&EPA
United States
Environmental Protection
Agency
EPA/600/B-20/242
July 2020
www.epa.gov/ord
WMOST
^^atershed Managemeot^B^-
Sv>PP
ortTool^
ScenCompare with Benefits
Module
WMOST Climate Scenario Viewer
and Comparison Post Processor
Version 2
Office of Research and Development
Center for Environmental Measurement and Modeling
-------
-------
EPA/600/B-20/242
ScenCompare with Benefits Module
WMOST Climate Scenario Viewer
and Comparison Post Processor
(Version 2; July 2020)
by
Naomi Detenbeck
Atlantic Coastal Environmental Sciences Division
Center for Environmental Measurement and Modeling
Narragansett, Rl 02882
Jessica Balukas
Elena Besedin
Alyssa Le
ICF, Inc
Cambridge, MA 02140
Center for Environmental Measurement and Modeling
Office of Research and Development
U.S. Environmental Protection Agency
Narragansett, Rl 02882
-------
Notice and Disclaimer
The views expressed in this User Guide are those of the authors and do not necessarily reflect the views
or policies of the U.S. Environmental Protection Agency. This document was subjected to the Agency's
ORD review and approved for publication as an EPA document. Mention of trade names or commercial
products does not constitute endorsement.
Acknowledgements
Version 1 of this tool was supported through EPA Contract EP-C-13-039 to Abt Associates, with
contributions from Alyssa Le, Annie Brown, and Justin Stein. An early draft of Version 2 of this tool was
supported through EPA Contract EP-W-17-009 to Abt Associates, with contributions from Olivia Griot,
Liz Mettetal, R. "Karthi" Karthikeyan, and Pearl Zheng. This tool was finalized through EPA Contract
68HE0C18D0001 to ICF, Incorporated. Former ORISE participant Timothy Stagnitta also contributed to
conceptual planning.
-------
Contents
Notice and Disclaimer ii
Acknowledgements ii
List of Figures iv
Abstract v
1 Workbook Organization 1
2 Controls 4
2.1 Load WMOST Scenario Data 4
2.2 Compare Decision Variables Across Scenarios 5
2.3 Compare Overall Costs across Scenarios 6
2.4 Compare Time Series Variables Across Scenarios 7
2.5 Compare Land Management Variables Across Scenarios 7
3 Benefits Module 9
3.1 Benefits and Co-benefits Calculated by the Module 10
3.1.1 Water Quality Benefits 12
3.1.2 Non-Water Quality Co-benefits 12
3.2 Using the Benefits Module 14
3.2.1 Navigation 14
3.2.2 Data Needs 15
3.2.3 Study Characteristics (Step 1) 15
3.2.4 Calculate Direct Benefits (Step 2) 17
3.2.5 Calculate Co-benefits (Step 3) 20
4 Example Application of ScenCompare for Wading-Threemile Watershed 24
4.1 Getting Started 24
4.1.1 Run WMOST Scenarios 25
4.1.2 Load WMOST Data to ScenCompare 27
4.2 Comparing Results Across Baseline and Climate Scenarios 29
4.2.1 Compare Model Input Data 29
4.2.2 Compare Cost and Decision Variables Across Scenarios 29
4.2.3 Compare Time Series Variables Across Scenarios 30
4.3 Land Use Optimization Scenarios 33
-------
4.3.1 Compare Robustness of Land Management Variables Decisions Across Climate
Scenarios 33
4.3.2 Compare Land Management Variables Across Scenarios 35
5 References 36
Appendix A. Data needs for the Benefits Module 38
Appendix B. Default tree canopy values 41
List of Figures
Figure 1: ScenCompare Data Flow 3
Figure 2: Example of selection of scenario for deletion 5
Figure 3: Example of filter selecting variables that take different values among three scenarios 6
Figure 4: Example of selected variables related to land use management decisions 9
Figure 5: Benefit and co-benefit categories and valuation methodologies included in the
Benefits Module 11
Figure 6: Example of the Study Characteristics Step 1A of the Benefits Module 15
Figure 7: Example of the Study Characteristics Step IB of the Benefits Module 16
Figure 8: Example of the Direct Benefits Step of the Benefits Module 17
Figure 9: Button for calculating direct benefits in the Benefits Module 17
Figure 10: Button for identifying relevant BMPs for co-benefit calculations within the
Benefits Module 21
Figure 11: Example of the co-benefits Step of the Benefits Module 22
Figure 12: Button for calculating co-benefits in Step 3 of the Benefits Module 22
Figure 13: Map of the ten AVERT regions in the contiguous United States 24
List of Tables
Table 1. WMOST management practice and assumed distance from residences for the change in
property values 13
Table 2. Canopy cover benefits and descriptions of the quantification/monetization sources 13
Table 3. Green roof benefits and descriptions of the quantification/monetization sources 14
Table A.l. Data needs for the Benefits Module 38
Table B.l. Default percent tree canopy values for each vegetated NLCD land cover class 41
-------
Abstract
An updated version of the Scenario Comparison Tool (ScenCompare) has been developed that
incorporates a Benefits Module for EPA's Watershed Management Optimization Support Tool (WMOST).
WMOST was developed by the United States Environmental Protection Agency (US EPA) to facilitate
integrated water resources management. ScenCompare allows comparison and evaluation of any set of
WMOST results to understand the effects of varying climate, land use, and other model inputs on the set
of management actions selected by WMOST to meet the specified management goal at the lowest cost.
The new Benefits Module for WMOST embedded within ScenCompare v.2 enables stakeholders to
calculate the value of additional water quality benefits associated with water resource management as
well as additional co-benefits. Water quality benefits (or costs) include both changes in costs of drinking
water treatment and total nonmarket benefits (i.e., use and nonuse) of water quality changes. Co-
benefits valued include (1) change in housing property value due to improved aesthetic quality of the
landscape from increases in green space, (2) air pollution removal and energy savings benefits related to
canopy cover, and (3) air pollution removal and energy savings benefits related to green roofs.
ScenCompare Instructions and User Guide
v
-------
-------
Introduction
ScenCompare is a MS-Excel application designed to view and compare scenario results from the
Watershed Management Optimization Support Tool (WMOST) v3.01 (Detenbeck et al. 2018 a,b,c). This
version of ScenCompare is compatible with MS-Excel (Windows versions 2010, 2013 and 2016) and
WMOST v3.01. The tool is specifically intended to allow comparison of WMOST results for different
climate scenarios, but ScenCompare more generally allows comparison and evaluation of any set of
WMOST results to understand the effects of varying climate, land use, and other model inputs on the set
of management actions selected by WMOST to meet the specified management goal at the lowest cost.
For example, ScenCompare can assist users interested in applying WMOST as part of Robust Decision
Making (RDM) approaches for identifying vulnerabilities, and managing goals and risks, in the face of
uncertain future conditions. Under RDM-type approaches, the outcome of a prescribed management
strategy, such as a Watershed Implementation Plan (WIP) to meet Total Maximum Daily Load (TMDL)
requirements, can be tested against multiple scenarios of future changes in temperature and rainfall
and then analyzed within ScenCompare to determine under what conditions the strategy might be
expected to fail to meet performance requirements. Robust management solutions are those most
effective across a wide range of future conditions (Lempert and Collins 2007). Further, ScenCompare
provides WMOST users access to all outputs generated by a WMOST run, thereby expanding on the set
of standard outputs visible in the WMOST v3 interface.
These instructions focus on the process for loading WMOST results into ScenCompare, generating
summary tables comparing decision variables, and generating time series plots of user-selected
variables across scenarios. The results module in WMOST generates a results.csv file during processing
of the optimization output text file produced by the optimization program, Bonmin, which is run on
NEOS, an online server for optimization programs (https://neos-server.org/neos/).
ScenCompare users should already be familiar with WMOST model outputs being processed. Please
refer to the WMOST documentation for details on variables and modeled components included in
WMOST output files and available for processing with ScenCompare (Detenbeck et al. 2018 b,c).
WMOST variables are described in detail in the Appendix C to the theoretical documentation report
(Detenbeck et al. 2018c).
1 Workbook Organization
ScenCompare is an Excel workbook that uses customized Visual Basic for Applications (VBA) code to
automate key tasks. The initial blank workbook includes:
• Introduction tab: Describes the purpose of ScenCompare and allows the user to navigate to
the Controls, Variable Definitions and Loaded Scenarios tabs (the user can also navigate to
the tabs by clicking on them at the bottom of the screen).
• Controls tab: Provides access to steps in compiling and analyzing WMOST data.
ScenCompare Instructions and User Guide
-------
• Variable Definitions tab: Provides a description of the WMOST variables in the output data.
• Loaded Scenarios tab: Provides the inventory of output files imported by the user
• Model Results tab: Provides the WMOST model results output for each loaded file
• Model Input Data tab: Provides the model input data for each loaded scenario
• Benefits tab: Calculates benefits related to outcomes targeted by a management practice
(direct benefits) and benefits that arise from other outcomes of implementing the
management practice selected to meet the target (co-benefits). The Benefits Module
evaluates benefits and co-benefits for a set of management options from a single
optimization scenario. In order to calculate benefits, you will need to load at least one
scenario using the Controls tab (Section 2.1) and access the Benefits tab on the Loaded
Scenarios tab.
-------
c/>
o
fi>
3
Q.
C
(D
0
c
01
(D
Figure 1: ScenCompare Data Flow
ScenCompare
WMOST
Results log file
(SpecsResults.csv)
Controls - Input
Data
Always available
Variable Definitions
Exce tab
Loaded Scenarios
i J
Benefits Module
Model Input Data
\
Model Results
Data Comparison
Available after
loading scenario(s)
CO
-------
Figure 1 summarizes the data flow from WMOST through ScenCompare, including data flow between
tabs.
Section 2 describes the steps involved in compiling and analyzing WMOST data. Note that you can also
use all standard Excel functions and capabilities from within ScenCompare to customize graphics or
tables, perform calculations, etc.
2 Controls
2.1 Load WMOST Scenario Data
To load WMOST Scenario Input Data and Results into ScenCompare, click on the leftmost button on the
Controls tab labeled "Load WMOST Scenario Data". When prompted, select the model Specifications
and Results file. Note that the Specifications and Results file is a log .csv file generated by WMOST
during a model run and saved to the same file folder that contains the WMOST model. The log file
contains the model inputs and results for the run. To facilitate comparisons, you may wish to copy the
appropriate set of log files to the same directory housing ScenCompare or to a separate subdirectory.
ScenCompare adds the WMOST scenario outputs to the Model Results tab, the WMOST input data to
the Model Input Data tab, and the name of the file and other model details to the Loaded Scenarios tab.
Repeat these steps to load the data for other WMOST model runs as many times as needed to import
the desired data. There is no set limit on the number of scenarios that can be loaded; however, you may
experience a slowdown in performance if an excessive number is loaded. Note that you may add results
to ScenCompare at any time.
ScenCompare verifies that any data file loaded after the first data file has the same number of land uses,
land use sets, water users and time steps. Differences in these variables lead to mismatches in the
variable order and incorrect comparisons of data. If a difference is found, ScenCompare gives the option
to keep or discard the data that was just loaded. It is recommended that you discard the data and rerun
the model with an appropriate setup, if the comparison is still desired.
ScenCompare automatically fills in the average annual precipitation and temperature if you used the
hydrology module when developing the WMOST scenarios or used the Hydro-Climate Automation
module (HCAM). If you developed the data manually, you can enter precipitation and temperature
statistics on the Loaded Scenarios tab. ScenCompare uses this information for some of the graphs (see
Section 2.3).
Note that you may also remove scenarios from the Loaded Scenarios tab by selecting the row(s) on this
tab and clicking the button on the right side of the tab labeled "Clear Selected Scenarios".1 See Figure 2
below for an example of how to select and delete data for a scenario. To delete Scenario 2, you would
1 Note that if you have created comparison tables and graphs in Steps 2 and 3 on the Controls tab (see Sections 2.2 and 2.3 for details) and later
delete a scenario, you will need to regenerate the data tables and time series to ensure that the tables reflect only the remaining scenarios.
-------
select the row that contains Scenario 2 or the "Data Name" for the second scenario listed, and then click
the "Clear Selected Scenarios" button.
Figure 2: Example of selection of scenario for deletion
1
Loaded Files: Data Name: Scenario Name jStudyAi Scenarii RunStar RunEnd StartDa EndDati ModelMode:
kH:\ERD\ANCHOR\AssignrMonponsett_AdjB_ASASR
Monpon AdjB_AS ###### ###### ###### ###### Hydrology Only
Select row to clear scenario data.
(a
j|i:\ERD\ANCHOR\Assignr Monponsett_AdjB_wl IBT100K
Monpon AdjB_wl ###### ###### ###### ###### Hydrology Only
V
H:\ERD\ANCHOR\Assignr Monponsett_AdjB_wl IBT_500K
Monpon AdjB_wl ###### ###### ###### ###### Hydrology Only
5
H:\ERD\ANCHOR\Assigrir MonponsettNoBrock NoBrockton
Monpon NoBrock ###### ###### ###### ###### Hydrology Only
^^lear Selected Scenario^
6
~
8 |
TIP: Note that the Model Results and Model Input Data tabs provide the detailed WMOST outputs and
inputs, respectively, for each scenario. Because many of the model variables are time series, the data
set is extensive (thousands of values). You can use standard Excel filter tools (accessible through the
Data menu) to hide/show a subset of variables you are interested in. You can also use standard
equations in Excel to quickly identify variables that take on different values across the scenarios. For
convenience, ScenCompare provides a pre-calculated column that determines whether differences exist
between scenarios.
TIP: You can use the "Data Difference" column on the Model Results and Model Input Data tabs to filter
for variables that assume the same or different numerical values across the scenarios, with Data
Difference flags of 0 and 1, respectively.
2.2 Compare Decision Variables Across Scenarios
ScenCompare can be used to compare decision variables across scenarios. These are variables
representing the least-cost combination of best management practices (BMPs) to meet the
management objective (e.g., streamflow minimum threshold). For example, for stormwater BMPs,
these are generally expressed as the acres of a given Hydrologic Response Unit (HRU) assigned to
treatment by a specific BMP such as a rain garden. See the WMOST theoretical documentation and user
guides for more details (Detenbeck et al. 2018 b,c).
To compare all decision variables across scenarios, click on the button on the Controls tab labeled
"Compare Scenario Decisions". This action will create a new tab called Table Comparison containing the
values of decision variables from all model runs loaded into ScenCompare. Note that the second column
("Description") describes each variable and the third column ("Units") specifies the unit of measure for
each variable.
The rightmost column in the sheet ("Data Difference") contains a flag identifying whether there are
differences in variable values across any of the scenarios. A Data Difference value of 1 indicates that at
least one of the scenarios differs from the others for that variable.
ScenCompare Instructions and User Guide
5
-------
Figure 3: Example of filter selecting variables that take different values among three scenarios, in this example,
the scenarios show differences in total operating costs, management approaches selected (e.g., stormwater BMPs
and ASR) and level of implementation, and associated costs.
Home
l/a) From Access
From Web
Insert Page Layout
1
From Other Existing Refresh
From Text Sources - Connections All -r
Formulas Data
01 Connections
Properties
rWMOST ScenCompare_012517.xlsm"?' Microsoft Excel
uance PDF
Get External Data
Edrt Links
Connections
Review
ar 1--^ I H H ^ ^ata Validation ' ^ Group ' *1 IP| D
pply ^Consolidate ^ Ungroup " ™f
Text to Remove rffl
Ivanced columns Duplicates ^ What-If Analysis - W Subtotal
Data Tools Outline fl i
H162
jl A B C
1 Variable ^ Description [T fUnits
2 objective Objective cost $
35 DALu33 Land Area - 0.6" Infiltrat ac
36 DALu43 Land Area - 0.6" Infiltrat ac
37 DALu53 Land Area - 0.6" Infiltrat ac
43 DALull3 Land Area - 0.6" Infiltrat ac
66 DALu45 Land Area -1" Infiltratio ac
67 DALu55 Land Area -1" Infiltratio ac
73 DALull5 Land Area -1" Infiltratio ac
111 DQAsrAddl Additional ASR capacity MGD
125 CLuSet3 0.6" Infiltration trench - $/yr
127 CLuSetS 1" Infiltration trench - Lc $/yr
135 CWtp Total cost of potable wa $/yr
146 CCAsr Capital cost of aquifer s1 $/yr
147 CAsr Total cost of aquifer stoi $/yr
150 ClbtW Total cost of potable int $/yr
154 CWMake Penalty for water deficit $/yr
156
18186396.09
468.9155099
188.44859
56.29038155
2.463176949
133.4065532
4.51856203
-3.26569E-16
186.1776694
160377.4126
70982.79257
466450.5466
16315025.63
17488585.34
0
0
466450.5466
0
0
0
0
0
0
0
0
0
0
466450.5466
0
0
0
0
F b
yj Data Difference
1635461.
