runoff attributable to soil hydrologic groups A + B:
ROab = (ROmtg * Aabcd/Aab)  (ROcd * Acd/Aab)
Adjusted unit recharge was calculated as P - RO - PET, where P = precipitation, RO =
runoff, and PET = potential evapotranspiration.
11

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Table 2. Translation of original Chesapeake Bay Watershed Model (CBWM) Hydrologic Response Units (HRUs) into WMOST
HRUs based on aggregations within pervious and impervious classes, combination of pervious and impervious units, disaggregation
by MS4 Permittee and hydrologic soil group. See Appendix C for CBWM class definitions.
Original CBWM
land-use classes
Aggregated
Land-use
Regulated Stormwater Permit
Soil Type
HRU
ID
Combined pervious/ impervious HRU
mir, mnr, mci
Impervious
Montgomery County
NA


mch, mtg
Turfgrass
Montgomery County
A/B
1
MC Turfgrass with impervious A/B
mch, mtg
Turfgrass
Montgomery County
C/D
2
MC Turfgrass with impervious C/D
mir, mnr, mci
Impervious
City of Rockville
NA


mch, mtg
Turfgrass
City of Rockville
A/B
3
RV Turfgrass with impervious A/B
mch, mtg
Turfgrass
City of Rockville
C/D
4
RV Turfgrass with impervious C/D
mir, mnr, mci
Impervious
MD State Highway Administration
NA


mch, mtg
Turfgrass
MD State Highway Administration
A/B
5
MSHA Turfgrass with impervious A/B
mch, mtg
Turfgrass
MD State Highway Administration
C/D
6
MSHA Turfgrass with impervious C/D
mir, mnr, mci
Impervious
Other Regulated
NA


mch, mtg
Turfgrass
Other Regulated
A/B
7
OReg Turfgrass with impervious A/B
mch, mtg
Turfgrass
Other Regulated
C/D
8
OReg Turfgrass with impervious C/D
for, hfr
Natural forested
NA
NA
9
Natural forested
osp, wto
Natural nonforested
NA
NA
10
Natural nonforested
wat
Water
NA
NA
11
Water
12

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Calibration and Validation Approach
Although the CBWM is calibrated for the entire watershed, the HRU time series are
generated at a relative coarse scale (CBWM land segment, approximately equivalent to
Montgomery County). Thus, we cannot expect a perfect fit for the smaller CJC. For
calibrations, we ran WMOST in simulation mode, with HRU areas fixed (min HRU
area = max HRU area in Land Use Conservation table options; Appendix D.
Screenshot D.l) and without any management options (stormwater BMPs, riparian
restoration, alternative or structural BMPs) enabled. In practice, this is achieved by
setting costs of all management actions to -9 within the model. This serves as a flag in
the WMOST code to include or exclude various management practices from optimization
equations. Although some stormwater control measures were in place in the CJC
watershed in 2014, the CBWM outputs reflect those managed conditions, so this level of
implementation was considered as zero added BMPs, rather than explicitly modelling
these initial BMPs in WMOST. All drinking water demand in the CJC watershed is met
through interbasin transfers so the number of water user classes was set to 1 and each
element of the unaccounted water (UAW) and user demand and consumption time series
was set to zero (Appendix D. Screenshot D.2). Virtually all CJC watershed is sewered so
percentage public water users on septic was set to 0% (Appendix D. Screenshot D.3).
Water demand could be set to zero since there is no public water utility in CJC, with
drinking water supplied through interbasin transfers (Appendix D. Screenshot D.2).
All lakes and wetlands were accounted for in the open water HRU with no downstream
reservoir modelled. Private withdrawals and private discharge time series to surface
water and groundwater were entered based on data obtained by MDE from withdrawal
and discharge permits (Appendix D. Screenshot D.4). Infiltration and inflow to the sewer
was set at 2.18 million gallons per day (MGD; City of Rockville Master Plan;
http://www.rockvillemd.gov/DociimentCenter/Yiew/904 Appendix D. Screenshot
D.5). Infrastructure modification options were set to -9 with associated costs set to zero
(Appendix D. Screenshot D.6).
There are no USGS gaging stations in Cabin John Creek. Therefore, we calibrated
WMOST for the pre-TMDL year of 2006 to observations from the nearby highly
urbanized Anacostia Creek watershed at USGS gage 01650500 (Northwest Branch
Anacostia River near Colesville, MD) and corrected for the ratio of Cabin John Creek:
Anacostia watershed areas. The year 2006 had relatively average annual precipitation
with the exception of two relatively large events (Figure 5). The initial "lumped"
groundwater recession coefficient was calculated within WMOST based on HRU-
specific groundwater recession coefficients from the CBWM, and then systematically
altered to improve the fit between WMOST modelled and CJC-adjusted observations as
determined by the Nash-Sutcliffe coefficient for streamflow (NSE, Moriasi et al. 2007).
13

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Annual Precipitation, Montgomery County
_ 9S C
199 C
:ccc
2CG5
:c:c
2 r: _ 5
:c2c
Figure 5. Annual (solid line) and average (dashed line) precipitation for Montgomery
County. The year 2006 was an average precipitation year overall over the last 100 years
but had the two highest 3-day precipitation totals in June.
Following calibration of the lumped groundwater recession coefficient, we calibrated the
water quality loading time series based on observed versus modelled concentrations for
total P and total N. Water quality observations were retrieved from the Maryland
Ambient Water Quality Monitoring System (AWQMS; sample location in Figure 6).
Suspended solids loading observations were very limited, so we also compared total
annual average loads from WMOST (based on TR-55 adjusted CBWM outputs) with
edge of stream loads generated by MDE for the sediment TMDL (MDE 2011) and used
these ratios to correct load time series. Robustness of WMOST baseline results was
checked using validation data for the year 2014 (starting point of optimization runs
corresponding to end of Phase I WIPs).
14

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N
Legend
0 0.5 1
	
2 Kilometers
J_l
 CJB0005
Figure 6. Location of water quality sampling station CJB0005 near mouth of Cabin John
Creek.
Data Sources for Management Action Implementation Areas, Costs and
Effectiveness
Riparian Buffer
We estimated three relative load groups for riparian buffer restoration measures using a
modified version of the Chesapeake Bay Riparian Analysis Toolbox
(http://ches.communitvmodeling.org/models.php). The toolbox enables calculation of
upland areas that drain to each riparian zone pixel (10 m segment). The toolbox was
modified to allow calculation of flow pathways based on 10-meter digital elevation
models (DEMs) from the beta high resolution NHD (1:24,000), using hydrologically-
corrected DEMs (https://www.usgs.gov/core-science-systems/ngp/national-
hydrography/nhdplus-high-resolution). Buffer zones were defined at 30-meter widths
perpendicular to the stream channel (3 10-meter pixel depth). Upland HRU areas
draining to each riparian pixel were converted to loads based on average annual unit
HRU load. Riparian pixels in potential restoration areas were ranked by receiving load
and then categorized into low, medium, and high relative load groups based on order-of-
magnitude differences (Figure 7; Appendix D. Screenshot D.7). Riparian buffer percent
load reductions were based on guidance from the Chesapeake Bay Program (Claggett and
TetraTech 2014). Restoration costs were based on estimates from MD agencies compiled
by MDE (Appendix E). Potential buffer restoration areas included turf grass and scrub
15

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shrub, open mixed, and barren areas but not impervious area in developed HRUs, as
removal of existing impervious areas is not necessarily practical and will significantly
increase costs. Other potential factors influencing site selection for riparian restoration
(e.g., those in the watershed resources registry;
https://watershedresourcesregistry.org/states/maryland.html) were not considered but
could be considered during implementation.
Legend
HM 2
Legend
 2
en*
Legend
HI2
Figure 7. Relative load groups for contribution of upland HRUs to riparian buffer
restoration areas for a) total N, b) total P, and c) total SS. Relative load group 1 has
highest contributing load to riparian buffers. Most of the watershed falls in the
intermediate relative load group. The highest relative load group overlaps for all three
parameters in a small area in the SW portion of the watershed.
Stormwater Control Measures, Alternative, and Nonstructural BMPs
Level of implementation of existing BMPs by year was compiled by MDE [Appendix F J.
We subtracted areas treated by existing BMPs in 2014 from total suitable MRU areas to
obtain maximum MRU area available for treatment (Appendix D. Screenshot D.8). We
calculated suitable areas for BMP placement based on siting criteria in MD stormwater
manuals (CWP and MDE 2009) using the EPA Best Management Practices (BMPs)
siting tool (https://www.epa.gov/water-research/best-management-practices-bmps-siting-
tool; Table 3). MDE obtained estimates of installation costs of SCMs, alternative, and
nonstructural BMPs from the City of Rockville, Montgomery County, and MD State
Flighway Administration (Appendix E). No operation and maintenance cost estimates
were available from these agencies, so we used default WMOST estimates. Removal
efficiencies for SCMs were calculated in WMOST via linkage to EPA's SUSTAIN tool;
these removal efficiencies include effects of both runoff volume reductions and
biogeochemical processes taking place within BMPs. In contrast, removal efficiencies
currently used by the CBPO in assigning credits for SCMs are based on average
reduction of runoff volume, and do not consider biogeochemical processes or interannual
variability due to weather. Removal efficiencies for alternative and nonstructural BMPs
were provided by MDE (MDE 2014; Appendix G).
16

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Table 3. Siting criteria for stormwater control measures in MD and suitable acres for SCMs by HRU.
Stormwater Control Measures based on MDE naming criteria
Dry Detention Ponds & Hydrodynamic Structures,
Filtering	Infiltration Permeable Dry Extended Detention Ponds, Dry Well,
Bioretention Practices Green Roof Practices Pavement Stormwater to the Maximum Extent Practicable
Stormwater Control Measures based on SUSTAIN categories

Siting requirements based on MDE
Stormwater Manual
Bioretention
Basin
(Acres)
Sand Filter
(Acres)
Biofiltration
w/UD
(Acres)
Infiltration
Basin
(Acres)
Porous
Pavement
w/UD
(Acres)
Extended Dry
Detention
Basin
(Acres)
Dry Pond current
implementation
for Dry Pond to
Wet Pond
Conversion
(Acres)
Wet Pond
(Acres)
Not modeled

Soil Characteristics
Made Soil
OK
No criteria
f >0.52 in/hr
or A and B
A and B
A-D SUSTAIN



Drainage Area (Acres)
<5
< 10
No criteria
< 10
<3 SUSTAIN
> 10 SUSTAIN



Slope (%)
None
None
No criteria
< 15%
< 1%
<15% SUSTAIN



Ultra Urban > 80% imperviousness
OK
Depends
No criteria
Not Practical
OK
Not Practical


HRU
Description



Suitable acres



HRU1
Turfgrass A/B Montgomery County
674.96
1603.41
0
1440.79
767.42
22.99
787.22
9.20
HRU2
Turfgrass C/D Montgomery County
99.55
215.09
0
6.32
3.53
11.77
122.46
6.40
HRU3
Turfgrass A/B City of Rockville
121.02
387.99
0
369.13
229.87
3.35
63.78
0.80
HRU4
Turfgrass C/D City of Rockville
30.78
91.72
0
4.88
3.63
3.80
17.63
1.50
HRU5
Turfgrass A/B MD State Highway
Administration
33.91
113.91
0
30.68
22.00
4.12
0
2.40
HRU6
Turfgrass C/D MD State Highway
Administration
8.27
14.04
0
0.30
0.23
0.49
0
0.30
HRU7
Turfgrass A/B Other Regulated
14.61
74.91
0
70.08
47.99
0.78
0
0.14
HRU8
Turfgrass C/D Other Regulated
5.99
21.89
0
2.21
0.96
0.29
0
1.50
17

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Sediment and Flow Loading Targets
The protocol for establishing annual suspended sediment loading targets for nontidal
segments in the state of Maryland was developed in 2006 and amended in 2011 (MDE
2006, 2011). The methodology relies on calculation of the median long-term average
load normalized to an all-forested condition associated with reference watersheds,
originally calculated based on output from the Phase V CBWM (MDE 2006). Reference
watersheds were defined based on the percentage of Maryland Biological Stream Survey
monitoring stations, translated into watershed stream miles, that have an Index of Biotic
Integrity Score (IBI) < 3 (MDE 2008). This methodology led to a sediment loading
target defined as 3.3 times the sediment loading rate expected from a fully forested
watershed.
Development of a total maximum daily load for Cabin John Creek required the
translation of annual average loads into a maximum daily load. Following EPA methods
(EPA 1991), the TMDL was estimated based on a 99th percentile probability of
exceedance, the average annual sediment TMDL, and the coefficient of variation (CV)
of the CJC reach simulation daily loads from the CBWM v5.2. MDE calculated a
coefficient of variation of 9.4 from the modeled daily time series from 1985 - 2005.
The maximum daily load for stormwater and nonpoint sources was estimated as the
long-term average annual load multiplied by a factor that accounts for expected
variability of daily loading values:
MDL = LTA* e(za-'5a2)
where
MDL = Maximum daily load
LTA = Long term average (average annual load)
Z = z-score associated with target probability level
o2 = ln(CV2 + 1)
CV = coefficient of variation based on arithmetic mean and standard deviation
The resulting dimensionless conversion factor from long term average annual loads is
14.7, with the daily load equivalent of 0.040 (14.7/365).
In evaluating different strategies for achieving the target annual and daily loads for CJC,
we considered both a model-based approach (using our TR-55 adjusted Phase 6 CBWM
inputs) and a flow-based empirical approach. The model-based approach assumes that
the mechanistic representation of sediment sources, delivery, and transport in the CBWM
is accurate. The flow-based empirical approach starts with the sediment loading target,
then translates that into a flow target, using the empirical relationship between suspended
sediment load and flow. Due to the lack of sufficient suspended sediment loading data
for CJC, we determined the peak flows associated with sediment loading targets based on
a sediment rating curve established for the Anacostia River sediment TMDL
(http://www.mde.maiyland.gov/programs/Water/TMBL/ApprovedFinalTMDl p/Docume
nts/www.mde.state.md.us/assets/document/AnacostiaSed_AppendixB_final.pdf). The
18

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rationale for application of the flow-based empirical approach stems from Maryland's
Biological Stressor Identification Analyses (BSIDs), which identify sediment/flow
related stressors to in-stream habitat for aquatic life. These sediment related impacts to
in-stream habitat are tied back to altered hydrologic regimes in Maryland's BSID reports.
Therefore, it is assumed that by decreasing peak flow rates, sediment loads will also
decrease, as will sediment impacts, to in-stream aquatic life.
Optimization Runs
Structural BMPs evaluated included Bioretention (SUSTAIN = Bioretention Basin),
Filtering Practices (SUSTAIN = Sand Filter), Infiltration Practices (SUSTAIN =
Infiltration Basin), Permeable Pavement (SUSTAIN = Porous Pavement w/UD), and Dry
Detention Ponds (SUSTAIN = Extended Dry Detention Basin) (Appendix D. Screenshot
D.9). We did not model wet pond creation because, given the siting criteria, there was
virtually no suitable area left to implement additional wet ponds. Instead, we tested dry
pond to wet pond conversions as a wet pond implementation using costs restricted to
conversions only. Potential alternative BMPs tested included streambank
stabilization/restoration (Appendix D. Screenshot D. 10) and outfall enhancements
(Appendix D. Screenshot. Nonstructural BMPs tested included street sweeping and tree
canopy over turf (Appendix D. Screenshot D. 11). The structural BMPs were tested with
and without riparian buffer restoration (Table 4).
Table 4. Best management practices evaluated for Cabin John Creek watershed.
Structural BMPs
Alternative BMPs
Nonstructural BMPs
Bioretention
Streambank
Stabilization/Restoration
Street Sweeping
Filtering Practices
Outfall Enhancement
Tree Canopy over Turf
Infiltration Practices
Riparian Buffer Restoration

Permeable Pavement


Dry Detention Ponds


Dry Pond to Wet Pond
Conversion


We based initial optimization runs on 2014 weather conditions. The year 2014 had
average precipitation levels overall with one large event. Estimated 2014 loads in the
absence of post-2014 BMPs were used to establish target load reductions. We conducted
an additional set of optimization runs using 2003 weather inputs. 2003 was a relatively
wet year, although the 2003 maximum daily load actually was less than the 2014
maximum daily load due to the one large event in 2014. We included one set of water
quality parameter constraints (TN or TP or TSS) per run, with both annual and maximum
daily load targets specified. For TSS, we included the following constraints:
21% reduction of baseline annual load target plus 21% reduction of baseline maximum
daily load (Method la) or
19

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21% reduction of baseline annual load target plus maximum daily load target equal to
0.04 * annual load target (Method lb)
The adjustment factor of 0.04 was derived by MDE as part of the CJC sediment TMDL
and includes corrections for conversion between annual and daily time scales as well as
adjustments for temporal variability (based on the coefficient of variation in a daily load
time series) (MDE 2011).
We also ran optimizations using peak flow targets to control sediment loads. Method 2a
involved finding representative daily discharge values corresponding to the 1-year 24-
hour storm and establishing that as a peak flow target. The MDE stormwater manual
establishes BMP designs to treat the 1-year 24-hour event. Method 2b involved
estimating pre-development peak flow as a target by running WMOST with all HRUs
converted to a forested state. Method 2c involved finding the peak flows associated with
sediment loading targets based on a sediment rating curve established for the Anacostia
River sediment TMDL
("http://www.mde.marvland.gOY/programsAVater/TMDL/ApprovedFinalTMDLs/Docume
nts/www.mde.state.md.us/assets/document/AnacostiaSed \npendt\H tmal.pdfl.
20

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Results
Calibration
Flow
Results for the final 2006 WMOST hydrology calibration run are shown in Figure 8. The
groundwater recession coefficient was adjusted to a value of 0.03. Calibration results
were satisfactory, with a Nash Sutcliffe Efficiency (NSE) coefficient of 0.57 (satisfactory
range 0.50 < NSE < 0.65) (Moriasi et al. 2007), r2 value of 0.59, and median bias of-1.9
(as compared to average measured flow of 34 cfs; Table 5). However, the adjusted
CBWM results significantly underestimated runoff for the one very large event
(189 mm precipitation) at the end of June.
Table 5. Fit statistics for WMOST calibration (2006) and validation (2014). Calibration
was based on comparison to flow record from USGS gaging station 01650500 on the
nearby highly urbanized Anacostia River, after correction for differences in watershed
area.
Model run
Average
Measured
Flow
NSE Value
R2 Value
Relative
Percent
Error
Average
Bias
Median Bias
Calibration
34.0
0.57
0.59
50.1
-0.6
-1.9
Validation
44.4
0.63
0.79
17.7
-5.4
-12.3
Measured & Modeled Flows
3' 1	Return to Intro
2,500.
2'oco '






lrX)
500.
Note: Baieflow moy be higher thon r nodetedm Ureom flow, to stream flow receivej boieflowbut ofeo hos wrthdfawois; therefore, fhuji flow in the
Hfeom may be lowet thon baseficw.
Figure 8. Results of WMOST 2006 model calibration for stream discharge (cfs).
Measured Flow
iti-stream flow
		S.vv-' flow
21

-------
Results of the model validation for baseline 2014 conditions are shown in Figure 9 and
Table 5. The NSE is at the top end of the range of "satisfactory" NSE values based on
Moriasi's criteria (Moriasi et al. 2007), and near the lower end of the "good" range.
Percent bias is also in the satisfactory range. As in 2006, modelled flows underestimated
the largest peak flow in late April, with percent error approximately 3x the error
associated with the peak June flow in 2006.
2014
3,500.
3,000.
2,500.
2,000.
U
3
II 1,500.
1,000.
500.
0.











.1. 1J J.l.

i Measured
Flow
In-stream
flow


Figure 9. Results of WMOST validation procedure for modelled discharge for 2014 (pre-
optimization).
TN
Results of the 2006 calibration run for TN are shown in Figures 10 and 11. Only twelve
observations were available for comparison, and unfortunately, all were collected during
baseflow conditions (Figure 10). The r2 value was 0.61 with a percent bias of only -6.4%
(considered very good), but the NSE was 0.25 (greater than zero so acceptable but less
than satisfactory according to Moriasi et al. 2007). A comparison of modeled versus
measured estimates of instantaneous TN load showed a negative bias for modeled loads
at low values (intercept of -24 kg N/day) but a slope close to 1 (Figure 11). Observed
loads were calculated as the product of instantaneous total nitrogen concentrations
sampled near the outlet of Cabin John Creek (CBJ0005; Figure 11) and flow
measurements from the USGS gaging station 01650500 in the nearby urbanized
Anacostia watershed adjusted for differences in watershed area.
22

-------

Figure 10. Results of 2006 calibration for total N, comparing WMOST measured
(circles) and modeled values.
2006
1800
y = 1.0773x-24.037
Rz = 0.9878 -X"
1600
1400
1200
1000
800
600
400
200
200
400
600
800
1000
1200
1400
1600
1800
-200
Measured Total Nitrogen (kg N/day)
Figure 11. Comparison of measured vs modeled instantaneous total N load from
WMOST for 2006 calibration period.
23

-------
For the 2014 TN validation, however, the NSE was poor (-1.3) and the r2 value was fair
(0.62), with poor fits particularly for fall months (Figure 12).
2014
6 i
5
4
 3
2
1
0
l/0i */Ot S/Oj 6/3o 8^9 l/28
Figure 12. Comparison of WMOST validation: measured (circles) versus modeled
concentration of TN in 2014.
TP
Results of the 2006 calibration run for TP are shown in Figure 13. Only twelve
observations were available for comparison, and unfortunately, all were collected during
baseflow conditions. Results of the model validation for baseline 2014 conditions are
shown in Figure 14. Modeled values are generally greater than measured in spring
through early summer but less than measured values in late summer through fall. Again,
measured values were generally only available for baseflow conditions.
24

-------
2006
0.70
0.60
0.50
OA 0.40
re 0.30

0.20
0.10
0.00
till
	 
l/l	2/2q Vli S/3i ?/20 9/8	10/28 12/1?
Figure 13. Calibration for TP in 2006, measured (circles) versus modeled TP.
2014

0.45

0.4

0.35
	1
0.3
CL*

E
0.25
CL

CO
0.2
M


0.15

0.1

0.05

0.
ULJ
I
H
uiMUULJI	|
V

~i	1	r
Date
Figure 14. Validation of TP measured (circles) versus modeled concentrations in 2014.
25

-------
TSS
Results of the 2006 calibration run for TSS are shown in Figure 15. Only six
observations were available for comparison, and unfortunately, all were collected during
baseflow conditions. Measured values were inadequate to calculate meaningful
calibration statistics.
2006
? 0,6

ULl
l/Ql 2/2 o	S/3j /2o SO# /?s
Figure 15. Time series of measured and modeled concentrations of total suspended
sediment (g/L) for the calibration year, 2006.
Optimization Runs
Overall Rankings of Management Actions to Achieve Annual and Maximum Daily
Load Targets
Table 6 provides the least-cost management options to achieve each of the parameter
goals by year : 2014 baseline (average annual precipitation but one large event) and 2003
as a representative wet year (higher than average annual precipitation but with events
more evenly distributed). For TN, the most cost-effective solution to achieve a 5%
reduction in annual load and (2003 or 2014) maximum daily load included riparian buffer
restoration for both 2014 (average/variable event) and 2003 (wet/even event) years. For
TP, the most cost-effective solutions to achieve a 4% reduction in annual load and (2003
or 2014) maximum daily load also included riparian buffer. The least-cost solutions
chosen to achieve a 21% reduction in annual and (2003 or 2014) maximum daily TSS
loads included implementation of infiltration basins, dry pond to wet pond conversions
and sand filters for both 2003 and 2014 weather regimes. When the MDE approach was
used to derive a maximum daily load target that considers daily variation in
26

-------
Table 6. Least-cost optimization selection of BMP management options for Cabin John Creek to meet maximum daily load and annual
load targets for total N, total P, or total suspended solids under 2014 climate (average precipitation year with one large event) or 2003
(above average annual precipitation year). Riparian implementation area is area of riparian buffer restored. Implementation area for
stormwater control measures (BMPs) represents area treated. First set of suspended sediment targets is based on 21% reduction of
actual maximum daily load for baseline year, while second set of suspended sediment targets includes factor accounting for temporal
variability of modelled maximum daily loads across years (MDE 2011). Colors correspond to BMP types.
All Management
Options
Considered
Max Daily
Load Target
(lb/day)
Max Annual
Load Target
(lb/year)
Target
Year
Selected
Options
Max
Daily
Load
(lb/day)
Annual Load
(lb/year)
Daily
Reduction
(%)
Annual
Reduction
(%)
Implemen-
tation
(Acres)
Sub-Cost
(Millions $)
Total Cost
Sum
(Millions $)
Peak
daily
flow
(cfs)
Peak
daily
flow
(mgd)
None


N
2003
Baseline
11398
298694
--
--
--
--
--
1472
792
7 structural BMPs
(Bioretention, sand
filter, Infiltration
Basin, Porous
Pavement, Dry
Detention Basin, Dry
pond to Wet pond
conversion) +
riparian buffers
10830
277471
N
2003
riparian buffers
10798
281668
5.26%
5.70%
74

0.514
940
506
None


N
2014
Baseline
18260
146456
--
--
--
--
--
1386
746
riparian buffers
17347
139133
N
2014
riparian buffers
16936
138869
7.25%
5.18%
114

0.914
1309
705
None


P
2003
Baseline
1012
17198
--
--
--
--
--
969
522
riparian buffers
972
16510
P
2003
riparian buffers
958
16326
5.30%
5.07%
24

0.166
948
510
None


P
2014
Baseline
1178
10333
--
--
--
--

1387
746
riparian buffers
1130
9920
P
2014
riparian buffers
1121
9890
4.84%
4.29%
24

0.166
1327
714
None


TSS
2003
Baseline
779657
12886780
--
--
--
--

1472
792
7 structural BMPs
(Bioretention, sand
filter, Infiltration
Basin, Porous
Pavement, Dry
Detention Basin, Dry
pond to Wet pond
conversion)
615929
10180556
TSS
2003
2.75 in
Infiltration Basin +
Dry Pond to Wet
Pond Conversion +
2.75" Sand Filter
615928
10173479
21.00%
21.05%
1893 + 982
+ 92
18.42 +
3.76 + 1.14
13.32
1271
684
27

-------
Table 6. (Continued)
All Management
Options
Considered
Max Daily
Load
Target
(lb/day)
Max Annual
Load Target
(lb/year)
Target
Year
Selected
Options
Max
Daily
Load
(lb/day)
Annual Load
(lb/year)
Daily
Reduction
(%)
Annual
Reduction
(%)
Implemen-
tation
(Acres)
Sub-Cost
(Millions $)
Total Cost
Sum
(Millions $)
Peak
daily
flow
(cfs)
Peak
daily
flow
(mgd)
None


TSS
2014
Baseline
662371
9305934
--
--
--
--

2059
1108
7 structural BMPs
(Bioretention, sand
filter, Infiltration
Basin, Porous
Pavement, Dry
Detention Basin, Dry
pond to Wet pond
conversion)
523273
7351688
TSS
2014
2.75 in Infiltration
Basin + Dry Pond to
Wet Pond
Conversion + 2.75"
Sand Filter
523272
7318198
21.00%
21.36%
1924 + 991
+ 81
18.79 + 3.76 +
1.00
23.55
1789
963
None


TSS
2003
Baseline
779657
12886780
--
--
--
--

1472
792
7 structural BMPs
(Bioretention, sand
filter, Infiltration
Basin, Porous
Pavement, Dry
Detention Basin, Dry
pond to Wet pond
conversion)
407,222.24
10,180,555.97
TSS
2003
2.75 in Infiltration
Basin + Dry Pond to
Wet Pond
Conversion + 2.75"
Sand Filti + >.75"
Bioretention Basin +
Riparian Buffer
Restoration
407222
6799471
47.77%
47.24%
1924 + 991
+ 1893 + 65
+ 393
18.79 + 3.76 +
24.6 + .7 +
1.37
50.31
1076
579
None


TSS
2014
Baseline
662371
9305934
--
--
--
--

2059
1108
7 structural BMPs
(Bioretention, sand
filter, Infiltration
Basin, Porous
Pavement, Dry
Detention Basin, Dry
pond to Wet pond
conversion)
294,067.51
7,351,687.80
TSS
2014
2.75 in Infiltration
Basin + Dry Pond to
Wet Pond
Conversion + 2.75"
Sand Filti + >.75"
Bioretention Basin +
Riparian Buffer
Restoration
294067
4095558
55.60%
55.99%
1924 + 991
+ 2463 + 817
+ 393
18.79 + 3.76 +
44.6 + 2.22
+ 1.37
90.78
1410
759
28

-------
TSS load, additional BMPs and level of implementation were required - a combination of
infiltration basins, dry pond to wet pond conversions, sand filters, bioretention basins,
and riparian buffer restoration.
Management Actions Required to Achieve Flow Targets Consistent with 21% Sediment
TMDL Target
Achieving the flow target associated with the 21% reduction in MDE-estimated
maximum daily loads requires the greatest level of BMP implementation. A flow target
based on estimates of pre-development 1 yr 24-hour events (11 - 13 cfs) was not
achievable with any level of BMP implementation. Peak flow targets based on
application of sediment rating curves (499 cfs in 2014, 535 cfs in 2003) were also not
achievable. The minimum achievable targets based on incremental scenarios of steadily
decreasing peak flow targets were 700 cfs for the 2014 weather regime and 525 cfs for
the 2003 weather regime. These flow targets required implementation of a combination
of sand filters, infiltration basins, and porous pavement for both 2003 and 2014 weather
regimes (Figure 16). However, peak flows for least-cost solutions for 21% reduction of
TSS annual loads described above were similar to the minimum peak flows possible
(Table 6).
The CBWM is designed to model edge of stream loads but may not fully account for
effects of peak flows on suspended sediment and bedload derived from stream bank
erosion. The latter are captured by sediment rating curves, i.e., the relationship between
discharge and suspended sediment load. Analyses performed by MDE for the Anacostia
River sediment TMDLs using sediment rating curves suggested that 75% of increased
sediment loads over the last 65 years were due to increases in peak flow events.
Although WMOST identified riparian buffer restoration as least-cost solutions to achieve
load reductions for TN and TP, these solutions only achieved 2-6% reductions in
maximum daily flows.
TN and TP Scenarios with TSS Optimal Management
The effects of optimal management solutions to meet 21% reductions in annual and
maximum daily TSS loads on TN and TP loads were evaluated by fixing these
management solutions as scenarios and running WMOST for TN and TP but without
targets. Optimal management for sediment (including infiltration basins, dry to wet pond
conversions, and sand filters) would achieve annual load reductions for TN of 12.5 -
12.6%) (Figure 17) and annual load reductions for TP of 15.5 - 15.6% (Figure 18). Even
greater reductions are predicted for maximum daily loads, of 17.4 - 17.9% for TN and
16.6 - 19.9%) for TP (Table 7). Peak daily flows would be reduced by 13 - 13.6%
(Figure 16).
29

