Hydrological modeling using remote sensing precipitation data in a Brazilian savanna basin. (April 2022)

• Record Type: Journal Article
• Authors: Junqueira, Rubens; Viola, Marcelo R.; Amorim, Jhones da S.; Camargos, Carla; de Mello, Carlos R.
• Is Part Of: Journal of South American earth sciences. Volume 115(2022)
• Publication Date: 2022-04
• Subjects: IMERG -- SUFI-2 -- SWAT -- TMPA -- Uncertainty analysis; Geology -- Latin America -- Periodicals; Earth sciences -- Latin America -- Periodicals; Geology -- Antarctica -- Periodicals; Earth sciences -- Antarctica -- Periodicals; Geology -- Caribbean Area -- Periodicals; Earth sciences -- Caribbean Area -- Periodicals; Géologie -- Amérique latine -- Périodiques; Sciences de la terre -- Amérique latine -- Périodiques; Géologie -- Antarctique -- Périodiques; Sciences de la terre -- Antarctique -- Périodiques; Géologie -- Caraïbes (Région) -- Périodiques; Sciences de la terre -- Caraïbes (Région) -- Périodiques; Earth sciences; Geology; Antarctica; Caribbean Area; Latin America; Periodicals; 550.5
• DOI: 10.1016/j.jsames.2022.103773
• Languages: English
• ISSNs: 0895-9811

Abstract

Abstract: Precipitation is the main input for hydrological models. However, due to limitations of rain gauge stations, satellite precipitation estimates have become a good alternative to precipitation information. In this context, this study aimed to validate the precipitation data with Tropical Rainfall Measuring Mission Multisatellite Precipitation Analysis (TMPA) and Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG) data, in addition to assessing the uncertainty and performance of the Soil and Water Assessment Tool (SWAT) using observed precipitation (OP), TMPA, and IMERG data. Statistical coefficients were used to validate TMPA and IMERG precipitation data. P-factor and r-factor were considered for the uncertainty analysis, while the Nash-Sutcliffe efficiency (NSE), its logarithmic version (LNSE), and the percent bias (PBIAS) were analyzed to characterize the model performance analysis in monthly time steps. There was an overestimation by TMPA and IMERG in the precipitation estimation, especially in the dry period. OP, TMPA, and IMERG setups presented satisfactory results for uncertainty and performance analysis in hydrological modeling. The IMERG setup generally showed better results than the TMPA setup, being a good alternative for hydrological modeling, especially in regions with scarce precipitation datasets. Highlights: TMPA and IMERG overestimate the rainfall in a Brazilian savanna basin. IMERG presented better results than TMPA in sensitivity and performance analysis. IMERG is a great alternative in regions with a lack of rain gauge.