United States
Environmental Protection
Agency
BASINS and WEPP Climate Assessment
Tools (CAT): Case Study Guide to
Potential Applications
United States Environmental Protection Agency
Office of Research and Development, National Center for
Dnmental Assessmenl
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EPA/600/R-11/123F
August 2012
BASINS and WEPP Climate Assessment Tools (CAT):
Case Study Guide to Potential Applications
National Center for Environmental Assessment
Office of Research and Development
U.S. Environmental Protection Agency
Washington, DC 20460
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DISCLAIMER
This document has been reviewed in accordance with U.S. Environmental Protection
Agency policy and approved for publication. Mention of trade names or commercial products
does not constitute endorsement or recommendation for use.
Preferred Citation: U.S. EPA (Environmental Protection Agency). (2012) BASINS and WEPP Climate
Assessment Tools (CAT): Case Study Guide to Potential Applications. National Center for
Environmental Assessment, Washington, DC; EPA/600/R-11/123F. Available online at
http ://epa. gov/ncea.
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CONTENTS
LIST OF TABLES iv
LIST OF FIGURES vii
LIST OF ABBREVIATIONS ix
FOREWORD xi
PREFACE xii
AUTHORS AND REVIEWERS xiii
EXECUTIVE SUMMARY xiv
1. INTRODUCTION 1
2. BASINS AND WEPP CLIMATE ASSESSMENT TOOLS 4
3. CASE STUDIES 12
3.1. Introduction 12
3.2. Streamflow and water quality sensitivity to climate change in the Raccoon
River, Iowa, using BASINS CAT with SWAT 14
3.3. Urban stormwater sensitivity to rainfall change and effectiveness of
management in the Upper Roanoke River, VA, using BASINS CAT with
SWMM 36
3.4. Agricultural sediment yield sensitivity to climate change and management
practices in Blue Earth County, MN, using WEPPCAT 47
3.5. Streamflow and water quality sensitivity to changes in precipitation
amount, frequency, and intensity in the Tualatin River, OR, using
BASINS CAT with HSPF 59
3.6. Streamflow sensitivity to dry weather events in Sespe Creek, CA, using
BASINS CAT with HSPF 66
3.7. Streamflow and water quality relative sensitivity to climate change vs.
impervious cover in the Western Branch of the Patuxent River, MD, using
BASINS CAT with HSPF 74
3.8. Limitations of case study simulations 83
4. CONCLUDING COMMENTS 85
5. REFERENCES 86
APPENDIX A A-l
in
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LIST OF TABLES
2-1. Summary of BASINS CAT options for adjusting meteorological time series to
create climate change scenarios and assess endpoint values based on model
simulations 6
3-1. Summary of case studies presented in this report 13
3-2. Land-use summary for Raccoon River watershed 16
3-3. Raccoon River model calibration statistics for annual streamflow, nutrient and
sediment loads 17
3-4. Mean annual streamflow (cms) for all combinations of temperature and
precipitation change as extracted from the BASINS CAT pivot table 20
3-5. Mean annual TN load (kg/ha/yr) for all combinations of temperature and
precipitation change as extracted from the BASINS CAT pivot table 21
3-6. Mean annual TP load (kg/ha/yr) for all combinations of temperature and
precipitation change as extracted from the BASINS CAT pivot table 21
3-7. Mean annual TSS load (tonnes/ha/yr) for all combinations of temperature and
precipitation change as extracted from the BASINS CAT pivot table 22
3-8. Mean annual evapotranspiration (cm/yr) for all combinations of temperature and
precipitation change as extracted from the BASINS CAT pivot table 23
3-9. NARCCAP regional and global climate models used to develop climate change
scenarios 25
3-10. Mean monthly streamflow (cms) for all scenarios 32
3-11. Mean monthly nitrogen load (kg/ha) for all scenarios 32
3-12. Mean monthly phosphorous load (kg/ha) for all scenarios 33
3-13. Mean monthly TSS load (tonnes/ha) for all scenarios 33
3-14. Total annual precipitation (mm), mean annual temperature (°C), mean annual
streamflow (cms), and mean annual loads of TSS (tonnes/ha), TN (kg/ha), and TP
(kg/ha) for all scenarios 34
3-15. Baseline land-use summary for the commercial redevelopment site 38
3-16. Event rainfall intensity (mm/hr), stormwater flow rate (cms), and concentrations
of TP (mg/L) and TSS (mg/L) for the baseline and three precipitation change
scenarios 40
IV
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LIST OF TABLES (continued)
3-17. Event mean flow rate (cms) under all precipitation change and stormwater
management scenarios 43
3-18. Soybean spring chisel plow land management specifications in WEPPCAT 50
3-19. Corn spring chisel plow land management specifications in WEPPCAT 50
3-20. Mean annual sediment yield (tonnes/ha/yr) for corn production under conditions
of changing climate 51
3-21. Mean annual sediment yields (tonnes/ha/yr) for soybean production under
conditions of changing climate 52
3-22. Corn fall mulch till management characteristics in WEPPCAT 55
3-23. Corn no-till management characteristics in WEPPCAT 55
3-24. Sediment yield (tonnes/ha/yr) resulting from corn production under all climate
change, land use, and management scenarios 56
3-25. Sediment yield (tonnes/ha/yr) results from a 2°C increase in temperature and a
20% increase in mean annual rainfall volume 58
3-26. Tualatin River HSPF model daily streamflow calibration and validation results 61
3-27. Tualatin River HSPF model monthly water quality calibration and validation
results 61
3-28. Precipitation, streamflow and loadings of TN and TSS for all climate scenarios 64
3-29. Land-use summary for Sespe Creek watershed 67
3-30. Sespe Creek HSPF model calibration and validation results for streamflow
volume (normalized by watershed area) 68
3-31. Simulation results for the Precip 0, Precip -10, and Precip -20 scenarios as
applied to historic period of low flow, 1959-1961 71
3-32. Simulation results for the Duration and Duration/Severity scenarios as applied to
the extended period of low flow, 1959-1964 72
3-33. Land-use summary for Western Branch of the Patuxent River watershed 75
3-34. Western Branch of the Patuxent model hydrology calibration and validation
statistics 76
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LIST OF TABLES (continued)
3-35. Summary of Western Branch of the Patuxent watershed land use by category for
each scenario 79
3-36. Annual streamflow and TSS load characteristics for the Western Branch of the
Patuxent simulations 80
VI
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LIST OF FIGURES
2-1. Overview of the EPA BASINS 4 modeling system (U.S. EPA, 2007) 5
2-2. Overview of the WEPP modeling system (from Flanagan et al., 1995) 10
3-1. The Raccoon River watershed and major tributaries 16
3-2. BASINS CAT option window for making multiple changes within specified range 18
3-3. BASINS CAT window displaying results for mean annual streamflow at the
outlet of the Raccoon River SWAT model in a pivot table 20
3-4. Contour plot showing percent change in mean annual streamflow for all
combinations of temperature and precipitation change 24
3-5. BASINS CAT window showing the modification of data on a monthly basis 26
3-6. Mean monthly precipitation (mm), temperature (°C), and streamflow (cms) for
the NARCCAP-derived climate change scenarios, CC-1, CC-2, CC-3, CC-4, and
the baseline scenario 28
3-7. Mean monthly TSS (tonnes/ha), TN (kg/ha), and TP (kg/ha) for the NARCCAP-
derived climate change scenarios, CC-1, CC-2, CC-3, CC-4, and the baseline
scenario 29
3-8. Mean monthly precipitation (mm), temperature (°C), and streamflow (cms) for
the CC-3, CC-5, CC-6, and baseline scenarios 30
3-9. Mean monthly TSS (tones/ha), TN (kg/ha), and TP (kg/ha) for the CC-3, CC-5,
CC-6, and baseline scenarios 31
3-10. The commercial redevelopment site in the Upper Roanoke River watershed in
Virginia, USA 37
3-11. Rainfall event intensity for the baseline rainfall event and three precipitation
change scenarios 39
3-12. Event rainfall intensity vs. stormwater flow rate at the site outlet for the baseline
and all precipitation change scenarios 41
3-13. BASINS CAT Endpoint window where users can specify endpoints, endpoint
statistics, and Highlight Values thresholds for color coding the results 42
3-14. Rainfall vs. flow dynamics at the site outlet for all precipitation change and
management scenarios 44
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LIST OF FIGURES (continued)
3-15. BASINS CAT window for the centralized management scenario showing the
color-coded mean stormwater flow rate values that are above the specified
maximum threshold defined in Figure 3-13 45
3-16. Mean annual sediment yield for Lasa soil at 2% slope under corn production for
all climate change scenarios 53
3-17. Mean annual sediment yield for the Lasa and Lerdal soil under corn production
for three climate change scenarios 53
3-18. Mean annual sediment yield for Lasa soil under corn and soybean production for
three climate change scenarios 54
3-19. Sediment yield (tonnes/ha/yr) under corn fall mulch till with a 3, 6, and 9 m grass
buffer 57
3-20. Tualatin River watershed location, Oregon, USA 60
3-21. BASINS CAT Modify Existing Data window specifying the criteria for
developing the intensity increase scenario 62
3-22. Example of precipitation event distribution for the three climate change scenarios 63
3-23. Location of the Sespe Creek watershed in California, USA 67
3-24. BASINS CAT window used to define precipitation adjustments for the Precip 0,
Precip -10, and Precip -20 scenarios 70
3-25. Mean monthly streamflow during drought periods for all scenarios 73
3-26. The Western Branch of the Patuxent River watershed and its location within the
Chesapeake Bay watershed, MD, USA 75
3-27. Observed and simulated TSS concentrations (mg/L) in the Western Branch of the
Patuxent River watershed 77
3-28. BASINS CAT window showing the selection of precipitation adjustments to
create a 20% increase in the largest 30% of events with no net change in annual
precipitation volume 78
3-29. Simulated sensitivity of stormwater runoff volume to changes in impervious
cover, precipitation volume, and precipitation intensity 81
3-30. Simulated sensitivity of TSS loads to changes in impervious cover, precipitation
volume, and precipitation intensity 82
Vlll
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LIST OF ABBREVIATIONS
ARS Agricultural Research Service
BASINS Better Assessment Science Integrating Point and Non-point Sources
BMP best management practice
CAT Climate Assessment Tool
CC-# climate change scenario
CMIP3 Coupled Model Intercomparison Project Phase 3
cfs cubic feet per second
cms cubic meters per second
EMC event mean concentration
ET evapotranspiration
FB forest buffer
GB grass buffer
GCM global climate model
GIS geographic information system
HRU hydrologic response unit
HSPF Hydrologic Simulation Program-FORTRAN
HUC Hydrologic Unit Code
I intensity
IPCC Intergovernmental Panel on Climate Change
LULC land use land cover
NARCCAP North American Regional Climate Change Assessment Program
NB no buffer
NCAR National Center for Atmospheric Research
NCDC National Climatic Data Center
NCEA National Center of Environmental Assessment
NLCD National Land Cover Data
NOAA National Oceanic and Atmospheric Administration
NRCS Natural Resource Conservation Service
NSE Nash-Sutcliffe Model Efficiency Coefficient
ORD Office of Research and Development
PB percent bias
PET potential evapotranspiration
R2 coefficient of determination
PRISM Parameter-elevation Regressions on Independent Slopes Model
RCM regional climate model
RMSE root mean square error
STATSGO State Soil Geographic Database
SWAT Soil Water Assessment Tool
SWMM Storm Water Management Model
TGICA Task Group on Data and Scenario for Impact and Climate Assessment
TN total nitrogen
TP total phosphorus
TSS total suspended solids
UMRB Upper Mississippi River Basin
IX
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LIST OF ABBREVIATIONS (continued)
USDA U.S. Department of Agriculture
U.S. EPA U.S. Environmental Protection Agency
V volume
USGS U.S. Geologic Survey
WEPP Water Erosion Prediction Project
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FOREWORD
Climate change poses significant challenges to water resources and the Environmental Protection
Agency's (EPA) National Water Program (NWP). EPA Office of Water's NWP 2012 Strategy:
Response to Climate Change addresses climate change in the context of our water programs.
The Office of Water is committed to working with the Office of Research and Development,
other water science agencies, and the water research community to further define needs and
develop research to support implementation of the 2012 Strategy, including providing the
decision support tools needed by water resource managers.
The modeling tools discussed in this report result from collaborations between EPA's Office of
Research and Development (ORD) and Office of Water (BASINS CAT) and the US Department
of Agriculture's Agricultural Research Service (WEPPCAT). The tools were developed to
facilitate the use of existing, widely used models for conducting scenario-based assessments of
climate change effects on water systems. To support the use of these tools, this report illustrates
how the tools can be applied in a variety of climatic and land use settings to gain an improved
understanding of system sensitivity, vulnerability, and the potential effectiveness of management
practices.
The EPA Office of Water thanks the authors, reviewers, and entire project team for their effort in
preparing this report. We look forward to continuing our collaboration as we strive to meet the
challenge of understanding and responding to climate change.
Karen Metchis, Policy Advisor for Climate Change
U.S. EPA Office of Water
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PREFACE
This report was prepared by EPA's Air, Climate and Energy (ACE) research program, located
within the Office of Research and Development (ORD). The ACE research program is designed
to address the increasingly complex environmental issues we face in the 21st century. The
overarching vision of ACE is to provide the cutting-edge scientific information and tools to
support EPA's strategic goals of protecting and improving air quality and taking action on
climate change in a sustainable manner.
EPA and partners recently developed two Climate Assessment Tools (CATs) that facilitate
scenario-based assessments using existing, widely used models in EPA's Better Assessment
Science Integrating point and Nonpoint Sources (BASINS) modeling system and USDA's Water
Erosion Prediction Project (WEPP) model. This report presents a set of short case studies
designed to illustrate potential application of these tools to address different questions about the
effects of climate variability and change on water and water quality. The final report reflects a
consideration of comments received on an External Review Draft report dated November, 2011
(EPA/600/R-1 l/123a), provided by an external letter peer review and a 30-day public comment
period.
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AUTHORS AND REVIEWERS
The National Center for Environmental Assessment (NCEA), Office of Research and
Development, was responsible for preparing this final report. An earlier draft report was
prepared by AQUA TERRA Consultants, under EPA Contract EP-C-06-029.
AUTHORS
AQUA TERRA Consultants, Decatur, GA
Paul Hummel
Paul Duda
Tong Zhai
Elizabeth Wolfram
U.S. Environmental Protection Agency, National Center for Environmental Assessment, Global
Change Research Program, Washington, DC
Thomas Johnson
Meredith Warren
REVIEWERS
This report was much improved by many excellent and thoughtful comments provided by
reviewers Douglas Beyerlein, Judith Meyer, Bethany Neilson, and Mark Southerland. We are
also grateful for comments on an earlier draft of this report provided by Steve Kramer, Philip
Morefield, Daniel Nover, Julie Reichert, and Christopher Weaver.
ACKNOWLEDGEMENTS
We thank the Global Change Research Program staff at EPA ORD NCEA and staff at EPA
Office of Water for their generous advice and support contributing to the development of the
BASINS CAT tool. We also are very grateful to the entire group at U.S. Department of
Agriculture (USDA) Agriculture Research Service (ARS) Southwest Watershed Research Center
and University of Arizona for their excellent work in developing, implementing, and providing
ongoing support for the WEPPCAT tool: Timothy Bayley, Averill Gate, Jr., Daniel Esselbrugge,
David Goodrich, Phil Guertin, and Mark Nearing.
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EXECUTIVE SUMMARY
Climate is changing. During the last century, the global average temperature increased 1.4°F
(IPCC, 2007). Changes in the form, amount, and intensity of precipitation have also been
observed, although with significant regional variability (IPCC, 2007; Groisman 2005). Climate
modeling experiments suggest these trends will likely continue or accelerate throughout the next
century (IPCC, 2007; Karl et al., 2009). There is increasing concern about the potential effects
of climate change on water resources. Potential effects of climate change include increased risk
of flooding and drought, changes in the quality and seasonal timing of runoff, loss of aquatic
habitat, and ecosystem impairment (Bates et al., 2008; Karl et al., 2009; U.S. EPA, 2008).
Many communities, states, and the federal government are considering adaptation strategies for
reducing the risk of harmful impacts resulting from climate change. Challenges remain,
however, concerning how best to incorporate diverse, uncertain, and often conflicting
information about future climate change into decision making. Despite continuing advances in
our understanding of climate science and modeling, we currently have a limited ability to predict
long-term (multidecadal) future climate at the local and regional scales needed by decision
makers (Sarewitz et al., 2000). It is therefore not possible to know with certainty the future
climatic conditions to which a particular region or water system will be exposed. Water
resources in many areas are also vulnerable to increasing water demand, land-use change, and
point-source discharges. Climate change will interact with these and other stressors in different
settings in complex ways.
Scenario analysis using computer simulation models is a useful and common approach for
assessing vulnerability to plausible but uncertain future conditions and guiding the development
of robust climate adaptation strategies (Lempert et al., 2006; Sarewitz et al., 2000; Volkery and
Ribeiro, 2009). A barrier to conducting this type of analysis is the effort required to create and
run meteorological inputs representing different climate change scenarios for different water
models. EPA and partners recently developed two assessment tools, Better Assessment Science
Integrating point and Non-point Sources (BASINS) Climate Assessment Tool (CAT) and Water
Erosion Prediction Project (WEPP) CAT, to facilitate the use of existing, well known models in
EPA's BASINS system (Hydrologic Simulation Program-FORTRAN [HSPF], Soil Water
Assessment Tool [SWAT], Storm Water Management Model [SWMM]) and USDA's WEPP
model for conducting scenario-based assessments. The tools provide flexible capabilities for
creating and running user-specified climate change scenarios to address a wide range of "what
if questions about how weather and climate could affect water and watershed systems.
Combined with the existing capabilities of the BASINS and WEPP models, the tools can be used
to explore the combined effects of changes in climate and land use on a range of streamflow and
water quality endpoints, as well as the potential effectiveness of management practices for
reducing impacts.
This report presents a series of short case studies designed to illustrate how these tools can be
used to address a range of different questions about the potential implications of climate change
on water and watershed systems. Climate change scenarios are created based on model
projections as well as historical data and past events. Land-use change and management
scenarios are also included to address questions related to the relative effects of land use versus
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climate change, and the effectiveness of management practices for reducing impacts. The six
case study assessments are:
Streamflow and water quality sensitivity to climate change in the Raccoon River, Iowa,
using BASINS CAT with the SWAT
Urban stormwater sensitivity to rainfall change and effectiveness of management in the
Upper Roanoke River, VA, using BASINS CAT with the SWMM
Agricultural sediment yield sensitivity to climate change and management practices in
Blue Earth County, MN, using WEPPCAT
Streamflow and water quality sensitivity to changes in precipitation amount, frequency,
and intensity in the Tualatin River, OR, using BASINS CAT with the HSPF
Streamflow sensitivity to dry weather events in Sespe Creek, CA, using BASINS CAT
with HSPF
Streamflow and water quality relative sensitivity to climate change versus impervious
ground cover in the Western Branch of the Patuxent River, MD, using BASINS CAT
with HSPF
The issue of climate change is complex and will challenge water managers to incorporate
diverse, uncertain, and often conflicting information about future climate change into water
resources decision making. Results of these case studies illustrate important differences in the
sensitivity of different Streamflow and water quality endpoints to changes in specific climate
drivers. An awareness of these differences highlights the need for tools like BASINS CAT and
WEPPCAT which can be used to increase understanding of system behavior, identify how we
are most vulnerable, and to guide management decisions for reducing risk. It should be noted,
however, that case studies in this report use preexisting models and may not represent all local
management and other factors in full detail. Case study results should thus be considered
qualitative and heuristic rather than absolute.
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1. INTRODUCTION
There is growing concern about the potential effects of climate change on water resources.
Changes in the amount, form, and intensity of precipitation, together with factors affecting
evaporative loss such as air temperature have a direct influence on the quantity, quality, and
timing of available water (Gleick and Adams, 2000). Water infrastructure is typically designed
and operated to maintain safe and reliable drinking water supplies, flood protection, wastewater
treatment, and urban drainage under anticipated climatic conditions. Climate change presents a
risk of harmful impacts to these and other components of water resources management designed
using historical data (Milly et al., 2008).
It is now generally accepted that human activities including the combustion of fossil fuels and
land-use change have resulted, and will continue to result in, long-term climatic change (IPCC,
2007; Karl et al., 2009). The 2007 Fourth Assessment Report of the Intergovernmental Panel on
Climate Change (IPCC) states that "warming of the climate system is unequivocal, as is now
evident from observations of increases in global average air and ocean temperatures, widespread
melting of snow and ice and rising global average sea level" (IPCC, 2007, p.2). During the last
century, the global average temperature has increased 1.4°F (IPCC, 2007). Changes in
precipitation patterns have also been observed, although with significant regional variability
(IPCC, 2007; Groisman, 2005). Climate modeling experiments suggest these trends will likely
continue or accelerate throughout the next century (IPCC, 2007; Karl et al., 2009).