0
0
52.40900839
0
0
0
0
0
62698.78515
0
283139.1895
0
0
183311.3571
1106311.856
TIP: You can use standard Excel filter tools (accessible through the Data menu) to hide/show a subset of
variables you are interested in, or to show only the variables that have Data Difference of 1.
2.3 Compare Overall Costs across Scenarios
Clicking on the "Make Climate Graphs" on the Table Comparison tab (see Section 2.2) creates a new tab
called ClimateGraph objective. This tab contains three climate plots:
1) a scatterplot that charts the objective cost versus average annual precipitation,
2) a scatterplot that charts the objective cost versus average annual temperature, and
3) a bubble plot that charts the objective costs versus total annual precipitation and average
annual temperature. The area of each bubble is scaled according to the total cost, with the
maximum size determined by the maximum cost in the data set
The "objective" value is the total annualized cost of all watershed management actions taken to meet
the specified objective (i.e., meeting water demand subject to physical constraints and water quantity
and/or quality targets).
As discussed in Section 2.1, if you did not use the hydrology module in WMOST, thejnode! Specifications
and Results file does not automatically include statistics for average annual precipitation and average
annual temperature. This means that ScenCompare cannot automatically extract the information
needed to create the climate graphs. To rectify this issue, you can enter the precipitation and
6
-------
temperature statistics for each scenario on the Loaded Scenarios tab before creating the tables and
ScenCompare will use those values to create the climate graphs.
2.4 Compare Time Series Variables Across Scenarios
ScenCompare can also be used to compare time series variables such as discharge (Q variables),
concentration (X variables), or loads (L variables) across scenarios. These are variables representing
time-dependent flows or stocks in the modeled watershed components, listed on the Controls tab.
To compare time series variables across scenarios, place the cursor in the cell containing the first
variable of interest in the list under Step 3 on the Controls tab, and click on the button labeled "Create
Tables and Graphs for Selected Variables"2 to the right of the data columns. This action creates a new
tab called Table nn where nn is the name of the selected variable. This new tab provides summary
statistics for the selected variables for each scenario in ScenCompare (e.g., minimum, maximum,
average, and number of observations greater than 0 [as the default threshold]) along with values for
each time step.
This action also creates three plots on the time series variable tab:
1) a time series plot of the variable over the time period,
2) a histogram of the count of time steps (e.g., number of days if WMOST was run using a
daily time step) for which the variable takes a value greater than the "Count Threshold"
for each scenario, and
3) a box-and-whisker plot that shows the minimum, 1st quartile, median, 3rd quartile, and
maximum for each scenario.
You can use the button on the right, "Make Climate Graphs", to create a new tab called
ClimateGraph nn that contains three climate graphs that compare the average value over the entire
time series across the scenarios. Refer to Section 2.3 for more details on the climate graphs and how
they are created.
TIP: You can change the "Count Threshold" from its default value of 0 to any number, and the tab
adjusts the count statistics and histogram to reflect the new threshold.
TIP: You can use standard Excel tools to add or modify the formatting of the basic plot generated
automatically by ScenCompare.
2.5 Compare Land Management Variables Across Scenarios
ScenCompare can also be used to compare least-cost land management solutions. The variables are
described in the second column on the Table Comparison tab and in the Variable Definitions tab based
on the management option and the HRU. For example, Land Area - 0.6" Infiltration trench, "Medium to
low density residential, Sand and Gravel" contains the acres of medium to low density residential land
2 Note that you may select multiple variables to be processed at the same time by holding the control key and clicking on all desired variable
names before clicking on the button.
ScenCompare Instructions and User Guide
7
-------
on sand and gravel (the HRU represented by the combination of land use and soil type) for which
WMOST decided to implement a 0.6" infiltration trench.
Each of the land use management options (HRU sets) has an associated cost variable that contains the
cost associated with the decision. The variables are named CLuSet#, where # is the number of the HRU
Set. Details on BMP performance and cost calculations are in the WMOST documentation (Detenbeck et
al. 2018 b,c).
To compare land use allocation variables, look at each land use management set individually. By
convention, the first land use management set represents land conservation decisions. All other sets
are related to stormwater management decisions.
• Land Conservation: One of the management options available in WMOST is the decision to
conserve undeveloped land. The decision essentially reallocates baseline land use to
undeveloped land uses, keeping the total land area the same. The final land area allocation is
reported through the first set of DALu variables (all DALu variables for HRU set 1). To determine
whether land area was conserved, you can first look at the CLUSetl variable to see if it is greater
than $0 for any of the scenarios, which would indicate that WMOST incurred costs to conserve
land.3 To determine how much land area was conserved, look at the DALu variables and
compare values to the baseline acres you had specified in your WMOST run.4 The resulting
difference is the change due to land conservation. A positive difference means more land was
conserved, and a negative difference means the land was converted to undeveloped areas or
conserved.5
• Stormwater Management: WMOST may also implement stormwater BMPs on developed HRU
areas. The areas managed using stormwater BMPs are reported in the remaining DALu variable
sets. The values represent the number of acres receiving the type of stormwater BMP defined
by the HRU set, e.g., acres of medium to low density residential on sand and gravel managed
using a 0.6" Infiltration trench. The number of acres will be a portion of the HRU area reported
in the first management set described above. WMOST may select multiple stormwater BMP
types for any given scenario but the total acres managed across the HRU sets cannot exceed the
total area, i.e., stormwater BMPs are mutually exclusive and WMOST applies only one type of
BMP to any given parcel of land.
TIP: To only view results that relate to land use management, you can use the MS-Excel filter tool to
select the relevant variables: DALu## and CLuSet#. The filter should be reset to full display before
applying other functions such as graphing, however. The MS-Excel sort function should not be used as it
may make comparisons across columns meaningless.
3 This presumes that you specified non-zero costs to acquire land for conservation in your WMOST inputs.
4 Note that the baseline HRU acres are reported in the Model Input Data tab, using the variable ALuBase and HRU number.
5 Note that decisions to conserve land will not be flagged as a change in the Data Difference column in Table Comparison tab unless the
allocations differed across scenarios.
-------
Figure 4: Example of selected variables related to land use management decisions. The screen shows differences
in the number of acres managed using 0.6" infiltration trenches for several MRUs.
[yfr A "J " WMOST ScenCompare_012517.xlsm - Microsoft ExceT^
I Home Insert Page Layout Formulas Data Review View Nuance PDF
1
n *
Calibri ~ 11
- A* a"
— = = Wrap Text
General
m m
m
a*3
1*
m
^ -
Paste
. j
Clipboard n
B I U - FR "
Font
3» - iAi-
5
W W ^ WW §i Merge & Center »
Alignment R
$ * % ' too i°0
Number r* |
Conditional Format as Cell
Formatting ' Table - Styles "
Styles
H3
-F
f*r
A
B
1 c 1
D
E
F
G
c
Variable^ Description
" Unil - wASR
M
Mo Brockton ~ wlBT
"*¦ Data Difference
-
V
"^Tand Area - Land Area with Conservation, "Forest, Sand and Gravel" ac
833.6582144
833.6582144
833.6582144
°L
4
DALu21
Land Area - Land Area with Conservation, "Open nonresidential, Sar ac
74.36874457
74.36874457
74.36874457
0
5
DALu31
Land Area - Land Area with Conservation, "Medium to low density re: ac
597.9906384
597.9906384
597.9906384
0
G
DALu41
Land Area - Land Area with Conservation, "High-density residential, ac
321.8551432
321.8551432
321.8551432
0
7
DALu51
Land Area - Land Area with Conservation, "Commercial-industrial-trc ac
60.80894358
60.80894358
60.80894358
0
8
DALuGI
Land Area - Land Area with Conservation, "Agriculture, Sand and Gr ac
10.34096625
10.34096625
10.34096625
0
9
DALu71
Land Area - Land Area with Conservation, "Forest, Till & fine-grainec ac
161.0035535
161.0035535
161.0035535
0
10
DALu81
Land Area - Land Area with Conservation, "Open nonresidential. Till ac
1.739357974
1.739357974
1.739357974
0
11
DALu91
Land Area - Land Area with Conservation, "Medium to low density re: ac
28.63970334
28.63970334
28.63970334
0
12
DALu101
Land Area - Land Area with Conservation, "High-density residential, ac
61.81480419
61.81480419
61.81480419
0
13
DALu111
Land Area - Land Area with Conservation, "Commercial-industrial-trc ac
2.463176949
2.463176949
2.463176949
0
14
DALu121
Land Area - Land Area with Conservation, "Agriculture, Till & fine-gr ac
12.6231485
12.6231485
12.6231485
0
15
DALu131
Land Area - Land Area with Conservation, "Cranberry bogs, Combint ac
340
340
340
0
1G
DALu141
Land Area - Land Area with Conservation, "Forested wetland, Combit ac
64.2
64.2
64.2
0
17
DALu151
Land Area - Land Area with Conservation, "Nonforested wetlands, Cc ac
47.3
47.3
47.3
0
18
DALu12
Land Area - 0.6" Bioretention w
th UD, "Forest, Sand and Gravel"
ac
0
0
0
0
19
DALu22
Land Area - 0.6" Bioretention w
th UD, "Open nonresidential. Sand ai ac
0
0
0
0
20
DALu32
Land Area - 0.6" Bioretention w
th UD, "Medium to low density reside ac
0
0
0
0
21
DALu42
Land Area - 0.6" Bioretention w
th UD, "High-density residential, San ac
0
0
0
0
22
DALu52
Land Area - 0.6" Bioretention w
th UD, "Commercial-industrial-transp ac
0
0
0
0
23
DALuG2
Land Area - 0.6" Bioretention w
th UD, "Agriculture, Sand and Gravel ac
0
0
0
0
24
DALu72
Land Area - 0.6" Bioretention w
th UD, "Forest, Till & fine-grained de| ac
0
0
0
0
25
DALu82
Land Area - 0.6" Bioretention w
th UD, "Open nonresidential. Till &
fi ac
0
0
0
0
26
DALu92
Land Area - 0.6" Bioretention w
th UD, "Medium to low density reside ac
0
0
0
0
27
DALu102
Land Area - 0.6" Bioretention w
th UD, "Hi gh-densi ty resi denti al, Ti 11 ac
0
0
0
0
28
DALu112
Land Area - 0.6" Bioretention w
th UD, "Commercial-industrial-transp ac
0
ol
0
0
29
DALu122
Land Area - 0.6" Bioretention w
th UD, "Agriculture, Till & fine-graine ac
0
0
0
0
30
DALu132
Land Area - 0.6" Bioretention w
th UD, "Cranberry bogs. Combined" ac
0
0
0
0
31
DALu142
Land Area - 0.6" Bioretention w
th UD, "Forested wetland. Combined' ac
0
0
0
0
32
DALu152
Land Area - 0.6" Bioretention w
th UD, "Nonforested wetlands, Combi ac
0
0
0
0
33
DALu13
Land Area - 0.6" Infiltration trench, "Forest, Sand and Gravel"
ac
0
0
0
0
34
DALu23
Land Area - 0.6" Infiltration trench, "Open nonresidential. Sand and I ac
J,
n
35
DALu33
Land Area - 0.6" Infiltration trench, "Medium to low density residents ac
468.9155099
0
0
1
36
DALu43
Land Area - 0.6" Infiltration trench, "High-density residential. Sand a ac
188.44859
0
0
1
37
DALu53
Land Area - 0.6" Infiltration trench, "Commercial-industrial-transport; ac
56.29038155
0
52.40900839
1
38
DALuG3
Land Area - 0.6" Infiltration trench, "Agriculture, Sand and Gravel"
ac
0
0
0
0
39
DALu73
Land Area - 0.6" Infiltration trench, "Forest, Till & fine-grained depos ac
0
0
0
0
40
DALu83
Land Area - 0.6" Infiltration trench, "Open nonresidential. Till & fine- ac
0
0
0
0
41
DALu93
Land Area - 0.6" Infiltration trench, "Medium to low density residents ac
0
0
0
0
42
DALu103
Land Area - 0.6" Infiltration trench. "Hiah-densitv residential. Till &
fi ac
0.
0
0
0
H
4 ~ ~!
Controls Variable Definitions
Loaded Scenarios / Model Results , Model Input Data J Table
_Comparison
ClimateGra
rfl <
Ready Filter Mode | | H
3 Benefits Module
The WMOST v3/v3.1 optimization model is currently driven by an objective function that seeks to
minimize total annual costs. From the various management options input by the user, WMOST helps
determine which options would achieve the specified water quantity and quality6 targets at the lowest
At this time, WMOST can only model one constituent at a time. For example, WMOST can optimize the least cost combination of
management options related to reducing in-stream phosphorus concentrations.
ScenCompare Instructions and User Guide
9
-------
total annual cost, where the total annual cost includes both annualized capital costs for initial
implementation and annual operation and maintenance (O&M) costs.
While WMOST accounts for the effects of management strategies pertaining to the specified targets,
some other direct and indirect or ancillary effects (benefits and co-benefits, respectively) of the
management strategies are not being considered or reported within WMOST. For example, WMOST
considers how land conservation may serve to reduce nitrogen loadings and help meet a nitrogen
target, but it does not currently consider how this same practice may also improve overall water quality
(other constituents besides nitrogen, including phosphorus and sediment - a benefit) or contribute to
carbon sequestration (a co-benefit).
The sections below include instructions on how to use the Benefits Module to calculate potential
economic benefits and co-benefits of WMOST-chosen management options. Section 3.1 provides an
overview of the benefit and co-benefit categories included in the Benefits Module. Section 3.2 outlines
step-by-step instructions for how to use the Benefits Module.
3.1 Benefits and Co-benefits Calculated by the Module
The Benefits Module calculates both benefits and co-benefits. Benefits are water-related outcomes
resulting from management options to meet water quality targets. Within the context of WMOST, all
benefits within the module are based on water quality changes.7 Co-benefits arise from the
implementation of the practice(s) selected to meet the target. Within WMOST, all co-benefits are
ancillary effects of management options that are not derived from water quality.
Section 3.1.1 describes the two benefit categories available in the Benefits Module. Section 3.1.2
describes the three co-benefit categories available in the Benefits Module. Figure 5 diagrams the
benefits valued within the Benefits Module and the valuation methodology. You will have the option to
tailor the categories of benefits and co-benefits that are calculated by the module.
The Benefits Module evaluates benefits and co-benefits for a set of management options from a single
optimization scenario. Calculations for certain benefits will require importing data for both baseline
conditions and conditions under the set of management options. It is recommended that you evaluate
the same watershed and set of management options throughout the module sections in order to
accurately evaluate and compare benefits and co-benefits.
7 Although none of the Benefits Module benefit or co-benefit categories monetize water quantity changes, WMOST does assess water quantity
changes. Comparison of WMOST results on WMOST's Results tab orScenCompare's Model Results tab can highlight ancillary benefits of
achieving water quantity targets. For example, since WMOST allows users to impose limits associated with flood damages, users can compare
scenario results to determine the avoided costs associated with flood risk reduction.
-------
Figure 5: Benefit and co-benefit categories and valuation methodologies included in the Benefits Module.
Environmental Outcome
Valuation Approach
Effect of Management
Options
Benefit Category
CQ
to
Meet water quality targets
based on chosen
management options
Willingness-to-pay for water
quality improvements
Changes in total nonmarket
benefits of water quality
changes
Changes in water treatment
costs
Changes to TSS concentrations
Changes to surface water
quality (TN, TP, and TSS
concentrations)
Avoided water treatment costs
Implementation of land
conservation, bioretention
basin, grass swale, gravel or
constructed wetland, riparian
buffers, or increases in tree
canopy cover
Implementation of land
conservation, riparian buffers,
or increases in tree canopy
cover or urban/community
trees
Green roof implementation
Improved aesthetic quality of
p.
