HOURLY STREAMFLOW FORECASTING FOR THE HUAYNAMOTA RIVER, NAYARIT, MEXICO, USING THE DISCRETE KALMAN FILTER
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Date
2020
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Abstract
Because of extreme rainfall events caused by climate change and of accelerated alteration of basins by population growth, it is important to forecast streamflow generated by precipitation events. The objective of this study was to predict hourly flows in the Huaynamota River basin using the Discrete Kalman Filter (DKF), together with the autoregressive exogenous input model (ARX). Initially, the Kalman filter parameters are defined then recalculated for defined periods; that is, the model parameter values are constantly updated. Flows were forecasted six steps ahead (L=1, 2, 3, 4, 5 and 6 hours). The basin studied is part of the Huynamota River, delimited by the Chapalangana hydrometric station, upstream from the Aguamilpa reservoir, Nayarit, Mexico. The Huaynamota River is a tributary of the Santiago River. Hourly data series were used for precipitation and flow from August to September 2017. The DKF-ARX forecasting model showed Nash-Sutcliffe efficiency indexes between 0.99 and 0.85, with L=1 and L=6, respectively. It is concluded that it is feasible to obtain a good forecast of hourly streamflow with the discrete Kalman filter.
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discrete Kalman filter, autoregressive models, flow prediction