An application of Ensemble Kalman Filter in integral-balance subsurface modeling

Data assimilation method provides a framework to decrease the uncertainty of hydrological modeling by sequentially incorporating observations into numerical model. Such a process involves estimating statistical moments of different order based on the evolution of conditional probability distribution function. Because of the nonlinearity of many hydrological dynamics, explicit and analytical solutions for moments of state distribution are often impossible. Evensen [J Geophys Res 99(c5): 10143-10162 (1994)] introduced Ensemble Kalman Filtering (EnKF) method to address such problems. We test and evaluate the performance of EnKF in fusing model predictions and observations for a saturated-unsaturated integral-balance subsurface model. We find EnKF improve the model predictions, and also we conclude a good estimate of state variance is essential for the success of EnKF.