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Motivation

Performance Comparison of an Energy-Budget and the Temperature Index-Based (Snow-17) Snow Models at SNOTEL Stations. Fan Lei 1 , Victor Koren 2 , Fekadu Moreda 3 , and Michael Smith 2 1 Riverside Technology, inc 2 Hydrology Laboratory, Office of Hydrologic Development, NWS/NOAA 3 MHW, Inc.

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Motivation

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  1. Performance Comparison of an Energy-Budget and the Temperature Index-Based (Snow-17) Snow Models at SNOTEL Stations Fan Lei1, Victor Koren2, Fekadu Moreda3, and Michael Smith21Riverside Technology, inc2Hydrology Laboratory, Office of Hydrologic Development, NWS/NOAA 3MHW, Inc

  2. Motivation • Improve National Weather Service (NWS) water resources forecasts by using energy budget models of snow accumulation and melt.

  3. Background • Simple degree-day, conceptual lumped model is currently used to model snow accumulation and melt for NOAA/NWS operational river forecasting. • Energy-budget snowmelt models are physically more consistent and they require no (or much less) calibration. • Without reliable driving fields of meteorological data, the application of energy-budget snowmelt models is limited so far.  • Emerging meteorological data may lead to better performance of energy-budget snowmelt models. • NWS Office of Hydrologic Development (OHD) is conducting research on transitioning from conceptual to energy-budget snowmelt modeling to improve current operational river forecasts.

  4. Model Description (1) • SNOW-17 • A current operational snowmelt component in the NWS River Forecast System (NWSRFS), Developed by Anderson (1973,1976); • Uses air temperature as index of major snow processes; • Model performs well after calibration; • Being tested in distributed mode (HL-RDHM, Moreda et al., 2005)

  5. Model Description (2) Energy-Budget Snowmelt Model (EBSM) One layer model linked to multilayer soil/vegetation scheme (a version of Eta-LSS, Koren et al [1999]); Energy forcings are described by meteorological fields, including: surface air temperature, surface downward short wave flux, surface downward long wave flux, surface wind and surface humidity; Model does not include conceptual type parameters, no (or very little) calibration is needed.

  6. Test Basin Nevada Carson River Basin California • Considering snow data availability, Carson River Basin is selected as the test basin. Pacific Ocean Carson River Basin elevation (units: m)

  7. Data (1) SNOpack TELemetry (SNOTEL) ground measurements Hourly temperature, precipitation since 1997; Daily snow water equivalent. North American Regional Reanalysis (NARR) Based on National Centers for Environmental Prediction (NCEP)'s mesoscale Eta forecast model and Eta Data Assimilation System (EDAS); 3-hourly 2m air temp., 2m relative humidity, surface downward long wave radiation, 10m surface wind, precipitation; 0.375 degree (about 32km) resolution.

  8. Data (2) • GEWEX [Global Energy and Water Cycle Experiment] Continental Scale International Project (GCIP) and GEWEX America Prediction Project (GAPP) Surface Radiation Budget (SRB) Data • Re-processedhourly averaged surface downward short wave flux; • 1/8 degree (about 16 km) resolution. • North American Land Data Assimilation System (NLDAS) • Surface albedo, Leaf Area Index (LAI), (Greeness FRACtion) GFRAC, Soil type, vegetation type, etc; • 1/8 degree (about 16 km) resolution across North America; • Some of the parameters are adjusted in energy-budget snow melt model.

  9. Experiment Design • 1999 water year was selected for experiments, based on data availability and quality; • Snow Water Equivalent (SWE) was selected as main snow property; • Extracted NLDAS data are used as EBSM model parameters. • LAI and GFRAC are manually adjusted to match the sites land cover. • Extracted NARR data, SNOTEL ground measured Temp. & Precip. were applied as model inputs; • NARR Temp. are adjusted for elevation. • Both models are run to generate: • Point SWE simulations, • Basin SWE simulations (on going), • Basin outlet hydrographs (in plan).

  10. Experiment Design (2) AccumulatedPrecipitation from NARR and SNOTEL Precip-SNOTEL Precip-NARR

  11. Results: Observed and simulated SWE using Snotel precip. EBSM-TSnotel SN17-TSnotel SWE-Measured EBSM-T2m-NARR SN17-T2m-NARR

  12. EBSM Results: Observed and simulated SWE using NARR precip. EBSM-TSnotel SN17-TSnotel SWE-Measured EBSM-T2m-NARR SN17-T2m-NARR

  13. Discussion • The two models show reasonable agreement with each other and with the ground measurements, given reasonable temperature and precipitation data. • Both models are very sensitive to temperature especially during accumulation periods. • The experiments indicate that with the elevationadjustment, the temperature data interpolated from NARR may be used to drive the EBSM, although some model fitting may be needed. • Given the highly spatially-variable nature of precipitation in mountainous areas, special treatment is necessary or other more reliable data sources need to be explored.

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