Gridded Rainfall Estimation for Distributed Modeling in Western Mountainous Areas 1. Introduction Estimation of precipitation in mountainous areas continues to be problematic. It is well known that radar-based methods are limited due to beam blockage. In these areas, in order to run a distributed model that accounts for spatially variable precipitation, we have generated hourly gridded rainfall estimates from gauge observations. These estimates will be used as basic data sets to support the second phase of the NWS-sponsored Distributed Hydrologic Model Intercomparison Project (DMIP 2). One of the major foci of DMIP 2 is to better understand the modeling and data issues in western mountainous areas in order to provide better water resources products and services to the Nation. In light of these goals, we perform several analyses to evaluate the gridded estimates for DMIP 2 experiments. These gridded inputs are used to generate mean areal precipitation (MAPX) time series for comparison to the traditional mean areal precipitation (MAP) time series derived by the NWS California-Nevada River Forecast Center (CNRFC) for model calibration. We use two of the DMIP 2 basins in California and Nevada: the North Fork of the American River (catchment area 885 sq. km) and the East Fork of the Carson River (catchment area 922 sq. km) as test areas. For operational forecasting, the basins are sub-divided into elevation zones. Analyses are preformed for a wider rectangular area (Figure 1) and for subbasins. For the rectangular area we compared PRISM and gridded products based on annual precipitation, monthly time series and regression of monthly time series. For the subbasins, we compared six-hourly mean areal precipitation time series derived from gridded products to CNRFC MAP time series. The analyses include typical-year monthly comparisons, regression fit, and overall statistics. Fekadu Moreda 1,3, Shuzheng Cong 1,2,, John Schaake 1,4, Michael Smith 1 1 OHD, 2 UCAR, 3 MHW, 4 RTi, Inc. Office of Hydrologic Development, NOAA National Weather Service 1325 East-West Highway, Silver Spring, MD 20910, U.S.A Study Area and Data 2. Study Area and Data References AGU 2006 Spring Meeting May , Baltimore, MD 3. Methods 4. Results - For the Rectangular Area – Test with PRISM data 4. Results Cont’d. Comparison of Precipitation Averages over Operational Scale Basins 5. Summary RiverSubbasinElevation Range (m) HRAP pixels Area (km 2 ) American River North ForkAMERUP American River North ForkAMERLW Carson River above Markleeville MARKUP Carson River above Markleeville MARKLW Carson River at GardnervilleGARDUP Carson River at GardnervilleGARDLW Table 1. Subdivision of the basins based on elevation zones H Hourly gages D Daily gages SD SNOTEL gages Center of HRAP grids Figure 3. Annual precipitation derived from grids matches the annual PRISM for the entire rectangular box Figure 4. Monthly precipitation time series of the gridded precipitation match the monthly PRISM time series for the rectangular area. Figure 5. As expected, high correlation exists between the monthly gridded precipitation and PRISM PGPG d1d1 d4d4 d2d2 d3d3 H D D Deriving Hourly Gridded Precipitation (1)Derive precipitation estimates using three data sources for the period of : 1) hourly cooperative observer (coop) gauges, 2) daily total coop gauges, and 3) SNOw pack TELemetry (SNOTEL) daily precipitation gauges. (2)The daily values are disaggregated to hourly using the nearest hourly gauge values. (3)The hourly values, expressed as fraction of normal, are then interpolated to approximately 4km Hydrologic Rainfall Analysis Project (HRAP) (Greene and Hudlow, 1982) grids using an inverse-distance method shown in Figure 2. (4)Parameter-elevation Regressions on Independent Slopes Model (PRISM) ( / ) monthly precipitation climatology grids are used to compute fractions of normal at gage locations prior to the inverse distance interpolation and to convert interpolated fractions of normal to precipitation amounts at each grid point. Deriving Six-hourly Mean Areal Precipitation (1)From the CNRFC, we obtained six-hourly MAP time series for each basin. The two basins are decomposed into subbasins based on elevation differences (Table 1). The CNRFC MAP time series were derived using procedures developed by Anderson (2002) employing pre-determined weights. (2) To derive an MAPX time series based on the gridded precipitation: Clip the subbasin shapefiles of the elevation zones to obtain HRAP points (center) in the subbasins Create list of HRAP points within a subbasin. For each of hourly gridded field of precipitation, obtain hourly average precipitation for the subbasins by averaging the value of all pixels in the subbasin The one hourly time series is then cumulated to obtain six-hourly time series Table 2. Overall statistics of the MAP and MAPX time series for period of Figure 8. Regression fit of six-hourly precipitation time series of the three basins: higher elevation zones Figure 9. Regression fit of six-hourly precipitation time series of the three basins: lower elevation zones Figure 6. Typical-year monthly comparison of the MAP and MAPX for higher elevation zone (North Fork American River) Figure 7. Typical-year monthly comparison of the MAP and MAPX for lower elevation zone (North Fork American American River) Figure 1. The study area covers the above rectangular area which encompasses the American and Carson River Basins Anderson, E.A., Calibration of Conceptual Hydrologic Models for Use in River Forecasting. Greene, D. R., Hudlow, M.D., Hydrometeorological grid mapping procedures, International Symposium on Hydrometeorology, American Water Resources Associations, Denver, Colorado., June SD (1) Overall differences between the MAP and MAPX are very small for the NF American River for both subbasins: higher and lower elevation zones. For the Carson River basins the overall difference is up to 10%. The difference tends to be higher with less correlation when the subbasin size is small. For example, the lower Carson River basin GRDNLW exhibits a 10% bias between the CNRFC MAP and the MAPX time series. (2) The comparison of the areal averaged gridded products validates the use of the generated data for distributed model simulations in DMIP 2. (3) The effect of the gridded input in terms of flow generation will be studied using several models in DMIP 2. Figure 2. Illustration of inverse distance weights MAPX (mm) MAP (mm) H23A