A model to forecast short-term snowmelt runoff using synoptic observations of streamflow, temperature, and precipitation

Wendell V. Tangborn

doi:10.1029/wr016i004p00778

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A model to forecast short-term snowmelt runoff using synoptic observations of streamflow, temperature, and precipitation

Wendell V. Tangborn

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Abstract

Snowmelt runoff is forecast with a statistical model that utilizes daily values of stream discharge, gaged precipitation, and maximum and minimum observations of air temperature. Synoptic observations of these variables are made at existing low- and medium-altitude weather stations, thus eliminating the difficulties and expense of new, high-altitude installations. Four model development steps are used to demonstrate the influence on prediction accuracy of basin storage, a preforecast test season, air temperature (to estimate ablation), and a prediction based on storage. Daily ablation is determined by a technique that employs both mean temperature and a radiative index. Radiation (both long- and short-wave components) is approximated by using the range in daily temperature, which is shown to be closely related to mean cloud cover. A technique based on the relationship between prediction error and prediction season weather utilizes short-term forecasts of precipitation and temperature to improve the final prediction. Verification of the model is accomplished by a split sampling technique for the 1960–1977 period. Short- term (5–15 days) predictions of runoff throughout the main snowmelt season are demonstrated for mountain drainages in western Washington, south-central Arizona, western Montana, and central California. The coefficient of prediction ( C p ) based on actual, short-term predictions for 18 years is for Thunder Creek (Washington), 0.69; for South Fork Flathead River (Montana), 0.45; for the Black River (Arizona), 0.80; and for the Kings River (California), 0.80.

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Wendell V. Tangborn. 2010-07-09. A model to forecast short-term snowmelt runoff using synoptic observations of streamflow, temperature, and precipitation. https://doi.org/10.1029/wr016i004p00778

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