Evaluation of areal monthly average precipitation estimates from MERRA2 and ERA5 reanalysis in a colombian caribbean basin

Global reanalysis dataset estimations of climate variables constitute an alternative for overcoming data scarcity associated with sparsely and unevenly distributed hydrometeorological networks often found in developing countries. However, reanalysis datasets require detailed validation to determine...

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Autores:
Vega-Durán, Jean
Escalante-Castro, Brigitte
Canales, Fausto
Acuña Robles, Guillermo Jesús
Kaźmierczak, Bartosz
Canales, Fausto Alfredo
Tipo de recurso:
Article of journal
Fecha de publicación:
2021
Institución:
Corporación Universidad de la Costa
Repositorio:
REDICUC - Repositorio CUC
Idioma:
eng
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oai:repositorio.cuc.edu.co:11323/9067
Acceso en línea:
https://hdl.handle.net/11323/9067
https://doi.org/10.3390/atmos12111430
https://repositorio.cuc.edu.co/
Palabra clave:
Rainfall
Reanalysis
ERA 5
MERRA 2
Thiessen polygons
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openAccess
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© 2021 by the authors. Licensee MDPI, Basel, Switzerland.
id RCUC2_f6b2d10bd1627bec204d38b735ecc877
oai_identifier_str oai:repositorio.cuc.edu.co:11323/9067
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network_name_str REDICUC - Repositorio CUC
repository_id_str
dc.title.eng.fl_str_mv Evaluation of areal monthly average precipitation estimates from MERRA2 and ERA5 reanalysis in a colombian caribbean basin
title Evaluation of areal monthly average precipitation estimates from MERRA2 and ERA5 reanalysis in a colombian caribbean basin
spellingShingle Evaluation of areal monthly average precipitation estimates from MERRA2 and ERA5 reanalysis in a colombian caribbean basin
Rainfall
Reanalysis
ERA 5
MERRA 2
Thiessen polygons
title_short Evaluation of areal monthly average precipitation estimates from MERRA2 and ERA5 reanalysis in a colombian caribbean basin
title_full Evaluation of areal monthly average precipitation estimates from MERRA2 and ERA5 reanalysis in a colombian caribbean basin
title_fullStr Evaluation of areal monthly average precipitation estimates from MERRA2 and ERA5 reanalysis in a colombian caribbean basin
title_full_unstemmed Evaluation of areal monthly average precipitation estimates from MERRA2 and ERA5 reanalysis in a colombian caribbean basin
title_sort Evaluation of areal monthly average precipitation estimates from MERRA2 and ERA5 reanalysis in a colombian caribbean basin
dc.creator.fl_str_mv Vega-Durán, Jean
Escalante-Castro, Brigitte
Canales, Fausto
Acuña Robles, Guillermo Jesús
Kaźmierczak, Bartosz
Canales, Fausto Alfredo
dc.contributor.author.spa.fl_str_mv Vega-Durán, Jean
Escalante-Castro, Brigitte
Canales, Fausto
Acuña Robles, Guillermo Jesús
Kaźmierczak, Bartosz
dc.contributor.author.none.fl_str_mv Canales, Fausto Alfredo
dc.subject.proposal.eng.fl_str_mv Rainfall
Reanalysis
ERA 5
MERRA 2
Thiessen polygons
topic Rainfall
Reanalysis
ERA 5
MERRA 2
Thiessen polygons
description Global reanalysis dataset estimations of climate variables constitute an alternative for overcoming data scarcity associated with sparsely and unevenly distributed hydrometeorological networks often found in developing countries. However, reanalysis datasets require detailed validation to determine their accuracy and reliability. This paper evaluates the performance of MERRA2 and ERA5 regarding their monthly rainfall products, comparing their areal precipitation averages with estimates based on ground measurement records from 49 rain gauges managed by the Institute of Hydrology, Meteorology, and Environmental Studies (IDEAM) and the Thiessen polygons method in the Sinu River basin, Colombia. The performance metrics employed in this research are the correlation coefficient, the bias, the normalized root mean square error (NRMSE), and the Nash–Sutcliffe efficiency (NSE). The results show that ERA5 generally outperforms MERRA2 in the study area. However, both reanalyses consistently overestimate the monthly averages calculated from IDEAM records at all time and spatial scales. The negative NSE values indicate that historical monthly averages from IDEAM records are better predictors than both MERRA2 and ERA5 rainfall products.
publishDate 2021
dc.date.issued.none.fl_str_mv 2021-10-29
dc.date.accessioned.none.fl_str_mv 2022-03-10T19:26:22Z
dc.date.available.none.fl_str_mv 2022-03-10T19:26:22Z
dc.type.spa.fl_str_mv Artículo de revista
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dc.type.content.spa.fl_str_mv Text
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dc.type.version.spa.fl_str_mv info:eu-repo/semantics/acceptedVersion
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dc.identifier.citation.spa.fl_str_mv Vega‐Durán, J.; Escalante‐Castro, B.; Canales, F.A.; Acuña, G.J.; Kaźmierczak, B. Evaluation of Areal Monthly Average Precipitation Estimates from MERRA2 and ERA5 Reanalysis in a Colombian Caribbean Basin. Atmosphere 2021, 12, 1430. https://doi.org/10.3390/atmos12111430
dc.identifier.uri.spa.fl_str_mv https://hdl.handle.net/11323/9067
dc.identifier.url.spa.fl_str_mv https://doi.org/10.3390/atmos12111430
dc.identifier.doi.spa.fl_str_mv 10.3390/atmos12111430
dc.identifier.eissn.spa.fl_str_mv 2073-4433
dc.identifier.instname.spa.fl_str_mv Corporación Universidad de la Costa
dc.identifier.reponame.spa.fl_str_mv REDICUC - Repositorio CUC
dc.identifier.repourl.spa.fl_str_mv https://repositorio.cuc.edu.co/
identifier_str_mv Vega‐Durán, J.; Escalante‐Castro, B.; Canales, F.A.; Acuña, G.J.; Kaźmierczak, B. Evaluation of Areal Monthly Average Precipitation Estimates from MERRA2 and ERA5 Reanalysis in a Colombian Caribbean Basin. Atmosphere 2021, 12, 1430. https://doi.org/10.3390/atmos12111430
10.3390/atmos12111430
2073-4433
Corporación Universidad de la Costa
REDICUC - Repositorio CUC
url https://hdl.handle.net/11323/9067
https://doi.org/10.3390/atmos12111430
https://repositorio.cuc.edu.co/
dc.language.iso.none.fl_str_mv eng
language eng
dc.relation.ispartofjournal.spa.fl_str_mv Atmosphere
dc.relation.references.spa.fl_str_mv 1. Bertoni, J.C.; Tucci, C.E.M. Precipitação. In Hidrologia: Ciência e Aplicação; Tucci, C.E.M., Ed.; Editora da UFRGS/ABRH: Porto Alegre, Brazil, 2004; pp. 177–241, ISBN 8570256639.
2. Canales, F.A.; Gwoździej‐Mazur, J.; Jadwiszczak, P.; Struk‐Sokołowska, J.; Wartalska, K.; Wdowikowski, M.; Kaźmierczak, B. Long‐Term Trends in 20‐Day Cumulative Precipitation for Residential Rainwater Harvesting in Poland. Water 2020, 12, 1932.
3. Nkiaka, E.; Nawaz, N.R.; Lovett, J.C. Evaluating global reanalysis precipitation datasets with rain gauge measurements in the Sudano‐Sahel region: case study of the Logone catchment, Lake Chad Basin. Meteorol. Appl. 2017, 24, 9–18.
4. Lozano Sandoval, G.; Monsalve Durango, E.A.; García Reinoso, P.L.; Rodríguez Mejía, C.A.; Gómez Ospina, J.P.; Triviño Loaiza, H.J. Environmental Flow Estimation Using Hydrological and Hydraulic Methods for the Quindío River Basin: WEAP as a Support Tool. Inge CUC 2015, 11, 34–48.
5. Dingman, S.L. Physical Hydrology; 3rd ed.; Waveland Press, Inc.: Long Grove, IL, USA, 2015; ISBN 9781478611189.
6. World Meteorological Organization Guide to Hydrological Practice. Volume I: Hydrology—From Measurement to Hydrological Information; World Meteorological Organization: Geneva, Switzerland, 2008; ISBN 9789263101686.
7. Condom, T.; Martínez, R.; Pabón, J.D.; Costa, F.; Pineda, L.; Nieto, J.J.; López, F.; Villacis, M. Climatological and Hydrological Observations for the South American Andes: In situ Stations, Satellite, and Reanalysis Data Sets. Front. Earth Sci. 2020, 8, 1–20.
8. Yu, Z.; Wu, J.; Chen, X. An approach to revising the climate forecast system reanalysis rainfall data in a sparsely‐gauged mountain basin. Atmos. Res. 2019, 220, 194–205.
9. Thornton, P.E.; Running, S.W.; White, M.A. Generating surfaces of daily meteorological variables over large regions of complex terrain. J. Hydrol. 1997, 190, 214–251.
10. Ramirez Camargo, L.; Gruber, K.; Nitsch, F. Assessing variables of regional reanalysis data sets relevant for modelling small‐ scale renewable energy systems. Renew. Energy 2019, 133, 1468–1478.
11. Chawla, I.; Mujumdar, P.P. Evaluating rainfall datasets to reconstruct floods in data‐sparse Himalayan region. J. Hydrol. 2020, 588.
12. Sun, S.; Shi, W.; Zhou, S.; Chai, R.; Chen, H.; Wang, G.; Zhou, Y.; Shen, H. Capacity of satellite‐based and reanalysis precipitation products in detecting long‐term trends across Mainland China. Remote Sens. 2020, 12, 2902.
13. Blacutt, L.A.; Herdies, D.L.; de Gonçalves, L.G.G.; Vila, D.A.; Andrade, M. Precipitation comparison for the CFSR, MERRA, TRMM3B42 and Combined Scheme datasets in Bolivia. Atmos. Res. 2015, 163, 117–131.
14. Bojanowski, J.S.; Vrieling, A.; Skidmore, A.K. A comparison of data sources for creating a long‐term time series of daily gridded solar radiation for Europe. Sol. Energy 2014, 99, 152–171.
15. Dee, D.; Fasullo, J.; Shea, D.; Walsh, J. National Center for Atmospheric Research The Climate Data Guide: Atmospheric Reanalysis: Overview & Comparison Tables. Available online: https://climatedataguide.ucar.edu/climate‐data/atmospheric‐ reanalysis‐overview‐comparison‐tables (accessed on 6 June 2021).
16. Hersbach, H.; Bell, B.; Berrisford, P.; Hirahara, S.; Horányi, A.; Muñoz‐Sabater, J.; Nicolas, J.; Peubey, C.; Radu, R.; Schepers, D.; et al. The ERA5 global reanalysis. Q. J. R. Meteorol. Soc. 2020, 146, 1999–2049.
17. Springer, A.; Eicker, A.; Bettge, A.; Kusche, J.; Hense, A.; Mahto, S.S.; Pandey, A.C.; Huang, B.; Cubasch, U.; Li, Y.; et al. Evaluation of the Water Cycle in the European COSMO‐REA6 Reanalysis Using GRACE. Water 2017, 9, 289.
18. Nguyen, T.H.; Masih, I.; Mohamed, Y.A.; van der Zaag, P. Validating rainfall‐runoff modelling using satellite‐based and reanalysis precipitation products in the sre pok catchment, the mekong river basin. Geosciences 2018, 8, 164.
19. Jurasz, J.; Canales, F.A.; Kies, A.; Guezgouz, M.; Beluco, A. A review on the complementarity of renewable energy sources: Concept, metrics, application and future research directions. Sol. Energy 2020, 195, 703–724.
20. Ramirez Camargo, L.; Schmidt, J. Simulation of multi‐annual time series of solar photovoltaic power: Is the ERA5‐land reanalysis the next big step? Sustain. Energy Technol. Assess. 2020, 42, 100829.
