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.

Most activities that support economic growth in the São Francisco Riber Basin (Brazil) need water. Allocation of the water resources to each competing use needs quantification in order to develop an integrated water management plan. Irrigation agriculture is the largest water consuming activity in the basin. It has produced large economic and social advancements in the region and has potential for further development. The local development agency in the São Francisco River has projected an increase of more than 500,000ha in irrigation developments distributed within the basin. Water requirements of the projected irrigation expansions and their effects on river flow were quantified. A semi-distributed model was constructed to simulate the water balance in 16 watersheds within the basin. The watersheds were hydrologically characterized by the average precipitation, atmospheric demand and runoff as well as their variability. Water requirements for increased irrigated agriculture were calculated using an agronomic mas balance. A Monte Carlo procedure generated the variability of irrigation requirements and resulting decreased river flows from the multidimensional probability distribution of the hydrologic variables of each watershed. Irrigation requirements were found to be more variable during the wet season because of weather variability. In contrast to what might be expected, in drier years, irrigation requirements were often larger during the wet season than in the dry season because the cropped area is largest in the wet months and variability of precipitation is greater. Increased irrigation shifted downward the distribution of river flows but not enough to affect other strategic water uses such as hydropower. Further irrigation expansion may be limited by wet season flows.200

Small reservoirs play a key role in the Brazilian savannah (Cerrado), making irrigation feasible and contributing to the economic development and social well-being of the population. A lack of information on factors, such as evaporative water loss, has an impact on the design and management of these reservoirs, as well as on regional water safety. Acquiring this information is crucial for hydrologists to develop more effective water resource management strategies and policies. This study assesses the performance of a diverse number of methods that are used to estimate evaporation and provides evaporation probability curves on a fortnightly period for small reservoirs in the Brazilian savannah region. Evaporation data were collected for a small water reservoir located in the Buriti Vermelho watershed, a typical dam of the Brazilian savannah region. Among the assessed methods, those of Kohler et al. (1955) and Linacre (1993) presented the best performances on both the daily and monthly scales for evaporation estimates. By simulating the evaporation rates for a timeseries, an increasing trend in evaporation was observed for the transition between the dry and wet seasons, jeopardizing double cropping in the region. The developed probability curves are an important tool for improving water resource planning and increasing the local water availability.

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.