Data assimilation for distributed hydrological catchment modeling via ensemble Kalman filter

a r t i c l e i n f o Catchment scale hydrological models are critical decision support tools for water resources management and environment remediation. However, the reliability of hydrological models is inevitably affected by limited measurements and imperfect models. Data assimilation techniques combine complementary information from measurements and models to enhance the model reliability and reduce predictive uncertainties. As a sequential data assimilation technique, the ensemble Kalman filter (EnKF) has been extensively studied in the earth sciences for assimilating in-situ measurements and remote sensing data. Although the EnKF has been demonstrated in land surface data assimilations, there are no systematic studies to investigate its performance in distributed modeling with high dimensional states and parameters. In this paper, we present an assessment on the EnKF with state augmentation for combined state-parameter estimation on the basis of a physical-based hydrological model, Soil and Water Assessment Tool (SWAT). Through synthetic simulation experiments, the capability of the EnKF is demonstrated by assimilating the runoff and other measurements, and its sensitivities are analyzed with respect to the error specification, the initial realization and the ensemble size. It is found that the EnKF provides an efficient approach for obtaining a set of acceptable model parameters and satisfactory runoff, soil water content and evapotranspiration estimations. The EnKF performance could be improved after augmenting with other complementary data, such as soil water content and evapotranspiration from remote sensing retrieval. Sensitivity studies demonstrate the importance of consistent error specification and the potential with small ensemble size in the data assimilation system. Understanding the response of a river catchment to atmospheric forcing is critically important to climate studies, agriculture (irrigation planning and vegetation and crop growth), natural hazards prevention and mitigation (e.g. floods and droughts), and other water resources managements (e.g. water transfer and storages). These studies are highly dependent on advanced simulation models and large amounts of environmental data that are increasingly being made available. Hydrological models are built based on a set of principles coupled with a number of assumptions and imperfectly defined parameters, and measurements are usually scarce in space and discontinuous in time. These result in great uncertainties about the measurements, model structures and parameters. In applications, numerical hydrological modeling generally requires estimation of model parameters through calibration with observed data to mitigate the output uncertainties. Many calibration methods have been developed, including the automatic calibration with multiple objectives or criteria [20,24,32]. But …