The watershed response to climate change will vary in different locations depending on the
specific types of change that occurs and the attributes of individual watersheds including
physiographic setting, land use, and human use and management of water. Effects could include
increases or decreases in available water supply, changes in the seasonal timing of supply,
increased risk of flooding and drought, increased water temperature, changes in pollutant
loading, loss of aquatic habitat, and ecosystem impairment (Bates et al., 2008; Karl et al., 2009;
U.S. EPA, 2008). In addition, water resources in many areas are stressed and vulnerable to
existing, non-climatic stressors including increasing demand, land-use change, point-source
discharges, and habitat degradation. Climate change will interact with these stressors in different
settings in complex ways that are not well understood. Where effects are large, current water
resource management may not be adequate to cope with the changes.
Responding to climate change is complicated by the scale, complexity, and inherent uncertainty
of the problem. Despite continuing advances in our understanding of climate science and
modeling, current climate models have a limited ability to predict long-term (multidecadal)
future climate at local and regional scales relevant to water managers (e.g., municipality,
drainage basin; Sarewitz et al., 2000). It is, therefore, not possible to know with certainty the
future climatic conditions to which a particular location or water system will be exposed. This
uncertainty should not, however, be considered a barrier to taking action. Current global and
regional climate models (GCMs, RCMs) are excellent tools for understanding the complex
interactions and feedbacks associated with future emissions scenarios and identifying a set of
plausible, internally consistent scenarios of future climatic conditions. Historical observations
and paleo records of climatic variability can also provide useful information about the type and
range of changes possible in different regions of the nation. This type of information can also be
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valuable for assessing the implications of plausible future climate conditions, to identify how we
are most vulnerable, and guide the development of robust strategies for reducing risk (Sarewitz
et al., 2000).
Assessing the risks and impacts of climate change (vulnerability assessment) can take many
forms depending on the ecological or social resource of interest, decision context, and projected
potential range of expected climate changes characteristics. Scenario analysis using computer
simulation models is a common approach for assessing vulnerability and evaluating the
effectiveness of adaptation measures (Lempert et al., 2006; Volkery and Ribeiro, 2009). A
number of water simulation models are available that are capable of representing the response of
watershed systems to changes in climatic, land-use, and management drivers. Many of these
models, such as those currently available in EPA's BASINS modeling system (HSPF, SWAT,
SWMM), are well validated and already used to support management decision making. Several
excellent references are available discussing the use of scenarios to assess climate change
impacts (e.g., see IPCC TGICA, 2007; WUCA, 2010; Brekke et al., 2009; U.S. EPA, 2011;
Brown, 2011; Johnson and Weaver, 2009). A barrier to conducting this type of analysis is the
level of effort required to create meteorological inputs representing multiple future climate
change scenarios to drive water models.
EPA and partners recently developed two assessment tools, the BASINS CAT and the
WEPPCAT, to facilitate scenario-based assessments using well known, existing models in
EPA's BASINS modeling system, and USDA's WEPP model, respectively. The tools provide
flexible capabilities for creating and running climate change scenarios to address a range of
"what if questions about how weather and climate could affect water and watershed systems
(e.g., how would increases in the intensity of rainfall events affect stormwater runoff, what type
of climate change would need to occur to increase stream water temperatures to a level harmful
to fish?). Combined with the existing capabilities of the BASINS and WEPP models, the tools
can be used to investigate the combined effects of potential changes in climate and land use on a
range of streamflow and water quality endpoints, as well as the potential effectiveness of
management practices for reducing impacts.
BASINS CAT was originally released in 2007 with EPA's BASINS modeling system version 4.0
and was originally available only with the HSPF watershed model (Johnson and Kittle, 2006;
Imhoff et al., 2007; U.S. EPA, 2009a). With the release of BASINS CAT Version 2 in 2012,
CAT capabilities will also be available with the SWAT and SWMM models.
WEPPCAT was released in 2010 in partnership with the U.S. Department of Agriculture
(USDA) Agricultural Research Service (ARS)
(http://typhoon.tucson.ars.ag.gov/weppcat/index.php). WEPPCAT provides an online platform
for creating and running climate change scenarios to assess potential implications for soil erosion
from agricultural lands using the USD A ARS WEPP model.
About This Report
This report presents a series of short case studies using the BASINS and WEPP climate
assessment tools. The case studies are designed to address three general objectives. First, the
case studies illustrate conceptually how scenarios based on different types of climate, land use,
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and management information can be used to address questions about the potential implications of
climate change on watersheds. Climate change scenarios are created based on model projections
as well as historical data and past events. Land-use change and management scenarios are also
included to address questions related to the relative effects of land use versus climate change,
and the effectiveness of management practices for reducing impacts. Second, the case studies
illustrate selected capabilities of the climate assessment tools when used with different models.
BASINS CAT and WEPPCAT provide users with capabilities for adjusting historical
meteorological data to create climate change scenarios to assess hydrologic and water quality
response to climate change using the BASINS and WEPP models. The case studies in this report
illustrate a number of these capabilities. Finally, case study simulations convey information
about the potential response of watersheds in different parts of the nation to future changes in
climate, land use, and management practices. It is important to note, however, that all case
studies use preexisting models and may not represent all local management and other factors
affecting hydrologic and water quality endpoints in full detail. Results should, thus, be
considered qualitative and heuristic rather than absolute. The specific case study locations were
selected to leverage the availability of existing models and to represent a range of physiographic,
hydrologic, and climatic conditions.
The intended audience of this report is water scientists, engineers, managers, and planners with a
basic knowledge of water and watershed modeling interested in using models to conduct
scenario-based assessments of the potential effects of climate change on water and watershed
systems. The report may be of particular interest to current users of BASINS or WEPP that want
to extend the scope of their modeling to include the potential effects of climate change. More
generally, the approaches for creating and using scenarios as described in this report and
implemented with the BASINS CAT and WEPPCAT tools are readily transferable for use with
any environmental model. We hope that the information in this report can stimulate creativity
and exploration of the different ways scenario analysis can be used to support management
decision making.
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2. BASINS AND WEPP CLIMATE ASSESSMENT TOOLS
This section provides a brief introduction to the BASINS and WEPP climate assessment tools.
More detailed documentation of BASINS CAT is available in the document "BASINS 4.0 CAT:
Supporting Documentation and User's Manual" (U.S. EPA, 2009a) and on the BASINS web
page (http://water.epa.gov/scitech/datait/models/basins/bsnsdocs.cfm). More detailed
documentation of WEPPCAT is available on the WEPPCAT web page
(http://typhoon.tucson.ars.ag.gov/weppcat/index.php).
BASINS CAT and WEPPCAT are each intended to facilitate application of existing simulation
models for conducting scenario-based assessments. The conceptual basis of these tools is
simple: to provide flexible capabilities for creating and running scenarios to address a wide range
of what-if questions about the potential effects of climate change on water and watershed
systems. It is important to note that BASINS CAT and WEPPCAT do not provide climate
change data for any particular region of the United States. Rather, the tools simply provide a
capability for users to create meteorological data reflecting any type of change they wish to
consider. In each case, climate change scenarios are created using the change factor or delta-
change approach, whereby user-selected historical meteorological data within a selected baseline
period (e.g., daily temperature, daily precipitation) are adjusted to create scenarios for input to
water models. These capabilities support a range of assessment goals, e.g., simple screening
analysis, systematic sensitivity analysis, or assessing more detailed scenarios based on climate
model projections.
Introduction to BASINS CAT
EPA's BASINS modeling system integrates environmental data, analytical tools, and watershed
modeling programs to support assessments of watershed land-use change, pollutant discharges,
and management practices on water quality (U.S. EPA, 2001; 2007;
http://www.epa.gov/waterscience/basins/). BASINS consists of four components: (1) a
comprehensive collection of national cartographic and environmental databases,
(2) environmental assessment tools and utilities (summarize results; establish pollutant
source/impact interrelationships; selectively retrieve data; import tool, download tool, grid
projector, post processor, and land use, soil classification and overlay tool); (3) automated
watershed characterization reports (for eight different data types); and (4) a suite of watershed
models including HSPF (Bicknell et al., 2005), SWAT (Neitsch et al., 2005), AQUATOX
(Clough and Park, 2006), SWMM (Rossman, 2010) and pollutant loading application (U.S. EPA,
2007). The main interface to BASINS is provided through Map Window, a nonproprietary, open-
source Geographic Information System (GIS). The GIS provides a framework for linking
BASINS modeling tools with environmental data (see Figure 2-1).
-------
BASINS
GI3
TIGER Line
and Ccniui
Dot*
U««r Supplied
DM*
BASINS 4.0 System Overview
Tools and Utilities
3 R«pOtt*
WattrnhKl Dtlintatlon
(Automatic or Manual)
Pawnttir E*!«m*tlon
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-
Watershed Modeling
HSPFAVtnHSPF
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Po s! Process i ng
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CHm«l« Analysis Tool
Figure 2-1. Overview of the EPA BASINS 4 modeling system (U.S. EPA,
2007).
BASINS CAT is not a stand-alone model. BASINS CAT is a plug-in available for use with pre-
existing, calibrated BASINS models. Specifically, BASINS CAT does the following: (U.S. EPA,
2009a):
• provides a flexible, pre-processing capability for creating meteorological time series
representing user-specified changes in climate for input to BASINS models using the
change factor approach (see Table 2-1);
• manages new meteorological data so that it is properly formatted for input into BASINS
models; and
• provides a post-processing capability for calculating user-specified streamflow and water
quality endpoints from BASINS model output (see Table 2-1).
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Table 2-1. Summary of BASINS CAT options for adjusting meteorological
time series to create climate change scenarios and assess endpoint values
based on model simulations.
Modifying
historical
precipitation
records
•Apply a multiplier to each value within selected months in a multiyear record
•Apply multiplier to each value within selected years in a multiyear record
•Represent storm intensification by applying multiplier to values (events) only
within a selected size class
•Represent changes in event frequency by adding or removing storm events to
observed historical precipitation time series
Modifying
historical air
temperatures
•Add or subtract from each value within selected months in a multiyear record
•Add or subtract from each value within selected years in a multiyear record
Creating complex
scenarios
'Combine multiple adjustments to precipitation and temperature time series to
create complex scenarios
'Create spatially variable climate change scenarios for multiple locations
Calculating
assessment
endpoints
•Calculate mean, max, min, sum, and other summary values from model output
time series
•Calculate summary values for specified range of concern in model output time
series (e.g., selected months, years)
•Calculate duration and frequency events based on model output time series
(e.g., 7-day average low streamflow with a 10-year return period,100-year
flood, 2-year flood)
• Export BASINS CAT time series data as text (ASCII) files
Climate change scenarios (i.e., the adjusted meteorological time series created using the tool) are
contained within the same BASINS Watershed Data Management (.wdm) file with the original,
historical meteorological records. In addition, BASINS CAT provides a view/export capability
that can be used to display the changes resulting from a specific adjustment or save the adjusted
weather record as an ASCII file.
BASISN CAT does not provide explicit capabilities for creating land-use change or management
scenarios. Creation of land-use change and management scenarios as part of a BASINS CAT
analysis is done by adjusting land-use and/or management definitions in input files used by the
watershed model selected for the analysis.
In BASINS CAT Version 2 (released in 2012), climate assessment capabilities are accessible for
three BASINS models: HSPF, SWAT, and SWMM. These three models provide general
capabilities for application to a wide range of issues in water management. Models differ in the
approaches used to represent key processes, input requirements, and endpoints simulated. It is
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the user's choice as to which model is most appropriate for a given assessment. The following is
a brief summary of the three BASINS models accessible to BASINS CAT.
HSPF
HSPF is a process-based, basin-scale model that provides a comprehensive package for
simulating watershed hydrology and water quality for a wide range of conventional and toxic
organic pollutants (http://www.epa.gov/ceampubl/swater/hspf/: Shoemaker et al., 2005). The
model simulates watershed hydrology, land and soil contaminant runoff, sediment-chemical
interactions, and in-stream fate and transport in one-dimensional stream channels. It can be
configured to represent all types of land uses and offers the ability to include land-use activities
and potential management controls. Since its inception in 1980, HSPF has been widely applied
in the planning, design, and operation of water resources systems and is arguably one of the best
verified watershed models currently available. HSPF can be applied to most watersheds using
available meteorological, land use, hydrography, management, streamflow, and water quality
data. The principal model outputs include streamflow runoff and mass loads or concentrations of
sediment, nutrients, pesticides, and toxic chemicals at selected points within a watershed.
HSPF represents a watershed as a group of various land uses all routed to a representative stream
segment. The modeling framework is defined in terms of subwatershed segments, one-
dimensional stream reach segments, and well-mixed reservoirs/lakes. The spatial scale for
simulation uses one-dimensional, lumped parameters on a land use or subwatershed basis.
Processes simulated for pervious and impervious land areas include water budget, sediment
generation and transport, and the generation and transport of other water quality constituents.
Hydrologic simulations include consideration of interception, infiltration, evapotranspiration
(ET), interflow, groundwater loss, and overland flow processes. Sediment production is based
on detachment and/or scour from a soil matrix and transport by overland flow in pervious areas,
whereas solids buildup and wash-off is simulated for impervious areas. HSPF also simulates the
in-stream fate and transport of a wide variety of pollutants, such as nutrients, sediments, tracers,
dissolved oxygen/biochemical oxygen demand, temperature, bacteria, and user-defined
constituents.
Selected HSPF model strengths include (Shoemaker et al., 2005):
• model setup can be simple or complex, depending on application, requirements, and data
availability;
• capable of simulating land and receiving water processes;
• a variety of simulation time steps can be used, including sub-hourly to 1 minute, hourly,
or daily; and
• enables user-defined model output options by defining the external targets block.
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SWAT
SWAT is a basin-scale, continuous simulation model that operates on a daily time step and is
designed to predict the nonpoint source loadings and resulting water quality impacts of runoff,
sediment, and agricultural chemicals (nutrients and pesticides) from a watershed (Neitsch et al.,
2005). In addition, the model includes capabilities and functionality to assess a wide variety of
impacts of alternative management practices and land-use changes (Gassman et al., 2007). The
model is physically based, computationally efficient, and capable of continuous simulations over
long periods of time, ranging from days to decades. Major model components include weather,
hydrology, erosion/sedimentation, soil temperature, plant growth, nutrients, pesticides, bacteria,
agricultural management, stream and pond/reservoir routing. The simulation of these
components is carried out within SWAT's basic building block, the Hydrologic Response Unit
(HRU). HRUs represent unique combinations of land use, soil characteristics, and management
within each sub-basin being modeled.
The SWAT model has comprehensive representation of all major watershed processes. It has a
particularly strong representation of agricultural land use. Hence, it is usually selected for
assessing nutrient loads from agricultural dominant watersheds. The model uses GIS
technology, topography, soils, precipitation, plant growth, and crop management information to
form a complete deterministic representation of the hydrology and water quality of a watershed.
SWAT has gained both national and international acceptance as an efficient and reliable
watershed modeling tool as demonstrated by hundreds of SWAT-related papers in the open
technical literature, presentations at international SWAT conferences, and its inclusion in EPA's
BASINS modeling system. Additional information regarding the development and use of
SWAT can be found at: http://www.brc.tamus.edu/swat.
Selected SWAT model strengths are as follows:
physically based model can be used to evaluate the relative impact of changes in
management practices, climate, and vegetation on water quality or other variables of
interest;
required minimum data for running simulations are commonly available;
ability to simulate crop and plant communities and provide crop yield and plant biomass;
and
computationally efficient allowing simulation of very large basins or a variety of
management strategies without excessive investment of time or money.
SWMM
The SWMM is a rainfall-runoff simulation model that can be used to simulate runoff quantity
and quality from primarily urban areas on a single event or long-term (continuous) basis (see
http://www.epa.gov/ednnrmrl/models/swmm/). SWMM is commonly used to inform decisions
related to stormwater management, combined sewer overflows, assessing nonpoint source
pollution loads, and low impact development techniques. Typical SWMM applications include
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the design and sizing of drainage system components for flood control, flood plain mapping of
natural channel systems, evaluating the effectiveness of best management practices (BMPs) for
reducing wet weather pollutant loadings, generating nonpoint source pollutant loadings for waste
load allocation studies, and designing control strategies for minimizing sewer overflows
(Rossman, 2010).
SWMM operates on time steps ranging from seconds to years. SWMM accounts for spatial
variability by dividing a study area into a collection of smaller, homogeneous subcatchment
areas, each containing its own fraction of pervious and impervious subareas. Overland flow can
be routed between subareas, between subcatchments, or between entry points of a drainage
system through a system of pipes, channels, storage/treatment devices, pumps, and regulators.
SWMM simulates the quantity and quality of runoff generated within each subcatchment, and
the flow rate, flow depth, and quality of water in each pipe and channel during a simulation
period.
Some key SWMM strengths are as follows:
accounts for all hydrologic processes that produce runoff from urban areas and the
production, transport, and treatment of pollutant loads associated with runoff;
accounts for interruption in natural stream transport network such as nonlinear reservoir
routing of overland flow;
contains a flexible set of hydraulic modeling capabilities dealing with industry standard
stormwater structures such as stormwater storage, divider, pumps, weirs, and orifices,
etc.; and
simulates different flow regimes such as backwater, surcharging, reverse flow, and
surface ponding.
Introduction to WEPPCAT
The WEPP is a process-based (mechanistic) model, available as a desktop model or through a
web-based interface, for simulating soil erosion and sediment yield from agricultural areas (see
Figure 2-2). WEPP can be used to assess how soil erosion rates are impacted by precipitation
events, soil type, vegetation type, topography, and number of commonly applied BMPs for
reducing soil loss. Simulations can be run at the hill slope or watershed scale (Flanagan et al.,
1995). Hill slope scale simulations are ideally suited for assessing the effectiveness of BMPs in
local settings such as a specific farm field. Watershed scale applications consist of linking hill
slopes via channels and impoundments (Flanagan et al., 1995). Recent developments allow
forested land cover, such as forested riparian buffers, to be represented in WEPP.
WEPPCAT is an online application of the WEPP model that provides flexible capabilities for
creating climate change scenarios to assess the potential effects of climate change on soil erosion
and sediment yield using the WEPP model. WEPPCAT was developed by the USD A ARS in
partnership with EPA ORD and is available for use at
http://typhoon.tucson.ars.ag.gov/weppcat/index.php (see Figure 2-2). WEPPCAT simulations
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are limited to the hill slope scale only. The WEPPCAT online interface allows users to input
field characteristics including soil series, field size, slope steepness, slope shape, and land
management (e.g., alfalfa with cutting, bluegrass with grazing, etc.). WEPPCAT outputs include
mean annual runoff, soil loss, and sediment yield. Users can also generate spatial sediment loss
data and sediment particle size profiles.
Climate
User
Spatial and
Temporal Dtttributlon
ol Croilon and
Sediment ¥ield
US€R
INTtftFflCC
INPUT
Sol Irrigation
Stop* Channel
Management Impoundment
Climate LUatsrihad
structure
Figure 2-2. Overview of the WEPP modeling system (from Flanagan et al.,
1995).
10
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WEPP uses the stochastic weather generator Cligen to generate daily meteorological data as
input to simulations based on monthly statistics from National Oceanic and Atmospheric
Administration (NOAA) National Climatic Data Center (NCDC) weather stations
(http://www.ars.usda.gov/Research/docs.htm?docid=l8094). Cligen outputs include daily time
series for precipitation (including storm parameters such as peak intensity), temperature, solar
radiation, and wind. WEPPCAT provides a capability to create climate change scenarios by
adjusting Cligen parameters to represent potential future changes in temperature and
precipitation. Available adjustments include increases and decreases in mean monthly
temperature, precipitation volume, and the number of wet days (i.e., the transition probabilities
of a wet day following a dry day, and a dry day following a dry day). These adjustments can be
made either uniformly for all months of the year, or individual adjustments can be made to
specific months or seasons of the year.
In addition to changing precipitation volume, Cligen parameters can also be adjusted to increase
the proportion of annual rainfall occurring in large magnitude events (i.e., to represent an
increase in event intensity independent of changes in total annual precipitation). WEPPCAT
provides a capability to increase the proportion of annual precipitation occurring in large
magnitude events up to 25%. Adjustments in precipitation intensity are made by applying the
user determined increase to the largest 5% of events, and simultaneously decreasing precipitation
in the lower 95% events by the same volume. This adjustment can only be made to all events
across the entire year, and is accomplished by altering the standard deviation of the distributions
of daily precipitation used by the climate generator. It is currently not possible to adjust the
intensity of events only in specific months of the year. Precipitation data can also be modified in
WEPPCAT based on elevation using the Parameter-elevation Regressions on Independent Slopes
Model (PRISM) climate database. Modifications are made by selecting precipitation values or
elevations for areas surrounding the selected weather station. All WEPPCAT simulations are
based on 100 years of daily meteorological data generated using Cligen. WEPPCAT results are
average values based on 100 years of simulation output.
