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Ensemble Kalman Filter for Hourly Streamflow Forecasting in Huaynamota River, Nayarit, México
Filtro de Kalman de Conjuntos para pronóstico de caudales horarios en el río Huaynamota, Nayarit, México
DOI:
https://doi.org/10.15446/ing.investig.90023Keywords:
Ensemble Kalman Filter, autoregressive models, short-term streamflow forecasting, data assimilation (en)Filtro de Kalman de Conjuntos, modelos autorregresivos, pronósticos de caudales a corto plazo, asimilación de datos (es)
Hydrological phenomena are characterized by the formation of a non-linear dynamic system, and streamflows are not unrelated to this premise. Data assimilation offers an alternative for flow forecasting using the Ensemble Kalman Filter, given its relative ease of implementation and lower computational effort in comparison with other techniques. The hourly streamflow of the Chapalagana station was forecasted based on that of the Platanitos station in northwestern México. The forecasts were made from one to six steps forward, combined with set sizes of 5, 10, 20, 30, 50, and 100 members. The Nash-Sutcliffe coefficients of the Discrete Kalman filter were 0,99 and 0,85 for steps one and six, respectively, achieving the best fit with a tendency to shift the predicted series, similar to the persistent forecast. The Ensemble Kalman Filter (EnKF) obtained 0,99 and 0,05 in steps one and six. However, it converges on the observed series with the limitation of considerable overestimation in higher steps. All three algorithms have equal statistical adjustment values in step one, and there are progressive differences in further steps, where ARX and DKF remain similar and EnKF is differentiated by the overestimation. EnKF enables capturing non-linearity in sudden streamflow changes but generates overestimation at the peaks.
Los fenómenos hidrológicos se caracterizan por conformar un sistema dinámico no lineal, y los caudales no son ajenos a esta premisa. La asimilación de datos ofrece una alternativa para el pronóstico de caudales mediante el Filtro de Kalman de Conjuntos, dada su relativa facilidad de implementación y menor esfuerzo computacional en contraste con otras técnicas. Se pronosticó el caudal horario de la estación Chapalagana en función del de la estación Platanitos en el noroeste de México. Los pronósticos se realizaron de uno a seis pasos hacia adelante, combinados con tamaños de conjunto de 5, 10, 20, 30, 50 y 100 miembros. Los coeficientes de Nash-Sutcliffe para el Filtro de Kalman Discreto fueron de 0,99 y 0,85 en los pasos uno y seis respectivamente, logrando el mejor ajuste con tendencia a desplazar la serie pronosticada, similar al pronóstico persistente. El Filtro de Kalman de Conjuntos (EnKF) obtuvo 0,99 y 0,05 en los pasos uno y seis. No obstante, este converge sobre la serie observada con la limitante de sobrestimación considerable en pasos superiores. Los tres algoritmos tienen igual valor de ajuste estadístico en el paso uno, y se dan diferencias progresivas en pasos sucesivos, donde ARX y DKF se mantienen similares y EnKF se diferencia por la sobrestimación. EnKF permite captar la no linealidad en los cambios bruscos de caudal, pero genera sobrestimación en los picos.
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Copyright (c) 2022 Ildefonso Nárvaez-Ortiz, Laura Alicia Ibáñez-Castillo, Ramón Arteaga-Ramírez, Mario Alberto Vázquez-Peña
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