-------
Table 7. Effect of applying optimal strategies for reducing TSS loads by 21% on achievement of daily maximum and annual loading
goals for total N and total P.
Max
All	Max Daily Annual
Management Load Load
Options	Target Target
Considered (lb/day) (lb/year) Target Year Model Run
Total Cost	Peak	Peak
Daily Annual Imple- Total Cost Sum	daily	daily
Max Daily Annual Reduction Reduction mentation (Millions (Millions	flow	flow
Load (lbs) Load (lbs) (%) (%) (Acres) $) $)	(cfs)	(mgd)



2.75 in






Simulate


Infiltration Basin






optimal
solution for
10830 277471 N
2003
+ Dry Pond to
Wet Pond
9417 261200
17.38%
12.55%
2.976.57
23.64
452.00
TSS control


Conversion +
2.75" Sand Filter









2.75 in






Simulate


Infiltration Basin






optimal
solution for
P
2003
+ Dry Pond to
Wet Pond
811 14539
19.88%
15.46%
2.976.57
23.64
452.00
TSS control


Conversion +
2.75" Sand Filter






Simulate









optimal
solution for
17346.62 139133.5 N
2014

14994.14 128177.5
17.88%
12.48%
2996.847
23.87769
652.4349
TSS control









Simulate









optimal
solution for
P
2014

982.4265 8720.247
16.57%
15.61%
2996.847
23.87769
652.4349
TSS control









30

-------
1200
a) 2014
1000
? 800
0)
s? 600
05
-C
u
	Baseline 	21%TSS load reduction 	700 max daily cfs
b) 2003
1200
1000
800


-------
on
c
T5
fO
O
20,000
13,000
16,000
14,000
12,000
10,000
8,000
6,000
4,000
2,000

* ~ ^ ^ ,aH
"^?0
	Baseline
a) 2014
jhA_
JLL	L
J a i i i iiuJL
S/3j -^O 5/	1(V?8
Optimized forTSS loading target
2 3/l?
>
(O
T3
tlD
C
T5
ro
O
20,000
18,000
16,000
14,000
12,000
10,000
8,000
6,000
4,000
2,000
*/l
b) 2003
jlJU-
Ji iULIlu L iii iiu
*#0 S/3j ?/2o 5/&
	Baseline 	Optimized forTSS loadingtarget
J2/j ?
Figure 17. Daily loads for total N (TN) under optimal management regime for meeting
21% sediment load reduction under a) 2014 weather conditions and b) 2003 conditions.
32

-------
>-
TO
o
or

(=0
c
"D
re
o
1,400
1,200
1,000
800
600
400
200
0
l/l
a) 2014
LJLUJl
JUlLlnJUL
7/2q 9/S */2s ^/ly
-Baseline 	Optimized for TSS loading target
>
re
-o
c"
.O
60
c
"D
re
o
1,400
1,200
1,000
800
600
400
200
0
1 1.L.L1
b) 2003
ft
UL
AMk
l/l 2/^q 4/jj S/jj ?/2q 9/g *0/28
 Baseline
Optimized for TSS loading target
Figure 18. Daily loads for total phosphorus (TP) under optimal management regime for
meeting 21% sediment load reduction under a) 2014 weather conditions and b) 2003
conditions.
33

-------
Comparison of Stormwater BMP and Riparian Implementation by HRU
Optimal solutions for 21% reductions in annual and maximum daily loads of TSS
required full implementation of infiltration basins on all suitable land and full conversion
of existing dry ponds to wet ponds across all permittee types, supplemented by an
implementation of sand filters to treat 81 acres, a small fraction of the total area that
could be treated by sand filters. Site constraints limited implementation based on soil
type (infiltration basins) and drainage area (infiltration basins and dry detention pond to
wet pond conversions), but not for sand filters (Table 3). Sand filters were preferentially
allocated to Montgomery County jurisdictions with Soil Hydrologic Groups Type A or B
(Table 8).
Table 8. Optimal treatment of HRU acres by stormwater control measures for 2014 vs
2003 input conditions to meet 21% TSS load reduction.
New Implementation New Implementation + Conversions




2.75 in
2.75 in
2.75 in
Dry Pond to


Base-line


Infiltration
Sand
Infiltration
Wet Pond
2.75 in
HRU1
Area (acres) EIA2
INFIL3
Basin
Filter
Basin
Conversion
Sand Filter
2014 inputs
Tg A/B MC
9,073
0.31
0.4
1441
937
1441
787
81
Tg C/D MC
1,465
0.31
0.05
6
0
6
122
0
Tg A/B Rv
1,544
0.37
0.4
369
0
369
64
0
Tg C/D Rv
372
0.37
0.05
5
0
5
18
0
Tg A/B SHA
445
0.68
0.4
31
0
31
0
0
Tg C/D SHA
130
0.68
0.05
0
0
0
0
0
Tg A/B Oth
333
0.23
0.4
70
0
70
0
0
Tg C/D Oth
122
0.23
0.05
2
0
2
0
0
2003 inputs
Tg A/B MC
9,073
0.31
0.4
1441
919
1441
787
92
Tg C/D MC
1,465
0.31
0.05
6
0
6
122
0
Tg A/B Rv
1,544
0.37
0.4
369
0
369
64
0
Tg C/D Rv
372
0.37
0.05
5
0
5
18
0
Tg A/B SHA
445
0.68
0.4
31
0
0
0
0
Tg C/D SHA
130
0.68
0.05
0
0
0
0
0
Tg A/B Oth
333
0.23
0.4
70
0
70
0
0
Tg C/D Oth
122
0.23
0.05
2
0
2
0
04
1	HRU = Hydrologic Response Unit, Tg = turfgrass + impervious, A+B = soil hydrologic groups A + B,
C + D = soil hydrologic groups C + D, MC = Montgomery County, Rv = Rockville,
SHA = State Highway Administration, Oth = Other regulated entity
2	EIA = effective impervious area
3	INF = infiltration capacity
34

-------
Under the more stringent requirements imposed by the MDE sediment TMDL targeting
approach (Method lb), greater implementation of sand filters was required for 2014
conditions with preference for jurisdictional implementation by Montgomery County >
City of Rockville > State Highway Administration > Other Regulated entities. Within
each jurisdiction, preference was given for implementation on Type A or B soils as
compared to Type C or D soils. Riparian buffer restoration recommendations were more
evenly split between Soil Hydrologic Groups A + B as compared to groups C + D (Table
9). Under the even more stringent requirements based on peak flow targets only,
preference was given to porous pavement implementation as a third option as compared
to conversion of dry ponds to wet ponds and addition of bioretention basins or riparian
restoration.
Table 9. Optimal treatment of HRU acres to meet 21% annual and maximum daily load
reductions for TSS, including MDE factor for temporal variability.

2.75 in
Dry Pond to

2.75 in
Riparian

Infiltration
Wet Pond
2.75 in
Bioretention
Buffer
HRU1
Basin
Conversion
Sand Filter
Basin
Restoration
2014 conditions
Tg A/B MC
1441
787
1603
675
133
Tg C/D MC
6
122
215
100
135
Tg A/B Rv
369
64
388
0
33
Tg C/D Rv
5
18
92
0
23
Tg A/B SHA
31
0
114
34
5
Tg C/D SHA
0
0
14
8
5
Tg A/B Oth
70
0
36
0
14
Tg C/D Oth
2
0
0
0
23
Natural-nonf
0
0
0
0
20
2003 conditions
Tg A/B MC
1441
787
1603
0
133
Tg C/D MC
6
122
215
65
135
Tg A/B Rv
369
64
0
0
33
Tg C/D Rv
5
18
0
0
23
Tg A/B SHA
31
0
0
0
5
Tg C/D SHA
0
0
0
0
5
Tg A/B Oth
70
0
75
0
14
Tg C/D Oth
2
0
0
0
23
Natural-nonf
0
0
0
0
20
1 HRU = Hydrologic Response Unit, Tg = turfgrass + impervious, A+B = soil hydrologic groups A + B,
C + D = soil hydrologic groups C + D, MC = Montgomery County, Rv = Rockville, SHA = State Highway
Administration, Oth = Other regulated entity
35

-------
Optimal solutions for TN or TP reductions alone differed from optimal solutions for TSS
reductions. If only new stormwater control measures were considered with no riparian
buffer restoration, infiltration basins alone would be the most cost-effective option to
meet TN or TP load reductions required for the Chesapeake Bay TMDL, implemented on
Montgomery County, Rockville, or Other Regulated jurisdictions for TN and on
Montgomery County and Other Regulated for TP. If retrofits are considered, then
conversion of existing extended detention dry ponds to wet ponds would be the least-cost
solution, with implementation in Montgomery County and Other Regulated jurisdictions
(Tables 10, 11). In general, placement of infiltration basins on Type A/B soils was
favored while retrofits to wet ponds were favored on Type C/D soils.
Table 10. Optimal treated acres of HRUs by BMPs for TN load reductions considering
1) all stormwater control measures (SCM), 2) all SCMs plus retrofits, or 3) riparian
buffer restoration.
New	Retrofit

Baseline


2.75 in
Dry Pond to


Area


Infiltration
Wet Pond
Riparian
HRU1
(acres)
EIA2
INFIL3
Basin
Conversion
buffer
2014 inputs
Tg A/B MC
9,073
0.31
0.4
33
0
10
Tg C/D MC
1,465
0.31
0.05
0
478
55
Tg A/B Rv
1,544
0.37
0.4
357
0
14
Tg C/D Rv
372
0.37
0.05
0
0
9
Tg A/B SHA
445
0.68
0.4
0
0
4
Tg C/D SHA
130
0.68
0.05
0
0
1
Tg A/B Oth
333
0.23
0.4
333
333
6
Tg C/D Oth
122
0.23
0.05
122
74
9
Natural-nonf
0
0
0
0
0
8
2003 inputs
Tg A/B MC
9,073
0.31
0.4
366
0
10
Tg C/D MC
1,465
0.31
0.05
0
294
55
Tg A/B Rv
1,544
0.37
0.4
0
0
6
Tg C/D Rv
372
0.37
0.05
0
0
3
Tg A/B SHA
445
0.68
0.4
0
0
0
Tg C/D SHA
130
0.68
0.05
0
0
0
Tg A/B Oth
333
0.23
0.4
333
333
0
Tg C/D Oth
122
0.23
0.05
0
122
0
Natural-nonf
0
0
0
0
0
0
1	HRU = Hydrologic Response Unit, Tg = turfgrass + impervious, A+B = soil hydrologic groups A + B, C
+ D = soil hydrologic groups C + D, MC = Montgomery County, Rv = Rockville, SHA = State Highway
Administration, Oth = Other regulated entity
2	EIA = effective impervious area
3	INF = infiltration capacity
36

-------
Table 11. Optimal treated acres of HRUs by BMPs for TP load reductions considering
1) all stormwater control measures (SCM), 2) all SCMs plus retrofits, or 3) riparian
buffer restoration.




New
Retrofit


Baseline


2.75 in
Dry Pond to


Area


Infiltration
Wet Pond
Riparian
HRU1
(acres)
EIA2
INFIL3
Basin
Conversion
buffer
2014 inputs
Tg A/B MC
9,073
0.31
0.4
377
0
10
Tg C/D MC
1,465
0.31
0.05
0
403
14
Tg A/B Rv
1,544
0.37
0.4
0
0
0
Tg C/D Rv
372
0.37
0.05
0
0
0
Tg A/B SHA
445
0.68
0.4
0
0
0
Tg C/D SHA
130
0.68
0.05
0
0
0
Tg A/B Oth
333
0.23
0.4
333
333
0
Tg C/D Oth
122
0.23
0.05
0
0
0
Natural-nonf
0
0
0
0
0
0
2003 inputs
Tg A/B MC
9,073
0.31
0.4
384
0
10
Tg C/D MC
1,465
0.31
0.05
0
298
14
Tg A/B Rv
1,544
0.37
0.4
0
0
0
Tg C/D Rv
372
0.37
0.05
0
0
0
Tg A/B SHA
445
0.68
0.4
0
0
0
Tg C/D SHA
130
0.68
0.05
0
0
0
Tg A/B Oth
333
0.23
0.4
333
333
0
Tg C/D Oth
122
0.23
0.05
0
122
0
Natural-nonf
0
0
0
0
0
0
1HRU = Hydrologic Response Unit, Tg = turfgrass + impervious, A+B = soil hydrologic groups A + B,
C + D = soil hydrologic groups C + D, MC = Montgomery County, Rv = Rockville,
SHA = State Highway Administration, Oth = Other regulated entity
2 EIA = effective impervious area
3INFIL = infiltration capacity
37

-------
Alternative and Nonstructural BMPs
Many of the alternative and nonstructural BMPs proved to be infeasible to meet
maximum daily and/or annual loads. Neither street sweeping, nor streambank
stabilization yielded feasible solutions for TN or TP in 2003 or 2014. Tree canopy
implementation could meet goals but at a much higher cost as compared to either
stormwater control measures or riparian restoration. Neither tree canopy, street
sweeping, nor streambank stabilization could meet TSS load reduction targets for 2003 or
2014 conditions (Table 12).
Distribution of Proposed Riparian Zone Restoration Among Relative Load Groups
Required riparian buffer restoration to meet TN or TP load reductions did not differ
between 2014 and 2003 conditions. Greater restoration would be required to meet TN
load reductions, split mainly between relative load groups 1 and 2, as compared to
restoration to meet TP load reductions, from relative load group 1 on Montgomery
County jurisdiction only (Table 13). Riparian buffer removal efficiencies are twice as
great for TP as for TN (Appendix G).
38

-------
Table 12. Least-cost optimization selection of alternative and nonstructural BMP management options for Cabin John Creek to meet
maximum daily load and annual load targets for total N, total P, or total suspended solids under 2014 climate (average precipitation
year with one large event) or 2003 (above average annual precipitation year). STSW = street sweeping (lOOx/yr), TC = tree canopy,
STST = streambank stabilization. Inf = infeasible.
Para-
meter
Options
Max Daily
Load
Target
(lb/day)
Max Annual
Load Target
(lb/year)
Target
Year
Max Daily
Load
(lb/day)
Annual
Load
(lb/year)
Daily
Reduction
(%)
Annual
Reduction
(%)
Implemen
-tation
(Acres)
Total Cost
(Millions
$)
reaK.
Daily
flow
(cfs)
TN
None


N
2003
11398
298694




807
TN
STSW
10830
277471
N
2003





Inf

TN
TC
10830
277471
N
2003
8939
249751
21.58%
16.39%
7180
175
807
TN
STST
10830
277471
N
2003





Inf

TN
None


N
2014
18260
146456




1154
TN
STSW
17347
139133
N
2014
-
-
-
-
-
Inf

TN
TC
17347
139133
N
2014
14299
125385
21.69%
14.39%
7180
175
1154
TN
STST
17347
139133
N
2014





Inf

TP
None


P
2003
1012
17198




807
TP
STSW
972
16510
P
2003





Inf

TP
TC
972
16510
P
2003
784
14284
22.50%
16.95%
7180
175
807
TP
STST
972
16510
P
2003





Inf

TP
None


N
2014
1178
10333




1154
TP
STSW
1130
9920
N
2014





Inf

TP
TC
1130
9920
N
2014
933
8526
20.74%
17.49%
7180
175
1155
TP
STST
1130
9920
N
2014





Inf

TSS
None


TSS
2003
779657
12886780




807
TSS
STSW
615,929
10,180,556
TSS
2003





Inf

TSS
TC
615,929
10,180,556
TSS
2003





Inf

TSS
STST
615,929
10,180,556
TSS
2003





Inf

TSS
None


TSS
2014
662371
9305934




1154
TSS
STSW
523,273
7,351,688
TSS
2014





Inf

TSS
TC
523,273
7,351,688
TSS
2014





Inf

TSS
STST
523,273
7,351,688
TSS
2014





Inf

39

-------
Table 13. Required conversion of HRUs to forested riparian buffer by relative load
group for TN or TP load reductions to meet Chesapeake Bay TMDL targets at least-cost.
Relative load group
1	2	3
HRU
conversions
TN
TP
TN
TP
TN
TP
Tg A/B MC
10.41
9.55
0
0
0.02
0
Tg C/D MC
13.07
14.39
41.54
0
0.05
0
Tg A/B Rv
6.26
0
7.10
0
0.20
0
Tg C/D Rv
2.75
0
6.32
0
0
0
Tg A/B SHA
0.96
0
1.02
0
0
0
Tg C/D SHA
0.76
0
0.00
0
0
0
Tg A/B Oth
0.58
0
5.15
0
0.02
0
Tg C/D Oth
0.72
0
8.00
0
0.47
0
Natural-nonf
0.75
0
6.88
0
0.61
0
40

-------
Discussion
Model Fits
In this application of WMOST, we used HRU-specific unit hydrology (runoff, recharge)
and pollutant loads from the Phase 6 CBWM. Time series were subsequently modified
using TR-55 to partition hydrology and loads between HRUs on soil hydrologic groups
A+B and groups C+D. Specifically, contributions from soil groups C+D were estimated
using TR-55 and subtracted from contributions from all soil groups to estimate
contributions from soil groups A+B to ensure that total loads per original HRU remained
the same. The CBWM was calibrated to USGS Weighted Regressions on Time,
Discharge and Season (WRTDS) loads by the Chesapeake Bay Program Office using
observations from watersheds ranging in area from 7.4 mi2 to 27,086 mi2
(ftp://ftp.chesapeakebaY.net/Modeline/Phase6/Phasi 1710/Watershed Model/WSM
Outputs/WRTDS Com.parison./). Model fits for the CBWM overall were excellent (TN
NSE = 0.998, TP NSE = 0.997, TSS NSE = 0.987). Estimated loads per unit area were
also very good (TN NSE = 0.955, TSS NSE = 0.965, TP NSE = 0.966). A comparison of
modelled vs estimated WRTDS loads for the NW Branch of the Anacostia River showed
modeled values that were 98.6 - 98.9% of estimated loads. However, model fits for
concentration were highly variable and worse than load comparisons for the NW Branch
of the Anacostia (r2 = 0.14 for TN concentrations; r2 = 0.72 for TN loads1; r2 = 0.55 for
TP concentrations2, r2 = 0.72 for TP loads; r2 = 0.36 for TSS concentrations, r2 = 0.55 for
TSS loads3). Given the goal of optimizing the CBWM for the entire basin, with a greater
emphasis on load estimation, it is not surprising that modeled versus measured
concentrations in Cabin John Creek showed some discrepancies.
For the current analysis, we chose not to attempt further calibration of WMOST to better
match modelled with measured concentrations because the goal of the current exercise
was meeting load reduction targets for Bay TMDLs, not non-tidal water quality targets
for criteria. In addition, most observed concentrations for TN, TP, and TSS for the
calibration year of 2006 were from base flow periods and did not provide a wide range of
concentrations or loads needed for a good calibration. Thus, we used modelled loads
based on Phase 6 CBWM time series, and only adjusted CJC TSS time series in WMOST
to match estimated loads from the non-tidal Cabin John Creek sediment TMDL. This
would have corrected for failure of the CBWM to fully account for inputs from
streambank erosion. (Note that continued improvements have been made to the CBWM
between the beta 3 version we used and the final version; however, improvements to the
1ftp://ftp.chesapeakebav.net/Modeling/Phase6/Phase 6 20.1.7.1.0/Watershed Model/WSM Outputs/Calibrat
ion Figures/04 TOTN IMAGES 15NOV20t I " J KO "H Pt'Ol TOTN WINDOW CONC 15N
OV20.1.7 1746.PS.Ddf
2ftp://ftp.chesapeakebav.net/Modeling/Phase6/Phase 6 20.1.7.1.0/Watershed Model/WSM Outputs/Calibrat
ion Figures/05 TOTP IMAGES 15NOV2Q17 2350/PI	000.1. TOTP WINDOW CO	[O
V20.1.7 0005.PS.pdf
3ftp://flp.chesapeakebav.net/Modeling/Phase6/Phase 6 20.1.7.1.0/Watershed Model/WSM Outputs/Calibrat
ion Figures/06 TSSX IMAGES 16NOV2'	9/PLO 45.1.0 000.1. TSSX WINDOW CONC 16NO
V20.1
41

-------
final Phase 6 model to fully account for all sediment sources may still be needed (Easton
et al. 2017).) We did conduct a parallel set of analyses after applying adjustment factors
of 1.5 to TN and 0.8 to TP to improve modeled vs observed concentrations. In general,
results were qualitatively similar, although greater levels of BMP implementation were
required to meet TN load targets, including both dry pond retrofits and the creation of
new infiltration basins.
Relative Cost of BMPs Per Unit Load Reduction
Costs per unit volume or pollutant load reduction were calculated for one representative
HRU (HRU1 = Montgomery County Turfgrass + Impervious on A+B soils) using BMP
costs per impervious acre adjusted for percent impervious area and differences between
annual baseline and WMOST calculated BMP-managed loads (Table 14). Although TN,
TP, and TSS load reductions calculated by WMOST via SUSTAIN were highest for sand
filters, infiltration basins and porous pavement, lower costs associated with dry to wet
pond conversions yielded greater cost-efficiencies for this practice. The secondary BMP
selected to meet 21% TSS load reductions, infiltration basins, was slightly less cost-
effective than sand filters when evaluated on an annual basis, suggesting that infiltration
basins were more effective in reducing maximum daily loads. However, sand filters were
added to the most cost-effective suite of practices to meet peak flow reduction
requirements, consistent with their greater cost effectiveness for annual runoff volume
reductions. Porous pavement was the next most cost-effective option for reducing annual
peak flows and was also added to the list of practices required for meeting maximum
peak flow reductions. Extended dry detention basins were ineffective (with greatest
cost/unit reduction) at reducing either annual runoff volume or pollutant loads and were
never selected as part of optimization strategies.
Resolution of Differences Among Solutions by Parameter
Optimum load reduction strategies were significantly different for non-tidal TSS TMDL
targets as compared to TN and TP targets for the Bay TMDL. TSS solutions were more
stringent than Bay TMDL target solutions, not because BMPs are less effective for TSS
(Table 14) but because required percentage load reductions for TSS are much higher. If a
flow-based strategy is followed, even greater implementation is required to account for
sediment inputs from stream bank erosion. However, implementation of solutions for
TSS based directly on loads or based on maximum possible peak flow reductions would
be more than sufficient for meeting the Bay load reduction targets for TN and TP.
Robustness of Solutions Across Weather Regimes
Optimizations of TSS load reductions (both load and flow-based) were more sensitive to
differences in inputs between the average/more variable precipitation year of 2014 and
the wet/less variable precipitation year of 2003 than optimizations of TN or TP load
reductions. This can be attributed to the fact that modeling of riparian buffer
effectiveness in WMOST includes application of a constant percent reduction efficiency
42

-------
Table 14. Estimated load or volume reductions from HRU1 (Montgomery County turfgrass + impervious on A+B soils) and cost per
unit reduction, with capital costs annualized over 10-year period at 5% interest rate. Lowest unit cost/year and pollutant is highlighted
in boldface. Mg = million gallons, lb = pounds, mt = metric ton.



2014


2003


BMP
Runoff Vol
TN
TP
TSS
Runoff Vol
TN
TP
TSS
Unit:
mg
lb
lb
mt
mgd
lb
lb
mt

6%
57%
69%
93%
4%
58%
68%
89%
2.75" Bioretention








Basin
$8,746
$1,920
$17,419
$12
$9,248
$937
$11,066
$9

98%
99%
98%
99%
97%
99%
98%
98%
2.75" Sand Filter








w/UD
$210
$401
$4,466
$4
$127
$198
$2,770
$3

4%
46%
59%
89%
2%
45%
56%
84%
2.75" Biofiltration








w/UD
$25,217
$3,825
$33,130
$20
$28,572
$1,928
$21,624
$15

99%
100%
100%
100%
100%
100%
100%
100%
2.75" Infiltration








Basin
$211
$404
$4,482
$4
$126
$200
$2,772
$3

100%
100%
100%
100%
100%
100%
100%
100%
2.75" Porous








Pavement w/UD
$1,432
$2,761
$30,615
$27
$859
$1,367
$18,949
$19

-1%
0%
0%
2%
-1%
0%
0%
2%
2.75" Extended Dry








Detention Basin

$218,910
$2,989,872
$15,985,423

$113,678
$2,051,471
$305

6%
68%
68%
89%
3%
70%
67%
85%
2.75" Wet Pond








Conversion
$897
$162
$1,792
$1
$977
$78
$1,118
$1
43

-------
regardless of precipitation regime. Most differences in solutions suggested greater
implementation would be required to meet goals under 2014 inputs due to constraints
imposed by the one large event.
Ancillary Benefits to Flow Regime
Optimization for TSS load reductions has ancillary benefits for low flow and peak flow
conditions. Figure 19 shows the WMOST estimate of "baseflow", the transfer between
groundwater and surface water (DQgwsw) for baseline conditions, 21% TSS reductions,
peak flow reductions, and 100% forested conditions (pre-development). The 21% TSS
reduction solution increases baseflow somewhat (18-23%) during the May - September
growing season over the baseline conditions for both 2014 and 2003 starting conditions.
The flow-optimized solutions increase baseflow much more (456 - 517%) during the
growing season, and to a lesser extent during fall and winter months. Effects are more
prolonged for the year with above average annual precipitation. Based on 2014 starting
conditions, predicted increases actually surpass expected base flows from a simulated
100%) forested watershed during fall and winter months.
Transferability of CJC Results to Other MD Watersheds
The CJC watershed is highly developed with little agriculture, complete sewering,
negligible point sources, and no water supply issues. Remaining forest land tends to be
concentrated along riparian zones of the main stem of the creek. For similar watersheds
in the Piedmont region of Maryland that also have non-tidal sediment TMDLs, we expect
the proposed solutions based on WMOST analyses for the CJC to be qualitatively similar.
Highly urbanized Maryland watersheds with sediment TMDLs, minimal agriculture,
similar percent effective impervious areas, and no WWTP point sources include Bynum
Run, Gwynns Falls, and Potomac River. Other similar watersheds but with one or more
WWTP point sources include Jones Falls, Rock Creek, and Anacostia River
(Appendix H, Figure 20). Watersheds with significant point sources could also have
issues with effluent-dominated low flows; these could be mitigated with solutions that
increase infiltration and maintain base flows to dilute effluents. However, watersheds
with high residual dissolved N concentrations in groundwater from historic agricultural
practices may also require implementation of BMPs that treat baseflow and promote
denitrification to a greater extent (Hopkins et al. 2017). Watersheds with less intact
riparian buffers than the CJC may require greater riparian buffer restoration, particularly
if stream temperatures are elevated.
Sources of Uncertainty, Quality and Use of Data
There are several sources of uncertainty related to the analyses presented here, including
deficiencies in available calibration and validation data specific to the CJC watershed,
uncertainties in urban soil attributes, transferability of BMP performance parameters
from New England to Maryland, adjustment of CBWM inputs for hydrologic soil group,
performance of riparian zones in urban settings, applicability of O&M costs in SUSTAIN
44

-------

c
a;
c
o
CL
E
o
u
5
_o
H-
OJ
V)
CO
CO
o
350
300
250
A


Baseline
Optimize TSS
Optimize Flow
100% Forest
350
 300
c
01
c
O
CL
E
o
g
150
a;
V)
fO
CO
o
250
200
100
50
nV,
M V\
ft
\
M
\f
|
J X
\

i
A
a
i\
\
u a|
IV*
i
K
2J/3/Oi 3/j/03 6/l8/03 9/2g/03 0l/4/04
Baseline
Optimize TSS
Optimize Flow
Figure 19. WMOST baseflow component (cfs) for baseline conditions, TSS load
reduction optimization, flow reduction optimization, and pre-development (100%
forested) conditions for a) 2014 and b) 2003 starting conditions.
45

-------
Legend
TSS TMDL similar to CJC but with WWTP
TSS TMDL most similar to CJC
Cabin John Creek
0
30
60
120 Kilometers
Other TSS TMDL watersheds
Maryland Counties
Figure 20. Maryland watersheds with Total Maximum Daily Loads for total suspended
solids (TSS) shaded to indicate similarity to Cabin John Creek watershed. See
"Transferability to other MD watersheds" for details.
to the CJC watershed, contribution of bank erosion in CJC to TSS loads, and robustness
of solutions under future climate change.
Infiltration rates for combined soil hydrologic groups A + B and C + D were assigned
based on the midrange of values reported in the CBWM Model documentation (US EPA
2010). However, actual infiltration rates for urban soil/fill material may be lower due to
compaction experienced during construction activity (Schwartz and Smith 2016); this
would provide overestimates of performance for infiltration-based BMPs.
In this project, we used the default first order decay coefficients provided in WMOST to
describe biogeochemical processes taking place within stormwater control measures.
These coefficients had been calibrated using actual monitoring data from instrumented
stormwater BMPs collected by the University of New Hampshire Stormwater Center but
have not been adjusted for differences in temperature regime between New Hampshire
and Maryland. Adjustment of coefficients for temperature effects on denitrification
would provide increased estimates of BMP performance.
Estimates of riparian buffer performance used in this analysis were based on expert
judgement and values from the literature (summarized in Claggett and TetraTech, 2014)
and do not reflect potential differences in performance between undeveloped,
agricultural, and urban settings. In urban settings, storm sewer inputs can bypass riparian
zones, potentially limiting their effectiveness in reducing loadings (trapping particulates
and processing interflow) from urban runoff (Roy et al. 2005). However, riparian zones
have been demonstrated to reduce nutrient concentrations during baseflow conditions
even in suburban settings (Stewart et al. 2006), presumably through enhanced
denitrification of groundwater inputs enriched from septic, sewer leakages, and residual
46

-------
inputs from historic agricultural practices. In nontidal floodplains of the Bay watershed,
denitrification potential increases rapidly with percent watershed urbanization up to 10%
urban land cover (Korol et al. 2019). The efficiency of denitrification in urban/suburban
settings can be reduced by stream downcutting and disconnection from the floodplain
(Groffman et al 2003). For floodplains that remain connected with streams in developed
watersheds, the stabilizing influence of intact riparian vegetation should still have some
effect on bank erosion and sediment trapping during flooding events and should also
enhance denitrification by reducing carbon-limitation of denitrification in floodplains
(Korol et al. 2019). Sediment budgets constructed for suburban watersheds in the
Piedmont have demonstrated that riparian floodplains are still capturing particulate N, P,
and sediment in suburban watersheds in the Piedmont (Hopkins et al. 2018) but the net
flux (upland + bank sources - bank erosion) varies with stream order and constituent.
Riparian zones are still acting as net sinks for particulate N and P deposition, but bank
erosion is exceeding floodplain deposition of fine sediments. Bank erosion is greatest
from headwater streams (Hopkins et al. 2018).
Operation and maintenance costs can vary significantly among BMP types,
municipalities, regions and local settings (e.g., existing development density,
accessibility) (ASCE 2017). Although we used BMP construction and design costs
specific to Montgomery County, the City of Rockville, and MD State Highway
Administration, these local agencies could not provide operation and maintenance
(O&M) costs so we relied on default values from SUSTAIN. Table 15 provides a
comparison of BMP O&M costs summarized for the state of Maryland with SUSTAIN
estimates (King and Hagan 2011). Estimates of O&M costs for Maryland counties
adjusted for Montgomery County (factor = 0.985) and updated to $2016 using the
consumer price index (factor of 1.07) are up to an order of magnitude lower than default
estimates used by SUSTAIN. However, variation in O&M costs across BMPs is much
lower than variation in design and construction costs, so it is unlikely that solutions
would differ substantially if generic MD O&M costs were substituted.
Calibration and validation of the WMOST application to measured hydrology and water
quality relied heavily on gauging station data from the nearby Anacostia River, a nearby
highly urbanized tributary to the Potomac, after adjustment for watershed area.
Calibration and validation fit statistics for discharge were satisfactory. Calibration and
validation fit statistics for concentrations of TN and TP were based on limited sampling
data in CJC, most of which was collected as grab samples during low flow conditions and
did not represent the full range of discharge regimes. For the current project, the ability
to simulate loadings to meet TMDL loading targets is more critical than the ability to
simulate concentrations for water quality criteria. The CBWM, which provided the basis
for baseline time series imported to WMOST, has been calibrated with water quality data
over a wide range of watershed areas and discharge levels. Overall, the Phase 6 CBWM
shows excellent predictions of TN, TP, and TSS loads. The limited instantaneous TN
loading data for CJC in 2006 showed a good relationship with WMOST modelled data,
with an r2 of 0.99 (driven by one large value), a low bias at low values (negative
intercept) and a slope close to 1. The fit for TSS was not as high (r2 = 0.55), possibly due
to challenges in incorporating inputs from stream bank erosion. (Improvements to the
47

-------
Table 15. Comparison of stormwater BMP O&M costs estimated by WMOST (based on
SUSTAIN defaults) as compared to generic MD county-level O&M costs per unit acre of
HRU1 (30.87% impervious) based on King and Hagan (2011).

