Difficult Run Modeling Study: Lessons Learned for Improved BMP Design

Transcription

1 Difficult Run Modeling Study: Lessons Learned for Improved BMP Design David J. Sample Associate Professor Nasrin Alamdari Biological Systems Engineering Click to edit Master subtitle style Presentation for the STAC Workshop Monitoring and Assessing Impacts of Changes in Weather and Extreme Events on BMP siting and Design September 7-8, 2017 Annapolis, MD

2 Outline Urban and CC impacts on water quality Case study description and model development Case study results Assessment of BMP failure modes Modeling analysis tools-rswmm Next steps Summary April 9, 2008

3 Urbanization Impacts on Urban Runoff Source: State of the Nation s River 2008, Potomac Stormwater Runoff, Potomac Conservancy.

4 Projected Climate Changes Changes in seasonal precipitation patterns Increasing intensity of extreme events Changes in temperature, changing evaporation rates Projected future changes in precipitation relative to the recent past Source: USGCRP 2009 Projected temperature change for mid-century and end-ofcentury Source: USGCRP 2009

5 Difficult Run Case Study Area selected as urban component of NSF WSC project Existing SWMM model previously developed in 2005 (Fairfax County WMP) Area: 150 km 2 Streams: 233 km Imperviousness: 18% 56% residential Over 900 BMPs, aggregated to 18 (1 in each subcatchment)

6 Difficult Run SWMM Model Dynamic rainfall-runoff-water quality model Link-node topology Simulation of runoff quantity and quality from urban areas Enhancements to existing model Updated from SWMM 4.4h to 5.0, linked with GIS, updated spatial analysis (slope, soils, elevation, land use, imperviousness) Added groundwater and evaporation submodels Added continuous simulation Added water quality simulation

7 SWMM can simulate inflow of user-defined pollutants by respective land cover, can input concentrations in: Rainfall Groundwater Direct infiltration/inflow Dry weather flow Land cover, e.g.: Residential Commercial Industrial Forest Processes EMC (example on right) Buildup Washoff Street cleaning Water Quality Submodel Parameter EMC (mg/l) TSP 0.19 PP 0.08 TN 2.13 TSS 40 April 9, 2008

8 Treatment Modeling removal of pollutants by assigning a set of treatment functions to a storage/treatment node Mathematical expression of a treatment function: The pollutant concentration entering the node The removals of other pollutants One or two following process variables: FLOW for flow rate into node DEPTH for water depth above node invert AREA for node surface area DT for routing time step HRT for hydraulic residence time April 9, 2008

9 Single scenario, single RCM: Special Report on Emissions Scenarios (SRES A2) North American Regional Climate Change Assessment Program, NARCCAP) MM5I-CCSM was selected based on its ability to accurately simulate historical temperature and precipitation. Averaging annual mean temperature and precipitation over the region for 9 of the NARCCAP models, and compared the means to the mean obtained from an observation-based dataset. Time periods, Historical: , Projected: Bias correction-eqiratio method, matches historical pdf of NWS gage Ensemble Approach: Climatologic Modeling Five selected Coupled Model Intercomparison Project Phase 5 (CMIP5) under two Representative Concentration Pathways (RCP 4.5 and 8.5) scenarios (MPI-ESM-LR, GISS-E2-R, CCSM4, CSIRO Mk3.6.0, and BCC-CSM1.1).

10 Water Quantity Calibration Model Performance Upstream USGS gage Downstream USGS gage Period NSE R 2 PBIAS NSE R 2 PBIAS Calibration % % Verification % %

11 Water Quality Calibration

12 Projected Changes by Season

13 Projected Changes by Season

14 Projected Changes by Season

15 Duration Curves for Nitrogen, Phosphorus

16 Results Constituent Ensemble 4.5 Ensemble 8.5 NARCCAP A2 Lower, % Upper, % Lower, % Upper, % Median, % TSS TN TP * *Significant difference at 95% confidence level.

17 BMP Performance Reduction Modes Mechanism Overtopping due to inadequate storage/sedimentation Overtopping due to inadequate hydraulics Warmer temperatures causing more TSP to mobilize and resuspend Dam breaching Potential Adaptation Strategy Enlarge impoundment or dredge, increase maintenance frequency, pretreat Redesign/replace outlet structure Smarter discharge controls (note monitoring data needed to assess) Reinforce/redesign identified weak spots (inspection) Source: Georgia Stormwater Manual (2003),

18 Benefits of the Modeling Tool Used in the Study SWMM is constantly being updated; so, use of a non-altered SWMM source code and ensures compatibility with future updates, and use of already developed models. Does not need to run inside of GIS, saving computer resources R was chosen for use in this study for development of a control module that could execute SWMM simulations repetitively, changing key parameters according to user needs. A basic version of RSWMM was enhanced by the authors for this study. The tool is capable of separating events, and includes a postprocessor to view duration curves and box plots of the output. Automatic calibration was added using a metaheuristic solver (NSGA-II). Sensitivity analysis was added to assess potential changes in inputs 9e.g., imperviousness, hydraulic conductivity) on treatment performance.

19 Examples of Outputs from Post-processor Tool

20 Assess performance of simulated BMPs under CC, evaluate strategies Link BMP cost spreadsheet calculation to RSWMM and optimizer Assess site-level, single- BMP performance (limited by calibration data) Extend modeling approach to Virginia Beach, incorporating Sea Level Rise Next Steps

21 Summary Modeled streamflow from the Difficult Run watershed for historical and projected precipitation and temperature. Single RCM-scenario found TSS, TN, and TP median increases of 7.6%, 7.1%, and 8.1%, respectively (P was statistically significant). Wide uncertainty bands for the 5 CMIP5/2 scenarios. RSWMM was useful in assisting this analysis, and can be used to assess BMP performance and failure modes. More monitoring and inspection data are needed to be able to assess watershed-wide.

22 Limitations Uncertainty in GCMs, including their inherent assumptions regarding future emission of greenhouse gases. Uncertainty in the representation of climatology at regional and local scales. Uncertainty of parameters required for input in development of hydrological models. Analysis of uncertainty propagating through the multiple models and processes is a recommendation. Using a single greenhouse emission scenario.

23 Acknowledgements Zach Easton of Virginia Tech Andrew Ross of Penn State Peter Steinberg of Continuum Ray Najjar, of Penn State University John Jastram of the U.S. Geological Survey Darold Burdick and Daniel Habete of Fairfax County Celso Ferreira of George Mason University National Science Foundation, Water Sustainability and Climate WSC-Category 1 Collaborative Project: Coupled Multi-Scale Economic, Hydrologic and Estuarine Modeling to assess Impacts of Climate Change on Water Quality Management, Grant #23032 Virginia Agricultural Experiment Station and the Hatch program of the National Institute of Food and Agriculture, U.S. Department of Agriculture Portions of this work have been published in Alamdari, N., Sample, D., Steinberg, P., Ross, A., & Easton, Z. (2017). Assessing the Effects of Climate Change on Water Quantity and Quality in an Urban Watershed Using a Calibrated Stormwater Model. Water 2017, Vol. 9, Page 464, 9(7),

24 Questions? David Sample, PhD, P.E., D. WRE Associate Professor Virginia Tech, Biological Systems Engineering Dept. Hampton Roads Agricultural Research and Extension Center 1444 Diamond Springs Rd Virginia Beach, VA