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| 雅鲁藏布江流域多源降水产品评估及其在水文模拟中的应用 | |
| 引用本文： | 孙赫,苏凤阁.雅鲁藏布江流域多源降水产品评估及其在水文模拟中的应用[J].地理科学进展,2020,39(7):1126-1139. |
| 作者姓名： | 孙赫 苏凤阁 |
| 作者单位： | 1. 中国科学院青藏高原研究所环境变化与地表过程重点实验室,北京 100101 2. 中国科学院大学,北京 100049 3. 中国科学院青藏高原地球科学卓越创新中心,北京 100101 |
| 基金项目： | 国家自然科学基金项目(91747201);国家自然科学基金项目(41871057) |
| 摘 要： | 论文对比分析了1980—2016年基于站点插值降水数据CMA(China Meteorological Administration)和APHRODITE(Asian Precipitation-Highly-Resolved Observational Data Integration Towards Evaluation)、卫星遥感降水数据PERSIANN-CDR(Precipitation Estimation from Remotely Sensed Information using Artificial Neural Network-Climate Data Record)和GPM(Global Precipitation Measurement)、大气再分析数据GLDAS(Global Land Data Assimilation System)以及区域气候模式输出数据HAR(High Asia Refined analysis)在雅鲁藏布江7个子流域的降水时空描述,利用国家气象站点数据对各套降水数据进行单点验证,并以这6套降水数据驱动VIC(Variable Infiltration Capacity)大尺度陆面水文模型反向评估了各套降水产品在雅鲁藏布江各子流域径流模拟中的应用潜力。结果表明：① PERSIANN-CDR和GLDAS年均降水量最高(770~790 mm),其次是HAR和GPM(650~660 mm),CMA和APHRODITE年均降水量最低(460~500 mm)。除GPM外,其他降水产品在各子流域都能表现季风流域的降水特征,约70%~90%的年降水量集中在6—9月份。② 除PERSIANN-CDR和GLDAS外,其他降水产品皆捕捉到流域降水自东南向西北递减的空间分布特征。其中,HAR数据空间分辨率最高,表现出更详细的流域内部降水空间分布特征。③ 与对应网格内的国家气象站降水数据对比显示,APHRODITE、GPM和HAR降水整体低估(低估10%~30%),且严重低估的站点主要集中在下游(低估40%~120%)。PERSIANN-CDR和GLDAS整体表现为高估上游流域站点降水(高估28%~60%),但低估下游流域站点降水(低估11%~21%)。④ 在流域径流模拟上,当前的6套降水产品在精度或时段上仍无法满足水文模型模拟的需求。⑤ 通过水文模型反向评估,6套降水产品中区域气候模式输出的HAR在流域平均降水量和季节分配上更合理。 |
| 关 键 词： | 降水评估 水文模拟 雅鲁藏布江 青藏高原 |
| 收稿时间： | 2019-05-09 |
| 修稿时间： | 2019-07-09 |
Evaluation of multiple precipitation datasets and their potential utilities in hydrologic modeling over the Yarlung Zangbo River Basin |
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| SUN He,SU Fengge.Evaluation of multiple precipitation datasets and their potential utilities in hydrologic modeling over the Yarlung Zangbo River Basin[J].Progress in Geography,2020,39(7):1126-1139. | |
| Authors: | SUN He SU Fengge |
| Institution: | 1. Key Laboratory of Tibetan Environment Changes and Land Surface Processes, Institute of Tibetan Plateau Research, CAS, Beijing 100101, China 2. University of Chinese Academy of Sciences, Beijing 100049, China 3. CAS Center for Excellence in Tibetan Plateau Earth Sciences, Beijing 100101, China |
| Abstract: | The gauge-based precipitation data from the National Climate Center, China Meteorological Administ-ration (CMA), Asian Precipitation-Highly-Resolved Observational Data Integration Towards Evaluation (APHRODITE), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Network-Climate Data Record (PERSIANN-CDR), Global Precipitation Measurement (GPM), Global Land Data Assimilation System (GLDAS), High Asia Refined analysis (HAR) are compared with each other and evaluated by the precipitation data from 16 national meteorological stations during 1980-2016 in the Yarlung Zangbo River and its sub-basins. The potential utilities of these multiple precipitation datasets are then systematically evaluated as inputs for the variable infiltration capacity (VIC) macroscale land surface hydrologic model. The results show that: 1) PERSIANN-CDR and GLDAS contain the largest precipitation estimates among the six datasets with mean annual precipitation of 770-790 mm, followed by the HAR and GPM (650-660 mm), while CMA and APHRODITE contain the lowest precipitation estimates with mean annual precipitation of 460-500 mm. All the products can detect the large-scale monsoon-dominated precipitation regime in the Yarlung Zangbo River and its sub-basins with 70%-90% of annual total precipitation occurring in June-September except the GPM. 2) The general spatial pattern of the annual mean precipitation fields is roughly in agreement among the six datasets, with a decreasing trend from the southeast to the northwest in the Yarlung Zangbo River Basin except the PERSIANN-CDR and GLDAS. 3) Relative to the data from the national meteorological stations, APHRODITE, GPM, and HAR generally underestimate precipitation by 10%-30%, while PERSIANN-CDR and GLDAS overestimate precipitation from stations in upstream sub-basins by 28%-60% and underestimate precipitation from stations in downstream sub-basins by 11%-21%. 4) The six precipitation datasets cannot satisfy the needs of hydrological simulation in term of accuracy or period in the basin. 5) HAR precipitation data—output of regional climate model—show more reasonable amount and seasonal pattern among the six datasets in the upper Brahmaputra according to the inverse evaluation by VIC hydrological model. |
| Keywords: | precipitation estimate hydrological simulation the Yarlung Zangbo River Tibetan Plateau |
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