WMOST O&M cost
MD O&M cost
BMP
($2016/acre HRU1)
($2016/acre HRU1)
2.75" Bioretention Basin
$4,765
$506
2.75" Sand Filter w/UD
$5,529
$539
2.75" Infiltration Basin
$1,923
$299
2.75" Porous Pavement w/UD
$1,640
$1,011
2.75" Extended Dry Detention Basin
$2,096
$407
2.75" Wet Pond conversion
$2,096
$252
Phase 6 CBWM have substantially increased the estimate of bank erosion inputs as
compared to the prior Phase 5 version
(https://www.chesapeakebav.net/what/piiblications/phase 6 modeling tools). In this
case, we chose to adjust the time series input by a factor to ensure that total annual loads
matched the edge-of-stream estimate from the non-tidal CJC TMDL. In addition, we
provided an alternative approach to estimating necessary load reductions based directly
on a sediment-discharge rating curve which should capture bank erosion inputs.
Sediment fingerprinting studies in suburban and urban watersheds partially or fully in the
Piedmont have shown that the bulk of fine sediments in watershed loads are derived from
bank erosion (58 - 91%) as compared to road surfaces (8 - 13%) (Devereux et al. 2010,
Cashman et al. 2018).
Comparison with Similar Optimization Studies
Due to the limited number of BMP optimization studies, range of climate regimes and
landscape settings, endpoints and combinations of BMPs analyzed, and constraints
included in studies, it is difficult to compare our current analyses with previous
evaluations. Seventeen studies were identified that had examined effectiveness of
different combinations of stormwater control practices including green infrastructure
practices in settings ranging from arid to subtropical climates (Appendix I). Only one
compared cost-effectiveness of structural vs nonstructural practices (Shoemaker et al.
2013). Most of these studies focused on some form of flow metrics, but a few included
water quality endpoints (E coli, phosphorus, or heavy metals). Within analyses focusing
on flow metrics, some found more cost-effective solutions for green infrastructure when
lower flow reduction targets were implemented, while addition of detention ponds was
necessary for peak flow reduction for design storms of greater magnitude (Damodoram et
al. 2010). Within assessments focusing on water quality, optimal solutions varied
depending on whether event mean concentration or loading targets were evaluated (Baek
et al. 2015). Some of these studies only included scenario comparisons rather than full
optimization analyses. However, other evaluations went beyond the scope of our
analyses, considering optimal size of BMPs, use of site-focused BMPs (rain gardens, rain
barrels) versus regional BMPs (detention, bioretention facilities) versus treatment trains,
or upstream vs. downstream placement of BMPs in the watershed. None included
48

-------
comparisons involving both a wide range of stormwater control measures (including
infiltration basins) and riparian zones.
Future Improvements
We have only started to address robustness of solutions by comparing BMP performance
between years with average and above average precipitation. Initial results suggest that
optimization can be influenced more heavily by single large events than by the annual
precipitation level. The MDE approach of developing TMDL targets for maximum daily
load does take into account temporal variability in daily loads. In the near future we will
improve analyses for CJC by further investigating robustness of results under future
climate change scenarios.
Conclusions
Optimal solutions differed among constituents considered, the degree of load reductions
required, and the relative importance of instream vs watershed sources. Meeting
sediment load reduction targets will require greater implementation of green
infrastructure stormwater control measures, particularly those with enhanced infiltration.
Modelling results show limited effectiveness for gray infrastructure (extended detention
basins) in meeting water quality goals.
Although our analysis suggested different least-cost solutions for TN and TP as compared
to TSS load reductions, optimal management for the non-tidal sediment TMDL should
surpass target reductions for TN and TP for the Bay TMDL. An additional focus on peak
flow reduction through infiltration practices would help to limit stream bank erosion, a
major contributor to sediment loads in Piedmont streams, and would greatly enhance
baseflow. Optimal solutions were relatively robust based on comparisons between 2014
(average precipitation year with one large event) and 2003 (above average precipitation
but smaller maximum event). The robustness of solutions was more sensitive to variation
in peak flows than to differences in annual precipitation levels
Unresolved uncertainties include the predicted effectiveness of forested riparian zones in
highly urbanized settings in reducing nutrient loads, variation in riparian processing of
soluble forms of nutrients in developed landscapes by stream order and flow status
(baseflow vs events), and importance of groundwater contributions. More information is
needed on the contribution of intact forested riparian zones to bank stabilization and
carbon limitation of denitrification processes, and potential interactive effects of riparian
zone restoration and upland BMPs.
49

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Appendix A. Full-size Maps of HRU Component Distributions


0	0.5 1	2 Kilometers
1	I I i I i i I I
Legend
Soil Hydrologic Group
A + B
C + D
Impervious
Water
56

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Legend
Aggregate Land Use
Impervious
Natural
Natural-forested
Tree Canopy over Turfgrass
Turfgrass
Water
2 Kilometers
57

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Legend
Stormwater Regulation
Montgomery County
| Other
| Rockville
I State Highway Administration
t
N
A
0	0.5 1	2 Kilometers
1	i i i I i i i I
58

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Legend
Final HRU
Impervious
MCo Tg AB
MCo Tg CD
Natural
Natural forested
Oth Tg AB
Oth Tg CD
Rk Tg AB
Rk Tg CD
SHATg AB
SHATg CD
Water
0	0.75 1.5	3 Kilometers
1	i i i I i i i I
N
A
59

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60

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Appendix B: Summaray of data sources for WMOST runs
WMOSTv3.01 Input Location
Input data
Baseline Hydrology Module (1A)
Baseline Hydrology Module (1A)
Baseline Hydrology Module (1A)
Baseline Hydrology Module (1A)
Baseline Hydrology Module (1A)
Baseline Hydrology Module (1A)
Unit runoff time series
Unit recharge time series
Unit runoff loading time series
Unit recharge loading time series
Precipitation time series
Potential evapotranspiration time series
Land Use tab (IB)
Land Use tab (IB)
Land Use tab (IB)
Stormwater Hydrology
Stormwater Hydrology
Stormwater Hydrology
Stormwater Hydrology
Stormwater Hydrology
Stormwater Hydrology
Stormwater Hydrology
Module (2A)
Module (2A)
Module (2A)
Module (2A)
Module (2A)
Module (2A)
Module (2A)
Land Use tab (2B)
Land Use tab (2B)
Land Use tab (2B)
Water Quality BMPs tab (2C)
Water Quality BMPs tab (2C)
Water Quality BMPs tab (2C)
Water Quality BMPs tab (2C)
Baseline area of HRUs
Percent effective impervious
Infiltration rate
Hourly time series
BMP design depths
First order decay rates
Managed unit runoff time series
Managed unit recharge time series
Managed unit runoff loading time series
Managed unit recharge loading time series
Maximum HRU areas treatable by each BMP
Stormater BMP initial costs
Stormater BMP O&M costs
Streambank Stabilization removal rates
Streambank Stabilization costs
Outfall enhancement removal rates
Outfall enhancement costs
Data source
Details
Post-processing using TR-55 to distribute runoff and recharge for
urban HRUs between units with soil hydrologic groups A+B versus
CBWM v6 beta 3 model output	C+D and to combine pervious and impervious HRU values
Post-processing using TR-55 to distribute runoff and recharge for
urban HRUs between units with soil hydrologic groups A+B versus
CBWM v6 beta 3 model output	C+D and to combine pervious and impervious HRU values
Post-processing using TR-55 to distribute runoff and recharge for
urban HRUs between units with soil hydrologic groups A+B versus
CBWM v6 beta 3 model output	C+D and to combine pervious and impervious HRU values
Post-processing using TR-55 to distribute runoff and recharge for
urban HRUs between units with soil hydrologic groups A+B versus
CBWM v6 beta 3 model output	C+D and to combine pervious and impervious HRU values
CBWM v6 beta 3 model output
CBWM v6 beta 3 model output
Chesapeake Conservancy high-
resolution land-cover dataset, SSURGO
soils, and MDE NPDES Regulated SW
Delineation for Phase 6 Chesapeake
Bay model
Chesapeake Conservancy high-
resolution land-cover dataset
Median of range by soil hydrologic
group (SWMM)
CBWM v6 beta 3 model output
MDE stormwater manual
WMOST defaults
Generated by Stormwater Module
Generated by Stormwater Module
Generated by Stormwater Module
Generated by Stormwater Module
Reduced to account for existing BMP implementation (MDE NIEN
data submission to CBP) and adjusted for site constraints based on
Areas of existing HRUs (see above) MD Stormwater Manual using BMP Siting tool
Compiled from Montgomery County, City of Rockville, and MD State
Highway Administration, including annual reports and ancillary data
MDE	provided by the juriscitions
WMOST defaults	Derived from SUSTAIN
MDE	CAST dataset
Compiled from Montgomery County, City of Rockville, and MD State
Highway Administration, including annual reports and ancillary data
MDE	provided by the juriscitions
MDE	CAST dataset
Compiled from Montgomery County, City of Rockville, and MD State
Highway Administration, including annual reports and ancillary data
MDE	provided by the juriscitions

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Water Quality BMPs tab (2C)
Street sweeping removal rates
Water Quality BMPs tab (2C)
Water Quality BMPs tab (2C)
Street sweeping costs
Tree canopy removal rates
Water Quality BMPs tab (2C)
Water Quality BMPs tab (2C)
Tree canopy costs
Urban nutrient management removal rates
Water Quality BMPs tab (2C)
Water Quality BMPs tab (2C)
Water Quality BMPs tab (2C)
Water Quality BMPs tab (2C)
Urban nutrient management costs
Maximum linear stream length
Maximum number of outfalls to be treated
Application area (street sweeping)
Water Quality BMPs tab (2C)
Water Quality BMPs tab (2C)
Application area (tree canopy over turf)
Application area (urban nutrient management)
Riparian Buffers (2D)
Riparian area by relative load group
Riparian Buffers (2D)
Riparian Buffers (2D)
Riparian Buffers (2D)
Initial cost to restore riparian area
O&M cost to restore riparian area
Riparian loss rates by HRU
Riparian Buffers (2D)
Water Use and Demand Management (3)
Water Use and Demand Management (3)
Water Use and Demand Management (3)
Water Supply Sources and Infrastructure (4): Surfacewater
Water Supply Sources and Infrastructure (4): Surfacewater
Water Supply Sources and Infrastructure (4): Surfacewater
Water Supply Sources and Infrastructure (4): Surfacewater
Water Supply Sources and Infrastructure (4): Surfacewater
Water Supply Sources and Infrastructure (4): Groundwater
Water Supply Sources and Infrastructure (4): Groundwater
Water Supply Sources and Infrastructure (4): Groundwater
Upland Area by relative load group
Potable demand
Nonpotable demand
Septic and Sewer Systems
Other Surfacewater Withdrawals
Other Surfacewater Discharges
Private TN/TP Point Source Loading time series
Target maximum daily load
Target annual load
Other groundwater withdrawals
Other groundwater discharges
Groundwater concentration
Measured Data
Measured Data
Flow
TN, TP, TSS concentrations
MDE
MDE
MDE
MDE
MDE
MDE
NHDPIus High Resolution
MDE
US Census Bureau TIGER roads
CB high resolution land-use and
canopy cover data layer
Urban HRU areas
Calculated using CB Riparian Analysis
Tool and loading data by HRU
MDE
NRCS
MDE
Calculated using CB Riparian Analysis
Tool and loading data by HRU
Set to zero
Set to zero
Septic set to zero
MDE
MDE
MDE
MD TMDL for CJC
Stage II WIP
MDE
MDE
Literature
Ambient Water Quality Monitoring
System database
Ambient Water Quality Monitoring
System database
CAST dataset
Compiled from Montgomery County, City of Rockville, and MD State
Highway Administration, including annual reports and ancillary data
provided by the juriscitions
CAST dataset
Compiled from Montgomery County, City of Rockville, and MD State
Highway Administration, including annual reports and ancillary data
provided by the juriscitions
CAST dataset
Compiled from Montgomery County, City of Rockville, and MD State
Highway Administration, including annual reports and ancillary data
provided by the juriscitions
Montgomery and MD SHA geodatabases
Chesapeake Conservancy high-resolution land-cover dataset
Compiled from Montgomery County, City of Rockville, and MD State
Highway Administration, including annual reports and ancillary data
provided by the juriscitions
CAST dataset
Met by interbasin transfers, balanced by wastewater exports
Met by interbasin transfers, balanced by wastewater exports
MDE Water Supply Program
MDE NPDES DB
MDE NPDES DB
MDE Water Supply Program
MDE Water Supply Program

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Appendix C. Phase 6 Land Use Classification
Introduction
There are 39 different land uses in the Phase 6 watershed model (Table C. 1) derived from
13 mapped land uses, the County-level Census of Agriculture, and overlays of Municipal
Separate Storm Sewer Systems and Combined Sewer Systems. These land uses are
further divided into federal and non-federal categories using an overlay of federal lands.
In addition, the CBP Partners will use an overlay of wastewater treatment plan service
areas to determine population on sewer and septic.
Table CI. Final Phase 6 Land Uses
Developed
Combined
Sewer System
Tree Canopy over Turf
Grass
cch
Tree Canopy over
Impervious
cci
Construction
ccn
Roads
cir
Buildings and Other
cnr
Turf Grass
ctg
Municipal
Separate Storm
Sewer Systems
Tree Canopy over Turf
Grass
mch
Tree Canopy over
Impervious
mci
Construction
men
Roads
mir
Buildings and Other
mnr
Turf Grass
mtg
Non-regulated
Developed
Areas
Tree Canopy over Turf
Grass
nch
Tree Canopy over
Impervious
nci
Roads
nir
Buildings and Other
nnr
Turf Grass
ntg
Natural

True Forest
for
Harvested Forest
hfr
Headwater/isolated
Wetland
wto
Non-tidal Floodplain
Wetland
wtf
Mixed Open
osp
Water
wat
61

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Agriculture
Commodity
Full Season Soybeans
soy

Crops
Grain without Manure
gom


Grain with Manure
gwm


Silage with Manure
swm


Silage without Manure
som


Small Grains and Grains
sgg


Small Grains and Soybeans
sgs


Other Agronomic Crops
oac

Hay and forage
Pasture
pas


Legume Hay
lhy


Other Hay
ohy

Specialty Crops
Specialty Crop High
sch


Specialty Crop Low
scl

Other
Ag Open Space
aop


Non-Permitted Feeding
Space
fnp


Permitted Feeding Space
fsp
Mapped Phase 6 Watershed Model Land Uses (with the exception of tidal wetlands)
1.	Impervious Roads (IR) = paved and unpaved roads and bridges.
2.	Impervious Non-Roads (INR) = buildings, driveways, sidewalks, parking lots,
runways, some private roads, railroads and rail right-of-ways, and barren lands
within industrial, transitional (early stages of construction), and warehousing land
uses.
3.	Forest (FOR) = trees farther than 30'-80' from non-road impervious surfaces and
forming contiguous patches greater than 1-acre in extent. The variable distance is
a result of filtering algorithms (e.g., focal moving windows) applied to the high-
resolution non-road impervious surface class.
4.	Tree Canopy over Impervious Surfaces (TCI) = trees over roads and non-road
impervious surfaces.
5.	Tree Canopy over Turf Grass (TCT) = trees within 30'-80' of non-road
impervious surfaces where the understory is assumed to be turf grass or otherwise
altered through compaction, removal of surface organic material, and/or
fertilization.
6.	Water (WAT) = all streams, ponds, swimming pools, canals, ditches, wet
detention basins, reservoirs, etc. mapped from the high-resolution imagery, NWI
ponds & lakes, and synthetic streams derived from a lOm-resolution National
62

-------
Elevation Dataset using at similar density to those mapped in the 1:24,000-scale
National Hydrography Dataset.
7.	Tidal Wetlands (WTT) = National Wetlands Inventory (NWI) non-pond, non-lake
wetlands, state designated wetlands, and state identified potential wetlands
divided into tidal, floodplain, and headwater subclasses. Tidal wetlands include
both saline wetlands (E2EM, ESFO, W2SS) and palustrine wetlands (PEM, PFO,
PSS) located in a freshwater tidal water regime that was flooded temporally,
seasonally, semi-permanently, or permanently. Tidal Wetlands will be included
in the water quality model and excluded from the watershed model.
8.	Floodplain Wetlands (WTF) = National Wetlands Inventory (NWI) non-pond,
non-lake wetlands, state designated wetlands, and state identified potential non-
tidal wetlands located within the FEMA designated 100-year floodplain or on
frequently flooded soils.
9.	Other Wetlands (WTO) = National Wetlands Inventory (NWI) non-pond, non-
lake wetlands, state designated wetlands, and state identified potential non-tidal,
non-floodplain wetlands. These are typically headwater wetlands or isolated
wetlands.
10.	Turf Grass (TG) = Herbaceous and barren lands that have been altered through
compaction, removal of organic material, and/or fertilization. These include all
herbaceous and barren lands within road right-of-ways and residential,
commercial, recreational, and other turf-dominated land uses (e.g., cemeteries,
shopping centers) and a portion of herbaceous and barren lands within federal
facilities, parks, institutional campuses, and large developed parcels.
11.	Mixed Open (MO) = All scrub-shrub and herbaceous and barren lands that have
been minimally disturbed (e.g., periodically bush hogged, meadows, etc.),
reclaimed, or that have internal and/or regulated drainage. These include active,
abandoned and reclaimed mines, landfills, beaches, waterbody margins, natural
grasslands, utility right-of-ways and a portion of herbaceous lands within
industrial, transitional (early stages of construction), and warehousing land uses.
Also included are potential agricultural lands that were not mapped as either
cropland or pasture in the NASS Cropland Data Layers (2008 through 2015).
12.	Cropland (CRP) = Herbaceous and barren lands that are not classed as turf grass
or mixed open. The portion of such lands that are crops is determined by the
frequency at which the lands are classified as crops in the NASS Cropland Data
Layers (2008 through 2015).
13.	Pasture/Hay (PAS) = Herbaceous and barren lands that are not classed as turf
grass or mixed open. The portion of such lands that are pasture/hay is determined
by the frequency at which the lands are classified as pasture/hay in the NASS
Cropland Data Layers (2008 through 2015).
63

-------
Overlays
1.	Federal facilities (FED) - all federally owned/managed properties
2.	Municipal Separate Storm Sewer Systems (MS4) - areas with Phase I or Phase II
stormwater permit. These areas typically drain into municipally owned/operated
storm sewer drainage networks within the 2010 Census Urban Areas.
3.	Combined Sewer Service Areas (CSS) - areas served by centralized combined
wastewater/stormwater treatment systems.
4.	Wastewater Treatment Plant Service Areas (SWR) - areas served by public or
private sewer utilities.
64

-------
Appendix D. Selected Screenshots from WMOST Calibration and Optimization Runs.
Screenshot D.l. Land Use tab in WMOST for CJC calibration run. HRU area is set to be
equal to baseline areas for land conservation options (min = max) and associated costs are
set to -9 to prevent WMOST from implementing any changes in HRU area to implement
conservation.
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Land Use arid Its Management
Naomi Detenbeck H 
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Land Usc| and Its Management

For management options that are not applicable or desired for an HRU, enter -9 for costs.




For Minimum and Maximum Areas, enter-9 if there is no limit on the area for a type of HRU.

Return to Input j Return to Baseline Hydrology J
Allocation of area among Managed HRU sets is mutually exclusive (i.e., one acre may only receive one management approach/BMP type).



O&M = Operations and maintenance







1 Data are automatically populated if you use the Hydrology Module.


Baseline HRU Characteristics




Data for Land Conservation Option




Percent
Infiltration



Initial Cost to




Baseline Area
Effective
Rate

Minimum Area
Maximum
Conserve
O&M Cost

HRU ID
HRU Name1
[acre]
Impervious1
[in/hr]1

[acre]
Area [acre]
[$/acre]
[$/acre/yr]


Turfgrass A/B Montgomery









HRU1B
County
8,580
30.9%
0.4

8580
8580
-9
-9


Turfgrass C/D Montgomery









HRU2B
County
1,385
30.9%
0.05

1385
1385
-9
-9


Turfgrass A/B City of









HRU3B
Rockville
1,460
36.8%
0.4

1460
1460
-9
-9


Turfgrass C/D City of









HRU4B
Rockville
352
36.8%
0.05

352
352
-9
-9


Turfgrass A/B MD State









HRU5B
Highway Administration
428
68.3%
0.4

428
428
-9
-9


Turfgrass C/D MD State









HRU6B
Highway Administration
125
68.3%
0.05

125
125
-9
-9


Turfgrass A/B Other









HRU7B
Regulated
209
22.7%
0.4

209
209
-9
-9


Turfgrass C/D Other









HRU8B
Regulated
61
22.7%
0.05

61
61
-9
-9

HRU9B
Natural
3,637
0.0%
0.12308

3637
3637
-9
-9

HRU10B
Water
35
0.0%
0

35
35
-9
-9



















1	1	J	1
0
Input J Land Use

= Hi
HI H
n 0
65

-------
Screenshot D.2. Potable demand tab in WMOST calibration mn. All drinking water
demand in the CJC watershed is met through interbasin transfers so the number of water
user classes was set to 1 on the Input Data Screen (not shown) and each element of the
unaccounted water (UAW) and user demand and consumption time series was set to zero.

a

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ai ~ ;
~ f.
Potable Demand


V I






Potable Demandl




A
Return to Input j
~
~


*

Enter data in blue input fields for available time period. Time series must be consecutive and complete, e.g., no skipped days.

For monthly time step, the day of the month does not matter.
Enter data in blue input fields for each water use for each month.

Date
Total Water Demand [million gallons /tin
Average Percent Consumptive Water Use (%)

(mm/dd/yyyy)
Unaccounted residential

Month
residential |

1/1/2006
0.00
0.00

January
0

1/2/2006
0.00
0.00

February
0

1/3/2006
0.00
0.00

March
0

1/4/2006
0.00
0.00

April
0

1/5/2006
0.00
0.00

May
0

1/6/2006
0.00
0.00

June
0

1/7/2006
0.00
0.00

July
0

1/8/2006
0.00
0.00

August
0

1/9/2006
0.00
0.00

September
0

1/10/2006
0.00
0.00

October
0

1/11/2006
0.00
0.00

November
0

1/12/2006
0.00
0.00

December
0

1/13/2006
0.00
0.00




1/14/2006
0.00
0.00




1/15/2006
0.00
0.00




1/16/2006
0.00
0.00

Enter average daily wastewater loadings of each constituent from each potable water user.

1/17/2006
0.00
0.00


Loadings (lbs/time step)

1/18/2006
0.00
0.00

Constituent
residential |

1/19/2006
0.00
0.00

TN


1/20/2006
0.00
0.00




1/21/2006
0.00
0.00




1/22/2006
0.00
0.00




1/23/2006
0.00
0.00




1/24/2006
0.00
0.00




1/25/2006
0.00
0.00



Ti

| Input Potable Demand 0

; eeh E
Ready




HUH	1	+ km*
66

-------
Screenshot D.3. Septic_Sewer tab in WMOST calibration run. Virtually all CJC
watershed is sewered so percentage public water users on septic was set to 0%.
0	?	WMOSTv3_12072017_caIibrate_TN_2006_adjustinput.xlsm - Excel Naomi Detenbeck S
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Enter the daily average effluent concentration
for septic treatment of each constituent-
Customers with Public Water & Septic Systems Recharjeonstituent Concentration (mg/L)
residential
Customers with Public Water & Septic Systems Rechar| Enter the daily average effluent concentration
for enhanced septic treatment of each constituent.
Constituent Concentration (mg/L)
TN	0
Note: Some user types discharge to the wastewater treatment
rows need to add to 100%. Each value is the percent septic use for a user type.
Enhanced Septic Treatment
Storm Sewer
Maximum capacity of storm sewer
A1
Septic System Users and Sewer Use
Septic Systerb Users and Sewer Use
Return to Input ]
O&M cost for enhanced septic capacity
$/MG

Maximum capacity of enhanced septic
MGD

To exclude limits on
-9|the storm sewer, enter -9.
Municipal flows that go directly to sewer system (%)
Month	Municipal
January
February
March
April
May
June
July
August
September
October
November
December
Sanitary Sewer
Maximum capacity of sanitary sewer
To exclude limits on the
_-9jsanitary sewer, enter -9.
Input
Potable Demand Septic_Sewer

Ready
no eq
67

-------
Screenshot D.4. Surface water tab in WMOST calibration run. Private withdrawals and
private discharge time series to surface water and groundwater were entered based 011
data obtained by MDE from withdrawal and discharge permits.
H	? WMOSTv3_12072017_calibrate_TN_200... Naomi Detenbeck S - ~ X
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B22
X ~ 0

















0

Other Sw Other Sw
External
Withdrawals Discharge to
Outflow from

~

Withdrawal Discharge
Sw Inflow from Reservoir Reservoir
Reservoir



Date (mm/dd/yyyy)
[MG/time step] [MG/time step] [cfs]
[MG/time step] [MG/time step] [MG/time step]


1/1/2006
ol
0
0
0

0
0.00


1/2/2006
0
0
0
0

0
0.00


1/3/2006
0
0
0
0

0
0.00


1/4/2006
0
0
0
0

0
0.00


1/5/2006
0
0
0
0

0
0.00


1/6/2006
0
0
0
0

0
0.00


1/7/2006
0
0
0
0

0
0.00


1/8/2006
0
0
0
0

0
0.00


1/9/2006
0
0
0
0

0
0.00










3/20/2006
0.218483871
0
0
0

0
0.00


3/21/2006
0.218483871
0
0
0

0
0.00


3/22/2006
0.218483871
0
0
0

0
0.00


3/23/2006
0.218483871
0
0
0

0
0.00


3/24/2006
0.218483871
0
0
0

0
0.00


3/25/2006
0.218483871
0
0
0

0
0.00


3/26/2006
0.218483871
0
0
0

0
0.00


3/27/2006
0.218483871
0
0
0

0
0.00


3/28/2006
0.218483871
0
0
0

0
0.00


3/29/2006
0.218483871
0
0
0

0
0.00


3/30/2006
0.218483871
0
0
0

0
0.00


3/31/2006
0.218483871
0
0
0

0
0.00


4/1/2006
0.152866667
0
0
0

0
0.00


4/2/2006
0.152866667
0
0
0

0
0.00


4/3/2006
0.152866667
0
0
0

0
0.00


4/4/2006
0.152866667
0
0
0

0
0.00


4/5/2006
0.152866667
0
0
0

0
0.00


4/6/2006
0.152866667
0
0
0

0
0.00

-
Input Potable Demand
Septic_Sewer
Surface Water
1 
: M
~
ra
| Ready



ffl H
H 
	1
+
1GD%
68

-------
Screenshot D.5. Groundwater tab in WMOST calibration run. Infiltration and inflow to
the sewer was set at 2.18 million gallons per day (MGD; City of Rockville Master Plan).

H - WMOSTv3_12072017_calibrate_TN_2006_a
-------
Screenshot D.6. Infrastructure tab for WMOST calibration run. Infrastructure
modification options were set to -9 with associated costs set to zero.

H - WMOSTv3_12072017_calibrate_TN_2006_a
i
n




Exclude New/Additiogal Capacity


Groundwater (Gw) Pumping
-9| HCF = hundred cubic feet

Capital cost for additional capacity
$/MGD
MGD = million gallons per day

Operation & Maintenance (O&M) costs
$/MG
MG = million gallons

Existing maximum capacity
0.00
MGD
O&M = operations and maintenant

Lifetime remaining on existing infrastructure
0
yrs
UAW = Unaccounted for Water

Lifetime of new construction
0
yrs




Exclude New/Additional


Surface Water (Sw) Pumping
-9I


Capital cost for additional capacity
$/MGD



O&M costs
$/MG



Existing maximum capacity
0.00
MGD



Lifetime remaining on existing infrastructure
0
yrs



Lifetime of new construction
0
yrs

Water Treatment P


Exclude New/Additional
Maximum daily infl

Water Treatment Plant (WTP)
s\ 4. 1
Average daily efflui

Capital cost for additional capacity
$/MGD



O&M costs
$/MG



Existing maximum capacity
0.00
MGD



Lifetime remaining on existing infrastructure
0
yrs

Upgraded Water Tr

Lifetime of new construction
0
yrs

Capital cost for add



Lifetime of new cor

Unaccounted-for-Water/ Potable water distribution system leak



Initial cost for survey & repair
0
$

Upgraded Water Tr

O&M costs for maintaining reduction in UAW
0
$/yr

Average daily efflui

Maximum percent UAW that can be fixed
%









u Input CSO Potable Demand Septic_Sewer
Surface Water Groundwater
Infrastructure | (+) 1 < I
E
Ready


H M ED -
	1	+ 100%
70

-------
Screenshot D.7. Riparian buffer BMP module for WMOST optimization ran. Riparian
pixels in potential restoration areas were ranked by receiving load and then categorized
into low, medium, and high relative load groups based on order-of-magnitude
differences.

