Chesapeake Bay Program Watershed Model Overview
The Chesapeake Bay Program's Watershed Model, presented to STAC in December 2019, provides insights on long-term average pollutant loads and land use dynamics in the Chesapeake Bay watershed. The model incorporates inputs such as land use acres, BMPs, and delivery mechanisms to estimate pollutant loads per land use area. It also factors in population growth projections to simulate the effects of urban development on land use change and pollutant loads. The model aims to offer a comprehensive understanding of watershed processes and results for effective environmental management.
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The Chesapeake Bay Program The Chesapeake Bay Program Watershed Model Watershed Model Processes and Results Processes and Results Gary Shenk CBPO Based on the work of: Lewis Linker, Gopal Bhatt, Isabella Bertani, Jeff Sweeney, Jessica Rigelman, Olivia Devereux, Matt Johnston, Andrew Sommerlot, Richard Tian, Danny Kaufman, Ruth Cassilly and many, many more Presentation to STAC 12/18/2019 This information is being provided to meet the need for timely best science. The information is provided on the condition that neither the U.S. Geological Survey nor the U.S. Government shall be held liable for any damages resulting from the authorized or unauthorized use of the information. 1
Keep It Simple Include Everything Average Load + Inputs * Sensitivity * Time-Averaged Model Land Use Acres * BMPs * Land to Water Stream Delivery * River Delivery * Load per land use per area 3
CAST Time averaged scenario output Estimates the long-term average load given a set of anthropogenic inputs Average Load + Inputs * Sensitivity * Time-Averaged Model Land Use Acres * BMPs * Connected to a dynamic model for calibration and to estimate some sensitivities Land to Water Stream Delivery * River Delivery * Load per land use per area 4
LOCAL POPULATION PROJECTIONS CBLCM v4 2010 - 2050 Modeling the Effects of Population Growth on Land Use Change and Pollutant Loads Modeling Assumption: Urban development results from growth in population and employment.
Chesapeake Bay Land Change Model v4 County-level Employment Projections County-level Population Projections Total Housing Demand Historic Infill Patterns Historic Development Patterns Potential Infill Development Land Conservation Demand Housing Land Demand Employment Land Demand Residential Development Land Conservation Commercial Development Iterative & Stochastic Present Land Cover Future Land Cover Summary Units (P6 modeling segments) Future Development Statistics
Multiple Stochastic Iterations Every county is simulated 101 times for each scenario and target year, i.e., 2025. Average of simulations by summary unit = future development Relative Standard Deviation = estimate of uncertainty 9
Point Sources Most WWTP use default values for load based on flow and technology about 10% use monitored values Makes up about 85% of the load Industrial sources are monitored States estimate industrial sources to increase based on population trend, linear trend, or no trend 10
Forecasted 2025 Growth on Sewer vs Septic in Maryland Sewer Service Area Residential Growth (sim.) Commercial Growth (sim.)
Atmospheric Deposition Model Regression model for history of wet deposition Hourly estimate based on Weather Physical landscape Upwind sources Preliminary Information-Subject to Revision. Not for Citation or Distribution 12
Atmospheric Deposition Model CMAQ National-scale EPA model Used for many national air regulations CBP uses dry deposition and changes in future wet and dry deposition due to policy Preliminary Information-Subject to Revision. Not for Citation or Distribution 13
Fine-scale spatial analysis to improve sediment prediction 14
Sources WQGIT determines inputs Ag Workgroup Ag Modeling Subcommittee Poultry Litter Subcommittee Riparian Pasture Deposition Subcommittee Urban workgroup Wastewater Treatment Workgroup 15
Soil P data Lancaster Soil Test Data Mass-Balance Process Model Bayesian Estimate 18
Nitrogen in Groundwater Used MODFLOW models of Potomac and Eastern Shore from Ward Sanford Coastal Plain PC regression model Others related to recharge and depth to groundwater 19
Flowpath Lags Used in dynamic model for calibration Not used in scenarios 20
Management Practices BMP Panel Results CBP partnership asks for a panel (WQGIT) Panel forms for 1-2 years At least six subject matter experts Academics Implementers Government scientists After approval by the partnership, it is included in the watershed model 21
BMPs 189 BMPs with specified percent reduction 104 are cover crops Cover Crop Traditional Rye Early Drilled Cover Crop Traditional Forage Radish Early Aerial Appalachian Plateau Siliciclastic TN 11% TP 6% Coastal Plain Dissected Uplands TN 14% TP 6% TSS 9% TSS 9% 22
Feed Additives Stream Fencing Lagoon Covers Manure Storage Manure transport Nutrient management 24
USGS Sparrow Model Nitrogen Land to Water factors Groundwater recharge Piedmont Carbonate Enhanced Vegetative Index Available water capacity Preliminary Information-Subject to Revision. Not for Citation or Distribution 25
Stream to River factors Temperature Travel time Reservoirs Nitrogen Preliminary Information-Subject to Revision. Not for Citation or Distribution 26
Down - scalin g Carbon Dioxide effect Support/Revise Flow--N relationship GCM Selecti on GCMs RCP Process- based response of flow and sediment On land Investigate Speciation changes Climate scenarios Soil P history effect Observed Precipitati on Trends Extrapolati on Watershed Climate Specified land nutrient response Effect on land use P sensitivity in developed Estuary Nutrient Inputs Effect on Ag Reasonable response to temperature? BMP effectiveness change Growth and respiration curve for phytoplankton Wetlands losses and gains BMPs CMAQ Reasonable response to sea level rise? Climate change effect Land to water factor effect Land to Water Wind effects Estuarine Model WQSTM Management Loads from flooding Estuarine Inputs Stream to River factors effect Stream s Sea level Rise Groundwater Travel Time effect Ocean Temp UNEC Reservoir Rules effects Rivers Included Not included But important Not included minor Diversion Effect WWTP and septic Effect Manageme nt Effort Adjustment STAC CC2.0 Framework recommendations CSO Effect Effect on water quality standards Project/Decision 27 Model Endpoint Data Set
How well does the model predict spatial differences in load? Simulated Per Acre Load 29 WRTDS Per Acre Load
How well does the model predict Temporal differences in load? CBP loads indicator and CAST scenarios
1985-2014 34 stations with long-term data WRTDS trends CAST scenarios with constant atmospheric deposition WRTDS more variable Average reduction similar 74% directionally correct Directionally correct 74% Nitrogen
1985-2014 15 stations with long-term data WRTDS trends CAST scenarios WRTDS more variable Average reduction similar when there are reductions 73% directionally correct Directionally correct 74% Phosphorus
1985-2014 319 5-year trends WRTDS trends CAST scenarios with constant atmospheric deposition WRTDS more variable 58% directionally correct Likely too short of a time frame Directionally correct 74% Nitrogen
1985-2014 180 5-year trends WRTDS trends CAST scenarios WRTDS more variable Average reduction similar 59% directionally correct Directionally correct 74% Phosphorus
Phosphorus 38
Phosphorus 40
Further investigation Include atmospheric deposition What is the effect of lag times Investigate uncertainty of both WRTDS and CAST Attempt to isolate factors => 41
