Using Multiple Gcm Models to Reduce Uncertainties in Assessment of the Effect of Future Climate Change on Cotton Growth and Water Consumption in China Article Swipe
Ruixue Yuan
,
Keyu Wang
,
Dandan Ren
,
Z. H. Chen
,
Baosheng Guo
,
Haina Zhang
,
Dan Li
,
Cunpeng Zhao
,
Shumin Han
,
Huilong Li
,
Shuling Zhang
,
De Li Liu
,
Yanmin Yang
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.20944/preprints202503.2226.v1
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.20944/preprints202503.2226.v1
Global Climate Models (GCMs) are a primary source of uncertainty in assessing climate change impacts on agricultural production, especially when relying on limited models. Considering China's vast territory and diverse climates, this study utilized 22 GCMs and selected three representative cotton-producing regions: Aral (northwest inland), Wangdu (Yellow River basin), and Changde (Yangtze River basin). Using the APSIM model, we simulated climate change effects on cotton yield, water consumption, uncertainties, and climatic factor contributions. Results showed significant variability driven by different GCMs, with uncertainty increasing over time and under radiation forcing. Spatial variations in uncertainty were observed: Wangdu exhibited highest uncertainties in yield and phenology, while Changde had the greatest uncertainties in ET and irrigation amount. Key factors affecting yield varied regionally—daily maximum temperature and precipitation dominated in Aral; precipitation was a major negative factor in Wangdu; and maximum temperature and solar radiation were critical in Changde. This study provides scientific support for developing climate change adaptation measures tailored to cotton production across different regions.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.20944/preprints202503.2226.v1
- https://www.preprints.org/frontend/manuscript/2b4f1414b4d23ad1def157cc78c8aa1b/download_pub
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
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All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4409016684Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.20944/preprints202503.2226.v1Digital Object Identifier
- Title
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Using Multiple Gcm Models to Reduce Uncertainties in Assessment of the Effect of Future Climate Change on Cotton Growth and Water Consumption in ChinaWork title
- Type
-
preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
-
2025-03-31Full publication date if available
- Authors
-
Ruixue Yuan, Keyu Wang, Dandan Ren, Z. H. Chen, Baosheng Guo, Haina Zhang, Dan Li, Cunpeng Zhao, Shumin Han, Huilong Li, Shuling Zhang, De Li Liu, Yanmin YangList of authors in order
- Landing page
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https://doi.org/10.20944/preprints202503.2226.v1Publisher landing page
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https://www.preprints.org/frontend/manuscript/2b4f1414b4d23ad1def157cc78c8aa1b/download_pubDirect link to full text PDF
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YesWhether a free full text is available
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-
greenOpen access status per OpenAlex
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https://www.preprints.org/frontend/manuscript/2b4f1414b4d23ad1def157cc78c8aa1b/download_pubDirect OA link when available
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GCM transcription factors, Climate change, China, Water consumption, Consumption (sociology), Environmental science, Natural resource economics, Environmental economics, Economics, General Circulation Model, Water resource management, Geography, Ecology, Social science, Archaeology, Sociology, BiologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.solar | 140 |
| abstract_inverted_index.study | 32, 147 |
| abstract_inverted_index.three | 38 |
| abstract_inverted_index.under | 87 |
| abstract_inverted_index.water | 66 |
