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Agricultural water allocation with climate change based on gray wolf optimization in a semi-arid region of China

BACKGROUND: We quantified and evaluated the allocation of soil and water resources in the Aksu River Basin to measure the consequences of climate change on an agricultural irrigation system. METHODS: We first simulated future climate scenarios in the Aksu River Basin by using a statistical downscali...

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Autores principales: Wang, Zhidong, Zhao, Xining, Wang, Jinglei, Song, Ni, Han, Qisheng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: PeerJ Inc. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9817936/
https://www.ncbi.nlm.nih.gov/pubmed/36620746
http://dx.doi.org/10.7717/peerj.14577
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author Wang, Zhidong
Zhao, Xining
Wang, Jinglei
Song, Ni
Han, Qisheng
author_facet Wang, Zhidong
Zhao, Xining
Wang, Jinglei
Song, Ni
Han, Qisheng
author_sort Wang, Zhidong
collection PubMed
description BACKGROUND: We quantified and evaluated the allocation of soil and water resources in the Aksu River Basin to measure the consequences of climate change on an agricultural irrigation system. METHODS: We first simulated future climate scenarios in the Aksu River Basin by using a statistical downscaling model (SDSM). We then formulated the optimal allocation scheme of agricultural water as a multiobjective optimization problem and obtained the Pareto optimal solution using the multi-objective grey wolf optimizer (MOGWO). Finally, optimal allocations of water and land resources in the basin at different times were obtained using an analytic hierarchy process (AHP). RESULTS: (1) The SDSM is able to simulate future climate change scenarios in the Aksu River Basin. Evapotranspiration (ET(0)) will increase significantly with variation as will the amount of available water albeit slightly. (2) To alleviate water pressure, the area of cropland should be reduced by 127.5 km(2) under RCP4.5 and 377.2 km(2) under RCP8.5 scenarios. (3) To be sustainable, the allocation ratio of forest land and water body should increase to 39% of the total water resource in the Aksu River Basin by 2050.
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spelling pubmed-98179362023-01-07 Agricultural water allocation with climate change based on gray wolf optimization in a semi-arid region of China Wang, Zhidong Zhao, Xining Wang, Jinglei Song, Ni Han, Qisheng PeerJ Agricultural Science BACKGROUND: We quantified and evaluated the allocation of soil and water resources in the Aksu River Basin to measure the consequences of climate change on an agricultural irrigation system. METHODS: We first simulated future climate scenarios in the Aksu River Basin by using a statistical downscaling model (SDSM). We then formulated the optimal allocation scheme of agricultural water as a multiobjective optimization problem and obtained the Pareto optimal solution using the multi-objective grey wolf optimizer (MOGWO). Finally, optimal allocations of water and land resources in the basin at different times were obtained using an analytic hierarchy process (AHP). RESULTS: (1) The SDSM is able to simulate future climate change scenarios in the Aksu River Basin. Evapotranspiration (ET(0)) will increase significantly with variation as will the amount of available water albeit slightly. (2) To alleviate water pressure, the area of cropland should be reduced by 127.5 km(2) under RCP4.5 and 377.2 km(2) under RCP8.5 scenarios. (3) To be sustainable, the allocation ratio of forest land and water body should increase to 39% of the total water resource in the Aksu River Basin by 2050. PeerJ Inc. 2023-01-03 /pmc/articles/PMC9817936/ /pubmed/36620746 http://dx.doi.org/10.7717/peerj.14577 Text en ©2023 Wang et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ) and either DOI or URL of the article must be cited.
spellingShingle Agricultural Science
Wang, Zhidong
Zhao, Xining
Wang, Jinglei
Song, Ni
Han, Qisheng
Agricultural water allocation with climate change based on gray wolf optimization in a semi-arid region of China
title Agricultural water allocation with climate change based on gray wolf optimization in a semi-arid region of China
title_full Agricultural water allocation with climate change based on gray wolf optimization in a semi-arid region of China
title_fullStr Agricultural water allocation with climate change based on gray wolf optimization in a semi-arid region of China
title_full_unstemmed Agricultural water allocation with climate change based on gray wolf optimization in a semi-arid region of China
title_short Agricultural water allocation with climate change based on gray wolf optimization in a semi-arid region of China
title_sort agricultural water allocation with climate change based on gray wolf optimization in a semi-arid region of china
topic Agricultural Science
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9817936/
https://www.ncbi.nlm.nih.gov/pubmed/36620746
http://dx.doi.org/10.7717/peerj.14577
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