0 *5- *
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P15	" |	fx Riparian Buffer Implementation Mode


B
C
D
E
F
G
H
,
t
M
N
P













2

| Return to Input
| | Return to
Stormwater Hydrology Module J
Riparian Buffer BMPs





4


Follow the step-by-step directions to apply riparian buffer land area conversions. Based on your selections, the model may convert the riparian
area land u
e to meet any specified targets.
5

1. Riparian Buffer Management: Convert riparian buffer land use to reduce or increas
riparian area and upland area
oadings.






6

1A. Riparian Buffer Management Conversion Sets









7






1. Select the HRU types that ar
available for land use convers
on in From HRU Name and To HRU Name.
8

Enter the number of land use
conversions:


The From HRU ID and To HRU ID will automatically populate from the baseline land use
table.

9


(max 10)
9
Setup Riparian Buffer
2. Enter the following data int
the tables below:



10




Tables

1) riparian
area available for land use co
nversion (acres) based on the relative loads the land area rece
11

Enter the number of relative loads groups:


2) the capital costs of converting the area
(S/acre),



12


{max 5}
3


3) the O&M
costs of converting the area (S/acre/yr),



13






4) the load adjustment efficiency that is applied to upgradient areas only, and


14






5) the upland areas associated with each
riparian area relative loads
.roup in Table IB.









Riparian
Riparian



Riparian








Riparian Area,
Area, Relative
Area, Relative



Buffer



Land Use




Relative Loads
Loads Group 2
Loads Group 3
Initial Cost to
O&M Cost
TN Load Adj
Implementati

15

Conversion No.
From HRU ID
To HRU ID
From HRU Name
To HRU Name
Group 1 [acre]
[acre]
[acre]
Convert [5/acre]
[S/acre/yr]
Efficiency [%]
on Mode

16

RBM1LC1
1
10
Turfgrass A/B Montgomery
Natural
25.73
107.38
0.04
20
0.22
-25
Optimization

17

RBM1LC2
2
10
Turfgrass C/D Montgomery
Natural
32.30
102.64
0.12
20
0.22
-25
Optimization

18

RBM1LC3
3
10
Turfgrass A/B CityofRockvi
Natural
15.^6
1734
0.50
20
0.22
-25
Optimization

19

RBM1LC4
4
10
Turfgrass C/D CityofRockvi
Natural
6.79
15.63
0.65
20
0.22
-25
Optimization

20

R8M1LC5
5
10
Turfgrass A/B MD State High
Natural
2.37
252
0.00
20
0.22
-25
Optimization

21

RBM1LC6
6
10
Turfgrass C/D MD State High
Natural
1.88
3.52
0.00
20
0.22
-25
Optimization

22

RBM1LC7
7
10
Turfgrass A/B Other Regulat
Natural
1.43
12.72
0.06
20
3.599
-25
Optimization

23

RBM1LC8
8
10
Turfgrass C/D Other Regulat
Natural
1.78
19.77
1.17
20
3.599
-25
Optimization

24

RBM1LC9
9
10
Natural-nonforested
Natural
1.86
17.01
131
20
0.22
-25
Optimization

?a

Note: a negative load adjustment
aiue represents a reduction in loadings, while a positive load adjustment value represents an increas
in loadings.




27













28

IB. Riparian Buffer Management Upland Land Us









29




















Upland Area,
Upland Area,
Upland Area,











Relative Loads
Relative
Relative











Group 1
Loads Group 2
Loads Group 3




30

HRU ID
HRU Name



[acres]
[acres]
[acres]




31

RBM1HRU1
Turfgrass A/B Montgomery County
991.18
1,990.48
1.28




32

RBM1HRU2
Turfgrass C/D Montgomery County
107.77
300.05
0.07




33

RBM1HRU3
Turfgrass A/B City of Rockville
328.55
123.37
0.82




34

RBM1HRU4
Turfgrass C/D City of Rockville
51.78
39.30
0.14




35

RBM1HRU5
Turfgrass A/B MD State Highway Administration
28.08
39.10
0.00




36

RBM1HRU6
Turfgrass C/D MD State Highway Administration
9.64
8.23
0.00




37

RBM1HRU7
Turfgrass A/B Other Regulated
31.78
76.86
6.27




38

RBM1HRU8
Turfgrass C/D Other Regulated
8.38
22.04
1.05




39

RBM1HRU9
Natural-nonforested
44.46
129.03
3.69




40

RBM1HRU10
Natural







41

RBM1HRU11
Water







81













Input | Riparian Buffers | ($)	|T| 	1 B
Ready	1 ' 1 0	|h	+ 85%
71

-------
Screenshot D.8. Land use tab from WMOST optimization run. We subtracted areas
treated by existing BMPs in 2014 from total suitable HRU areas to obtain maximum
J LRU area available for treatment.
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A1 *" V fx Land Use arid Its Management


V








A
B
C
D
E
	 r 	>
G , H E
60
First Set of Managed Land Uses and Their Limits  Q
2.75" Bioretention Basin
jjnput name of management practice





*
Initial Cost
" 





Minimum
Maximum
to Conserve



61
HRU ID
HRU Name
Area [acre]
Area [acre]
[$/acre]
O&M Cost [$/acre/yr]




Turfgrass A/B Montgomery






62
HRU1M1
County
0
9039.852282
168.8
4.764613537




Turfgrass C/D Montgomery






63
HRU2M1
County
0
1459.363887
168.8
4.764613537




Turfgrass A/B City of






64
HRU3M1
Rockville
0
1543.954752
382.319279
5.684277173




Turfgrass C/D City of






65
HRU4M1
Rockville
0
371.7947004
382.319279
5.684277173




Turfgrass A/B MD State






66
HRU5M1
Highway Administration
0
444.7499534
211.195216
10.54770447




Turfgrass C/D MD State






67
HRU6M1
Highway Administration
0
129.9628793
211.195216
10.54770447




Turfgrass A/B Other






68
HRU7M1
Regulated
0
332.7820299
260.489834
3.500094808




Turfgrass C/D Other






69
HRU8M1
Regulated
0
121.8529445
260.489834
3.500094808


70
HRU9M1
Natural
0
0
-9
-9


71
HRU10M1
Water
0
0
-9
-9


112







113
Second Set of Managed Land Uses and Their Limits


2.75" Sand Filter w/UD
< Input name of management practice






Initial Cost






Minimum
Maximum
to Conserve



114
HRU ID
HRU Name
Area [acre]
Area [acre]
[$/acre]
O&M Cost [$/acre/yr]




Turfgrass A/B Montgomery






115
HRU1M2
County
0
8894.416254
52.5
5.528924118




Turfgrass C/D Montgomery






116
HRU2M2
County
0
1436.741212
52.5
5.528924118




Turfgrass A/B City of






117
HRU3M2
Rockville
0
1517.889666
207.737802
6.59611465




Turfgrass C/D City of






118
HRU4M2
Rockville
0
364.5897929
207.737802
6.59611465

~


| Input Land Use
J 



i M If M
Ready




m
ni h	1	+ io
72

-------
Screenshot D.9. Stormwater Hydrology and Loadings Module from WMOST
optimization run. Structural BMPs tested included Bioretention (SUSTAIN =
Bioretention Basin), Filtering Practices (SUSTAIN = Sand Filter), Infiltration Practices
(SUSTAIN = Infiltration Basin), Permeable Pavement (SUSTAIN = Porous Pavement
w/UD), and Dry Detention Ponds (SUSTAIN = Extended Dry Detention Basin).

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| Return to Input
Stormwater Hydrology and Loadings Module
Follow the step-by-step directions to process baseline hydrology and loadings data and obtain stormwater managed hydrology and loadii
	Based on your selections, the model will populate input fields on the appropriate tabs.	
To use this Stormwater Module, you MUST enter baseline hydrology and loadings data first. You may do so manually or via the Hydrology Module.
1. Did you use the Baseline Hydrology and Loadings Module or manually enter data?	| Entered Own Data
If you used the Baseline Hydrology and Loadings Module, use the Import Hourly Time Series button to
automatically populate hourly data.
Import Hourly Time Series
If you entered data manually, use the Hourly Time Series t
and provide hourly data.
Man
2. Model time step
Did you run a daily or monthly model? [ Daily
3. Select the best management practices (BMPs) that you would like to model and enter the design depth for each BMP.
Indicate the decay rate of water quality constituents for each BMP. Enter 0 for no constituent decay. Use the Import Default Decay Rates button to use default values.
Seethe User's GMria for descriptions of the BMPs and guidance on sizing for different water management objectives and default BMP design values. Each BMP will be sized and run fo;
Agricultural BM^^^yjn exception and will be run for all undeveloped HRUs (EIA = 0%).
BMP Type
Bioretention Basin
Sand Filter w/UD
Biofiltration w/UD
Infiltration Basin
Porous Pavement w/UD
Extended Dry Detention Basin
Design Depth For
BMP [in]	
TN 1st Order
Decay Rate [1/hr]
Import Default
Decay Rates
For Sediment Basins only:
Area Managed by
Agriculture BMP [acres]
TN Settling Velocity
[in/s]


















4. Agricultural BMPs
If you want to model agricultural BMPs, use the button to the right to show the inputs cells for additional BMP parameter inputs.
5. Use the buttons below to perform the indicated processes. If you plan to use any agricultural BMPs, seethe additional inputs in Step 6 before creating the SUSTAIN input files.
Input j Stormwater Land Use 0
Ready
ID ED --
73

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Screenshot D. 10. Water Quality BMP module from WMOST optimization run. Potential
alternative BMPs tested included streambank stabilization/restoration.
Q	v	WMOSTv3.01_09282018_ofrtimize_TN_2014_noadj_7BMPstrbnkrestn_gwrpt03.xlsm - Excel	Naomi Detenbeck E9  ~ X
File Home Insert Page Layout Formulas Data Review View Developer Help Q Tell me what you want to do	[$ Share
Follow the step-by-step directions to apply water quality BMPs to the stream, reservoir, or land use. Based on your selections, the model will populate input fields on the appropriate
	tabs.	
ijjjjiIiihj ' "I!'1' "Hjii'imsn
Apply a loadings credit to the loadings target in the stream or reservoir.
ilting from a streambank project.	
	 0.000205479|lb/ft/time step
Enterthe cost per stream length treated and the maximum stream length that can be treated.
Cost per linear stream length	0.44710541 S/foot thous$
Maximum linear stream length
| In-Stream Loadings Target ~|
Specify whether the stabilization project applies to the in-stream or reservoir loading target.
from an outfall project.
Enterthe cost per outfall treated and the maximum number of outfalls that can be treated.
Enterthe application area, cost of application, and load adjustment for each constituent in the tables below.
If an HRU does not receive any treatment from the management practice, enter 0 for the load removal rate.
I. Runoff Loadings Direct Reduction Management Land Uses:
Ready
74

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Screenshot D. 11. Water Quality BMP module for WMOST optimization run.
Nonstructural BMPs tested included street sweeping and tree canopy over turf.
0	V	WM0STv3.01_09282018.optimize_TN_2014_noadj_7BMPtrcan_gwrpt03j(lsm - Excel	Naomi Detenbeck E  ~ X
File Home Insert Page Layout Formulas Data Review View Developer Help Q Tell me what you want to do	Share
A1	\ X ~ fit








Return to Input
Return to Storm water
Hydrology Module

Water Quality BMPs

Follow the step-by-step directions to apply water quality BMPs to the stream, reservoir, or land use. Based on your selections, the model will populate input fields on the appropriate





tabs.


1. Loadings Target Adjustment BMPs: Apply a loadings credit to the loadings target in the stream or reservoir.
*" ri	
i -*
...*=	





2. Runoff Loadings Direct Reduction BMPs: Apply direct runoff loadings reduction to an HRU area by implementing a direct reduction BMP.
2A. Runoff Loadings Direct Reduction Management Sets







You can model the following three management options:

Select the name of the direct reduction BMP(s)

1)	Street Sweeping
2)	Tree Canopy over Impervious/Turf Area
Select the number of direct reduction Setup Direct
iBgfNoT
Direct Reduction Management Name

3) Urban Nutrient Management

managed sets: I 1 I Heduaon Tables
ty*Mi
Tree Canopy over Impervious/Turf

DRM3

If an HRU does not receive any treatment from the management practice, enter 0 for the load removal rate.








2B. Runoff Loadings Direct Reduction Management Land Uses:












First Set of Direct Reduction Application Areas and their Costs


c
Tree Canopy over Impervious/Turf - Set 1 )







	'



Application
Initial Cost of
O&M Cost
TN Load Removal

HRU ID
HRU Name
Area [acre]
Application [$/acre]
[$/acre/yrl
Rate [%]

HRU1DRM1
Turfgrass A/B Montgomery County
4555.486135
23.01
1.38
23.80

HRU2DRM1
Turfgrass C/D Montgomery County
724.9813413
23.01
1.38
23.80

HRU3DRM1
Turfgrass A/B City of Rockville
980.8894522
23.01
1.38
18.16

HRU4DRM1
Turfgrass C/D City of Rockville
250.8852159
23.01
1.38
18.16


Turfgrass A/B MD State Highway





HRU5DRM1
Administration
338.4843239
23.01
1.38
13.34


Turfgrass C/D MD State Highway





HRU6DRM1
Administration
106.3402503
23.01
1.38
13.34

HRU7DRM1
Turfgrass A/B Other Regulated
158.3656952
23.01
1.38
20.33

HRU8DRM1
Turfgrass C/D Other Regulated
64.39972174
23.01
1.38
20.33

HRU9DRM1
Natural
0.00
0.00
0.00
0.00

HRU10DRM1
Water
0.00
0.00
0.00
0.00









WQ BMPs
W 0
Ready "
75

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Append!: Summary of costs of best management practices for structural and
nonstructural (alternative) applications by regulated entity in Montgomery County, MP.
Appendix E2. Raw data used in calculating summary of costs of best management
practices for structural and nonstructural (alternative) applications by regulated entity in
Montgomery County, MP.
Appendix F. 2014 HRU areas and history of implementation of existing stormwater
BMPs in Cabin John Creek
Appendr	timates of pollutant removal efficiencies for structural, alternative,
and nonstructural stormwater BMPs.
Appendix H. Maryland watersheds with Total Maximum Paily Loads for total
suspended solids and similarities to Cabin John Creek.
Appendix 1. Summary of urban stormwater BMP scenario and optimization studies.
Appendix J. Example WMOST v3.01 run set-ups
Calibration for TN (2006)
Calibration for TP (2006)
Optimization for TP (2003) including stormwater BMPs and riparian buffer
Optimization for TP (2014) including stormwater BMPs and riparian buffer
76

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Appendix El. Summary of costs of best management practices for structural and nonstructural (alternative) applic
Costs were compiled from Montgomery County, City of Rockville, and Maryland State Highway Administration

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Retrofit BMP
Bioretention
Bioretention
Bioretention
Bioretention
Bioswale
Bioswale
Bioswale
Bioswale
Dry Detention Ponds and Hydrodynamic Structures
Dry Detention Ponds and Hydrodynamic Structures
Dry Detention Ponds and Hydrodynamic Structures
Dry Detention Ponds and Hydrodynamic Structures
Dry Extended Detention Ponds
Dry Extended Detention Ponds
Dry Extended Detention Ponds
Dry Extended Detention Ponds
Filter Strip
Filter Strip
Filter Strip
Filter Strip
Filtering Practices
Filtering Practices
Filtering Practices
Filtering Practices
Infiltration Practices
Infiltration Practices
Infiltration Practices
Infiltration Practices
Permeable Pavement
Permeable Pavement
Permeable Pavement
Permeable Pavement
Stormwater to the Maximum
Stormwater to the Maximum
Stormwater to the Maximum
Stormwater to the Maximum
Vegetated Open Channels
Vegetated Open Channels
Vegetated Open Channels
Vegetated Open Channels
Wet Ponds and Wetlands
Wet Ponds and Wetlands
Wet Ponds and Wetlands
Wet Ponds and Wetlands
Drypond to Wetpond conversion
Drypond to Wetpond conversion
Soils
Permittee
Cost/Acre
All
City of Rockville
$382,319
All
Montgomery County
$168,800
All
SHA
$211,195
All
Other Regulated
$260,490
All
City of Rockville
N/A
All
Montgomery County
$202,076
All
SHA
$202,076
All
Other Regulated
$202,076
All
City of Rockville
N/A
All
Montgomery County
N/A
All
SHA
N/A
All
Other Regulated
N/A
All
City of Rockville
N/A
All
Montgomery County
N/A
All
SHA
N/A
All
Other Regulated
N/A
All
City of Rockville
N/A
All
Montgomery County
N/A
All
SHA
N/A
All
Other Regulated
N/A
All
City of Rockville
$207,738
All
Montgomery County
$52,500
All
SHA
$211,195
All
Other Regulated
$199,168
All
City of Rockville
N/A
All
Montgomery County
$59,850
All
SHA
$59,850
All
Other Regulated
$59,850
All
City of Rockville
$2,073,745
All
Montgomery County
$435,000
All
SHA
$1,891,662
All
Other Regulated
$1,891,662
All
City of Rockville
$707,730
All
Montgomery County
$557,500
All
SHA
$684,618
All
Other Regulated
$684,618
All
City of Rockville
N/A
All
Montgomery County
$202,076
All
SHA
$202,076
All
Other Regulated
$202,076
All
City of Rockville
$19,614
All
Montgomery County
$15,000
All
SHA
$18,955
All
Other Regulated
$18,955
All
City of Rockville
$22,875
All
Montgomery County
$12,000
Extent Practicable
Extent Practicable
Extent Practicable
Extent Practicable

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Drypond to Wetpond conversion	All	SHA	$81,318
Drypond to Wetpond conversion	All	Other Regulated	$44,077

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Notes
Average of City, County, and SHA costs
City does not have any bioswales or vegetated open channels
Current = SHA costs
Current = SHA estimate.
City says they have not implemented any filter strips
None
None
Average of City, County, and SHA costs
City does not have any infiltration practices
Current = county costs
Current = County costs.
None
None
Current = average of city/county costs
Average of city/county costs
None
None
Current = average of city/county costs
Average of city/county costs.
City does not have any bioswales or vegetated open channels
Current = SHA costs
Current = SHA estimate.
None
None
Current = city/county costs
Current = city/county costs.
None
None

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None
Average of City, County, and SHA costs

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:ations by regulated entity in Montgomery County, MD.

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Alternative BMP
Units
Cost/Acre
Urban Forest Buffers
Acres
$22,821
Urban Nutrient Management
Acres/Yr
N/A
Street Sweeping
Acres/Year
$658
Urban Stream Restoration
Linear Feet
$586.00
Outfall Stabilization
Outfalls
$436,322
Tree Canopy over Turf
Acres
$23,009
Tree Canopy over Impervious
Acres
$23,009
Impervious Reduction
Acres
$253,590
Reforestation
Acres
$22,821
MO = Montgomery County
CBP = Chesapeake Bay Program
SHA = State Highway Administration

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Notes
Assumed forest buffer costs = reforestation costs
This version of UNM is a default based on State legislation
current cost estimate is from MO County. City of Rockville said they do not sweep enough to receive any CBP cred
SHA estimate.
SHA estimate and City of Rockville estimate used. County provided reforestation costs.
Applied generic tree planting costs. Assumed cost same whether over pervious/impervious.
County/SHA average cost. City says they have not done any impervious surface reduction.
Current cost estimates are from MO county (Schueler 2007), and assume that SHA and Rockville tree planting cost:

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it, but we are double checking this. Also, checked with SHA about their sweeping program and waiting to hear bac
s = reforestation costs.

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:k (wondering if their inlet cleaning estimates include street sweeping solids too).

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Appendix E2. Raw data used in calculating summary of costs of best management practices for structural and non

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structural (alternative) applications by regulated entity in Montgomery County, MD.

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Alternative BMP
Units
Permittee
Cost/Unit
Notes
Advanced IDDE Program
Yes/No
City of Rockville
N/A

Advanced IDDE Program
Yes/No
Montgomery County
N/A

Advanced IDDE Program
Yes/No
SHA
N/A

Advanced IDDE Program
Yes/No
Other Regulated
N/A
Use city/county/SHA costs if applicable
Urban Forest Buffers
Acres
City of Rockville
$20,000
MO county estimate. S
Urban Forest Buffers
Acres
Montgomery County
$20,000

Urban Forest Buffers
Acres
SHA
$20,000
MO county estimate.
Urban Forest Buffers
Acres
Other Regulated
$20,000

Urban Nutrient Management
Acres/Yr
City of Rockville
N/A

Urban Nutrient Management
Acres/Yr
Montgomery County
N/A

Urban Nutrient Management
Acres/Yr
SHA
N/A

Urban Nutrient Management
Acres/Yr
Other Regulated
N/A
Use avg. city/county/SHA cost
Street Sweeping
Acres/Year
City of Rockville
$658
MO county estimate.
Street Sweeping
Acres/Year
Montgomery County
$658

Street Sweeping
Acres/Year
SHA
$658
MO county estimate.
Street Sweeping
Acres/Year
Other Regulated
$658
Avg. city/county/SHA cost
Enhanced Septic System
# Systems
City of Rockville
N/A
Very few systems in watershed
Enhanced Septic System
# Systems
Montgomery County
N/A
Very few systems in watershed
Enhanced Septic System
# Systems
SHA
N/A
Very few systems in watershed
Enhanced Septic System
# Systems
Other Regulated
N/A
Use avg. city/county/SHA costs, if needed and applicable
Urban Stream Restoration
Linear Feet
City of Rockville
$542.95

Urban Stream Restoration
Linear Feet
Montgomery County
$200.00

Urban Stream Restoration
Linear Feet
SHA
$598.36

Urban Stream Restoration
Linear Feet
Other Regulated
$447.11
Avg. city/county/SHA cost
Outfall Enhancement
Acres
City of Rockville
N/A

Outfall Enhancement
Acres
Montgomery County
N/A

Outfall Enhancement
Acres
SHA
N/A

Outfall Enhancement
Acres
Other Regulated
N/A
Use avg. city/county/SHA cost
Outfall Stabilization
Linear Feet
City of Rockville
$2,182
SHA estimate.
Outfall Stabilization
Linear Feet
Montgomery County
$2,182
SHA estimate.
Outfall Stabilization
Linear Feet
SHA
$2,182

Outfall Stabilization
Linear Feet
Other Regulated
$2,182
Avg. city/county/SHA cost
Tree Canopy overTurf
Acres
City of Rockville
$42,346
SHA estimate used. Rockville provided no reforestation or tree
planting costs.
Tree Canopy overTurf
Acres
Montgomery County
$42,346
SHA estimate used. County provided no tree planting costs (only
reforestation).
Tree Canopy overTurf
Acres
SHA
$42,346

Tree Canopy overTurf
Acres
Other Regulated
$42,346
Avg. city/county/SHA cost
Tree Canopy over Impervious
Acres
City of Rockville
$42,346
Applied generic tree planting costs. Assumed cost same whether over
pervious/impervious
Tree Canopy over Impervious
Acres


Applied generic tree planting costs. Assumed cost same whether over
Montgomery County
$42,346
pervious/impervious
Tree Canopy over Impervious
Acres


Applied generic tree planting costs. Assumed cost same whether over
SHA
$42,346
pervious/impervious
Tree Canopy over Impervious
Acres
Other Regulated
$42,346
Applied generic tree planting costs. Assumed cost same whether over
pervious/impervious
Impervious Reduction
Acres
City of Rockville
$173,150
County/SHA average cost.
Impervious Reduction
Acres
Montgomery County
$72,600

Impervious Reduction
Acres
SHA
$273,700

Impervious Reduction
Acres
Other Regulated
$173,150
Avg. city/county/SHA cost
Reforestation
Acres
City of Rockville
$20,000
MO county estimate.
Reforestation
Acres
Montgomery County
$20,000

Reforestation
Acres
SHA
$20,000
MO county estimate.
Reforestation
Acres
Other Regulated
$20,000
Avg. city/county/SHA cost
BMP = best management practice
IDDE = illicit discharge detection and elimination
SHA = State Highway Administration
SW = stormwater permitee
SWM = stormwater management

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Retrofit BMP
Bioretention
Bioretention
Bioretention
Bioretention
Bioswale
Bioswale
Bioswale
Bioswale
Dry Detention Ponds and Hydrodynamic Structures
Dry Detention Ponds and Hydrodynamic Structures
Dry Detention Ponds and Hydrodynamic Structures
Dry Detention Ponds and Hydrodynamic Structures
Dry Extended Detention Ponds
Dry Extended Detention Ponds
Dry Extended Detention Ponds
Dry Extended Detention Ponds
Filter Strip
Filter Strip
Filter Strip
Filter Strip
Filtering Practices
Filtering Practices
Filtering Practices
Filtering Practices
Infiltration Practices
Infiltration Practices
Infiltration Practices
Infiltration Practices
Permeable Pavement
Permeable Pavement
Permeable Pavement
Permeable Pavement
Stormwater to the Maximum Extent Practicable
Stormwater to the Maximum Extent Practicable
Stormwater to the Maximum Extent Practicable
Stormwater to the Maximum Extent Practicable
Vegetated Open Channels
Vegetated Open Channels
Vegetated Open Channels
Vegetated Open Channels
Wet Ponds and Wetlands
Wet Ponds and Wetlands
Wet Ponds and Wetlands
Wet Ponds and Wetlands
Drypond to Wetpond conversion
Drypond to Wetpond conversion
Drypond to Wetpond conversion
Drypond to Wetpond conversion
Soils
Permittee
Cost/Acre
Notes
All
City of Rockville
$382,319

All
Montgomery County
$168,800

All
SHA
$211,195

All
Other Regulated
$254,105
Avg. city/county/SHA cost
All
City of Rockville
$202,076
SHA estimate.
All
Montgomery County
$202,076
SHA estimate.
All
SHA
$202,076

All
Other Regulated
$202,076
Avg. city/county/SHA cost
Not an acceptable restoration
All
City of Rockville
N/A
scenarios in WMOST
Not an acceptable restoration
All
Montgomery County
N/A
scenarios in WMOST
Not an acceptable restoration
All
SHA
N/A
scenarios in WMOST
Not an acceptable restoration
All
Other Regulated
N/A
scenarios in WMOST
Not an acceptable restoration
All
City of Rockville
N/A
scenarios in WMOST
Not an acceptable restoration
All
Montgomery County
N/A
scenarios in WMOST
Not an acceptable restoration
All
SHA
N/A
scenarios in WMOST
Not an acceptable restoration
All
Other Regulated
N/A
scenarios in WMOST
All
City of Rockville
N/A
No data.
All
Montgomery County
N/A
No data.
All
SHA
N/A
No data.
All
Other Regulated
N/A
Use avg. city/county/SHA cost
All
City of Rockville
$207,738

All
Montgomery County
$209,467
City/SHA estimate.
All
SHA
$211,195

All
Other Regulated
$209,467
Avg. city/county/SHA cost
All
City of Rockville
N/A
No data.
All
Montgomery County
N/A
No data.
All
SHA
N/A
No data.
All
Other Regulated
N/A
Use avg. city/county/SHA cost
All
City of Rockville
$2,073,745

All
Montgomery County
$435,000

All
SHA
$1,254,373
City/county estimate.
All
Other Regulated
$1,254,373
Avg. city/county/SHA cost
All
City of Rockville
$707,730

All
Montgomery County
$557,500

All
SHA
$632,615
City/county estimate.
All
Other Regulated
$632,615
Avg. city/county/SHA cost
All
City of Rockville
$202,076
SHA estimate.
All
Montgomery County
$202,076
SHA estimate.
All
SHA
$202,076

All
Other Regulated
$202,076
Avg. city/county/SHA cost
All
City of Rockville
$19,614

All
Montgomery County
$15,000

All
SHA
$17,307
City/county estimate.
All
Other Regulated
$17,307
Avg. city/county/SHA cost
All
City of Rockville
$22,875

All
Montgomery County
$12,000

All
SHA
$81,318

All
Other Regulated
$38,731
Avg. city/county/SHA cost
practice, so not included for future
practice, so not included for future
practice, so not included for future
practice, so not included for future
practice, so not included for future
practice, so not included for future
practice, so not included for future
practice, so not included for future
SHA = State Highway Administration
SW = stormwater permitee
SWM = stormwater management
WMOST = Watershed Management Optimization Support Tool

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Permittee	Source	Project/Category Name	Retrofit/BMP Type
City of Rockville
City of Rockville cost spreadsheet f
i CI P Book
Fallsgrove Exec Plaza

bioretention & BaySaver pre-treatment for bioretention
City of Rockville
City of Rockville cost spreadsheet f
CI P Book
St. Raphael's Parish School

bioretention
City of Rockville
City of Rockville cost spreadsheet f
CI P Book
The Village At Rockville

micro-bioretention
City of Rockville
City of Rockville cost spreadsheet f
CI P Book
The Village At Rockville

micro-bioretention
City of Rockville
City of Rockville cost spreadsheet f
CI P Book
The Village At Rockville

micro-bioretention
City of Rockville
City of Rockville cost spreadsheet f
CI P Book
PNC Bank Hungerford

2 micro-bioretentionPlanter Boxes (no separate entries)
City of Rockville
City of Rockville cost spreadsheet f
CI P Book
275 N Washington St

micro-bioretention
Montgomery County
Montgomery County 2016 Annual Report
ESD Retrofits for Larger Parkinglots
and Rooftops
Bioretention
Montgomery County
Montgomery County 2016 Annual Report
ESD Retrofits for Larger Parkinglots
and Rooftops
Large Bioretention Retrofits
Montgomery County
Montgomery County 2016 Annual Report
ESD Retrofits in Street ROW

Central Business District
Montgomery County
Montgomery County 2016 Annual Report
ESD Retrofits in Street ROW

Suburban w/curbs
Montgomery County Montgomery County 2016 Annual Report	ESD Retrofits in Street ROW	Suburban w/o curbs
SHA	SHA 2016 Annual Report & Imp. Plans	TC56-TMDL AT VARIOUS LOCATIONS IN DIST 7	SWM
SHA	SHA 2016 Annual Report & Imp. Plans	TC56-AT VARIOUS LOCATIONS IN DIST 5	SWM
SHA	SHA 2016 Annual Report & Imp. Plans	TC70-SWM AT VARIOUS LOCATIONS IN DIST 5	SWM
SHA
SHA 2016 Annual Report & Imp. Plans
TC 11-LEGACY PAVEMENT IMP-D 1ST 2/DIST4
SWM
Impervious Area (Acres) Cost- Design ($) Cost - Construction ($) Cost -Total ($) Unit Cost ($/Acre) Notes
0.4	$187,426	$56,228	$243,653	$624,751
0.7	$138,115	$41,434	$179,549	$264,043
0.3	$22,952	$6,886	$29,838	$99,460
0.1	$33,393	$10,018	$43,411	$482,344
0.0	$14,841	$4,452	$19,294	$482,350
0.1	$16,583	$4,975	$21,558	$307,971
0.3	$105,426	$31,628	$137,054	$415,315
N/A	N/A	N/A	N/A	$200,000
N/A	N/A	N/A	N/A	$57,000
Assumed ESD retrofits in ROW = bioretention. Could also
be filtering or infiltration practices. County only provided
N/A	N/A	N/A	N/A	$250,000 cost on unit/area basis
Assumed ESD retrofits in ROW = bioretention. Could also
N/A	N/A	N/A	N/A	$200,000 be filtering or infiltration practices
Assumed ESD retrofits in ROW = bioretention. Could also
N/A	N/A	N/A	N/A	$137,000 be filtering or infiltration practices
Average unit cost was determined from SHA annual report,
which included a general category of SWM. In SHA's
implementation plans, they indicate that they have installed
or will be installing "bioretention" and "microbioretention"
retrofits. It was assumed these bioretention retrofits fall
32.9	$1,048,097	$5,013,520	$6,061,617	$184,188 under the "SWM" classification.
Average unit cost was determined from SHA annual report,
which included a general category of SWM. In SHA's
implementation plans, they indicate that they have installed
or will be installing "bioretention" and "microbioretention"
retrofits. It was assumed these bioretention retrofits fall
12.9	$500,038	$1,706,160	$2,206,198	$170,891 under the "SWM" classification.
18.9	$166,191	$3,332,597	$3,498,788
30.4	$1,245,680	$4,995,307	$6,240,987
Average unit cost was determined from SHA annual report,
which included a general category of SWM. In SHA's
implementation plans, they indicate that they have installed
or will be installing "bioretention" and "microbioretention"
retrofits. It was assumed these bioretention retrofits fall
$185,514 under the "SWM" classification.
Average unit cost was determined from SHA annual report,
which included a general category of SWM. In SHA's
implementation plans, they indicate that they have installed
or will be installing "bioretention" and "microbioretention"
retrofits. It was assumed these bioretention retrofits fall
$205,026 under the "SWM" classification.

