The Kalman filter theory is coupled with a distributed hydrological model to update spatially distributed state variables by using several techniques proposed here. To acquire the total water storages of a basin from discharge observations at the outlet, a Q-S curve is used as an observation equation. After updating total water storage with the Kalman filter, the ratio method is introduced to reset the distributed storage amount of a basin, maintaining the spatially distributed pattern. A Monte Carlo simulation is adopted to predict state variables and error variance propagations. A distributed model coupled with the Kalman filter theory gives updated simulation results with improved forecasting accuracy.