11
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3. CASE STUDIES
3.1. INTRODUCTION
This section presents a series of six case studies designed to illustrate selected capabilities and
approaches for conducting scenario-based assessments using BASINS CAT and WEPPCAT (see
Table 3-1). The case studies include applications of four different watershed models; encompass
a range of spatial and temporal scales; include climate change, land-use change, and
management scenarios; and evaluate hydrologic and water quality endpoints of concern.
The scenarios used in each analysis were determined by available information and the goals of a
specific assessment activity. Multiple scenarios were employed to capture the full range of
underlying uncertainties associated with future climate, land use, and management practices on
water resources. Three different types of climate scenarios were employed: synthetic scenarios,
analog scenarios, and scenarios based on outputs from climate models (see IPCC TGICA [2007]
for a more complete discussion). Synthetic scenarios are created by incrementally modifying
climatic attributes within a predetermined, plausible range of future change. For example,
adjustments of historical temperatures by 1, 2, and 3°C and historical precipitation by 5, 10, and
15% could be applied in various combinations to create nine different climate change scenarios
(IPCC TGICA, 2007). Analog scenarios are constructed by identifying a time or geographic
location that has a climate similar to anticipated future conditions in the location of interest.
These records can be obtained either from the past (temporal analogs) or from a different
geographic location (spatial analogs). Model-based scenarios are developed using output from
climate modeling experiments that simulate the response of the climate system to changes in
greenhouse gas emissions and other climatic forcing.
Non-climatic watershed stressor scenarios, included land-use change and water resource
management practices, were considered in much the same way as climate change. Land-use
scenarios were based on a range of context-dependant information such as population growth,
land-use regulations, and economic factors, among other things. The management scenarios
were designed to explore the effectiveness of existing practices in the context of climate change
adaptation. In each case, consideration of multiple scenarios is desirable to capture the full range
of underlying uncertainties associated with future climate, land use, and management practices.
The intent of the case studies in this report is to illustrate the use of BASINS CAT and
WEPPCAT in a variety of applications. All case studies using HSPF, SWAT, or SWMM were
conducted using preexisting, calibrated models. In certain cases, minor modifications such as
performing additional calibration were made. In each case, however, models may not represent
in full detail all management practices and other factors influencing the hydrologic behavior of
case study watersheds. WEPP simulations did not require a preexisting model and were
developed independently. In addition, analysis and discussion of simulation results are brief and
not comprehensive. Results should, therefore, not be considered absolute.
12
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Table 3-1. Summary of case studies presented in this report.
Section
Topic
Analysis Approach
3.2
Streamflow and water
quality sensitivity to climate
change in the Raccoon
River, IA, using BASINS
CAT with SWAT
PART A: Assess scenarios based on different combinations of
assumed temperature and precipitation change within plausible
ranges of future change; changes uniform for each month of the
year
PART B: Assess scenarios based on downscaled climate model
projections, North American Regional Climate Change
Assessment Program (NARCCAP) for temperature and
precipitation for mid-21'
of the year
century; changes vary among months
3.3
Urban stormwater sensitivity
to rainfall change and
effectiveness of
management in the Upper
Roanoke River, VA, using
BASINS CAT with SWMM
PART A: Assess scenarios based on different assumed changes
in precipitation (single event) within a plausible range of future
change
PART B: Assess performance of two stormwater management
strategies under precipitation change scenarios developed in
PART A
3.4
Agricultural sediment yield
sensitivity to climate change
and management practices
in Blue Earth County, MN,
using WEPPCAT
PART A: Assess scenarios based on different combinations of
assumed changes in temperature, precipitation volume, and
precipitation event intensity; changes uniform for each month of
the year
PART B: Assess performance of land management practices for
reducing sediment loss from corn fields under climate change
scenarios developed in PART A
3.5
Streamflow and water
quality sensitivity to changes
in precipitation amount,
frequency, and intensity in
the Tualatin River, OR,
using BASINS CAT with
HSPF
Assess scenarios based on different combinations of assumed
increases in precipitation annual volume, precipitation event
intensity (proportion of annual total in occurring in large
magnitude events), and precipitation event frequency (number of
precipitation events per year)
3.6
Streamflow sensitivity to dry
weather events in Sespe
Creek, CA, using BASINS
CAT with HSPF
Assess scenarios based on targeted adjustments to a historical
period of dry weather; scenarios represent increased severity of
historical dry period, increased duration of historical dry period,
and increased severity and duration of historical dry period
3.7
Streamflow and water
quality relative sensitivity to
climate change vs.
impervious ground cover in
the Western Branch of the
Patuxent River, MD, using
BASINS CAT with HSPF
Assess scenarios based on different combinations of assumed
increases in precipitation annual volume, precipitation event
intensity (proportion of annual total in occurring in large
magnitude events), and impervious ground cover
13
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3.2. STREAMFLOW AND WATER QUALITY SENSITIVITY TO CLIMATE
CHANGE IN THE RACCOON RIVER, IOWA, USING BASINS CAT WITH SWAT
Case Study Overview
This case study illustrates the use of BASINS CAT with a SWAT watershed model to
assess the sensitivity of streamflow and total nitrogen (TN), total phosphorus (TP), and
total suspended solids (TSS) loads to potential climate change in an agriculturally-
dominated watershed.
PART A is a general sensitivity analysis using a matrix of climate change scenarios based
on multiple combinations of potential temperature and precipitation changes within a
user-defined range. Climate change scenarios were created by increasing historical mean
annual temperatures +0 to +5°C by increments of 1°C and adjusting mean annual
precipitation volume -10 to +20% by increments of 5%.
PART B evaluates more detailed scenarios created using BASINS CAT to represent
climate model projections. Climate change scenarios were created by applying
adjustments to average monthly temperature and precipitation volumes based on
projections from four regionally downscaled climate models. Two additional scenarios
were created by adjusting average monthly precipitation volumes from one climate model
projection to assess the influence of different seasonal distributions of observed changes
on pollutant loading and streamflow.
Introduction
Nutrient pollution is an ongoing water quality issue in the Mississippi River basin, leading to
such problems as extensive algae growth and hypoxia in the Gulf of Mexico (Rabalais, 2001).
The Upper Mississippi River Basin (UMRB), a major agricultural region in the United States, is
a significant net exporter of nutrients to the Mississippi River and Gulf of Mexico. Future
climate change is projected to result in warming temperatures and changes in precipitation
regimes, and it is possible these changes could influence pollutant loading in the Mississippi
River Basin (Rossi et al., 2009). Managers and decision makers interested in quantifying future
nutrient loads from the UMRB will likely need to consider the potential impacts of climate
change in addition to other factors that impact water quality (e.g., land use, fertilizer application,
public policy, pollution abatement technology, etc.).
In this case study, a SWAT model of the Raccoon River in IA, a subbasin within the greater
UMRB, was used to simulate potential watershed response to projected climate change.
BASINS CAT was used to create an array of climate change scenarios for model simulations and
assess endpoints. The sensitivity of the Raccoon River was assessed in two different ways.
In PART A of this case study, adjustments to precipitation and temperature were applied
uniformly to the entire duration of the simulation using the BASINS CAT multiple changes
within specified range feature to assess potential changes to mean annual streamflow and TN,
14
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TP, and TSS loadings. However, changes to climate seldom follow a definitive and uniform
pattern across the year, especially for precipitation; therefore, this case study was taken a step
further in PART B.
PART B of this case investigates how seasonal changes to precipitation volume and temperature
can impact monthly and mean annual streamflow and TN, TP, and TSS loads. Spatial variability
of climate change was also represented by applying distinct adjustments to temperature and
precipitation data from the two NCDC weather stations included in the model. The BASINS
CAT months/years adjustment feature was used to develop the climate change scenarios based
on simulations for four RCMs, and the tool was used to assess sensitivity of endpoints.
Location Description
The Raccoon River watershed encompasses an area of roughly 9,400 km2 in central Iowa (see
Figure 3-1). It contains two 8-digit Hydrologic Unit Codes (HUCs). The northern portion, HUC
07100006, contains the North Raccoon branch and the main Raccoon River. The southern
portion, HUC 07100007, contains the Middle and South branches, which flow into the main
Raccoon River at the HUC outlet. The Raccoon River watershed drains into the Des Moines
River at the city of Des Moines, IA. Land use in the watershed is predominantly agricultural,
with minimal urban development and forested areas (see Table 3-2).
Water Model Setup
A preexisting, calibrated SWAT model of the Raccoon River was extracted from a previous
modeling effort investigating the impacts of ethanol corn production in the UMRB (U.S. EPA,
2010). The Raccoon River SWAT model uses the 2001 National Land Cover Data (NLCD,
http://www.mrlc.gov/nlcd2001.php) and 2004-2006 Cropland Data Layer
(http://www.nass.usda.gov/research/Cropland/metadata/meta.htm) for the land use coverage, and
the USDA-Natural Resource Conservation Service (NRCS) State Soil Geographic Database
(STATSGO; http://soils.usda.gov/survey/geography/statsgo/) for soils data. Data from the
Conservation Tillage Information Center and the 1997 and 2002 USD A Census of Agriculture
were used to identify the cropping rotation and management practices for the agricultural land
areas. Each subwatershed was assigned appropriate management and tillage practices. The
model was set up to run using 1960-2001 weather data developed by Di Luzio (2008). These
weather data, developed for modeling and assessments, are gridded data sets of daily
precipitation and temperature (maximum and minimum) spatially interpolated using slope,
elevation, and aspect as spatial covariates. Grid cells are 4 km2 (2 km on each side) and cover
the conterminous United States.
Initial SWAT parameters for the Raccoon River model were acquired from a national database
developed as part of a previous UMRB SWAT model (U.S. EPA, 2010). Additional, more
detailed calibration of the Raccoon River SWAT model was carried out using available
streamflow, TN, TP, and TSS data at the watershed outlet at Van Meter, IA. The entire 42-year
simulation duration, 1960-2001, was used to conduct the model calibration. Calibration results
for streamflow, nutrient, and sediment loads are shown in Table 3-3. The model was limited by
exclusion of point sources, which influences streamflow and pollutant loads. Quantitative results
should, therefore, not be considered absolute, but rather as indicative of the relative changes
resulting from the scenarios considered in this case study.
15
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N
^| Raccoon Rivei
Figure 3-1. The Raccoon River watershed and major tributaries.
Table 3-2. Land-use summary for Raccoon River watershed.
Land Use
Corn
Soybeans
Other Agriculture
Urban/Developed
Forest
Wetland
Portion of Watershed (%)
42
33
13
8
2
2
16
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Table 3-3. Raccoon River model calibration statistics for annual streamflow,
nutrient and sediment loads. R2: coefficient of determination; NSE: Nash-
Sutcliffe Efficiency coefficient (Nash and Sutcliffe, 1970); PB: percent bias;
RMSE: root mean square error.
Endpoint
R2
NSE
PB
RMSE
Streamflow (cms)
0.93
0.99
16.5
17
TN (kg/ha/yr)
0.93
0.47
39.8
24,657
TP (kg/ha/yr)
0.90
0.49
37.4
2,224
TSS (kg/ha/yr)
0.40
0.07
24.4
3,880
PART A: Annual Sensitivity Analysis
Scenario Development: PART A
A total of 42 SWAT model simulations were completed. Climate change scenarios included 1
baseline scenario and 41 climate change scenarios. No land-use or management scenarios were
included.
Climate Change Scenarios
The focus of PART A was to assess the sensitivity of the Raccoon River to changes in mean
annual precipitation and temperature. Information about potential future changes in temperature
and precipitation in the Raccoon River watershed was obtained from an ensemble of statistically
downscaled climate change data acquired from The Nature Conservancy's Climate Wizard web
site (www.climatewizard.org). ClimateWizard is a portal for accessing and visualizing summary
statistics for projected future changes in temperature and precipitation at any location within the
United States based on 16 GCM projections and three greenhouse gas emissions scenarios
(Special Report on Emissions Scenarios A2, A1B, and Bl) archived by the Program for Climate
Model Diagnosis Coupled Model Intercomparison Project Phase 3 (CMIP3). Summary
information is presented for two future periods: mid-century (2050s) and end-of-century (2080s).
Climate change scenarios were developed to fall within the ensemble range of projected end-of-
century (2080s) temperature and precipitation changes for the Raccoon River watershed.
Projected changes in mean annual temperature ranged from approximately 2 to 6.5°C, and
projected changes in annual precipitation volume ranged from -22 to +30%. Spatial variability
in projected changes across the watershed was relatively small.
Climate scenarios were created using a change factor or delta change methodology (Anandhi et
al., 2011). Climate change scenarios for the SWAT model were developed using BASINS
CAT's ability to create multiple changes within specified range (see Figure 3-2). This feature
automates the creation of multiple climate adjustments for selected variables by specifying a
range of change and step increment within the range (e.g., to change temperature by 0 to 3°C by
increments of 1°C). When two or more variables are selected, this feature creates scenarios
reflecting each possible combination of changes for selected variables. The following
17
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adjustments were made using change factors to the Raccoon River temperature and precipitation
records for 1960-2001:
Modify Existing Data
- n x
Precp
base 1 AREA (and 1 more)
Multiply Existing Values by a Number (eg Precipitation)
Modification Name:
Existing Data to Modify:
How to Modify:
Number to multiply existing data by
O Single Change (*! Multiple changes within specified range
Minimum 0.9 multiplication factor
multiplication factor
Maximum:
Increment:
1.2
n IT:
Events
EJ Vary values only in the following Events
Exceeding threshold |0
Allow gaps up to
Sum of values exceeding threshold 0
Total duration above |0
Months/Years
Vary only in selected
None
Ok
Cancel
Figure 3-2. BASINS CAT option window for making multiple changes
within specified range. Modification Name is user defined; Existing Data to
Modify is the time series to be modified; How to Modify is the type of data
modification; Multiple changes within specified range allows the user to
specify the criteria for the multipliers including minimum, maximum and
incremental values.
Average daily temperatures increased by 0 to 5°C at increments of 1°C
Average annual precipitation volume adjusted by -10 to +20% at increments of 5%
Figure 3-2 shows how the precipitation volume adjustments were made using the BASINS CAT
interface. In the section labeled number to multiply existing data by, the option multiple
changes within specified range is selected to automate the creation of multiple scenarios within
18
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a user-specific range. The minimum and maximum multipliers within the range are defined as
0.9 and 1.2. The step increment is 0.05. BASINS CAT will, thus, create and run multiple
precipitation change scenarios representing -10, -5, 0, 5, 15, and 20% change in precipitation
volume.
Identical adjustments were applied to two weather stations included in the model, one located in
each subwatershed. The SWAT model used the modified temperature and precipitation inputs to
internally recompute potential evapotranspiration (PET) using the Penman-Monteith algorithm
for simulation of each scenario. This differs from other BASINS CAT models (i.e., HSPF,
SWMM) where PET is recomputed external to the model by BASINS CAT, and then provided to
the model as an input variable in the same manner as temperature and precipitation.
Land-use Scenarios
Land-use and land cover (LULC) data were held constant for all model runs. While it is likely
that LULC in the Raccoon River will change in the future, holding it constant allowed for the
assessment of potential impacts from climate change only.
Management Scenarios
Management scenarios were not specifically evaluated in this case study. BMPs or other
management practices may have been included in the original SWAT model, but no adjustments
were made in order to focus on potential climate change impacts only.
Endpoint Selection: PART A
The endpoints for this study consisted of mean annual streamflow and loadings of TN, TP, and
TSS. A cursory assessment of these constituents was considered appropriate given the goal of
evaluating general watershed sensitivity to climate change in a largely agricultural watershed.
Results: PART A
A useful method for analyzing results from a series of climate scenarios created with BASINS
CAT is the pivot table capability. The tool allows users to interactively build pivot tables by
specifying row, column, and cell variables (see Figure 3-3). The pivot tables are displayed in the
BASINS CAT interface and may also be saved in a form readily available for use by common
spreadsheet tools. Model results for mean annual streamflow and loadings of TN (kg/ha/yr), TP
(kg/ha/yr), and TSS (tonnes/ha/yr) extracted using the pivot table option are shown in Tables 3-4
to 3-7. The combination of 0% change in precipitation and 0°C change in temperature represents
the baseline conditions of the model (historical climate).
Results show changes in streamflow were proportional to changes in precipitation, and inversely
proportional to changes in air temperature. Changes in pollutant loads were directly proportional
to changes in streamflow. For example, a 5% increase in precipitation and a 0°C increase in
temperature resulted in a mean annual streamflow of 56 cms, while a 5% increase in
precipitation with a 5°C increase in temperature resulted in a mean annual streamflow of 33 cms.
19
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, Climate Assessment Tool 2.0
00®
File Edit Options Help
Model || Results Piv
:t Table
Rows At
em Add Current Value
Columns Precp Multiply Current Value
Cells FIc
0.9
0 30.731
1 26.35
2 22.609
3 19.159
4 16.211
5 13.697
iwQut Mean base REACH 2FLOW_OUT
0.95 1 1.05 1.1 1 1.15
-
v
II
1.2
38.789 47.149 56.028 65.373 74.687 84.19
33.959 41.96 50.585 59.559 68.633 77.974
29.756 37.367 45.604 54.258 63.015 72.052
25.82 32.971 40.868 49.218 57.713 66.45
22.288 28.996 36.399 44.391 52.57 61.038
19.269 25.593 32.652 40.277 48.159 56.418
Figure 3-3. BASINS CAT window displaying results for mean annual
streamflow at the outlet of the Raccoon River SWAT model in a pivot table.
Table 3-4. Mean annual streamflow (cms) for all combinations of
temperature and precipitation change as extracted from the BASINS CAT
pivot table. Result for the baseline condition (historical climate) is
highlighted in grey in the first column.
Precipitation
Change
-10%
-5%
0%
5%
10%
15%
20%
Mean Annual Streamflow (cms)
Temperature Change
0°C
31
39
47
56
65
75
84
1°C
26
34
42
51
60
69
78
2°C
23
30
37
46
54
63
72
3°C
19
26
33
41
49
58
66
4°C
16
22
29
36
44
53
61
5°C
14
19
26
33
40
48
56
20
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Table 3-5. Mean annual TN load (kg/ha/yr) for all combinations of
temperature and precipitation change as extracted from the BASINS CAT
pivot table. Result for the baseline condition (historical climate) is
highlighted in grey in the first column.
Precipitation
Change
-10%
-5%
0%
5%
10%
15%
20%
Mean Annual TN (kg/ha/yr)
Temperature Change
0°C
7.9
9.8
11.6
13.5
15.4
17.1
18.9
1°C
7.1
9.0
10.9
13.0
14.9
16.7
18.6
2°C
6.3
8.1
10.0
12.1
14.1
15.9
17.8
3°C
5.6
7.4
9.2
11.3
13.5
15.5
17.5
4°C
5.0
6.8
8.6
10.8
13.1
15.2
17.3
5°C
4.4
6.1
7.9
9.9
12.2
14.4
16.6
Table 3-6. Mean annual TP load (kg/ha/yr) for all combinations of
temperature and precipitation change as extracted from the BASINS CAT
pivot table. Result for the baseline condition (historical climate) is
highlighted in grey in the first column.
Precipitation
Change
-10%
-5%
0%
5%
10%
15%
20%
Mean Annual TP (kg/ha/yr)
Temperature Change
0°C
0.57
0.71
0.83
0.97
1.12
1.26
1.42
1°C
0.52
0.64
0.76
0.92
1.06
1.19
1.34
2°C
0.46
0.59
0.72
0.87
1.02
1.14
1.31
3°C
0.42
0.55
0.67
0.82
0.97
1.11
1.27
4°C
0.40
0.52
0.65
0.80
0.96
1.11
1.27
5°C
0.35
0.49
0.62
0.77
0.93
1.09
1.25
21
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Table 3-7. Mean annual TSS load (tonnes/ha/yr) for all combinations of
temperature and precipitation change as extracted from the BASINS CAT
pivot table. Result for the baseline condition (historical climate) is
highlighted in grey in the first column.
Precipitation
Change
-10%
-5%
0%
5%
10%
15%
20%
Mean Annual TSS (tonnes/ha/yr)
Temperature Change
0°C
0.58
0.77
0.98
1.22
1.49
1.77
2.08
1°C
0.47
0.65
0.84
1.07
1.32
1.58
1.87
2°C
0.40
0.55
0.74
0.95
1.18
1.43
1.71
3°C
0.33
0.48
0.64
0.84
1.06
1.31
1.57
4°C
0.29
0.41
0.57
0.76
0.97
1.20
1.46
5°C
0.24
0.36
0.51
0.68
0.89
1.11
1.35
Evapotranspiration for the climate scenarios was analyzed to determine its potential role on
endpoint values. BASINS CAT was used to generate a pivot table of modeled
evapotranspiration from the land surface for each of the climate scenarios (see Table 3-8).
