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Article

Assessment of Remote Sensing and Re-Analysis Estimates of Regional Precipitation over Mato Grosso, Brazil

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Programa de Pós-Graduação em Física Ambiental, Instituto de Física, Universidade Federal de Mato Grosso, 2367, Av. Fernando Corrêa da Costa, Cuiabá, MT 78060-900, Brazil
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Instituto de Física, Universidade Federal de Mato Grosso, 2367, Av. Fernando Corrêa da Costa, Cuiabá, MT 78060-900, Brazil
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Instituto Federal de Mato Grosso, Av. Juliano da Costa Marques, Cuiabá, MT 78050-560, Brazil
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Biological Sciences Department, California State University San Marcos, 333 S. Twin Oaks Valley Rd., San Marcos, CA 92096, USA
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New Mexico Water Resources Institute and Department of Animal and Range Sciences, New Mexico State University, Las Cruces, NM 88003, USA
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Instituto de Educação Agricultura e Ambiente, Universidade Federal do Amazonas, 786, Rua 29 de Agosto, Humaitá, AM 69800-000, Brazil
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Authors to whom correspondence should be addressed.
Academic Editor: Evangelos Rozos
Water 2021, 13(3), 333; https://doi.org/10.3390/w13030333
Received: 16 November 2020 / Revised: 24 January 2021 / Accepted: 25 January 2021 / Published: 29 January 2021
(This article belongs to the Special Issue Remote Sensing in Water Cycle Management)
The spatial and temporal distribution of precipitation is of great importance for the rain-fed agricultural production and the socioeconomics of Mato Grosso (MT), Brazil. MT has a sparse network of ground rain gauges that limits the effective use of precipitation information for sustainable agricultural production and water resources in the region. Several gridded precipitation products from remote sensing and reanalysis of land surface models are currently available that can enhance the use of such information. However, these products are available at different spatial and temporal resolutions which add some challenges to stakeholders (users) to identify their appropriateness for specific applications (e.g., irrigation requirements, length of growing season, and drought monitoring). Thus, it is necessary to provide an assessment of the reliability of these precipitation estimates. The objective of this work was to compare regional precipitation estimates over MT as provided by the Global Land Data Assimilation (GLDAS), Modern-Era Retrospective Analysis for Research and Applications (MERRA), Tropical Rainfall Measurement Mission (TRMM), Global Precipitation Measurement (GPM), and the Global Precipitation Climatology Project (GPCP) with ground-based measurements. The comparison was conducted for the 2000–2018 period at eleven ground-based weather stations that covered different climate zones in MT using daily, monthly, and annual temporal resolutions. The comparison used the Pearson correlation index–r, Willmott index–d, root mean square error—RMSE, and the Wilks methods. The results showed GPM and GLDAS estimates did not differ significantly with the measured daily, monthly, and annual precipitation. TRMM estimates slightly overestimated daily precipitation by about 4.7% but did not show significant difference on the monthly and annual scales when compared with local measurements. The GPCP underestimated annual precipitation by about 7.1%. MERRA underestimated daily, monthly, and annual precipitation by about 22.9% on average. In general, all products satisfactorily estimated monthly precipitation, and most of them satisfactorily estimated annual precipitation; however, they showed low accuracy when estimating daily precipitation. The TRMM, GPM, GPCP, and GLDAS estimates had the highest performance, from high to low, while MERRA showed the lowest performance. The findings of this study can be used to support the decision-making process in the region in application related to water resources management, sustainability of agriculture production, and drought management. View Full-Text
Keywords: GLDAS; MERRA; TRMM; GPM and GPCP; spatial and temporal variability; South America; surface observations GLDAS; MERRA; TRMM; GPM and GPCP; spatial and temporal variability; South America; surface observations
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MDPI and ACS Style

Junior, A.L.P.; Biudes, M.S.; Machado, N.G.; Vourlitis, G.L.; Geli, H.M.E.; Santos, L.O.F.d.; Querino, C.A.S.; Ivo, I.O.; Neto, N.L. Assessment of Remote Sensing and Re-Analysis Estimates of Regional Precipitation over Mato Grosso, Brazil. Water 2021, 13, 333. https://doi.org/10.3390/w13030333

AMA Style

Junior ALP, Biudes MS, Machado NG, Vourlitis GL, Geli HME, Santos LOFd, Querino CAS, Ivo IO, Neto NL. Assessment of Remote Sensing and Re-Analysis Estimates of Regional Precipitation over Mato Grosso, Brazil. Water. 2021; 13(3):333. https://doi.org/10.3390/w13030333

Chicago/Turabian Style

Junior, Altemar L.P., Marcelo S. Biudes, Nadja G. Machado, George L. Vourlitis, Hatim M.E. Geli, Luiz O.F.d. Santos, Carlos A.S. Querino, Israel O. Ivo, and Névio L. Neto. 2021. "Assessment of Remote Sensing and Re-Analysis Estimates of Regional Precipitation over Mato Grosso, Brazil" Water 13, no. 3: 333. https://doi.org/10.3390/w13030333

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