21. Canales, F.A.; Jurasz, J.K.; Guezgouz, M.; Beluco, A. Cost‐reliability analysis of hybrid pumped‐battery storage for solar and wind energy integration in an island community. Sustain. Energy Technol. Assess. 2021, 44, 101062.
22. Kapica, J.; Canales, F.A.; Jurasz, J. Global atlas of solar and wind resources temporal complementarity. Energy Convers. Manag. 2021, 246, 114692.
23. Hurtado‐Montoya, A.F.; Mesa‐Sánchez, Ó.J. Reanalysis of monthly precipitation fields in Colombian territory. Dyna 2014, 81, 251.
24. Dinku, T.; Funk, C.; Peterson, P.; Maidment, R.; Tadesse, T.; Gadain, H.; Ceccato, P. Validation of the CHIRPS satellite rainfall estimates over eastern Africa. Q. J. R. Meteorol. Soc. 2018, 144, 292–312.
25. Urrea, V.; Ochoa, A.; Mesa, O. Seasonality of Rainfall in Colombia. Water Resour. Res. 2019, 55, 4149–4162.
26. Fernandes, K.; Muñoz, A.G.; Ramirez‐Villegas, J.; Agudelo, D.; Llanos‐Herrera, L.; Esquivel, A.; Rodriguez‐Espinoza, J.; Prager, S.D. Improving seasonal precipitation forecasts for agriculture in the orinoquía Region of Colombia. Weather Forecast. 2020, 35, 437–449.
27. Urrea, V.; Ochoa, A.; Mesa, O. Validación de la base de datos de precipitación CHIRPS para Colombia a escala diaria, mensual y anual en el período 1981–2014. In Proceedings of the XXVII Congreso Latinoamericano de Hidráulica, Lima, Peru, 28–30 September 2016; p. 11.
28. Morales‐Acuña, E.; Linero‐Cueto, J.R.; Canales, F.A. Assessment of Precipitation Variability and Trends Based on Satellite Estimations for a Heterogeneous Colombian Region. Hydrology 2021, 8, 128.
29. Funk, C.; Peterson, P.; Landsfeld, M.; Pedreros, D.; Verdin, J.; Shukla, S.; Husak, G.; Rowland, J.; Harrison, L.; Hoell, A.; et al. The climate hazards infrared precipitation with stations—A new environmental record for monitoring extremes. Sci. Data 2015, 2, 1–21.
30. Jurasz, J.; Beluco, A.; Canales, F.A. The impact of complementarity on power supply reliability of small scale hybrid energy systems. Energy 2018, 161, 737–743.
31. Canales, F.A.; Jurasz, J.; Kies, A.; Beluco, A.; Arrieta‐Castro, M.; Peralta‐Cayón, A. Spatial representation of temporal complementarity between three variable energy sources using correlation coefficients and compromise programming. MethodsX 2020, 7, 100871.
32. Canales, F.A.; Jurasz, J.; Beluco, A.; Kies, A. Assessing temporal complementarity between three variable energy sources through correlation and compromise programming. Energy 2020, 192, 116637.
33. Copernicus Climate Change Service (C3S) ERA5: Fifth generation of ECMWF atmospheric reanalyses of the global climate Available online: https://cds.climate.copernicus.eu/cdsapp#!/home (accessed on 25 May 2021).
