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Predicting the impact of climate change in Srepok river basin by LARS-WG model with CMIP5 scenarios

Thi Thao Nhi Thi Thao 1, *
Dao Nguyen Khoi 2
  1. Institute for Computational Science and Technology, HCMC DOS, Vietnam
  2. Faculty of Environment, University of Science, VNUHCM, Vietnam
Correspondence to: Thi Thao Nhi Thi Thao, Institute for Computational Science and Technology, HCMC DOS, Vietnam. Email: nhi.ptt@icst.org.vn.
Volume & Issue: Vol. 5 No. 2 (2021) | Page No.: 1101-1111 | DOI: 10.32508/stdjns.v5i2.970
Published: 2021-04-30

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This article is published with open access by Viet Nam National University Ho Chi Minh City, Viet Nam. This article is distributed under the terms of the Creative Commons Attribution License (CC-BY 4.0) which permits any use, distribution, and reproduction in any medium, provided the original author(s) and the source are credited.

Abstract

Global warming has significantly challenged the natural environment and livelihood conditions. Understanding potential future changes of climate variables, such as temperature and precipitation, is critical for water resource management in this region. This study proposed an approach to applying LARS-WG model in CMIP5 scenarios, 9 monitoring stations with 25-year series of dately data used for calibration and validation, which were the input data of the model. Combining data from 5 outputs of general periodic model (GCMs) for the periods 2021–2020, 2041–2060, and 2061– 2080, the results showed that the CMIP5 scenarios could be used successfully in the LARS-WG model and the model worked well under the climatic conditions of the Srepok river basin (Central Highlands of Vietnam). Comparing future weather scenarios with current weather data showed that warmer transitions and reduced rainfall would be generally in the future. Future temperature and precipitation trends showed an increase in both the magnitude and frequency of extreme events. So the affects of climate change are enormous on the management of related projects. Water resources need to be considered a lot in the local decision-making.

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