Combining Observations with a Distributed Hydrological Model for Imporved Streamflow Forecasting

Abstract:

The Iowa Flood Center operates a real-time flood forecasting system for the state of Iowa based upon a distributed hydrological model. This model partitions the landscape into individual control volumes called hillslopes, which are determined from a 90 meter DEM. In addition to the results of this hydrological model, streamflow observations are available at more than 300 locations, including measurements from USGS operated streamflow gauges and Iowa Flood Center operated bridge sensors. Augmenting the model outputs with available observations can improve forecast accuracy. Combining these sources of information requires computing sensitivities of model states at each location to upstream states. These sensitivities greatly increase the number of computations and require additional computational power to maintain real-time usability.

This presentation documents developments with a real-time distributed streamflow forecasting model with assimilated data. The forecasting system applied to the State of Iowa (about 140,000 square kilometers) will be detailed. A comparison of streamflow forecasts with model states influenced by observations to forecasts without influence by observations is given to show the effectiveness of our methods.