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Impact of climate change on snowmelt runoff in a Himalayan basin, Nepal

Authors
  • Thapa, Samit1, 1
  • Li, Haizhi2
  • Li, Bo1, 1
  • Fu, Donglei1, 1
  • Shi, Xiaofei1, 1, 3
  • Yabo, Stephen1, 1
  • Lu, Lu1, 1
  • Qi, Hong1, 1
  • Zhang, Wei1
  • 1 Harbin Institute of Technology, Harbin, China , Harbin (China)
  • 2 Heilongjiang Provincial Environmental Monitoring Center Station, Harbin, China , Harbin (China)
  • 3 CASIC Intelligence Industry Development Co. Ltd, Beijing, 100854, China , Beijing (China)
Type
Published Article
Journal
Environmental Monitoring and Assessment
Publisher
Springer-Verlag
Publication Date
Jun 08, 2021
Volume
193
Issue
7
Identifiers
DOI: 10.1007/s10661-021-09197-6
Source
Springer Nature
Keywords
License
Yellow

Abstract

The Hindu Kush Himalaya (HKH) is one of the major sources of fresh water on Earth and is currently under serious threat of climate change. This study investigates the future water availability in the Langtang basin, Central Himalayas, Nepal under climate change scenarios using state-of-the-art machine learning (ML) techniques. The daily snow area for the region was derived from MODIS images. The outputs of climate models were used to project the temperature and precipitation until 2100. Three ML models, including Gated recurrent unit (GRU), Long short-term memory (LSTM), and Recurrent neural network (RNN), were developed for snowmelt runoff prediction, and their performance was compared based on statistical indicators. The result suggests that the mean temperature of the basin could rise by 4.98 °C by the end of the century. The annual average precipitation in the basin is likely to increase in the future, especially due to high monsoon rainfall, but winter precipitation could decline. The annual river discharge is projected to upsurge significantly due to increased precipitation and snowmelt, and no shift in hydrograph is expected in the future. Among three ML models, the LSTM model performed better than GRU and RNN models. In summary, this study depicts severe future climate change in the region and quantifies its effect on river discharge. Furthermore, the study demonstrates the suitability of the LSTM model in streamflow prediction in the data-scarce HKH region. The outcomes of this study will be useful for water resource managers and planners in developing strategies to harness the positive impacts and offset the negative effects of climate change in the basin.

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