In many hydrologic models, determination of precipitation type is indexed to surface air temperature, and the selection of the maximum snow and minimum rain thresholds are chosen empirically, by calibration or using published values (e.g. U.S. Army Corps of Engineers, 1956), or are selected arbitrarily (e.g., Bowling et al., 2003). Some models use one fixed temperature as a division between rain and snow, rather than using a range with mixed (frozen and solid) precipitation (e.g. Bicknell et al., 2002). The NWSRFS implementation of the Sacramento model (Office of Hydrologic Development, 2002) is rare in allowing the incorporation of freezing level data. The following questions take advantage of the availability of radar-based freezing level observations in the study region to look for opportunities for improving streamflow simulations in regions of complex topography and strong orographic influence: 1)How well do surface temperature-based methods work for determining whether precipitation is falling as rain, snow, or a mixture? 2)Does the radar-detected 0°C level differ substantially from the air-temperature-based method? 3)Can the observed radar-based 0°C level be used to improve streamflow simulations during the events studied during IMPROVE-2? ABSTRACT One of the greatest challenges in hydrologic modeling in areas with significant orographic influences is accurate simulation of the precipitation fields, since this drives the streamflow response. In the northwest United States, where most of the precipitation occurs during the cool season, another major factor in streamflow simulation is the determination of whether the precipitation is falling as rain or snow, since these strongly influence the timing of the resulting runoff. The partitioning of precipitation in distributed hydrologic models into rain, snow, or a mixture of the two is often based on surface air temperature, since this is included in the station observation records that provide the precipitation and other meteorological data used to force the model. This study examines the adequacy for hydrologic modeling of using surface air temperature to determine this partitioning of rain and snow in the Santiam River basin, Oregon. The western slopes of the Cascade mountain range in Oregon, specifically an area including the South Fork of the Santiam River, was the geographical focus for the second phase of the research effort dubbed Improvement of Microphysical PaRameterization through Observational Verification Experiment (IMPROVE-2). This intensive field observation campaign was carried out from 26 November through 22 December 2001, with measurements used to perform comprehensive verification of cloud and precipitation microphysical processes parameterized in mesoscale models. Included in the suite of IMPROVE-2 observations were both scanning and vertically pointing radar. While scanning radar observations in areas of complex terrain, such as the western Cascades, are problematic due to ground clutter and beam blocking, vertically pointing radar does not suffer from this. We show that, by replacing the surface air temperature-based algorithm in a distributed hydrologic model with a freezing level determined with S-band radar supplemented by other observations, significant improvement in the simulated hydrograph can be obtained. IMPROVE-2 Overview and River Basin for Study 2 Meteorology in South Santiam Basin during IMPROVE Observation Period 3 REFERENCES Bicknell, B.R., J.C. Imhoff, J.L. Kittle Jr., A.S. Donigian, Jr. and R.C. Johanson Hydrological Simulation Program -- FORTRAN, User's Manual for Version 11. EPA/600/R-97/080. U.S. EPA, National Exposure Research Laboratory, Athens, GA. Bowling, L.C., D.P. Lettenmaier, B. Nijssen, L.P. Graham, et al. 2003, Simulation of high latitude hydrological processes in the Torne-Kalix basin: PILPS Phase 2(e) 1: Experiment description and summary intercomparisons, Journal of Global and Planetary Change, 38(1-2), Houze, R.A. and S. Medina, 2002: comparison of orographic precipitation in MAP and IMPROVE II, In: Preprints: 10 th Conference on mountain meteorology and MAP meeting, Park City, UT, June, Office of Hydrologic Development, 2002: National Weather Service Forecast System Model User Manual, Section 3.3-RSNWELEV, National Weather Service. U.S. Army Corps of Engineers, 1956: Summary Report of the snow investigations – Snow Hydrology, North Pacific Division, Portland, OR, June Wigmosta, M.S., L.W. Vail, and D.P. Lettenmaier, 1994, A distributed hydrology-soil-vegetation model for complex terrain, Water Resour. Res. 30, Summary 4 Effect of Precipitation Type Determination on Hydrologic Simulations Partitioning of precipitation into rain and snow in distributed hydrologic simulations in the Western Cascades, Oregon, USA. Edwin P. Maurer 1, Jasmine Cetrone 2, Socorro Medina 2, and Clifford Mass 2 1. Civil Engineering Department, Santa Clara University, Santa Clara, CA Department of Atmospheric Sciences, University of Washington, Seattle, WA AMS Annual Meeting 2004 Poster Session 3: Poster P3.5 IMPROVE is aimed at comprehensively checking and improving the parameterization schemes currently implemented in the Penn State/NCAR Mesoscale Model (MM5), a mesoscale model that has been extensively used for both research and operational forecasting. The primary goal of IMPROVE is to utilize quantitative measurements of cloud microphysical parameters in a variety of mesoscale features to improve the representation of cloud and precipitation processes in mesoscale models. The IMPROVE-2 field study, focused on orographic clouds and precipitation in the Oregon Cascade Mountains, was conducted 26 November through 22 December For more details, see: The IMPROVE-2 domain overlaps largely with the South Santiam River basin, shown here, which has a total basin area of 1,440 km 2. This catchment provides a spatial integrator for the observed and modeled precipitation, and a valuable validation tool for assessing precipitation fields simulated by forecast models. Focus of This Study 1 The South Santiam basin, during the IMPROVE-2 period, included many observational assets. The subset of observations used in this study included a vertically pointing S-Band