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DEVELOPMENT AND TESTING OF A SNOWMELT‐RUNOFF FORECASTING TECHNIQUE 1
Author(s) -
Rango Albert,
Katwijk Victor
Publication year - 1990
Publication title -
jawra journal of the american water resources association
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.957
H-Index - 105
eISSN - 1752-1688
pISSN - 1093-474X
DOI - 10.1111/j.1752-1688.1990.tb01358.x
Subject(s) - snowmelt , environmental science , surface runoff , hydrology (agriculture) , meteorology , snow , engineering , geotechnical engineering , physics , ecology , biology
The snowmelt‐runoff model (SRM) was used to produce accurate simulations of streamfiow during the snowmelt period (April‐September) for ten years on the Rio Grande Basin (3419 km 2 ) near Del Norte, Colorado, U.S.A. In order to use SRM in the forecast situation, it was necessary to develop a family of snow cover depletion curves for each elevation zone based on accumulated snow water equivalent on April 1. Selection of an appropriate curve for a particular year from snow course measurements allows input of the daily snow cover extent to SRM for forecast purposes. Data from three years (1980, 1981, and 1985) were used as a quasi‐forecast test of the procedure. In these years forecasted snow cover extent data were input to SRM, but observed temperature and precipitation data were used. The resulting six‐month hydrographs were very similar to the hydrographs in the ten simulation years previously tested based on comparisons of performance evaluation criteria. Based on this result, the Soil Conservation Service (SCS) requested SRM forecasts for 1987 on the Rio Grande. Using the same procedure but with SCS estimated temperature and precipi‐tation data, SRM produced a forecast hydrograph that had a r 2 = 0.82 and difference in seasonal volume of 4.4 percent. To approximate actual operational conditions, SRM computed daily flows were updated every seven days with measured flows. The resulting forecast hydrograph had a R 2 = 0.90 and a difference in volume of 3.5 percent. The method developed needs to be refined and tested on additional years and basins, but the approach appears to be applicable to operational runoff forecasting using remote sensing data.