Change in property values
r Increased green space
~
the landscape
Reduced criteria air pollutant
->¦ levels from increased tree
canopy cover
Carbon sequestration from
increased tree canopy cover
Reduced urban heat island
-~ effect from increased urban/
community trees or green roofs
Avoided human health damages •
Human health benefits per
hectare of tree cover
(canopy cover) _|
Increased carbon sequestration
Social cost of carbon
(canopy cover & green roofs)
Avoided emissions from power
Human health benefits per ton
of pollutant avoided
(canopy cover & green roofs)
plants
t
Reduction in heating/cooling
Electricity savings
needs
(canopy cover & green roofs)
-------
3.1.1 Water Quality Benefits
Water quality benefits are water-related outcomes resulting from management options to meet water
quality targets. The Benefits Module includes two benefit categories: (1) change in water treatment
costs, and (2) total nonmarket benefits (i.e., use and nonuse) of water quality changes.
Change in water treatment costs: The water treatment cost benefit is based on changes in total
suspended solids (TSS) concentrations. Changes in TSS concentrations translate to changes in the
amount of chemical coagulant required to reduce turbidity at water treatment plants. The methodology
used within the module to calculate the change in water treatment costs is described in detail in the
2009 Environmental Impact and Benefits Assessment for Final Effluent Guidelines and Standards for the
Construction and Development Category (U.S. EPA, 2009).
Total nonmarket benefits of water quality changes: Total nonmarket benefits of water quality changes
are based on the changes in ecosystem services provided by surface water that are valued by humans,
including water-based recreation (e.g., swimming, fishing, wildlife viewing, boating/kayaking), aquatic
biodiversity, wildlife support, aesthetic (e.g., water clarity/color), and non-use (e.g., existence and
bequest values). The Benefits Module uses a meta-regression model of surface water valuation studies
(U.S. EPA, 2015) to estimate the total nonmarket benefits of water quality changes.
3.1.2 Non-Water Quality Co-benefits
Co-benefits are ancillary benefits that arise from the implementation of the practice(s) selected to meet
water quality targets. The Benefits Module includes three co-benefit categories: (1) change in housing
property value due to improved aesthetic quality of the landscape from increases in green space, (2) air
pollution removal and energy savings benefits related to canopy cover, and (3) air pollution removal and
energy savings benefits related to green roofs. The three co-benefit categories are also referred to as
change in property values, canopy cover benefits, and green roof benefits, respectively, in the Benefits
Module and throughout Section 3 of this User Guide. The Theoretical Documentation for the Benefits
Module (U.S. EPA, 2020) provides details about how the Benefits Module monetizes these co-benefits.
Change in property values (valuation approach designated with a solid gray line in Figure 5): Increased
green space from BMP implementation is often expected to enhance nearby property values by
improving the aesthetic quality of the landscape. Potential changes in nearby property value are
estimated using a meta-regression model of existing hedonic property valuation studies (Mazzotta et al.
2014). It is not recommended to calculate this benefit if the chosen BMPs will not occur within a certain
distance from residential properties. For grass swale, direct reduction tree canopy, and riparian buffer,
the appropriate buffer distance is 0-250 meters. For land conservation, bioretention basin, gravel
wetland, and riparian buffer, the appropriate buffer distance is 250-500 meters (see Table 1 for detail).
-------
Table 1. WMOST management practice and assumed distance from residences for the change in property values
WMOST management practice
0-250-meter buffer
250-500-meter buffer
Land Conservation
X
Bioretention Basin
X
Grass Swale
X
Gravel Wetland
X
Direct Reduction Tree Canopy
X
Riparian Buffer*
X
X
*Riparian buffers are unique in that the assumed distance from residences can be estimated using a riparian contribution
lookup table included within ScenCompare. This lookup table was developed by EPA and helps to define what portion of
the chosen riparian buffer area falls within each buffer distance of residential homes.
Canopy cover benefits (valuation approaches designated with dashed and solid black lines in Figure 5):
Canopy cover benefits are based on increased acres of overall canopy cover and whether this increase
includes urban/community trees (see Figure 5). Increased acres of canopy cover results in increased
carbon sequestration and increased removal of criteria air pollutants8 that cause negative human health
impacts (N02, S02, 03, PM2.5). Increased acres of urban/community trees also result in energy cost
savings for nearby buildings and subsequent reductions in criteria air pollutants (NOx, S02, and PM2.5)
and carbon dioxide from avoided power plant emissions.9 Table 2 describes the canopy cover benefits in
more detail, along with the sources used to quantify and monetize each benefit and the region type of
each source (e.g., national, state-level, local).
Table 2. Canopy cover benefits and descriptions of the quantification/monetization sources
Environmental
Outcome
Benefit
Source(s)
Region Type
Increased acres
of canopy cover
Increased carbon sequestration
Social Cost of Carbon:
Global: IWGSCC (2016)
Domestic: U.S. EPA (2019a)
National
Avoided human health damages
resulting from tree removal of air
pollutants (NO2, SO2, O3, PM2.5)
Nowak et al. (2014)
National (regressions);
county-level
(population density)
Increased acres
of urban/
community trees
Electricity savings
Nowak et al. (2017); personal
communication with authors
State-level
Avoided human health damages
from avoided NOx, SO2, and PM2.5
emissions from power plants
Quantification: Nowak and
Greenfield (2012);
Nowak et al. (2017)
Monetization: U.S. EPA (2018)
Quantification: State-
level
Monetization: National
Avoided CO2 emissions from
power plants
Quantification: Nowak and
Greenfield (2012);
Nowak et al. (2017)
Monetization: IWGSCC (2016),
U.S. EPA (2019a)
Quantification: State-
level
Monetization: National
8 EPA has established national ambient air quality standards (NAAQS) for six of the most common air pollutants—carbon monoxide (CO), lead
(Pb), ground-level ozone (O3), particulate matter (PM2.5), nitrogen dioxide (NO2; NOx), and sulfur dioxide (SO2; SOx) — known as "criteria" air
pollutants. The primary NAAQS are set to protect public health.
9 Avoided human health damages for acres of tree canopy overall and acres of urban/community trees differ because power plants do not emit
ground-level ozone.
ScenCompare Instructions and User Guide
13
-------
Green roof benefits (valuation approaches designated with solid black lines in Figure 5): Green roof
benefits are based on energy savings of the affected buildings and subsequent reductions in air
pollutants and carbon dioxide from avoided power plant emissions (see Figure 5). You can use Arizona
State University's Green Roof Energy Calculator tool10 to determine green roof energy savings from the
potential green roof(s). The Benefits Module then applies the energy savings estimate to calculate cost
savings from changes in cooling and heating needs, human health benefits from avoided emissions of
criteria air pollutants, and benefits from avoided carbon dioxide emissions. Table 3 describes the
benefits from green roof implementation in more detail, along with the sources used to quantify and
monetize each benefit and the region type of each source (national, state-level, local).
Table 3. Green roof benefits and descriptions of the quantification/monetization sources
Benefit
Source(s)
Region Type
Electricity savings
State-level: U.S. EIA (2018)
State-level (you can provide
local values)
Avoided human health damages
from avoided N0X, S02, and PM2.5
emissions from power plants
Quantification: U.S. EPA (2019b)
Monetization: U.S. EPA (2018)
Quantification: Regional
(AVERT regions)
Monetization: National
Avoided C02 emissions from
power plants
Quantification: U.S. EPA (2019b)
Monetization: IWGSCC (2016),
U.S. EPA (2019a)
Quantification: Regional
(AVERT regions)
Monetization: National
3.2 Using the Benefits Module
3.2.1 Navigation
To populate the Benefits Module with the scenario information you loaded into ScenCompare, click the
"Calculate Management Option Benefits" button. As mentioned previously, the Benefits Module
evaluates benefits and co-benefits for a set of management options from a single optimization scenario.
If you end up adding or deleting model runs for the same optimization scenario, navigate through
ScenCompare using the various tabs at the bottom of the screen and click the "Update List of Model
Runs" button on the Benefits Module tab. If you would like to evaluate a different optimization
scenario, navigate through ScenCompare using the "Return to Loaded Scenarios" and "Calculate
Management Option Benefits" buttons.
The Benefits Module interface is organized by different
colored cells (see summary on the left)
• Green cells are typically data descriptions and units,
• blue cells are locations where users should define
inputs specific to their scenario,
• yellow cells will be automatically filled in by the Benefits Module as you progress through the
module, and
• purple cells summarize the benefit/co-benefit totals as calculated by the module.
10 https://sustainability.asu.edu/urban-climate/green-roof-calculator/
3.2.1.1 Color Key
Data Description and Units
User Inputs
Benefits Module Outputs
Benefit/Co-benefit Totals
14
-------
3.2.2 Data Needs
The Benefits Module requires data from multiple sources in addition to WMOST results to calculate
benefits and co-benefits. The additional data needs include a mix of user knowledge of the study
watershed and data from external data sources. Table A.l in Appendix A describes specific data needs
and any associated data sources and benefit or co-benefit categories.
The following sections describe how users provide the information needed to run the Benefits Module.
The Benefits Module is divided into three steps: Study Characteristics (Section 3.2.3), Calculation of
Direct Benefits (Section 3.2.4), and Calculation of Co-benefits (Section 3.2.5).
3.2.3 Study Characteristics (Step 1)
3.2.3.1 Step 1A
Step 1A of the Study Characteristics section of the Benefits Module includes data inputs that affect all
benefit and co-benefit calculations (Figure 6).
Figure 6: Example of the Study Characteristics Step lAof the Benefits Module.
1. Study Characteristics
1A. Specify the year of the analysis.
12019
Calculate Social Cost of Carbon
If calculating management option co-benefits (see Section 3), choose if you would like to calculate co-benefits using the global or domestic social cost of carbon-
Global social cost of carbon
$/metric ton; delete either the global or domestic cost as appropriate
Domestic social cost of carbon
$ 6.67
$/metric ton; delete either the global or domestic cost as appropriate
Specify the dollar year in which you'd like to evaluate benefits.
$2017
Upload GDP Deflator
Under Step 1A, select the year of the analysis and the dollar year. The year of the analysis should
correspond with the first year of BMP implementation or the first year you expect benefits to begin
accruing. The year of the analysis drop-down includes years 2016 through 2030, which correspond to
the first and last years for which Benefit per Ton (BPT) values (U.S. EPA, 2018) are available to value
human health benefits of reductions in air pollutants (see Section 3.1.2). The dollar year is also used to
standardize all benefit and co-benefit estimates into the same dollar year (e.g., 2019 dollars) using the
Gross Domestic Product (GDP) deflator (included as a separate .csv - GDP_PriceDeflators.csvn). The
dollar year drop-down includes years 2016 through 2019.12 The GDP deflator file can be uploaded by
pressing the "Upload GDP Deflator" and selecting the GDP deflator file. This file will then be used across
the co-benefit and benefit calculations to convert values into the selected dollar year.
To monetize reductions in carbon (carbon sequestration), the Benefits Module can use either global
(IWGSCC, 2016) or domestic (U.S. EPA, 2019a) social cost of carbon (SC-C02) values, depending on your
selection. U.S. EPA is currently using interim values of the domestic SC-C02to inform their federal
regulatory analyses, but if you are simulating a non-regulatory scenario, you can decide whether
domestic or global SC-C02 values are appropriate for your analysis. Global social cost of carbon values
11 Keeping the GDP deflator file separate from the background Benefits Module calculations allows for easier updates when the BEA releases
annual GDP deflator index values for future years (year 2020 and beyond). GDP updates from the BEA are available here (select modify to
show annual values instead of quarterly values): https://apps.bea.gov/iTable/iTable.cfm?reqid=19&step=3&isuri=l&nipa table list=13
12 Future updates of the Benefits Module can incorporate additional dollar years in the drop-down as the Bureau of Economic Analysis (BEA)
releases annual GDP deflator index values for future years (year 2020 and beyond).
ScenCompare Instructions and User Guide
15
-------
consider global benefits of reducing C02 emissions, or avoided damages worldwide, whereas domestic
social cost of carbon values only consider domestic benefits.
To calculate SC-C02 values, click the "Calculate Social Cost of Carbon" button after selecting the year of
the analysis and dollar year. The Benefits Module will calculate both global and domestic SC-C02 values.
Decide which value is most appropriate for the analysis and then delete the other value.
3.2.3.2 Step 1B
Step IB of the Study Characteristics section of the Benefits Module requires input information about
HRUs in the study watershed as well as the proportion of HUC12s located within the study watershed
(Figure 7).
Figure 7: Example of the Study Characteristics Step IB of the Benefits Module.
IB. The Benefits Module uses several databases to calculate management option direct benefits and co-benefits. The databases are based on USGS1 fourth level of hydrologic unit classification (HUC12).
The Module also requires land use-specific information about the land uses found within your watershed.
Select a model below that represents your watershed using the drop-down list.
|Model run representing your watershed. |TN_Scenario
Press the button below to populate the table with the HRU names from your watershed. After setting up the HRU table, enterthe number of HUC12s that make up your study area (up to 20).
II I1
Setup HRU Table
J Setup HUC12 Table
Select each data header for more information.
HRU Name
Agricultural land
Residential land
Green space
percentage
HUC12 ID
Proportion of HUC12 (percentage)
Turfgrass A/B Montgomery County
TurfgrassC/D Montgomery County
r0
r0
1
1
80
50
520700081003
"ioo
Turfgrass A/B City of Rockvillc
¦b
2
70
TurfgrassC/D City of Rockville
ro
0
0
Turfgrass A/B MD State Highway
Turfgrass C/D MD State Highway
1
1
0
0
0
0
Turfgrass A/B Other Regulated
r0
0
0
Turfgrass C/D Other Regulated
r0
0
0
Natural
"o
0
0
Water
wo
0
0
Prior to completing the HRU and HUC12 tables, select a model run that represents the study watershed.
The drop-down will include all scenario names on the "Loaded Scenarios" tab (see Sections 1 and 2.1).
After selecting the model run, select the "Setup HRU Table" button, which will fill the table with HRU
names based on the selected model run. Add values for agricultural land by identifying which HRUs can
be considered agricultural. Add values for residential land by identifying which HRUs can be considered
residential. Add green space percentages for each HRU as appropriate. These designations should all be
made based on your knowledge of model land uses. You can also consult external data sources to help
determine the appropriate variable values for each HRU, as described in Appendix A. Clicking on the
variable headings provides instructions for each variable:
• Agricultural land: 1 if agricultural, 0 otherwise
• Residential land: 1 if low-density, 2 if medium-density, or 3 if high-density residential; 0
otherwise13
• Green space percentage: % of residential land use area that is green space (for HRUs with a 1, 2,
or 3 in the residential land column); 0 otherwise. You do not need to specify green space
percentages for land uses that are not residential.
After completing the HRU table, complete the HUC12 table. First, enter the number of HUC12s that
comprise the study watershed.14 Second, select the "Setup HUC12 Table" button to add more lines to
13 See https://www.mrlc.gov/data/legends/national-land-cover-database-2016-nlcd2016-legend for more detailed definitions of low-density,
medium-density, and high-density residential land use.
14 HUC12 IDs can have leading zeros so it is recommended to either manually enterthe HUC12 ID or Paste As Values to maintain the cell's data
type as text.
16
-------
the HUC12 table, as necessary. Third, enter the HUC12 IDs and the proportion of the HUC12 that falls
within the study watershed, based on your knowledge of the study watershed. You can also consult
external sources to help determine the appropriate values for the HUC12 table, as described in
Appendix A.
3.2.4 Calculate Direct Benefits (Step 2)
The "Calculation of Direct Benefits" section of the Benefits Module includes data inputs necessary to
calculate the two direct benefit categories: change in water treatment costs and the total nonmarket
value of water quality changes (Figure 8; Section 3.1.1) You have the option to calculate either or both
direct benefit categories. If you do not want to calculate values for one or both direct benefit
categories,15 leave the model run choices blank for the non-applicable benefit category or categories.