Evapotranspiration is much less sensitive to changes in precipitation than temperature. As
temperature increases, evapotranspiration as expected increases, resulting in reduced streamflow
and pollutant loads. For example, as temperature increases from 0 to 5°C, holding the
precipitation increase constant at 5%, streamflow decreases from 56 to 33 cms, while at the same
time, evapotranspiration increases from 60.5 to 69.2 cm/yr.
In addition to pivot tables, contour plots can provide a visual display of results from the climate
scenarios, a presentation useful for climate vulnerability assessment and decision support. While
BASINS CAT does not directly generate contour plots, model output can be exported as text
files for use with any graphics and plotting software. Figure 3-4 is a contour plot of the
simulated change in streamflow for each combination of temperature and precipitation
adjustment. Contours were generated by interpolation from the original 42 scenario endpoints,
indicated as dots on the plot, using plotting software external to BASINS. The impact of
warming temperatures on mean annual streamflow can be seen by moving vertically from the
point labeled "Current Climate." Similarly, the impact of changes in precipitation can be seen by
moving horizontally in the plot.
22
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Table 3-8. Mean annual evapotranspiration (cm/yr) for all combinations of
temperature and precipitation change as extracted from the BASINS CAT
pivot table. Result for the baseline condition (historical climate) is
highlighted in grey in the first column.
Precipitation
Change
-10%
-5%
0%
5%
10%
15%
20%
Mean Annual Evapotranspiration (cm/yr)
Temperature Change
0°C
58.7
59.4
60.0
60.5
61.0
61.4
61.8
1°C
60.4
61.3
61.9
62.5
63.1
63.6
64.0
2°C
61.9
62.9
63.7
64.4
65.0
65.6
66.1
3°C
63.3
64.4
65.3
66.1
66.9
67.5
68.1
4°C
64.6
65.9
66.9
67.8
68.7
69.4
70.0
5°C
65.8
67.1
68.2
69.2
70.2
71.0
71.7
23
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Change in Mean Annual Flow. %
-5
10
15
Change in Mean Annual Precipitation. %
Figure 3-4. Contour plot showing percent change in mean annual
streamflow for all combinations of temperature and precipitation change.
PART B: Seasonal Sensitivity Analysis
Scenario Development: PART B
A total of seven model simulations were completed. Scenarios included one baseline climate
scenario and six climate change scenarios. No land-use change or management scenarios were
included.
Climate Change Scenarios
In PART A, climate change scenarios were created without consideration of seasonal differences
in how climate changes. Adjustments were made uniformly to all temperature and precipitation
values within the historical baseline period. Although not well understood, it is possible that
climate change will vary seasonally. For example, there may be increases in winter precipitation
with little change during the summer months. Similarly, greater warming may occur during the
winter months than summer, altering the timing of spring snowmelt and streamflow. BASINS
CAT provides the capability to apply change factors to only selected months of the year. This
capability allows scenarios representing changes that vary on a seasonal basis to be created.
24
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For PART B, climate change scenarios were developed to investigate how seasonal changes in
precipitation patterns can impact mean monthly and annual endpoint values. As in PART A,
climate scenarios were developed using the change factor method. The change factors were
developed using data from dynamically downscaled GCM model projections from the
NARCCAP (http://www.narccap.ucar.edu). The NARCCAP model projections are a series of
high resolution climate simulations developed by nesting RCMs within coarser resolution
GCMs. The NARCCAP climate simulations cover 1971-2000 (baseline) and 2041-2070
(future). The change factors applied to the SWAT model weather data represented the difference
in average monthly values between the baseline and the future NARCCAP simulations. The
change factors were used to adjust the mean monthly values of temperature and precipitation for
the entire 1960 to 2001 Racoon River weather time series.
Four climate change scenarios, CC-1, CC-2, CC-3, CC-4, were developed using four NARCCAP
climate model projections (see Table 3-9). Two additional scenarios, CC-5 and CC-6, were
created by applying additional adjustments to one of these NARCCAP scenarios, CC-3.
Scenarios CC-5 and CC-6 each maintain the same mean annual precipitation and temperature as
CC-3, but monthly precipitation values were altered to represent two different seasonal patterns
of change.
Table 3-9. NARCCAP regional and global climate models used to develop
climate change scenarios.
Climate Scenario
CC-1
CC-2
CC-3
CC-4
CC-5
CC-6
NARCCAP RCM and GCM model combinations used to develop climate
change scenarios (RCM_GCM)
crcm_cgcm3
rcm3_cgcm3
rcm3_gfdl
wrfg_ccsm
rcm3_gfdl
rcm3_gfdl
Canadian Regional Climate Model nested in the Canadian
Global Climate Model version 3
National Center for Atmospheric Research (NCAR)
Regional Climate Model version 3 nested in the Canadian
Global Climate Model version 3
NCAR Regional Climate Model version 3 nested in the
Geophysical Fluid Dynamics Laboratory Climate Model
version 2
Weather Research and Forecasting Grell Model nested in
the NCAR Community Climate Model version 3
Same RCM_GCM projection as CC-3 but monthly
precipitation values are adjusted to represent an alternative
pattern of seasonal variability
Same RCM_GCM projection as CC-3 but monthly
precipitation values are adjusted to represent an alternative
pattern of seasonal variability
The BASINS CAT Months/Years adjustment feature was used to modify the monthly
temperature and precipitation climate data in the SWAT model for each of the simulations (see
Figure 3-5). Adjustments were made using monthly change factors representing the difference
between baseline conditions and projected future change for each climate change scenario.
Precipitation change factors were applied as multipliers to each record within a given month in
25
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the baseline precipitation time series. Temperature change factors were applied as constant
degree changes to each record within a given month in the baseline temperature time series.
_. Modify Existing Data
- n x
Modification Name:
Existing Data to Modify:
How to Modify:
base PCP2 PR EC
Multiply Existing Values by a Number (eg Precipitation]
Number to multiply existing data by
(.*.) Single Change O Multiple changes within specified range
Value 1.2102
multiplication factor
Events
O Vary values only in the following Events
Exceeding threshold
Allow gaps up to
Sum of values exceeding threshold 10
Total duration above 0
Months/Years
0 Vary only in selected Month
•Jul
Feb Aug
Mar Sep
Apr Del
May Nov
Jun Dec
Ok
Cancel
Figure 3-5. BASINS CAT window showing the modification of data on a
monthly basis. Modification Name is user defined; Existing Data to Modify is
the time series to be modified; How to Modify specifies the type of time series
modification; Single Change under Number to multiply existing data by is the
multiplier applied to the time series specified; the Months/Years adjustment
specifies to which month in the time series the multiplier will be applied.
In PART A of this case study, it was assumed that the spatial variability of climate change within
the study watershed was negligible, and identical change factors were applied to temperature and
precipitation data from each of these locations for each scenario considered. In many cases,
however, such as in large or topographically complex watersheds, spatial differences in climate
change may need to be represented. Spatial variability in climate change can be represented in
26
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BASINS CAT by applying different sets of change factors to meteorological data from stations
in different locations.
In PART B spatial variability was represented in all climate change scenarios by applying
different monthly change factors to the two meteorological data locations used by the SWAT
model, one representing the northern and the other the southern subwatersheds. Using the
BASINS CAT's Months/Years adjustment feature, adjustments were first applied to temperature
and precipitation data from one location, followed by application of a different set of adjustments
to data from the second location.
Land-use Scenarios
LULC data were held constant for all model runs. While it is unlikely LULC in the Raccoon
River will not change in the future, holding it constant allowed for the assessment of potential
impacts from climate change only.
Management Scenarios
Management scenarios were not specifically evaluated in this case study. BMPs or other
management practices may have been included in the original SWAT model, but no adjustments
were made in order to focus on potential climate change impacts only.
Endpoint Selection: PART B
The endpoints for this study consisted of mean annual and monthly streamflow and loadings of
TN, TP, and TSS.
Results: PART B
Model simulation results are shown in Figures 3-6 to 3-9 and Tables 3-10 to 3-13. The BASINS
CAT ability to specify monthly time subsets for endpoint analysis was used to extract the mean
monthly values. Table 3-14 presents the annual totals for precipitation (mm), TN (kg/ha), TP
(kg/ha), TSS (kg/ha), mean annual streamflow (cms), and temperature (°C) for all scenarios. All
climate change scenarios have higher mean annual temperature and total annual precipitation
than the baseline. However, the mean monthly precipitation and temperature values for
Scenarios CC-1 to CC-4 vary in direction throughout the year compared to the baseline. The
scenarios tend to have higher mean monthly precipitation in the spring (March to May) and fall
(September to November), but lower mean monthly precipitation in the late summer (June to
August) (see Figures 3-6 and 3-7 and Tables 3-10 to 3-13). Simulation results for scenarios
representing different monthly climate adjustments (seasonal variability) illustrate a high
sensitivity of streamflow and pollutant loadings to the distribution of rainfall and temperature
changes within the year.
27
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10
11
12
Figure 3-6. Mean monthly precipitation (mm), temperature (°C), and
streamflow (cms) for the NARCCAP-derived climate change scenarios, CC-
1, CC-2, CC-3, CC-4, and the baseline scenario.
28
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0.35 -i
10
11
12
Month
Figure 3-7. Mean monthly TSS (tonnes/ha), TN (kg/ha), and TP (kg/ha) for
the NARCCAP-derived climate change scenarios, CC-1, CC-2, CC-3, CC-4,
and the baseline scenario.
29
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30 -i
Baseline
CC-3, CC-5,
CC-6
10
11
12
Figure 3-8. Mean monthly precipitation (mm), temperature (°C), and
streamflow (cms) for the CC-3, CC-5, CC-6, and baseline scenarios. The
monthly temperature was exactly the same for all scenarios since only
precipitation was adjusted; therefore, CC-3, CC-5, and CC-6 are represented
by the same line.
30
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0.00
Figure 3-9. Mean monthly TSS (tones/ha), TN (kg/ha), and TP (kg/ha) for
the CC-3, CC-5, CC-6, and baseline scenarios.
31
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Table 3-10. Mean monthly streamflow (cms) for all scenarios.
Climate Scenario
Baseline
CC-1
CC-2
CC-3
CC-4
CC-5
CC-6
Jan
22
32
37
25
32
31
68
Feb
34
26
28
21
33
30
101
Mar
54
29
35
54
45
46
103
Apr
64
44
61
83
68
66
78
May
78
83
74
124
87
111
71
Jun
91
113
94
94
70
141
48
Jul
63
51
48
38
43
65
26
Aug
31
17
21
15
20
24
8
Sep
37
19
44
30
63
27
26
Oct
38
21
34
41
60
31
40
Nov
31
23
27
20
53
34
46
Dec
22
20
22
17
37
30
57
Mean
47
40
44
47
51
53
56
Table 3-11. Mean monthly nitrogen load (kg/ha) for all scenarios.
Climate Scenario
Baseline
CC-1
CC-2
CC-3
CC-4
CC-5
CC-6
Jan
0.6
0.9
0.9
0.6
0.9
0.8
1.7
Feb
0.7
0.6
0.6
0.5
0.8
0.6
2.1
Mar
1.0
0.7
0.7
1.1
0.9
0.9
2.2
Apr
0.9
0.9
1.2
1.7
1.4
1.3
1.9
May
1.6
2.3
2.1
3.5
2.5
3.1
2.3
Jun
2.3
2.9
2.6
3.1
2.3
3.9
1.8
Jul
1.8
1.8
1.6
1.8
1.5
2.3
1.0
Aug
0.9
0.7
0.8
0.7
0.7
1.0
0.3
Sep
0.6
0.4
0.9
0.6
1.2
0.6
0.5
Oct
0.4
0.3
0.5
0.6
0.9
0.5
0.6
Nov
0.3
0.3
0.4
0.3
0.8
0.5
0.6
Dec
0.5
0.5
0.5
0.4
0.9
0.7
1.2
Mean
0.9
1.0
1.1
1.3
1.2
1.3
1.4
to
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Table 3-12. Mean monthly phosphorous load (kg/ha) for all scenarios.
Climate Scenario
Baseline
CC-1
CC-2
CC-3
CC-4
CC-5
CC-6
Jan
0.04
0.10
0.10
0.06
0.06
0.07
0.18
Feb
0.08
0.07
0.07
0.05
0.08
0.07
0.28
Mar
0.14
0.08
0.08
0.16
0.11
0.12
0.26
Apr
0.08
0.09
0.12
0.17
0.12
0.12
0.10
May
0.10
0.20
0.13
0.27
0.17
0.25
0.10
Jun
0.13
0.19
0.16
0.13
0.10
0.26
0.05
Jul
0.08
0.05
0.06
0.03
0.05
0.07
0.03
Aug
0.03
0.01
0.02
0.01
0.02
0.01
0.01
Sep
0.06
0.03
0.09
0.06
0.14
0.04
0.06
Oct
0.04
0.03
0.04
0.06
0.09
0.04
0.07
Nov
0.02
0.02
0.02
0.01
0.04
0.03
0.04
Dec
0.03
0.03
0.03
0.02
0.04
0.04
0.10
Mean
0.07
0.07
0.08
0.09
0.09
0.09
0.11
Table 3-13. Mean monthly TSS load (tonnes/ha) for all scenarios.
Climate Scenario
Baseline
CC-1
CC-2
CC-3
CC-4
CC-5
CC-6
Jan
0.03
0.05
0.06
0.03
0.04
0.04
0.13
Feb
0.05
0.04
0.04
0.02
0.04
0.04
0.22
Mar
0.10
0.05
0.05
0.10
0.07
0.08
0.26
Apr
0.11
0.07
0.11
0.17
0.12
0.12
0.14
May
0.14
0.18
0.14
0.29
0.18
0.25
0.13
Jun
0.18
0.27
0.21
0.20
0.13
0.36
0.08
Jul
0.13
0.11
0.10
0.06
0.09
0.14
0.05
Aug
0.05
0.02
0.03
0.02
0.03
0.03
0.01
Sep
0.07
0.03
0.09
0.06
0.15
0.04
0.05
Oct
0.07
0.03
0.06
0.08
0.12
0.05
0.08
Nov
0.04
0.03
0.03
0.02
0.08
0.04
0.07
Dec
0.02
0.02
0.02
0.02
0.05
0.04
0.09
Mean
0.08
0.07
0.08
0.09
0.09
0.10
0.11
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Table 3-14. Total annual precipitation (mm), mean annual temperature (°C), mean annual streamflow (cms),
and mean annual loads of TSS (tonnes/ha), TN (kg/ha), and TP (kg/ha) for all scenarios.
Climate
Scenario
Baseline
CC-1
CC-2
CC-3
CC-4
CC-5
CC-6
Total Annual
Precipitation, mm
813.0
835.9
853.5
857.9
884.0
884.2
884.3
Mean Annual
Temp., °C
8.83
11.85
11.61
11.50
11.22
11.22
11.22
Mean Annual
Streamflow, cms
47.1
39.8
43.7
46.9
51.0
52.9
55.7
Total Annual
TSS, tonnes/ha
0.98
0.88
0.94
1.07
1.11
1.23
1.30
Total Annual
TN, kg/ha
11.6
12.4
12.9
15.1
14.7
16.2
16.2
Total Annual
TP, kg/ha
0.83
0.89
0.90
1.03
1.01
1.11
1.27
-------
The influence of monthly variation in precipitation and temperature is further illustrated by the
comparisons of CC-3 and two synthetically adjusted versions, CC-5 and CC-6. All three
scenarios have the same mean annual rainfall and temperature, but the seasonal distribution of
precipitation is modified in CC-5 and CC-6 (see Figure 3-8). The modified scenarios indicate
additional potential impacts on mean monthly and annual streamflow and pollutant loads. For
example, in Scenario 6, more than doubling the baseline precipitation in the winter and early
spring resulted in a significant increase in all endpoints during those same months (see
Figures 3-8 and 3-9). This scenario also had the highest mean annual streamflow and pollutant
loadings, indicating potential risk from higher precipitation volumes in the winter and spring (see
Table 3-14).
Summary
This study assessed the sensitivity of a predominantly agricultural watershed, the Raccoon River,
to climate change. The primary BASINS CAT features used in this study were the ability to
automate the adjustment of temperature and precipitation time series data for the SWAT
watershed model on a monthly and annual basis, and to apply these changes spatially. This
allowed an array of climate change scenarios to be automatically generated and used as inputs
for model simulations. The pivot table feature was used to generate endpoint result tables for all
temperature and precipitation change combinations considered. This capability made it possible
to quickly develop and evaluate watershed sensitivity to the climate change scenarios on a
seasonal and annual basis.
The climate scenarios applied in PARTS A and B were developed using synthetic adjustments
and climate model projections. While simple, they were effective in showing that the Raccoon
River watershed is indeed sensitive to changes in both precipitation and temperature. PART A
presented a general watershed sensitivity analysis whereby uniform adjustments to precipitation
and temperature were applied across the entire watershed and simulation time series. The results
enabled the development of a contour plot, a simple guide for assessing watershed response
across a range of temperature and precipitation changes. In the context of watershed
management, a simple assessment such as the one presented may be adequate, providing enough
detail to inform the underlying watershed management goal. This type of modeling and analysis
approach can also be extended to assess the general sensitivity of land-use change or land
management practices.
PART B investigates how seasonal shifts in climate change (mainly in terms of varied
precipitation) can affect mean monthly and annual endpoint results. Although subject to
uncertainty, seasonal shifts in precipitation due to climate change are likely. Further, a shift in
the timing or seasonality of climate patterns could lead to unexpected outcomes that are not
visible when examining the trends at an annual scale only. To illustrate the effect of seasonal
variability, monthly adjustments to precipitation and temperature in the form of change factors
were applied to the baseline weather data of the Raccoon River watershed SWAT model. The
comparison of the climate scenarios to each other and the baseline illustrates the potential effects
of seasonal shifts of climate on streamflow and pollutant loadings in the Raccoon River
watershed, especially precipitation as in Scenarios CC-5 and CC-6.
35
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3.3. URBAN STORMWATER SENSITIVITY TO RAINFALL CHANGE AND
EFFECTIVENESS OF MANAGEMENT IN THE UPPER ROANOKE RIVER, VA,
USING BASINS CAT WITH SWMM
Case Study Overview
This case study illustrates the use of BASINS CAT with an event-scale SWMM model to
assess the sensitivity of urban stormwater runoff and pollutant loading to climate change.
PART A evaluates stormwater runoff, nutrient, and TSS concentrations from an urban
redevelopment site under a set of event scenarios. BASINS CAT was used to increase the
total volume of a hypothetical design rainfall event by 10, 20, and 30%.
PART B evaluates the effectiveness of two management scenarios to meet a hypothetical
stormwater goal under the same precipitation change scenarios as in PART A. The
management scenarios considered in PART B were a no management (baseline) scenario, a
distributed management scenario, and a centralized stormwater management scenario.
Introduction
The impacts of urban stormwater runoff on local water bodies have made it a high management
priority in many cities. Impervious cover associated with roads, rooftops and compacted soil can
alter hydrologic processes resulting in increased stormwater runoff, channel erosion, reduced
groundwater recharge and decreased baseflow during dry weather. Stormwater runoff can also
carry urban pollutants into stormwater systems and nearby streams. Urbanized watersheds
generally exhibit a high sensitivity to rainfall and snowmelt events. Changes in precipitation
could significantly alter stormwater runoff volumes and pollutant loading from urban
environments.
PART A of this case study investigates the sensitivity of stormwater runoff from a commercial
redevelopment site to precipitation change at the event scale. SWMM was used to simulate the
urbanized watershed's response, and BASINS CAT was used to develop the precipitation change
scenarios. The BASINS CAT capability to create multiple changes within specified range was
used to modify rainfall event volumes, creating an array of climate scenarios for use in the model
simulations. Endpoints analyzed included stormwater flow rate and event mean concentrations
(EMCs) of TP and TSS at the site outlet.
PART B assessed two management scenarios where stormwater BMPs were employed to
investigate the benefits of alternative stormwater management options under precipitation
change. Such analysis can be a cost effective way of evaluating climate change adaptation
strategies. BASINS CAT was used to develop the precipitation change scenarios and assess the
results from the model simulations.
36
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Location Description
The study site was a 0.2 km2 commercial redevelopment project located in the headwaters of the
Upper Roanoke River (HUC 03010101) in southwest Virginia (see Figure 3-10; Young et al.,
2009). Prior to redevelopment, the site was comprised of a few small commercial buildings and
a motel. Since 2008, it has undergone two phases of redevelopment. Phase I involved the
construction of a shopping mall, theater, restaurants, and stormwater detention facility in the
southern portion covering approximately 0.05 km2 of the site. Phase II called for a big-box retail
development in the northern portion covering approximately 0.1 km2 of the site. The entire site
drains from the northwest to southeast corner.
