Open AccessImpact of seasonal changes in vegetation on the river model prediction accuracy and real‐time flood control performance
Author(s) -
Vermuyten Evert,
Meert Pieter,
Wolfs Vicent,
Willems Patrick
Publication year - 2020
Publication title -
journal of flood risk management
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.049
H-Index - 36
ISSN - 1753-318X
DOI - 10.1111/jfr3.12651
Subject(s) - vegetation (pathology) , environmental science , flood control , flood myth , data assimilation , seasonality , hydrology (agriculture) , conceptual model , meteorology , computer science , geography , geology , medicine , geotechnical engineering , archaeology , pathology , database , machine learning
The vegetation along a river reach varies throughout a year. Seasonal vegetation affects the hydrodynamic behaviour of the river system. Accordingly, flood studies should take this temporal variation into account. This also applies to real‐time flood forecasting and control. This paper studies the impact of seasonal vegetation when considering real‐time flood control performance, based on a model predictive control (MPC) scheme. The scheme makes use of a conceptual river model to limit the computational times, as well as a reduced genetic algorithm (RGA) for the optimization of the flood control gates. The impact of seasonal vegetation on the conceptual model accuracy was analysed and a flexible data assimilation approach developed, to adjust the model predictions to different vegetation scenarios. This method can successfully improve the efficiency of a control strategy, by strongly predicting and reducing the impact of seasonal vegetation changes on river conditions.