Flood Prediction with Two-Dimensional Shallow Water Equations: A Case Study of Tongo-Bassa Watershed in Cameroon
Source abstract
Abstract
As a result of urbanization, combined with the anthropogenic effects of climate change, natural events such as floods are showing increasingly adverse impacts on human existence. This study proposes a new model, based on shallow water equations, that is able to predict these floods and minimize their impacts. The first-order finite volume method (FVM), the Harten Lax and van Leer (HLL) scheme, and the monotone upwind scheme for conservation laws (MUSCL) are applied in the model. In addition, a virtual boundary cell approach is adopted to achieve a monotonic solution for both interior and boundary cells and flux computations at the boundary cells. The model integrates the infiltration parameters recorded in the area, as well as the Manning coefficient specific to each land-cover type of the catchment region. The results provided were mapped to highlight the potential flood zones and the distribution of water heights throughout the catchment region at any given time, as well as that at the outlet. It has been observed that when standard infiltration and the Manning parameters were selected, the floodable surface increased, as expected, with the increasing rainfall intensity and duration of the simulation. With sufficient infiltration, only a portion of the water tends to stagnate and flow off on the surface toward the outlet. A sensitivity analysis of certain parameters, such as rainfall data and the final infiltration coefficient in the lower watershed of the littoral region, was conducted; the results show that the model simulates well the general character of water flow in the watershed. Finally, the model’s validation using field-collected parameters during the flood of 25 July 2017 and 18 to 22 July 2016 in the Grand Ouaga basin in Burkina reveals Nash–Sutcliffe values of 0.7 and 0.73, respectively.
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Research context
OpenAlex topics: Flood Risk Assessment and Management · Hydrology and Watershed Management Studies · Urban Stormwater Management Solutions
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
College of Water Conservancy and Hydropower Engineering, Hohai University, Nanjing 210098, China
College of Water Conservancy and Hydropower Engineering, Hohai University and Yangtze Institute for Conservation and Development, 1 Xikang Road, Nanjing 210098, China
Department of Civil Engineering, University of Toronto, 35 St. George St., Toronto, ON M5S 1A4, Canada
College of Harbour Coastal and Offshore Engineering, Hohai University, Nanjing 210098, China
Funding
National Natural Science Foundation of China · Fok Ying Tong Education Foundation · China Scholar Council
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