| abstract_inverted_index.while | 104 |
| abstract_inverted_index.yield | 101, 118 |
| abstract_inverted_index.(GCMs) | 3 |
| abstract_inverted_index.Global | 0 |
| abstract_inverted_index.Models | 2 |
| abstract_inverted_index.Wangdu | 45, 96 |
| abstract_inverted_index.across | 161 |
| abstract_inverted_index.change | 13, 61, 154 |
| abstract_inverted_index.cotton | 64, 159 |
| abstract_inverted_index.driven | 77 |
| abstract_inverted_index.factor | 71, 133 |
| abstract_inverted_index.model, | 57 |
| abstract_inverted_index.showed | 74 |
| abstract_inverted_index.source | 7 |
| abstract_inverted_index.varied | 119 |
| abstract_inverted_index.yield, | 65 |
| abstract_inverted_index.(Yellow | 46 |
| abstract_inverted_index.Changde | 50, 105 |
| abstract_inverted_index.Climate | 1 |
| abstract_inverted_index.Results | 73 |
| abstract_inverted_index.Spatial | 90 |
| abstract_inverted_index.Wangdu; | 135 |
| abstract_inverted_index.amount. | 114 |
| abstract_inverted_index.basin), | 48 |
| abstract_inverted_index.basin). | 53 |
| abstract_inverted_index.climate | 12, 60, 153 |
| abstract_inverted_index.diverse | 29 |
| abstract_inverted_index.effects | 62 |
| abstract_inverted_index.factors | 116 |
| abstract_inverted_index.highest | 98 |
| abstract_inverted_index.impacts | 14 |
| abstract_inverted_index.limited | 22 |
| abstract_inverted_index.maximum | 121, 137 |
| abstract_inverted_index.models. | 23 |
| abstract_inverted_index.primary | 6 |
| abstract_inverted_index.relying | 20 |
| abstract_inverted_index.support | 150 |
| abstract_inverted_index.(Yangtze | 51 |
| abstract_inverted_index.Changde. | 145 |
| abstract_inverted_index.climatic | 70 |
| abstract_inverted_index.critical | 143 |
| abstract_inverted_index.forcing. | 89 |
| abstract_inverted_index.greatest | 108 |
| abstract_inverted_index.inland), | 44 |
| abstract_inverted_index.measures | 156 |
| abstract_inverted_index.negative | 132 |
| abstract_inverted_index.provides | 148 |
| abstract_inverted_index.regions. | 163 |
| abstract_inverted_index.regions: | 41 |
| abstract_inverted_index.selected | 37 |
| abstract_inverted_index.tailored | 157 |
| abstract_inverted_index.utilized | 33 |
| abstract_inverted_index.affecting | 117 |
| abstract_inverted_index.assessing | 11 |
| abstract_inverted_index.climates, | 30 |
| abstract_inverted_index.different | 79, 162 |
| abstract_inverted_index.dominated | 125 |
| abstract_inverted_index.exhibited | 97 |
| abstract_inverted_index.observed: | 95 |
| abstract_inverted_index.radiation | 88, 141 |
| abstract_inverted_index.simulated | 59 |
| abstract_inverted_index.territory | 27 |
| abstract_inverted_index.(northwest | 43 |
| abstract_inverted_index.adaptation | 155 |
| abstract_inverted_index.developing | 152 |
| abstract_inverted_index.especially | 18 |
| abstract_inverted_index.increasing | 83 |
| abstract_inverted_index.irrigation | 113 |
| abstract_inverted_index.phenology, | 103 |
| abstract_inverted_index.production | 160 |
| abstract_inverted_index.scientific | 149 |
| abstract_inverted_index.variations | 91 |
| abstract_inverted_index.Considering | 24 |
| abstract_inverted_index.production, | 17 |
| abstract_inverted_index.significant | 75 |
| abstract_inverted_index.temperature | 122, 138 |
| abstract_inverted_index.uncertainty | 9, 82, 93 |
| abstract_inverted_index.variability | 76 |
| abstract_inverted_index.agricultural | 16 |
| abstract_inverted_index.consumption, | 67 |
| abstract_inverted_index.precipitation | 124, 128 |
| abstract_inverted_index.uncertainties | 99, 109 |
| abstract_inverted_index.contributions. | 72 |
| abstract_inverted_index.representative | 39 |
| abstract_inverted_index.uncertainties, | 68 |
| abstract_inverted_index.China's | 25 |
| abstract_inverted_index.cotton-producing | 40 |
| abstract_inverted_index.regionally—daily | 120 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 13 |
| citation_normalized_percentile.value | 0.11735969 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |