Precipitation estimations based on remote sensing compared with data from weather stations over agricultural region of Argentina pampas
Global patterns of precipitation have changed due to the increase in temperature as a result of climate change. Measuring the amount of precipitation at a given location using surface instruments is relatively simple. However, the great spatial and temporal variability of the intensity, type and occ...
| Autores principales: | , , , , |
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| Formato: | Artículo |
| Lenguaje: | Inglés |
| Publicado: |
Elsevier
2021
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| Materias: | |
| Acceso en línea: | http://hdl.handle.net/20.500.12123/10833 https://www.sciencedirect.com/science/article/pii/S2352938521001257 https://doi.org/10.1016/j.rsase.2021.100589 |
| _version_ | 1855484664523784192 |
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| author | Ovando, Gustavo Gabriel Sayago, Silvina Bellini Saibene, Yanina Noemi Belmonte, María Laura Bocco, Mónica |
| author_browse | Bellini Saibene, Yanina Noemi Belmonte, María Laura Bocco, Mónica Ovando, Gustavo Gabriel Sayago, Silvina |
| author_facet | Ovando, Gustavo Gabriel Sayago, Silvina Bellini Saibene, Yanina Noemi Belmonte, María Laura Bocco, Mónica |
| author_sort | Ovando, Gustavo Gabriel |
| collection | INTA Digital |
| description | Global patterns of precipitation have changed due to the increase in temperature as a result of climate change. Measuring the amount of precipitation at a given location using surface instruments is relatively simple. However, the great spatial and temporal variability of the intensity, type and occurrence of this phenomenon, makes direct and uniformly calibrated measurements difficult in large regions. Satellite information is an important alternative to describe precipitation events; the Global Precipitation Measurement (GPM) mission estimates precipitation, considering different time periods, with three products Integrated Multi-Satellite Retrievals for GPM (IMERG), in near real time. This study evaluates and quantifies, temporal and spatially, the monthly precipitation estimated by Early (IMERG-E), Late (IMERG-L) and Final (IMERG-F) products compared with data from weather stations located in agricultural areas of the Pampas region in Argentina. Data of precipitation belonging to meteorological stations located at four provinces: Buenos Aires, Córdoba, La Pampa and Santa Fe, for 2014–2018 periods, were considered. The spatial performance of IMERG was evaluated using statistical coefficients and Taylor diagrams, considering at region, province and stations level. The adjustment of the products increased from IMERG-E to IMERG–F, obtaining R2 values between 0.86 and 0.95 and RMSE from 14.2 to 29.3 mm, the best results corresponding to Córdoba and the worst to La Pampa. The performance of GPM products varies temporally; IMERG-F presented a higher correlation coefficient and a lower percent root mean square error in warm than in cold seasons. The results indicate that GPM can effectively capture the amount and patterns of monthly precipitation over the Pampas region of Argentina, which is important for its application to agricultural production and disaster prevention. |
| format | Artículo |
| id | INTA10833 |
| institution | Instituto Nacional de Tecnología Agropecuaria (INTA -Argentina) |
| language | Inglés |
| publishDate | 2021 |
| publishDateRange | 2021 |
| publishDateSort | 2021 |
| publisher | Elsevier |
| publisherStr | Elsevier |
| record_format | dspace |
| spelling | INTA108332021-12-02T13:03:07Z Precipitation estimations based on remote sensing compared with data from weather stations over agricultural region of Argentina pampas Ovando, Gustavo Gabriel Sayago, Silvina Bellini Saibene, Yanina Noemi Belmonte, María Laura Bocco, Mónica Precipitación Atmosférica Teledetección Meteorología Precipitation Remote Sensing Meteorology Región Pampeana Taylor Diagram Global patterns of precipitation have changed due to the increase in temperature as a result of climate change. Measuring the amount of precipitation at a given location using surface instruments is relatively simple. However, the great spatial and temporal variability