After completing all of the data inputs, as described in Sections 3.2.4.1 and 3.2.4.2, click the "Calculate
Direct Benefits" button (Figure 9), which is located above the data input fields. The benefit estimates
will appear in a column along the right-hand side of the Direct Benefits section. If you chose to estimate
both direct benefit categories, the Benefits Module will provide separate estimates for "water
treatment cost changes" and "annual willingness-to-pay for water quality changes" as well as a total
direct benefits value.
Figure 8: Example of the Direct Benefits Step of the Benefits Module.
Choose the appropriate baseline and
managed model runs below.
Fill out the blue input cells to compute the direct benefit value of...
Change in water treatment costs
Baseline Model Run
Estimated ratio of turbidity to TSS
1.5
1.5 by default
TSS BaselineWTP
$ 440.00
$/ton
Managed Model Run
Cost of alum
2018
Dollaryear
TSS ScenWTP
Total nonmarket benefits of water quality changes
Baseline Model Run - TN
TN Baseline
Indicate below if your case study falls into any of the three defined US regions.
If your case study does not fall into any of the three, leave all values as zero.
Baseline Model Run - TP
Northeast
0
1 if located in the Northeast (ME, NH, VT, MA, Rl, CT, and NY); 0 otherwise
TP Baseline
Central
0
1 if located in the Central US (OH, Ml, IN, IL, Wl, MN, IA, MO, ND, SD, NE, KS, MT,
WY, UT, and CO); 0 otherwise
Baseline Model Run - TSS
South
0
1 if located in the South (NC, SC, GA, FL, KY, TN, MS, AL, AR, LA, OK, TX, and NM);
TSS BaselineWTP
Model Run Estimating TN Changes
TN_Scenario
Upload Income and Population Data
Upload BOD, DO, and FC Data
Model Run Estimating TP Changes
TP Scenario
Specify the dollaryearof the income data.
Specify the data frequency of BOD, DO, and FC data
Model Run Estimating TSS Changes
12018
Monthly|
TSS ScenWTP
Figure 9: Button for calculating direct benefits in the Benefits Module.
2. Calculation of Direct Benefits
^Onc^nou^alue^Tav^eei^ntere^below, use this button to calculate the value of direct benefits.
Calculate Direct Benefits
T^in^nn^n^cujenefi^alcuTation^ire inappropriate for your study area, leave the model run choices blank under step 2A.
15 You may not want to calculate values for a benefit category for various reasons: (1) you are not interested in the category for your analysis,
(2) you do not have the inputs required to run the analysis, or (3) you believe the benefit category is inappropriate for your study
area/analysis (e.g., change is drinking water treatment costs likely isn't appropriate since all BMPs will occur downstream from any drinking
water treatment intakes).
ScenCompare Instructions and User Guide
17
-------
3.2.4.1 Change in Water Treatment Costs
Calculations for the change in water treatment costs will require importing data for both baseline
conditions and conditions under the set of management options. To calculate the change in water
treatment costs, select baseline and managed model runs from the drop-down menus on the left-hand
side that are aligned with the "Change in water treatment costs" variable box. The model run drop-
downs contain all scenario names on the "Loaded Scenarios" tab (see Sections 1 and 2.1). Since the
benefit is related to change in water treatment costs for treating TSS/turbidity, it is recommended that
you only select TSS model runs.
You will also need to provide values for two additional variables: (1) the estimated ratio of turbidity to
TSS and (2) the cost of aluminum sulfate (alum) used to reduce turbidity.
(1) Calculating changes in water treatment costs requires converting TSS results from WMOST
model runs into turbidity using an equation provided in U.S. EPA (2009). The regulatory analysis
used ratio values of 0.8, 1.5, and 2.2 for high, midpoint, and low treatment cost estimates,
respectively. The benefits module uses 1.5 as the default ratio value but you can adjust the
value in the interface. As the value of the ratio decreases, a given level of TSS generates more
turbidity, requiring higher doses of chemical coagulants for treatment.
(2) Aluminum sulfate is the primary coagulant used to treat turbidity. If available, you can enter
alum cost values based on actual per-ton expenditures at drinking water treatment plants within
your study area. If you do not have actual alum expenditure information, U.S. EPA (2005)
provides alum values for dry stock ($300/ton) and liquid stock alum ($230/ton) in 1998$. It is
recommended that you select the value based on the type of alum used at the drinking water
treatment plant(s) with water intakes within the study watershed. You also need to provide the
dollar year of the alum cost estimate. If the estimate is based on current site-specific alum
costs, specify the latest available dollar year (currently year 2019).
3.2.4.2 Total Nonmarket Benefits of Water Quality Changes
To calculate total nonmarket benefits of water quality changes, you will need to run WMOST for each
combination of the pollutant (total nitrogen [TN], total phosphorus [TP], and TSS) and analytic scenario
(i.e., baseline and managed conditions), for a total of six separate runs.
• For the baseline TN, TP, and TSS runs, you will need to run WMOST in simulation mode (see
Section 2.1 of WMOST User Guide) to obtain time series representative of baseline conditions
for each constituent.
• For the managed condition runs, you will first run WMOST in optimization mode for a parameter
of interest (e.g., TN) to determine cost-effective BMPs that help to meet your desired water
quality target(s). Then, you will need to run WMOST in simulation mode two additional times to
determine how the selected BMPs will affect the two other parameters (e.g., TP and TSS).
Once those models have been loaded into ScenCompare as listed on the "Loaded Scenarios" tab within
the Benefits Module, select baseline and managed model runs from the drop-down menus on the left-
hand side that are aligned with the "Total nonmarket benefits of water quality changes" variable box.
-------
The model run drop-downs contain all scenario names on the "Loaded Scenarios" tab (see Sections 1
and 2.1). If you are calculating both changes in water treatment cost benefits and total nonmarket
benefits of water quality changes, select the same TSS baseline and scenario models for both benefit
categories.
In addition to selecting model results for TN, TP, and TSS, you will need to: (1) provide values for
regional variables, (2) upload income and population data, and (3) upload data for three other water
quality metrics (biochemical oxygen demand [BOD], dissolved oxygen [DO], and fecal coliform [FC]).
(1) The regional variables are dummy variables based on the state(s) in which the study watershed
falls. If the study watershed is within one of the states described for each regional variable, set
the variable to 1 (and to 0 otherwise). If the study watershed does not fall within any of the
states described for the three regional variables, set all the regional variables to 0. The three
regions are defined as follows:
o Northeast: Connecticut, Maine, Massachusetts, New Hampshire, New York, Rhode
Island, and Vermont
o Central: Colorado, Illinois, Indiana, Iowa, Kansas, Michigan, Minnesota, Missouri,
Montana, Nebraska, North Dakota, Ohio, South Dakota, Utah, Wisconsin, and Wyoming
o South: North Carolina, South Carolina, Georgia, Florida, Kentucky, Tennessee,
Mississippi, Alabama, Arkansas, Louisiana, Oklahoma, Texas, and New Mexico
(2) Estimation of the total nonmarket benefits of water quality changes also requires mean income
and number of households for the counties that intersect the study watershed. Data for mean
income and number of households should correspond with the year of the analysis. If the year
of the analysis is a future year for which mean income and number of households is not yet
available, use data for the closest available year (e.g., 2018 data for year 2021). If more than
one county intersects the study watershed, average the mean income and sum the total number
of households in the affected counties.16 The American Community Survey (ACS) 5-year
estimates provide both values in one table: mean income in the past 12 months and the number
of households (TablelD: S1901).17 Compile the income and household data using the same
format as the "Population_lncome_Template.csv" template included with the ScenCompare
16 Averaging the mean income across intersecting counties enables the calculation of one household willingness-to-pay (WTP) value for the
study watershed instead of county-specific WTP values. The Benefits Module then applies the household WTP value to affected households,
or the number of households residing in counties intersecting the study watershed. Applying the WTP value to only households in
intersecting counties is a conservative application since households beyond the intersecting counties may also have a WTP forthe water
quality changes.
"To obtain the ACS mean income and number of households data, go to https://data.census.gov/cedsci/advanced and type "S1901" into the
Table ID data field. Under "Browse Filters," select the appropriate geography and years for your study area and analysis. For instance, if you
need data for Apache County, Arizona, click the Geography filter and then select County > Arizona > Apache County, Arizona. You can select
more than one county from more than one state, if needed. To select the year of the data, select the year filter and then the desired year.
Table S1901 is available for years 2010 to 2018. After adding the table ID number and the geography/year filters, select "Search" in the
bottom right-hand corner. Select the table in the search results to view in full-screen. Underneath the table title, select the 5-year estimates
from the "Product" drop-down. 5-year estimates are based on the largest sample size and are the most reliable. You can download the data
by clicking the "Download" button in the upper left-hand corner of the screen, checking the box next to the table, and clicking "Download
Selected." A box will appear that provides the option to download 1-year or 5-year estimates (or both) in either CSV or PDF format. Select 5-
year estimates in CSV format to most easily match the "Population_lncome_Template.csv" format. Select "Download" in the bottom right-
hand corner and then "Download Now" once the file is ready. In the data CSV file (file name contains "data_with_overlays"), each selected
geographic region will appear as a row. Use the column names to find the total number of households and mean income estimates.
ScenCompare Instructions and User Guide
19
-------
download and upload the dataset by selecting the "Upload Income and Population Data"
button. You will also need to provide the dollar year for the income data. For ACS data, the
dollar year is based on the last year included in the estimates (e.g., 2013-2017 ACS 5-Year
Estimates are reported in 2017$).
(3) Three additional water quality parameters—BOD, DO, and FC—are needed to calculate the
Water Quality Index (WQI). As described above, the Benefits Module uses a meta-regression
model of surface water valuation studies (U.S. EPA, 2015) to estimate benefits of water quality
changes measured on the 100-point WQI. You can download BOD (mg/L), DO (mg/L), and FC
(cfu/100 mL) data for your study watershed from the water quality portal.18 The BOD, DO, and
FC data should match the time frame of your model run. If water quality portal data for your
study watershed are unavailable or you are unsure of which data to use, reference the
spreadsheet included with the ScenCompare download (BOD_DO_FC_ByHUCs.xlsx).19 Compile
the BOD, DO, FC data using the same format as the "BOD_DO_FC_Template.csv" template,
which is included with the ScenCompare download, and upload the dataset by selecting the
"Upload BOD, DO, and FC Data" button. Since BOD, DO, and FC data are available at different
frequencies (e.g., monthly, daily), use the drop-down below the "Upload BOD, DO, and FC Data"
button to indicate the frequency of the uploaded data. If using the provided default data, the
data frequency should be set to "Annually".
3.2.5 Calculate Co-benefits (Step 3)
To calculate co-benefits, first select a model run from the drop-down in Step 3 of the Benefits Module,
which will include all scenario names on the "Loaded Scenarios" tab (see Sections 1 and 2.1; Figure 10).
Select a model run that includes the management option(s) that you want to evaluate. After selecting
the model run, select the "Identify Relevant BMPs" button to identify the BMPs chosen by the model.
The selected BMPs will appear in bold along the left-hand side of the Co-benefits section (see red box in
Figure 11). The WMOST results (yellow cells in Figure 11) for each benefit category will also auto-
populate upon clicking the "Identify Relevant BMPs" button. If you do not want to calculate values for
one or several of the co-benefit categories,20 unbold (i.e., highlight, go to Home menu, and select bold
format) the associated BMP(s) under Step 3. You cannot bold any BMPs not identified by the "Identify
Relevant BMPs" button because it indicates that that BMP was not chosen during the least-cost
optimization and, therefore, would be inaccurate to include in the benefits analysis.
18 https://www.waterqualitydata.us/portal/
19 This spreadsheet summarizes annual average BOD, DO, and FC data from the National Water Information System over various geographies
(HUC8, HUC6, and HUC4) from 2007 to 2018. You should select data that corresponds with your watershed. For example, if your watershed
corresponds with HUC 020700081003, look for data for all three parameters in either 02070008, 020700, or 0207.
20 You may not want to calculate values for a co-benefit category for various reasons: (1) you are not interested in the category for your
analysis, (2) you do not have the inputs required to run the analysis, or (3) you believe the benefit category is inappropriate for your study
area/analysis (e.g., none of the BMPs are anticipated to be implemented within the specified distances from residential properties that
would lead to changes in property values).
-------
Figure 10: Button for identifying relevant BMPs for co-benefit calculations within the Benefits Module.
3. Calculation of Co-benefits
Select a model below using the drop-down list.
|Model run to calculate co-benefits
|TN_Scenario
Once a model is selected, use this button to identify which BMPs were chosen by Your model.
Identify Relevant BMPs
After identifying relevant BMPs chosen by your model and determining which co-benefit categories to
estimate, you will need to provide the required inputs for calculating the associated benefits in Step 3 of
the "Calculation of Co-benefits" section (Figure 11). Step includes data inputs necessary to calculate the
three co-benefit categories: (1) change in housing property value due to improved aesthetic quality of
the landscape from increases in green space (change in property values), (2) air pollution removal,
energy savings, and air emission reduction benefits related to canopy cover (canopy cover benefits), and
(3) energy savings and air emission reduction benefits related to green roofs (green roof benefits; see
Section 3.1.2).
After completing all of the data inputs, as described in Sections 3.2.5.1, 3.2.5.2, and 3.2.5.3, click the
"Calculate Co-benefits" button, which is located above the data input fields (Figure 12). If you leave the
"Price of electricity for residential customers" field blank (see Section 3.2.5.3), the Benefits Module will
also prompt to you to select the "ElectricityPrice_byState.csv" file. The co-benefit estimates will then
appear in a column along the right-hand side of the Co-benefits section. The Benefits Module will
provide separate values for each co-benefit category that you elect to estimate (change in property
values, canopy cover benefits, and green roof benefits) as well as a total co-benefits value. If you unbold
any BMPs, the WMOST results (yellow cells: acres of increased green space, acres of increased canopy
cover, acres of green roof) will also update to reflect acreage from the bold BMPs upon selecting the
"Calculate Co-benefits" button.
Note: Estimates of canopy cover benefits, green roof benefits, and total co-benefits have lower and
upper bound values. The range of values results from using BPT values for the Electricity Generating
Units sector (U.S. EPA, 2018) to estimate human health benefits resulting from reductions in air
pollutants due to avoided power plant emissions. Estimates vary based on the epidemiology study used
as the basis for premature mortality estimates, with the lower bound estimates based on Krewski et al.
(2009) and the higher bound estimates based on Lepeule et al. (2012). If human health benefits
resulting from reductions in air pollutants due to avoided power plant emissions are not calculated
within the module, the lower and upper bound values will be the same.
ScenCompare Instructions and User Guide
21
-------
Figure 11: Example of the co-benefits Step of the Benefits Module.
BMP^hoset^jwounTTode^re^^^
Change in housing property value due to
Land Conservation
Bioretention Basin
Grass Swale
Gravel Wetland
Direct Reduction Tree Canopy
Riparian Buffer
Air pollution removal and energy savings I nefits related to canopy cover
Land Conservation
Direct Reduction Tree Canopy
Riparian Buffer
Air pollution removal and energy savings I nefits related to green roofs
Fill out the blue input cells to compute the co-benefit value of...
proved aesthetic quality of the landscape from increases in green space
Acres of increased green space
59
WMOST results
Wetland
1
1 if wetland area is expected to increase; 0 otherwise
Recreational
1
1 if recreational amenities are included in development; 0 otherwise
Import Default Values - Percent Tree Cover
Acres of increased canopy cover
59.16|lwMOST results
Population density
2,115|people/square mile
Acres of green roof
0
WMOST results
Direct energy savings
kWh/year
Location
AVERT region
Source of energy savings
AVERT emission factor category
Price of electricity for residential customers
$/MWh
Dollar year
Figure 12: Button for calculating co-benefits in Step 3 of the Benefits Module.
3. Calculation of Co-benefits
Select a model below using the drop-down list.
Model run to calculate co-benefits
TNScenario
Once a model is selected, use this button to identify which BMPs were chosen by your model.
Identify Relevant BMPs
If any of the co-benefit calculations are inappropriate for your study area, unbold the associated BMP under Step 3A.
Once input values have been entered below, use this button to calculate the value of co-benefits.
