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This study aimed to understand the perception of drought among farmers, in order to support decision-making in the water allocation process. This study was carried out in the Tabuleiro de Russas irrigated perimeter, in northeast Brazil, over the drought period of 2012–2018. Two analyses were conducted: (i) drought characterization, using the Standardized Precipitation Index (SPI) based on drought duration and frequency criteria; and (ii) analysis of farmers’ perceptions of drought via selection of explanatory variables using the Random Forest (RF) and the Decision Tree (DT) methods. The 2012–2018 drought period was defined as a meteorological phenomenon by local farmers; however, an SPI evaluation indicated that the drought was of a hydrological nature. According to the RF analysis, four of the nine study variables were more statistically important than the others in influencing farmers’ perception of drought: number of cultivated land plots, farmer’s age, years of experience in the agriculture sector, and education level. These results were confirmed using DT analysis. Understanding the relationship between these variables and farmers’ perception of drought could aid in the development of an adaptation strategy to water deficit scenarios. Farmers’ perception can be beneficial in reducing conflicts, adopting proactive management practices, and developing a holistic and efficient early warning drought system.

Drought is widely known as a complex natural hazard, not just by its climatological features but also by human experiences and socio-economical impacts. Drought preparedness is the only way a society can mitigate effects and better cope with droughts. Here we present a methodological approach to guide the implementation of proactive drought plans, specially designed for hydrossystems and cities scales. We highlight strategies to engage local stakeholders in constructing such plans and build a participatory methodology. The preparedness drought plan methodology was developed and applied to two hydrosystems and two cities located in the Piranhas-Açu river basin, a drought-prone area of Brazilian Semi-arid. Our ndings suggest that participatory socio-technical methodologies, built only from the system operators' tacit knowledge, can achieve good results when data and resources are limited. Still, results can be enhanced by hydrologic and hydraulic modeling to assess vulnerability, scenarios and strategies. We illustrate and analyze the process by storytelling to develop a meaningful and convincing narrative that speaks to theory and practice, and we provide recommendations to facilitate this approach.

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Climate variability and change, associated with increasing water demands, can have significant implications for water availability. In the Brazilian semi-arid, eutrophication in reservoirs raises the risk of water scarcity. The reservoirs have also a high seasonal and annual variability of water level and volume, which can have important effects on Chlorophyll-a concentration (Chla). Assessing the influence of climate and hydrological variability on phytoplankton growth can be important to find strategies to achieve water security in tropical regions with similar problems. This study explores the potential of machine learning models to predict Chla in reservoirs and to understand their relationship with hydrological and climate variables. The model is based mainly on satellite data, which makes the methodology useful for data-scarce regions. Tree-based ensemble methods had the best performances among six machine learning methods and one parametric model. This performance can be considered satisfactory as classical empirical relationships between Chla and phosphorus may not hold for tropical reservoirs. Water volume and the mix-layer depth are inversely related to Chla, while mean surface temperature, water level, and surface solar radiation have direct relationships with Chla. These findings provide insights on how seasonal climate prediction and reservoir operation might influence water quality in regions supplied by superficial reservoirs.