Baseline land-use categories for the site are shown in Table 3-15. For this small urban
watershed, there are three distinct land-use categories: green space, impervious surfaces, and
rooftop. The impervious surfaces represent any paved surface such as roads or parking lot.
Green space represents any naturally occurring or manmade pervious land cover. Rooftop
represents all rooftop surfaces, including both conventional and vegetated rooftops.
West Virginia
North Carolina
Reaches in 0301 01 01
First and Main Site
HUC 0301 01 01
-V
D
0.125
I
0.25
0.375
I
0.5
Scale in Miles
Figure 3-10. The commercial redevelopment site in the Upper Roanoke
River watershed in Virginia, USA.
37
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Table 3-15. Baseline land-use summary for the commercial redevelopment
site.
Land Use
Green space
Impervious cover
Rooftop
Portion of Site, %
43
41
16
Water Model Setup
Stormwater modeling requires consideration of individual rainfall-runoff events. A preexisting
SWMM model was acquired from a previous study to evaluate an optimization tool for
improving site development and Stormwater BMP selection in Virginia (Young et al., 2009).
The SWMM model, while capable of running on a continuous time-scale, was developed for
running event-based simulations. The original study included consideration of a baseline
scenario with no runoff control measures and two alternative Stormwater management scenarios.
The model included subwatershed delineation and hydrologic discretization of the SWMM
hydraulic schematic.
The SWMM model used in this case study was not calibrated. Rather, the baseline scenario was
used as a basis against which the two alternative Stormwater management scenarios were
compared. The model uses a custom designed SCS (Soil Conservation Service) Type II storm
that has a 31.7-mm/hr rainfall intensity for a 1-hour duration. The model simulation duration is
2 days with a 1-hour design event in the beginning hour of the simulation period. Temperature
data were acquired from the nearby NCDC weather station in Blacksburg, VA (440766). Initial
assessment of model simulations indicated that temperature was not a significant factor in
determining endpoint values given the short timescale of the event. Therefore, while changes in
temperature are included in climate change scenarios, results are presented only for changes in
precipitation.
PART A: Runoff Sensitivity Analysis
Scenario Development: PART A
A total of four model simulations were completed. Scenarios included one baseline precipitation
scenario and three precipitation change scenarios. No land use or management scenarios were
included.
Precipitation Change Scenarios
Precipitation change scenarios were developed to fall within the ensemble range of projected
end-of-century (2080s) precipitation changes for this region based on statistically downscaled
data from 16 CMIP3 climate models acquired from the Climate Wizard web site. See
Section 3.2 for more information on these models. Projected changes in mean annual
precipitation ranged from -17 to +27%.
The climate scenarios for input to SWMM were developed using the BASINS CAT capability to
create multiple changes within specified range. This feature automates the creation of multiple
38
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adjustments for selected variables by specifying a range and step increment within the range
(e.g., to change temperature from 0 to 3°C by increments of 1°C). In this study, the design event
rainfall intensity was increased 10 to 30% by increments of 10%, which together with the
baseline scenario resulted in a total of four precipitation change scenarios. The event rainfall
intensity for the baseline and three precipitation change scenarios are shown in Figure 3-11. For
each model run, BASINS CAT was used to generate a revised PET record based on the revised
temperature record using the Hamon method (Hamon, 1961). The revised PET record was then
provided as an input variable to the SWMM model in the same manner as the revised
temperature and precipitation records.
200
£ 150
E
E_
>
^ 100
c
'5 50
a*
Baseline Rainfall Event (31.75 mm/hr)
10% Increase
20% Increase
30% Increase
10 20 30 40
Time (minute)
50
60
Figure 3-11. Rainfall event intensity for the baseline rainfall event and three
precipitation change scenarios.
Land-use Scenarios
Part A of this case study does not include land-use scenarios. LULC was held constant to assess
the impacts of precipitation change only.
Management Scenarios
Part A of this case study does not include management scenarios. Stormwater BMPs were not
included to assess the impacts of precipitation change only.
Endpoint Selection: PART A
The endpoints for this study included the event mean stormwater flow rate and the EMCs of TP
and TSS at the site outlet. The original study by Young et al. (2009) included TP and TSS, both
reported as major pollutants in the State of Virginia. Using BASINS CAT, the endpoint time
series and desired statistics at event or months/years timescales can be reported.
39
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Results: PART A
Table 3-16, developed using the BASINS CAT pivot table capability, shows the resulting event
values for the baseline and three precipitation scenarios. Precipitation depths (mm/hr) are
presented as sum totals for the event, while stormwater flow rate (cms), TSS (mg/L), and TP
(mg/L) are event means. The rainfall event dynamic and flow hydrograph for all scenarios are
shown in Figure 3-12. The increase in rainfall volume in the simulated design storm was found
to increase the flow rate and pollutant concentrations during the event. The flow increases of 14,
28, and 38% followed a nearly linear response to the 10, 20, and 30% increase in rainfall
volume, respectively. While TSS and TP concentrations increased, the rate of increase
diminished as precipitation volume increased. This response is expected given that first flush of
pollutants would be washed away at a much faster rate for larger events and pollutant
concentrations will become diluted as runoff volumes increase.
Table 3-16. Event rainfall intensity (mm/hr), stormwater flow rate (cms),
and concentrations of TP (mg/L) and TSS (mg/L) for the baseline and three
precipitation change scenarios.
Scenarios
Baseline
+10%
+20%
+30%
Rainfall and Endpoints
Event Rainfall Intensity (mm/hr)
31.75
34.90
38.10
41.30
Flow (cms)
0.021
0.024
0.027
0.029
TSS (mg/L)
0.98
1.02
1.05
1.05
TP (mg/L)
0.42
0.47
0.50
0.51
PART B: Management Options Assessment
Scenario Development: PART B
A total of 12 model simulations were completed. Scenarios included 1 baseline precipitation
scenario and 3 precipitation change scenarios, and 3 management scenarios. No land-use
scenarios were considered.
Precipitation Change Scenarios
Same as PART A.
Land-Use Scenarios
PART B of this case study does not include land-use scenarios. LULC was held constant to
assess the impacts of the precipitation change and alternative stormwater management options
only.
Management Scenarios
A baseline scenario with no stormwater BMPs (PART A) and two scenarios representing
different stormwater management strategies were included:
40
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[I
20 40 60 80 100 120 140 160 180
[I
1 50"
•^-
& 100 -
I
I 150 -
.3
200
I +30% Rain - Flow
"!
i
| H
• +10°/
• Base
o Rain -
line Rain
Flow
- Flow
0.0
0 20 40 60 80 100 120 140 160 180
Time (minute)
Figure 3-12. Event rainfall intensity vs. stormwater flow rate at the site
outlet for the baseline and all precipitation change scenarios.
Baseline: Pre-redevelopment conditions
Centralized management: strategy that consists of installing a small number of
conventional mass storage/detention structures to collect storm runoff. These detention
structures are designed to capture runoff from large drainage areas within the entire
watershed and release through control structures such as weirs. Such a mass
storage-delayed release approach serves both to reduce the peak flow rate and to help
reduce pollutant loading in runoff through filtration and gravitational settling
Distributed management: strategy that consists of a large number of storage structures
of low capacity, combined with infiltration structures, such as pervious pavement and
green rooftops, throughout the headwater areas of the watershed. By spreading out
multiple source-control BMPs throughout the whole drainage basin, this approach can
achieve in-situ runoff volume reduction while minimizing pollutant movement off-site
Endpoint Selection: PART B
PART B focused only on stormwater runoff at the redevelopment site outlet. An arbitrary
management target of maintaining stormwater runoff below 0.02 cms was selected (defined as
0.7 cfs in Figure 3-13 because BASINS CAT uses the native units for the selected model, in this
case SWMM uses English units) to illustrate a BASINS CAT capability for flagging simulation
41
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, Endpoint
EndpointName: T otal I nflow E vt
Data set: FandM Centralized Manual SD11 Total Inflow
Attribute:
Mean
Manage Attributes
Highlight Values
Default Color:
Color Lower Values: D eepS kvB lue
Maximum Value: 0.7
Color H igher Values: 0 rangeR ed
Events
0 Only include values in the following Events
Exceeding threshold
Allow gaps up to lO
Sum of values exceeding threshold 10
Total duration above
«
Months/Years
Q Only include values in selected
All
Ok
None
Cancel
Figure 3-13. BASINS CAT Endpoint window where users can specify
endpoints, endpoint statistics, and Highlight Values thresholds for color
coding the results. Endpoint Name is user specified; Data set is the time series
to be modified; Attribute is the end point statistic to be considered; Highlight
Values indicates the endpoint threshold value used for color coding the
results, in this case study, 0.7 cfs (0.02 cms).
42
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results above or below a user-specific threshold. This capability can be useful for screening
results when a large number of scenarios are evaluated. BASINS CAT allows users to specify
such thresholds and color code endpoint values that exceed it using the Highlight Values option
(see Figure 3-13).
Results: PART B
Changes in event rainfall intensity resulted in increased stormwater flow rates across the baseline
and alternative management scenarios (see Table 3-17). The simulation results indicated that the
mean flow rate at the outlet is the highest with the baseline, followed by centralized
management, and then distributed management. The event rainfall dynamic and runoff
hydrograph for the baseline and alternative management scenarios are shown in Figure 3-14.
Increases in the design event rainfall intensity were shown to increase the flow rate in an almost
linear fashion for all three management scenarios. Both centralized and distributed management
helped reduce peak flow rate significantly compared to the baseline scenario. The centralized
management scenario also prolonged flow duration (at a very low flow rate) to beyond 400
minutes after the onset of the design event (not shown in Figure 3-14) while flow essentially
ended after 180 minutes for the baseline and the distributed management scenarios. The
distributed management scenario utilized a series of source-control BMPs that have limited
storage capacity (in contrast to the large detention structures in the centralized management
scenario), which can be overcome in larger storms, leading to the delayed "second peak" in its
hydrograph.
Figure 3-15 illustrates the option within BASINS CAT to display simulation results with
endpoint values color-coded based on a user-specified threshold criterion. In this case, 0.02 cms
was chosen (arbitrarily) as the ceiling stormwater flow rate above which the cell containing the
endpoint value is highlighted in orange.
Table 3-17. Event mean flow rate (cms) under all precipitation change and
stormwater management scenarios.
Scenario
Precipitation (mm/hr)
Baseline (no management)
Centralized managment
Distributed managment
Baseline
31.7
0.021
0.016
0.004
+10%
34.9
0.024
0.019
0.006
+20%
38.1
0.027
0.022
0.008
+30%
41.3
0.029
0.025
0.011
43
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20
40
80
100
120
140
160
180
.5
S
a
c
50 -
& ioo H
|
I 150
.3
a
« 200
2.0
""•
.
I
• +30% Ram - Flow
• +20% Rain - Flow
• + 10% Rain- Flow
Baseline Rain - Flow
1.5
1.0
0.5
0.0
Baseline (no management)
«•
••-•
£
o
E
•«-•
S
0
E
i. U
1.5
1.0
0.5
0.0
2.0
1.5
1.0
0.5
0.0
1
Centralized management
i i ~~ ~ i
^^ -• ^^^^^^^^
^^^^^^•—
^^-
i i i i
Distributed management
1 1 1 1 1 1 1 1 1
] 20 40 60 80 100 120 140 160 18
Time (minute)
Figure 3-14. Rainfall vs. flow dynamics at the site outlet for all precipitation
change and management scenarios.
44
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Climate Assessment Tool 2.0 - BASINSCAT SWMM
File Edit Options Help
Model Climate Data Assessment Endpoints Results Pivot Table
Run Rain
Run the model
Refresh results from the last model run
TempSep Total Inflow Evt
Show Progress of Each Run
Clear Results on Start
Saved Results
I Multiply
Add
Mean
Current Value Current Value FandM Centralized Manual SD11 +
base
1
2
3
4
5
6
7
1.1
1.1
1.1
1.2
1.2
1.2
1.3
1.3
1.3
1.3
3.6
5.4
1.8
3.6
5.4
1.3
3.6
5.4
0.57263
0.67018
0.67018
0.67018
0.76939
0.76939
0.76939
0.86622
0.86622
0.86622
G:\CAT\ModelCentralized\Modified-001
GACAT \ModelCentralized\Modified-002
G:\CAT\ModelCentralized\Modified-003
G: \CAT \M odelCentralizedW odified-004
G:\CAT\ModelCentralized\Modified-005
G:\CAT \ModelCentralized\Modified-006
GACAT \ModelCentralizedSModified-007
G: \CAT \M odelCentralizedW odified-008
G:\CAT\ModelCentralized\Modified-Q09
Finished runs
Figure 3-15. BASINS CAT window for the centralized management scenario
showing the color-coded mean stormwater flow rate values that are above
the specified maximum threshold defined in Figure 3-13. Temperature
(TempSep) was included in the model, but it did not have an impact on the
endpoints.
Summary
This study assessed the sensitivity of stormwater quantity and quality in a small urban watershed
in Virginia to precipitation change using SWMM. The assessment utilized BASINS CAT to
process adjustments to event rainfall intensity. This allowed an array of precipitation scenarios
to be automatically generated and used as input for model simulations. The pivot table feature of
BASINS CAT was used to generate endpoint result tables for the precipitation changes
considered. The Endpoint definition dialog allows the user to specify maximum and minimum
threshold values by which the resulting values can be color-coded accordingly in the Results
grid. This can be a very helpful feature for quickly identifying the scenarios that exceed
management targets when using BASINS CAT.
45
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PART A was a general sensitivity analysis of the baseline site conditions that indicated
increasing rainfall event intensity increased both stormwater flow rate and pollutant EMCs.
PART B assessed stormwater sensitivity to climate change from a management perspective. The
assessment included three management scenarios, baseline with no stormwater management,
centralized management, and distributed management. Increasing precipitation resulted in an
almost linear increase in stormwater flow at the outlet for all management scenarios. On an
event basis, for any given rainfall intensity, the two alternative stormwater management
approaches significantly lowered the peak stormwater flow rate; the centralized approach
resulted in the longest duration of stormwater flow.
While the climate change scenarios evaluated in this case study were relatively simple, they
provide a screening-level understanding of stormwater runoff sensitivity to climate change, and
the potential effectiveness of stormwater management strategies for reducing climate change
impacts. Evaluation of more detailed climate change or management scenarios is also possible.
These capabilities can be an important addition to the tools used by stormwater managers to
design, manage, and maintain stormwater infrastructure.
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3.4. AGRICULTURAL SEDIMENT YIELD SENSITIVITY TO CLIMATE CHANGE
AND MANAGEMENT PRACTICES IN BLUE EARTH COUNTY, MN, USING
WEPPCAT
Case Study Overview
This case study uses WEPPCAT to conduct a sensitivity analysis of sediment yield from
agricultural fields under different climate change, land use, and crop management scenarios.
In PART A, the sensitivity of soil erosion to changes in land use, crop management, and
climate are evaluated. Land-use scenarios include a30m x 30m field with either Lasa or
Lerdal soil series with a slope of 2 or 5%. Crop management scenarios include corn spring
chisel plow and soybean spring chisel plow. Climate change scenarios considered are:
• temperature increases of 0, 2, and 4°C.
• precipitation volume adjustments of-10, 0, +20%.
• precipitation intensity increases of 10%.
PART B evaluated the effectiveness of additional management practices, specifically
alternative tillage practices and riparian filter strips, for reducing soil erosion under a single
land use scenario and the same climate change scenarios evaluated in PART A.
Introduction
Agricultural crop production can be disruptive to soil structure, resulting in significant soil
erosion during runoff events. In regions of the country where agriculture constitutes a significant
percentage of land use, soil erosion can have a significant impact on water quality. Land
managers often employ crop management practices to reduce agricultural impacts on surface
water including cover crops, alternative tillage, and vegetated riparian filter strips that reduce soil
erosion and remove sediment from runoff.
Climate change could have a significant influence on erosion processes in agricultural areas.
This case study investigates the sensitivity of agricultural fields under corn and soybean
production in Blue Earth County, MN, and the potential effectiveness of different crop
management practices for reducing sediment yields from agricultural fields under a range of
future climate change scenarios. In PART A, WEPPCAT was used to assess the sensitivity of
farm fields under conventional corn and soybean production in Blue Earth County, MN to
potential changes in climate. Climate change scenarios included adjustments to temperature,
precipitation volume, and precipitation event intensity. PART B expanded on the analysis in
PART A to evaluate the potential effectiveness of alternative crop management practices,
including filter strips and alternative tillage methods, for reducing climate change impacts on
sediment yields.
47
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Location Description
Blue Earth County, MN is located in the south-central part of Minnesota, one of the top
producing regions of corn and soybeans in the United States (USDA, 2012a). The County is
suitable for corn and soybean production given the generally flat topography, good soil quality,
and ample precipitation (USDA, 2012b). Local soils are generally well drained and classified
under Hydrologic Groups A and B (high infiltration and water-holding capacity) (USDA,
2012b). Topographic slopes in the County range from 0 to over 15%.
Model Setup
All WEPPCAT simulations in this case study were conducted online at
http://typhoon.tucson.ars.ag.gov/weppcat/index.php. Data inputs required to run WEPPCAT
include site/field characteristics (field length and width, hillslope shape, slope, and soil type),
crop management, and riparian filter strip characteristics. Required meteorological inputs were
acquired internally by WEPPCAT by selecting an appropriate NCDC weather station. One
hundred years of daily meteorological inputs representing each climate change scenario are
generated internally in WEPPCAT using the Cligen weather generator. WEPPCAT results are
average values for 100-year simulations. The WEPP model does not require calibration.
PART A: General Sensitivity Analysis
Scenario Development: PART A
A total of 144 model simulations were completed. Scenarios included 1 baseline climate
scenario and 17 climate change scenarios, 4 land-use scenarios, and 2 crop management
scenarios.
Climate Change Scenarios
Climate change scenarios were developed to fall within the ensemble range of projected
end-of-century (2080s) temperature and precipitation changes for this region of the nation based
on statistically downscaled data from 16 CMIP3 climate models acquired from the
Climate Wizard web site. See Section 3.2 for more information about these data. Projected
changes in temperature ranged from approximately 2°C to 7°C, and projected changes in
precipitation volume ranged from approximately -23 to +33%.
Daily meteorological data for WEPPCAT simulations are generated by the Cligen stochastic
weather generator using monthly weather statistics at NOAA NCDC weather stations. Climate
change scenarios are created in WEPPCAT by adjusting monthly weather statistics inputs to
Cligen. Available adjustments include increases and decreases in mean monthly temperature,
precipitation volume, and the transition probabilities of a wet day following a dry day, and a dry
day following a dry day (i.e., number of wet days). These adjustments can be made either
uniformly among months of the year, or individual adjustments can be made to specific months
of the year. In addition, WEPPCAT also has the capability to adjust Cligen parameters to
increase the proportion of annual rainfall occurring in large magnitude events. The proportion of
annual precipitation occurring in large magnitude events can be increased up to 25%. These
adjustments in precipitation intensity are made by applying the user-determined increase to the
largest 5% of events, and simultaneously decreasing precipitation in the lower 95% of events
such that there is no or negligible net change in annual precipitation volume.
48
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In this case study, WEPPCAT was used to modify precipitation volume, precipitation intensity,
and temperature. Baseline meteorological inputs for simulations were obtained from the NCDC
Winnebago weather station given its proximity to Blue Earth County and the two soil series of
interest. The climate change scenarios consisted of a matrix of adjustments representing
different combinations of potential changes in temperature, precipitation volume, and
precipitation intensity. The meteorological data were adjusted in the following manner:
• Average annual temperature was increased by 0, 2, and 4°C
• Average annual precipitation volume by -10, 0, and +20%. These scenarios are
designated as volume (V)(-10), V(0), and V(+20), respectively
Precipitation was then adjusted to assess the effects of increased event intensity (proportion of
annual precipitation occurring in large magnitude events). This was accomplished by first
adjusting the annual precipitation volume by -10, 0, and +20%, and then increasing proportion
of annual precipitation occurring in the largest 5% of events by 10%. This scenario is designated
as intensity (!)(+! O)1.
Land-use Scenarios
PART A included a total of four land-use scenarios for a 30 m x 30 m farm field. Land-use
scenarios included different combinations of two Blue Earth County soil series, Lasa and Lerdal,
and two uniform topographic slope gradients, 2 and 5%. The Lasa soil series is classified as
Hydrologic Group A (high infiltration and water-holding capacity), and the Lerdal soil series is
classified as Hydrologic Group C (low infiltration and water-holding capacity).
Crop Management Scenarios
The two crop management scenarios evaluated are corn spring chisel plow and soybean spring
chisel plow. Land management options that can be represented in WEPPCAT simulations are
predefined and fixed in terms of tilling, planting, and harvesting dates and methods (see Tables
3-18 and 3-19).