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spelling Vega-Durán, JeanEscalante-Castro, BrigitteCanales, FaustoAcuña Robles, Guillermo JesúsKaźmierczak, BartoszCanales, Fausto Alfredovirtual::577-12022-03-10T19:26:22Z2022-03-10T19:26:22Z2021-10-29Vega‐Durán, J.; Escalante‐Castro, B.; Canales, F.A.; Acuña, G.J.; Kaźmierczak, B. Evaluation of Areal Monthly Average Precipitation Estimates from MERRA2 and ERA5 Reanalysis in a Colombian Caribbean Basin. Atmosphere 2021, 12, 1430. https://doi.org/10.3390/atmos12111430https://hdl.handle.net/11323/9067https://doi.org/10.3390/atmos1211143010.3390/atmos121114302073-4433Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/Global reanalysis dataset estimations of climate variables constitute an alternative for overcoming data scarcity associated with sparsely and unevenly distributed hydrometeorological networks often found in developing countries. However, reanalysis datasets require detailed validation to determine their accuracy and reliability. This paper evaluates the performance of MERRA2 and ERA5 regarding their monthly rainfall products, comparing their areal precipitation averages with estimates based on ground measurement records from 49 rain gauges managed by the Institute of Hydrology, Meteorology, and Environmental Studies (IDEAM) and the Thiessen polygons method in the Sinu River basin, Colombia. The performance metrics employed in this research are the correlation coefficient, the bias, the normalized root mean square error (NRMSE), and the Nash–Sutcliffe efficiency (NSE). The results show that ERA5 generally outperforms MERRA2 in the study area. However, both reanalyses consistently overestimate the monthly averages calculated from IDEAM records at all time and spatial scales. The negative NSE values indicate that historical monthly averages from IDEAM records are better predictors than both MERRA2 and ERA5 rainfall products.20 páginasapplication/pdfengMDPI Multidisciplinary Digital Publishing InstituteSwitzerland© 2021 by the authors. Licensee MDPI, Basel, Switzerland.Atribución 4.0 Internacional (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Evaluation of areal monthly average precipitation estimates from MERRA2 and ERA5 reanalysis in a colombian caribbean basinArtículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARTinfo:eu-repo/semantics/acceptedVersionhttps://www.mdpi.com/2073-4433/12/11/1430ColombiaAtmosphere1. Bertoni, J.C.; Tucci, C.E.M. Precipitação. In Hidrologia: Ciência e Aplicação; Tucci, C.E.M., Ed.; Editora da UFRGS/ABRH: Porto Alegre, Brazil, 2004; pp. 177–241, ISBN 8570256639.2. Canales, F.A.; Gwoździej‐Mazur, J.; Jadwiszczak, P.; Struk‐Sokołowska, J.; Wartalska, K.; Wdowikowski, M.; Kaźmierczak, B. Long‐Term Trends in 20‐Day Cumulative Precipitation for Residential Rainwater Harvesting in Poland. Water 2020, 12, 1932.3. Nkiaka, E.; Nawaz, N.R.; Lovett, J.C. Evaluating global reanalysis precipitation datasets with rain gauge measurements in the Sudano‐Sahel region: case study of the Logone catchment, Lake Chad Basin. Meteorol. Appl. 2017, 24, 9–18.4. Lozano Sandoval, G.; Monsalve Durango, E.A.; García Reinoso, P.L.; Rodríguez Mejía, C.A.; Gómez Ospina, J.P.; Triviño Loaiza, H.J. Environmental Flow Estimation Using Hydrological and Hydraulic Methods for the Quindío River Basin: WEAP as a Support Tool. Inge CUC 2015, 11, 34–48.5. Dingman, S.L. Physical Hydrology; 3rd ed.; Waveland Press, Inc.: Long Grove, IL, USA, 2015; ISBN 9781478611189.6. World Meteorological Organization Guide to Hydrological Practice. Volume I: Hydrology—From Measurement to Hydrological Information; World Meteorological Organization: Geneva, Switzerland, 2008; ISBN 9789263101686.7. Condom, T.; Martínez, R.; Pabón, J.D.; Costa, F.; Pineda, L.; Nieto, J.J.; López, F.; Villacis, M. Climatological and Hydrological Observations for the South American Andes: In situ Stations, Satellite, and Reanalysis Data Sets. Front. Earth Sci. 2020, 8, 1–20.8. Yu, Z.; Wu, J.; Chen, X. An approach to revising the climate forecast system reanalysis rainfall data in a sparsely‐gauged mountain basin. Atmos. Res. 2019, 220, 194–205.9. Thornton, P.E.; Running, S.W.; White, M.A. Generating surfaces of daily meteorological variables over large regions of complex terrain. J. Hydrol. 1997, 190, 214–251.10. Ramirez Camargo, L.; Gruber, K.; Nitsch, F. Assessing variables of regional reanalysis data sets relevant for modelling small‐ scale renewable energy systems. Renew. Energy 2019, 133, 1468–1478.11. Chawla, I.; Mujumdar, P.P. Evaluating rainfall datasets to reconstruct floods in data‐sparse Himalayan region. J. Hydrol. 2020, 588.12. Sun, S.; Shi, W.; Zhou, S.; Chai, R.; Chen, H.; Wang, G.; Zhou, Y.; Shen, H. Capacity of satellite‐based and reanalysis precipitation products in detecting long‐term trends across Mainland China. Remote Sens. 2020, 12, 2902.13. Blacutt, L.A.; Herdies, D.L.; de Gonçalves, L.G.G.; Vila, D.A.; Andrade, M. Precipitation comparison for the CFSR, MERRA, TRMM3B42 and Combined Scheme datasets in Bolivia. Atmos. Res. 2015, 163, 117–131.14. Bojanowski, J.S.; Vrieling, A.; Skidmore, A.K. A comparison of data sources for creating a long‐term time series of daily gridded solar radiation for Europe. Sol. Energy 2014, 99, 152–171.15. Dee, D.; Fasullo, J.; Shea, D.; Walsh, J. National Center for Atmospheric Research The Climate Data Guide: Atmospheric Reanalysis: Overview & Comparison Tables. Available online: https://climatedataguide.ucar.edu/climate‐data/atmospheric‐ reanalysis‐overview‐comparison‐tables (accessed on 6 June 2021).16. Hersbach, H.; Bell, B.; Berrisford, P.; Hirahara, S.; Horányi, A.; Muñoz‐Sabater, J.; Nicolas, J.; Peubey, C.; Radu, R.; Schepers, D.; et al. The ERA5 global reanalysis. Q. J. R. Meteorol. Soc. 2020, 146, 1999–2049.17. Springer, A.; Eicker, A.; Bettge, A.; Kusche, J.; Hense, A.; Mahto, S.S.; Pandey, A.C.; Huang, B.; Cubasch, U.; Li, Y.; et al. Evaluation of the Water Cycle in the European COSMO‐REA6 Reanalysis Using GRACE. Water 2017, 9, 289.18. Nguyen, T.H.; Masih, I.; Mohamed, Y.A.; van der Zaag, P. Validating rainfall‐runoff modelling using satellite‐based and reanalysis precipitation products in the sre pok catchment, the mekong river basin. Geosciences 2018, 8, 164.19. Jurasz, J.; Canales, F.A.; Kies, A.; Guezgouz, M.; Beluco, A. A review on the complementarity of renewable energy sources: Concept, metrics, application and future research directions. Sol. Energy 2020, 195, 703–724.20. Ramirez Camargo, L.; Schmidt, J. Simulation of multi‐annual time series of solar photovoltaic power: Is the ERA5‐land reanalysis the next big step? Sustain. Energy Technol. Assess. 2020, 42, 100829.21. Canales, F.A.; Jurasz, J.K.; Guezgouz, M.; Beluco, A. Cost‐reliability analysis of hybrid pumped‐battery storage for solar and wind energy integration in an island community. Sustain. Energy Technol. Assess. 2021, 44, 101062.22. Kapica, J.; Canales, F.A.; Jurasz, J. Global atlas of solar and wind resources temporal complementarity. Energy Convers. Manag. 