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Near Real-Time Runoff Estimation Using Spatially Distributed Radar

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Near Real-Time Runoff Estimation Using Spatially Distributed Radar NearNear RealReal--TimeTime RunoffRunoff EstimationEstimation UsingUsing SpatiallySpatially DistributedDistributed RadarRadar RainfallRainfall DataData Jennifer Hadley 22 April 2003 IntroductionIntroduction n Water availability has become a major issue in Texas in the last several years, and the population is expected to double in the next 50 years (Texas Water Development Board, 2000). n Thus, there is a need for real-time weather data processing and hydrologic modeling which can provide information useful for planning, flood and drought mitigation, reservoir operation, and watershed and water resource management practices. n Traditionally, hydrologic models have used rain gauge networks which are generally sparse and insufficient to capture the spatial variability across large watersheds, and are unable to provide data in real-time. IntroductionIntroduction n Obtaining accurate rainfall data, in particular, is extremely important in hydrologic modeling because rainfall is the driving force in the hydrologic process. n NEXRAD radar can provide data for planning and management with spatial and temporal variability in real-time over large areas. PurposePurpose The objective of this study is to evaluate several variations of the Natural Resource Conservation Service (NRCS – formerly known as the Soil Conservation Service – SCS) curve number (CN) method for estimating near real-time runoff, using high resolution radar rainfall data for watersheds in various agro-climatic regions of Texas. IssuesIssues withwith CNCN AssignmentAssignment n Hawkins (1998) and Hawkins and Woodward (2002) state that CN tables should be used as guidelines and that actual CNs should be determined based on local and regional data. n Price (1998) determined that CN could be variable due to seasonal changes. n Ponce and Hawkins (1996) stated that values for initial abstractions (Ia) could be interpreted as a regional parameter to improve runoff estimates. n According to Hawkins et al. (2002) and Jiang (2001) an Ia value of 0.05 was generally a better fit than a value of 0.2. In 252 of 307 cases, a higher r2 was produced with the 0.05 value. DatasetsDatasets n LULC: 1992 USGS National Land Cover Data (NLCD), 30m resolution n SOILS: USDA-NRCS State Soil Geographic (STATSGO) Database, 200m resolution n Weather: n NEXRAD – West Gulf River Forecasting Center (WGRFC) of the National Weather Service (NWS), 1999-2001, 4km resolution n Rain gauge – National Climatic Data Center (NCDC) of the NWS n Stream Flow: USGS stream flow data will be downloaded and passed through a filter program obtained from the SWAT website OBJECTIVESOBJECTIVES n Select study areas based on the size of the watershed, landuse, soil hydrologic group, rainfall pattern/agro-climatic region, and stream gauge location; then evaluate several variations of the NRCS curve number method in the selected study areas, with NEXRAD radar and long-term rain gauge rainfall data. OBJECTIVESOBJECTIVES n Run all variations for all study areas with CN grids at several different resolutions to account for spatial variability. n Compare the modeled runoff for NEXRAD and rain gauge data with observed stream gauge data to determine the most appropriate method for estimating runoff in various regions of Texas. Size,Size, StreamStream GaugeGauge Location,Location, && LanduseLanduse 1. No existing reservoirs. 2. Stream gauge at watershed outlet. 3. Stream gauge has sufficient historical data (03/01/1956 – 09/30/2001). 4. Drainage is approximately 683 mi2. 