1. Performance Comparison of an Energy- Budget and the Temperature Index-Based (Snow-17) Snow Models at SNOTEL Stations Fan Lei, Victor Koren 2, Fekadu Moreda 3, and Michael Smith 2 1 Riverside Technology, inc 2 Hydrology Laboratory, Office of Hydrologic Development, NWS/NOAA 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 3 MHW, 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.  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),A current operational snowmelt component in the NWS River Forecast System (NWSRFS), Developed by Anderson (1973,1976); Developed by Anderson (1973,1976); Uses air temperature as index of major snow processes;Uses air temperature as index of major snow processes; Model performs well after calibration;Model performs well after calibration; Being tested in distributed mode (HL-RDHM, Moreda et al., 2005)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]);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;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.Model does not include conceptual type parameters, no (or very little) calibration is needed.

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

7. Data (1)  SNOpack TELemetry (SNOTEL) ground measurements Hourly temperature, precipitation since 1997; Hourly temperature, precipitation since 1997; Daily snow water equivalent. 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); 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; 3-hourly 2m air temp., 2m relative humidity, surface downward long wave radiation, 10m surface wind, precipitation; 0.375 degree (about 32km) resolution. 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-processed hourly averaged surface downward short wave flux; Re-processed hourly averaged surface downward short wave flux; 1/8 degree (about 16 km) resolution. 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; Surface albedo, Leaf Area Index (LAI), (Greeness FRACtion) GFRAC, Soil type, vegetation type, etc; 1/8 degree (about 16 km) resolution across North America; 1/8 degree (about 16 km) resolution across North America; Some of the parameters are adjusted in energy-budget snow melt model. 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.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.NARR Temp. are adjusted for elevation.  Both models are run to generate: Point SWE simulations, Point SWE simulations, Basin SWE simulations (on going), Basin SWE simulations (on going), Basin outlet hydrographs (in plan). Basin outlet hydrographs (in plan).

10. Accumulated Precipitation from NARR and SNOTEL Experiment Design (2) Precip-NARRPrecip-SNOTEL

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

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

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 elevation adjustment, 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.

14. Future Plans  Lengthen analysis period to confirm initial results.  Investigate sensitivity of EBSM to meteorological forcings.  Conduct error propagation tests of EBSM.  Expand experiments to distributed version at basin scales.  Develop recommendations on potential use of available and emerging meteorological data for operational EBSM application. This work is supported under NWS AHPS Contract DG133W-03-CQ-0021 with Riverside Technology, inc.