-------
SHA	SHA 2016 Annual Report & Imp. Plans	TC11-LEGACY PAVEMENT IMP-DISTRICT 3	SWM
SHA	SHA 2016 Annual Report & Imp. Plans	TC 11-LEGACY PAVEMENT IMP-DISTRICT 5	SWM
SHA	SHA 2016 Annual Report & Imp. Plans	LEGACY PAVEMENT IMPDIST 7/SOME DIST 6	SWM
SHA	SHA 2016 Annual Report & Imp. Plans	TC70-SWM AT VARIOUS LOCATION IN DIST 3	SWM
Summary

Permitee
Unit Cost ($/'Acre)
City of Rockville
$382,319
Montgomery County
$168,800
SHA
$211,195
Other Regulated SW
N/A
BMP = best management practice
ROW = right of way
SHA = State Highway Administration
SW = storm water permitee
SWM = stormwater management

Average unit cost was determined from SHA annual report,
which included a general category of SWM. In SHA's
implementation plans, they indicate that they have installed
or will be installing "bioretention" and "microbioretention"
retrofits. It was assumed these bioretention retrofits fall
$419,335	$1,994,609	$2,413,944	$401,655 under the "SWM" classification.
Average unit cost was determined from SHA annual report,
which included a general category of SWM. In SHA's
implementation plans, they indicate that they have installed
or will be installing "bioretention" and "microbioretention"
retrofits. It was assumed these bioretention retrofits fall
$1,257,081	$1,257,081	$225,688 under the "SWM" classification.
Average unit cost was determined from SHA annual report,
which included a general category of SWM. In SHA's
implementation plans, they indicate that they have installed
or will be installing "bioretention" and "microbioretention"
retrofits. It was assumed these bioretention retrofits fall
$327,282	$3,146,602	$3,473,884	$151,433 under the "SWM" classification.
Average unit cost was determined from SHA annual report,
which included a general category of SWM. In SHA's
implementation plans, they indicate that they have installed
or will be installing "bioretention" and "microbioretention"
retrofits. It was assumed these bioretention retrofits fall
$161,555	$1,698,237	$1,859,792	$165,168 under the "SWM" classification.

-------
Permittee
Project/Category Name
mperviousArea (Acresj Cost- Design ($) Cost - Construction ($) Misc. Cost ($) Cost -Total ($) Unit Cost ($/Acre)
City of Rockville
City of Rockville
City of Rockville cost spreadsheet & CIP Book
City of Rockville cost spreadsheet & CIP Book
Montgomery County Montgomery County 2016 Annual Report
SHA 2016 Annual Report
Northeast Park Pond
Horizon Hills SWM retrofits
Retrofits Of Exsisting BMP's
23
80
N/A
Drainage improvements at various locations in District 3
$117,000
$270,000
N/A
$30,000
$253,880
$2,100,000
N/A
$4,246,372
N/A
N/A
N/A
$10,265
$370,880
$2,370,000
N/A
$4,286,637
$16,125
$29,625
$12,000
$85,391
unit/area basis from MO
county
Misc. cost for SHA = ROW cost
(assumed to be purchase of
add'I. ROW area). Projects are
characterized as "retrofits" in
SHA annual report, so
assumed retrofits meant pond
conversions.
SHA 2016 Annual Report
TMDL Stormwater Facility Enhancement in District 5 - Design E
N/A
$4,608,534
$108,076
$4,716,610
$77,245
M isc. cost for SHA = ROW cost
(assumed to be purchase of
add'I. ROW area). Projects are
characterized as "retrofits" in
SHA annual report, so
assumed retrofits meant pond
conversions.
Summary

Permitee
Unit Cost ($/Acre)
City of Rockville	$22,875
Montgomery County	$12,000
SHA	$81,318
Other Regulated SW N/A
BMP = best management practice
SHA = State Highway Administration
SW = stormwater permitee
SWM = stormwater management

-------
Permittee
Source
Project/Category Name
Retrofit/BMP Type


Impervious Area (Acres)
Cost- Design ($)
Cost - Construction ($)
Misc. Cost ($)
Cost -Total ($)
Unit Cost ($/Acre)

Notes
Montgomery County
Montgomery County 2016 Annual Report
Pavement removal
Impervious Cover Reduction

N/A
N/A
N/A
N/A
N/A
$72,600
Only cost provided on unit/area basis from MO county
SHA
SHA Imp. Plans
Catoctin Creek
Impervious Surface Elim
nat
on
0.5
N/A
N/A
N/A
$139,000
$278,000
Derived from IA remova
otals per watershed in Imp. Plans
SHA
SHA Imp. Plans
Double Pipe Creek
Impervious Surface Elim
nat
on
0.2
N/A
N/A
N/A
$43,000
$215,000
Derived from IA remova
otals per watershed in Imp. Plans
SHA
SHA Imp. Plans
Liberty Reservoir
Impervious Surface Elim
nat
on
0.2
N/A
N/A
N/A
$48,000
$240,000
Derived from IA remova
otals per watershed in Imp. Plans
SHA
SHA Imp. Plans
Little Patuxent River
Impervious Surface Elim
nat
on
0.5
N/A
N/A
N/A
$141,000
$282,000
Derived from IA remova
otals per watershed in Imp. Plans
SHA
SHA Imp. Plans
Lower Monocacy
Impervious Surface Elim
nat
on
3.4
N/A
N/A
N/A
$967,000
$284,412
Derived from IA remova
otals per watershed in Imp. Plans
SHA
SHA Imp. Plans
Patapsco River Lower
Impervious Surface Elim
nat
on
0.2
N/A
N/A
N/A
$68,000
$340,000
Derived from IA remova
otals per watershed in Imp. Plans
SHA
SHA Imp. Plans
Potomac River
Impervious Surface Elim
nat
on
0.3
N/A
N/A
N/A
$88,000
$293,333
Derived from IA remova
otals per watershed in Imp. Plans
SHA
SHA Imp. Plans
Seneca Creek
Impervious Surface Elim
nat
on
0.4
N/A
N/A
N/A
$102,000
$255,000
Derived from IA remova
otals per watershed in Imp. Plans
SHA
SHA Imp. Plans
Upper Monocacy River
Impervious Surface Elim
nat
on
0.9
N/A
N/A
N/A
$248,000
$275,556
Derived from IA remova
otals per watershed in Imp. Plans
Summary

Permitee
Unit Cost ($/Acre)
City of Rockville
N/A
Montgomery County
$72,600
SHA
$273,700
Other Regulated SW
N/A
SHA = State Highway Administration
SW = stormwater permitee

-------
Permittee
Source

Project/Category Name
Retrofit/BMP Type
SHA
SHA Imp.
Plans
Antietam Creek
Outfal
Stabilization
SHA
SHA Imp.
Plans
Bynum Run
Outfal
Stabilization
SHA
SHA Imp.
Plans
Cabin John Creek
Outfal
Stabilization
SHA
SHA Imp.
Plans
Conococheague Creek
Outfal
Stabilization
SHA
SHA Imp.
Plans
Double Pipe Creek
Outfal
Stabilization
SHA
SHA Imp.
Plans
Liberty Reservoir
Outfal
Stabilization
SHA
SHA Imp.
Plans
Little Patuxent River
Outfal
Stabilization
SHA
SHA Imp.
Plans
Lower Monocacy River
Outfal
Stabilization
SHA
SHA Imp.
Plans
Patapsco River Lower North Branch
Outfal
Stabilization
SHA
SHA Imp.
Plans
Patuxent River Upper
Outfal
Stabilization
SHA
SHA Imp.
Plans
Potomac River Montgomery
Outfal
Stabilization
SHA
SHA Imp.
Plans
Seneca Creek
Outfal
Stabilization
SHA
SHA Imp.
Plans
Upper Monocacy River
Outfal
Stabilization
Summary

Permitee
Unit Cost ($/ft)
City of Rockville
N/A
Montgomery County
N/A
SHA
$2,182
Other Regulated SW
N/A
CIP = Construction Industry Publication
SHA = State Highway Administration
Linear Feet (ft) Cost-Total ($) Unit Cost ($/Ton) Notes
1,000
$2,182,000
$2,182
Derived from
planned totals
per watershed
in
Imp.
Plans
400
$873,000
$2,183
Derived from
planned totals
per watershed
in
Imp.
Plans
1,200
$2,618,000
$2,182
Derived from
planned totals
per watershed
in
Imp.
Plans
400
$873,000
$2,183
Derived from
planned totals
per watershed
in
Imp.
Plans
2,000
$4,363,000
$2,182
Derived from
planned totals
per watershed
in
Imp.
Plans
2,400
$5,235,000
$2,181
Derived from
planned totals
per watershed
in
Imp.
Plans
2,400
$5,235,000
$2,181
Derived from
planned totals
per watershed
in
Imp.
Plans
6,200
$13,523,000
$2,181
Derived from
planned totals
per watershed
in
Imp.
Plans
3,800
$8,288,000
$2,181
Derived from
planned totals
per watershed
in
Imp.
Plans
1,800
$3,926,000
$2,181
Derived from
planned totals
per watershed
in
Imp.
Plans
2,400
$5,235,000
$2,181
Derived from
planned totals
per watershed
in
Imp.
Plans
400
$873,000
$2,183
Derived from
planned totals
per watershed
in
Imp.
Plans
2,400
$5,235,000
$2,181
Derived from
planned totals
per watershed
in
Imp.
Plans

-------
Permittee
Source

Project/Category Name
Retrofit/BMP Type
City of Rockville
City of Rockville cost spreadsheet
CIP Book
Shapiro & Duncan
Eco-Stone Pave

City of Rockville
City of Rockville cost spreadsheet
CIP Book
610 Lofstrand Lane
perv
ous concre
e
City of Rockville
City of Rockville cost spreadsheet
CIP Book
610 Lofstrand Lane
perv
ous concre
e
City of Rockville
City of Rockville cost spreadsheet
CIP Book
Rollins Center
perv
ous concre
e
City of Rockville
City of Rockville cost spreadsheet
CIP Book
Rollins Center
perv
ous concre
e
City of Rockville
City of Rockville cost spreadsheet
CIP Book
Rollins Center
perv
ous concre
e
City of Rockville
City of Rockville cost spreadsheet
CIP Book
West Montgomery Alley Pervious Concrete Paving
perv
ous concre
e
City of Rockville
City of Rockville cost spreadsheet
CIP Book
Wootton Pkwy Sidewalk
perv
ous concre
e
MontgomeryCounty MontgomeryCounty2016AnnualReport	ESDRetrofitsforLargerParkinglotsand Roofto ps	PermablePavers
Summarv

Permitee
Unit Cost ($/Acre)
City of Rockville
$2,073,745
Montgomery County
$435,000
SHA
N/A
Other Regulated SW
N/A
CIP = Construction Industry Publication
SHA = State Highway Administration
Impervious Area (Acres) Cost- Design ($) Cost - Construction ($) Cost -Total ($) Unit Cost ($/Acre)
0.16	$2,185	$7,284	$9,469	$59,181
0.11	$91,296	$304,319	$395,615	$3,596,500
0.09	$91,296	$304,319	$395,615	$4,395,722
0.12	$91,296	$304,319	$395,615	$3,296,792
0.15	$91,296	$304,319	$395,615	$2,637,433
0.18	$91,296	$304,319	$395,615	$2,197,861
1	N/A	$82,260	$82,260	$82,260
0.19	$11,000	$50,600	$61,600	$324,211
Only cost provided on
unit/area basis from
N/A	N/A	N/A	N/A	$435,000 MO county

-------
Permittee
Source
Retrofit/BMP Type
Montgomery County Montgomery County 2016 Annual Report Reforestation from turf
Summarv

Permitee
Unit Cost ($/Acre)
City of Rockville
N/A
Montgomery County
$20,000
SHA
N/A
Other Regulated SW
N/A
SHA = State Highway Administration
Unit Cost ($/Acre) Notes
$20,000	Only cost provided on unit/area basis from MO county

-------
Permittee
City of Rockville
City of Rockville
City of Rockville
City of Rockville
City of Rockville
City of Rockville
City of Rockville
City of Rockville
City of Rockville
City of Rockville
City of Rockville
City of Rockville
City of Rockville
City of Rockville
City of Rockville
City of Rockville
City of Rockville
City of Rockville
City of Rockville
City of Rockville
City of Rockville
City of Rockville
City of Rockville
City of Rockville
City of Rockvi
City of Rockvi
City of Rockvi
City of Rockvi
City of Rockvi
City of Rockvi
City of Rockvi
City of Rockvi
City of Rockvi
City of Rockvi
City of Rockvi
City of Rockvi
City of Rockvi
City of Rockvi
City of Rockvi
City of Rockvi
City of Rockvi
City of Rockvi
City of Rockvi
City of Rockvi
City of Rockvi
City of Rockvi
City of Rockvi
City of Rockvi
spreadshee
spreadshee
spreadshee
spreadshee
spreadshee
spreadshee
spreadshee
spreadshee
spreadshee
spreadshee
spreadshee
spreadshee
spreadshee
spreadshee
spreadshee
spreadshee
spreadshee
spreadshee
spreadshee
spreadshee
spreadshee
spreadshee
spreadshee
spreadshee
Project/Category Nome
610 Lofstrand Lane
Mt Calvary Baptist Church
Mt Calvary Baptist Church
Mt Calvary Baptist Church
Twinbrook Station East - (Phas
Twinbrook Station East - (Phas
Twinbrook Station East - (Phas
Twinbrook Station East - (Phas
Twinbrook Commc
Twinbrook Commc
Twinbrook Commc
National Lutheran Home
Twinbrook Place
Westat Cafeteria
Upper Rock, Blocks G&H
Upper Rock, Blocks G&H
Kol Snalom -Pha:
East (Phas
East (Phas
East (Phas

Millers Ale House
275 N Washington St
Anderson Ave Sidewalk
Anderson Ave Sidewalk
Avery Road sidewalk
Mt Calvary Baptist Church
Mt Calvary Baptist Church
Retrofit/BMP Type
ormfilte
ormfilte
ormfilte
ormfilte
ormfilte
ormfilte
ormFilte
ormfilte
ormfilte
ormfilte
ormFilte
ormFilte
ormFilte
ormFilte
ormFilte
ormceptor pre-t
ormFilte
ormfilte
ormfilte
ormFilte
ormfilte
ormfilte
t UG pipe storage for WQv
and HydroDynamic Pre-Treatment Structure
ind StormFilter
Si Vorsentry Pre-Treatment
al Report & Imp. Plans
TC56-TMDL AT VARIOUS LOCATIONS IN DIST 7
TC56-AT VARIOUS LOCATIONS IN DIST 5
TC70-SWM AT VARIOUS LOCATIONS IN DIST 5
al Report & Imp. Plans
TCI 1-LEGACY PAVEMENT IMP-DIST 2/DIST 4
al Report & Imp. Plans
TCI 1-LEGACY PAVEMENT IMP-DISTRICT 3
TCI 1-LEGACY PAVEMENT IMP-DISTRICT 5
LEGACY PAVEMENT IMPDIST 7/SOME DIST 6
al Report & Imp. Plans
TC70-SWM AT VARIOUS LOCATION IN DIST 3
City of Rockville
Montgomery County
Other Regulated SW
CIP = Construction Industry Publicat
SHA = State Highway Administratior
SWM = storm water management
UG = underground
WQv = water quality volume
a (Acres) Cost- Design ($)
- Construction ($)
Cost -Total ($)
Unit Cost ($/Acre)
$75,886
$98,652
$193,435
$15,523
$20,179
$96,090
$15,523
$20,179
$100,895
$11,403
$14,824
$123,533
$106,444
$138,378
$162,798
$93,922
$122,098
$162,797
$260,476
$338,618
$162,797
$155,284
$201,869
$162,798
$141,508
$183,961
$162,797
$176,572
$229,544
$162,797
$72,633
$94,422
$162,797
$195,550
$254,215
$306,283
$90,396
$117,514
$123,699
$56,717
$73,733
$171,472
$345,052
$448,567
$81,262
$393,218
$511,184
$162,797
$72,666
$94,466
$68,454
$294,491
$382,839
$1,320,134
$53,681
$69,785
$268,404
$140,000
$140,000
$155,556
N/A
N/A
N/A
$44,700
$44,700
$124,167
$63,524
$82,581
$183,513
$73,243
$95,216
$158,693
$22,766
$4,657
$4,657
$3,421
$31,933
$28,176
$78,143
$46,585
$42,453
$52,972
$21,790
$58,665
$27,119
$17,015
$103,515
$117,965
$21,800
$88,347
$16,104
$19,057
$21,973
$3,332,597	$3,498,7E
$4,995,307	$6,240,987
$1,994,609	$2,413,944
$1,257,081	$1,257,081
$3,146,602	$3,473,8
$1,698,237	$1,859,792
Average unit cost was determined from SHA annus
category of SWM. In SHA's implementation plans,
will be installing "other filtering practice" retrofits,
practice" retrofits fall under the "SWM" dassificati'
Average unit cost was determined from SHA annua
category of SWM. In SHA's implementation plans,
will be installing "other filtering practice" retrofits,
practice" retrofits fall under the "SWM" dassificati-
Average unit cost was determined from SHA annua
category of SWM. In SHA's implementation plans,
will be installing "other filtering practice" retrofits,
practice" retrofits fall under the "SWM" dassificati-
Average unit cost was determined from SHA annua
category of SWM. In SHA's implementation plans,
will be installing "other filtering practice" retrofits,
practice" retrofits fall under the "SWM" dassificati-
Average unit cost was determined from SHA annua
category of SWM. In SHA's implementation plans,
will be installing "other filtering practice" retrofits,
practice" retrofits fall under the "SWM" dassificati-
Average unit cost was determined from SHA annua
category of SWM. In SHA's implementation plans,
will be installing "other filtering practice" retrofits,
practice" retrofits fall under the "SWM" dassificati-
Average unit cost was determined from SHA annua
category of SWM. In SHA's implementation plans,
will be installing "other filtering practice" retrofits,
practice" retrofits fall under the "SWM" dassificati-
Average unit cost was determined from SHA annua
category of SWM. In SHA's implementation plans,
will be installing "other filtering practice" retrofits,
practice" retrofits fall under the "SWM" dassificati-
si report, which included a general
they indicate that they have installed or
Itwas assumed these"other filtering
si report, which included a general
they indicate that they have installed or
Itwas assumed these"other filtering
si report, which included a general
they indicate that they have installed or
Itwas assumed these"other filtering
si report, which included a general
they indicate that they have installed or
Itwas assumed these"other filtering
si report, which included a general
they indicate that they have installed or
Itwas assumed these"other filtering
si report, which included a general
they indicate that they have installed or
Itwas assumed these"other filtering
si report, which included a general
they indicate that they have installed or
Itwas assumed these"other filtering
si report, which included a general
they indicate that they have installed or
Itwas assumed these"other filtering

-------
Permittee
City of Rockvil
City of Rockvil
City of Rockvil
City of Rockvil
City of Rockvil
City of Rockvil
City of Rockvil
City of Rockvil
City of Rockvil
City of Rockvil
City of Rockvil
Project/Category Nome
Source
City of Rockville cost spreadsheet & CIP Book	Kol Shalom - Phase 1 only
City of Rockville cost spreadsheet & CIP Book	275 N Washington St
City of Rockville cost spreadsheet & CIP Book	SFD-720 Beall Ave
City of Rockville cost spreadsheet & CIP Book	SFD-306 Lincoln Ave
City of Rockville cost spreadsheet & CIP Book	SFD for 3 houses - 200, 202, 204 Crabb Ave
City of Rockville cost spreadsheet & CIP Book	SFD-201 Upton St
City of Rockville cost spreadsheet & CIP Book	SFD-110 Monument St
City of Rockville cost spreadsheet & CIP Book	2 SFDs-114,116 Crabb Ave
City of Rockville cost spreadsheet & CIP Book	SFD - 23 Wall St
City of Rockville cost spreadsheet & CIP Book	SFD-213 N Van Buren St
City of Rockville cost spreadsheet & CIP Book	SFD-337 Seth Place
Retrofit/BMP Type
Green Roof
Green Roof
Micro-scale ESDforSFD
Micro-scale ESDforSFD
Micro-scale ESDforSFD
Micro-scale ESDforSFD
Micro-scale ESDforSFD
Micro-scale ESDforSFD
Micro-scale ESDforSFD
Micro-scale ESDforSFD
Micro-scale ESDforSFD
Montgomery County Montgomery County 2016 Annual Report
Green Roofs and Tanks
ESD Parkinglot/Rooftop Retrofits
Montgomery County Montgomery County 2016 Annual Report	Voluntary LID Projects
*SW MEP in this analysis just made to represent small scale micro ESD practices
Rain Gardens, Rain Barrells
Summarv

Permitee
Unit Cost ($/Acre)
City of Rockville	$707,730
Montgomery County	$557,500
SHA N/A
Other Regulated SW N/A
CIP = Construction Industry Publication
ESD = environmental site design
LID = low impact development
MO = Montgomery
SFD= single family dwelling
SHA= State Highway Administration
SW = stormwater permitee
Impervious Area (Acres) Cost- Design ($) Cost - Construction ($) Cost -Total ($) Unit Cost ($/Acre) Notes
0.19	$128,884	$429,614	$558,499	$2,939,468
0.17	$91,296	$304,319	$395,615	$2,327,147
0.06	$1,967	$6,556	$8,523	$142,050
0.05	$3,405	$11,352	$14,757	$295,140
0.14	$6,426	$21,420	$27,845	$198,893
0.07	$2,419	$8,063	$10,482	$149,743
0.11	$4,156	$13,854	$18,010	$163,727
0.2	$4,141	$13,802	$17,943	$89,715
0.05	$2,472	$8,240	$10,712	$214,240
0.08	$2,905	$9,682	$12,587	$157,338
0.09	$23,003	$76,678	$99,681	$1,107,567
Only cost provided on
unit/area basis from MO
N/A	N/A	N/A	N/A	$817,000 county
Only cost provided on
unit/area basis from MO
N/A	N/A	N/A	N/A	$298,000 county

-------
Permittee
Source

City of Rockville
City of Rockville cost spreadsheet & CIP Book
City of Rockville
City of Rockville cost spreadsheet & CIP Book
City of Rockville
City of Rockville cost spreadsheet & CIP Book
City of Rockville
City of Rockville cost spreadsheet & CIP Book
City of Rockville
City of Rockville cost spreadsheet & CIP Book
Montgomery County
Montgomery County 2016 Annual Report
SHA
SHA 2016 Annual
Report
SHA
SHA 2016 Annual
Report
SHA
SHA 2016 Annual
Report
SHA
SHA 2016 Annual
Report
SHA
SHA 2016 Annual
Report
SHA
SHA 2016 Annual
Report
SHA
SHA 2016 Annual
Report
SHA
SHA 2016 Annual
Report
SHA
SHA 2016 Annual
Report
SHA
SHA 2016 Annual
Report
SHA
SHA 2016 Annual
Report
SHA
SHA 2016 Annual
Report
SHA
SHA 2016 Annual
Report
SHA
SHA 2016 Annual
Report
SHA
SHA 2016 Annual
Report
SHA
SHA 2016 Annual
Report
Summary


Permitee

Unit Cost ($/ft)
City of Rockville

$543
Montgomery County

$200
SHA

$598
Other Regulated SW

N/A
CIP = Construction Industry Publication
MO = Montgomery
SHA = State Highway Administration
SW = stormwater permitee

-------
Project/Category Name
Retrofit/BMP Type
College Gardens Park stream restoration
Stream
Restoration
Rockcrest Trib. & Aleutian Ave stream restoration
Stream
Restoration
Woodley Gardens Park stream restoration
Stream
Restoration
Glenora Trib stream restoration (Bouldercrest reach)
Stream
Restoration
Horizon Hills stream restoration
Stream
Restoration
Beyond Simple Stream
Stream
Restoration
1-97 SB WEST OF EASTWEST BOULEVARD
Stream
Restoration
SRI - BROAD CREEK STREAM RESTORATION
Stream
Restoration
Upper Little Patuxent - TC 12
Stream
Restoration
Plumtree Run Stream Restoration
Stream
Restoration
STREAM RESTORATION OF CRICKET LAND TRIBUTARY (NW-4)
Stream
Restoration
RESTORATION OF PB-119, PB-109
Stream
Restoration
RESTORATION OF NW-170
Stream
Restoration
RESTORATION OF IC-62
Stream
Restoration
RESTORATION OF PB-37,PB-108, PB-8
Stream
Restoration
RESTORATION OF PB-85
Stream
Restoration
RESTORATION OF RC-2
Stream
Restoration
RESTORATION OF NB-1
Stream
Restoration
RESTORATION OF SC-2 - Goshan Branch
Stream
Restoration
MD 23 Magness Farm Stream Restoration at Tributary of Deer Creek
Stream
Restoration
MD 100 Red Hill Branch Brampton Hills
Stream
Restoration
Dorsey Run
Stream
Restoration

-------
Impervious Area (Acres) Linear Feet (ft) Cost- Design ($) Cost - Construction ($) Cost -Total ($)
5
500
$79,430
$145,196
$224,626
36
3600
$193,000
$875,000
$1,068,000
51
5100
$272,000
$951,000
$1,223,000
11
1100
$205,162
$1,099,529
$1,304,691
13
1300
$130,000
$1,100,000
$1,230,000
N/A
N/A
N/A
N/A
N/A
11
1100
N/A
N/A
$2,560,337
23
2300
N/A
N/A
$2,002,368
45
4500
N/A
N/A
$2,285,492
21
2100
N/A
N/A
$1,487,880
51.71
5171
N/A
N/A
$2,886,457
27.26
2726
N/A
N/A
$1,184,596
60.11
6011
N/A
N/A
$3,431,044
12.09
1209
N/A
N/A
$145,947
53.61
5361
N/A
N/A
$1,078,402
64.5
6450
N/A
N/A
$2,668,256
48.55
4855
N/A
N/A
$1,590,079
89.1
8910
N/A
N/A
$2,379,935
39.91
3991
N/A
N/A
$3,958,336
11.6
1160
N/A
N/A
$204,957
10.44
1044
N/A
N/A
$579,272
19.73
1973
N/A
N/A
$1,069,708

-------
Unit Cost ($/ft)
Notes
$449
$297
$240
$1,186
$946
$200
$2,328
$871
$508
$709
$558
$435
$571
$121
$201
$414
$328
$267
$992
$177
$555
$542
Only cost provided on unit/area basis from MO county

-------
Permittee	Source	Retrofit/BMP Type
Montgomery County Montgomery County 2016 Annual Report Enhanced Street Sweeping
*1 curb mile = 1 acre
**SHA provides inlet cleaning estimates in their implementation plan, not street sweeping. Didn't expli
Summary

Permitee
Unit Cost ($/acre/yr)
City of Rockville
N/A
Montgomery County
$658
SHA
N/A
Other Regulated SW
N/A
MO = Montgomery
SHA = State Highway Administration
SW = stormwater permitee

-------
Unit Cost ($/curb mile/yr)	Notes
$658	Only cost provided on unit/area basis from MO county
citly include inlet cleaning in WMOST

-------
Permittee
Source
SHA
SHA 2016 Annual Report
SHA
SHA 2016 Annual Report & Imp. Plans
SHA
SHA 2016 Annual Report & Imp. Plans
SHA
SHA 2016 Annual Report & Imp. Plans
SHA
SHA 2016 Annual Report & Imp. Plans
SHA
SHA 2016 Annual Report & Imp. Plans
SHA
SHA 2016 Annual Report & Imp. Plans
SHA
SHA 2016 Annual Report & Imp. Plans
SHA
SHA 2016 Annual Report & Imp. Plans
Summary

Permitee
Unit Cost ($/acre)
City of Rockville
N/A
Montgomery County
N/A
SHA
$202,076
Other Regulated SW
N/A
SHA = State Highway Administration
SW = stormwater permitee

-------
Project/Category Name
Retrofit/BMP Type Impervious Area (Acres)
Grass Swale, Attenuation Swale or Dry Swale
TC56-TMDL AT VARIOUS LOCATIONS IN DIST7
TC56-AT VARIOUS LOCATIONS IN DIST 5
TC70-SWM AT VARIOUS LOCATIONS IN DIST 5
TC11-LEGACY PAVEMENT IMP-DIST 2/DIST 4
TC11-LEGACY PAVEMENT IMP-DISTRICT 3
TC11-LEGACY PAVEMENT IMP-DISTRICT 5
LEGACY PAVEMENT IMPDIST 7/SOME DIST 6
TC70-SWM AT VARIOUS LOCATION IN DIST 3
Swales
20.67
SWM
32.9
SWM
12.9
SWM
18.9
SWM
30.4
SWM
6.0
SWM
5.6
SWM
22.9
SWM
11.3

-------
Cost-Design ($) Cost - Construction ($) Cost -Total ($) Unit Cost ($/Acre) Notes
$187,634
$2,481,288
$2,668,922
$129,121

$1,048,097
$5,013,520
$6,061,617
$184,188
Average unit cost w
$500,038
$1,706,160
$2,206,198
$170,891
Average unit cost w
$166,191
$3,332,597
$3,498,788
$185,514
Average unit cost w
$1,245,680
$4,995,307
$6,240,987
$205,026
Average unit cost w
$419,335
$1,994,609
$2,413,944
$401,655
Average unit cost w

$1,257,081
$1,257,081
$225,688
Average unit cost w
$327,282
$3,146,602
$3,473,884
$151,433
Average unit cost w
$161,555
$1,698,237
$1,859,792
$165,168
Average unit cost w

-------
as determined from SHA annual report, which included a
as determined from SHA annual report, which included a
as determined from SHA annual report, which included a
as determined from SHA annual report, which included a
as determined from SHA annual report, which included a
as determined from SHA annual report, which included a
as determined from SHA annual report, which included a
as determined from SHA annual report, which included a
general category of SWM. In SHA's implementation plan
general category of SWM. In SHA's implementation plan
general category of SWM. In SHA's implementation plan
general category of SWM. In SHA's implementation plan
general category of SWM. In SHA's implementation plan
general category of SWM. In SHA's implementation plan
general category of SWM. In SHA's implementation plan
general category of SWM. In SHA's implementation plan

-------
s, they indicate that they have installed or will be installing
s, they indicate that they have installed or will be installing
s, they indicate that they have installed or will be installing
s, they indicate that they have installed or will be installing
s, they indicate that they have installed or will be installing
s, they indicate that they have installed or will be installing
s, they indicate that they have installed or will be installing
s, they indicate that they have installed or will be installing
Other
open
channel
system'
retrofits.
It was assumed
Other
open
channel
system'
retrofits.
It was assumed
Other
open
channel
system'
retrofits.
It was assumed
Other
open
channel
system'
retrofits.
It was assumed
Other
open
channel
system'
retrofits.
It was assumed
Other
open
channel
system'
retrofits.
It was assumed
Other
open
channel
system'
retrofits.
It was assumed
Other
open
channel
system'
retrofits.
It was assumed

-------
these "other open channel system" retrofits fal
these "other open channel system" retrofits fal
these "other open channel system" retrofits fal
these "other open channel system" retrofits fal
these "other open channel system" retrofits fal
these "other open channel system" retrofits fal
these "other open channel system" retrofits fal
these "other open channel system" retrofits fal
under the "SWM" classification,
under the "SWM" classification,
under the "SWM" classification,
under the "SWM" classification,
under the "SWM" classification,
under the "SWM" classification,
under the "SWM" classification,
under the "SWM" classification.

