Assessing the performance of near real-time rainfall products to represent spatiotemporal characteristics of extreme events

case study of a subtropical catchment in south-eastern Brazil

Journal article (2018)

Authors

M.A. Laverde Barajas IHE Delft Institute for Water Education, Water Resources - CEG
Gerald Corzo Perez IHE Delft Institute for Water Education
J. G. Dalfré Filho University of Campinas
D.P. Solomatine IHE Delft Institute for Water Education, Water Resources - CEG
Research Group
Water Resources (CEG) (TU Delft)
Copyright: © 2018 M.A. Laverde Barajas, G. A. Corzo Perez, J. G. Dalfré Filho, D.P. Solomatine
DOI
help_outline
https://doi.org/10.1080/01431161.2018.1475773
More Info
expand_more

Published Date

2018

Language

English

Reuse Rights

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.

Faculty

Civil Engineering & Geosciences

Department

Water Management

Research Group

Water Resources

Abstract

This study evaluates the performance of four Near Real-Time (NRT) satellite rainfall products in estimating the spatiotemporal characteristics of different extreme rainfall events in a subtropical catchment in south-eastern Brazil. The Climate Prediction Centre Morphing algorithm (CMORPH), Tropical Rainfall Measuring Mission, Multisatellite Precipitation Analysis in real time (TMPA-RT), the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Global Cloud Classification System (PERSIANN-GCCS), and the Hydro-Estimator are evaluated for monsoon seasons, based on their capability to represent four types of rainfall events distinguished for: (1) local and short duration, (2) long-lasting event, (3) short and spatial extent, and (4) spatial extent and long lasting. Since the events are defined relative to a percentile, the relative performance variation at different threshold levels (75th, 90th, and 95th) is also evaluated. The data from the 13 Automatic Weather Stations (AWSs) for the period from 2007 to 2014 are used as the reference. The results show that the product performance highly depends on the spatiotemporal characteristics of rainfall events. All four products tend to overestimate intense rainfall in the study area, especially in high altitude zones. CMORPH had the best overall performance to estimate different types of extreme spatiotemporal events. The results allow for developing a better understanding of the accuracy of the NRT products for the estimation of different types of rainfall events.