Results, cont. Discussion Future Directions Study Watershed and Model Results Study Overview The Role of Roadside Ditch Networks in Short-Circuiting Natural Hydrologic Pathways: implications for nonpoint source pollution transport Soil & Water Research Group Cornell University SOIL & WATER LAB Cornell University Integrating "poor-man's" ensemble weather risk forecasts into spatial hydrologic modeling systems for small stream flooding and hazard assessment Fuka, D.R., Easton, Z.M., Buchanan, B.P., Walter, M.T., and Steenhuis, T.S Biological & Environmental Engineering Issues in Hydrologic Modeling Spatial Issues Introduction Hydrologic modeling at daily or sub daily resolution requires accurate precipitation data, which is particularly important when assessing flood risk in small runoff dominated watersheds. Precipitation gage data pose a variety of challenges, including the fact that they are located outside of many watersheds of interest and thus, may not accurately represent the actual rainfall in the watershed. We show that by integrating global forecast products from the atmospheric modeling community into the hydrologic models commonly used that we can circumvent some of the issues with point-of-measure rain gauges. Traditionally in hydrologic forecasting, a precipitation forecast is used from a nearby precipitation station on which the hydrologic model has been calibrated. In this study, we compare and contrast calibrating a watershed model (SWAT) using derived statistical representations of precipitation forecasts from "poor- man's" ensembles of raw gridded atmospheric models interpolated to the center of subbasins vs using the closest precipitation gage measurement and NWS forecasts for that gage location. In addition, we look at what scales and radii using direct gridded model output may introduce equal or less error to watershed modeling projects than using the closest station. Closest Station KALB Assuming nearest real- time reporting station for PPT. • 30km apart, PPT r 2 .23 • Mehta, V.K. et. al. 2004. Finding a set of stations to run with real-time at 30km is unlikely. This type of agreement would be a dream situation for us! US has: • ~60km spacing between real-time stations Ethiopia • 1 semi operational real-time weather station • From 2-4 recording sites; data in bad photo copies • 600km-1000km regularly spaced stations in region Data Quality Issues We want to develop “live” management tools. • Waste spreading risk areas. Has to be applicable world wide. Need consistent forcing variables (ppt, temp, etc). We would like to produce short term forecasts. • Agricultural waste spreading time windows. Gridded weather models have some skill, thus substituting short range forecasts centered in the watershed for weather station data should better represent conditions in the watershed than stations at some X distance away. Question: What is this distance? TIGGE Ensemble Archive. • ECMWF • NCEP Closest Stations. • KALB • KBGM • KMSV Variables. • PPT, as well as Max and Min Temperature. Date range: 4/1/2007 - 11/1/2008 • 0Z runs, model hours 06 though 30 ECMWF Ensemble. • .45deg Res. Global. • 50 perturbations. • Median from 50 member pt forecast. NCEP Global Ensemble Forecast System (GEFS). • 1.0deg Res. Global. • 20 perturbations. • Median from 20 member pt. forecast. 0 20 40 60 80 100 120 140 160 Pert1 Pert2 Pert3 Pert4 Pert5 Pert6 Pert7 Pert8 Pert9 Pert10 Pert11 Pert12 Pert13 Pert14 Pert15 Town Brook watershed. • Catskill Mountain range. • New York State. • 37km catchment of Cannonsiville basin. • Drains into West Branch of the Delaware River. • 500m to 1000m elevation. SWAT 2005, initialized with ArcSWAT. Landuse • ~Half Ag, Half Forest Slope • Half below 17% 3 Sub-basins • 44 HRUs Summer 2007 0 0.5 1 1.5 2 2.5 3 4/29/07 5/6/07 5/13/07 5/20/07 5/27/07 6/3/07 6/10/07 6/17/07 6/24/07 7/1/07 7/8/07 7/15/07 7/22/07 7/29/07 8/5/07 Date Flow KALB ECMWF Ens NCEP GEFS Comb. Mea Summer 2007 KALB 0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0 0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2 Measured Summer 2007 ECMWF Ens y = 1.139x R 2 = 0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0 0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2 Measured Summer 2007 NCEP GEFS y = 0.7452x R 2 = 0.6118 0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0 0.00 0.20 0.40 0.60 0.80 1.00 1.20 1.40 1.60 1.80 2.00 Measured Summer 2007 NCEP ECMWF Combined Mean y = 1.0294x 0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0 0.00 0.20 0.40 0.60 0.80 1.00 1.20 1.40 1.60 1.80 2.00 Measured Goals Hypothesis Data Overview Gridded Forecast Models from TIGGE It appears there is validity to using short term forecast archives in place of weather station data to force watershed models. The distance to the where the model skill produces the same accuracy as the weather stations is closer than we thought. We need to further investigate the watershed calibrations independent for individual forcing data sets. We need to be careful when applying this method, and should use statistical summaries of the ensembles sparingly. Variables from the atmospheric models are physically linked, and we should likely keep them that way. Obligatory Hydrograph Hr 6-30 Forecasts Combined Forecast Continuing the study in a watershed with larger number of near-by met stations to determine true value of X. Repeating the study in Lake Tana Basin, where current Met records are hard to obtain. Studying effect of additional variables available in models that are not available at most met stations. Acknowledgements and Interesting Side Points Authors would like to acknowledge ECMWF, who supplied the data for this study via the TIGGE online user interface. http://tigge-portal.ecmwf.int/ All computation for this study was performed in the publicly accessible Amazon EC2 grid. Authors are looking for collaborators who might wish to run similar studys without the pain of extracting the datasets themselves.