Estimation of sediment load for Himalayan Rivers: Case study of Kaligandaki in Nepal

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作者
Pennan Chinnasamy
Aditya Sood
机构
[1] Indian Institute of Technology Bombay,
[2] International Water Management Institute,undefined
来源
关键词
Himalayan rivers; sediment load; hydropower; Nepal; SWAT;
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学科分类号
摘要
Himalayan regions have increasing sediment yield due to undulating topography, slope and improper watershed management. However, due to limited observation data, and site accessibility issues, less studies have quantified sedimentation loads in the Himalayas, especially Nepal. This has hindered the investments on run-of-river hydropower projects as high and unpredicted sedimentation has increased losses in hydropower production. Therefore, there is a need to understand key physical processes driving sedimentation in these regions, with the available data. This study used the Soil and Water Assessment Tool (SWAT) to estimate the sedimentation yields in the Kaligandaki basin of Nepal, which is an important tributary that drains into the Ganges. Multi-source data from field observations, remote sensing platforms, surveys and government records were used to set up and run the SWAT model for the Kaligandaki basin from 2000 to 2009. Results for the 10-year model run indicate that 73% of the total sediment load is estimated to come from the upstream regions (also known as High Himalayan region), while only 27% is contributed from the Middle and High Mountain regions (where land management-based interventions were deemed most feasible for future scenarios). The average sediment concentration was 1986 mg/kg (ppm), with values of 8432 and 12 mg/kg (ppm) for maximum and minimum, respectively. Such high sedimentation rates can impact river ecosystems (due to siltation), ecosystem services and hydropower generation. In addition, model results indicate the need for better high frequency observation data. Results from this study can aid in better watershed management, which is aimed at reducing sedimentation load and protecting Himalayan rivers.
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