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Keywords = Tulijá River Basin

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23 pages, 10000 KiB  
Article
Assessment of PERSIANN Satellite Products over the Tulijá River Basin, Mexico
by Lorenza Ceferino-Hernández, Francisco Magaña-Hernández, Enrique Campos-Campos, Gabriela Adina Morosanu, Carlos E. Torres-Aguilar, René Sebastián Mora-Ortiz and Sergio A. Díaz
Remote Sens. 2024, 16(14), 2596; https://doi.org/10.3390/rs16142596 - 16 Jul 2024
Cited by 2 | Viewed by 1565
Abstract
Precipitation is a fundamental component of the Earth’s hydrological cycle. Therefore, monitoring precipitation is paramount, as accurate information is needed to prevent natural hydrological disasters, such as floods and droughts. However, measuring precipitation using rain gauges is complicated due to their sparse spatial [...] Read more.
Precipitation is a fundamental component of the Earth’s hydrological cycle. Therefore, monitoring precipitation is paramount, as accurate information is needed to prevent natural hydrological disasters, such as floods and droughts. However, measuring precipitation using rain gauges is complicated due to their sparse spatial distribution. Satellite precipitation products (SPPs) are an alternative source of rainfall data. This study aimed to evaluate the performance of PERSIANN-CCS and PDIR-Now SPPs over the Tulijá River Basin (Chiapas, Mexico) using scatter plots, categorical statistics, descriptive statistics, and decomposing total bias. Additionally, bias correction was performed using the quantile mapping (QM) method. QM is a technique used to improve the fit of SPPs with respect to rainfall observations through a transfer function, aiming to reduce systematic errors in SPPs. The results indicate that the PDIR-Now product tends to overestimate rainfall to a large extent, thus showing better performance in detecting rain events. Meanwhile, PERSIANN-CCS underestimates precipitation to a lesser extent. The findings of this study demonstrate that correcting the bias of SPPs improves estimations of rainfall records, thereby reducing the percentage bias and root mean square error. Full article
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