Estimation of seasonal precipitation memory curves for major rivers in the Tibetan Plateau based on GRACE satellites data Article Swipe
YOU?
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· 2024
· Open Access
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· DOI: https://doi.org/10.1016/j.ejrh.2024.101942
Study region: River basins in the Tibetan Plateau, including the Yarlung Zangbo River (YZR) basin, the Salween River (SWR) basin, the Lancang River (LCR) basin, the Yangtze River (YTR) basin, the Yellow River (YLR) basin, and the Tarim River (TRM) basin. Study focus: In recent years, the concept of seasonal catchment memory has emerged as a pivotal element in the field of hydrological science. Precipitation memory curve (PMC) is a good method to describe seasonal precipitation memory process, which is the key function connecting precipitation and terrestrial water storage change (TWSC). The model simulated TWSC with precipitation is proposed based on PMC. According to the performance of the model, this study investigates optimal curve functions to describe seasonal catchment memory in the Tibetan Plateau. New hydrological insights for the region: It is found that in most basins, the shape of PMC is consistent with the Boussinesq function or Maillet function. In the SWR basin, PMC varies between warm season and cold season. However, due to relatively less precipitation in the cold season compared to the warm season, the segmented-representation PMC has only a slight improvement on the TWSC simulation. In the TRM basin, the temperature-index method can improve the performance of the model. The PMC of the TRM basin during the warm season exhibits a short duration of precipitation memory of approximately three months.
Related Topics
- Type
- article
- Language
- en
- Landing Page
- http://doi.org/10.1016/j.ejrh.2024.101942
- OA Status
- gold
- References
- 62
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- OpenAlex ID
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Raw OpenAlex JSON
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https://openalex.org/W4402301169Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1016/j.ejrh.2024.101942Digital Object Identifier
- Title
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Estimation of seasonal precipitation memory curves for major rivers in the Tibetan Plateau based on GRACE satellites dataWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
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2024Year of publication
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2024-09-06Full publication date if available
- Authors
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Haiting Gu, Li Liu, Yue‐Ping Xu, Di Ma, Jingkai Xie, Xinting YuList of authors in order
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https://doi.org/10.1016/j.ejrh.2024.101942Publisher landing page
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goldOpen access status per OpenAlex
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https://doi.org/10.1016/j.ejrh.2024.101942Direct OA link when available
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Plateau (mathematics), Precipitation, Seasonality, Estimation, Geography, Climatology, Satellite, Remote sensing, Environmental science, Physical geography, Meteorology, Geology, Statistics, Mathematics, Engineering, Aerospace engineering, Systems engineering, Mathematical analysisTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.in | 4, 58, 120, 134, 168 |
| abstract_inverted_index.is | 68, 79, 97, 131, 141 |
| abstract_inverted_index.of | 48, 61, 106, 139, 200, 205, 217, 220 |
| abstract_inverted_index.on | 100, 185 |
| abstract_inverted_index.or | 147 |
| abstract_inverted_index.to | 72, 103, 115, 164, 173 |
| abstract_inverted_index.New | 124 |
| abstract_inverted_index.PMC | 140, 154, 179, 204 |
| abstract_inverted_index.SWR | 152 |
| abstract_inverted_index.TRM | 191, 207 |
| abstract_inverted_index.The | 91, 203 |
| abstract_inverted_index.and | 35, 85, 159 |
| abstract_inverted_index.can | 196 |
| abstract_inverted_index.due | 163 |
| abstract_inverted_index.for | 127 |
| abstract_inverted_index.has | 52, 180 |
| abstract_inverted_index.key | 81 |
| abstract_inverted_index.the | 5, 9, 15, 20, 25, 30, 36, 46, 59, 80, 104, 107, 121, 128, 137, 144, 151, 169, 174, 177, 186, 190, 193, 198, 201, 206, 210 |
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| abstract_inverted_index.TWSC | 94, 187 |
| abstract_inverted_index.cold | 160, 170 |
| abstract_inverted_index.good | 70 |
| abstract_inverted_index.less | 166 |
