| Issue |
BIO Web Conf.
Volume 115, 2024
2nd Edition of the International Conference on “Natural Resources and Sustainable Development” (RENA23)
|
|
|---|---|---|
| Article Number | 01007 | |
| Number of page(s) | 8 | |
| Section | Satellite Remote Sensing for an Effective Natural Resource Management | |
| DOI | https://doi.org/10.1051/bioconf/202411501007 | |
| Published online | 25 June 2024 | |
Use of geomatics and multi-criteria methods to assess water erosion in the Tigrigra watershed (Azrou region, Morocco)
1 Water Science and Environmental Engineering Team, Faculty of Sciences, Moulay Ismail University, Meknes, Morocco.
2 ONEE, National Office for Electricity and Drinking Water, Meknes
3 Anassi High School (Annex 2), Ministry of National Education, Meknes
4 Department of Geomorphology and Geomatics, Scientific Institute, Mohammed V University, Rabat
In Morocco, the capacity of dam reservoirs has decreased in recent years due to water erosion. This study aims to identify the sub-watersheds most vulnerable to soil erosion in the Tigrigra watershed by utilizing morphometric analysis of linear, landscape, and shape parameters and various multi-criteria decision models. These approaches allow for the prioritization of areas or sub-watersheds at high erosion risk. In the study area, erosion assessment is conducted using multi-criteria decision support models (MCDM) such as MOORA, VIKOR, TOPSIS, COPRAS, WASPAS, and SAW within a GIS environment. This approach highlights the significant role of morphometric parameters and multi-criteria methods in identifying sub-watersheds susceptible to erosion. Overall, the results indicate that morphometric parameters are highly effective in identifying erosion-prone areas. The Tigrigra watershed generally exhibits low to medium sensitivity to erosion, except for certain sub-watersheds. Subcatchment 28 showed significant erosion in most methods used.
© The Authors, published by EDP Sciences, 2024
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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