Calculate Co-benefits
3.2.5.1 Change in Housing Property Value Due to Improved Aesthetic Quality of the
Landscape from Increases in Green Space (Change in Property Values)
To calculate the change in property values, you will need to provide values for two variables, one
indicating whether wetland area is expected to increase and the other indicating whether recreational
amenities (e.g., park, greenway, trail, path) are included in the implementation of the WMOST-chosen
BMPs. This should be based on your knowledge of potential BMP implementation. You can also consult
external data sources to help determine the appropriate values for these two variables, as described in
Appendix A.
You will also need to import values for the percentage of new green space that is tree cover. Selecting
the "Import Default Values - Percent Tree Cover" button adds percent tree cover input rows applicable
to the identified BMPs and adds default values for bioretention basin, grass swale, gravel wetland, and
riparian buffer BMPs (as described in Appendix B). If WMOST identified land conservation as a cost-
effective BMP, you will need to manually enter tree cover percentages for each of the watershed HRUs
based on your knowledge of the study watershed. You can also consult external data sources to help
determine the appropriate values for tree cover percentages, as described in Appendix A.
22
-------
3.2.5.2 Air Pollution Removal and Energy Savings Benefits Related to Canopy Cover
(Canopy Cover Benefits)
To calculate canopy cover benefits, you need to provide population density (people/square mile) for the
county(s) that intersects your study watershed. You can calculate population density using the following
two resources: 1) the most recent ACS 5-year population estimates21 and 2) the most recent Census land
area values in square miles.22 If you know that county boundaries have changed since the most recent
Census, you can use alternative sources, such as GIS shapefiles or state-level estimates, to determine
the appropriate land area values for intersecting counties. To account for multiple intersecting counties,
average the population density values. If you have information about the proportion of the study
watershed that falls within each county, you can calculate a weighted average population density using
the intersection proportions as weights or use the population density associated with the county that
intersects with the majority of your study watershed. If you do not have information about the
intersection proportions, you can either use the population density for the county that accounts for the
majority of the watershed or use a standard average population density value.
3.2.5.3 Air Pollution Removal and Energy Savings Benefits Related to Green Roofs
(Green Roof Benefits)
To calculate green roof benefits, first determine green roof energy savings by inputting information
about potential green roofs into Arizona State University's Green Roof Energy Calculator tool.23 The
calculator tool requires green roof characteristics and location (select the closest available city to your
study watershed).
To quantify reductions in criteria air pollutants (S02, NOx, PM2.5) and carbon dioxide (C02) from reduced
energy consumption due to the installation of green roofs, the Benefits Module applies regional AVERT24
emission rates (U.S. EPA, 2019b) to convert energy savings into avoided emission of criteria air
pollutants (in lbs of S02, NOx, and PM2.5) and carbon dioxide (in tons of C02). The contiguous United
States is divided into ten AVERT regions (Figure 13). Four different types of regional AVERT emission
rates are available: wind, utility-scale photovoltaic, portfolio energy efficiency, and uniform energy
efficiency. Use the uniform energy efficiency values, which represent consistent energy savings
throughout the year, unless you have reason to believe that one of the other types is more appropriate
for your study area.25 Consult U.S. EPA (2019b) for additional details about the four types of AVERT
emission rates.
21 https://data.census.gov/cedsci/air?t=Populations%20and%20People&y=2018&tid=ACSSTlY2018.S0101&hidePreview=false
22 https://www.census.gov/quickfacts/fact/table/US/PST045219
23 https://sustainability.asu.edu/urban-climate/green-roof-calculator/
24 Avoided Emissions and geneRation Tool
25 Portfolio energy efficiency rates are most appropriate for assessing energy savings from a wide range of energy efficiency programs. Uniform
energy efficiency rates are most appropriate if energy savings are consistent throughout the year. Wind and utility-scale photovoltaic are
most appropriate if the savings can be attributed to the associated renewable energy technology type.
ScenCompare Instructions and User Guide
23
-------
Figure 13: Map of the ten AVERT regions in the contiguous United States.
Northwest
(NW)
' Rocky
Mountain
(RM)
Atlantic (E
Lower
Midwest
California
(vA)
Lastly, you need to provide the price of a megawatt hour of residential electricity ($/MWh) and the
dollar year of the provided value. You can use local values for the price of a megawatt hour of
residential electricity if available. However, if you leave the cell blank, you can load state-level average
prices for residential electricity from the U.S. Energy Information Administration (2018;
"ElectricityPrice_byState.csv" file included with the ScenCompare download. The Benefits Module will
prompt you to upload the state-level average prices for residential electricity after selecting the
"Calculate Co-benefits" button (see Section 1.1.1.1).
4 Example Application of ScenCompare for Wading-Threemile
Watershed
Below is an example of an application of ScenCompare using a WMOST case study on the Wading-
Threemile River Watershed in the Taunton Basin in Massachusetts. The example provides a guide for
setting up and loading scenario runs in ScenCompare (Section 4.1), using the tool's functions and
evaluating the scenario data (Section 4.2), and analyzing the various land use management decisions in
WMOST (Section 4.3).
4.1 Getting Started
The primary purpose of ScenCompare is to provide users with an interface and tool for comparing
WMOST results for different future climate scenarios. Therefore, this example details the differences in
WMOST least-cost management decision solutions between the baseline and future climate scenarios.
24
-------
However, in general, the functions of this tool can be used to evaluate any set of WMOST results and
help you to understand the effects of model inputs on the management actions selected by the WMOST
optimization model, which meets specified management goals at the lowest cost. The following section
discusses the development of the WMOST scenarios for the Wading-Threemile River Watershed, and
how those scenarios are loaded into ScenCompare to prepare the tool for analysis.
4.1.1 Run WMOST Scenarios
Data for this example come from the Wading-Threemile subwatershed in the upper Taunton River basin
in Massachusetts (https://www.mass.gov/service-details/taunton-river-watershed). The Taunton River
watershed is the second largest watershed in Massachusetts and the largest freshwater contributor to
Narragansett Bay. The Taunton River is the longest undammed tidal river in New England, supporting
the largest herring run in the state. In 2009 it was designated as a Partnership Wild and Scenic River by
the National Park Service. In these Partnership Wild and Scenic Rivers, communities protect their own
outstanding rivers and river-related resources through a collaborative approach. Challenges faced by
communities in the Taunton include protection of outstanding natural resource areas, flooding, sea level
rise and storm surges, water body impairments related to eutrophication, water supply constraints, and
the need to protect the downstream Mount Hope Bay (RTI 2014). This case study was developed in
cooperation with a consortium of regional development agencies (Southeast Regional Planning and
Development District, SERPDD and the Metropolitan Area Planning Council, MAPC) and
nongovernmental organizations (Manomet, the Nature Conservancy, and Mass Audubon) which had
received funding from EPA Region 1 from the Healthy Communities Grant Program to assess the
benefits of green infrastructure within the watershed and to educate the public about those benefits.
The EPA ORD team has applied WMOST v3 to the two subwatersheds within the upper Taunton. A two-
stage objective was established: first, to minimize costs (capitol plus operations and maintenance) for
near term planning, and second, to minimize future costs under projected growth and climate scenarios.
Goals and constraints considered in Stage 1 included ecoregional targets for total phosphorus in lakes
and flowing waters, a reduction in total nitrogen loads to the Mt Hope Bay, and maintenance of
minimum low flows for a stable water supply and to support fish populations. Management options
under consideration include land conservation, stormwater best management practices (BMPs,
including green infrastructure), forested riparian buffer restoration, repair of water infrastructure leaks,
upgrades in wastewater treatment, water conservation, and aquifer storage and recharge. A
comparison of different traditional ("gray") and nature-based ("green) stormwater BMPs showed that
infiltration basins were the most cost-effective option to meet water quality goals. Initial results were
shared with the Resilient Taunton Watershed Network (RTWN).
In this case, we apply WMOST v3.01 to future growth and climate scenarios to identify the most cost-
effective management actions. Future projections of mean annual temperature and mean annual
precipitation were obtained from the general circulation models (GCMs) included in the 5th Coupled
Model Intercomparison Project (CMIP5) for two of the representative concentration pathways (rcp-4.5
and rcp-8.5) adopted by the Intergovernmental Panel on Climate Change (IPCC) in its 5th Assessment
Report (IPCC 2014). The pathways in this report correspond to changes in radiative forcing relative to
pre-industrial values (i.e. +2.6, +4.5, +6.0, +8.5 W/m2) that are possible in year 2100 based on
ScenCompare Instructions and User Guide
25
-------
projections of greenhouse gas emissions (IPCC 2014). These data were corrected for bias and
statistically downscaled to a regional scale (Brekke et al. 2013). Four combinations of changes in
temperature and precipitation (AT, AP) were selected for this study to roughly bound the extremes of AT
and AP reflected by the collection of GCMs, thereby representing a range of possible future climate
scenarios, and an average of these scenarios was also calculated. Bounding scenarios were identified
using the US EPA LASSO tool (Morefield 2016), focusing on 21 of the models that had been shown to
perform well for New England in hindcasting exercises (Sheffield et al. 2015). The average projection
was AT = +4.4°F/AP = +10.1%. The bounding scenarios were based on the FGOALS-s2, realization 3
(GCM AT = +6.2°F/AP = +19.4%), IPSL-CM5A_LR, realization 1 (GCM AT = +5.0°F/AP = -1.7%), MPI-ESM-LR
realization 2 (GCM AT = 3.7°F/ AP = -2.2%), and CSIRO-Mk3-6-0 realization 1 (GCM AT = +3.3°F/AP =
+19.5%) model runs26.
A new set of input hourly temperature and precipitation data was generated for each scenario by
uniformly adjusting the baseline temperature and precipitation records by the corresponding AT
(absolute) and AP (percentage) values, respectively. Using the adjusted temperature and precipitation
data, hourly runoff rates were generated for input to WMOST for each scenario using SWMM. Similarly,
the temperature, precipitation, and runoff data were used in SWMM to generate four new sets of
hourly nitrogen and phosphorus loading rates for input to WMOST. In the following, climate change
scenarios are labeled as General Circulation Model (GCM) AT (°F)/ AP (%).
One of the objectives of the WMOST Wading-Three mile case study is to analyze the robustness of
WMOST management decisions over these future climate scenarios. To do this, we created a series of
WMOST runs based on a historical dry year (2002) and each of the five climate scenarios described
above (one median projection and four bounding scenarios).
We ran the historical climate and five future climate scenarios (four bounding and one median) for three
types of comparisons:
1. Baseline (baseline land use with no land management options);
2. Optimal stormwater BMP implementation for 2002 (fixed set of optimal stormwater land use
BMPs for climate scenario runs); and
3. Optimal riparian zone implementation for 2002 (selection of 10 potential riparian buffer land
conversions from developed land to forest).
The TN loading target (1,156 lbs N) was turned off for the baseline run (for the historical scenarios and
the climate scenarios), as well as in the climate scenarios for the stormwater BMP and riparian zone
runs to see if the future scenarios would meet the target given optimal BMPs selected for 2002. This set
of runs was designed to test how robust the original solution was to a range of plausible, future climate
conditions.
In addition to the three comparisons listed above, we also compared the differences in decisions
between the historical baseline and two climate scenarios (the median and extreme) with the TN
26 CSIRO-Mk3-6-0 is from the Commonwealth Scientific and Industrial Research Organisation in collaboration with the Queensland Climate
Change Centre of Excellence; FGOALS-s2 is from the LASG, Institute of Atmospheric Physics, Chinese Academy of Sciences; IPSL-CM5A-LR is
from Institut Pierre-Simon Laplace; MPI-ESM-LR is from Max Planck Institute for Meteorology (MPI-M).
-------
loading target turned on and a stormwater BMP set available (Section 4.3.2). This set of comparisons
evaluated whether optimal management practices would change given climate change scenarios.
Go to Controls
Go to Variable
Definitions
4.1.2 Load WMOST Data to ScenCompare
The following instructions describe how you would use the example result log files
generated for WMOST scenarios as described above to assess differences in scenario
results using ScenCompare. Once you have completed your scenario runs and
prepared the Scenario Log Files in WMOST (see WMOST v3 User Guide27 for more Loaded
Scenarios
details), open the ScenCompare application. From the Introduction tab, navigate to
the Controls tab. You can also use the Introduction tab to navigate to the Variable Definitions tab and
the Loaded Scenarios tab.
Step 1: Load data from selected WMOST log files
Load WMOST Scenario
Data
After opening your file, another
dialog box will pop up, prompting
you to name this scenario. Enter a
name in the text box that will help
you quickly identify the scenario, if
you would like one (this step is
optional). Then, click "OK" to load
the scenario.
Scenario Name
Name for this scenario (optional):
On the Controls tab, click the "Load WMOST Scenario Data"
button to open a file selection dialog box. In the dialog box,
select the Scenario Log File you created for your baseline run and
click "Open".
gil File Open
l-E-l
Wading_Threemile ~ Baseline
Search Baseline
Organize ~ New folder
a
& OneDrive
3il Libraries
|_*| Documents
Ifl My Docurr
Public Doc
Music
B Pictures
Q Videos
0 | Wading3Mile_baseline3.3.9.5_SpecsResults.csv
Q ] Wading3Mile_baseline3.7m2.2_SpecsResults.csv
l|^] Wa d i n g3 M i I e_basel i n e4.410 l_SpecsResu Its. c sv
Wading3Mile_baseline5.0ml.7_SpecsResults.csv
© Wading3Mile_baseline6.219.4_SpecsResults.csv
Type
Microsoft Excel Comma
Microsoft Excel Comma
Microsoft Excel Comma
Microsoft Excel Comma
Microsoft Excel Comma
|l£?ij Wading3mile_baselineTN2002_SpecsResults.csv Microsoft Excel Comma
*¦ Computer
£, OSDisk (CO .
J
Filename: Wading3mile_baselineTN2002_Sp< * [ All Files [V)
Tools * Open
Once the data is loaded, you can view the summary information for the scenario on the Loaded
Scenarios tab, which includes the average annual precipitation and average temperature statistics for
the model run. If these columns have "NA" for the statistics, enter the average annual precipitation and
average temperature statistics for the scenario in their given columns. This tab also includes
information, including the file path of the data file, study area name, scenario name, and start and end
dates.
27 Available from https://www.epa.gov/ceam/wmost-30-download-page
ScenCompare Instructions and User Guide 27
-------
Loaded Scenarios tab
A B
C
D
E F
G
H
1 J
K
1
2
Loaded Files: Data Name:
Scenario Name fedit here):
StudvAreaName:
ScenarioName: RunStartTime:
StartDate:
EndDate:
ModelMode: Ave Annual PreciD Ave Temuerature
\\camfile01.corp.abtas Wad i ng3mi 1 e_TP
Baseline2002
Wading3mile
TN2002baselin 7/1/201812:35
1/1/2002
12/31/2002
Hydrology & Loa 42.05
52.697
3
You can view the input data and results in the Model Input Data and Model Results tabs, respectively,
for the scenario you just loaded.
Model Input Data tab
Model Results tab
Repeat the steps above to load as many
scenarios as you would like. You can
use the "Return to Controls" button on
the Loaded Scenarios tab to easily
return to the Controls tab to load
additional scenario data files. If you
want to remove a scenario, select the
row of that scenario and click the "Clear
Selected Scenarios" button. A message
box will pop up asking you if you are
sure you want to delete the data for
that scenario.
In the example below, there are five
climate scenarios loaded in addition to the baseline run. The
precipitation change and temperature change columns
calculate the difference between the average annual
precipitation and average temperature of the climate scenario
and the baseline run. For example, if the average temperature change is positive, then the climate
scenario has a greater average temperature than the baseline run for the time period.
