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Runoff projection in the Tibetan Plateau using a long short-term memory network-based framework under various climate scenarios.

Authors :
Chu, Haibo
Wei, Jiahua
Wang, Hao
Zhou, Jinjun
Source :
Journal of Hydrology. Mar2024, Vol. 632, pN.PAG-N.PAG. 1p.
Publication Year :
2024

Abstract

• A long short-term memory (LSTM)-based framework was developed for runoff projection. • Runoff over the Tibetan Plateau under different climate scenarios was projected at grid scale. • Future runoff represents an increase trend over the TP region. Runoff projections in the Tibetan Plateau (TP) can provide a valuable basis for making decisions regarding water resource management and facilitating the assessment of potential risks to water security. This study estimated the runoff of the Tibetan Plateau (TP) at a grid resolution of 0.5°for the near-term period (2022–2051) and the long-term period (2052–2082) under various climate scenarios. A framework based on long short-term memory (LSTM) was used to project the impact of climate change on runoff by integrating input selection, LSTM modelling and runoff projection application. This novel framework was used to determine the variables that contribute to runoff occurrence in different grid cells, analyze the relationship between these variables and runoff, and predict how runoff may change in the future due to precipitation, temperature, and antecedent runoff under various climate scenarios. The projected results showed that the runoff would be approximately 450 mm/a for the period from 2022 to 2051 and approximately 465 mm/a during the period from 2052 to 2082, representing an increase of about 46 % compared to the period from 1982 to 2012. Future runoff will increase in a decreasing pattern from the southwest to the northeast across the TP region. These findings regarding the projected increase in runoff will assist in the development of proactive adaptation strategies and support long-term economic growth through effective water resource management plans. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
00221694
Volume :
632
Database :
Academic Search Index
Journal :
Journal of Hydrology
Publication Type :
Academic Journal
Accession number :
176296729
Full Text :
https://doi.org/10.1016/j.jhydrol.2024.130914