doi:10.12202/j.0476-0301.2020068马秋梅^1,,,
熊立华²,
张验科¹,
纪昌明¹
1.华北电力大学可再生能源学院，北京，102206
2.武汉大学水资源与水电工程科学国家重点实验室，湖北武汉，430072
基金项目:国家自然科学基金资助项目（51709105）；中央高校基本科研基金资助项目（2019MS031）

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通讯作者:马秋梅（1988—），女，华北电力大学博士后. 研究方向：水文水资源. e-mail：simonemaqm@163.com

中图分类号:TV25；P339

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出版历程

收稿日期:2019-10-27
网络出版日期:2020-03-02
刊出日期:2020-04-01

Multi-source uncertainties in streamflow modeling driven by TRMM satellite precipitation

Qiumei MA^1,,,
Lihua XIONG²,
Yanke ZHANG¹,
Changming JI¹
1. Renewable Energy School, North China Electric Power University,102206， Beijing , China
2. State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, 430072，Wuhan , Hubei，China



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摘要
摘要:具有较高时空分辨率的卫星降水估计（satellite precipitation estimate，SPE）越来越多地被应用于水文模拟。但是由于包含误差，SPE在水文应用中会带来输入不确定性，相关问题尚未研究清楚。本研究旨在量化两种降水输入：卫星降水产品(热带降水观测任务 tropical rainfall measurement mission,TRMM)和相应的站点降水，分别驱动两个水文模型（集总式Ge′nie rural (GR)模型和分布式coupled routing and excess storage (CREST)模型，进行降雨-径流过程模拟时的多源不确定性。使用方差分解法对多源不确定性组分进行划分。为验证所提框架的有效性，选择赣江流域外洲水文站的径流进行模拟。结果表明：卫星降水和站点降水驱动下CREST模拟所得总不确定性，均低于对应情况下GR模拟所得结果；且在两种降水产品和两个水文模型结合的四种情景中，卫星降水驱动CREST模型模拟所得输入不确定性最低。上述结果表明，分布式CREST模型比集总式GR模型能更好地利用TRMM卫星降水的空间分布信息。
关键词:不确定性分析/
卫星降水/
TMPA/
水文应用效果
Abstract:Satellite Precipitation Estimate (SPE) with high spatio-temporal resolutions is frequently applied to hydrological modeling. However, uncertainty in hydrological modeling due to bias and errors has not been investigated. In the present work multi-source uncertainties in rainfall-runoff of two hydrological models (i.e., lumped GR model and distributed CREST model) forced by two precipitation inputs (Tropical Rainfall Measurement Mission post-processed 3B42v7 product and corresponding gauge rainfall) were quantified. The multi-source uncertainty components were partitioned by variance decomposition. To verify effect of proposed framework, streamflow at Waizhou outlet in the Ganjiang River Basin was simulated. Total uncertainty in CREST modeling driven by both SPE and gauge precipitation was found to be lower than in Ge′nie Rural (GR) modeling. Among 4 scenarios for 2 precipitation inputs combined with 2 hydrological models, input uncertainty of Coupled Routing and Excess Storage (CREST) modeling driven by SPE was found to be the lowest. These data indicate that distributed CREST model is better than lumped GR model to take advantage of the spatial information in TRMM satellite precipitation data.
Key words:uncertainty analysis/
satellite precipitation/
TMPA/
hydrological application