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Assessment of TRMM rainfall data for flood modelling in three contrasting catchments in Java, Indonesia

Authors :
Suroso Suroso
Purwanto Bekti Santoso
Stephen Birkinshaw
Chris Kilsby
Andras Bardossy
Edvin Aldrian
Source :
Journal of Hydroinformatics, Vol 25, Iss 3, Pp 797-814 (2023)
Publication Year :
2023
Publisher :
IWA Publishing, 2023.

Abstract

This study investigates the use of the Tropical Rainfall Measurement Mission's (TRMM) rainfall data for predicting water flows and flood events in three catchments on the island of Java, Indonesia, namely, Ciliwung, Citarum and Bengawan Solo. The Shetran model was used for rainfall-runoff simulations, with rainfall input obtained from measured rain gauges (hourly and daily) and TRMM (3-hourly and daily). The daily Nash Sutcliffe Efficiency (NSE) values for the model calibration period were 0.75, 0.70 and 0.85 using rain gauge data and 0.44, 0.44 and 0.75 using the TRMM rainfall data. For the validation period, the NSE values were 0.71, 0.62 and 0.89 using rain gauge data and 0.26, 0.61 and 0.58 for the TRMM data. The Critical Success Index for predicting flooding events was improved using rain gauge data compared to using TRMM data. The results demonstrate that rain gauge data are systematically superior to TRMM rainfall data when used for simulating discharges and predicting flooding events. These findings suggest that rain gauge data are preferred for flood early warning systems in tropical rainfall regimes and that if TRMM or similar satellite rainfall data are used, the evaluated flood risks should be treated with extreme caution. HIGHLIGHTS This study investigates the quality of the Tropical Rainfall Measurement Mission's (TRMM) rainfall data using the Shetran hydrological model.; Two rainfall products (observed rainfall and TRMM rainfall) have been used to find out which one has the greatest potential to predict flooding.; The observed rainfall measurement data produce a better simulation discharge than the one using TRMM rainfall data.;

Details

Language :
English
ISSN :
14647141 and 14651734
Volume :
25
Issue :
3
Database :
Directory of Open Access Journals
Journal :
Journal of Hydroinformatics
Publication Type :
Academic Journal
Accession number :
edsdoj.1d7324bf31e44f3d8f8f16f4b93de493
Document Type :
article
Full Text :
https://doi.org/10.2166/hydro.2023.132