2021, 246, 114692.23. Hurtado‐Montoya, A.F.; Mesa‐Sánchez, Ó.J. Reanalysis of monthly precipitation fields in Colombian territory. Dyna 2014, 81, 251.24. Dinku, T.; Funk, C.; Peterson, P.; Maidment, R.; Tadesse, T.; Gadain, H.; Ceccato, P. Validation of the CHIRPS satellite rainfall estimates over eastern Africa. Q. J. R. Meteorol. Soc. 2018, 144, 292–312.25. Urrea, V.; Ochoa, A.; Mesa, O. Seasonality of Rainfall in Colombia. Water Resour. Res. 2019, 55, 4149–4162.26. Fernandes, K.; Muñoz, A.G.; Ramirez‐Villegas, J.; Agudelo, D.; Llanos‐Herrera, L.; Esquivel, A.; Rodriguez‐Espinoza, J.; Prager, S.D. Improving seasonal precipitation forecasts for agriculture in the orinoquía Region of Colombia. Weather Forecast. 2020, 35, 437–449.27. Urrea, V.; Ochoa, A.; Mesa, O. Validación de la base de datos de precipitación CHIRPS para Colombia a escala diaria, mensual y anual en el período 1981–2014. In Proceedings of the XXVII Congreso Latinoamericano de Hidráulica, Lima, Peru, 28–30 September 2016; p. 11.28. Morales‐Acuña, E.; Linero‐Cueto, J.R.; Canales, F.A. Assessment of Precipitation Variability and Trends Based on Satellite Estimations for a Heterogeneous Colombian Region. Hydrology 2021, 8, 128.29. Funk, C.; Peterson, P.; Landsfeld, M.; Pedreros, D.; Verdin, J.; Shukla, S.; Husak, G.; Rowland, J.; Harrison, L.; Hoell, A.; et al. The climate hazards infrared precipitation with stations—A new environmental record for monitoring extremes. Sci. Data 2015, 2, 1–21.30. Jurasz, J.; Beluco, A.; Canales, F.A. The impact of complementarity on power supply reliability of small scale hybrid energy systems. Energy 2018, 161, 737–743.31. Canales, F.A.; Jurasz, J.; Kies, A.; Beluco, A.; Arrieta‐Castro, M.; Peralta‐Cayón, A. Spatial representation of temporal complementarity between three variable energy sources using correlation coefficients and compromise programming. MethodsX 2020, 7, 100871.32. Canales, F.A.; Jurasz, J.; Beluco, A.; Kies, A. Assessing temporal complementarity between three variable energy sources through correlation and compromise programming. Energy 2020, 192, 116637.33. Copernicus Climate Change Service (C3S) ERA5: Fifth generation of ECMWF atmospheric reanalyses of the global climate Available online: https://cds.climate.copernicus.eu/cdsapp#!/home (accessed on 25 May 2021).2011112RainfallReanalysisERA 5MERRA 2Thiessen polygonsPublication48a40323-4c39-4859-bb1c-eeeb97a2c4dfvirtual::577-148a40323-4c39-4859-bb1c-eeeb97a2c4dfvirtual::577-1https://scholar.google.com.pr/citations?user=mBTX4IAAAAAJ&hl=esvirtual::577-10000-0002-6858-1855virtual::577-1ORIGINALEvaluation of Areal Monthly Average Precipitation Estimates.pdfEvaluation of Areal Monthly Average Precipitation Estimates.pdfapplication/pdf3378208https://repositorio.cuc.edu.co/bitstreams/80ca4678-8c20-4202-b044-3b8c3d85d8ce/downloadd841c2e02ba2da83a2fee86cd8e4e322MD51LICENSElicense.txtlicense.txttext/plain; charset=utf-83196https://repositorio.cuc.edu.co/bitstreams/552df60e-a7c9-4a6c-ab92-357805460a28/downloade30e9215131d99561d40d6b0abbe9badMD52TEXTEvaluation of Areal Monthly Average Precipitation Estimates.pdf.txtEvaluation of Areal Monthly Average Precipitation Estimates.pdf.txttext/plain77989https://repositorio.cuc.edu.co/bitstreams/137c7cb1-4dce-41b6-ad23-0f5441533bb3/download6c75a8b52ea66fff527e59f68e06b429MD53THUMBNAILEvaluation of Areal Monthly Average Precipitation Estimates.pdf.jpgEvaluation of Areal Monthly Average Precipitation Estimates.pdf.jpgimage/jpeg15474https://repositorio.cuc.edu.co/bitstreams/009a7f7a-d37e-4f07-8433-14f18ff77576/downloada414989a8ad31ebe4e4c8661caec679bMD5411323/9067oai:repositorio.cuc.edu.co:11323/90672025-02-20 17:56:02.185https://creativecommons.org/licenses/by/4.0/© 2021 by the authors. Licensee MDPI, Basel, Switzerland.open.accesshttps://repositorio.cuc.edu.coRepositorio de la Universidad de la Costa CUCrepdigital@cuc.edu.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