Endpoint Selection: PART A
The endpoints simulated by WEPPCAT are the same as the WEPP model: sediment loss and
sediment yield. Sediment loss is the total amount of soil displaced along the length of a field due
to runoff as measured at the bottom of the slope. Sediment yield is the amount of soil displaced
(sediment loss) as measured at the bottom of the slope minus any retained by a filter strip (if
applicable).
1 "Rainfall intensification is accomplished here by altering the standard deviation of the distributions of daily
precipitation used by the climate generator" (WEPPCAT, 2011). This approach results in a slight change in average
annual rainfall even if changes to the overall volume are not defined by the model user. In this case study, it
resulted in a minor 1-2 % decrease in average annual rainfall. For example, in the V(-10) scenario, a 10% decrease
in volume results in 26.8 inches of rain per year, while a 10% decrease in rainfall plus a 10% increase in rainfall
intensity in the largest 5% of events results in 26.2 inches of average annual rainfall. This difference was deemed
insignificant and actually resulted in more conservative TSS loads due to a decrease in annual runoff versus the
volume-only adjustment in annual precipitation.
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Table 3-18. Soybean spring chisel plow land management specifications in
WEPPCAT.
Date
4/5
4/10
5/10
5/10
6/10
10/15
Operation Type
Tillage
Tillage
Tillage
Plant-Annual
Tillage
Harvest
Operation Name
Chisel plow
Field cultivator, secondary tillage, after duckfoot points
Planter, double disk openers
Soybeans — medium fertilization level
Cultivator, row, multiple sweeps per row
Soybeans — medium fertilization level
Table 3-19. Corn spring chisel plow land management specifications in
WEPPCAT.
Date
4/15
4/25
5/1
5/10
5/10
6/5
10/15
Operation Type
Tillage
Tillage
Tillage
Tillage
Plant-Annual
Tillage
Harvest
Operation Name
Chisel plow
Field cultivator, secondary tillage, after duckfoot points
Tandem disk
Planter, double disk openers
Corn, Jefferson IA, high production 125 bu/acre
Cultivator, row, multiple sweeps per row
Corn, Jefferson IA, high production 125 bu/acre
Results: PART A
Simulation results are shown in Tables 3-20 and 3-21 and Figures 3-16 to 3-18. Results illustrate
the sensitivity of sediment yield to increases in precipitation volume and intensity. The greatest
change was observed for the scenario V(+20) + I(+10), with a simulated sediment yield close to
double the yield under the baseline scenario (see Tables 3-20 and 3-21 and Figure 3-16). This
illustrates the synergistic effect of increasing precipitation volume and intensity on sediment
yield. Results also suggest that increases in volume have a greater impact on the overall increase
in sediment yield versus intensity alone. For example, under historic weather conditions (V(0)),
the Lasa soil at a 2% slope under corn production yielded 4.9 tonnes/ha/yr of sediment (see
Table 3-20). Increasing the precipitation volume 20% resulted in a 7.4 tonnes/ha/yr sediment
yield, a 51% increase, while the combined effect of increasing the precipitation volume 20% and
event intensity 10% resulted in 8.3 tonnes/ha/yr, a 69% increase (see Table 3-20).
Field slope, soil hydrologic group, and crop type influenced sediment yield under all climate
scenarios. For example, as expected, a 2% slope resulted in a lower sediment yield versus a 5%
slope (see Figures 3-17 and 3-18). Lasa soil also resulted in a lower sediment yield versus
Lerdal, likely due to the soil properties affecting infiltration and water-holding capacity
(hydrologic group classification) (see Figure 3-17). Finally, corn production resulted in a much
lower sediment yield versus soybeans (see Figure 3-18).
50
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Table 3-20. Mean annual sediment yield (tonnes/ha/yr) for corn production
under conditions of changing climate2. Scenarios named to reflect changes in
precipitation volume and intensity: V = volume, I = intensity, numerical
value reflects percent change from baseline. Sediment yield values
highlighted in grey indicate the baseline scenarios.
Soil Type
and Slope
Temp.
Increase,
°C
Rainfall, mm
Lasa 2%
Lasa 5%
Lerdal 2%
Lerdal 5%
0
2
4
0
2
4
0
2
4
0
2
4
Precipitation Scenarios
V(-10)
656.6
3.8
3.8
4.0
7.6
8.5
9.4
5.6
5.6
6.1
8.7
10.1
11.9
V(-10) +
l(+10)
641.9
4.3
4.3
4.5
8.5
9.4
10.5
6.3
6.5
6.9
10.1
11.7
13.9
V(0)
725.2
4.9
4.9
5.2
9.9
10.8
12.1
7.2
7.4
8.1
11.2
13.0
15.5
V(0) +
l(+10)
712.95
5.4
5.6
5.8
10.8
12.1
13.5
8.1
8.3
9.0
12.6
14.6
17.5
V(+20)
869.75
7.4
7.6
7.8
14.6
16.1
18.2
10.8
10.8
11.7
16.4
18.6
22.4
V(+20) +
l(+10)
852.6
8.3
8.3
8.7
15.9
17.7
19.7
11.9
11.9
12.8
18.2
20.8
25.1
: See Footnote 1 for explanation of discrepancy in annual rainfall values resulting from intensity adjustments.
51
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Table 3-21. Mean annual sediment yields (tonnes/ha/yr) for soybean
production under conditions of changing climate3. Scenarios named to
reflect changes in precipitation volume and intensity: V = volume,
I = intensity, numerical value reflects percent change from baseline.
Sediment yield values highlighted in grey indicate the baseline scenarios.
Soil Type
and Slope
Temp.
Increase, °C
Rainfall, mm
Lasa 2%
Lasa 5%
Lerdal 2%
Lerdal 5%
0
2
4
0
2
4
0
2
4
0
2
4
Precipitation Scenarios
V(-10)
656.6
6.7
6.7
7.2
16.8
16.8
17.7
9.4
9.6
10.5
23.1
23.5
25.1
V(-10) +
l(+10)
641.9
7.4
7.4
7.8
18.4
18.4
19.3
10.5
10.8
11.9
26.0
26.7
28.5
V(0)
725.2
8.5
8.5
9.2
21.3
21.1
22.2
12.1
12.6
13.7
30.0
30.7
32.7
V(0) +
l(+10)
712.95
9.2
9.4
10.1
22.9
22.9
24.2
13.2
13.9
15.0
33.0
34.3
36.8
V(+20)
869.75
12.6
12.8
13.7
30.7
30.9
32.5
17.9
18.6
20.4
45.3
46.6
50.2
V(+20) +
l(+)10
852.6
13.7
13.9
14.8
33.2
33.2
35.0
19.5
20.4
22.2
49.3
51.1
54.9
1 See Footnote 1 for explanation of discrepancy in annual rainfall values resulting from intensity adjustments.
52
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1_
1" Q O
> t~
Ql v
c
C c
0 0
•^ A
2
gj , A
> 4 t
+j
01
E-)
'o
QJ
to
0
o
•
A
A
»
2
Change in Temperature °C
O
A
A
4
V-10
Ov-io+no
A VO
AVO+IIO
• V20
Ov?n+nn
Figure 3-16. Mean annual sediment yield for Lasa soil at 2% slope under
corn production for all climate change scenarios.
~f!r
.c
-
C
09
£
«
1S.O
16.0 -
id n
1 ~) n
inn
o n
On -
0
O Lasa V-10
Q £ * Lasa VO
• LasaVZO
Lcrdal V-10
° OLcrdal VO
OLerdal V20
2 5
Fie Id Slope, %
Figure 3-17. Mean annual sediment yield for the Lasa and Lerdal soil under
corn production for three climate change scenarios.
53
-------
es/ha/yr
c
£
•u
ID
>-
1=
4)
E
S
V
w
35.0 -i
30.0 -
25.0
20.0 -
15.0
10.0
5.0
0.0
A
A
i
1
2
Fie Id Slope, %
A
A
A
A
5
ACornV-10+110
ACornVO+110
AComV20+llO
ASoyV-10+110
ASoyVChllO
AcLovV?nino
1
Figure 3-18. Mean annual sediment yield for Lasa soil under corn and
soybean production for three climate change scenarios.
PART B: Managing Soil Erosion under Climate Change
Scenario Development: PART B
A total of 108 model simulations were completed. Scenarios included 1 baseline climate
scenario, 5 climate change scenarios, 1 land-use scenario, and 18 management scenarios.
Climate Change Scenarios
PART B used a subset of the climate change scenarios evaluated in PART A. The climate
change scenarios consisted of a matrix of different combinations of potential changes in
temperature and precipitation. Mean monthly temperatures were increased by 0, 2, and 4°C.
Mean monthly precipitation volumes were increased by 0 and 20%. Precipitation was further
adjusted to assess the impact of increasing intensity. Similarly to PART A, this was
accomplished by first increasing the precipitation volume by 20% and then increasing the
intensity by 10%.
Land-Use Scenarios
PART B evaluates only one land-use scenario, a30m x 30m farm field with Lerdal soil at a 5%
uniform slope. Lerdal is a Hydrologic Group C soil, meaning it has both a low infiltration rate
and water-holding capacity. This soil series and slope were selected because they represent a
"worst case" scenario for a field under crop production in Blue Earth County, MN based on the
results from PART A.
54
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Crop Management Scenarios
PART B was designed to assess the effectiveness of implementing additional crop management
practices including alternative tillage practices and grass and forest filter strips for reducing
sediment yield under a range of climate change scenarios. As a baseline, the corn spring chisel
plow crop management scenario from PART A was selected. Two scenarios representing
additional crop management practices were selected from a predetermined set of options
available in WEPPCAT for evaluation: corn no-till and corn fall mulch till (see Tables 3-19, 3-
22, and 3-23). WEPPCAT also provides the option of including a riparian filter strip to assess
potential reductions in sediment yields. Six scenarios representing grass and forest filter strips 3,
6, and 9 m wide by 30 m long were also evaluated. A baseline scenario with no filter strip was
also included for comparisons under each climate change scenario and tilling practice.
Table 3-22. Corn fall mulch till management characteristics in WEPPCAT.
Date
4/25
5/5
5/10
5/10
6/5
10/15
11/1
Operation Type
Tillage
Tillage
Tillage
Plant-Annual
Tillage
Harvest
Tillage
Operation Name
Field cultivator, secondary tillage, after duckfoot points
Tandem disk
Planter, double disk openers
Corn, Jefferson IA, high production 125 bu/acre
Cultivator, row, multiple sweeps per row
Corn, Jefferson IA, high production 125 bu/acre
Chisel plow, straight with spike pts
Table 3-23. Corn no-till management characteristics in WEPPCAT.
Date
5/10
5/10
10/15
Operation Type
Tillage
Plant-Annual
Harvest
Operation Name
Planter, no-till with fluted coulter
Corn, Jefferson IA, high production 125 bu/acre
Corn, Jefferson IA, high production 125 bu/acre
Endpoint Selection: PART B
The endpoints simulated by WEPPCAT are the same as the WEPP model: sediment loss and
sediment yield. Sediment loss is the total amount of soil displaced along the length of a field due
to runoff as measured at the bottom of the slope. Sediment yield is the amount of soil displaced
(sediment loss) as measured at the bottom of the slope minus any retained by a filter strip (if
applicable).
Results: PART B
The model simulations provide a general assessment of the potential sediment yield associated
with varying degrees of climate change and different crop management options (see Table 3-24).
Generally, increases in precipitation volume and intensity and temperature resulted in increased
sediment yields under all management scenarios. Sediment yield decreased as filter strip width
55
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increased; however, there were diminishing marginal returns with sediment reduction as the filter
strip width increased from 3 to 9 m (see Figure 3-19).
Table 3-24. Sediment yield (tonnes/ha/yr) resulting from corn production
under all climate change, land use, and management scenarios. Buffers
named to reflect cover and width: NB = no buffer, GB = grass buffer,
FB = forest buffer, numerical value signifies width. Precipitation scenarios
named to reflect changes in volume and intensity: V = volume, I = intensity,
numerical value reflects percent change from baseline. Values highlighted in
grey indicate baseline scenarios.
Temp
(°C)
Increase
0°C
2°C
4°C
Buffer
NB
GB3
GB6
GB9
FB3
FB6
FB9
NB
GB3
GB6
GB9
FB3
FB6
FB9
NB
GB3
GB6
GB9
FB3
FB6
FB9
Corn Fall Mulch
V(0)
8.1
4.7
3.6
2.7
4.3
2.9
2.2
9.4
5.2
3.6
2.9
4.5
3.1
2.2
11.7
6.1
4.0
2.9
5.4
3.4
2.5
V(+20)
11.7
7.2
5.4
4.3
6.3
4.5
3.6
13.5
7.8
5.6
4.3
6.9
4.7
3.6
17.0
9.2
6.3
4.7
8.1
5.4
3.8
V(+20) +
l(+10)
13.0
8.1
6.1
4.9
7.2
4.9
4.0
15.2
9.0
6.3
4.9
7.8
5.4
4.0
18.6
10.3
7.2
5.2
9.2
5.8
4.3
Corn No Till
V(0)
1.3
1.3
1.3
1.1
1.3
1.3
1.1
1.3
1.3
1.3
1.1
1.3
1.3
1.3
1.6
1.6
1.3
1.3
1.6
1.3
1.3
V(+20)
2.0
1.8
1.8
1.8
1.8
1.8
1.8
2.0
2.0
2.0
1.8
2.0
2.0
1.8
2.2
2.2
2.0
2.0
2.2
2.0
2.0
V(+20) +
l(+10)
2.0
2.0
2.0
1.8
2.0
2.0
2.0
2.2
2.2
2.2
2.0
2.2
2.0
2.0
2.5
2.5
2.2
2.0
2.2
2.2
2.2
Corn Spring Chisel
V(0)
11.2
6.3
4.5
3.4
5.4
3.6
2.7
13.0
6.7
4.7
3.4
5.8
3.8
2.7
15.5
7.6
4.9
3.6
6.5
4.0
2.7
V(+20)
16.4
9.6
6.9
5.4
8.5
5.6
4.3
18.6
10.3
7.2
5.4
9.0
5.8
4.3
22.4
11.9
7.8
5.6
9.0
6.3
4.5
V(+20) +
l(+10)
18.4
10.8
7.8
6.1
10.3
6.5
4.9
21.1
11.9
8.3
6.1
11.2
6.7
4.9
25.3
13.5
9.0
6.5
13.0
7.2
4.9
56
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14.0
-vo
-V20
V20H10
0.0
GB3 GB6
FilterStrip Scenario
GB9
Figure 3-19. Sediment yield (tonnes/ha/yr) under corn fall mulch till with a
3, 6, and 9 m grass buffer. Buffers named to reflect cover and width:
NB = no buffer and GB = grass buffer, numerical value signifies width.
Precipitation scenarios named to reflect changes in volume and intensity:
V = volume, I = intensity, numerical value reflects percent change from
baseline.
The simulation results can be used to characterize sensitivity to climate change (see Table 3-24).
For example, the simulation for a field under corn spring chisel plow and current climate
conditions without a buffer resulted in a sediment yield of 11.2 tonnes/ha/yr (see Table 3-24). If
climate change resulted in a 20% increase in annual rainfall (V(+20) scenario) and a 2°C
increase in temperature, the sediment yield would be 18.6 tonnes/ha/yr, a 66% increase over
current yields (see Table 3-24). A land owner could also use the model simulations to determine
potential options for not only maintaining current sediment yield, but also identifying ways to
reduce sediment yield under current and altered climate regimes. As indicated in Table 3-25,
certain crop management practices and/or filter strips could meet both of these management
goals. If, for example, the land owner wanted to maintain a sediment yield of 6 tonnes/ha/yr or
less under the V(+20) precipitation scenario, a number of options may exist. No-till for corn
production was by far the superior management practice for reducing sediment yield under the
V(+20) scenario (see Table 3-25). A landowner could also maintain current tillage practices and
install a 6 to 9 m forest buffer or 9 m grass buffer and reduce sediment yields below 6
tonnes/ha/yr (see Table 3-25).
57
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Table 3-25. Sediment yield (tonnes/ha/yr) results from a 2°C increase in
temperature and a 20% increase in mean annual rainfall volume. Buffers
named to reflect cover: NB = no buffer, GB = grass buffer, and FB = forest
buffer, numerical value signifies width. Grayed cells signify tillage and filter
strip combinations producing 6 tonnes/ha/year or less of sediment.
Buffer
NB
GB3
GB6
GB9
FB3
FB6
FB9
Corn Fall Mulch
V(+20)
13.5
7.8
5.6
4.3
6.9
4.7
3.6
Corn No Till
V(+20)
2.0
2.0
2.0
1.8
2.0
2.0
1.8
Corn Spring Chisel
V(+20)
18.6
10.3
7.2
5.4
9.0
5.8
4.3
Summary
In this case study, WEPPCAT was used to investigate the sensitivity of sediment yields from
farm fields to climate change and alternative crop management practices. WEPPCAT enables
users to efficiently create and run a large number of climate change and crop management
scenario combinations to assess potential changes in sediment yields. The results from PART A
indicated a relatively high degree of sensitivity of agriculture land in Blue Earth County, MN to
climate change. The sediment yields from fields with both Lasa and Lerdal soil under the most
extreme climate change scenarios were almost double compared to the baseline scenarios
evaluated in this study. The finding also indicated that crop type and slope play a significant role
in determining sediment yield under all climate change scenarios.
PART B of this case study evaluates the effectiveness of alternative crop management options
for reducing sediment yields under a range of climate change scenarios. The findings indicated
that sediment yields could potentially be reduced or prevented under the most extreme climate
change scenarios if certain management practices are employed. This type of information can be
used to identify locations within watersheds that are vulnerable to increased sediment loading,
and to develop appropriate management strategies for adapting agricultural land to climate
change.
58
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3.5. STREAMFLOW AND WATER QUALITY SENSITIVITY TO CHANGES IN
PRECIPITATION AMOUNT, FREQUENCY, AND INTENSITY IN THE
TUALATIN RIVER, OR, USING BASINS CAT WITH HSPF
Case Study Overview
This case study used BASINS CAT with HSPF to assess the sensitivity of streamflow, TN,
and TSS loads to three different types of potential precipitation change. BASINS CAT
capabilities for modifying precipitation were used to increase precipitation by 10 and 20% in
the following ways:
• constant percent increase applied to all precipitation events (constant increase).
• increase applied to the largest 30% of precipitation events (intensity increase).
• increase in total number of annual precipitation events (frequency increase).
All change scenarios also included a constant temperature increase of 2°C. Potential
evapotranspiration was recalculated with BASINS CAT to reflect this change.
Introduction
Climate change is anticipated to result in regionally variable changes in precipitation amount,
frequency, and intensity throughout the nation (IPCC, 2007). While subject to uncertainty, many
regions are expected to see an overall increase in precipitation volume and average event
intensity. The effects of precipitation change on streamflow and water quality endpoints will
vary depending on the specific type of change that occurs and local watershed physiographic,
land-use, and water management conditions.
This case study investigates the effect of different types of precipitation change on streamflow
and water quality endpoints in the Tualatin River, OR, using BASINS CAT with an HSPF
model. It highlights BASINS CAT capabilities for creating precipitation change scenarios
representing changes in precipitation amount, event frequency, or average event intensity.
Location Description
The Tualatin River (HUC 17090010) drains 1844 km2 in northwest Oregon and is a tributary of
the Willamette River (see Figure 3-20). Land use includes densely populated areas, agriculture,
and the forests of Oregon's Coast Range, Tualatin, and Chehalem Mountains. Most of the fast-
growing urban population, approximately 500,000 residents, resides on 15% of the watershed's
area. About 35% of the watershed is used for agriculture, and about 50% of the watershed is
forested.
59
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Figure 3-20. Tualatin River watershed location, Oregon, USA.
Water Model Setup
A preexisting, calibrated HSPF model of the Tualatin River was acquired from an earlier study
of the entire Willamette watershed (Johnson et al., 2011). Model segmentation was based on
intersections of land use, hydrologic soil group, and available NCDC weather stations. Soils
data were from the STATSGO data set, and land-use was from the 2001 NLCD. Meteorological
data used as input to the model were from NCDC weather stations at Beaverton (350595),
Buxton (351222), and Forest Grove (352997).
The baseline model data were for 1980 through 2005. A hydrology calibration period of
10/01/1995 to 09/30/2005 and validation period of 10/01/1985 to 09/30/1995 were used for the
Tualatin stream gage at the basin outlet. The water quality calibration and validation periods
were 10/01/1991 to 9/30/1995 and 10/01/1986 to 9/30/1990, respectively. Brief summaries of
calibration and validation results are provided in Tables 3-26 and 3-27.
60
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Table 3-26. Tualatin River HSPF model daily streamflow calibration and
validation results. NSE: Nash-Sutcliffe Efficiency coefficient (Nash and
Sutcliffe, 1970); E' (Garrick et al., 1978); R2: Coefficient of Determination.
