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Last modified
8/16/2009 2:33:35 PM
Creation date
8/21/2008 8:15:48 AM
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Board Meetings
Board Meeting Date
5/20/2008
Description
CWCB Director's Report
Board Meetings - Doc Type
Memo
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COLORADO ENHANCED SNOWPACK ASSESSMENT <br />"NOAA-SNODAS adaptation to Colorado's Watersheds " <br />May 2008 <br />Joe Busto & Michelle Garrison, Colorado Water Conservation Board <br />Rich Stodt, U.S. Bureau of Reclamation <br />Jay Day, Riverside Technology, inc. <br />T~TF NFF11 <br />Snowpack runoff is an extremely important part of the hydrologic cycle in Colorado. It constitutes 80% <br />of reservoir storage and is responsible for occasional springtime flooding. In order to predict runoff <br />quantitatively, we need to know characteristics of Snowpack and answer questions like: How much snow water <br />equivalent (SWE) does it contain? What is its spatial and elevation distribution? What are the effects of <br />weather, winds, sun, climate, and soils on the snowmelt, etc.? A limitation in forecasts has been a lack of detail <br />of the spatial and temporal extent of SWE in the mountains. <br />The CWCB, USBR, and RTi are working together to develop enhanced Snowpack data for forecasts and <br />assessments. There is always a temporal gap between the April 1 Snowpack and the reservoir fill/spill cycle <br />during the summer, and this gap causes uncertainty in estimating reservoir inflows. The goal will be to see if <br />there is value added to River Basin Forecast Center and NRCS products <br />THE SNOW DATA ASSIMILATION SYSTEM (SNODAS) <br />The Snow Data Assimilation System (SNODAS) is an experimental model recently developed by the <br />National Operational Hydrologic Remote Sensing Center (NOHRSC). Information about it can be found at <br />httpa/www.nohrsc.nws.gov/technology. SNODAS incorporates SNOTEL and snow course data, satellite data, <br />radar data and a numerical weather model to <br />estimate Snowpack spatially at a 1-km resolution Example SNODAS manning for Western U.S. <br />for the entire United States. Outputs include <br />snow depth, SWE, snowmelt at base of <br />Snowpack, and sublimation. Combining all the <br />different types of data input helps reduce errors <br />and uncertainties related to each component data <br />set and provides spatially continuous rather than <br />point data, ultimately providing an improved <br />assessment of Snowpack. <br />SNODAS integrates remotely sensed, <br />observed, and modeled data sets into the "best <br />estimate" of various Snowpack state variables. <br />SNODAS includes a physically based, near real- <br />time, energy-and-mass-balanced, spatially <br />uncoupled, vertically distributed, multi-layer <br />snow model. Downscaled analysis and forecast <br />fields from a mesoscale numerical weather <br />prediction model, surface weather observations, satellite-derived solar radiation data, and radar-derived <br />precipitation data drive the snow model. The model is run once each day, for the previous 24-hour period and <br />fora 12-hour forecast period, at high spatial (1 km) and high temporal (1 hour) resolutions across the <br />coterminous United States. The snow model state variables are updated periodically using satellite, airborne, and <br />ground-based snow observations. <br />The model is cast in an assimilation framework and serves: (1) to organize various snow and other <br />hydro-meteorological observations, and (2) to track Snowpack evolution between available observations. <br />SNODAS permits more frequent and timely product generation; near real-time model analyses and forecasts; <br />
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