In this paper a high‐resolution linked hydroeconomic model is demonstrated for drought conditions in a Brazilian river basin. The economic model of agriculture includes 13 decision variables that can be optimized to maximize farmers' yearly net revenues. The economic model uses a multi‐input multioutput nonlinear constant elasticity of substitution (CES) production function simulating agricultural production. The hydrologic component is a detailed physics‐based three‐dimensional hydrodynamic model that simulates changes in the hydrologic system derived from agricultural activity while in turn providing biophysical constraints to the economic system. The linked models capture the effects of the interactions between the hydrologic and the economic systems at high spatial and temporal resolutions, ensuring that the model converges to an optimal economic scenario that takes into account the spatial and temporal distribution of the water resources. The operation and usefulness of the models are demonstrated in a rural catchment area of about 10 km2 within the São Francisco River Basin in Brazil. Two droughts of increasing intensity are simulated to investigate how farmers behave under rain shortfalls of different severity. The results show that farmers react to rainfall shortages to minimize their effects on farm profits, and that the impact on farmers depends, among other things, on their location in the watershed and on their access to groundwater.

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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.