Assessment of Remote Sensing and Re-Analysis Estimates of Regional Precipitation over Mato Grosso, Brazil
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 agricult...
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2021-01-01
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Article |
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DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Altemar L. Pedreira Junior Marcelo S. Biudes Nadja G. Machado George L. Vourlitis Hatim M. E. Geli Luiz Octávio F. dos Santos Carlos A. S. Querino Israel O. Ivo Névio Lotufo Neto |
spellingShingle |
Altemar L. Pedreira Junior Marcelo S. Biudes Nadja G. Machado George L. Vourlitis Hatim M. E. Geli Luiz Octávio F. dos Santos Carlos A. S. Querino Israel O. Ivo Névio Lotufo Neto Assessment of Remote Sensing and Re-Analysis Estimates of Regional Precipitation over Mato Grosso, Brazil Water GLDAS MERRA TRMM GPM and GPCP spatial and temporal variability South America |
author_facet |
Altemar L. Pedreira Junior Marcelo S. Biudes Nadja G. Machado George L. Vourlitis Hatim M. E. Geli Luiz Octávio F. dos Santos Carlos A. S. Querino Israel O. Ivo Névio Lotufo Neto |
author_sort |
Altemar L. Pedreira Junior |
title |
Assessment of Remote Sensing and Re-Analysis Estimates of Regional Precipitation over Mato Grosso, Brazil |
title_short |
Assessment of Remote Sensing and Re-Analysis Estimates of Regional Precipitation over Mato Grosso, Brazil |
title_full |
Assessment of Remote Sensing and Re-Analysis Estimates of Regional Precipitation over Mato Grosso, Brazil |
title_fullStr |
Assessment of Remote Sensing and Re-Analysis Estimates of Regional Precipitation over Mato Grosso, Brazil |
title_full_unstemmed |
Assessment of Remote Sensing and Re-Analysis Estimates of Regional Precipitation over Mato Grosso, Brazil |
title_sort |
assessment of remote sensing and re-analysis estimates of regional precipitation over mato grosso, brazil |
publisher |
MDPI AG |
series |
Water |
issn |
2073-4441 |
publishDate |
2021-01-01 |
description |
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. |
topic |
GLDAS MERRA TRMM GPM and GPCP spatial and temporal variability South America |
url |
https://www.mdpi.com/2073-4441/13/3/333 |
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doaj-2445862826d24d57a79be57755c37a0e2021-01-30T00:04:08ZengMDPI AGWater2073-44412021-01-011333333310.3390/w13030333Assessment of Remote Sensing and Re-Analysis Estimates of Regional Precipitation over Mato Grosso, BrazilAltemar L. Pedreira Junior0Marcelo S. Biudes1Nadja G. Machado2George L. Vourlitis3Hatim M. E. Geli4Luiz Octávio F. dos Santos5Carlos A. S. Querino6Israel O. Ivo7Névio Lotufo Neto8Programa de Pós-Graduação em Física Ambiental, Instituto de Física, Universidade Federal de Mato Grosso, 2367, Av. Fernando Corrêa da Costa, Cuiabá, MT 78060-900, BrazilInstituto de Física, Universidade Federal de Mato Grosso, 2367, Av. Fernando Corrêa da Costa, Cuiabá, MT 78060-900, BrazilInstituto Federal de Mato Grosso, Av. Juliano da Costa Marques, Cuiabá, MT 78050-560, BrazilBiological Sciences Department, California State University San Marcos, 333 S. Twin Oaks Valley Rd., San Marcos, CA 92096, USANew Mexico Water Resources Institute and Department of Animal and Range Sciences, New Mexico State University, Las Cruces, NM 88003, USAPrograma de Pós-Graduação em Física Ambiental, Instituto de Física, Universidade Federal de Mato Grosso, 2367, Av. Fernando Corrêa da Costa, Cuiabá, MT 78060-900, BrazilInstituto de Educação Agricultura e Ambiente, Universidade Federal do Amazonas, 786, Rua 29 de Agosto, Humaitá, AM 69800-000, BrazilPrograma de Pós-Graduação em Física Ambiental, Instituto de Física, Universidade Federal de Mato Grosso, 2367, Av. Fernando Corrêa da Costa, Cuiabá, MT 78060-900, BrazilPrograma de Pós-Graduação em Física Ambiental, Instituto de Física, Universidade Federal de Mato Grosso, 2367, Av. Fernando Corrêa da Costa, Cuiabá, MT 78060-900, BrazilThe 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.https://www.mdpi.com/2073-4441/13/3/333GLDASMERRATRMMGPM and GPCPspatial and temporal variabilitySouth America |