Hydrological forecast in the Misantla basin (Mexico) using the discrete Kalman filter
DOI:
https://doi.org/10.24850/j-tyca-2026-02-05Keywords:
Hydrological forecasting, floods, Runoff, mathematical models, stochastic processes, MexicoAbstract
In Mexico, extreme precipitation events have increased in recent years, especially in the south, causing abrupt changes in river flows and severe flooding. Hydrological information is essential for managing floods, dams, and disasters. To predict river flows, methods like neural networks, artificial intelligence, and data assimilation techniques, including the Discrete Kalman Filter (DKF), are used. This filter performs stochastic estimations through a correction algorithm, providing unbiased linear estimates with minimal variance from noisy data, and updates the system with each new observation. Its effectiveness in hydrology has been proven in several studies. This work implements the DKF in a watershed in the Gulf of Mexico to predict flows, evaluating two response functions: the Instantaneous Unit Hydrograph (IUH) and the Linear Tank Model (TL). During the analysis of 20 flood events in 1987, an extreme flood in September was detected, with a flow of 271.6 m³/s. The results showed that the IUH was more accurate than the TL, especially during flood events. This behavior is attributed to both the nature of the response functions and the Kalman filter equations, which improve error estimation and the representation of stochastic processes. In conclusion, the DKF with these functions is efficient for flow prediction.
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