Abstract A spatiotemporal object-based rainfall analysis method is used to evaluate the hydrological response of two systematic satellite error sources for storm estimation in the Capivari catchment, Brazil. This method is called Spatiotemporal Contiguous Object-based Rainfall Analysis (ST-CORA) specifically evaluates the error structure of satellite-based rainfall products using a 3D pattern clustering algorithm. Errors due to location and magnitude in the Near Real-time (NRT) CMORPH product are subtracted by adjusting the shift and the intensity distribution with respect to a storm object obtained from gauge-adjusted weather radar. Synthetic scenarios of each error source are used as forcing for hourly calibrated distributed hydrological ‘wflow-sbm’ model to evaluate the main sources of systematic errors in the hydrological response. Two types of storm events in the study area are evaluated: short-lived and a long-lived storm. The results indicate that the spatiotemporal characteristics obtained by ST-CORA clearly reflect the main source of errors of the CMORPH storm detection. It is found that location is the main source of error for the short-lived storm event, while volume is the main source in the long-lived storm event. The subtraction of both errors leads to an important reduction of the simulated streamflow in the catchment. The method applied can be useful in bias correction schemes for satellite estimations especially for extreme precipitation events.

中文翻译：

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在洪水估计中分解基于卫星的降雨误差：使用基于时空对象的验证方法的水文响应

摘要 使用基于时空对象的降雨分析方法来评估巴西卡皮瓦里流域风暴估计的两个系统卫星误差源的水文响应。这种方法称为时空连续基于对象的降雨分析（ST-CORA），专门使用 3D 模式聚类算法评估基于卫星的降雨产品的误差结构。近实时 (NRT) CMORPH 产品中由位置和幅度引起的误差通过调整偏移和强度分布相对于从仪表调整的天气雷达获得的风暴对象来减去。每个误差源的合成情景被用作每小时校准的分布式水文“wflow-sbm”模型的驱动力，以评估水文响应中系统误差的主要来源。评估了研究区域内两种类型的风暴事件：短期和长期风暴。结果表明，ST-CORA获得的时空特征清晰地反映了CMORPH风暴检测的主要误差来源。发现位置是短期风暴事件的主要误差来源，而体积是长期风暴事件的主要误差来源。减去这两个误差会导致流域中模拟流量的显着减少。所应用的方法可用于卫星估计的偏差校正方案，特别是对于极端降水事件。结果表明，ST-CORA获得的时空特征清晰地反映了CMORPH风暴检测的主要误差来源。发现位置是短期风暴事件的主要误差来源，而体积是长期风暴事件的主要误差来源。减去这两个误差会导致流域中模拟流量的显着减少。所应用的方法可用于卫星估计的偏差校正方案，特别是对于极端降水事件。结果表明，ST-CORA获得的时空特征清晰地反映了CMORPH风暴检测的主要误差来源。发现位置是短期风暴事件的主要误差来源，而体积是长期风暴事件的主要误差来源。减去这两个误差会导致流域中模拟流量的显着减少。所应用的方法可用于卫星估计的偏差校正方案，特别是对于极端降水事件。