Use of a stochastic weather generator in a watershed model for streamflow simulation

Current methods of streamflow forecasting rely on historic climate sequences that are inadequate in length and have statistical relationships that are difficult to fit and condition. Coupling a stochastic weather generator with a deterministic watershed model can provide better streamflow forecasts. This study presents a technique to couple a k-nearest neighbor stochastic weather generator and the Precipitation-Runoff Modeling System (PRMS) watershed model to simulate historic streamflow statistics and provide a framework for forecasting flows. The weather generator uses weather data in the Upper Truckee River Basin on the California and Nevada border (USA) to produce a simulated dataset. The simulated dataset sufficiently preserves the statistics of the historic record of precipitation and maximum and minimum temperature. Simulated weather variables were used as input to PRMS, which adequately simulated modeled historic streamflows. A conditioned forecast based on wet/dry years was used to demonstrate the utility of the forecasting framework.