A B
C D
E F
1 |Wadir ~ jwadiii -
Wadiij -1 Wadiil ~
mile_TN2002baseline
2 Variable Identifie
Value Units
3 AvgAnnu; None
42.05 total inches per year
4 AvgTemp None
52-697 deg F
5 NDateHy None
365 time steps in model
6 j Dt None
1 days in time step
7 NLu None
16 # HRUs
8 ; NLuNamf 1
Forest sa -
9 NLuNamf 2
open nor-
10 NLuNamf 3
MLD res:-
11 NLuNamf 4
MHHDre-
12 | NLuNamf 5
comindtr-
13 NLuNarru 6
ag sand -
14 NLuNamf 7
forest til -
15 NLuNamf 8
open nor -
16 1 NLuNamf 9
MLD res 1 -
17 j NLuNamf 10 MHHD re -
IS NLuNamf 11 comindtr-
19 NLuNamf 12 ag till -
20 NLuNamf 13 cranbenv
21 NLuNamf 14 forested -
22 NLuNamf 15
nonfores-
23 iNLuNamf 16 water -
24 | ALu Base 1
13647 acre
25 ALuBase 2
2296.36 acre
26 ALuBase 3
5948.68 acre
27 ALuBase 4
973.221 acre
23 ALuBase 5
2536.94 acre
29 ALuBase 6
634.234 acre
30 ALuBase 7
12553.5 acre
31 'ALuBase 8
1082.57 acre
32 ALuBase 9
2821.25 acre
A B
C D
E
F
1
Wadir- Wadir
Wadir ' Wadir -
mile_1j-
002base!
ne
2
Variable Identifie
Value Units
3
objective None
2261.31 S/yr
4
DALull None
13647 ac
5
DALu21 None
2296.36 ac
6
DALu31 None
5948.68 ac
7
DALu41 None
973.221 ac
8
DALuSl None
2536.94 ac
9
DALu61 None
634.234 ac
10
DALu71 None
125533 ac
11
DALuSl None
1082.57 ac
12
DALu91 None
2821.25 ac
13
DALulOl None
448.338 ac
14
DALulll None
1104.44 ac
15
DALU121 None
240.572 ac
16
DALU131 None
98.4156 ac
17
DALU141 None
6473.56 ac
18
DALul51 None
2132.34 ac
19
DALul61 None
1330.48 ac
20
DQSwExt
1
131.162 MGD
21
DQSwExt
2
99.0065 MGD
22
DQSwExt
3
105.615 MGD
23
DQSwExt
4
99.6948 MGD
24
DQSwExt
5
93.6522 MGD
25 DQSwExt
6
93.4212 MGD
26 DQSwExt
7
128.981 MGD
27 DQSwExt 8
96.3411 MGD
28
DQSwExt
9
97.379 MGD
29
DQSwExt
10
99.5948 MGD
30
DQSwExt
11
119.877 MGD
31 DQSwExt 12
100.46 MGD
Clear Selected Scenariosl
r
( Return to Controls 1
KZ. Js
A B
C
D
E
F
G
H 1
J
K
L
M
ScenarioName:
RunStartTime:
EndDate: ModelMode:
Ave Temperature
Precipitation Change
Temperature Change
H:\ERD\ANCHOR\Assi| Wading3mi!e If
Baseline2002
Wading3mile
TN2002baselin
7/1/2018 12:35
1/1/2002
12/31/2002 Hydrology &
oa 42.05
52.697
H:\ERD\ANCHOR\Assi| Wading3M
Baseline3.3
Wading3Miie
baseline3.3.9.E
6/29/2018 13:25
1/1/2002
12/31/2002 Hydrology &
oa 50.32
55.997
8.27
3.3
H:\ERD\ANCHOR\Assi] Wading3M_2
H:\ERD\ANCHOR\AssijWading3M 3
Basel ine3.7
Baseline4.4
Wading3Mile
Wading3Mile
baseline3.7m2
baseline4.410.
6/29/2018 13:35
6/29/2018 13:42
1/1/2002
1/1/2002
12/31/2002 Hydrology &
12/31/2002 Hydrology &
oa 41.182
oa 46.258
56.397
57.097
-0.868
4.208
3.7
4.4
H:\ERD\ANCHOR\AssiiWading3M 4
Baseline5.0
Wading3Miie
baselineS.Oml
6/30/2018 15:32
1/1/2002
12/31/2002 Hydrology &
oa 41.348
57.697
-0.702
5
H:\ERD\ANCHOR\Assij Wading3M_5
Basel ine6.2
Wading3Mile
baseline6.219.'
6/29/2018 14:05
1/1/2002
12/31/2002 Hydrology &
oa 50.287
58.897
8.237
6.2
In this example, the precipitation difference between the climate scenarios and the baseline run varies,
with less annual precipitation in the GCM AT = +3.7°F/AP = -2.2% and AT = +5.0°F/AP = -1.7% climate
scenarios (-0.868 and -0.702 in/year, respectively) and more precipitation in all the other climate
scenarios compared to 2002 (up to from +4.208 and +8.237 in/year). The average temperature in the
future climate scenarios is always greater than the baseline year of 2002, varying from +3.3°F to +6.2°F.
28
-------
4.2 Comparing Results Across Baseline and Climate Scenarios
In this section, we detail how to use the functions in ScenCompare to compare model input data (e.g.,
baseline hydrology and loading time series) and model results across scenarios. Function buttons can be
found under Step 2 and Step 3 on the Controls tab to facilitate the creation of tables and graphs, and all
Excel functionality can be used with the Model Input Data and Model Results tabs to facilitate data value
comparisons.
4.2.1 Compare Model Input Data
First, we look at the model input data. As an example,
we will consider the first comparison type in our list: the
baseline historical run versus the future climate
scenarios with no management targets or land use
decisions. In this run, the only varying model input data
is the hydrology data inputs (runoff, recharge, runoff
loadings, and recharge loadings). To see this, we
navigate to the Model Results tab and filter the "Data
Difference" column to show only values of "1". Values
of "1" indicate that the input data values are different in
at least one scenario. Values of "0" indicate that the
input data values are all the same.
After filtering the "Data Difference" column for values of
"1", we see that only the climate statistics (AvgAnnualPrecip and AvgTemp) and the monthly runoff and
recharge hydrology and loadings statistics28 (QRuT, QReT, LRuT, and LReT [not shown in above image])
differ between the scenarios.
4.2.2 Compare Cost and Decision Variables Across Scenarios
Next, we look at the difference in decision variables across the scenario results. To do this, go to the
Controls tab and select the "Compare Scenario Decisions" button under Step 2.
This button will generate the
Table Comparison tab, which displays all the
cost and decision variables from the model
results. Cost variables begin with a "C" and
decision variables begin with a "D".
This table first shows the objective cost across scenarios, followed by the specific cost and decision
variables. The image below shows the objective cost (total annual cost for watershed management) and
the 16 HRU land use decisions for the baseline land area set. To the right of the scenario comparison,
u
V
W
X
Y
Wading3M_5 - Wadir -
Wadiij ~
Wading3M_5
Data Difference -J
AvgAnnualPrecip
None
50.287
total inches per year
1
AvgTemp
None
58.897
deg F
1
QRuT
1;1
8.9818
in/acre/month
1
QRuT
2;1
5.95246
in/acre/month
1
QRuT
3;1
15.0658
in/acre/month
1
QRuT
4;1
10.6832
in/acre/month
1
QRuT
5,1
17.6976
in/acre/month
1
QRuT
6,1
11.9563
in/acre/month
1
QRuT
7;1
0.95592
in/acre/month
1
QRuT
8;1
6.34416
in/acre/month
1
QRuT
9;1
19.1135
in/acre/month
1
QRuT
10;1
12.068
in/acre/month
1
QRuT
11;1
18.4752
in/acre/month
1
QRuT
12;1
16.9107
in/acre/month
1
QReT
1,1
37.0396
in/acre/month
1
QReT
2;1
21.9068
in/acre/month
1
QReT
3,1
58.6548
in/acre/month
1
QReT
4,1
28.8215
in/acre/month
1
QReT
5,1
47.4223
in/acre/month
1
QReT
6,1
19.4081
in/acre/month
1
QReT
9,1
26.7919
in/acre/month
1
QReT
10,1
34.0932
in/acre/month
1
QReT
11,1
78.5421
in/acre/month
1
QReT
12;1
75.2742
in/acre/month
1
LRuT
1;1;1
2.38654
Ibs/acre/month
1
LRuT
2,1,1
1.90926
Ibs/acre/month
1
Step 2. Compare decision variables across scenarios
This step will compare all decision variables across scenarios:
Storm water BMPs .
Land conservation 1
Leak repairs
Compare Scenario A
. Decisions M
Additional infrastructure capacity
etc.
28 The statistics represent the monthly sum of runoff or recharge per acre for all land area for each managed set.
ScenCompare Instructions and User Guide
29
-------
there is a "Data Difference" column, which can be used to filter varying values, and buttons to navigate
back to the Controls tab or generate graphs that compare the objective cost to the climate statistics.
Variable
Description
Units
Baseline2002
Baseline_GCM-3.3
Baseline_GCM-3.7
Baseli ne_GCM-4.4
Baseline_GCM-5.0
Baseline_GCM-6.21 Data Difference
1
objective
Objective cost
s/y
4524.5
4524.5
4524.5
4524.5
4524.5
4524.5
0
DALull
Land Area - Land Area w/ Conservation, forest sand
ac
13647.0
13647.0
13647.0
13647.0
13647.0
13647.0
0
Controls
DALu21
Land Area - Land Area w/ Conservation, open nonres sand
ac
2296.4
2296.4
2296.4
2296.4
2296.4
2296.4
0
DALu31
Land Area - Land Area w/ Conservation, MLD res sand
ac
5948.7
5948.7
5948.7
5948.7
5948.7
5948.7
0
DALu41
Land Area - Land Area w/ Conservation, MHHD resid sand
ac
973.2
973.2
973.2
973.2
973.2
973.2
0
DALu51
Land Area - Land Area w/ Conservation, comindtrsand
ac
2536.9
2536.9
2536.9
2536.9
2536.9
2536.9
0
DALu61
Land Area - Land Area w/ Conservation, ag sand
ac
634.2
634.2
634.2
634.2
634.2
634.2
0
Make Climate
DALU71
Land Area - Land Area w/ Conservation, forest till
ac
12553.5
12553.5
12553.5
12553.5
12553.5
12553.5
0
Graphs
DALuSl
Land Area - Land Area w/ Conservation, open nonres till
ac
1082.6
1082.6
1082.6
1082.6
1082.6
1082.6
0
DALu91
Land Area - Land Area w/ Conservation, MLD res till
ac
2821.2
2821.2
2821.2
2821.2
2821.2
2821.2
0
DALulOl
Land Area - Land Area w/ Conservation, MHHD resid till
ac
448.3
448.3
448.3
448.3
448.3
448.3
0
DALulll
Land Area - Land Area w/ Conservation, comindtrtill
ac
1104.4
1104.4
1104.4
1104.4
1104.4
1104.4
0
DALul21
Land Area - Land Area w/ Conservation, ag till
ac
240.6
240.6
240.6
240.6
240.6
240.6
0
DALul31
Land Area - Land Area w/ Conservation, cranberry bog
ac
98.4
98.4
98.4
98.4
98.4
98.4
0
D ALu 141
Land Area - Land Area w/ Conservation, forested wetland
|ac
6473.6
6473.6
6473.6
6473.6
6473.6
6473.6
0
DALU151
Land Area - Land Area w/ Conservation, nonforested wetlnd
ac
2132.3
2132.3
2132.3
2132.3
2132.3
2132.3
0
DALul61
Land Area - Land Area w/ Conservation, water
ac
1330.5
1330.5
1330.5
1330.5
1330.5
1330.5
0
For this table, when we try to filter the "Data Difference" column for varying values, we find that there
are no differences across the scenarios for the objective cost and all of the other variables. This result is
not surprising because there were no management targets (flow or loadings) set for any of the scenarios
in the baseline run. We used the baseline run to determine the flows and costs associated with the
watershed and model time period, and checked whether we could achieve the same targets under
future climate scenarios.
Next, using the "Make Climate Graphs" button, we generated three climate graphs to
compare the objective costs across the scenarios. This button produces a new tab
titled ClimateGraph_objective, which has a table of the climate statistics and the
objective cost for all scenarios, and three graphs: 1) Objective cost vs. precipitation, 2)
Objective Cost vs. temperature, and 3) Objective cost vs. temperature and
precipitation. The Objective cost vs. temperatun
and precipitation graph shows a bubble plot of
the objective cost, where the area of the bubble
related to the magnitude of the objective cost
and graphed with the temperature statistics on
the y-axis and the precipitation statistic on the x-
axis, to show how costs vary by climate. In this
example, the objective cost bubbles are uniform
because the objective costs do not vary by
scenario.
4.2.3 Compare Time Series Variables Across Scenarios
Finally, we look at the comparisons available for the results time series variables such as the discharge
from the watershed. Under Step 3, on the Controls tab, you can select one or more variables you would
like to compare on their own tab. Select the variables of interest in the "Series Variables" column, and
click the "Create Tables and Graphs from Selected Variables" button to generate a new tab for each
variable. In this case, we selected DQSwExt, the flow time series of surface water flowing outside the
watershed. The flow regime from surface water to the external watershed is an indicator of watershed
health because it represents the volume of water available to the stream and downstream watersheds
after the water is used for human demand.
Make Climate
Graphs
Objective cost vs. temp and precipitation
is
2. 57 '
a 56
50 60
Precipitation (in)
30
-------
Step 3. Compare time series across scenarios.
Series Variables iseiect below)
Catesorv
Descriotion
COmAsr
O&M Costs
Aquifer storage and recovery (ASR) W Create Tables and ^
COmESep
O&M Costs
Enhanced septic treatment 1 Graphs from Selected I
COmGwPump
O&M Costs
Groundwater pumping Variables
COmlbtW
O&M Costs
Interbasin transfer (1BT) potable water —
COmlbtWw
O&M Costs
iBT wastewater
COmNpdist
O&M Costs
Nonpotable distribution system
COmOS
O&M Costs
Operation cost of offline storage use r|rnr Tn|l|„ ^
COmRes
O&M Costs
Reservoir management Graphs
CQmSwPump
O&M Costs
Surface water pumping
COmWrf
O&M Costs
COmWtp
O&M Costs
Water treatment
COmWwtp
O&M Costs
Wastewater treatment
DQCSOS
Flow
Combined sewer to offline storage
DQGwExt
Flow
Groundwater to external
DQGwMake
Flow
Groundwater deficits
DQGwWtp
Flow
Groundwater to water treatment plant
DQIbtWUseNp
Flow
IBT potable water to nonpotabie water use
DQIbtWUseP
Flow
IBT potable water to potable water use
DQOSWwtp
Flow
Offline storage to wastewater treatment plant
DQResAsr
Flow
Reservoir to ASR
DQResWtp
Flow
Reservoir to water treatment plant
DQSwAsr
Surface water to ASR
DQSwExt Igow I
Surface water to external
DQSwWtp jprnK
Surface water to water treatment plant
After selecting the button, a new tab is created entitled Table DQSwExt. On the left side of this tab,
there is a table of the time series for all scenarios, as well as the minimum, average, and maximum
statistics for the time series, and a time step count threshold. The count threshold defaults to zero, but
it can be edited to calculate the count of time steps above a certain value. In the example below, we
can see that the flow out of the watershed exceeds zero for all days in the time period.
Surface water to enter
Baseline2002 Baseline GCM-3.3 Baseline GCM-3.7 Baseline GCM-4.4 Baseline GCM-5.0 Baseline GCM-6.2
MIN
4.91604281
4.923838995
4.923838995
4.852392972
4.629905929
4.890133854
AVERAGE
113.7807581
140.0302234
140.0302234
124.4070025
105.762474
136.9017693
MAX
394.1073415
486.2379146
486.2379146
439.6757833
382.8236989
483.7298046
COUNT>0
365
365
365
365
365
365
Count Threshold (edit):
o|
1/1/2002
131.4222063
131.1075806
131.1075806
131.3151699
131.2154625
131.1097389
1/2/2002
98.65000245
98.94297298
98.94297298
98.70411448
98.82435458
98.80215908
1/3/2002
105.5583947
105.3437909
105.3437909
105.1340121
105.0347991
105.0600119
1/4/2002
99.60279215
99.29465405
99.29465405
98.97426677
98.91342454
98.89097536
1/5/2002
93.50104626
93.18199199
93.18199199
92.67792243
92.67217684
92.67959862
1/6/2002
92.91998335
93.10734564
93.10734564
92.24913241
92.28638843
92.52314579
1/7/2002
129.9370666
141.3680892
141.3680892
134.5667786
126.534404
140.5475422
As an example, we changed the count threshold to 114 MGD (the average flow of the baseline scenario)
and found that most of the scenarios had more time steps with flow out of the watershed exceeding the
baseline average compared to the baseline. However, the GCM 5.0 to -1.7 scenario did not follow this
trend, with fewer time steps exceeding the baseline average compared to the baseline scenario.
