Assimilating multi-site measurements for semi-distributed hydrological model updating

Accurate estimates of the uncertainties associated with hydrological model are essential for better streamflow simulation. This paper explores the Ensemble Kalman Filter (EnKF), an ensemble data assimilation method, for semi-distributed hydrological model updating. The semi-distributed model is very practical and often used for moderate and large basin streamflow forecasting and water resources management. The studied area in this study is a large basin of Baohe, upper branch of Hanjiang River. The semi-distributed Xinanjiang model states are updated by assimilating several spatially distributed measurement points within the whole basin. The spatial pattern and ensemble of model states such as soil water content are derived. A lumped model updating case is taken for comparison. The results show that the semi-distributed model case does better in high flow simulation than the lumped case, with 16% and 25% improvement to the simulation performance at peak flow in two periods of heavy rain processes. The smaller streamflow uncertainty at main basin outlet is also found in the semi-distributed updating case.