-------
Permittee
Source

SHA
SHA 2016 Annual
Report
SHA
SHA 2016 Annual
Report
SHA
SHA 2016 Annual
Report
SHA
SHA 2016 Annual
Report
SHA
SHA 2016 Annual
Report
SHA
SHA 2016 Annual
Report
SHA
SHA 2016 Annual
Report
SHA
SHA 2016 Annual
Report
SHA
SHA 2016 Annual
Report
SHA
SHA 2016 Annual
Report
SHA
SHA 2016 Annual
Report
SHA
SHA 2016 Annual
Report
SHA
SHA 2016 Annual
Report
SHA
SHA 2016 Annual
Report
SHA
SHA 2016 Annual
Report
Summary


Permitee
Unit Cost ($/acre)
City of Rockville
N/A

Montgomery County
N/A

SHA
$42,346
Other Regulated SW
N/A

SHA = State Highway Administration
SW = stormwater permitee

-------
Project/Category Name
Retrofit/BMP Type
TREE PLANTING IN WASHINGTON COUNTY S
Tree
Planting
SRI-TREE PLANTING-VAR LOC BALTIMORE CO
Tree
Planting
SRI-AT VARIOUS LOCATION - D4
Tree
Planting
TREE PLANTING-VAR LOC IN AA AND CH
Tree
Planting
SRI-TREE PLANTING-VAR LOC IN CECIL CO
Tree
Planting
TC70-CHESAPEAKE BAY WATERSHED PROGRAM-D4
Tree
Planting
TC70-CHESAPEAKE BAY WATERSHED PROGRAM-D7
Tree
Planting
TC70-CHESAPEAKE BAY WATERSHED PROG D-3,5
Tree
Planting
TC70-CHESAPEAKE BAY WATERSHED PROGRAM-D6
Tree
Planting
SRI-TREE PLANTING AT VAR LOC IN D-7
Tree
Planting
TREE PLANTING AT VARIOUS LOC - DIST 7
Tree
Planting
TREE PLANTING AT VARIOUS LOC - DIST 3
Tree
Planting
SRI-TREE PLANT-VAR LOC IN DISTRICT 3
Tree
Planting
Tree Plantings Associated with Various Landscape/Sustainability Projects
Tree
Planting
Tree Plantings for Million Tree Initiative (Partnership with DNR)
Tree
Planting

-------
Impervious Area (Acres) Cost-Design ($) Cost - Construction ($) Cost-Total ($) Unit Cost ($/Acre)
19.2
N/A
$1,281,794
$1,281,794
$66,656
45.4
N/A
$1,312,486
$1,312,486
$28,909
22.2
N/A
$1,769,630
$1,769,630
$79,857
14.8
N/A
$638,333
$638,333
$43,218
8.9
N/A
$682,896
$682,896
$76,558
33.0
N/A
$1,568,585
$1,568,585
$47,533
42.9
N/A
$2,912,940
$2,912,940
$67,869
23.0
N/A
$729,320
$729,320
$31,751
31.3
N/A
$1,212,257
$1,212,257
$38,767
53.2
N/A
$2,674,541
$2,674,541
$50,236
57.2
N/A
$113,285
$113,285
$1,980
4.7
N/A
$4,736
$4,736
$1,012
21.7
N/A
$1,045,072
$1,045,072
$48,116
57.4
N/A
N/A
N/A
N/A
133.9
N/A
$1,389,650
$1,389,650
$10,380

-------
Notes

-------
Permittee
Source
City of Rockville
City of Rockville
City of Rockville
City of Rockville
City of Rockville
City of Rockville
Montogmery County
City of Rockville cost
City of Rockville cost
City of Rockville cost
City of Rockville cost
City of Rockville cost
City of Rockville cost
Montgomery County
spreadsheet & CIP Book
spreadsheet & CIP Book
spreadsheet & CIP Book
spreadsheet & CIP Book
spreadsheet & CIP Book
spreadsheet & CIP Book
2016 Annual Report
Summary

Permitee
Unit Cost ($/acre)
City of Rockville	$19,614
Montgomery County	$15,000
SHA	N/A
Other Regulated SW	N/A
CIP = Construction Industry Publication
CPv = Channel protection volume
QplO = 10th percentile flow
SHA = State Highway Administration
SW = Stormwater permitees
SWM = stormwater management

-------
Project/Category Name
Retrofit/BMP Type
College Gardens Park SWM Pond
Maryvale II SWM Pond
Twinbrook Station East - (Phase 1A)
Twinbrook Commons East (Phase IB)
Twinbrook Commons East (Phase 1C)
Kol Shalom - Phase 1 only
Traditional Retrofit
SWM Retrofit-Wet Ponds
SWM Retrofit-Wet Ponds
Structural Water Quantity (QplO and CPv)
Structural Water Quantity (QplO and CPv)
Structural Water Quantity (QplO and CPv)
Structural Water Quantity (CPv Only)
New Pond Retrofit

-------
Impervious Area (Acres) Cost-Design ($) Cost - Construction ($) Cost-Total ($) Unit Cost ($/Acre)
51
$176,111
$795,104
$15,484
$302
89
$162,100
$883,600
$9,928
$112
18
$432,969
$129,891
$562,860
$30,590
30
$208,120
$62,436
$270,556
$9,088
2
$34,245
$10,273
$44,518
$21,823
2
$220,254
$23,423
$101,498
$55,768
$15,000

-------
Notes
Only cost provided on unit/area basis from MO county

-------
Appendix F. Distribution of area by Hydrologic Response Unit (land-use) in Cabin John Creek (CJC) and inventory of

-------
: existing best management practices in CJC watershed by year.

-------
HRU
1
2
3
4
5
6
7
8
9
10
11
12
13
14
Land-Use
g SW Pern
Soil Type
Acres
Impervious
County
N/A
3253.389
Turfgrass
County
A/B
1754.258
Turfgrass
County
C/D
272.821
Impervious
Rockville
N/A
705.7872
Turfgrass
Rockville
A/B
412.08
Turfgrass
Rockville
C/D
113.9071
Impervious
SHA
N/A
288.2842
Turfgrass
SHA
A/B
139.3445
Turfgrass
SHA
C/D
17.51663
Impervious
Other
N/A
103.1084
Turfgrass
Other
A/B
82.89283
Turfgrass
Other
C/D
36.76424
Natural
N/A
N/A
9208.164
Water
N/A
N/A
35.33004

-------
Appendix G. Removal efficiencies for best management practices.

-------
Reduction (%)
BMP
Reporting Units
77V
TP
TSS
Advanced Grey Infrastructure Nutrient Discovery Program
Acres
0%
0%
0%
Bioretention/raingardens - A/B soils, no underdrain
Acres
80%
85%
90%
Bioretention/raingardens - A/B soils, underdrain
Acres
70%
75%
80%
Bioretention/raingardens - C/D soils, underdrain
Acres
25%
45%
55%
Bioswale
Acres
70%
75%
80%
Dry Detention Ponds and Hydrodynamic Structures
Acres
5%
10%
10%
Dry Extended Detention Ponds
Acres
20%
20%
60%
Erosion and Sediment Control Level 1
Acres
25%
40%
40%
Erosion and Sediment Control Level 2
Acres
25%
40%
65%
Erosion and Sediment Control Level 3
Acres
25%
40%
77%
Filter Strip Runoff Reduction
Acres
20%
54%
56%
Filter Strip Stormwater Treatment
Acres
0%
0%
22%
Filtering Practices
Acres
40%
60%
80%
Forest Buffers
Acres
25%
50%
50%
Infiltration Practices w/ Sand, Veg. - A/B soils, no underdrain
Acres
85%
85%
95%
Infiltration Practices w/o Sand, Veg. - A/B soils, no underdrain
Acres
80%
85%
95%
MS4 Permit-Required Stormwater Retrofit
Acres
25%
35%
65%
Nutrient Management Maryland Commercial Applicators
Acres
9%
0%
0%
Nutrient Management Maryland DIY
Acres
5%
0%
0%
Nutrient Management Plan
Acres
9%
5%
0%
Nutrient Management Plan High Risk Lawn
Acres
20%
10%
0%
Nutrient Management Plan Low Risk Lawn
Acres
6%
3%
0%
Permeable Pavement w/ Sand, Veg. - A/B soils, no underdrain
Acres
80%
80%
85%
Permeable Pavement w/ Sand, Veg. - A/B soils, underdrain
Acres
50%
50%
70%
Permeable Pavement w/ Sand, Veg. - C/D soils, underdrain
Acres
20%
20%
55%
Permeable Pavement w/o Sand, Veg. - A/B soils, no underdrain
Acres
75%
80%
85%
Permeable Pavement w/o Sand, Veg. - A/B soils, underdrain
Acres
45%
50%
70%
Permeable Pavement w/o Sand, Veg. - C/D soils, underdrain
Acres
10%
20%
55%
Stormwater Management by Era 1985 to 2002 MD
Acres
17%
30%
40%
Stormwater Management by Era 2002 to 2010 MD
Acres
30%
40%
80%
Stormwater to the Maximum Extent Practicable (SW to the MEP)
Acres
50%
60%
90%
Vegetated Open Channels - A/B soils, no underdrain
Acres
45%
45%
70%
Vegetated Open Channels - C/D soils, no underdrain
Acres
10%
10%
50%
Wet Ponds and Wetlands
Acres
20%
45%
60%

-------
BMP
Reporting Units
Effectiveness Value
77V
Urban Forest Buffers
Acres
Reduction %
25%
Urban Nutrient Management
Acres
Reduction %
9%
Street Sweeping
Acres
Reduction %
See Street Sweeping
Tab
Urban Stream Restoration
Linear Feet
Reduction/Linear ft.
0.075
Outfall Enhancement
Acres
Reduction %
25%
Outfall Stabilization
Linear Feet
Reduction/Linear ft.
0.075
Tree Canopy over Turf
Acres
Reduction %
24%
Tree Canopy over Impervious
Acres
Reduction %
9%
Impervious Reduction
Acres
LU Change
Convert to Turf
Reforestation
Acres
LU Change
Covert to Natural
Urban Forest Buffers
Acres
LU Change
Covert to Natural

-------
Reduction
TP
TSS	Notes
Upstream Efficiency Only. Jurisdictions don't report upstream
50%	50%	drainage acres to urban forest buffers. If included in
jurisdiction's data, need to calculate upstream drainage acres
Should be applied to all turf HRUs in baseline conditions
4.5%	0%	,
scenario. Legislation went into effect in 2013.
See Street Sweeping See Street Sweeping Investigated seperation into fine vs. coarse grained efficiencies,
Tab	Tab	but not technically feasible.
Investigated seperation into fine vs. coarse grained efficiencies,
0.068	44.88
but not technically feasible.
45%	55%	SPSCs or RSCs. Counties have been modeling as bioretention.
0.068	44.88	Reported as Stream Restoration
24%	6%
11%	7%
Convert to Turf Convert to Turf
Covert to Natural Covert to Natural
Covert to Natural Covert to Natural

-------
Description
Passes/week
Passes/Yr
R
TSS
Advanced Sweeping
2 per week
100
21.0%
Advanced Sweeping
1 per week
50
16.0%
Advanced Sweeping
1 per 2 weeks
25
11.0%
Advanced Sweeping
1 per 4 weeks
10
6.0%
Advanced Sweeping
1 per 8 weeks
6
4.0%
Advanced Sweeping
1 per 12 weeks
4
2.0%
Advanced Sweeping
1 per week or 2 weeks in spring; monthly all other seasons
15
7.0%
Advanced Sweeping
1 per week or 2 weeks in fall; monthly all other seasons
20
10.0%
Mechanical Broom
2 per week
100
0.7%
Mechanical Broom
1 per week
50
0.5%
Mechanical Broom
1 per 4 weeks
10
0.1%

-------
eduction (%)

TN
TP
4.0%
10.0%
3.0%
8.0%
2.0%
5.0%
1.0%
3.0%
0.7%
2.0%
0.0%
1.0%
1.0%
4.0%
2.0%
5.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%

-------
WMOST IIMDE REF BMP NAM ENTITY IMP
TURFA/B TURFB/C LAT
LONG
BUILT YEA
MDE_454
MDE_449
MDE_450
MDE_452
MDE_451
MDE_455
MDE_453
MDE_500
MDE_501
MDE_502
MDE_503
MDE_504
MDE_505
MDE_506
MDE_507
MDE_508
MDE_509
MDE_510
MDE_511
MDE_512
MDE_513
MDE_514
MDE_515
MDE_516
MDE_517
MDE_518
MDE_519
MDE_520
MDE_521
MDE_522
MDE_523
MDE_524
MDE_525
MDE_526
MDE_527
MDE_528
MDE_529
MDE_572
MDE_593
MDE_553
MDE_576
MDE_552
MDE_549
MDE_548
MDE_582
MDE 583
590
591
592
603
656
1663
1763
150027
150061
150176
150182
150250
150252
150253
150254
150255
150256
150258
150414
150415
150644
150645
150646
150647
150648
150650
150651
150657
150661
150662
150667
150668
150669
150670
150740
150741
150742
MDE_BMP
MDE_BMP
MDE_BMP
MDE_BMP
MDE_BMP
MDE_BMP
MDE_BMP
MDE_BMP
MDE BMP
Dry Detent
Dry Detent
Dry Detent
Dry Detent
Wet Ponds
Wet Ponds
Dry Detent
Wet Ponds
Infil
Infil
Infil
Infil
Infil
Infil
Infil
Infil
Infil
Infil
Infil
Infil
Infil
Infil
Infil
Infil
Infil
Infil
Infil
Infil
Infil
Infil
Infil
Itration
Itration
Itration
Itration
Itration
Itration
Itration
Itration
Itration
Itration
Itration
Itration
Itration
Itration
Itration
Itration
Itration
Wet Ponds
Wet Ponds
Itration
Itration
Itration
Itration
Itration
Itration
Wet Ponds
Wet Extenc
Filtering Pr
Filtering Pr
Dry Detent
Filtering Pr
Infiltration
Dry Detent
Infiltration
Wet Extenc
Wet Extenc
Filtering Pr
Filtering Pr
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
SHA
SHA
SHA
SHA
SHA
SHA
SHA
SHA
SHA
SHA
SHA
SHA
SHA
SHA
SHA
SHA
SHA
SHA
SHA
SHA
SHA
SHA
SHA
SHA
SHA
SHA
SHA
SHA
SHA
SHA
City of Rod
City of Rod
City of Rod
City of Rod
City of Rod
City of Rod
City of Rod
City of Rod
City of Rod
5.81
12.7919
18.5479
4.26892
54.35
3.7
2.21382
0.624239
1.579745
0.068013
0.128156
0.134267
0.086118
0.142263
0.506337
0.293959
0.355072
0.094602
1.402119
1.594578
0.020414
0.077
0.025391
0.040314
0
14.52285
15.38899
0.578002
0.369524
0.613468
0.448342
0.492173
0.237217
3.654066
5.638111
0.729564
1.910854
0.229194
2.120041
0.601633
2.635726
0.103137
105.7155
1.375162
0.412548
0.630282
4.831507
10.56676
50.8361
8.075226
121.4432
8.195449
2.748703
1.954956
1.184479
0.065347
0.133699
0.290202
0.233654
0.522414
0.645725
0.013535
1.753054
0.402553
3.899191
3.258726
0.118456
0.339327
0.150224
0.28341
0.019454
14.32634
34.59103
0.360055
1.198738
0.611979
0.316014
0.439053
0.307969
6.739271
1.515901
0.354003
0.646837
0.133817
1.237805
0.351269
1.538892
0.060218
61.72296
0.8029
0.24087
0.367996
0.751393
1.643336
7.905997
1.255854
18.88677
1.274551
0.427477
0.245753
0.148898
0.008215
0.016807
0.036481
0.029372
0.065671
0.081173
0.001701
0.220373
0.050604
0.490159
0.409647
0.014891
0.042656
0.018884
0.035627
0.002446
1.800933
4.348361
0.045262
0.150691
0.07693
0.039725
0.055192
0.038714
0.847179
0.190561
0.044501
0.081313
0.03699
0.342154
0.097098
0.42538
0.016645
17.06145
0.221938
0.066581
0.101721
39.04698
39.05035
39.0382
39.03684
39.0546
39.02545
39.03843
39.05545
39.03327
39.03129
39.03183
38.9829
38.98684
38.98777
38.9942
38.99464
38.99816
39.00049
39.00208
39.0022
39.03323
39.03314
39.03297
39.03288
39.0328
39.03223
39.03066
39.02126
39.02062
39.02126
39.02215
39.03094
39.0315
39.0327
39.0489
39.05244
39.05403
39.07861
39.06901
39.08273
39.05942
39.07039
39.06762
39.07861
39.07311
39.07311
-77.1267
-77.1285
-77.1856
-77.1812
-77.1293
-77.1632
-77.1841
-77.1533
-77.1396
-77.1418
-77.1421
-77.1652
-77.1575
-77.1573
-77.1576
-77.1576
-77.1584
-77.1576
-77.1589
-77.159
-77.1419
-77.1418
-77.1411
-77.1409
-77.1402
-77.1295
-77.1328
-77.1414
-77.143
-77.1441
-77.1453
-77.1418
-77.1421
-77.1426
-77.1195
-77.1181
-77.1179
-77.1477
-77.1406
-77.1459
-77.1194
-77.1318
-77.1529
-77.1494
-77.1529
-77.1529
1996
1989
1993
1996
1995
1998
1996
1989
1992
1994
1994
1988
1988
1988
1988
1988
1988
1988
1988
1988
1994
1994
1994
1994
1994
2000
2000
2001
2001
2001
2001
1994
1994
2001
2007
2009
2007
1999
2002
1999
1999
1997
1999
1999
2000
2000

-------
2002
2002
2002
2002
2002
2002
2002
2002
2002
2002
2003
2003
2008
2004
2004
2004
2004
2006
2006
2006
2006
2005
2006
2006
2006
2006
2006
2006
2006
2006
2006
2006
2010
2008
2008
2008
2009
2008
2008
2008
2007
2014
2014
2014
2014
2014
2014
MDE_
.BMP
Filtering Pr
C
ty of
MDE_
.BMP
Dry Detent
C
ty of
MDE_
.BMP
Dry Detent
C
ty of
MDE_
.BMP
Filtering Pr
C
ty of
MDE_
.BMP
Filtering Pr
C
ty of
MDE_
.BMP
Filtering Pr
C
ty of
MDE_
.BMP
Dry Detent
C
ty of
MDE_
.BMP
Filtering Pr
C
ty of
MDE_
.BMP
Dry Detent
C
ty of
MDE_
.BMP
Dry Detent
C
ty of
MDE_
.BMP
Dry Detent
C
ty of
MDE_
.BMP
Filtering Pr
C
ty of
MDE_
.BMP
Dry Detent
C
ty of
MDE_
.BMP
Dry Detent
C
ty of
MDE_
.BMP
Filtering Pr
C
ty of
MDE_
.BMP
Dry Detent
C
ty of
MDE_
.BMP
Filtering Pr
C
ty of
MDE_
.BMP
Filtering Pr
C
ty of
MDE_
.BMP
Dry Detent
C
ty of
MDE_
.BMP
Dry Detent
C
ty of
MDE_
.BMP
Dry Detent
C
ty of
MDE_
.BMP
Dry Detent
C
ty of
MDE_
.BMP
Dry Detent
C
ty of
MDE_
.BMP
Dry Detent
C
ty of
MDE_
.BMP
Dry Detent
C
ty of
MDE_
.BMP
Dry Extend
C
ty of
MDE_
.BMP
Dry Extend
C
ty of
MDE_
.BMP
Dry Extend
c
ty of
MDE_
.BMP
Dry Detent
c
ty of
MDE_
.BMP
Dry Detent
c
ty of
MDE_
.BMP
Dry Detent
c
ty of
MDE_
.BMP
Dry Detent
c
ty of
MDE_
.BMP
Infiltration
c
ty of
MDE_
.BMP
Dry Detent
c
ty of
MDE_
.BMP
Dry Detent
c
ty of
MDE_
.BMP
Filtering Pr
c
ty of
MDE_
.BMP
Dry Detent
c
ty of
MDE_
.BMP
Dry Detent
c
ty of
MDE_
.BMP
Dry Detent
c
ty of
MDE_
.BMP
Filtering Pr
c
ty of
MDE_
.BMP
Dry Detent
c
ty of
MDE_
.BMP
Dry Well
c
ty of
MDE_
.BMP
Dry Well
c
ty of
MDE_
.BMP
Dry Well
c
ty of
MDE_
.BMP
Green Roo1
c
ty of
MDE_
.BMP
Bioretentic
c
ty of
MDE_
.BMP
Bioretentic
c
ty of
Roc
3.644178
2.127686
0
Roc
3.644178
2.127686
0
Roc
0.36671
0.214107
0
Roc
0.859476
0.501813
0
Roc
0.687581
0.40145
0
Roc
0.991262
0.578757

Roc
0.991262
0.578757

Roc
1.816359
1.060498
0
Roc
1.816359
1.060498
0
Roc
1.375162
0.8029
0
Roc
1.489758
0.869809
0
Roc
1.489758
0.869809
0
Roc
1.191807
0.695847
0
Roc
1.810629
1.057152
0
Roc
2.303396
1.344858
0
Roc
2.303396
1.344858
0
Roc
1.810629
1.057152
0
Roc
0.676121
0.394759
0
Roc
0.441198
0.257597
0
Roc
0.108867
0.063563

Roc
0.200544
0.11709
0
Roc
0.893855
0.521885
0
Roc
1.08867
0.635629
0
Roc
2.005444
1.170896
0
Roc
2.349234
1.371621
0
Roc
2.635726
1.538892

Roc
2.120041
1.237805
0
Roc
3.838993
2.24143
0
Roc
0.378169
0.220798
0
Roc
0.085948
0.050181
0
Roc
0.37244
0.217452
0
Roc
0.057298
0.033454
0
Roc
7.105002
4.148318
1
Roc
0.882395
0.515194

Roc
4.062457
2.371901
0
Roc
0.882395
0.515194

Roc
1.174617
0.685811
0
Roc
0.882395
0.515194

Roc
4.062457
2.371901
0
Roc
0.882395
0.515194

Roc
0.996992
0.582103
0
Roc
0.01719
0.010036
0
Roc
0.01146
0.006691
0
Roc
0.01146
0.006691
0
Roc
0.063028
0.0368
0
Roc
0.108867
0.063563

Roc
0.166165
0.097017
0
.588135	39.05911	-77.1529
.588135	39.05911	-77.1529
.059183	39.05911	-77.1529
.138711	39.069	-77.1476
.110969	39.07038	-77.1406
0.15998	39.06488	-77.1494
0.15998	39.06488	-77.1494
.293143	39.06488	-77.1494
.293143	39.06488	-77.1494
.221938	39.069	-77.1476
.240432	39.06625	-77.1511
.240432	39.06625	-77.1511
.192346	39.05938	-77.1532
.292218	39.06216	-77.1282
.371745	39.06216	-77.1282
.371745	39.06216	-77.1282
.292218	39.06216	-77.1282
.109119	39.08054	-77.1455
.071205	39.08438	-77.1448
0.01757	39.08767	-77.1508
.032366	39.08767	-77.1508
.144259	39.06866	-77.1293
.175701	39.05942	-77.1247
.323659	39.05942	-77.1247
.379143	39.05942	-77.1247
0.42538	39.05942	-77.1247
.342154	39.05942	-77.1247
.619576	39.05942	-77.1247
.061033	39.05942	-77.1247
.013871	39.05942	-77.1247
.060108	39.05942	-77.1247
.009247	39.05942	-77.1247
.146678	39.07999	-77.1448
0.14241	39.06418	-77.1607
.655641	39.06418	-77.1607
0.14241	39.06418	-77.1607
.189572	39.07642	-77.1388
0.14241	39.06569	-77.1607
.655641	39.06541	-77.1603
0.14241	39.06514	-77.1605
.160905	39.0586	-77.1219
.002774	39.05684	-77.1264
.001849	39.05684	-77.1264
.001849	39.05684	-77.1264
.010172	39.05684	-77.1264
0.01757	39.05684	-77.1264
.026817	39.05684	-77.1264

-------
2014
2013
2013
2013
2013
2014
2013
2014
2014
2014
2003
1998
2001
2003
1999
1998
1998
1993
1999
1991
1994
1989
1994
1999
1996
1994
1997
1998
1998
2004
1994
2009
1995
1996
1987
1998
2001
1994
2002
1995
1996
2002
1994
1991
1991
1996
1994
MDE_BMP
MDE_BMP
MDE_BMP
MDE_BMP
MDE_BMP
MDE_BMP
MDE_BMP
MDE_BMP
MDE_BMP
MDE_BMP
M010019
M010058
MO10273
MO10281
M010300
M010306
MO10313
MO10361
M010380
M010430
MO10459
MO10461
M010510
MO10539
M010550
MO10555
MO10574
MO10624
MO10637
MO10663
MO10713
M010720
MO10741
MO10744
MO10775
MO10792
MO10799
M010809
MO10815
MO10852
MO10854
MO10857
MO10858
MO10864
MO10871
MO10873
MO10877
Infiltration
Green Roo1
Filtering Pr
Filtering Pr
Filtering Pr
Permeable
Dry Well
Dry Well
Dry Well
Dry Well
Dry Detent
Bioretentic
Dry Detent
Dry Detent
Dry Detent
Dry Detent
Dry Detent
Infil
Infil
Infil
Infil
Infil
Infil
Infil
Infil
Infil
Infil
Infil
Infil
Infil
Infil
Infil
Infil
Infil
Infil
Itration
Itration
Itration
Itration
Itration
Itration
Itration
Itration
Itration
Itration
Itration
Itration
Itration
Itration
Itration
Itration
Itration
Itration
Dry Detent
Dry Detent
Dry Detent
Dry Detent
Wet Ponds
Dry Detent
Dry Detent
Dry Detent
Dry Detent
Dry Detent
Dry Detent
Dry Detent
City of Rod
City of Rod
City of Rod
City of Rod
City of Rod
City of Rod
City of Rod
City of Rod
City of Rod
City of Rod
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
0.063028
0.292222
0.045839
0.051569
0.034379
0.280762
0.080218
0.01146
0.01146
0.01146
2.6977
1.286344
1.496377
2.172928
3.051544
0.338719
2.193639
0.17354
0.406354
0.232923
0.314374
0.973525
0.113542
1.461515
0.470043
0.430674
2.231698
0.253901
0.207743
5.528003
3.545892
1.167102
0.972658
0.552152
1.876287
2.688617
6.097891
6.516619
3.113838
108.8714
5.798395
2.052435
9.769961
3.249576
2.71252
3.453112
6.079685
0.0368
0.170616
0.026763
0.030109
0.020073
0.163925
0.046836
0.006691
0.006691
0.006691
0.895906
0.427195
0.496947
0.721629
1.013418
0.112489
0.728507
0.057633
0.13495
0.077354
0.104404
0.323308
0.037707
0.485369
0.156101
0.143027
0.741147
0.084321
0.068991
1.83585
1.177591
0.387595
0.32302
0.18337
0.623115
0.89289
2.02511
2.164169
1.034106
36.15618
1.925647
0.681614
3.244604
1.079184
0.900828
1.146778
2.019064
0.010172
0.047162
0.007398
0.008323
0.005548
0.045312
0.012946
0.001849
0.001849
0.001849
0.139379
0.06646
0.077312
0.112267
0.157661
0.0175
0.113337
0.008966
0.020995
0.012034
0.016242
0.050298
0.005866
0.075511
0.024285
0.022251
0.115303
0.013118
0.010733
0.28561
0.183202
0.060299
0.050253
0.028527
0.09694
0.13891
0.315054
0.336688
0.16088
5.624948
0.29958
0.106041
0.504775
0.167893
0.140145
0.178408
0.314113
39.05684
39.08539
39.08539
39.08539
39.08539
39.05833
39.08475
39.08749
39.08749
39.08749
39.03755
39.02611
38.98597
39.03753
39.0102
38.98381
39.01267
38.98948
38.98079
39.01194
39.03121
39.00454
39.03119
38.99034
39.03809
39.03148
39.05383
39.01249
38.98409
39.05673
38.98941
39.02658
39.01869
39.0395
39.04502
39.01247
39.05211
39.04466
39.02674
39.0546
39.0469
39.02858
39.04276
39.02106
39.02101
39.03843
39.04215
-77.1264
-77.1486
-77.1486
-77.1486
-77.1486
-77.125
-77.1561
-77.1559
-77.1559
-77.1559
-77.1441
-77.1469
-77.1683
-77.144
-77.1732
-77.1419
-77.1561
-77.1432
-77.1332
-77.1595
-77.1701
-77.1475
-77.1702
-77.1513
-77.124
-77.1701
-77.1388
-77.1561
-77.1414
-77.1255
-77.1323
-77.132
-77.1283
-77.1222
-77.128
-77.1558
-77.1259
-77.1558
-77.1645
-77.1293
-77.1274
-77.1622
-77.154
-77.1467
-77.1499
-77.1841
-77.1552

