Geostatistical approach in estimating the capacity volume of the mudflow reservoir

Abstract

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.