Surface water to exteri Baseline2002
Baseline GCM-3.3
Baseline GCM-3.7
Baseline GCM-4.4
Baseline GCM-5.0
Baseline GCM-6.2
MIN
4.91604281
4.923838995
4.923838995
4.852392972
4.629905929
4.890133854
1 n
AVERAGE
113.7807581
140.0302234
140.0302234
124.4070025
105.762474
136.9017693
"
MAX
394.1073415
486.2379146
486.2379146
439.6757833
382.8236989
483.7298046
COUNT > 114
155
196
196
168
137
186
Count Threshold (edrt}:| 114
4,2.3.1 Time Series Graphs
The time series comparison tab also has graphing functions available. On the right side of this tab, there
are three graphs: 1) a time series graph, 2) a count threshold histogram, and 3) a box plots graph.
ScenCompare Instructions and User Guide
31
-------
The time series graph (right) shows the flow or loadings time
series for the model period and all scenarios. Depending on the
number of scenarios, you may need to change the order of the
time series to better see the comparison between flows. In this
example, we brought the baseline and GCM 5.0/-1.7 to the
forefront to see the magnitude of the flows in comparison to
scenarios with larger flows, like GCM 6.2/19.4.
Surface water to external
_ GOO
O
!| 500
TS 400
e
s 300
« 200
r ioo
i—I—lr
J—1—-
kl
IW w
y
>>>>>>>>>>>>
¦ Baseline_QCM-3.3
¦ Basel in e_GCM-3.7
¦ Basel in e_GCM-4.4
Basel in e_GCM-6.2
¦ Basel in e2002
¦ Basel in e_GCM-5.0
COUNT > 114
250
200
¦ COUNT > 114
The count threshold histogram (left) shows a column chart of
the number of threshold exceedances for each scenario. The
histogram changes whenever the count threshold is edited. The
box plots graph (right)
shows box plots
displaying the
minimum, first quartile,
Surface water to external: max, min,
median
600
0 500
!2 400
£• 300
1 200
? A ^
r»-
median, third quartile, and maximum flows for each scenario. It
shows how the distribution of flow magnitudes vary by scenario.
We see that the GCM 3.3/19.5 scenario has the largest spread,
though it is similar to other scenarios.
4.2.3.2 Climate Graphs
The time series comparison tab also has climate graphing functionality. On the
right side of the tab, you can use the "Make Climate Graph" button, to create a
new tab titled ClimateGraph DQSwExt. This tab creates a similar tab as seen in
Section 4.2.2, with three climate graphs showing the average time series value
for all scenarios versus the climate statistics (average annual precipitation and
average temperature).
J-
Make CM mate
Graphs
Surface water to external vs. precipitation
_ 1.6E+02
J 1.4E+02
j" 1.2E+02
¦ 1.0E+02
\ 8.0E+01
|
\ 6.0E+01
j 4.0E+01
j 2.0E+01
¦ 0.0E+00
~ Avg Surface water to external
Precipitation (in)
Surface water to external vs. temperature
1.6E+02
1.4E+02
1.2E+02
1.0E+02
8.0E+01
6.0E+01
4.0E+01
2.0E+01
O.OE+OO
~ Avg Surface water to external
Temperature (deg F)
The images above show the average flow out of the watershed compared to precipitation and
temperature. In the precipitation graph, we see a trend with increasing annual precipitation and larger
flows, although the GCM AT = +3.7°F/AP = -2.2% appears to be an outlier. We see no clear trend
between temperature and flows, which indicates that annual precipitation likely has a larger effect on
stream flows.
32
-------
4.3 Land Use Optimization Scenarios
In this section, we discuss the two land use optimization scenarios developed for the Wading-Threemile
watershed, the optimal stormwater BMP and the optimal riparian zone implementation, as well as how
to compare and analyze land management variables within ScenCompare.
4.3.1 Compare Robustness of Land Management Variables Decisions Across Climate
Scenarios
The second and third comparison types for this case study, the optimal stormwater BMP and optimal
riparian zone implementation, were developed to test the robustness of the WMOST land use
management decisions in future climate scenarios, i.e., whether the optimal set would change in the
future. We show how the model decisions varied by climate scenario for each run in the following
sections.
4.3.1.1 Stormwater BMP Optimization Scenarios
For the stormwater BMP comparison, we modeled a fixed set of stormwater land use BMPs optimized
for the historical baseline run with a user-specified stream loadings target, and modeled the five future
climate scenarios with the stormwater land use BMPs fixed at the 2002 solution and with no stream
loadings target. For the future climate scenarios, although WMOST had no decision variables with
respect to BMP implementation, there were still decision variables related to meeting water demand.
The optimal stormwater BMP selected was 1,088 acres of infiltration basins with a 0.6" design depth on
Commercial/Industrial/Transportation land use on a till and fine-grained deposits soil type. Using this
stormwater BMP set, we found that the optimization models for the future climate scenarios
determined a least-cost objective value of about $6,414/year, which is slightly lower than the historical
baseline scenario objective value of $6,430/year.
Variable
Description
Units
- OptBMPs Baseline '
OptBMPs GCM-3.7 ~
OptBMPs GCM-4.4 -
OptBMPs GCM-5.0 -
OptBMPs GCM-6.2 -
optbmps3.39.5 ~ | Data Difference •*"]
objective
Objective cost
S/yr
6430.9395
6414.6307
6414.6307
6414.6307
6414.6307
6414.6307 1
CGwPump
Total cost of groundwater pumping
490.0586873
291.19066
291.19066
291.19066
291.19066
291.19066 1
CSwPump
Total cost of surface water pumping
S/vr
117.8049938
124.0212959
124.0212959
124.0212959
124.0212959
124.0212959 1
ClbtW
Total cost of IBT potable water
S/vr
362.3837596
538.7266703
538.7266703
538.7266703
538.7266703
538.7266703 1
The cost difference between the baseline and climate scenarios occur in the model's usage of
groundwater pumping, surface water pumping, and interbasin transfer of potable water.
We used the "Create Tables and Graphs from Selected Variables" button to tabulate and graph the
loadings time series LSwRes, which is the loadings flow from the stream to the reservoir, and the flow
upon which the stream loadings target is based. By changing the Count Threshold to 1,156 lbs (the
baseline stream loading target), we found that the stream loadings for two of the five future climate
scenarios achieved the baseline loadings target. The other three scenarios exceeded the loadings target
on only one time step in the model period.
Surface water to resen ODtBMPs Baseline OotBMPs GCM-3.3 OotBMPs GCM-3.7 ODtBMPs GCM-4.4 ODtBMPs GCM-5.0 ODtBMPs GCM-6.2
MIN
42.59782234
138.3093323
131.7356999
134.8816899
131.3388132
138.0435979
AVERAGE
392.9978659
428.0723073
404.0083285
417.1245325
403.8378292
427.2204664
MAX
1147.533395
1188.52981
1150.731899
1172.097451
1151.979989
1189.114321
COUNT > 1156
0
1
0
1
0
1
Count Threshold (edit):
11561
ScenCompare Instructions and User Guide
33
-------
When comparing the loadings in the surface water to the climate statistics, we found that, in general,
the average surface water loadings increased with increasing precipitation and increasing temperature.
As shown in the images below, the linear relationship between stream loadings and precipitation is
stronger than the relationship between stream loadings and temperature, with an r2 value of 0.8778 for
precipitation versus an r2 value of 0.401 for temperature.
s i
2
Surface water to reservoir (Constituent 1)
4.3E+02
4.3E+02
4.2E+02
4.2E+02
4.1E+02
4.1E+02
4.0E+02
4.0E+02
3.9E+02
vs. precipitation
Avg Surface water to
reservoir (Constituent 1)
-Linear (Avg Surface water
r (Constituent 1))
20 40
Precipitation (in)
Surface water to reservoir (Constituent 1)
vs. temperature
4.3E+02
4.3E+02
4.2E+02
4.2E+02
4.1E+02
4.1E+02
4.0E+02
4.0E+02
3.9E+02
Temperature (deg F)
Avg Surface water to
reservoir {Constituent 1)
- Linear {Avg S urface water to
reservoir {Constituent 1})
4.3.1.2 Riparian Buffer Optimization Scenarios
For the riparian buffer run, we modeled a selection of 10 potential riparian buffer land conversions from
developed land to forest with the same climate scenarios and stream loadings targets as the stormwater
BMP run (i.e., with a loading target for the historical baseline run and no loadings target for the future
climate scenarios). The optimization model selected the optimal riparian buffer land use conversion
with the least cost.
We found that the model selected all of the same riparian conversion sets, except the baseline run did
not select the conversion from HRU 4 (medium/high-density residential on sand-and-gravel soil type) to
HRU 1 (forest on sand-and-gravel soil type) for loads group three, as indicated by the zero value for
CRipSetl33, which resulted in a lower total riparian conversion cost (CRipTotal).
Variable '
Description
L3
Unit -
OptRipe -
OptRipi "
OptRipj '
OptRipe -
OptRipe T
OptRipi -
Data Difference
objective
Objective cost
S/yr
574.0
5533.1
5533.1
5533.1
5533.1
5533.1
1
CRipSetlll
LU Conv: From HRU2 To HRU1 - Cost of riparian buffer land use convers
on set for loads group 1
S/yr
3.6
3.6
3.6
3.6
3.6
3.6
0
CRipSetll2
LU Conv: From HRU2 To HRU1 - Cost of riparian buffer land use convers
on set for loads group 2
S/yr
62.0
62.0
62.0
62.0
62.0
62.0
0
CRipSetll3
LU Conv: From HRU2 To HRU1 - Cost of riparian buffer land use convers
on set for loads group 3
s/yr
0.0
0.0
0.0
0.0
0.0
0.0
0
CRipSetl21
LU Conv: From HRU3 To HRU1 - Cost of riparian buffer land use convers
on set for loads group 1
S/vr
3.6
3.6
3.6
3.6
3.6
3.6
0
CRipSetl22
LU Conv: From HRU3 To HRU1 - Cost of riparian buffer land use convers
on set for loads group 2
S/yr
210.0
210.0
210.0
210.0
210.0
210.0
0
CRipSetl23
LU Conv From HRU3 To HRU1 - Cost of riparian buffer land use convers
on set for loads group 3
S/yr
0.0
0.0
0.0
0.0
0.0
0.0
0
rRinRptl-O
CRipSetl33
LU Conv: From HRU4 To HRU1 - Cost of riparian buffer land use convers
on set for loads group 3
S/yr
0.0
48.0
48.0
48.0
48.0
48.0
1
LKtp5etl41
lu conv: f-rom HKUb io hkui - tost ot riparian outter tana use convers
on set tor loaas group 1
W
CRipSetl42
LU Conv From HRU5 To HRU1 - Cost of riparian buffer land use convers
on set for loads group 2
S/yr
184.0
184.0
184.0
184.0
184.0
184.0
0
CRipSetl43
LU Conv From HRU5 To HRU1 - Cost of riparian buffer land use convers
on set for loads group 3
S/yr
0.0
0.0
0.0
0.0
0.0
0.0
0
CRipSetl52
LU Conv: From HRU6 To HRU1 - Cost of riparian buffer land use convers
on set for loads group 2
S/yr
12.5
12.5
12.5
12.5
12.5
12.5
0
CRipSetl53
LU Conv: From HRU6T0 HRU1 - Cost of riparian buffer land use convers
on set for loads group 3
S/yr
44.7
44.7
44.7
44.7
44.7
44.7
0
CRipSetlSl
LU Conv: From HRU8 To HRU7 - Cost of riparian buffer land use convers
on set for loads group 1
S/yr
4.1
*1
4.1
4.1
4.1
4.1
0
CRipSetl62
LU Conv From HRU8 To HRU7 - Cost of riparian buffer land use convers
on set for loads group 2
S/yr
62.0
62.0
62.0
62.0
62.0
62.0
0
CRipSetl63
LU Conv: From HRU8T0 HRU7 - Cost of riparian buffer land use convers
on set for loads group 3
S/yr
0.0
0.0
0.0
0.0
0.0
0.0
0
CRipSetl71
LU Conv: From HRU9 To HRU7 - Cost of riparian buffer land use convers
on set for loads group 1
S/yr
12.9
12.9
12.9
12.9
12.9
12.9
0
CRipSetl72
LU Conv: From HRU9To HRU7 - Cost of riparian buffer land use convers
on set for loads group 2
S/yr
122.3
122.3
122.3
122.3
122.3
122.3
0
CRipSetl73
LU Conv: From HRU9 To HRU7 - Cost of riparian buffer land use convers
n set for loads group 3
S/yr
0.0
0.0
s.e
0.0
0.0
0.0
0
CRipSetlSl
LU Conv: From HRUlOTo HRU7 - Cost of riparian buffer land use conver
on set for loads group 1
S/yr
1.9
1.9
L9
1.9
1.9
1.9
0
CRipSetl82
LU Conv: From HRUlOTo HRU7 - Cost of riparian buffer land use conver
on set for loads group 2
S/yr
45.8
45.8
45.8
45.8
45.8
45.8
0
CRipSetl83
LU Conv: From HRU10 To HRU7 - Cost of riparian buffer land use conver
on set for loads group 3
S/yr
0.0
0.0
0.0
0.0
0.0
0.0
0
CRipSetl91
LU Conv From HRUllTo HRU7 - Cost of riparian buffer land use conver
on set for loads group 1
S/yr
1.6
1.6
1.6
1.6
1.6
1.6
0
CRipSetl92
LU Conv: From HRUllTo HRU7 - Cost of riparian buffer land use conver
on set for loads group 2
S/yr
103.4
103.4
103.4
103.4
103.4
103.4
0
CRipSetl93
LU Conv From HRU11 To HRU7 - Cost of riparian buffer land use conver
on set for loads group 3
S/yr
0.0
0.0
0.0
0.0
0.0
0.0
0
CRipSetll02 LU Conv: From HRU12To HRU7 - Cost of riparian buffer land use conver
on set for loads group 2
S/yr
55
5.5
55
5.5
5.5
5.5
0
fRinSptlKH
1 U C.nnv Frnm HRU1? Tn HRLI7 - Crvtt nf rinarian huffpr land u*p rnnupr
nn <,pffnr load1;, ornim 3
S/w
00
on
no
on
00
00
^0
CRipTotal
Total cost of applying riparian buffer land management sets
s/yr
960.6
1008.5
1008.5
1008.5
1008.5
1008.5
1
34
-------
4.3.2 Compare Land Management Variables Across Scenarios
In this section, we provide an example of how to compare the least-cost type and magnitude of
application of stormwater BMPs identified by WMOST to meet the user-specified loading target under
the historical baseline and two future climate scenarios (GCM 4.4/10.1 [the median scenario] and GCM
6.2/19.4 [an extreme bounding scenario]). In these cases, the optimization model is allowed to decide
how much land area to allocate to a stormwater BMP. In this run, the stream loadings target for TN was
applied for all scenarios.
We viewed the different stormwater BMP decisions made by using the "Compare Scenario Decisions"
button to create the Table Comparison tab. After filtering the "Data Difference" column for values of
"1", we found that, for all scenario runs, to meet the loading target at least cost, the model selected an
Infiltration Basin with a design depth of 0.6" to be implemented on the
Commercial/Industrial/Transportation land use on the sand-and-gravel soil type and till-and-fine-grained
deposits soil type (DALu52 and DALull2, respectively).
Variable
[j Description [zJ Units
j BMPs_Baseline200; -
BMPs_GCM-4.4
BMPs_GCM-6.2 | - |Data Difference
objective
Objective cost $/yr
6427.4676
6543.0085
6649.0394 1
DALu52
Land Area - 0.6" Infill ac
0.0000174
53.16350656
114.7482124 1
DALull2
Land Area - 0.6" Infill ac
1090.555717
1104.442104
1104.442104 1
CLuSet2
0.6" Infiltration Basil $/yr
1894.536918
2010.916898
2117.786745 1
CGwPump
Total cost of groundv$/yr
479.1857416
483.2004344
489.1591315 1
CSwPump
Total cost of surface $/yr
120.8216609
121.1416244
121.46158791 1
ClbtW
Total cost of IBT potc$/yr
362.3282006
357.1545276
350.0369083 1
The land use allocation for this BMP changed across all three climate scenarios. The baseline scenario
selected the BMP only on the till and fine-grained deposits soil type (the value for DALu52 is negligible),
and the future climate scenarios selected the BMP for both the sand-and-gravel and till-and-fine-grained
deposits soil types, with a larger allocation of overall land area for the BMP in the extreme climate
scenario (GCM 6.2/19.4).
