Journal article

Publication year: 2022

Journal: Journal of South American Earth Sciences

Volume number: 115

ISSN: 0895-9811

eISSN: 1873-0647

Open access status: Bronze

DOI Link: https://doi.org/10.1016/j.jsames.2022.103773

Publisher: Elsevier

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.

Harvard Citation style: Junqueira, R., Viola, M., Amorim, J., Camargos, C. and de Mello, C. (2022) Hydrological modeling using remote sensing precipitation data in a Brazilian savanna basin, Journal of South American Earth Sciences, 115, Article 103773. https://doi.org/10.1016/j.jsames.2022.103773

APA Citation style: Junqueira, R., Viola, M., Amorim, J., Camargos, C., & de Mello, C. (2022). Hydrological modeling using remote sensing precipitation data in a Brazilian savanna basin. Journal of South American Earth Sciences. 115, Article 103773. https://doi.org/10.1016/j.jsames.2022.103773