a Institut für Wasserwirtschaft, Universität Hannover, Callinstrasse 32, D-3000, Hannover, West Germany
b Ingenieurbüro Dilger, Im Büttelwoog 2, D6783, Dahn, West Germany
Abstract:
Hydrologic models, as well as measurements of hydrologic processes, are corrupted by noise. The Kalman filter is a convenient tool to estimate the true but unknown state of a hydrologic system. It is, however, difficult to specify the necessary error covariances. A procedure is proposed to estimate the error covariances recursively in a combined state and parameter filter. Applications of the procedure yield meaningful results for two hydrologic data series of very different character. A major benefit of the proposed algorithm seems to be its robustness against instability.