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Estimating river discharge from rainfall satellite data through simple statistical models

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Abstract

Quantitative knowledge of river discharge measurements is essential for understanding coastal and estuarine dynamics and salinity variations. However, direct measurements are scarce for a large portion of rivers in Brazil. In this study, five simple models based on remote sensing and local rainfall datasets (MERGE) for the Ribeira de Iguape catchment are used to estimate the Valo Grande Channel (VGC) discharge on the southeastern coast of Brazil. These models use linear, quadratic, exponential, and two different multiple linear regression methods. The predicted VGC discharge time series resulting from each model is compared with the estimated time series based on in situ data from the Water and Electric Energy Department (DAEE in Portuguese). The estimated time series presented reasonable results, with skills varying from 0.84 to 0.92 and Nash–Sutcliffe efficiency (NSE) indices varying from 0.54 to 0.75, with the highest values corresponding to the multiple linear regression models. This methodology allowed us to reproduce longer time series at a daily frequency, as well as monthly averages between 2000 and 2020.

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Data availability

The MERGE datasets used and analyzed during the current study are available at http://ftp.cptec.inpe.br/modelos/tempo/MERGE/GPM/DAILY. For more information about the data results for the implemented models, please contact Paula Birocchi.

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Acknowledgements

The authors thank the CPTEC/INPE for providing the MERGE dataset, downloaded through the FTP server at http://ftp.cptec.inpe.br/modelos/tempo/MERGE/GPM/DAILY. We also thank the CNPQ (Conselho Nacional de Desenvolvimento Científico e Tecnológico) for providing the PhD scholarship for Paula Birocchi during the development of this study and Ligia Dias Araujo for helping to create Fig. 1.

Code availability

The Python codes used here can be requested via email to paula.birocchi@usp.br.

Funding

Author Paula Birocchi has received a PhD scholarship from CNPQ (Conselho Nacional de Desenvolvimento Científico e Tecnológico).

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  1. Instituto Oceanográfico, Universidade de São Paulo, São Paulo, Brazil

    Paula Birocchi, Danilo Augusto Silva, Dalton Kei Sasaki & Marcelo Dottori

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Contributions

All authors contributed to the study’s conception, design, and investigation. The methodology was chosen and the formal analysis was made by Paula Birocchi, Danilo Augusto Silva, and Dalton Kei Sasaki. The software usage and validation were made by Paula Birocchi. The visual elements of the paper were made by Paula Birocchi and Dalton Kei Sasaki. The first draft of the manuscript was written by Paula Birocchi and all authors commented on previous versions of the manuscript. All authors contributed to writing, reviewing, and editing the manuscript. All authors read and approved the final manuscript.

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Correspondence to Paula Birocchi.

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Birocchi, P., Silva, D.A., Sasaki, D.K. et al. Estimating river discharge from rainfall satellite data through simple statistical models. Theor Appl Climatol 153, 241–261 (2023). https://doi.org/10.1007/s00704-023-04459-4

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