PISCOeo_pm, a reference evapotranspiration gridded database based on FAO Penman-Monteith in Peru

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A new FAO Penman-Monteith reference evapotranspiration gridded dataset is introduced, called PISCOeo_pm. PISCOeo_pm has been developed for the 1981–2016 period at ~1 km (0.01°) spatial resolution for the entire continental Peruvian territory. The framework for the development of PISCOeo_pm is based...

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Detalles Bibliográficos
Autores: Huerta, Adrian, Bonnesoeur, Vivien, Cuadros-Adriazola, José, Gutierrez Lope, Leonardo, Ochoa-Tocachi, Boris F., Román-Dañobeytia, Francisco, Lavado-Casimiro, W.
Formato: artículo
Fecha de Publicación:2022
Institución:Servicio Nacional de Meteorología e Hidrología del Perú
Repositorio:SENAMHI-Institucional
Lenguaje:español
OAI Identifier:oai:repositorio.senamhi.gob.pe:20.500.12542/2254
Enlace del recurso:https://hdl.handle.net/20.500.12542/2254
https://doi.org/10.1038/s41597-022-01373-8
Nivel de acceso:acceso abierto
Materia:Evapotranspiration
Climatology
Hidrometeorología
https://purl.org/pe-repo/ocde/ford#1.05.11
https://purl.org/pe-repo/ocde/ford#1.05.10
precipitacion - Clima y Eventos Naturales
Descripción
Sumario:A new FAO Penman-Monteith reference evapotranspiration gridded dataset is introduced, called PISCOeo_pm. PISCOeo_pm has been developed for the 1981–2016 period at ~1 km (0.01°) spatial resolution for the entire continental Peruvian territory. The framework for the development of PISCOeo_pm is based on previously generated gridded data of meteorological subvariables such as air temperature (maximum and minimum), sunshine duration, dew point temperature, and wind speed. Different steps, i.e., (i) quality control, (ii) gap-filling, (iii) homogenization, and (iv) spatial interpolation, were applied to the subvariables. Based on the results of an independent validation, on average, PISCOeo_pm exhibits better precision than three existing gridded products (CRU_TS, TerraClimate, and ERA5-Land) because it presents a predictive capacity above the average observed using daily and monthly data and has a higher spatial resolution. Therefore, PISCOeo_pm is useful for better understanding the terrestrial water and energy balances in Peru as well as for its application in fields such as climatology, hydrology, and agronomy, among others.
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