Evaluating the Use of Microwave Radiance and Snow Water Equivalent Data in Streamflow Prediction

Accurate estimation of the quantity of water stored in seasonal snow cover and the streamflow resulting from snowmelt, particularly in the mountainous Western United States, is very important information for water resources managers. Challenges in the estimation of Snow Water Equivalent (SWE) arise from uncertain model forcing data, model structure/parameter error, poor spatial resolution of in-situ measurements and uncertainties in remotely sensed observations. In order to overcome these issues, this study implements data assimilation techniques to show the usefulness of remotely sensed and in-situ data for the improvement of snow water equivalent and streamflow prediction. Snow data assimilation is performed at the point scale, using in-situ data, and in a distributed form, using remotely sensed microwave radiance data. The point scale experiments in this study are used to compare the relative merit of different filtering and sampling techniques to improve accuracy and uncertainty estimation, with respect to SWE. SNOTEL data is used in these experiments because it provides point forcing data and observation, which creates a useful scenario for validation of assimilation techniques. The distributed experiment displays a framework for improving snow prediction, through remotely sensed data, which can enhance the estimation of streamflow in the National Weather Service River Forecast System.