ETo‐Brazil: A Daily Gridded Reference Evapotranspiration Data Set for Brazil (2000–2018). Issue 7 (20th July 2020)

• Record Type: Journal Article
• Authors: Althoff, Daniel; Dias, Santos Henrique Brant; Filgueiras, Roberto; Rodrigues, Lineu Neiva
• Is Part Of: Water resources research. Volume 56:Issue 7(2020)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-07-20
• Subjects: climate parameters -- neural networks -- regression trees -- machine learning -- satellite meteorology -- Penman‐Monteith; Hydrology -- Periodicals; 333.91
• DOI: 10.1029/2020WR027562
• Languages: English
• ISSNs: 0043-1397

Abstract

Abstract: The reference evapotranspiration (ETo) has long been used as a climate parameter for many studies in climatology and hydrology. However, many regions suffer from shortage of both meteorological monitoring stations and historical information on ETo. Thus, the objective of this study was to develop a daily gridded reference evapotranspiration data set for Brazil that matches the period and grid cells of the Global Precipitation Measurement (GPM) data. ETo was calculated using data from 849 weather stations over the period from 1 June 2000 to 31 December 2018. The features used to model ETo were the GPM daily data set, WorldClim averages monthly, and two engineered features. Among the machine learning algorithms assessed, the Cubist presented the best performance‐computation cost trade‐off in a subset of the entire data and, therefore, was selected to model ETo daily. The developed data set presented root mean square error of 0.65 mm day −1, or 16% lower than previous ETo data set developed for Brazil using interpolation techniques. The GPM and engineered features showed higher importance for the models trained during the wet season, while the WorldClim maximum temperature averages monthly were more important during the dry and cold season. The new gridded reference evapotranspiration data set for Brazil (ETo‐Brazil) was made freely available to the community. Key Points: Daily reference evapotranspiration is modeled daily using machine learning and based on remote sensing and climate data sets The importance of the variables for the models is assessed for all daily models The developed data set covers the same period and matches the resolution and positioning of the Global Precipitation Measurement data