Models for Prediction of the Effects of Hydrological and Basin Characteristics on Reservoir Sedimentation for Water Management in Thailand

抄録

Owing to recent climate and land use changes, which affect reservoir sedimentation, reductions in reservoir capacity and shortening of reservoir lifespan have occurred. Meanwhile, water resource management and planning strategies have becomes less effective. Such issues are likely to worsen unless sediment deposition can be predicted precisely and sedimentation can be prevented or controlled appropriately. Accordingly, a model for predicting changes in reservoir capacity due to changes in hydrological characteristics is necessary. The present study aims to assess the factors contributing to reservoir sedimentation in Thailand and to derive a predictive model of reservoir sedimentation using geomorphological factors and forest cover from 25 reservoirs throughout Thailand. In particular, mathematical statistical models predicting reservoir sedimentation (RS) were formulated through multiple regression analysis using data from these 25 reservoirs. The results of the study demonstrate that sediment volumes range from 0.0072 to 4.7218 million m3/year, with an average of 0.49 million m3/year. Six variables were found to have a significant effect on sedimentation in the model: annual volume of inflow (Q), average annual rainfall (R), drainage area (DA), relief ratio (Sr), compactness coefficient (Kc), and stream length ratio (Si). The most applicable equation for predicting RS for reservoirs in Thailand appears to be as follows: RS=exp{3.19007 + 0.00176R - 0.00087DA - 0.00065Cap + 0.03364WSA + 16.44675Sr - 1.29256Kc - 1.07294Si} R0.99986 DA0.00014 This equation produced the highest adjusted R2 (0.9125) with the smallest standard error of estimate (0.4963) and highly significant in prediction. Thus, this model can be applied to predict annual sedimentation in other reservoirs in Thailand. This equation can also be applied to forecast the volume of sediment deposition in other reservoirs and ascertain the real water supply of a given reservoir. Accordingly, it can achieve reductions in operational costs through reservoir capacity surveys, reducing government budgets by an average of 1,900,000 baht per project, and can reduce the time required for each survey by an average of 14 months. The prediction results can be used in the simulation of reservoir operations, improving the efficiency of irrigation operations and ensuring the sustainable management of water resources.