Ensemble Kalman Filter for Hourly Streamflow Forecasting in Huaynamota River, Nayarit, México

Hydrological phenomena are characterized by the formation of a non-linear dynamic system, and streamflows not unrelated to this premise. Data assimilation offers an alternative for flow forecasting using Ensemble Kalman Filter, given its relative ease implementation lower computational effort in comparison with other techniques. The hourly streamflow Chapalagana station was forecasted based on that Platanitos northwestern México. forecasts were made from one six steps forward, combined set sizes 5, 10, 20, 30, 50, 100 members. Nash-Sutcliffe coefficients Discrete filter 0,99 0,85 six, respectively, achieving best fit tendency shift predicted series, similar persistent forecast. Filter (EnKF) obtained 0,05 six. However, it converges observed series limitation considerable overestimation higher steps. All three algorithms have equal statistical adjustment values step one, there progressive differences further steps, where ARX DKF remain EnKF is differentiated overestimation. enables capturing non-linearity sudden changes but generates at peaks.