Decomposing satellite-based rainfall errors in flood estimation
Hydrological responses using a spatiotemporal object-based verification method
M. Laverde-Barajas (TU Delft - Water Resources, IHE Delft Institute for Water Education)
G. A. Corzo Perez (IHE Delft Institute for Water Education)
F. Chishtie (Spatial Informatics Group, LLC, SERVIR-Mekong)
A. Poortinga (SERVIR-Mekong, Spatial Informatics Group, LLC)
R. Uijlenhoet (Wageningen University & Research)
D. P. Solomatine (TU Delft - Water Resources, IHE Delft Institute for Water Education)
More Info
expand_more
Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.
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.