We also see that the objective cost increased from the baseline scenario to the future climate scenarios,
with the highest objective cost occurring in the extreme climate scenario. The climate comparison
graphs show an extremely close linear relationship between objective cost and increasing precipitation
and temperature with r2 values of 0.9999 and 0.9554, respectively.
Objective cost vs. precipitation
b./t+03
— 6.7E+03
R< = 0.9999
Q 6.6E+03
6.6E+03
b.bt+03
(i.4l K) '¦
Objective cost
- Linear (Objective cost)
Precipitation (in)
Objective cost vs. temperature
b./t+U3
~ 6.7E+03
R' = 0.9554
— 6.6E+03
..j- - _
b.bt+03
Objective cost
- Linear (Objective cost)
Temperature (deg F)
ScenCompare Instructions and User Guide
35
-------
5 References
Brekke, L., B.L. Thrasher, E.P. Maurer, and T. Pruitt. (2013). Downscaled CMIP3 and CMIP5 Climate
Projections: Release of Downscaled CMIP5 Climate Projections, Comparison with Preceding Information,
and Summary of User Needs. Avail. At: http://gdo-dcp.ucllnl.org/downscaled cmip projections/.
Detenbeck, N., A. Piscopo, M. ten Brink, C. Weaver, A. Morrison, T. Stagnitta, R. Abele, J. LeClair, T.
Garrigan, V. Zoltay, A. Brown, A. Le, J. Stein, and I. Morin. (2018a). Watershed Management
Optimization Support Tool v3. U.S. Environmental Protection Agency, Washington, DC, EPA/600/C-
18/001.
Detenbeck, N., A. Piscopo, M. ten Brink, C. Weaver, A. Morrison, T. Stagnitta, R. Abele, J. LeClair, T.
Garrigan, V. Zoltay, A. Brown, A. Le, J. Stein, and I. Morin. (2018b). Watershed Management
Optimization Support Tool (WMOST) v3: User Guide. US EPA Office of Research and Development,
Washington, DC, EPA/600/R-17/255.
Detenbeck, N., M. ten Brink, A. Piscopo, A. Morrison, T. Stagnitta, R. Abele, J. LeClair, T. Garrigan, V.
Zoltay, A. Brown, A. Le, J. Stein, and I. Morin. (2018c). Watershed Management Optimization Support
Tool (WMOST) v3: Theoretical Documentation. U.S. Environmental Protection Agency, Washington, DC,
EPA/600/R-17/220.
Interagency Working Group on Social Cost of Carbon (IWGSCC), United States Government. (2016).
Technical Support Document: Technical Update of the Social Cost of Carbon for Regulatory Impact
Analysis Under Executive Order 12866. Revised August 2016.
Intergovernmental Panel on Climate Change (2014). Climate Change 2014: Synthesis Report.
Contribution of Working Groups I, II, and III to the Fifth Assessment Report of the Intergovernmental
Panel on Climate Change [Core Writing Team, R.K. Pachauri and L.A. Meyer (eds.)]. IPCC, Geneva,
Switzerland, 151.
Krewski D., M. Jerrett, R.T. Burnett, R. Ma, E. Hughes, and Y. Shi, et al. (2009). Extended follow-up and
spatial analysis of the American Cancer Society study linking particulate air pollution and mortality.
Research report. Health Effects Institute, 2009, 5-114 (discussion 115-36).
Lempert, Robert J., and Myles Collins. (2007). Managing the Risk of Uncertain Threshold Responses:
Comparison of Robust, Optimum, and Precautionary Approaches, Risk Analysis 27(4):1009 -1026.
Lepeule, J., F. Laden, D. Dockery, and J. Schwartz. (2012). Chronic exposure to fine particles and
mortality: an extended follow-up of the Harvard Six Cities study from 1974 to 2009. Environmental
Health Perspectives 120(7), 965-70.
Mazzotta, M.J, E. Besedin, and A.E. Speers. (2014). A Meta-Analysis of Hedonic Studies to Assess the
Property Value Effects of Low Impact Development. Resources, 3(1), 31-61.
Morefield, P. (2016). Locating and Selecting Scenarios On-line (LASSO). Presentation to the STAC
Climate Change Scenarios Workshop, March 7-8, 2016. Avail, at:
http://www.chesapeake.org/stac/presentations/258 Morefield climate tool STAC scenarios%20work
shop v2.pdf
-------
Nowak, D. J., and E. J. Greenfield. (2012). Tree and impervious cover in the United States. Landscape
and Urban Planning, 107(1), 21-30.
Nowak, D.J., S. Hirabayashi, A. Bodine, and E. Greenfield. (2014). Tree and forest effects on air quality
and human health in the United States. Environmental Pollution, 193, 119-129.
Nowak, D.J., N. Appleton, A. Ellis, and E. Greenfield. (2017). Residential building energy conservation
and avoided power plant emissions by urban and community trees in the United States. Urban Forestry
& Urban Greening, 21, 158-165.
RTI. 2014. Strengthening the Resilience of the Taunton River Watershed: A Tool to Prioritize Local
Action. Prepared by RTI International for the U.S. Environmental Protection Agency. Available at:
https://www.epa.gov/sites/production/files/2015-ll/documents/hwptauntonriver.pdf.
Sheffield, J. et al. (2015). North American Climate in CMIP5 Experiments. Part I: Evaluation of Historical
Simulations of Continental and Regional Climatology. Journal of Climate 26: 9209-9245.
United States Energy Information Administration (U.S. EIA). (2018). Average retail price of electricity
(cent per kilowatthour). Retrieved from:
https://www.eia.gov/electricitv/data/browser/#/topic/7?agg=1.0&geo=vvvvvvvvvvvvo&endsec=8&freq
=A&start=2001&end=2018&ctvpe=linechart<vpe=pin&rtvpe=s&maptvpe=0&rse=0&pin=
United States Environmental Protection Agency (U.S. EPA). (2005). Technologies and Costs Document
for the Final Long Term 2 Enhanced Surface Water Treatment Rule and Final Stage 2 Disinfectants and
Disinfection Byproducts Rule. EPA 815-R-05-013. U.S. EPA, Office of Water, Washington, DC. December
2005.
United States Environmental Protection Agency (U.S. EPA). (2009). Environmental Impact and Benefits
Assessment for Final Effluent Guidelines and Standards for the Construction and Development Category
(EPA Document 821-R-09-012). Washington, D.C.: U.S. EPA Office of Water.
United States Environmental Protection Agency (U.S. EPA). (2015). Benefit and Cost Analysis for the
Effluent Limitations Guidelines and Standards for the Steam Electric Power Generating Point Source
Category (EPA Document 821-R-15-005). Washington, D.C.: U.S. EPA Office of Water.
United States Environmental Protection Agency (U.S. EPA). (2018). Estimating the Benefit per Ton of
Reducing PM2.5 Precursors from 17 Sectors. Technical Support Document. February 2018.
United States Environmental Protection Agency (U.S. EPA). (2019a). Regulatory Impact Analysis for
Repeal of the Clean Power Plan; Emission Guidelines for Greenhouse Gas Emissions from Existing
Electric Utility Generating Units; Revisions to Emission Guidelines Implementing Regulations. EPA-
452/R-19-003. June 2019
United States Environmental Protection Agency (U.S. EPA). (2019b). AVERT Avoided Emission Factors
2007-2018. Retrieved from: https://www.epa.gov/sites/production/files/2019-
05/documents/avert emission factors 05-30-19 508.pdf
United States Environmental Protection Agency (U.S. EPA). (2020 - in review). WMOST ScenCompare
Tool with Benefits Module: Theoretical Documentation. EPA/600/R-20/244.
ScenCompare Instructions and User Guide
37
-------
Appendix A. Data needs for the Benefits Module
Table A.l describes the Benefits Module data requirements to calculate all available benefit and co-benefit categories. If you do not plan to
calculate all possible benefit and co-benefit categories, use the "Associated Benefit" column to determine which data needs are required for your
selected benefit and co-benefit categories.
Table A.l Data needs for the Benefits Module
Data Inputs
Data Source
Associated Benefit
Analysis Year
Based on the management option implementation year. User chooses
the analysis year in the Benefits Module.
All
Dollar Year
User chooses the dollar year in the Benefits Module.
All
Agricultural land
designation
Based on your knowledge of model land uses. You can intersect WBD
watersheds (https://www.usgs.gov/core-science-svstems/ngp/national-
hvdrographv/access-national-hvdrographv-products) and NLCD data
Total nonmarket benefits of water quality
changes
(https://www.mrlc.gov/data) or reference StreamCat
(https://www.epa.gov/national-aquatic-resource-survevs/streamcat).
Residential land
designation
Based on your knowledge of model land uses. You can intersect WBD
watersheds (https://www.usgs.gov/core-science-svstems/ngp/national-
hvdrographv/access-national-hvdrographv-products) and NLCD data
Change in housing property value due to
improved aesthetic quality of the
landscape from increases in green space
(https://www.mrlc.gov/data) or reference StreamCat
(https://www.epa.gov/national-aauatic-resource-survevs/streamcat).
Green space
percentage
Based on your knowledge of model land uses. You can reference
EnviroAtlas (https://www.epa.gov/enviroatlas) for percent green space
by census block group.
Change in housing property value due to
improved aesthetic quality of the
landscape from increases in green space
Number of HUC12s;
HUC12 ID; Proportion
of HUC12
Based on your knowledge of model study area. You can use the "Science
in Your Watershed" resource
(https://water.usgs.gov/wsc/map index.html) to narrow down to the
HUC8.
Change in housing property value due to
improved aesthetic quality of the
landscape from increases in green space;
Air pollution removal and energy savings
benefits related to canopy cover and
green roofs
-------
Table A.l (Continued)
Data Inputs
Data Source
Associated Benefit
Estimated ratio of
turbidity to TSS
U.S. EPA (2009)
Change in water treatment costs
Cost of alum,
including dollar year
Based on your knowledge of the water treatment system. You can use
EPA values (U.S. EPA, 2005).
Change in water treatment costs
Northeast; Central-
South
Based on your knowledge of model study area. You can intersect WBD
watersheds (https://www.usgs.gov/core-science-svstems/ngp/national-
hvdrographv/access-national-hvdrographv-products) and state
Total nonmarket benefits of water quality
changes
boundaries (https://www.census.gov/geographies/mapping-files/time-
series/geo/tiger-line-file.html).
Income and
population data
U.S. Census (https://www.census.gov/data.html)
Total nonmarket benefits of water quality
changes
BOD, DO, and FC
data
Water quality portal (https://www.waterqualitvdata.us/portal/) or using
the spreadsheet downloaded with ScenCompare
(BOD_DO_FC_ByHUCs.xlsx)
Total nonmarket benefits of water quality
changes
Wetland
Based on your knowledge of BMP implementation. You can reference the
National Wetland Inventory
(https://www.fws.gov/wetlands/nwi/Overview.html) or hvdrologic model
land use definitions to understand if BMP implementation will expand
existing wetlands within the study area
Change in housing property value due to
improved aesthetic quality of the
landscape from increases in green space
Recreational
Based on your knowledge of BMP implementation. You can reference
resources from your local natural resources or environmental
management department.
Change in housing property value due to
improved aesthetic quality of the
landscape from increases in green space
Percent tree cover
Hard coded for bioretention basin, grass swale, gravel wetland, and
riparian buffer. If WMOST identified land conservation as a cost-effective
BMP, you will need to manually enter tree cover percentages for each of
the watershed HRUs based on your knowledge of model land uses and
informed by Appendix B.
Change in housing property value due to
improved aesthetic quality of the
landscape from increases in green space
-------
Table A.l (Continued)
Data Inputs
Data Source
Associated Benefit
Population density
U.S. Census (https://www.census.gov/data.html)
Air pollution removal and energy savings
benefits related to canopy cover
Direct energy savings
Green roof calculator: https://sustainabilitv.asu.edu/urban-
climate/green-roof-calculator/
Air pollution removal and energy savings
benefits related to green space
Location
Based on your knowledge of model study area (see Figure 13).
Air pollution removal and energy savings
benefits related to green space
Source of energy
savings
Based on your knowledge of energy source for green roof building
Air pollution removal and energy savings
benefits related to green space
Price of electricity for
residential customers
Based on your knowledge of model population or using the spreadsheet
downloaded with ScenCompare (ElectricityPrice_byState.csv)
Air pollution removal and energy savings
benefits related to green space
-------
Appendix B. Default tree canopy values
Table B.l Default percent tree canopy values for each vegetated NLCD land cover class29
Land Cover Class
Land Cover Description
Percent Tree Canopy
11
Open Water - areas of open water
0%
12
Perennial Ice/Snow - areas characterized by a perennial
cover of ice and/or snow
0%
31
Barren Land - areas of desert pavement, scarps, talus,
slides, volcanic material, glacial debris, sand dunes, strip
mines, gravel pits and other accumulations of earthen
material.
0%
41
Deciduous Forest - areas dominated by trees generally
greater than 5 meters tall, and greater than 20% of total
vegetation cover. More than 75% of the tree species
shed foliage simultaneously in response to seasonal
change.
100%
42
Evergreen Forest - areas dominated by trees generally
greater than 5 meters tall, and greater than 20% of total
vegetation cover. More than 75% of the tree species
maintain their leaves all year. Canopy is never without
green foliage.
100%
43
Mixed Forest - areas dominated by trees generally
greater than 5 meters tall, and greater than 20% of total
vegetation cover. Neither deciduous nor evergreen
species are greater than 75% of total tree cover.
100%
51
Dwarf Scrub - Alaska only areas dominated by shrubs less
than 20 centimeters tall with shrub canopy typically
greater than 20% of total vegetation. This type is often
co-associated with grasses, sedges, herbs, and non-
vascular vegetation.
0%
52
Shrub/Scrub - areas dominated by shrubs; less than 5
meters tall with shrub canopy typically greater than 20%
of total vegetation. This class includes true shrubs, young
trees in an early successional stage or trees stunted from
environmental conditions.
30%
71
Grassland/Herbaceous - areas dominated by graminoid or
herbaceous vegetation, generally greater than 80% of
total vegetation. These areas are not subject to intensive
management such as tilling, but can be utilized for
grazing.
0%
72
Sedge/Herbaceous - Alaska only areas dominated by
sedges and forbs, generally greater than 80% of total
vegetation. This type can occur with significant other
grasses or other grass like plants, and includes sedge
tundra, and sedge tussock tundra.
0%
29 We did not report default percent tree canopy values for developed land cover types as they are unlikely to be conserved within WMOST.
ScenCompare Instructions and User Guide
41
-------
Table B.l (Continued)
Land Cover Description
Percent Tree Canopy
73
Lichens - Alaska only areas dominated by fruticose or
foliose lichens generally greater than 80% of total
vegetation.
0%
74
Moss - Alaska only areas dominated by mosses, generally
greater than 80% of total vegetation.
0%
81
Pasture/Hay - areas of grasses, legumes, or grass-legume
mixtures planted for livestock grazing or the production
of seed or hay crops, typically on a perennial cycle.
Pasture/hay vegetation accounts for greater than 20% of
total vegetation.
0%
82
Cultivated Crops - areas used for the production of
annual crops, such as corn, soybeans, vegetables,
tobacco, and cotton, and also perennial woody crops
such as orchards and vineyards. Crop vegetation
accounts for greater than 20% of total vegetation. This
class also includes all land being actively tilled.
0%
90
Woody Wetlands - areas where forest or shrubland
vegetation accounts for greater than 20% of vegetative
cover and the soil or substrate is periodically saturated
with or covered with water.
100%
95
Emergent Herbaceous Wetlands - areas where perennial
herbaceous vegetation accounts for greater than 80% of
vegetative cover and the soil or substrate is periodically
saturated with or covered with water.
0%
------- |