Operational implementation of satellite-rain gauge data merging for hydrological modeling.
Systems exposed to hydroclimatic variability, such as the integrated electric system in Uruguay, increasingly require real-time multiscale information to optimize management. Monitoring of the precipitation field is key to inform the future hydroelectric energy availability. We present an operationa...
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| Médium: | article |
| Jazyk: | angličtina |
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2021
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| On-line přístup: | https://www.mdpi.com/2073-4441/13/4/533 https://hdl.handle.net/20.500.12008/38410 |
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| _version_ | 1868890100209811456 |
|---|---|
| author | De Vera, Alejandra |
| author2 | Alfaro, Pablo Terra, Rafael |
| author2_role | author author |
| author_browse | Alfaro, Pablo De Vera, Alejandra Terra, Rafael |
| author_facet | De Vera, Alejandra Alfaro, Pablo Terra, Rafael |
| author_role | author |
| collection | COLIBRI |
| dc.contributor.none.fl_str_mv | De Vera Alejandra, Universidad de la República (Uruguay). Facultad de Ingeniería. Alfaro Pablo, MotionSoft Consulting Terra Rafael, Universidad de la República (Uruguay). Facultad de Ingeniería. |
| dc.coverage.none.fl_str_mv | Uruguay 2020 |
| dc.creator.none.fl_str_mv | De Vera, Alejandra Alfaro, Pablo Terra, Rafael |
| dc.date.none.fl_str_mv | 2021 2023-07-26T12:37:28Z 2023-07-26T12:37:28Z |
| dc.format.none.fl_str_mv | 17 p. application/pdf |
| dc.identifier.none.fl_str_mv | De Vera, A., Alfaro, P. y Terra, R. "Operational implementation of satellite-rain gauge data merging for hydrological modeling". Water. [en línea]. 2021, vol.13, no. 4, p. 1-17. DOI: 2073-4441/13/4/533 2073-4441 https://www.mdpi.com/2073-4441/13/4/533 https://hdl.handle.net/20.500.12008/38410 10.3390/w13040533 |
| dc.language.none.fl_str_mv | en eng |
| dc.publisher.none.fl_str_mv | MDPI |
| dc.relation.none.fl_str_mv | Water, vol.13, no. 4, february 2021, p. 1-17. |
| dc.rights.none.fl_str_mv | info:eu-repo/semantics/openAccess Licencia Creative Commons Atribución (CC - By 4.0) |
| dc.source.none.fl_str_mv | reponame:COLIBRI instname:Universidad de la República instacron:Universidad de la República |
| dc.subject.none.fl_str_mv | Daily precipitation Satellite-based estimates Precipitation data merging Geostatistical methods Hydrological modeling Hydropower generation Operational modeling |
| dc.title.none.fl_str_mv | Operational implementation of satellite-rain gauge data merging for hydrological modeling. |
| dc.type.none.fl_str_mv | Artículo info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion |
| description | Systems exposed to hydroclimatic variability, such as the integrated electric system in Uruguay, increasingly require real-time multiscale information to optimize management. Monitoring of the precipitation field is key to inform the future hydroelectric energy availability. We present an operational implementation of an algorithm that merges satellite precipitation estimates with rain gauge data, based on a 3-step technique: (i) Regression of station data on the satellite estimate using a Generalized Linear Model; (ii) Interpolation of the regression residuals at station locations to the entire grid using Ordinary Kriging and (iii) Application of a rain/no rain mask. The operational implementation follows five steps: (i) Data download and daily accumulation; (ii) Data quality control; (iii) Merging technique; (iv) Hydrological modeling and (v) Electricity-system simulation. The hydrological modeling is carried with the GR4J rainfall-runoff model applied to 17 sub-catchments of the G. Terra basin with routing up to the reservoir. The implementation became operational at the Electricity Market Administration (ADME) on June 2020. The performance of the merged precipitation estimate was evaluated through comparison with an independent, dense and uniformly distributed rain gauge network using several relevant statistics. Further validation is presented comparing the simulated inflow to the estimate derived from a reservoir mass budget. Results confirm that the estimation that incorporates the satellite information in addition to the surface observations has a higher performance than the one that only uses rain gauge data, both in the rainfall statistical evaluation and hydrological simulation. |
| eu_rights_str_mv | openAccess |
| format | article |
| id | anni_782d5ebc595168e6c8a7ed6b1da8e9cb |
| identifier_str_mv | De Vera, A., Alfaro, P. y Terra, R. "Operational implementation of satellite-rain gauge data merging for hydrological modeling". Water. [en línea]. 2021, vol.13, no. 4, p. 1-17. DOI: 2073-4441/13/4/533 2073-4441 10.3390/w13040533 |
| instacron_str | Universidad de la República |
| institution | Universidad de la República |
| instname_str | Universidad de la República |
| language | eng |
| language_invalid_str_mv | en |
| network_acronym_str | anni |
| network_name_str | oai-lr-anni |
| oai_identifier_str | oai:colibri.udelar.edu.uy:20.500.12008/38410 |
| publishDate | 2021 |
| publishDateSort | 2021 |
| publisher.none.fl_str_mv | MDPI |
| reponame_str | COLIBRI |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | Licencia Creative Commons Atribución (CC - By 4.0) |
| spelling | Operational implementation of satellite-rain gauge data merging for hydrological modeling.De Vera, AlejandraAlfaro, PabloTerra, RafaelDaily precipitationSatellite-based estimatesPrecipitation data mergingGeostatistical methodsHydrological modelingHydropower generationOperational modelingSystems exposed to hydroclimatic variability, such as the integrated electric system in Uruguay, increasingly require real-time multiscale information to optimize management. Monitoring of the precipitation field is key to inform the future hydroelectric energy availability. We present an operational implementation of an algorithm that merges satellite precipitation estimates with rain gauge data, based on a 3-step technique: (i) Regression of station data on the satellite estimate using a Generalized Linear Model; (ii) Interpolation of the regression residuals at station locations to the entire grid using Ordinary Kriging and (iii) Application of a rain/no rain mask. The operational implementation follows five steps: (i) Data download and daily accumulation; (ii) Data quality control; (iii) Merging technique; (iv) Hydrological modeling and (v) Electricity-system simulation. The hydrological modeling is carried with the GR4J rainfall-runoff model applied to 17 sub-catchments of the G. Terra basin with routing up to the reservoir. The implementation became operational at the Electricity Market Administration (ADME) on June 2020. The performance of the merged precipitation estimate was evaluated through comparison with an independent, dense and uniformly distributed rain gauge network using several relevant statistics. Further validation is presented comparing the simulated inflow to the estimate derived from a reservoir mass budget. Results confirm that the estimation that incorporates the satellite information in addition to the surface observations has a higher performance than the one that only uses rain gauge data, both in the rainfall statistical evaluation and hydrological simulation.Este artículo resulta de la acumulación de trabajo realizado en el marco del “Proyecto PRONOS”, financiado por el Banco de Desarrollo de América Latina (CAF), el “Proyecto DACC” con el Ministerio de Ganadería, Agricultura y Pesca (MGAP), financiado por el Banco Mundial y una colaboración técnica financiada por la Administración del Mercado Eléctrico (ADME, Uruguay).MDPIDe Vera Alejandra, Universidad de la República (Uruguay). Facultad de Ingeniería.Alfaro Pablo, MotionSoft ConsultingTerra Rafael, Universidad de la República (Uruguay). Facultad de Ingeniería.2023-07-26T12:37:28Z2023-07-26T12:37:28Z2021Artículoinfo:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersion17 p.application/pdfDe Vera, A., Alfaro, P. y Terra, R. "Operational implementation of satellite-rain gauge data merging for hydrological modeling". Water. [en línea]. 2021, vol.13, no. 4, p. 1-17. DOI: 2073-4441/13/4/5332073-4441https://www.mdpi.com/2073-4441/13/4/533https://hdl.handle.net/20.500.12008/3841010.3390/w13040533reponame:COLIBRIinstname:Universidad de la Repúblicainstacron:Universidad de la RepúblicaenengWater, vol.13, no. 4, february 2021, p. 1-17.Uruguay2020Las obras depositadas en el Repositorio se rigen por la Ordenanza de los Derechos de la Propiedad Intelectual de la Universidad de la República.(Res. Nº 91 de C.D.C. de 8/III/1994 – D.O. 7/IV/1994) y por la Ordenanza del Repositorio Abierto de la Universidad de la República (Res. Nº 16 de C.D.C. de 07/10/2014)info:eu-repo/semantics/openAccessLicencia Creative Commons Atribución (CC - By 4.0)oai:colibri.udelar.edu.uy:20.500.12008/384102026-04-14T10:35:22Z |
| spellingShingle | Operational implementation of satellite-rain gauge data merging for hydrological modeling. De Vera, Alejandra Daily precipitation Satellite-based estimates Precipitation data merging Geostatistical methods Hydrological modeling Hydropower generation Operational modeling |
| status_str | publishedVersion |
| title | Operational implementation of satellite-rain gauge data merging for hydrological modeling. |
| title_full | Operational implementation of satellite-rain gauge data merging for hydrological modeling. |
| title_fullStr | Operational implementation of satellite-rain gauge data merging for hydrological modeling. |
| title_full_unstemmed | Operational implementation of satellite-rain gauge data merging for hydrological modeling. |
| title_short | Operational implementation of satellite-rain gauge data merging for hydrological modeling. |
| title_sort | Operational implementation of satellite-rain gauge data merging for hydrological modeling. |
| topic | Daily precipitation Satellite-based estimates Precipitation data merging Geostatistical methods Hydrological modeling Hydropower generation Operational modeling |
| url | https://www.mdpi.com/2073-4441/13/4/533 https://hdl.handle.net/20.500.12008/38410 |