4.6 Article

Erosion and flood susceptibility evaluation in a catchment of Kopet-Dagh mountains using EPM and RFM in GIS

Journal

ENVIRONMENTAL EARTH SCIENCES
Volume 81, Issue 20, Pages -

Publisher

SPRINGER
DOI: 10.1007/s12665-022-10598-0

Keywords

Erosion and sedimentation; Flood hazard; Susceptibility models; Environmental parameters; Qarasu watershed; Iran

Funding

  1. Khorramabad Azad University

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This study evaluated erosion and flood susceptibility using empirical models and prioritized GIS-based prone zones in the Kopet-Dagh Mountains catchment area. The results showed that the upstream catchments are most susceptible to erosion and floods. In addition, the models demonstrated good performance and sensitivity, as confirmed by the validation process.
Erosion and flood events can damage soils, water, quality, and sediment transportation, causing many cumulative hazards. In developing countries, such as Iran, the empirical models, which are low-cost procedures to mitigate environmental hazards, are necessary to plan the watersheds. Hence, the main aim of this study is to evaluate erosion and flood susceptibility using empirical models of erosion potential method (EPM) and rational flood model (RFM) to prioritize the GIS-based prone zones in a catchment of the Kopet-Dagh Mountains. The results revealed that the heavy classes of erosion and flood susceptibility include 40.4-58.2% of the total study area, dominantly in the upstream catchments. The correlation test revealed a strong, significant, and direct association (R equal to 0.705) between W and Qp at the 99% confidence level. Consequently, the results of our research indicated the prioritization of the three sub-catchments based on their slight sensitivity and susceptibility to occurrences of soil erosion and flood events through future spatial developments. Ultimately, the model validity explained the AUC (area under the curve) values averagely equal to 0.898 and 0.917 for erosion and flood susceptibility evaluations (i.e., EPM and RFM), explaining the very good performance of the models and excellent sensitivities.

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