-------
1988
1993
2003
2004
1996
1989
2005
1987
1997
2005
2001
2001
2000
1998
1992
1992
1988
2000
1989
1993
1994
1990
1998
1991
1992
1989
1998
1992
1995
1994
1998
1998
1993
1992
1998
1998
1998
1995
1989
2006
1990
1995
1998
1989
1994
1998
1988
MO10879
M010901
MO10939
MO10953
MO10997
MOllOOO
M011006
M011118
M011181
M011218
M011246
M011267
MO11306
M011438
MO11450
M011451
M011467
M011485
MO11605
MO11650
M011659
M011691
MO11703
M011716
M011721
M011726
M011746
M011747
M011753
M011764
M011794
M011836
M011885
M011892
M011924
M011966
MO12028
MO12065
MO12071
MO12076
MO12081
M012121
M012128
M012129
M012137
M012183
M012185
Dry Detent
Dry Detent
Dry Detent
Infiltration
Dry Detent
Dry Detent
Dry Detent
Dry Detent
Dry Detent
Dry Extend
Dry Detent
Dry Detent
Wet Ponds
Wet Extenc
Wet Extenc
Wet Extenc
Wet Extenc
Wet Extenc
Dry Detent
Dry Detent
Dry Detent
Dry Detent
Dry Detent
Dry Detent
Dry Detent
Dry Detent
Dry Detent
Dry Detent
Dry Detent
Dry Detent
Dry Detent
Dry Detent
Dry Detent
Dry Detent
Dry Detent
Dry Detent
Dry Detent
Dry Detent
Dry Detent
Filtering Pr
Filtering Pr
Dry Detent
Dry Detent
Dry Detent
Dry Detent
Dry Detent
Dry Detent
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
2.434692
50.38515
2.335541
0.645449
9.436222
18.43553
9.519405
2.412579
4.493999
7.828145
5.891354
1.107383
2.900217
19.74661
63.84092
91.34797
19.7914
22.11032
1.380669
1.165181
0.925877
0.634716
0.453493
1.169734
4.126176
0.578013
1.277776
1.741736
2.118303
2.483539
0.333967
0.230602
0.435229
0.6146
1.079324
1.167098
0.890134
0.972658
1.164404
3.246968
3.545892
0.783003
0.888712
0.393947
2.17112
2.460693
0.445535
0.808561
16.73291
0.775633
0.214353
3.133769
6.12244
3.161395
0.801217
1.492457
2.599727
1.956519
0.367762
0.963162
6.55785
21.20157
30.33666
6.572724
7.342836
0.45852
0.386957
0.307484
0.210789
0.150605
0.388468
1.370303
0.191958
0.424349
0.57843
0.703489
0.824783
0.110911
0.076583
0.14454
0.204109
0.358443
0.387593
0.295614
0.32302
0.386698
1.078318
1.177591
0.260035
0.295141
0.13083
0.721029
0.817196
0.147962
0.125791
2.603199
0.120668
0.033348
0.487532
0.95249
0.49183
0.124648
0.232187
0.404449
0.304383
0.057214
0.149843
1.020229
3.298405
4.719584
1.022543
1.142352
0.071334
0.0602
0.047836
0.032793
0.02343
0.060435
0.213183
0.029864
0.066018
0.089989
0.109444
0.128315
0.017255
0.011914
0.022487
0.031754
0.055764
0.060299
0.04599
0.050253
0.06016
0.167758
0.183202
0.040455
0.045916
0.020354
0.112173
0.127134
0.023019
39.0227
39.0382
39.03802
39.01961
39.03684
39.05035
39.01795
39.02934
39.01718
39.02025
39.04889
38.98833
39.05183
39.02546
39.02561
39.02262
39.02829
39.0356
39.0487
39.04838
39.02803
39.04631
39.03984
38.99152
39.0287
38.99361
39.04138
39.02784
39.03254
39.02819
39.04104
39.02667
38.98979
39.02686
39.03958
39.03954
39.03773
39.01874
38.99382
39.00975
38.98949
39.03293
39.02667
38.99367
39.02881
39.03851
39.04032
-77.1535
-77.1856
-77.1689
-77.138
-77.1812
-77.1285
-77.167
-77.1589
-77.1731
-77.1701
-77.1313
-77.1335
-77.134
-77.1632
-77.1538
-77.1397
-77.196
-77.167
-77.116
-77.1147
-77.1449
-77.1136
-77.1606
-77.1476
-77.1458
-77.1278
-77.1612
-77.1457
-77.1591
-77.1358
-77.1597
-77.1398
-77.1423
-77.1458
-77.1606
-77.1619
-77.1603
-77.1284
-77.1276
-77.1278
-77.1324
-77.1608
-77.1399
-77.127
-77.1358
-77.1618
-77.122

-------
1995
2001
1999
1995
2002
2002
2002
1995
2001
2002
2001
1998
1997
2002
1998
1998
2002
1998
1999
1999
2002
1998
1998
2003
1997
1991
1993
1999
2009
1988
1998
1998
2006
1993
1995
2005
1993
1992
1994
1994
1999
2002
2002
1999
2007
1998
2005
M012211
M012235
M012237
M012243
M012317
M012321
M012337
M012344
M012377
M012393
MO12440
M012451
M012514
M012537
M012539
MO12540
M012551
M012559
MO12560
M012583
M012588
M012592
MO12608
M012649
MO12705
M012716
M012718
M012719
M012739
M012759
M012766
M012769
MO12780
M012786
M012856
M012876
MO12902
M012923
M012925
M012926
M012942
M012946
M012947
M012949
M012965
M012973
MO12980
Dry Detent
Infiltration
tering Pr
tering Pr
tering Pr
tering Pr
tering Pr
tering Pr
tering Pr
tering Pr
tering Pr
tering Pr
tering Pr
Dry Detent
Dry Detent
Dry Detent
Dry Detent
Dry Detent
Dry Detent
Dry Detent
Dry Detent
Dry Detent
Dry Detent
Dry Detent
Dry Detent
Dry Detent
Dry Detent
Dry Detent
Dry Detent
Dry Detent
Dry Detent
Dry Detent
Dry Detent
Dry Detent
Dry Detent
Dry Detent
Dry Detent
Dry Detent
Dry Detent
Dry Detent
Dry Detent
Dry Detent
Dry Detent
Dry Detent
Dry Detent
Dry Detent
Dry Detent
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
2.002388
1.496377
1.307735
0.321328
1.102589
7.514783
2.052348
0.321328
1.627756
2.747085
0.959657
0.825765
1.117473
0.468571
0.452042
0.313795
0.819909
0.80005
3.051544
1.106762
0.144733
2.44752
0.816304
1.947138
1.117473
1.948243
0.133154
3.051544
0.218356
0.924677
1.296549
1.196271
3.246968
0.288876
2.002388
0.381242
50.38525
1.741736
5.71408
12.17149
0.93037
1.456068
0.800607
1.106762
4.8539
1.025864
4.346967
0.664993
0.496947
0.434299
0.106713
0.36617
2.495659
0.681585
0.106713
0.540578
0.912307
0.318702
0.274237
0.371113
0.155612
0.150123
0.104211
0.272292
0.265696
1.013418
0.367556
0.048066
0.812822
0.271095
0.646645
0.371113
0.647011
0.04422
1.013418
0.072516
0.307085
0.430584
0.397281
1.078318
0.095936
0.664993
0.12661
16.73294
0.57843
1.897646
4.042151
0.308976
0.48356
0.265881
0.367556
1.61198
0.340689
1.443628
0.103455
0.077312
0.067565
0.016602
0.056966
0.388259
0.106037
0.016602
0.0841
0.141931
0.049582
0.042664
0.057735
0.024209
0.023355
0.016213
0.042361
0.041335
0.157661
0.057182
0.007478
0.126454
0.042175
0.100601
0.057735
0.100658
0.00688
0.157661
0.011282
0.047774
0.066987
0.061807
0.167758
0.014925
0.103455
0.019697
2.603204
0.089989
0.295224
0.628852
0.048068
0.075229
0.041364
0.057182
0.250782
0.053002
0.22459
39.0514
38.98603
39.04019
38.98857
39.02291
39.02338
39.02885
38.98861
39.01502
39.02718
39.03281
39.02405
39.01065
39.048
39.02563
38.98376
39.02407
39.02619
39.01016
39.04634
39.05157
39.01263
39.04912
39.05383
39.01071
38.99075
39.04836
39.01024
38.99866
39.03096
39.02661
39.02728
39.00979
38.98967
39.05145
39.01469
39.03851
39.02778
39.02912
39.03001
39.04007
39.02717
39.02873
39.0463
39.02894
39.04209
39.01388
-77.1628
-77.1684
-77.1587
-77.1488
-77.1403
-77.1396
-77.1622
-77.1489
-77.1569
-77.1649
-77.1253
-77.1254
-77.1766
-77.1185
-77.1473
-77.1413
-77.1372
-77.1473
-77.1733
-77.1147
-77.1152
-77.1561
-77.1191
-77.1197
-77.1767
-77.1276
-77.1147
-77.1733
-77.1109
-77.1333
-77.1394
-77.1409
-77.1279
-77.1426
-77.163
-77.1612
-77.1864
-77.1457
-77.1364
-77.1366
-77.1583
-77.1646
-77.1626
-77.1146
-77.1403
-77.1614
-77.1926

-------
158
157
133
132
131
163
164
165
162
156
154
152
175
170
172
171
166
167
169
168
102
106
105
104
191
188
189
190
155
153
151
150
148
149
145
144
179
181
180
182
185
184
196
197
198
201
200
2005
2005
2004
2004
2004
2005
2005
2005
2005
2005
2005
2005
2006
2005
2005
2005
2005
2005
2005
2005
2001
2001
2001
2001
2007
2007
2007
2007
2005
2005
2005
2005
2005
2005
2004
2004
2006
2006
2006
2007
2007
2007
2008
2008
2008
2008
2008
M012981
M012982
M012992
M012993
M012994
M013112
M013113
M013114
M013115
MO13170
M013172
M013174
M013221
M013298
M013299
M013300
MO13301
MO13302
MO13303
MO13304
M013383
M013411
M013412
M013413
M013532
M013533
M013534
M013535
M013561
M013562
M013585
M013586
M013623
M013624
M013696
M013697
M013799
M013800
MO13801
M013828
M013847
M013848
M013923
M013924
M013925
MO13950
M013951
Filtering Pr
Dry Detent
Dry Extend
Filtering Pr
Dry Detent
Dry Detent
Dry Detent
Dry Detent
Infiltration
Dry Detent
Dry Detent
Dry Detent
Bioretentic
Dry Detent
Dry Detent
Dry Detent
Dry Detent
Dry Detent
Dry Detent
Dry Detent
Dry Detent
Filtering Pr
Dry Detent
Dry Detent
Filtering Pr
Dry Detent
Dry Detent
Dry Detent
Dry Detent
Dry Detent
Dry Detent
Dry Detent
Dry Detent
Filtering Pr
Dry Detent
Filtering Pr
Dry Detent
Infiltration
Dry Detent
Bioretentic
Dry Detent
Dry Detent
Dry Detent
Dry Detent
Dry Detent
Infiltration
Bioretentic
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
2.121883
1.364546
1.247837
0.215679
0.158983
1.030814
1.030814
1.030814
0.233089
0.634272
0.381556
0.252717
0.611865
1.442068
2.242049
1.917687
0.325127
0.325127
1.253826
0.325127
1.551195
5.846635
4.981291
0.296847
1.583896
0.119422
0.211235
0.308445
0.381556
0.252717
0.656945
0.149017
1.201791
2.061417
1.223432
1.223432
0.324759
0.997091
0.324759
2.663115
1.818716
0.648982
0.076636
0.076636
0.076636
0.421823
0.307572
0.704677
0.453166
0.414407
0.071627
0.052798
0.342333
0.342333
0.342333
0.077409
0.210642
0.126715
0.083927
0.2032
0.478911
0.744584
0.636864
0.107975
0.107975
0.416396
0.107975
0.515152
1.941667
1.654287
0.098583
0.526012
0.03966
0.070151
0.102435
0.126715
0.083927
0.218171
0.049489
0.399115
0.684597
0.406302
0.406302
0.107852
0.331134
0.107852
0.884421
0.603996
0.215527
0.025451
0.025451
0.025451
0.140088
0.102145
0.109629
0.070501
0.064471
0.011143
0.008214
0.053258
0.053258
0.053258
0.012043
0.03277
0.019713
0.013057
0.031613
0.074506
0.115838
0.099079
0.016798
0.016798
0.06478
0.016798
0.080144
0.302072
0.257363
0.015337
0.081834
0.00617
0.010914
0.015936
0.019713
0.013057
0.033942
0.007699
0.062092
0.106505
0.06321
0.06321
0.016779
0.051516
0.016779
0.137593
0.093966
0.03353
0.003959
0.003959
0.003959
0.021794
0.015891
39.01376
39.01373
39.03271
39.03098
39.03081
38.98346
38.98337
38.98354
38.98373
38.98605
38.98623
38.98596
39.02778
39.0301
39.03005
39.03004
39.02982
39.02989
39.0297
39.02991
39.01499
39.0521
39.05202
39.05199
39.04971
39.04915
39.04952
39.04918
38.98615
38.98603
39.04005
39.03991
38.99923
38.99922
38.99381
38.99385
39.04603
39.04569
39.04604
39.0474
38.99153
38.99165
39.05058
39.05055
39.05054
39.04876
39.04943
-77.1927
-77.1923
-77.1518
-77.1489
-77.149
-77.1368
-77.1365
-77.1368
-77.1367
-77.1452
-77.1453
-77.1451
-77.2008
-77.1317
-77.1311
-77.131
-77.1285
-77.1284
-77.1285
-77.1283
-77.1566
-77.1254
-77.1254
-77.1258
-77.1203
-77.1202
-77.1201
-77.1209
-77.1453
-77.1452
-77.1229
-77.1226
-77.1749
-77.1749
-77.1259
-77.1257
-77.192
-77.1922
-77.1921
-77.121
-77.1478
-77.1478
-77.1142
-77.1142
-77.1143
-77.1306
-77.1301

-------
2008
2008
2006
2004
2004
2004
2004
2004
2004
2004
2004
2005
2005
2009
2009
2009
2004
2004
2009
2009
2010
2010
2010
2010
2010
2010
2010
2010
2010
2010
2010
2010
2010
2010
2011
2010
2011
2012
2010
2011
2012
2012
2012
2012
2012
2002
2010
M013952
MO14049
MO14064
M014164
M014165
M014166
M014167
M014168
M014169
MO14170
M014171
M014263
M014264
M014265
M014338
M014339
M014414
M014417
M014583
M014584
M014776
M014777
M014778
M014779
MO14780
M014781
M014782
M014783
M014846
M014847
M014848
M014849
MO14850
M014851
M014887
MO14901
M014966
M014967
M014984
M014985
M015168
M015169
MO15170
M015171
M015172
M015226
M015491
Bioretentic
Dry Detent
Infiltration
Dry Detent
Filtering Pr
Dry Detent
Dry Detent
Filtering Pr
Dry Detent
Filtering Pr
Dry Detent
Infiltration
Dry Detent
Infiltration
Infiltration
Filtering Pr
Wet Ponds
Filtering Pr
Dry Detent
Dry Detent
Bioretentic
Dry Detent
Dry Detent
Dry Detent
Dry Detent
Dry Detent
Dry Detent
Bioretentic
Dry Detent
Dry Detent
Dry Detent
Dry Detent
Dry Detent
Dry Detent
Infiltration
Permeable
Dry Swale
Dry Swale
Dry Detent
Dry Detent
Dry Well
Dry Well
Dry Well
Dry Well
Dry Well
Dry Detent
Dry Detent
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
0.194003
2.22044
0.249454
2.287209
1.675962
1.675962
1.891092
1.891092
1.891092
0.232811
0.232811
4.096847
4.625239
1.229619
0.305461
0.514804
33.36683
0.413462
1.167097
1.16706
0.421958
0.385948
0.339652
0.262415
0.451356
0.052099
0.100722
0.483162
0.115477
1.256105
0.277459
0.337085
0.089479
0.471334
0.208392
0.015097
0.006432
0.022352
3.097869
0.148969
0.015445
0.008085
0.01016
0.012139
0.009674
0.468571
0.185394
0.064428
0.737408
0.082844
0.759582
0.556587
0.556587
0.628032
0.628032
0.628032
0.077317
0.077317
1.360563
I.536042
0.408357
0.101444
0.170966
II.08112
0.137311
0.387593
0.38758
0.140132
0.128173
0.112798
0.087148
0.149895
0.017302
0.03345
0.160458
0.03835
0.417152
0.092144
0.111946
0.029716
0.15653
0.069207
0.005014
0.002136
0.007423
1.028802
0.049473
0.005129
0.002685
0.003374
0.004031
0.003213
0.155612
0.061569
0.010023
0.114721
0.012888
0.118171
0.08659
0.08659
0.097705
0.097705
0.097705
0.012028
0.012028
0.211668
0.238968
0.06353
0.015782
0.026598
1.723931
0.021362
0.060299
0.060297
0.021801
0.01994
0.017548
0.013558
0.02332
0.002692
0.005204
0.024963
0.005966
0.064898
0.014335
0.017416
0.004623
0.024352
0.010767
0.00078
0.000332
0.001155
0.160055
0.007697
0.000798
0.000418
0.000525
0.000627
0.0005
0.024209
0.009579
39.04974
39.02997
39.05541
39.01025
39.01135
39.01138
39.00977
39.00978
39.00973
39.01
39.01045
39.02408
39.02417
39.02599
39.04747
39.04653
39.00048
39.0037
39.0267
39.02659
39.03233
39.03207
39.03202
39.0318
39.03179
39.03157
39.0315
39.03341
39.03209
39.03184
39.0321
39.03177
39.03163
39.0315
39.04442
38.99914
38.98531
39.04661
39.03162
39.04516
38.98218
38.98205
38.98188
38.98193
38.98203
39.04818
39.04684
-77.1295
-77.1967
-77.1196
-77.1789
-77.1788
-77.1787
-77.1779
-77.1781
-77.178
-77.1782
-77.1783
-77.1323
-77.1324
-77.1288
-77.1443
-77.1446
-77.1753
-77.1778
-77.132
-77.1319
-77.1696
-77.1693
-77.1695
-77.169
-77.1682
-77.1681
-77.1684
-77.1689
-77.1694
-77.169
-77.1693
-77.1683
-77.1681
-77.1684
-77.1279
-77.1313
-77.126
-77.1855
-77.1686
-77.1278
-77.1457
-77.146
-77.1458
-77.1457
-77.1456
-77.1186
-77.1174

-------
2011
2011
2011
2011
2011
2010
2010
2011
2011
2011
2011
2011
2011
2011
2010
2011
2011
2011
2010
2010
2010
2010
2010
2010
2010
2010
2010
2010
2010
2010
2010
2010
2010
2010
2010
2010
2010
2010
2007
2010
2010
2010
2010
2010
2010
2010
2010
MO15508
MO15509
MO15510
M015511
M015541
M015549
M015622
M015635
M015636
M015637
M015646
M015648
M015649
MO15650
M015671
M015681
M015682
M015683
M015686
M015687
M015688
M015689
MO15690
M015691
M015692
M015693
M015694
M015698
M015699
M015700
MO15701
MO15702
MO15703
MO15704
MO15708
MO15709
MO15710
M015711
MO15930
M015942
M015943
M015944
M015945
M015946
M015947
M015948
MO16048
Dry Detent
Dry Detent
Dry Detent
Dry Detent
Infiltration
Infiltration
Dry Detent
Dry Detent
Dry Detent
Dry Detent
Dry Swale
Dry Swale
Dry Swale
Dry Swale
Dry Detent
Dry Detent
Dry Detent
Dry Detent
Dry Detent
Dry Detent
Dry Swale
Dry Swale
Dry Detent
Dry Detent
Dry Detent
Dry Detent
Dry Detent
Dry Detent
Infiltration
Infiltration
Infiltration
Infiltration
Dry Detent
Dry Detent
Dry Detent
Dry Detent
Dry Detent
Dry Detent
Filtering Pr
Dry Detent
Dry Detent
Dry Detent
Dry Detent
Dry Detent
Dry Detent
Dry Detent
Dry Detent
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
0.454759
0.295748
0.317132
0.439336
0.27399
0.185394
0.185394
0.454759
0.439336
0.317132
0.061411
0.039249
0.146477
0.125275
0.178637
0.454759
0.439336
0.317132
1.205653
1.237923
1.602383
0.471102
1.237923
0.934562
1.205698
3.030221
0.623001
1.237923
4.777871
0.290797
0.203982
4.777873
0.623001
0.934562
0.934562
0.934562
3.030348
0.934562
1.583896
6.799396
0.590856
1.986199
0.826673
1.473844
1.953323
0.208247
6.799396
0.151026
0.098218
0.105319
0.145903
0.090992
0.061569
0.061569
0.151026
0.145903
0.105319
0.020395
0.013035
0.048645
0.041604
0.059325
0.151026
0.145903
0.105319
0.400397
0.411114
0.532151
0.156453
0.411114
0.310368
0.400412
1.006336
0.206899
0.411114
1.586731
0.096574
0.067742
1.586732
0.206899
0.310368
0.310368
0.310368
1.006379
0.310368
0.526012
2.258079
0.196223
0.659617
0.274538
0.489463
0.648698
0.069159
2.258079
0.023496
0.01528
0.016385
0.022699
0.014156
0.009579
0.009579
0.023496
0.022699
0.016385
0.003173
0.002028
0.007568
0.006472
0.009229
0.023496
0.022699
0.016385
0.062291
0.063959
0.082789
0.02434
0.063959
0.048285
0.062294
0.156559
0.032188
0.063959
0.246853
0.015024
0.010539
0.246854
0.032188
0.048285
0.048285
0.048285
0.156566
0.048285
0.081834
0.351298
0.030527
0.102619
0.042711
0.076148
0.10092
0.010759
0.351298
39.05722
39.05705
39.05687
39.05718
39.01144
39.04676
39.0468
39.0572
39.05717
39.0568
38.99327
38.99327
38.99343
38.9977
39.04663
39.05724
39.0572
39.05684
38.99093
38.99259
38.99095
38.99217
38.9926
38.99189
38.99084
38.9912
38.99141
38.99257
38.99255
38.99077
38.99076
38.99271
38.99135
38.99179
38.99164
38.99197
38.99119
38.99146
39.05053
39.05104
39.05104
39.0509
39.05276
39.05371
39.05406
39.0526
39.05099
-77.1197
-77.1191
-77.1194
-77.1192
-77.1783
-77.1177
-77.1173
-77.1196
-77.1192
-77.1193
-77.1096
-77.1096
-77.1094
-77.1207
-77.1172
-77.1197
-77.1192
-77.1193
-77.1532
-77.1549
-77.1533
-77.1537
-77.1548
-77.1553
-77.1532
-77.1544
-77.1542
-77.1549
-77.1552
-77.1535
-77.1532
-77.155
-77.1543
-77.1551
-77.1549
-77.1552
-77.1543
-77.1546
-77.1196
-77.1273
-77.1291
-77.1316
-77.1359
-77.136
-77.143
-77.1395
-77.1274

-------
2010
2010
2010
2010
2010
2010
2010
2010
2010
2010
2011
2011
2010
2010
2010
2010
2010
2010
2010
2010
2010
2010
2009
2010
2010
2010
2010
2010
2011
2011
2009
2010
2010
2010
2011
2011
2010
2010
2010
2010
2010
2010
2010
2010
2010
2010
2010
MO16049
M016050
MO16051
MO16052
MO16053
MO16056
MO16057
MO16058
MO16059
M016060
MO16061
MO16062
MO16065
MO16066
MO16067
MO16068
MO16069
MO16071
MO16072
MO16073
MO16074
MO16075
MO16076
MO16077
MO16078
MO16079
M016080
MO16081
MO16084
MO16085
MO16086
MO16088
MO16089
M016090
MO16091
MO16092
MO16095
M016194
M016195
M016196
M016197
M016198
M016199
M016200
MO16201
M016216
M016217
Dry Detent
Dry Detent
Dry Detent
Dry Detent
Dry Detent
Dry Detent
Dry Detent
Dry Detent
Dry Detent
Dry Detent
Dry Detent
Dry Detent
Dry Detent
Dry Detent
Dry Detent
Dry Detent
Dry Detent
Dry Detent
Filtering Pr
Filtering Pr
Filtering Pr
Filtering Pr
Filtering Pr
Filtering Pr
Filtering Pr
Filtering Pr
Filtering Pr
Filtering Pr
Dry Detent
Dry Detent
Dry Swale
Dry Detent
Dry Detent
Dry Detent
Dry Detent
Dry Detent
Dry Detent
Dry Detent
Dry Detent
Dry Detent
Dry Detent
Dry Detent
Dry Detent
Dry Detent
Dry Detent
Infiltration
Infiltration
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
0.590856
1.986199
0.826673
1.953323
0.688548
0.989622
0.647086
0.22921
0.388353
2.363872
1.771441
1.408116
6.865287
1.704168
2.732647
6.970461
3.15087
1.289613
0.989622
0.647086
0.22921
2.363872
0.630631
6.605883
1.480202
2.605763
6.659892
2.897583
1.408116
1.771441
0.91123
0.647086
0.537813
2.343397
1.408116
1.771441
0.331353
0.34043
1.143187
0.663228
1.67566
1.672132
3.941033
1.733594
4.54517
1.67566
1.672132
0.196223
0.659617
0.274538
0.648698
0.228667
0.328653
0.214897
0.07612
0.128972
0.785042
0.588295
0.467635
2.279962
0.565954
0.907512
2.31489
1.046404
0.428281
0.328653
0.214897
0.07612
0.785042
0.209433
2.193814
0.491575
0.865374
2.21175
0.962287
0.467635
0.588295
0.302619
0.214897
0.178608
0.778242
0.467635
0.588295
0.110042
0.113057
0.379652
0.220258
0.556487
0.555315
1.308817
0.575726
1.509451
0.556487
0.555315
0.030527
0.102619
0.042711
0.10092
0.035575
0.05113
0.033432
0.011842
0.020065
0.122132
0.091523
0.072752
0.354702
0.088048
0.141185
0.360136
0.162793
0.066629
0.05113
0.033432
0.011842
0.122132
0.032582
0.3413
0.076476
0.134629
0.34409
0.149707
0.072752
0.091523
0.04708
0.033432
0.027787
0.121074
0.072752
0.091523
0.01712
0.017589
0.059064
0.034266
0.086575
0.086392
0.203617
0.089568
0.234831
0.086575
0.086392
39.05104
39.05101
39.05277
39.05409
39.05374
39.05134
39.05142
39.05342
39.05535
39.05549
38.99609
38.99572
39.05118
39.05122
39.05082
39.05222
39.05392
39.05243
39.05135
39.05139
39.05333
39.05532
39.01616
39.05112
39.05119
39.05081
39.05258
39.05384
38.99562
38.99592
39.01577
39.05151
39.05336
39.05571
38.99571
38.996
39.03125
39.0314
39.0316
39.03159
39.03043
39.03
39.03036
39.03084
39.0316
39.03031
39.02986
-77.129
-77.1316
-77.1359
-77.1431
-77.143
-77.1332
-77.1335
-77.1345
-77.1482
-77.1479
-77.1754
-77.1754
-77.1281
-77.1286
-77.1321
-77.1361
-77.1432
-77.1395
-77.1334
-77.1337
-77.134
-77.148
-77.1559
-77.1274
-77.1291
-77.1317
-77.1361
-77.1428
-77.1752
-77.1753
-77.1553
-77.1341
-77.1345
-77.1491
-77.1753
-77.1753
-77.1367
-77.1369
-77.1369
-77.1371
-77.1397
-77.1397
-77.1383
-77.1377
-77.1386
-77.1401
-77.1396

-------
Appendix H. Maryland watersheds with TMDLs for total Suspended Solids.

