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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...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
PeerJ Inc.
2023
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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. |
format | Online Article Text |
id | pubmed-9817936 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | PeerJ Inc. |
record_format | MEDLINE/PubMed |
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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