radar, daily and hourly cooperative observer stations, SNOTEL stations, precipitation gauges installed for this study (labeled “IMPROVE”), and USGS streamflow gauges, shown on this map. Sample reflectivity data from the NOAA/ETL S- band vertically pointing radar. The data shown are for 2215 UTC 13 December UTC 14 December Note the bright band in red, the top of which is typically associated with 0°C temperatures. (Houze and Medina, 2002) Vertically-pointing S-Band Radar Daily Cooperative Obs. Hourly Cooperative Obs. SNOTEL Station IMPROVE Precipitation USGS Stream Gauge JUMP_OFF_JOELITTLE_MEADOWS Net decrease in snow water equivalent (swe) Net accumulation of snow water equivalent Observations show: Surface air temperature is not a good indicator of whether precipitation is falling as rain or snow. This is especially evident for lower elevation JUMP_OFF_JOE site, closer to valley bottom, where there is essentially no correlation between air temperature during a precipitation even and whether snow is accumulating or melting. Even at LITTLE_MEADOWS, closer to the ridge, at air temperatures between 2° and 4°C during this period, air temperature is a poor indicator of precipitation type. There is a wide discrepancy between the 2 locations in the air surface temperatures associated with both rain and snow, indicating the use of one index for the basin could be problematic. Examination of P/T/SWE relationships at 2 SNOTEL sites in basin Observed 0° Level Based on Bright Band Identification The average mb wind speed for the entire IMPROVE-2 period is 13.5 ms -1, thus applying the 0° level at the S-Band location to the entire basin introduces at most a 1 hour timing error for any point in the basin on average. Elevation and surface air temperature at each pixel (interpolated from observations with a lapse rate of -5.5 °C/km) were combined with the radar- observed freezing level to illustrate the variability in surface temperatures associated with rain and snow. At each time step, the radar detected 0° level was projected across the basin. The surface air temperatures for pixels with elevations Minimum during IMPROVE-2 period Maximum during IMPROVE-2 period Average during IMPROVE-2 period T min (Rain) T max (Snow) This example, from LITTLE_MEADOWS, highlights 2 periods where the air temperature indexing and radar 0°C levels can give different results. Here two periods are highlighted where the freezing level is well above the station elevation (indicating rain), while the surface air temperature is below zero (indicating at least partial snow). Given the above differences in air temperature-based versus radar-based discrimination of rain and snow, we investigate the sensitivity of streamflow simulations in IMPROVE to incorporation of freezing level data, using the DHSVM model (Wigmosta et al., 1994), modified to ingest freezing level data. Trial #1 – Using air temperature-based indexing of 2°C T max (snow) and 0°C T min (rain) (following Bowling, et al., 2003; Office of Hydrologic Development, 2002) Trial #2 – Using air temperature-based indexing, as in Trial #1, but with average vales inferred from radar-detected level (see box 3 above): -2.4°C T max (snow) and -4.9°C T min (rain) Trial #3 – Using elevation indexing: Observed 0° Level Based on Bright Band Identification (see plot in Box 3 above), varies with time: Gauge Gauge Trial #14357 Trial #240 Trial #339 RMSE for peak events (observed flows > 60 m 3 /s) Based on surface observations of air temperature and snow accumulation, the surface air temperature is not a strong indicator of accumulation or melt of snowpack (or whether precipitation type is rain or snow). The first method of partitioning precipitation into rain and snow used radar detected 0° elevation as that below which snow begins to melt; 300 m below this elevation marked that below which all precipitation is rain. A second method combined radar data with the elevations and surface air temperatures at each pixel and time step in the basin. Populations of T min (rain) and T max (snow) (see Box 3 for definitions) were derived, the average of which provided temperature indices for partitioning precipitation into rain, snow, or a mixture. The use of the second method in a hydrologic model (see Trial 2, Box 4) produced measurable improvements in simulated peak flows over using values of T min (rain) and T max (snow) from literature. Using the first method (see Trial 3, Box 4) produced further improvements in the simulation of one flood peak, but the incremental improvement over using the second method was small. At two locations where snow was observed, the simulations by the hydrologic model were better with the first method than the second, though local effects complicate the accurate simulation of snow at one site. Trial #3 Trial #2 Trial #1 Using the radar detected 0°C level, either directly (Trial #3) or combining it with the basin DEM and surface air temperatures to estimate a T max (snow) and T min (rain) (Trial #2) produces simulated improved hydrographs compared to literature-based T max (snow) and T min (rain) values (Trial #1), as reflected in the RMSE values above. Trial #2 achieves most of the decrease in RMSE, implying that “calibrating” T max (snow) and T min (rain) using radar data may be possible and beneficial to hydrologic simulations. The simulation of snow at the 2 SNOTEL sites in the basin is more problematic. The use of the variable radar-detected freezing level (trial #3) improves the snow water equivalent simulations at these points compared to trial #1, while trial #2 is not a consistent improvement. None of the trials could reproduce the complete removal of snow at the Jump_off_Joe site, indicating that other local factors are important. JUMP_OFF_JOELITTLE_MEADOWS within 10m of this 0° level, were taken as samples of the minimum surface air temperature at which any rain occurs, T min (rain). At a distance 300 meters below the 0° level all melt is assumed complete, and pixels with elevations close to this are representative of the maximum surface air temperature at which snow occurs, T max (snow). A linear mixture is assumed between these levels. Surface Air Temperatures for Snow/Rain Inferred from Radar 0°C Level