of the intensity, type and occurrence of this phenomenon, makes direct and uniformly calibrated measurements difficult in large regions. Satellite information is an important alternative to describe precipitation events; the Global Precipitation Measurement (GPM) mission estimates precipitation, considering different time periods, with three products Integrated Multi-Satellite Retrievals for GPM (IMERG), in near real time. This study evaluates and quantifies, temporal and spatially, the monthly precipitation estimated by Early (IMERG-E), Late (IMERG-L) and Final (IMERG-F) products compared with data from weather stations located in agricultural areas of the Pampas region in Argentina. Data of precipitation belonging to meteorological stations located at four provinces: Buenos Aires, Córdoba, La Pampa and Santa Fe, for 2014–2018 periods, were considered. The spatial performance of IMERG was evaluated using statistical coefficients and Taylor diagrams, considering at region, province and stations level. The adjustment of the products increased from IMERG-E to IMERG–F, obtaining R2 values between 0.86 and 0.95 and RMSE from 14.2 to 29.3 mm, the best results corresponding to Córdoba and the worst to La Pampa. The performance of GPM products varies temporally; IMERG-F presented a higher correlation coefficient and a lower percent root mean square error in warm than in cold seasons. The results indicate that GPM can effectively capture the amount and patterns of monthly precipitation over the Pampas region of Argentina, which is important for its application to agricultural production and disaster prevention. EEA Anguil Fil: Ovando, Gustavo. Universidad Nacional de Córdoba. Facultad de Ciencias Agropecuarias; Argentina Fil: Sayago, Silvina. Universidad Nacional de Córdoba. Facultad de Ciencias Agropecuarias; Argentina Fil: Bellini Saibene, Yanina. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Anguil; Argentina Fil: Belmonte, María Laura. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Anguil; Argentina Fil: Bocco, Monica. Universidad Nacional de Córdoba. Facultad de Ciencias Agropecuarias; Argentina 2021-12-02T12:48:00Z 2021-12-02T12:48:00Z 2021-08-01 info:ar-repo/semantics/artículo info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion http://hdl.handle.net/20.500.12123/10833 https://www.sciencedirect.com/science/article/pii/S2352938521001257 2352-9385 https://doi.org/10.1016/j.rsase.2021.100589 eng info:eu-repo/semantics/restrictedAccess application/pdf Elsevier Remote Sensing Applications: Society and Environment 23 : Article 100589. (August 2021) |
| spellingShingle | Precipitación Atmosférica Teledetección Meteorología Precipitation Remote Sensing Meteorology Región Pampeana Taylor Diagram Ovando, Gustavo Gabriel Sayago, Silvina Bellini Saibene, Yanina Noemi Belmonte, María Laura Bocco, Mónica Precipitation estimations based on remote sensing compared with data from weather stations over agricultural region of Argentina pampas |
| title | Precipitation estimations based on remote sensing compared with data from weather stations over agricultural region of Argentina pampas |
| title_full | Precipitation estimations based on remote sensing compared with data from weather stations over agricultural region of Argentina pampas |
| title_fullStr | Precipitation estimations based on remote sensing compared with data from weather stations over agricultural region of Argentina pampas |
| title_full_unstemmed | Precipitation estimations based on remote sensing compared with data from weather stations over agricultural region of Argentina pampas |
| title_short | Precipitation estimations based on remote sensing compared with data from weather stations over agricultural region of Argentina pampas |
| title_sort | precipitation estimations based on remote sensing compared with data from weather stations over agricultural region of argentina pampas |
| topic | Precipitación Atmosférica Teledetección Meteorología Precipitation Remote Sensing Meteorology Región Pampeana Taylor Diagram |
| url | http://hdl.handle.net/20.500.12123/10833 https://www.sciencedirect.com/science/article/pii/S2352938521001257 https://doi.org/10.1016/j.rsase.2021.100589 |
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