| abstract_inverted_index.most | 135 |
| abstract_inverted_index.only | 181 |
| abstract_inverted_index.that | 133 |
| abstract_inverted_index.this | 109 |
| abstract_inverted_index.warm | 157, 175, 211 |
| abstract_inverted_index.with | 95, 143 |
| abstract_inverted_index.(LCR) | 23 |
| abstract_inverted_index.(PMC) | 67 |
| abstract_inverted_index.(SWR) | 18 |
| abstract_inverted_index.(TRM) | 39 |
| abstract_inverted_index.(YLR) | 33 |
| abstract_inverted_index.(YTR) | 28 |
| abstract_inverted_index.(YZR) | 13 |
| abstract_inverted_index.River | 2, 12, 17, 22, 27, 32, 38 |
| abstract_inverted_index.Study | 0, 41 |
| abstract_inverted_index.Tarim | 37 |
| abstract_inverted_index.based | 99 |
| abstract_inverted_index.basin | 208 |
| abstract_inverted_index.curve | 66, 113 |
| abstract_inverted_index.field | 60 |
| abstract_inverted_index.found | 132 |
| abstract_inverted_index.model | 92 |
| abstract_inverted_index.shape | 138 |
| abstract_inverted_index.short | 215 |
| abstract_inverted_index.study | 110 |
| abstract_inverted_index.three | 222 |
| abstract_inverted_index.water | 87 |
| abstract_inverted_index.which | 78 |
| abstract_inverted_index.Yellow | 31 |
| abstract_inverted_index.Zangbo | 11 |
| abstract_inverted_index.basin, | 14, 19, 24, 29, 34, 153, 192 |
| abstract_inverted_index.basin. | 40 |
| abstract_inverted_index.basins | 3 |
| abstract_inverted_index.change | 89 |
| abstract_inverted_index.during | 209 |
| abstract_inverted_index.focus: | 42 |
| abstract_inverted_index.memory | 51, 65, 76, 119, 219 |
| abstract_inverted_index.method | 71, 195 |
| abstract_inverted_index.model, | 108 |
| abstract_inverted_index.model. | 202 |
| abstract_inverted_index.recent | 44 |
| abstract_inverted_index.season | 158, 171, 212 |
| abstract_inverted_index.slight | 183 |
| abstract_inverted_index.varies | 155 |
| abstract_inverted_index.years, | 45 |
| abstract_inverted_index.(TWSC). | 90 |
| abstract_inverted_index.Lancang | 21 |
| abstract_inverted_index.Maillet | 148 |
| abstract_inverted_index.Salween | 16 |
| abstract_inverted_index.Tibetan | 6, 122 |
| abstract_inverted_index.Yangtze | 26 |
| abstract_inverted_index.Yarlung | 10 |
| abstract_inverted_index.basins, | 136 |
| abstract_inverted_index.between | 156 |
| abstract_inverted_index.concept | 47 |
| abstract_inverted_index.element | 57 |
| abstract_inverted_index.emerged | 53 |
| abstract_inverted_index.improve | 197 |
| abstract_inverted_index.months. | 223 |
| abstract_inverted_index.optimal | 112 |
| abstract_inverted_index.pivotal | 56 |
| abstract_inverted_index.region: | 1, 129 |
| abstract_inverted_index.season, | 176 |
| abstract_inverted_index.season. | 161 |
| abstract_inverted_index.storage | 88 |
| abstract_inverted_index.However, | 162 |
| abstract_inverted_index.Plateau, | 7 |
| abstract_inverted_index.Plateau. | 123 |
| abstract_inverted_index.compared | 172 |
| abstract_inverted_index.describe | 73, 116 |
| abstract_inverted_index.duration | 216 |
| abstract_inverted_index.exhibits | 213 |
| abstract_inverted_index.function | 82, 146 |
| abstract_inverted_index.insights | 126 |
| abstract_inverted_index.process, | 77 |
| abstract_inverted_index.proposed | 98 |
| abstract_inverted_index.science. | 63 |
| abstract_inverted_index.seasonal | 49, 74, 117 |
| abstract_inverted_index.According | 102 |
| abstract_inverted_index.catchment | 50, 118 |
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| abstract_inverted_index.functions | 114 |
| abstract_inverted_index.including | 8 |
| abstract_inverted_index.simulated | 93 |
| abstract_inverted_index.Boussinesq | 145 |
| abstract_inverted_index.connecting | 83 |
| abstract_inverted_index.consistent | 142 |
| abstract_inverted_index.relatively | 165 |
| abstract_inverted_index.improvement | 184 |
| abstract_inverted_index.performance | 105, 199 |
| abstract_inverted_index.simulation. | 188 |
| abstract_inverted_index.terrestrial | 86 |
| abstract_inverted_index.hydrological | 62, 125 |
| abstract_inverted_index.investigates | 111 |
| abstract_inverted_index.Precipitation | 64 |
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| abstract_inverted_index.precipitation | 75, 84, 96, 167, 218 |
| abstract_inverted_index.temperature-index | 194 |
| abstract_inverted_index.segmented-representation | 178 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 6 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/13 |
| sustainable_development_goals[0].score | 0.6800000071525574 |
| sustainable_development_goals[0].display_name | Climate action |
| citation_normalized_percentile.value | 0.19500774 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |