Continental-Scale Estimates of Runoff Using Future Climate Storm Events

Transcription

1 Continental-Scale Estimates of Runoff Using Future Climate Storm Events 2017 AWRA Summer Specialty Conference: Climate Change Solutions Tysons, VA June 25-28, 2017 Peter Cada, Tetra Tech Megan Mehaffey and Anne Neale, USEPA ORD NERL, EnviroAtlas

2 The Issue Recent storms & runoff events have had serious impacts to both natural ecosystems and human infrastructure. Pocahontas, AR Black River - May 2017 Climate change is expected to change patterns of both precipitation & runoff. For both planning/research changing storm intensities and large-scale runoff estimates are needed. Fenton, MO Meramec River May

3 Methods - Overview Estimate Runoff with SCS-Curve Number (CN) method Use existing CONUS precipitation event data and downscaled climate projection data. Why this scale? Support the EnviroAtlas web-based platform allowing non-technical users to understand the interactions between people and the environment. Also, provides large-scale data sets and perspectives to decision-makers, other researchers, analysts, and educators.

4 Methods - Detailed SCS-CN CN Method (USDA, 1986) = ( ) where, mm/hr Modified from Shi, et al, 2009, Catena Q surf R I a S Continuing Abstraction - related to retention parameter (S) time = accumulated runoff or rainfall excess in millimeters (mm) = rainfall depth in mm = initial abstractions in mm (surface storage, interception, and infiltration). = retention parameter in mm = where, 25.4 = conversion from inches to mm CN = Curve Number, a function of soil characteristics, land use and land cover, and antecedent soil moisture conditions

5 CONUS Curve Number (CN) Grid Creation Considered Land Cover/Use (NLCD 2006 CDL 2010) Condenses 127 unique land cover/use classes into 45 CN classes SWAT inputs used for all Land Covers except Urban TR-55 for Urban-Type Land Uses Methods - Detailed Hydrologic Soil Group (HSG) from SSURGO

6 Precipitation (Rainfall Depth) Grid Creation NOAA-2, NOAA-14, and Texas USGS county-scale data Methods - Detailed

7 Methods - Detailed Precipitation Grid Creation NOAA-2, NOAA-14, and Texas USGS county-scale data

8 Future Precipitation Grid Creation Methods - Detailed NASA Earth Exchange (NEX) average annual temperatures (IPCC AR5 s RCP 8.5) Historic ( ) Future (2090- Clausius-Clapeyron (CC) equation & ideal gas law Indicates maximum potential precipitation based on future temperature Assumes maximum atmospheric saturation So not really going to work well in arid regions Use ideal gas law and CC equation to create a scalar grid to estimate potential maximum intensity of future precipitation. (Kao and Ganguly, 2011, Journal of Geophysical Research) Scalar Value =.... where, T 2 is the future, predicted temperature T 1 is historic average temperature

9 Methods - Detailed Future Precipitation Grid Creation Scalar grid outputs from CC and ideal gas law methodology Percent Increase from Existing Precipitation (Scalar Values * 100) 22% 40% 60%

10 Methods/Results Future Precipitation Grid Creation Scalar grid applied to NOAA 2, NOAA-14, and Texas existing precip. data Recall that Existing ranged from 23.0 down to 1.4

11 Results HUC-12 Scale Summary *** SCS-CN method does not include consideration of annual snow pack, snow melt events, subsurface flow contributions/withdrawals, other water management activities Percent Increase in Runoff (Historic to Future) 100-yr, 24-hour Storm Event 0% 60% 100+ Percent Change in Runoff Response at the HUC-12 Scale 100yr, 24hour Statistic Event Minimum th Percentile Median th Percentile Maximum Average Standard Deviation 15.60

12 Scalar Comparison to Local, Statistically-Based Studies Tetra Tech Project - Localized GEV and Equidistant Quantile Mapping (EQM) Method Grand Rapids, Michigan: 1.50 (2090- CC & IGL Method (2050) GEV and EQM Method Dauphin County, PA 1.43 ( (2085)

13 Tetra Tech Project - Localized GEV and Equidistant Quantile Mapping Method Portland, Oregon 1.34 ( ( ) Scalar Comparison to Local, Statistically-Based Studies St. Paul, Minnesota 1.53 ( (2050) Zhu, et al., 2013 Climatic Change Bayesian Model Averaging Grand Rapids, Michigan: 1.50 ( (2050) Albany, New York 1.48 ( ( ) Las Vegas, Nevada 1.38 ( ( ) Dauphin County, PA 1.43 ( (2085) Burbank, California 1.31 ( ( ) Denver, Colorado 1.43 ( ( ) Dallas, Texas 1.37 ( ( ) Orlando, Florida 1.27 ( ( )

14 Limitations The SCS-CN method does not include consideration of annual snow pack, snow melt events, subsurface flow contributions/withdrawals, other water management activities. The Clausius-Clapeyron equation & ideal gas law estimate maximum potential precipitation based on future temperature Most applicable to convective storm events where maximum atmospheric saturation drives storm event intensities and totals. No decrease seen under future climate condition estimates. Localized modeling and conditions may estimate that some areas will have a decrease.

15 Conclusions Landscape-scale estimate of runoff response changes can guide decision-makers and continued research. Great Lakes and Rocky Mountain regions show the highest potential for increases. Planning in these regions should take note. Both water resource and general infrastructure.

16 Funding for this work was provided by U.S. EPA, Office of Research and Development, National Exposure Research Lab for support of the EnviroAtlas Project. The views expressed in this presentation represent those of the authors and do not necessarily reflect the views or policies of the U.S. Environmental Protection Agency. Contact Related Information EPA EnviroAtlas Project URL - Acknowledgments