NSE
£'
R2
Calibration
(1995-2005)
0.799
0.731
0.726
Validation
(1985-1995)
0.811
0.702
0.769
Table 3-27. Tualatin River HSPF model monthly water quality calibration
and validation results. Relative Percent Error is the average of
observed-simulated divided by observed comparisons. Median Percent
Error is the median of observed-simulated comparisons divided by average
of observed values.
Endpoint Statistic
TSS Load Relative Percent Error
TSS Concentration Median Percent Error
Total N Load Relative Percent Error
Total N Concentration Median Percent Error
Calibration
(1991-1995)
3
-7.8
2
-16.8
Validation
(1986-1990)
5
10
-6
-19.2
Scenario Development
A total of seven model simulations were completed. Scenarios included one baseline climate
scenario and six climate change scenarios. No land use or management scenarios were included.
Climate Change Scenarios
Climate change scenarios were developed to fall within the ensemble range of projected mid-
century temperature and precipitation changes for this region based on statistically downscaled
data from 16 CMIP3 climate models acquired from the Climate Wizard web site. Section 3.2
provides additional information about these climate model projections. Projected mid-century
temperature changes for this region ranged from approximately 1°C to 2.5°C, and projected
changes in annual precipitation from approximately -10 to +18%.
Six climate change scenarios were created by applying change factors to baseline historical
temperature and precipitation data using BASINS CAT. Each scenario included a constant
temperature increase of 2°C applied to each temperature value in the baseline record. PET
records were also revised using the BASINS CAT Penman-Monteith option to account for
temperature changes. Six precipitation changes were created by increasing annual precipitation
volume by 10 and 20% in three different ways (see Figure 3-21):
61
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• applying a constant percent increase to all values in the baseline record using the
Multiply Existing Values by a Number option (constant increase)
• defining events and applying a constant percent increase to all values within the largest
30% of events using the Multiply Large/Small Events by a Number option (intensity
increase)
• randomly generating and adding values to the baseline record using the Add/Remove
Storm Events option (frequency increase)
, Modify Existing Data
Modification Name:
Preciplntensity
Existing D ata to Medify: COMPUTED OR350595 PREC (and 2 more]
How to Modify:
Multiply large/small events by a number
Percent Change in Volume
O Single Change v.*'1 Multiple changes within specified range
Minimum |lO %
Maximum:
Increment:
120
[To"
Events
0 Vary values only in the following Events
Exceeding threshold
Change 30 % of volume
0
Allow gaps up to
Sum of values exceeding threshold 0
Total duration above 10
Months/Years
Vary only in selected
Figure 3-21. BASINS CAT Modify Existing Data window specifying the
criteria for developing the intensity increase scenario. Modification Name
identifies the scenario; Existing Data to Modify is the time series to be
modified; How to Modify is the type of modification applied to the time
series; Percent Change in Volume is where the range and increments of
change are specified; Events is where the specific events to be modified are
defined.
62
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A time-series plot for a small portion of the modeled precipitation record for each climate change
scenario is shown in Figure 3-22. The figure shows an example of how a 20% increase in
precipitation is achieved using each of the three different precipitation adjustment capabilities.
The plot contains curves for each pattern change method: a dashed line for the constant increase,
a dotted line for intensity increase, and a light solid line for frequency increase. The three
precipitation events shown in Figure 3-22 illustrate the differences between the three methods.
The event beginning on October 28, 1986 at 16:00 hours is a new event added by the frequency
increase scenario. The event beginning October 29, 1986 at 0:00 hours shows the changes
applied for a 2-hour event that is in the top 30% of the original record. The lowest values (light
solid line) represent the frequency increase scenario, but this event is actually from the original
record. The dashed line represents the constant increase scenario and is, thus, 20% higher than
the original storm volume. The dotted line shows the intensity increase applied to this large
event, one that falls within the largest 30% of events. The event on October 29, 1986 beginning
at 07:00 hours is from the original record and is a small event that is increased only by the
constant increase scenario. The intensity increase scenario modification does not apply because
it does not fall within the largest 30% of events.
07
Oe
"5T
01
5 0.5
c
E
o o j
1
a.
0 0 3
ai
00
01
-
-;
h
H
j |
J
14:00 16:00 18:00 20:00 22:00 0:00 2:00 4:00 6:00 8:00 10:00
1986Oct28 ' 19860ct29
Time
Figure 3-22. Example of precipitation event distribution for the three
climate change scenarios. Legend: Increase all events = constant increase
scenario; Add event = frequency increase scenario; Increase large events =
intensity increase scenario.
63
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Land-Use Scenarios
Land-use scenarios were not evaluated in this case study. While it is unlikely land use in the
Tualatin River watershed will not change in the future, land-use was held constant to evaluate the
effects of potential climate change only.
Management Scenarios
Management scenarios were not evaluated in this case study. BMPs or other management
practices may have been included in the original HSPF model, but no adjustments were made to
evaluate the effects of potential climate change only.
Endpoint Selection
Mean annual streamflow, TSS, and TN were selected as endpoints to evaluate sensitivity of
streamflow and water quality to changes in precipitation and temperature.
Results
Simulation results for mean annual streamflow and loadings of TN and TSS are shown in Table
3-28. A key difference among the precipitation change scenarios can be seen in the depth of
precipitation of the maximum precipitation event. For both the 10 and 20% increase in annual
precipitation volume, the intensity increase scenario resulted in the greatest change in the size of
the maximum event, followed by the constant and frequency increase approaches (see Table 3-
28). A constant temperature increase of 2°C was also included in all scenarios but is not shown
to simplify presentation.
Table 3-28. Precipitation, streamflow and loadings of TN and TSS for all
climate scenarios.
Scenario
Baseline
Frequency
Constant
Intensity
Frequency
Constant
Intensity
Precipitation
Volume
Increase, %
0
10
10
10
20
20
20
Annual
Precipitation,
mm
1,014
1,115
1,115
1,115
1,217
1,217
1,217
Max
Precipitation
Event, mm
20.2
20.2
22.2
26.9
20.2
24.2
33.6
Mean
Streamflow,
cms
34.5
38.4
39.3
38.6
44.2
45.3
43.9
Annual
Load TN,
kg/ha/yr
17.7
19.3
19.5
18.8
21.5
21.6
20.2
Annual
Load
TSS,
tonnes/ha/yr
0.50
0.57
0.66
0.78
0.67
0.86
1.19
Mean annual streamflow, TSS, and TN increase in all scenarios, but results suggest different
sensitivities of the endpoints to the different types of precipitation change represented.
Streamflow (cms) shows the largest response to the constant increase, followed by the frequency
increase, and then the intensity increase scenario. Similarly, TN (tonnes/ha/yr) is less impacted
by the intensity increase with more substantial, and very similar, responses from the constant
64
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increase and the frequency increase. TSS (tonnes/ha/yr) was found to be highly sensitive to the
climate change scenarios but responded differently than TN and streamflow. The frequency
increase scenario yielded a 14 and 35% increase in TSS (tonnes/ha/yr) for the 10 and 20%
scenarios, respectively. The responses to the constant increases are more substantial (33 and
74%, respectively) and the increases in TSS (tonnes/ha/yr) in response to the intensity increases
are nearly double those of the constant increase (57 and 140%, respectively). These results
suggest increasing precipitation will generally increase TSS loads, however, increasing event
intensity has the greatest potential impact.
Summary
This case study illustrates the sensitivity of streamflow and water quality endpoints to changes in
precipitation patterns. Scenario analysis using environmental models such as those in BASINS
CAT are well suited for this type of analysis. Results indicate that even if annual precipitation
volume remains constant, other specific changes in how and when precipitation occurs can have
a significant influence on watershed streamflow and water quality endpoints. Of particular note
was the response of TSS loads to changes in proportion of annual precipitation occurring as large
magnitude events (intensity increase scenario). Further analysis is required. The analysis of
additional endpoints, either in the form of new constituents (e.g., TP) or hydrologic response
(e.g., peak flow value) may provide further insights into watershed sensitivity to changing
precipitation patterns.
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3.6. STREAMFLOW SENSITIVITY TO DRY WEATHER EVENTS IN SESPE CREEK,
CA, USING BASINS CAT WITH HSPF
Case Study Overview
This case study used BASINS CAT with an HSPF watershed model of Sespe Creek, CA, to
examine the sensitivity of streamflow to changes in the severity and duration of dry weather
events (meteorological drought). A relatively dry period in the historical record
(1959-1961) was identified and adjusted using BASINS CAT to create alternative drought
scenarios. BASINS CAT was used to increase the average annual temperatures by 2°C and
alter precipitation by:
• increasing the severity of the historical dry period by adjusting annual precipitation
volume during these years by 0, -10, and -20%.
• extending the duration of the historical dry period by reducing precipitation in two
wet years that immediately followed the 1959-1961 dry period.
• extending the duration of the historical dry period as above and increasing the
severity of drought by adjusted annual precipitation volume by -10% during these
years.
Introduction
Managing the impacts of drought on water supply is an important goal of watershed
management. Climate change in many parts of the nation could result in warmer, dryer
conditions leading to increased drought risk. Responding to this challenge will require an
improved understanding of the implications of climate change for drought, and the development
of management strategies for reducing drought impacts.
In this case study, BASINS CAT and an HSPF watershed model of Sespe Creek, CA were used
to assess the sensitivity of water supply to increased severity of dry weather events
(meteorological drought) in Sespe Creek, CA. In the context of this case study, we defined
drought severity to include the magnitude and duration of precipitation deficit. The simulation
endpoints evaluated include mean annual and low-flow streamflow statistics.
Location Description
The Sespe Creek watershed covers an area of approximately 700 km2 in southwestern California
(see Figure 3-23). It flows east through relatively pristine, mountainous, and remote terrain of
the Los Padres National Forest. Then it bends south through a bedrock-confining gorge before
widening out into a broad alluvial fan near the City of Fillmore until its confluence with the
Santa Clara River. Elevations range from more than 2,000 m in the headwaters and upper
reaches to about 120 m above mean sea level at the mouth. The typical hydrologic pattern
includes peak flows in late winter/early spring in response to winter rains and spring snowmelt
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followed by very dry conditions in summer and fall. The watershed is primarily undeveloped,
with dominant land uses being forest and shrub land (see Table 3-29).
Figure 3-23. Location of the Sespe Creek watershed in California, USA.
Table 3-29. Land-use summary for Sespe Creek watershed.
Land Use
Forest
Shrub
Open/Grassland
Agriculture
Developed
Watershed %
14
80
3
2
1
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Model Setup
A preexisting, calibrated HSPF model of the Sespe Creek watershed was extracted from a larger
HSPF model of the Santa Clara River developed as part of the Santa Clara River Watershed
Management effort by the Ventura County Watershed Protection District, Los Angeles County
Department of Public Works, and U.S. Army Corp of Engineers Los Angeles District (AQUA
TERRA Consultants, 2009). The Sespe Creek portion of the model was calibrated to historic
streamflow data at the mouth of the watershed (Sespe Creek near Fillmore, U.S. Geologic
Survey [USGS] gage 11113000) for the period of 10/1/1996 through 9/30/2005 and validated for
10/1/1993 through 9/30/1996. Statistical results of the calibration/validation are shown in
Table 3-30.
Table 3-30. Sespe Creek HSPF model calibration and validation results for
streamflow volume (normalized by watershed area). R2= coefficient of
determination.
Gage Location
Streamflow
(cms)
Simulated
Observed
Volume Error (%)
Daily
Monthly
R
R2
R
R2
Daily Peak Difference (%)
Fillmore (11 11 3000)
Calibration
(1996-2005)
26.2
27.7
-6.1
0.96
0.92
0.99
0.98
-5.5
Validation
(1993-1996)
26.7
24.9
7.0
0.92
0.84
0.97
0.94
9.6
In the original Sespe Creek model, PET was based on observed pan evaporation data. In this case
study, it was necessary to replace the observed pan data with computed PET regenerated by
BASINS CAT for each climate change scenario. This was accomplished using the Penman-
Monteith option for estimating PET in BASINS CAT. Model performance was validated after
making this change by comparing baseline simulations from the original model to simulations using
the PET generated by BASINS CAT in place of the observed pan evaporation data. Differences in
total streamflow volumes were less than 1%, differences in the lowest 10% of streamflow were 3%,
and differences in the highest 1% of streamflow were 2%. The original model calibration was, thus,
considered acceptable for use in the case study.
Scenario Development
A total of six model simulations were completed. Scenarios included one baseline climate
scenario and five climate change scenarios. No land-use or management scenarios were
included.
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Climate Change Scenarios
Climate change scenarios were developed to fall within the ensemble range of projected mid-
century (2050s) temperature and precipitation changes for this region based on statistically
downscaled data from 16 CMIP3 climate models acquired from the Climate Wizard web site.
Additional information about these climate projections is provided in Section 3.2. Projected
mid-century changes in temperature for this region ranged from approximately 1°C to 3°C, and
projected changes in precipitation ranged from approximately -40 to +35%.
Five climate change scenarios were created to represent potential future changes in the severity
and duration of drought (here defined as a period precipitation deficit). A period of historically
dry weather in 1959-1961 was identified based on streamflow records for Sespe Creek during a
baseline period, 1950-2001. BASINS CAT was then used to create scenarios of increased
drought severity, duration, and combined severity and duration by adjusting observed
temperature and precipitation values during and immediately following this historically dry
period. The full baseline period used in all model simulations was 1952-2001. The following
five climate change scenarios were created:
Three scenarios representing increased drought severity were created by decreasing
precipitation during the observed low flow period from 1959-1961 by 0, 10, and 20%.
These scenarios are hereafter referred to as "Precip 0," "Precip -10," and "Precip -20,"
respectively.
A scenario representing increased drought duration was created by decreasing rainfall in
two relatively wet years that immediately followed the dry period, 1962-1963.
Precipitation in 1962-1963 was decreased such that mean annual precipitation in these
years was equal to the mean of precipitation occurring in 1961 and 1964. Precipitation in
1964 was also relatively low. This scenario, thus, represents a hypothetical drought
period of 6 years, 1959-1964, and is hereafter referred to as "Duration."
A scenario representing both increased drought severity and increased duration. This
scenario was created by applying the same adjustments as in the Duration scenario
together with a 10% precipitation decrease applied to all 6 years of the extended drought
period. This scenario is hereafter referred to as "Duration/Severity."
A constant increase of 2°C was included in each climate change scenario to represent projected
warming in this region. Temperature increases were applied to the entire baseline period,
1952-2001, used in model simulations. PET values were revised by BASINS CAT accordingly
using the Penman-Monteith method.
Figure 3-24 shows the BASINS CAT window used to create the precipitation adjustments for the
scenarios Precip 0, Precip -10, and Precip -20. The three fields at the top define which input
records will be adjusted and by what method (i.e., "Multiply Existing Values ..."). In this
example, the BASINS CAT capability for creating multiple changes within specified range was
made to produce the three adjustments. The bottom frame defines the time of year
(Months/Years) during which the adjustments were applied (e.g., 1959-1961). BASINS CAT
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was then used to combine these three precipitation adjustments with the temperature and PET
adjustments described above to create three scenarios of varying drought severity.
Modify Existing Data
i. n x
Modification Name:
Existing Data to Modify: |OBSERVED VC152 PREC (and 4 more]
How to Modify:
Multiply Existing Values by a Number (eg Precipitation)
v
Number to multiply existing data by
O Single Change (.**' Multiple changes within specified range
Minimum 0.8
Maximum: 1
Increment:
0.1
multiplication factor
multiplication factor
Events
Vary values only in the following Events
Exceeding threshold 0
Allow gaps up to
Sum of values exceeding threshold 0
Total duration above
I
Months //ears
Vary only in selected
Calendar Year
1950
1951
1952
1953
1954
<
1955
1956
1957
1958
1962
1963
11964
1965
|1966
1967
1968
1969
1970
1971
1972
1973
1974
1975
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
All
None
Ok
Cancel
Figure 3-24. BASINS CAT window used to define precipitation adjustments
for the Precip 0, Precip -10, and Precip -20 scenarios. Modification Name is
the user defined scenario name; Existing Data to Modify identifies the time
series to be modified; How to Modify defines how time series values will be
adjusted; Number to Multiply Existing Data by indicates the number of
multiplies, their range, and increment between each multiplier; Month/Years
allows for the selection of specific years in the time series to apply the defined
changes.
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Land-use Scenarios
LULC was not evaluated in this case study. While it is unlikely that land use in the Sespe Creek
watershed will not change in the future, land use was held constant to focus the analysis on the
potential effects of climate change only.
Management Scenarios
Management scenarios were not specifically evaluated in this case study. BMPs or other
management practices may have been included in the original HSPF model, but no adjustments
were made to focus the analysis on the potential effects of climate change only.
Endpoint Selection
The simulation endpoints considered in this case study are mean annual streamflow and mean
annual 7-day low flow. Additionally, mean monthly streamflow values from each scenario are
plotted for comparison.
Results
Endpoint values for all model simulations are presented in Tables 3-31 and 3-32. Table 3-31
shows results for the Precip 0, Precip -10, and Precip -20 scenarios, and Table 3-32 shows results
for Duration and Duration/Severity scenarios. Note that the values reported in each table are
calculated only for the respective drought period under consideration: 1959-1961 for drought
severity scenarios, and 1959-1964 for drought duration and duration/severity scenarios.
The Precip 0 scenario shows only a small decrease (roughly 5%) in streamflow resulting from a
2°C temperature increase with no change in precipitation. However, combining the temperature
increase with decreases in precipitation during the dry period of 1959-1961 had a significant
impact on both mean flow and 7-day low flow. The Precip -10 scenario led to decreases in
mean annual streamflow (cms) and mean annual 7-day low flow (cms) of 38 and 30%,
respectively, from the baseline. For the Precip -20 scenario, decreases from the baseline were
62% for mean annual streamflow (cms) and 39% for mean annual 7-day low flow (cms).
Table 3-31. Simulation results for the Precip 0, Precip -10, and Precip -20
scenarios as applied to historic period of low flow, 1959-1961.
Scenario
Baseline
Precip 0
Precip -10
Precip -20
Change in
Temperature
°C
0
2
2
2
Change in
Precipitation
%
0
0
-10
-20
Mean Annual
Streamflow
(1959-1961)
cms
12.60
12.10
7.83
4.85
Mean Annual 7-Day Low
Flow (1959-1 961)
cms
0.61
0.58
0.43
0.37
The Duration and Duration/Severity scenarios also impacted streamflow (see Table 3-32). In
this case, reducing precipitation in 1962-1963 to represent an increased duration of drought led
71
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to decreases in streamflow relative to baseline conditions. Mean annual streamflow (cms) in the
Duration scenario decreased by 61%, and mean annual 7-day low flow (cms) decreased by 43%
for the extended drought period. The Duration/Severity scenario led to an additional 13%
decrease in mean annual streamflow (cms) and an additional 12% decrease in mean annual 7-day
low flow (cms).
Table 3-32. Simulation results for the Duration and Duration/Severity
scenarios as applied to the extended period of low flow, 1959-1964.
Scenario
Baseline
Duration
Duration and Severity
Change in
Temperature
°C
0
2
2
Change in
Precipitation
%
0
0
-10
Mean Annual
Streamflow
(1959-1964)
cms
48.60
19.00
12.80
Mean Annual 7-Day
Low Flow
(1959-1964)
cms
1.41
0.80
0.63
Using the BASINS CAT option to save input and output files for all simulations, BASINS
analysis tools were used to generate a plot of mean monthly streamflow for the five scenarios
(see Figure 3-25). This plot provides further insight into the results in Tables 3-31 and 3-32.
While the Duration/Severity scenario yielded the greatest decrease from the comparable baseline
values, it is notable that endpoint values from this scenario (see final row of Table 3-32) were
still higher than baseline values for the original drought period (see first row of Table 3-31). The
Duration and Duration/Severity scenarios were developed by decreasing all rainfall events in
1962-1963 by the same amount, yet the early 1962 precipitation remained substantial in these
scenarios (see 'Duration' and 'Duration/Severity' curves in Figure 3-25). Thus, the mean annual
streamflow (cms) and 7-day low flow (cms) remained at levels above the original baseline
drought (see first row of Table 3-31).
Summary
This case study illustrates the use of BASINS CAT to create scenarios for assessing the potential
effects of increased drought severity and duration on streamflow. Scenarios were created by
identifying a period of dry weather in the historical record, then using BASINS CAT to adjust
temperature and precipitation values during and immediately following this period to represent
increased drought severity, duration, and the combined effects of increased drought severity and
duration.
Several BASINS CAT features were used to create the scenarios. First, the ability to modify
data within specific seasonal or annual time periods allowed for precipitation changes to be
applied only during drought periods. Second, the capability for creating multiple changes
within specified range was used to decrease precipitation. Finally, the ability to combine
adjustments was used to create the scenario where both drought duration and severity were
increased.
