Ensemble Streamflow Forecasting via data assimilation

Accurate streamflow forecasting is required for effective water resources management. The present research focuses on development of a streamflow forecasting methodology through a data-assimilation technique, known as Ensemble Kalman filter (EnKF). A spatially distributed hydrologic model is forced with an ensemble of meteorological inputs, thus producing an ensemble of streamflow forecasts. A digital elevation model along with the temperature and precipitation lapse rates are used to represent the spatial variability for the input meteorology within the watershed. The model structural uncertainty is handled by sampling parameters from a feasible parameter space obtained through the calibration process. The model snow states are updated using SNODAS Snow Water Equivalent (SWE) estimates, where as the soil moisture states are updated using ground-based streamflow observations, within the EnKF framework. The methodology is tested in watersheds in the Pacific Northwest. The short-term and seasonal streamflow forecasts are compared to the observations and the currently employed regression based approaches.