5. Subwatershed is delineated according to the 4km x 4km NEXRAD grid. 6. Landuse is determined to be 73% rangeland. WeatherWeather StationStation LocationsLocations The nearest rain gauge information is used for each 4km x 4km grid cell within the subwatershed boundary. NEXRAD stations are known for each cell. NRCSNRCS CNCN MethodMethod VariationsVariations /* Function: Calculates total runoff for X number of days using the basic SCS /* CN method with Texas curve numbers and 0.2S, 0.1S, or 0.05S, based on Julian Day. /* Produces a summary table of runoff and rainfall inputs for further analysis. &s file1 = c:\research\runs\rnge_%year%.txt Writes runoff and rainfall outputs to &s file2 = c:\research\runs\rnge_RF_%year%.txt text files for each year. GRID SETCELL rngbndry Sets resolution SETWINDOW rngbndry Generates initial abstraction and initial = 25.4 * ( (1000 / (cn1_con4k)) - 10 ) rainfall = shapegrid (%weather%, %date%, 4000) rainfall grids if (%julian% <= 115) then runoff = con(rainfall > (0.2 * initial), Sqr((rainfall) - 0.2 * (initial)) / ((rainfall) + 0.8 * (initial)), 0) else if (%julian% > 115 && %julian% <= 273) then runoff = con(rainfall > (0.1 * initial), Sqr((rainfall) - 0.1 * (initial)) / ((rainfall) + 0.9 * (initial)), 0) else if (%julian% > 273) then runoff = con(rainfall > (0.05 * initial), Sqr((rainfall) - 0.05 * (initial)) / ((rainfall) + 0.95 * (initial)), 0) endif final_runoff = ZONALSUM (rngbndry, runoff) total_rain = ZONALSUM (rngbndry, rainfall) Runs CN method variations Summarizes runoff based on Julian Day and rainfall by zone FlowFlow (mm) (mm) 0.5 1.5 2.5 0.5 1.5 2.5 0 1 2 3 0 1 2 3 1/1/19991/1/1999 Runoff /RainfallOutputs 1/8/19991/8/1999 Runoff /RainfallOutputs 1/15/19991/15/1999 1/22/19991/22/1999 1/29/19991/29/1999 2/5/19992/5/1999 Estimated Runoff 2/12/1999 Estimated Runoff 2/12/1999 2/19/19992/19/1999 2/26/19992/26/1999 3/5/19993/5/1999 3/12/19993/12/1999 3/19/19993/19/1999 NEXRAD Rainfall NEXRAD Rainfall 3/26/19993/26/1999 4/2/19994/2/1999 4/9/19994/9/1999 4/16/19994/16/1999 4/23/19994/23/1999 4/30/19994/30/1999 5/7/19995/7/1999 5/14/19995/14/1999 5/21/19995/21/1999 5/28/19995/28/1999 40 35 30 25 20 15 10 5 0 40 35 30 25 20 15 10 5 0 Flow (cfs) Flow (cfs) 0.00E+00 5.00E+02 1.00E+03 1.50E+03 2.00E+03 2.50E+03 3.00E+03 3.50E+03 4.00E+03 0.00E+00 5.00E+02 1.00E+03 1.50E+03 2.00E+03 2.50E+03 3.00E+03 3.50E+03 4.00E+03 1/1/1970 1/1/1970 1/8/1970 1/8/1970 1/15/1970 1/15/1970 1/22/1970 1/22/1970 Base FlowFilter Base FlowFilter 1/29/1970 1/29/1970 2/5/1970 2/5/1970 2/12/1970 2/12/1970 Stream Flow Stream Flow 2/19/1970 2/19/1970 2/26/1970 2/26/1970 3/5/1970 3/5/1970 3/12/1970 Filter Pass2 3/12/1970 Filter Pass2 3/19/1970 3/19/1970 3/26/1970 3/26/1970 4/2/1970 4/2/1970 Runoff Runoff 4/9/1970 4/9/1970 4/16/1970 4/16/1970 4/23/1970 4/23/1970 4/30/1970 4/30/1970 5/7/1970 5/7/1970 5/14/1970 5/14/1970 5/21/1970 5/21/1970 5/28/1970 5/28/1970 FurtherFurther AnalysisAnalysis n Repeat this process for 8-10 subwatersheds throughout the state at various resolutions. n Complete estimation efficiency and regression analysis for each output dataset. n Make recommendations for CN variation to be used in each agro-climatic region. PotentialPotential SourcesSources ofof ErrorError // LimitationsLimitations n NLCD dataset is from 1992, which may not reflect the true land use for the study periods. n Point source discharge to streams, especially non-daily or storm water discharge, could increase runoff portion of stream flow unpredictably. n Runoff data displays characteristics of storm intensity, where as rainfall data is daily and does not account for intensity. PotentialPotential SolutionsSolutions n Remove upper and lower 10% of values to remove influence from outlying data points. n Plot both natural and ordered pairs for runoff estimates… in ordering the dataset, the P and Q values should have approximately the same return time. In other words, the frequency of these values will be matched..
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