TSS TMDL
Countyl County2

# WWTP
Riverine Systems



Cabin John Cr
Montgomery
non-tidal
0
Riverine Systems Most Similar to Cabin John Creek


Bynum Run
Harford
non-tidal
0
Gwynns Falls
Baltimore Baltimore City
non-tidal
0
Potomac R
Montgomery
non-tidal
0
Riverine Systems Similar to Cabin John Creek but with WWTP discharges

Jones Falls
Baltimore Baltimore City
non-tidal
1
Rock Creek
Montgomery
non-tidal
1
Anacostia
Montgome Anne Arundel, Prin tidal, non-tidal
2
Other Riverine Systems



Upper Pocomoke
Worcester Wicomico
non-tidal
2
Lower Monacacy
Frederick Montgomery
non-tidal
11
Patapsco River LNB
Baltimore Howard
non-tidal
1
Patuxent R U
Anne Arum Prince Georges
non-tidal
3
L Patuxent
Howard Anne Arundel
1 non-tidal/4 tidal
1
Seneca Creek
Montgomery
non-tidal
2
Upper Monocacy
Frederick Carroll
non-tidal
10
Double Pipe Creek
Frederick Carroll
non-tidal
5
Catoctin Cr
Frederick
non-tidal
6
Potomac R WC
Washington
non-tidal
0
Antietam Cr
Washington
non-tidal
12
Conococheague Cr
Washington
non-tidal
1
Evitts Cr
Allegheny
non-tidal
2
Wills Cr
Allegheny
non-tidal
0
Georges Cr
Allegheny Garrett
non-tidal
2
Youghiogheny R
Garrett
non-tidal
1
L Youghiogheny
Garrett
non-tidal
1
Lakes and Reservoirs



Clopper Lake
Montgomery


Big Millpond
Worcester


Centennial Lake
Howard


Rocky Gorge and Triadelphia
Reservoirs Howard Montgomery


Loch Raven Reservoir
Baltimore


Johnson Pond
Wicomico


Tony Tank
Wicomico


Lake Linganore
Frederick


Adkins Pond
Wicomico


Urieville Lake
Kent


Liberty Reservoir
Carroll



-------
Percent Percent	Percent	Percent
urban urban pervious urban impervious %imp/%perv cropland
90.40%	73.30%	16.90%	019	0.6
71.20% 60.90% 9.70% 0.14 7
87.50% 54.00% 32.70% 0.38 2.7
93.20%	34.80%	6.90%	017	0
73.90%	53.50%	20.20%	0.27	2.7
79.80%	59.90%	19.40%	0.24	3.4
75	58.5	16.5	0.22	5
5.70%
2.4
1.3
0.35
28.90%
31.7
25.3
6
0.19
25.8
59.20%
43.30%
15.80%
0.27
4.6
40.50%
30.30%
9.70%
0.24
8.7
19.90%



11.2
38.50%
30.50%
7.50%
0.20
20.7
14.80%
12.90%
1.80%
0.12
33.1
18.70%
16.40%
2.10%
0.11
41.4
16.60%
15.20%
1.20%
0.07
25.9
11.90%
10.80%
1.10%
0.09
26.3
11.40%
20.00%
5.20%
0.21
35.5
9.90%
22.10%
6.00%
0.21
32.9
21.30%
19.60%
1.60%
0.08
9.5
20.60%
18.60%
2.00%
0.10
4.9
15.70%
14.60%
1.10%
0.07
4.9
9.50%
9.00%
40.00%
0.82
9.6
20.00%
17.50%
2.30%
0.12
17.5

-------
Notes
1 WTP
minor wwtps
minor univ
17-26% connected impervfor nontidal
minor local wwtp
one water reclamation + addl minor wwtp
minor wwtp; most stormwater unregulated
plus minor wwtps
upstream PS from Double Pipe and PA
WTPs
minor, 1 WTP
CSO
1 major, + 2 CSO, 1 WTP
minor
minor WWTP, minor WTP

-------
2010
2010
2010
2010
2010
2010
2010
2010
2010
2011
2011
2012
2011
2011
2011
2011
2010
2010
2011
2009
2010
2011
2011
2011
2011
2011
2009
2010
2010
2011
2011
2009
2011
2009
2009
2009
2009
2010
2010
2011
2011
2011
2009
2011
2011
2009
2009
M016256
M016257
M016258
M016259
MO16260
M016261
M016262
M016295
M016469
M016691
M016692
M016693
M016694
M016876
MO16880
M016881
M016882
M017181
M017182
MO17201
M017211
M017217
M017218
M017219
M017222
M017223
M017224
M017236
M017237
MO17240
M017241
M017441
M017445
M017471
M017472
M017473
M017479
M017484
M017496
M017513
M017514
M017516
M017548
M017549
M017552
MO17580
M017611
Filtering Pr
Filtering Pr
Filtering Pr
Filtering Pr
Filtering Pr
Filtering Pr
Filtering Pr
Dry Detent
Dry Detent
Dry Swale
Dry Swale
Dry Swale
Dry Swale
Permeable
Permeable
Permeable
Rain Garde
Permeable
Permeable
Dry Swale
Dry Detent
Dry Detent
Dry Detent
Dry Detent
Dry Detent
Dry Detent
Dry Detent
Bioretentic
Bioretentic
Bioretentic
Bioretentic
Dry Detent
Dry Detent
Dry Detent
Dry Detent
Dry Detent
Dry Detent
Dry Detent
Infiltration
Infiltration
Infiltration
Bioretentic
Wet Ponds
Wet Ponds
Permeable
Filtering Pr
Filtering Pr
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
1.67566
1.672132
1.733594
1.733594
4.54517
0.931602
0.446977
1.733594
1.056333
0.060115
0.087581
0.097184
0.060858
0.064327
0.017645
0.021359
0.209828
0.087828
0.168635
0.337797
1.174769
1.101314
0.06048
0.067986
0.434013
0.547331
0.752652
0.23876
0.358473
0.384388
1.006958
0.752652
0.854885
0.676716
0.465257
4.012055
0.338357
0.23876
1.079114
0.147104
0.086197
0.956744
39.54277
2.816016
0.09241
29.77858
0.676716
0.556487
0.555315
0.575726
0.575726
1.509451
0.309385
0.148441
0.575726
0.350808
0.019964
0.029086
0.032275
0.020211
0.021363
0.00586
0.007093
0.069684
0.029168
0.056004
0.112182
0.390141
0.365746
0.020085
0.022578
0.144136
0.181769
0.249956
0.079292
0.119049
0.127655
0.334411
0.249956
0.283907
0.224737
0.154512
1.332404
0.112368
0.079292
0.358374
0.048853
0.028626
0.317735
13.13215
0.935199
0.030689
9.889467
0.224737
0.086575
0.086392
0.089568
0.089568
0.234831
0.048132
0.023093
0.089568
0.054576
0.003106
0.004525
0.005021
0.003144
0.003324
0.000912
0.001104
0.010841
0.004538
0.008713
0.017453
0.060696
0.0569
0.003125
0.003513
0.022424
0.028278
0.038887
0.012336
0.018521
0.01986
0.052026
0.038887
0.044169
0.034963
0.024038
0.207287
0.017482
0.012336
0.055754
0.0076
0.004453
0.049431
2.043017
0.145492
0.004774
1.53854
0.034963
39.03035
39.02997
39.03054
39.03079
39.03166
39.03156
39.03145
39.03072
39.01282
38.98533
38.98862
38.98721
38.98551
38.98982
38.98982
38.98956
38.99445
39.04828
39.00825
39.04987
39.05376
39.02882
39.02763
39.02775
39.0274
39.02711
39.02971
39.06011
39.06045
39.02957
39.00306
39.02971
39.00292
38.99107
38.9913
38.99243
39.04729
39.06008
39.00026
39.00268
39.00273
39.01844
39.03096
39.02868
38.98456
39.02974
38.9912
-77.1399
-77.1396
-77.1382
-77.1379
-77.1387
-77.137
-77.1367
-77.1378
-77.1526
-77.1243
-77.124
-77.1257
-77.1243
-77.1104
-77.1105
-77.1105
-77.1383
-77.1617
-77.1369
-77.1599
-77.1428
-77.1373
-77.1386
-77.1374
-77.1358
-77.1362
-77.1314
-77.1562
-77.1563
-77.1379
-77.1272
-77.1313
-77.1275
-77.1501
-77.15
-77.1246
-77.1147
-77.1562
-77.1883
-77.1267
-77.1274
-77.1871
-77.1329
-77.137
-77.1341
-77.1314
-77.15

-------
2009
2009
2009
2009
2011
2011
2009
2009
2009
2011
2010
2003
2003
2003
2003
2012
2012
2012
2012
2012
2012
2007
2007
2013
2013
2014
2014
2014
2014
2014
2013
2013
2013
2013
2013
2013
2012
2012
2012
2012
2013
2013
2012
2014
2014
2014
2014
M017612
M017613
M017614
MO17620
M017637
M017648
MO17660
M017661
M017664
M017668
M017671
M017694
M017716
MO17720
M017721
M017729
MO17750
M017761
M017772
M017776
MO17804
M017838
MO17870
M017915
M017916
M017923
M017924
M017925
M017926
M017927
M017958
M017966
M017967
M017968
M017969
MO17970
M017995
M017996
M017997
M017998
M017999
M018010
MO18011
MO18022
MO18023
MO18024
MO18025
Filtering Pr
Dry Detent
Dry Detent
Dry Detent
Dry Swale
Dry Swale
Dry Detent
Dry Detent
Dry Detent
Dry Detent
Dry Detent
Bioretentic
Dry Detent
Dry Detent
Filtering Pr
Bioretentic
Dry Detent
Green Roo1
Filtering Pr
Filtering Pr
Bioretentic
Dry Detent
Dry Detent
Bioretentic
Bioretentic
Bioretentic
Bioretentic
Bioretentic
Bioretentic
Bioretentic
Green Roo1
Infiltration
Bioretentic
Bioretentic
Bioretentic
Bioretentic
Filtering Pr
Filtering Pr
Filtering Pr
Filtering Pr
Dry Detent
Dry Detent
Dry Detent
Bioretentic
Bioretentic
Bioretentic
Bioretentic
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
0.465257
4.012055
4.012055
0.338357
0.318321
0.07981
0.465257
1.236235
1.236235
0.136035
0.23876
0.214729
2.562706
1.493198
0.877047
0.472349
0.474242
0.456021
1.308214
0.474242
0.304957
2.199745
2.559776
0.472628
0.47105
0.920115
0.341919
0.333237
0.127375
0.134528
0.656868
0.457587
0.033493
0.021212
0.040652
0.062203
2.062539
2.701441
0.688609
4.208922
0.931056
0.931056
4.208922
0.344332
1.627122
0.848637
0.586452
0.154512
1.332404
1.332404
0.112368
0.105714
0.026505
0.154512
0.410554
0.410554
0.045177
0.079292
0.071312
0.851075
0.495891
0.291267
0.156867
0.157496
0.151444
0.434458
0.157496
0.101276
0.730535
0.850101
0.15696
0.156436
0.30557
0.113551
0.110668
0.042301
0.044677
0.218146
0.151965
0.011123
0.007045
0.0135
0.020658
0.684969
0.897149
0.228687
1.397783
0.309204
0.309204
1.397783
0.114353
0.540367
0.281832
0.194761
0.024038
0.207287
0.207287
0.017482
0.016446
0.004123
0.024038
0.063871
0.063871
0.007028
0.012336
0.011094
0.132405
0.077148
0.045314
0.024404
0.024502
0.023561
0.06759
0.024502
0.015756
0.113652
0.132253
0.024419
0.024337
0.047539
0.017666
0.017217
0.006581
0.006951
0.033938
0.023642
0.00173
0.001096
0.0021
0.003214
0.106563
0.139573
0.035578
0.217458
0.048104
0.048104
0.217458
0.01779
0.084067
0.043846
0.0303
38.99143
38.99237
38.9924
39.04736
39.05833
38.99512
38.99146
38.99242
38.99235
39.0475
39.0601
38.98739
38.9883
38.9883
38.98794
39.04782
39.04741
39.04721
39.04636
39.04743
39.04294
39.04158
39.04152
39.05157
39.05279
39.03181
39.03154
39.03104
39.03119
39.0312
39.05199
39.05219
39.05136
39.05183
39.05224
39.0524
39.01223
39.01237
39.01262
39.01269
39.05187
39.05176
39.01305
39.00873
39.00897
39.00925
39.00883
-77.1499
-77.1246
-77.1246
-77.1146
-77.1677
-77.112
-77.1498
-77.1248
-77.1248
-77.1207
-77.1562
-77.1736
-77.1732
-77.1735
-77.1731
-77.1454
-77.1449
-77.1453
-77.1447
-77.145
-77.1387
-77.16
-77.1597
-77.1773
-77.1755
-77.1715
-77.1723
-77.1723
-77.1716
-77.1715
-77.1765
-77.1771
-77.1769
-77.1773
-77.1755
-77.1757
-77.1616
-77.1608
-77.1608
-77.1606
-77.1759
-77.1762
-77.1607
-77.1287
-77.1291
-77.1286
-77.1278

-------
2014
2014
2014
2014
2014
2014
2014
2014
2007
2007
2007
2012
2012
2012
2012
2012
2013
2012
2012
2012
2012
2012
2012
2012
2012
2012
2012
2012
2012
2012
2012
2012
2012
2012
2014
2010
2010
2011
2011
2010
2010
2010
2010
2010
2010
2010
2010
MO18026
MO18045
MO18046
MO18048
MO18160
M018161
M018162
M018163
M018257
M018273
M018282
M018373
M018374
M018382
M018386
M018387
MO 1840 2
M019219
MO19230
M019248
MO19270
M019314
M019344
M019345
M019357
M019371
M019374
M019376
M019378
M019379
MO19380
M019383
M019395
M019396
M020097
SWM105
SWM116
SWM158
SWM165
SWM176
SWM177
SWM178
SWM179
SWM180
SWM181
SWM182
SWM183
Bioretentic
Dry Detent
Dry Detent
Green Roo1
Bioretentic
Bioretentic
Bioretentic
Bioretentic
Dry Detent
Dry Detent
Dry Detent
Dry Detent
Dry Detent
Bioretentic
Bioretentic
Bioretentic
Bioretentic
Dry Detent
Green Roo1
Infiltration
Bioretentic
Wet Ponds
Dry Detent
Dry Detent
Dry Swale
Dry Swale
Dry Swale
Dry Swale
Dry Swale
Dry Swale
Dry Swale
Dry Swale
Dry Detent
Dry Detent
Permeable
Infiltration
Dry Detent
Wet Ponds
Dry Detent
Filtering Pr
Dry Detent
Filtering Pr
Dry Detent
Filtering Pr
Dry Detent
Filtering Pr
Dry Detent
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
1.961304
1.961304
0.586452
0.2795
0.412619
0.993899
1.768243
3.705814
2.049748
2.049748
0.354579
1.614444
0.327337
0.026774
0.93408
1.42037
0.02219
1.614444
0.098549
0.149752
0.282477
2.766904
1.614444
1.614444
0.049569
0.40909
0.126222
1.062874
0.12654
0.527842
0.119186
0.117071
1.614444
3.104751
0.575322
0.027841
0.190616
0.3486
0.212239
0.085932
0.224142
0.147105
0.126882
1.3784
0.309616
1.08827
0.002998
0.651349
0.651349
0.194761
0.092822
0.137031
0.330074
0.587234
1.230701
0.680721
0.680721
0.117756
0.536157
0.108709
0.008892
0.310208
0.471705
0.007369
0.536157
0.032728
0.049733
0.093811
0.918889
0.536157
0.536157
0.016462
0.135859
0.041918
0.35298
0.042024
0.175296
0.039582
0.038879
0.536157
1.031088
0.191065
1.491675
1.64574
3.653803
0.007405
2.004534
0.120859
3.532849
0.068416
5.028769
0.166946
3.128559
0.353381
0.101333
0.101333
0.0303
0.014441
0.021318
0.051351
0.091358
0.191465
0.105902
0.105902
0.01832
0.083412
0.016912
0.001383
0.04826
0.073385
0.001146
0.083412
0.005092
0.007737
0.014594
0.142955
0.083412
0.083412
0.002561
0.021136
0.006521
0.054914
0.006538
0.027272
0.006158
0.006049
0.083412
0.16041
0.029725
0.231984
0.255944
0.568237
0.001152
0.311744
0.018796
0.549426
0.01064
0.782071
0.025964
0.486551
0.054958
39.00954
39.00959
39.0088
39.00887
39.02433
39.02466
39.02383
39.02437
38.9921
38.99207
38.99171
38.99223
39.03125
39.03088
39.05522
39.05465
39.01969
38.99219
39.03139
38.98868
39.03986
39.03176
38.99228
38.99225
39.03101
39.05495
38.98383
39.05447
39.03076
39.03164
38.98383
39.0011
38.99229
38.99208
39.00127
39.00026
39.05243
39.02868
39.0475
39.05119
39.05122
39.05081
39.05082
39.05258
39.05222
39.05384
39.05392
-77.1278
-77.1278
-77.1278
-77.1287
-77.1276
-77.1294
-77.1291
-77.1293
-77.1452
-77.1452
-77.1453
-77.1606
-77.1403
-77.1407
-77.1242
-77.1243
-77.1307
-77.1605
-77.1401
-77.1201
-77.1567
-77.1407
-77.1605
-77.1605
-77.1407
-77.1241
-77.1242
-77.1242
-77.1408
-77.1403
-77.1244
-77.1238
-77.1607
-77.1603
-77.123
-77.1883
-77.1395
-77.137
-77.1207
-77.1291
-77.1286
-77.1317
-77.1321
-77.1361
-77.1361
-77.1428
-77.1432

-------
2010
2010
2010
2010
2010
2010
2010
2010
2011
2011
2011
2011
2011
2010
2010
2010
2010
2011
2011
2011
2012
2012
2012
2010
2010
2010
2010
2010
2010
Dry Detent
Infiltration
Infiltration
Infiltration
Infiltration
Dry Detent
Dry Detent
Dry Detent
Infiltration
Dry Detent
Dry Detent
Dry Detent
Dry Detent
Filtering Pr
Dry Detent
Infiltration
Dry Detent
Dry Detent
Infiltration
Dry Swale
Infiltration
Infiltration
Dry Swale
Dry Detent
Dry Detent
Dry Detent
Dry Detent
Dry Detent
Dry Detent
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
Montgome
0.002793
0.085189
0.637333
0.005671
0.050694
0.622992
0.17294
0.39648
0.057427
0.479994
0.711711
0.697345
0.512887
0.824311
0.260197
0.185394
0.299693
0.002108
0.111026
0.003617
0.02742
0.040769
0.017338
0.035173
0.124975
0.046071
0.000089
0.499821
0.187604
0.007503
4.896795
1.191877
0.117065
0.242257
0.335921
1.160517
0.213781
0.335159
0
0.022804
0.013613
0.001593
8.568173
0.1403
0.099965
0.315247
0.207424
0.196633
0.005904
0.014785
0.021983
0.016392
0.390438
0.004883
0.024842
0.130693
0.698568
0.316639
0.001167
0.761546
0.18536
0.018206
0.037676
0.052237
0.180483
0.033248
0.052124
0
0.003547
0.002117
0.000248
1.332516
0.02182
0.015545
0.049027
0.032258
0.03058
0.000918
0.002299
0.003419
0.002549
0.060721
0.000759
0.003863
0.020325
0.108641
0.049243
39.04663
38.99271
38.99255
38.99077
38.99076
38.99135
38.99179
38.9912
39.01144
39.05705
39.0572
39.05717
39.0568
39.05112
39.05118
39.04676
39.0315
39.04516
39.04442
38.98531
38.98211
38.98215
39.04661
39.03162
39.03184
39.0321
39.03163
38.99119
39.03154
-77.1172
-77.155
-77.1552
-77.1535
-77.1532
-77.1543
-77.1551
-77.1544
-77.1783
-77.1191
-77.1196
-77.1192
-77.1193
-77.1274
-77.1281
-77.1177
-77.1684
-77.1278
-77.1279
-77.126
-77.146
-77.1459
-77.1855
-77.1686
-77.169
-77.1693
-77.1681
-77.1543
-77.1691

-------
DEVELOPMENT
RF
RF
RF
RF
RF
RF
RF
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
RF
RF
ND
RF
ND
ND
RF
RF

-------
RF
ND
ND
RF
RF
RF
ND
RF
ND
ND
ND
RF
ND
ND
RF
ND
RF
RF
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
RF
ND
ND
RF
ND
ND
ND
RF
ND
RF
RF
RF
RF
RF
RF

-------
RF
RF
RF
RF
RF
RF
RF
RF
RF
RF
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND

-------
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND

-------
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND

-------
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND

-------
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND

-------
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND

-------
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND

-------
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND

-------
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND

-------
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
RF
RF
RF
RF
RF
RF
RF
RF
RF
RF
RF
RF

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RF
RF
RF
RF
RF
RF
RF
RF
RF
RF
RF
RF
RF
RF
RF
RF
RF
RF
RF
RF
RF
RF
RF
RF
RF
RF
RF
RF
RF

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WMOSTJl MDE_REF_ BMPJMAM ENTITY Units Value LAT	LONG BUILT_DATE
MDE_493 1754 Stream Res Montgome linear ft	125 39.04014 -77.1842 ########
MDE_498 1813 Stream Res Montgome linear ft	200 38.98727 -77.1495 ########
MDE_499 375 Stream Res Montgome linear ft	4646 38.98245 -77.1567 ########

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Appendix H. Maryland watersheds with TMDLs for total Suspended Solids.

TSS TMDL
Countyl County2

# WWTP
Riverine Systems



Cabin John Cr
Montgomery
non-tidal
0
Riverine Systems Most Similar to Cabin John Creek


Bynum Run
Harford
non-tidal
0
Gwynns Falls
Baltimore Baltimore City
non-tidal
0
Potomac R
Montgomery
non-tidal
0
Riverine Systems Similar to Cabin John Creek but with WWTP discharges

Jones Falls
Baltimore Baltimore City
non-tidal
1
Rock Creek
Montgomery
non-tidal
1
Anacostia
Montgome Anne Arundel, Prin tidal, non-tidal
2
Other Riverine Systems



Upper Pocomoke
Worcester Wicomico
non-tidal
2
Lower Monacacy
Frederick Montgomery
non-tidal
11
Patapsco River LNB
Baltimore Howard
non-tidal
1
Patuxent R U
Anne Arum Prince Georges
non-tidal
3
L Patuxent
Howard Anne Arundel
1 non-tidal/4 tidal
1
Seneca Creek
Montgomery
non-tidal
2
Upper Monocacy
Frederick Carroll
non-tidal
10
Double Pipe Creek
Frederick Carroll
non-tidal
5
Catoctin Cr
Frederick
non-tidal
6
Potomac R WC
Washington
non-tidal
0
Antietam Cr
Washington
non-tidal
12
Conococheague Cr
Washington
non-tidal
1
Evitts Cr
Allegheny
non-tidal
2
Wills Cr
Allegheny
non-tidal
0
Georges Cr
Allegheny Garrett
non-tidal
2
Youghiogheny R
Garrett
non-tidal
1
L Youghiogheny
Garrett
non-tidal
1
Lakes and Reservoirs



Clopper Lake
Montgomery


Big Millpond
Worcester


Centennial Lake
Howard


Rocky Gorge and Triadelphia
Reservoirs Howard Montgomery


Loch Raven Reservoir
Baltimore


Johnson Pond
Wicomico


Tony Tank
Wicomico


Lake Linganore
Frederick


Adkins Pond
Wicomico


Urieville Lake
Kent


Liberty Reservoir
Carroll



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Percent Percent	Percent	Percent
urban urban pervious urban impervious %imp/%perv cropland
90.40%	73.30%	16.90%	019	0.6
71.20% 60.90% 9.70% 0.14 7
87.50% 54.00% 32.70% 0.38 2.7
93.20%	34.80%	6.90%	017	0
73.90%	53.50%	20.20%	0.27	2.7
79.80%	59.90%	19.40%	0.24	3.4
75	58.5	16.5	0.22	5
5.70%
2.4
1.3
0.35
28.90%
31.7
25.3
6
0.19
25.8
59.20%
43.30%
15.80%
0.27
4.6
40.50%
30.30%
9.70%
0.24
8.7
19.90%



11.2
38.50%
30.50%
7.50%
0.20
20.7
14.80%
12.90%
1.80%
0.12
33.1
18.70%
16.40%
2.10%
0.11
41.4
16.60%
15.20%
1.20%
0.07
25.9
11.90%
10.80%
1.10%
0.09
26.3
11.40%
20.00%
5.20%
0.21
35.5
9.90%
22.10%
6.00%
0.21
32.9
21.30%
19.60%
1.60%
0.08
9.5
20.60%
18.60%
2.00%
0.10
4.9
15.70%
14.60%
1.10%
0.07
4.9
9.50%
9.00%
40.00%
0.82
9.6
20.00%
17.50%
2.30%
0.12
17.5

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Notes
1 WTP
minor wwtps
minor univ
17-26% connected impervfor nontidal
minor local wwtp
one water reclamation + addl minor wwtp
minor wwtp; most stormwater unregulated
plus minor wwtps
upstream PS from Double Pipe and PA
WTPs
minor, 1 WTP
CSO
1 major, + 2 CSO, 1 WTP
minor
minor WWTP, minor WTP

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Appendix I. Summary of urban stormwater BMP scenario and optimization studies
BR = bioretention, GR = green roof, IT = infiltration trench, PP = porous pavement, RB = rain barrel/cistern, GS = grassed swale, WP = wet pond, IB = infiltration BMPs, BF = biofiltration, GW = gravel wetland, RDB = retention/detention basins, SS = street sweeping, X = xeriscaping, PW = pet waste management, DP = detention pond, VF =
vegetated filter strip, FB = forested buffer, ESD = environmental site design
Ann Temp	Watershed size
Climate Ann Prec (c (deg C)	Event or year	(km2)	% IA or % dev	Site constraints? BMPs considered Aggregate BMPs? OPTimization or SCENario Cost components
Optn targets
Most cost-effective Comments
Gwangju, Korea
subtropical
not reported
Foshan, Guangdong
Province, China
subtropical
Aberjona R, MA, USA temperate
Representative
event (32.4 mm,
13.7 8.6h)
2 y 2 hr design
storm (24.8 mm) +
10 events from 10
yr record w
exceedance freq of
0.2 - 40%
4 events 21 - 81.4
22 mm
Optimal size of BR,
0.0125 85% IA	No	GR, IT, PP, RB	No
Optimal type, #,
placement:
mixed land-use Yes (limited area) RB,PP,GR	No
PP, GR, PP + GR Yes
4 TT: RB, GR -> BR
or PP -> GS ->
WP	Yes
OPT placement
MFF (mass first
flush)
Green roofs in lower
subcatchments
Peak flow, runoff
volume targets:
combination of GR,
PP in lower
watershed; HFR:
combination
throughout
watershed
Multicontrol
treatment trains
more expensive but
better control of
Constraint 60% flow flow volume, peak
volume reduction flow
Peak flow reduction
Optimal size differs
for EMC as
compared to
loading target; SS
load decreases w
BMP size for all LID,
EMC lowest at
intermediate BMP
size; min EMC for IT,
PP, GS
cost + (peak flow,
runoff volume or
Hydro logic
Footprint
Residence, HFR)
Baeketal. 2015
Chen etal. 2017
Giacomini and Joseph 2017
Mao et al. 2017
Perez-Pedini et al. 2005
Ipswich R, MA
Charles R, MA
temperate
temperate,
coastal	108.4
structural +
nonstructural
site: IB,BF,GS,PP;
neighborhood:
GW,RDP	yes
construction only
Increase instream
flow
P load reduction
Drinking water price
increase, leak
repairs for lower
instream flow
targets; added
Aquifer Storage and
Recovery and BR for
full instream flow
target	Zoltay et al. 2010
BMPs w higher P area and depth RO
efficiency in areas of treated varied by
high P loads sized HRU and site
larger	constraints	TetraTech 2009

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Appendix I. Summary of urban stormwater BMP scenario and optimization studies
BR = bioretention, GR = green roof, IT = infiltration trench, PP = porous pavement, RB = rain barrel/cistern, GS = grassed swale, WP =
vegetated filter strip, FB = forested buffer, ESD = environmental site design
wet pond, IB = infiltration BMPs, BF = biofiltration, GW = gravel wetland, RDB = retention/detention basins, SS = street sweeping, X = xeriscaping, PW = pet waste management, DP = detention pond, VF =
Ann Temp Watershed size
Ann Prec (c (deg C)	Event or year	(km2)	% IA or % dev	Site constraints? BMPs considered Aggregate BMPs? OPTimization or SCENario Cost components
Optn targets
Most cost-effective Comments
Texas A&M campus arid
18 - 45 mm events,
2y24h (114 mm),
10y24h (185 mm),
lOOy 24h (279 mm)
DP vs LID
(GR,PP,RB) vs
combined
not considered
peak flow
reduction, timing
LID more effective
for small events, DP
better than LID for 3
design storms for
peak flow reduction
but LID better
simulates pre-
development timing;
comb'n scenario
most closely
matches
predevelopment
Damodoram et al. 2010
nationwide simulations
Middle Rio Grande arid
Allegheny Co, PA
retention vs
retention + WQ
treatment vs WQ
treatment only
SS,PW,RB,X,DB
BR/RG, CW/WP,
GW/BW, IB/IT, PP,
VFS/GB
85% deer CSO
events
Results differed for
development vs
redevelopment and
commercial vs
multifamily vs
residential; ESD
benefits in reducing
SCM requirements
Newport 2014 (unpubl)
Shamsi et al 2014
Little Bear Creek,
Snohomish Co, WA
Gorst Creek + Bremertoi humid
26.6
% opportunities
Temperature
criteria not met
until riparian
buffers added;
Area: Buffer > LID >
RG/BR, VF,PP,
BS,IP,WP/WT,
nonstructural, FB
yes, by LU type
BOTH: Retrofit BMPs+/-
additional development
BMP reqts +/- canopy
flow (based on B-IBI used: GS > RG > BR > DP > WQ filtration ;
targets),
temperature
RB, BR, PP, DP
peak flow redn
FS PP, DP, add'l
dev BMPs
RB throughout at
site scale, DP at
regional scale but
not in lower
watershed due to
peak
synchronization
effects
Cost: DP > LID > WQ
filtr > FB	NHC2017
King Co
Green-Duwamish R, WA humid
720 km2
(modelled 135
hypothetical
0.404 km2
catchment: 5 LU
x 3 Soil Type x 2
Slope x 3 preen x
2 land costs)
SCEN: New + redev +/- road	decreased hi pulse
retrofits +/- nonroad	Cap + O&M + Insp/Enf + count, TSS, Tot Cu,
retrofits	Land costs	TotZn
King Co

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Appendix I. Summary of urban stormwater BMP scenario and optimization studies
BR = bioretention, GR = green roof, IT = infiltration trench, PP = porous pavement, RB = rain barrel/cistern, GS = grassed swale, WP = wet pond, IB = infiltration BMPs, BF = biofiltration, GW = gravel wetland, RDB = retention/detention basins, SS = street sweeping, X = xeriscaping, PW = pet waste management, DP = detention pond, VF =
vegetated filter strip, FB = forested buffer, ESD = environmental site design
Ann Temp	Watershed size
Location	Climate Ann Prec (c (deg C)	Event or year	(km2)	% IA or % dev	Site constraints? BMPs considered Aggregate BMPs? OPTimization or SCENario Cost components	Optn targets	Most cost-effective Comments	Reference
Albuquerque, NM
RB,X,DP,PW,SS
Minimize E coli
RB, X
Results sensitive to
cost of street
sweeping; Low end
cost infreq. SS
(monthly) not cost
effective compared
w RB; High cost SS:
No street sweeping,
added centralized
WP	i
Lower Grand R, OH temperate
Taiwan, Yuanshanyan R
1996-1998 (wet, 0.6 km2, 0.404
dry avg prec) km2	18.1-19.8% IA Yes
mixed; 18% res'l,
17% agr
BR, PP, RB, WP, DP,
TT
BR, GS, PP
design, constrn +
contingency
Ann vol reduction
(incr low flow, deer
hi flows)
TP, TN, TSS, BOD
% potential
utilization varied w
flow target: RG > RB Minimal peak flow
(lo target only) >
block BR > PP (more
imp for higher flow
reduction targets)
GS for open space +
PP for schools
showed greatest
load reduction
reduction seen but
incremental low
flow volume targets
met
SS redn optimal for
LID sized at 1% of
wshd area (BR, PP)
Chen et al. 2014

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