| Issue |
E3S Web of Conf.
Volume 590, 2024
6th Annual International Scientific Conference on Geoinformatics - GI 2024: “Sustainable Geospatial Solutions for a Changing World”
|
|
|---|---|---|
| Article Number | 07003 | |
| Number of page(s) | 10 | |
| Section | Geology, Hydrology, Hydrogeology, Hydrotechnical Facilities | |
| DOI | https://doi.org/10.1051/e3sconf/202459007003 | |
| Published online | 13 November 2024 | |
Geostatistical approach in estimating the capacity volume of the mudflow reservoir
1 "Tashkent Institute of Irrigation and Agricultural Mechanization Engineers" National Research University, Tashkent, 100000, Uzbekistan.
2 Kyrgyz State Technical University named after I. Razzakov, Bishkek, Kyrgyzstan
3 International horse breeding academy named after Aba Annaev, Arkadag city, Turkmenistan
4 National University of Uzbekistan named after Mirzo Ulugbek, Tashkent, Uzbekistan
* Corresponding author: kh.khasanov@outlook.com
Mudflow reservoirs play a crucial role in mitigating flood risks triggered by natural events like heavy rains and snowmelt, safeguarding surrounding areas from potential inundation. However, sedimentation poses a significant challenge by reducing the capacity and effectiveness of these mudflow reservoirs over time. This study focused on estimating the capacity of the Kalkama mudflow reservoir, constructed in 1987, using a geostatistical approach. Bathymetric survey data were analyzed using various interpolation methods. Kriging (Ordinary Kriging) provided the best performance with the lowest RMSE (0.28) and a high R² (0.99), indicating it is the most accurate method for this dataset. Based on this method, a spatial model of the mudflow reservoir was developed to assess its current capacity. Findings indicate a capacity loss of 2.33 million m³ (23.6%) over 36 years, alongside a 22% reduction in surface area at Full Storage Level, and the dead volume was completely filled with sediment.
© 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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