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100
Precip -10
Precip -20
Duration
Duration/Severity
0.01 -
1958 1959 1960 1961 1962 1963 1964
AVERAGE MONTHLY FLOW at RCH728
1965 1966
Figure 3-25. Mean monthly streamflow during drought periods for all
scenarios.
BASINS CAT was used to assess changes in mean annual streamflow and 7-day low flow. In all
simulations, the increased temperature and increased duration and severity of the drought period
translated to decreased streamflow and 7-day low flows. This type of analyses based on a
historical event can be very useful to water managers interested in exploring the potential
implications of extreme events water management.
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3.7. STREAMFLOW AND WATER QUALITY RELATIVE SENSITIVITY TO
CLIMATE CHANGE VS. IMPERVIOUS COVER IN THE WESTERN BRANCH
OF THE PATUXENT RIVER, MD, USING BASINS CAT WITH HSPF
Case Study Overview:
This case study evaluates the relative sensitivity of stormwater runoff and sediment loads
(TSS) to changes in precipitation volume, changes in precipitation event intensity, and
changes in watershed impervious cover using BASINS CAT with an HSPF model. The case
study combines climate change scenarios created using BASINS CAT and land-use change
scenarios created outside of BASINS CAT by adjusting HSPF input files. The following
change scenarios were considered:
increased precipitation volume of all events by 0, 10, and 20%
increased proportion of annual precipitation occurring in selected large magnitude
events (70th percentile and greater) events by 0, 10, and 20%
increased current watershed impervious cover of 8.6% to 15 and 25%
Introduction
Imperious surfaces in urban areas such as roofs, parking lots, roads, and sidewalks are a
significant hydrologic alteration commonly resulting in impairment of local water bodies. Many
of the effects are interrelated and often difficult to quantify, but two of the most significant
causes of the impairment of urban streams are increased stormwater runoff and runoff pollution.
Climate change in many parts of the nation is expected to increase the frequency and intensity of
large magnitude storm events. These changes present a risk of increased stormwater runoff,
pollutant loads, and flooding. In urban and suburban areas, there is also the potential for
cumulative, synergistic effects on stormwater runoff resulting from the interaction of climate
change and increased impervious cover associated with development. In such cases, changes in
land use could exacerbate the impacts of stormwater runoff and water quality impairment on
adjacent water bodies (Pyke et al., 2011). An improved understanding of these relationships can
help to inform management strategies for protecting water quality and aquatic ecosystems.
Location Description
The Western Branch of the Patuxent River (HUC 02060006) drains an area of 230 km2 east of
Washington D.C. and is a tributary of the Patuxent River and Chesapeake Bay (see Figure 3-26).
Land use is mixed with significant fractions in forest, urban development, and agriculture (see
Table 3-33).
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Legend
| Western Branch, Patuxent Rive
Reaches
4 Miles
Figure 3-26. The Western Branch of the Patuxent River watershed and its
location within the Chesapeake Bay watershed, MD, USA.
Table 3-33. Land-use summary for Western Branch of the Patuxent River
watershed.
Land Use
Forest
Urban/Developed
Agricultural
Wetland
Barren
Portion of Watershed, %
39
34
25
2
<1
Model Setup
A preexisting, calibrated HSPF model of the Western Branch of the Patuxent River was
extracted from a larger model of the Patuxent River watershed developed in the early 1990s for
the U.S. Geologic Survey and the state of Maryland (AQUA TERRA Consultants, 1994). The
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original model used GIRAS land use data. The Western Branch model used in this case study
was revised to use land use data from 2001 NLCD.
Model calibration and validation were checked after making the conversion to NLCD to ensure
the model would yield reasonable results. The calibration period was the same as for the original
model, 10/1/1985 through 9/30/1988. The validation period was 10/1/1995-9/30/2005.
Calibration and validation results are shown in Table 3-34. The overall streamflow balances are
very good, with errors in total volume less than 1%, and the storm peaks are well simulated, with
errors less than 6% for calibration and nearly 2% for validation.
Table 3-34. Western Branch of the Patuxent model hydrology calibration
and validation statistics. NSE= Nash-Sutcliffe Efficiency coefficient (Nash
and Sutcliffe, 1970); R2= coefficient of determination.
Daily Values R2
Daily Values NSE
Monthly Values R2
Monthly Values NSE
% Error in Total Volume
% Error in Storm Peaks
Calibration
(1985-1988)
0.50
0.47
0.74
0.73
-0.9
-5.8
Validation
(1995-2005)
0.56
0.52
0.81
0.81
0.9
2.1
Limited data were available for calibrating TSS on the Western Branch, but a time series plot of
the simulated and observed TSS concentrations shows that the simulation captures the overall
range and distribution of TSS concentrations at the sampling location (see Figure 3-27). While
these checks should not be construed as a complete calibration, the model was considered
acceptable for representing the relative changes across various model input scenarios, and for the
primarily illustrative purpose of the case study.
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3,000
+ OBSERVED at 01594526
WESTBRAN at RCH5
1996
Time
Figure 3-27. Observed and simulated TSS concentrations (mg/L) in the
Western Branch of the Patuxent River watershed.
Scenario Development
A total of nine model simulations were completed' including six climate change scenarios and
three land-use scenarios. Land-use scenarios represent only changes in developed land
(impervious cover). No management scenarios were included.
Climate Change Scenarios
Climate change scenarios represented changes to precipitation only. No temperature adjustments
were made. Precipitation change scenarios were based on an ensemble of 16 statistically
downscaled climate change projections acquired from The Nature Conservancy's Climate Wizard
web site (www.climatewizard.org). Section 3.2 provides additional information about these
climate projections. Projected changes in mean annual precipitation in this region by end-of-
century (2080s) ranged from about 0 to 20%.
A total of six precipitation change scenarios were created. Three of the scenarios represented
increases in annual precipitation volume of 0, 10, and 20% (precipitation volume scenarios).
BASINS CAT was used to adjust the magnitude of all events in the record by applying a
constant percent increase to all values in the baseline record. The remaining three scenarios
represented increases in the proportion of annual precipitation occurring in large magnitude
events by 0, 10, and 20% (precipitation intensity scenarios). BASINS CAT was used to define
events in the baseline precipitation record, apply a constant percent increase to all values within
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the largest 30% of events, and apply a constant percent multiplier to decrease all events below
this threshold to create no net change in annual precipitation volume.
The creation of the scenarios was facilitated by the BASINS CAT capability for combining
multiple adjustments to meteorological time series to create complex scenarios. Figure 3-28
shows the BASINS CAT window for selecting the two adjustments used to develop the 20%
increase in the largest 30 percent of events (selection of Preciplnt Intensity 20) with no net
change in annual precipitation volume (selection of Preciplnt Multiply 0.833).
Climate Assessment Tool 2.0 - BASINSCAT HSPF Patuxent
File Edit Options Help
Model Climate Data Assessment Endpoints Results Pivot Table
Base Model
New Model
G:\CAT\CS5 • Patuxent West BranchVCalibratedModelWestBran.uci
Modified
Add
Remove
Edit
Copy
View
Prepared
^| PrecipMult Multiply from 1.1 to 1.2 step 0.1
^| Preciplnt Intensify 10
^\ Preciplnt Multiply 0.909
~7\ Preciplnt Intensify 20
3 Preciplnt Multiply 0.933
Total iterations selected = 1 (0:00)
Figure 3-28. BASINS CAT window showing the selection of precipitation
adjustments to create a 20% increase in the largest 30% of events with no net
change in annual precipitation volume. Preciplnt intensify 20 = increase
selected events by 20%, Preciplnt Multiply 0.833 = apply a 0.833 multiplier to
events below the threshold event.
Land-use Scenarios
Three land-use scenarios were created to represent current (approximately 2001) watershed
impervious cover and two potential future development scenarios with increasing impervious
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cover. Current impervious cover was estimated to be 8.6%. Future development scenarios
assumed impervious cover of 15 and 25%.
BASINS CAT does not provide explicit capabilities for creating land-use change scenarios. The
increases in impervious cover were represented in the HSPF model through a proportional
decrease in each pervious land-use category. The percent increases in impervious cover were
obtained by shifting land use primarily from forest and agriculture to urban, as well as by
increasing in the amount of urban land that is considered impervious (i.e., increased urban
density). The choice of 15 and 25% imperviousness for the future scenarios is not based on local
information but falls within the range for moderate to highly developed watersheds in the
mid-Atlantic region (U.S. EPA, 2009b). Table 3-35 shows the percentages of each land-use
category for each of the three land-use scenarios in this case study.
Table 3-35. Summary of Western Branch of the Patuxent watershed land
use by category for each scenario.
Land Use Category
Forest
Urban/Developed
Agricultural
Wetland
Barren
8.6% Impervious
Cover
39
34
25
2
<1
15% Impervious
Cover
36
39
23
2
<1
25% Impervious
Cover
32
46
20
2
<1
Management Scenarios
Management scenarios were not evaluated in this case study.
Endpoint Selection
The endpoints considered in this case study are mean annual streamflow and mean annual TSS
loads. While not comprehensive of all impairment, these endpoints are considered representative
of potential hydrologic and water quality impacts due to changes in precipitation and impervious
cover.
Results
Results for mean annual streamflow (cms) and TSS loads (tonnes/ha/yr) are shown in Table 3-
36. Percent changes are expressed relative to the baseline conditions: 0% increase in
precipitation volume, 0% increase in precipitation intensity, and 8.6% impervious cover. Results
suggest that streamflow in the Western Branch watershed is most sensitive to increases in
precipitation volume. A 20% increase in overall precipitation volume leads to a 46% increase in
mean annual streamflow. Increases in impervious cover also resulted in significant changes to
mean annual streamflow; an increase to 25% impervious cover resulted in a 28% increase in
mean annual streamflow. Increases in precipitation intensity did not have nearly as large an
effect on mean annual streamflow as did increases in precipitation volume and impervious cover.
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Table 3-36. Annual streamflow and TSS load characteristics for the Western
Branch of the Patuxent simulations.
Scenario
Precipitation
Volume
Precipitation
Intensity
Impervious
Cover
Precipitation
Increase,
%
0
10
20
0
10
20
8.6
15
25
Mean Annual
Streamflow,
cms
2.81
3.43
4.11
2.81
2.89
2.97
2.81
3.12
3.60
Change in
Mean Annual
Streamflow
from Baseline,
%
-
21
46
-
3
6
-
11
28
Mean
Annual TSS
Load,
tonnes/ha/yr
0.056
0.073
0.090
0.056
0.067
0.078
0.056
0.053
0.050
Change in
Mean Annual
TSS Load
from
Baseline, %
-
30
61
-
20
39
-
-5
-11
Results suggest that TSS (tonnes/ha/yr) in the Western Branch watershed is most sensitive to
increases in precipitation volume. A 20% increase in overall precipitation volume led to a 62%
increase in annual TSS load. A major increase in TSS load also resulted from increases in
precipitation intensity. Results also suggest that TSS loads decrease with increases to
impervious cover. While this could result from reductions in watershed agricultural land, this
relationship is complex, and the true cause is not known.
Figure 3-29 is a simple heuristic model illustrating the relative sensitivity of stormwater runoff to
changes in precipitation volume, precipitation intensity, and watershed impervious cover. Note
that each scenario is presented while holding other variables constant (e.g., the precipitation
intensity scenarios represent changes in precipitation intensity while holding impervious cover
and precipitation volume at baseline levels). These results, though based on limited data, suggest
that when expressed on a constant percent basis, mean annual stormwater runoff is most
sensitive to changes in precipitation volume, followed by changes in impervious cover and
precipitation intensity in the Western Branch watershed.
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50
40 •
• Precipitation volume
•Precipitation intensity
Impervious cover
5 10 15 20
Change in Impervious Cover, Volume, and Intensity (%)
25
Figure 3-29. Simulated sensitivity of stormwater runoff volume to changes in
impervious cover, precipitation volume, and precipitation intensity.
Figure 3-30 shows a similar comparison for TSS. Results suggested that when expressed on a
constant percent basis, mean annual TSS loads are most sensitive to changes in precipitation
volume followed by changes in precipitation intensity. Mean annual TSS load is inversely
related to changes in impervious cover. It should be noted that TSS loading from developed land
is complex, and the relationship seen here is not universal and is likely the result of specific
land-use change characteristics included in this model.
Summary
This study assessed the relative sensitivity of streamflow and TSS loads to changes in
precipitation volume, precipitation intensity, and impervious cover for a mixed-use watershed in
Maryland. The primary BASINS CAT feature illustrated in this case study was the ability to
create a matrix of climate change scenarios representing different combinations of potential
temperature and precipitation change within user-defined ranges for input to a watershed model.
This case study also illustrates the approach of combining climate change scenarios with land-
use change scenarios.
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TJ
ra
o
0)
^
0)
CO
0)
O)
ra
^
O
70 •
60 •
50
40
30 H
20
10 H
o
-10
-20 J
•Precipitation volume
•Precipitation intensity
Impervious cover
Change in Impervious Cover, Volume, and Intensity (%)
Figure 3-30. Simulated sensitivity of TSS loads to changes in impervious
cover, precipitation volume, and precipitation intensity.
This case study illustrates the potential synergistic effects of climate change and urbanization on
stormwater. While the scenarios applied were relatively simple, results suggest that when
expressed on a constant percent basis, mean annual streamflow in the Western Branch watershed
is more sensitive to changes in precipitation volume and impervious cover than to event
intensity. TSS loads appear to be more sensitive to annual precipitation volume and event
intensity versus impervious cover. Even relatively minor changes represented in the scenarios
had notable impacts on either mean annual streamflow or annual TSS load, or both. Results
suggest that improved development strategies have the potential to reduce or offset the effects of
climate change. Management practices such as low-impact development that reduces impervious
cover in new and existing development could be used to compensate for increased stormwater
runoff associated with climate change (e.g., see http://www.epa.gov/owow/NPS/lid/).
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3.8. LIMITATIONS OF CASE STUDY SIMULATIONS
The case studies in this report illustrate selected capabilities of the BASINS and WEPP climate
assessment tools for conducting scenario-based analyses of watershed response to changes in
climate, land-use, and management practices. The scientific approach supported by these tools,
i.e., scenario analysis, can be useful for understanding system behavior, identifying
vulnerabilities, and evaluating the effectiveness of management responses to inform decision
making. The tools presented in this report are just one step forward in building our capacity for
understanding and responding to climate change. Application of hydrologic models in this way
has limitations, many of which are not well understood (Ghosh et al., 2010; Ludwig et al., 2009;
Najafi et al., 2011; Vaze et al., 2010). Further study is required to better assess, refine, and
develop our current modeling and scenario development capabilities. The following discussion
briefly identifies several issues, current limitations, and future needs associated with using
hydrologic models for impacts assessments.
Use of a hydrologic model assumes that scenarios do not alter watershed behavior in a way that
invalidates the model parameterization achieved through calibration. This issue exists in any
modeling analysis (Donigian, 2002; Donigian and Love, 2003) but is of particular concern when
considering climate change scenarios (Vaze et al., 2010). In many cases, climate change
scenarios will fall outside the range of historical observations used to calibrate the model. It is
reasonable to assume that at some point, large changes imposed by scenarios will affect model
calibration. It is difficult to know where this point is, however, or what the implications are for
results. BASINS CAT and WEPPCAT, for the most part, impose no constraints on the type and
magnitude of climate changes made. Users must, therefore, be cautious to consider the validity
of model simulations, particularly when assessing change scenarios falling outside the range of
observed climatic variability.
At the decadal scale, climate change may alter groundwater storage and recharge, potentially
impacting streamflow and water quality. The HSPF and SWAT models contain simple
representations of the shallow groundwater system, including percolation recharge, storage,
discharge to streams, losses to deep aquifers, and loading of pollutants such as nitrate, but
models do not provide a complete representation of groundwater pathways including exchanges
with deeper aquifers. Where these exchanges with deeper aquifers are represented in the models,
they are typically held constant, and their sensitivity to climate change is not simulated. Thus, a
complete picture of any long-term trends attributable to future climate may not be fully
represented by a given watershed model.
A major component of the water budget, ET, is directly sensitive to climate. ET is also strongly
influenced by land cover, which is in turn influenced by climate. Changes in ET have a
significant influence on the occurrence, distribution, and movement of water including soil
moisture, groundwater recharge, and streamflow. The method used to calculate ET, or more
commonly the reference potential evapotranspiration (PET), is, thus, a key process in simulating
the watershed response to climate change. The models in the case studies each have one or more
options for representing PET. Many watershed modeling efforts perform well with simplified
approaches to estimating PET, such as the Hamon method, which depends primarily on
temperature. The robustness of watershed model calibrations conducted with simplified PET is
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suspect under conditions of climate change, because a variety of other factors that influence PET,
such as wind speed, relative humidity, and cloud cover, are also likely to change. It is advisable
to use a full energy balance method for PET, such as Penman-Monteith PET (Jensen et al.,
1990), yet little is known about the proper specification of climate-altered input variables such as
wind and solar radiation.
Atmospheric carbon dioxide (CO2) concentration has increased steadily throughout the last
century. The trajectory of future CO2 concentration will vary depending on human efforts to
reduce emissions but could plausibly exceed 500 ppm (per volume) by 2050 (compared to about
370 ppm per volume in 2000). Increases in atmospheric CC>2 concentrations effectively reduce
the stomatal conductance of plant leaves, thus reducing water loss through transpiration. Limited
research has been conducted on the potential effects of increased atmospheric CO2 on
ecosystems, but the initial findings indicate that when CC>2 levels increase, ET decreases (Leakey
et al., 2009). CC>2 effects on plant growth could also influence nutrient uptake, litter fall, and
other processes that can affect water quality. Incorporation of CC>2 fertilization into a model is a
potentially significant factor affecting simulation of watershed response to climate change. The
SWAT watershed model includes a plant growth module that can account for the effects of
increased CO2, but the other models available with BASINS CAT and WEPPCAT do not.
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4. CONCLUDING COMMENTS
This report is a guide to the application of two modeling tools recently developed by EPA and
partners, BASINS CAT and WEPPCAT. The tools are not stand-alone models, rather they
facilitate application of existing water models (e.g., HSPF, SWAT, SWMM, and WEPP) to
assess questions about climate change impacts on water and watershed systems. Many
communities, states, and the federal government are considering adaptation strategies for
reducing the potential risks of climate change4. The challenges of how to incorporate diverse,
uncertain, and often conflicting information about future conditions into decision making are
significant. The scientific approach supported by BASINS CAT AND WEPPCAT, i.e., scenario
analysis, can help inform our adaptation decisions by increasing our understanding system
behavior, identifying vulnerabilities, and evaluating the effectiveness of management responses.
The six case studies in this report are designed to illustrate how BASINS CAT and WEPPCAT
can potentially be used to address a range of practical, real-world questions of interest to water
and watershed managers. The tools presented in this report, however, are just one step forward
in building our capacity for understanding and responding to climate change. We hope that these
tools can inspire and support ongoing research and applications to help meet this challenge.
4 For examples, see: Climate Change Adaptation Task Force:
http://www.whitehouse.gov/administration/eop/ceq/initiatives/adaptation
http://www.whitehouse.gov/sites/default/files/microsites/ceq/201 l_national_action_plan.pdf
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APPENDIX A.
Selected Sources of Climate Change Information
The case studies in this report utilized climate model projections and other information to
develop the climate scenarios. The information used is just a small subset of the climate data
currently available. Selected additional sources of climate change information, data, and
guidance concerning the use of climate change data are listed below. Most sources provide
climate change projections developed from climate modeling experiments using global GCMs or
RCMs. Regional information and guidance about climate change can be obtained from other
sources including government agencies and universities. Over time, climate change data will
become more readily available as climate models are improved, new modeling experiments are
conducted, new monitoring is completed, and research better reveal historical patterns of climate
variability and change.
Bias Corrected and Downscaled WCRP CMIP3 Climate and Hydrology Projections
Lawrence Livermore National Lab oratory/Bureau of Reclamation/Santa Clara University
http://gdo-dcp.ucllnl.org/downscaled_cmip3_projections/dcplnterface.html
Climate Wizard
http://www.climatewizard.org
Conservation International
http://futureclimates.conservation.org/
Data Basin
http: //datab asin. org/
Earth System Grid gateway
http://pcmdi3.llnl.gov/esgcet/home.htm
IPCC Data Distribution Centre
http://www.ipcc-data.org/
Lawrence Livermore National Laboratory, Program for Climate Model Diagnosis and
Intercomparison
http://www-pcmdi.llnl.gov/ipcc/about_ipcc.php
National Center for Atmospheric Research, North American Regional Climate Change
Assessment Program (NARCCAP)
http://www.narccap.ucar.edu/data/index.html
http://www.narccap.ucar. edu/results/index.html#climate-change
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SERVIR—Regional Visualization and Monitoring System
http ://www. servir. net/en/
USDA Forest Service
http://www.fs.fed.us/rm/data_archive/dataaccess/contents_datatype.shtml
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