Weather forecasting and Flood Simulation for Sustainable Land Use Management: Bentota River Basin in Sri Lanka
Flood simulation modeling related to land use management is very important for mitigation and integration of disaster risk reduction in the development process. Flood estimation data obtained through gauging stations, Geographic Position System (GPS) devices and participatory-based mapping are poor in accuracy, and current software for flood simulation is costly and requires a vast amount of input data. Hence, it is essential to have a proper method to simulate flooding in the context of changes in rainfall patterns in a relatively fast and accurate manner for flood-prone areas of Sri Lanka. This study analyzes the time series characteristics of total monthly rainfall and maximum daily rainfall of the Bentota River basin applying Mann–Kendall (MK) tests to rainfall trends as major input data for a flood simulation model which has been developed applying Arc Geographic Information System (GIS) software and Python scripting. The model combined various factors such as rainfall, slope, hydrology, soil, land use, storm water drainage, and human behavior factors. The developed flood simulation model showed a good level of consistency between observed and simulated results, with 64.03% accuracy. Maximum daily rainfall of this area shows a general increasing trend, whereas total monthly rainfall shows a general decreasing trend. According to the results of this study, there will be an extreme variability of rainfall once every 5 years during any month from April to July resulting in a minor flood situation in the area. Introducing riparian buffers, a flood resistive green home gardening model, green paving, rain water harvesting, drenching inland waterways, and converting selected marshy lands as park areas could be implemented as sustainable land management strategies for flood disaster risk reduction in the area. Farmers and the local community will be the main beneficiaries of the findings of this study. Moreover, decision makers could make decisions based on this prediction relating to future flood occurrences, vulnerable areas, and flood levels. The approach adopted in this study will also be useful for other researchers, agriculturalists, and planners to identify future climatological influences and to develop flood simulation models for other river catchment areas.
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