Fifty-four water samples were collected between July and December 2019 at nine monitoring stations and fifteen parameters were analysed to provide an updated diagnosis of the Piabanha River water quality. Further, forty years of monitoring were analysed, including government data and previous research projects. A georeferenced database was also built containing water management data. The Water Quality Index from the National Sanitation Foundation (WQI_NSF) was calculated using two datasets and showed an improvement in overall water quality, despite still presenting systematic violations to Brazilian standards. Principal components analysis (PCA) showed the most contributing parameters to water quality and enabled its association with the main pollution sources identified in the geodatabase. PCA showed that sewage discharge is still the main pollution source. The cluster analysis (CA) made possible to recommend the monitoring network optimization, thereby enabling the expansion of the monitoring to other rivers. Finally, the diagnosis provided by this research establishes the first step towards the Framing of water resources according to their intended uses, as established by the Brazilian National Water Resources Policy.

This study aims to identify the best machine learning (ML) approach to predict concentrations of biochemical oxygen demand (BOD), nitrate, and phosphate. Four ML techniques including Decision tree, Random Forest, Gradient Boosting and XGBoost were compared to estimate the water quality parameters based on biophysical (i.e., population, basin area, river slope, water level, and stream flow), and physicochemical properties (i.e., conductivity, turbidity, pH, temperature, and dissolved oxygen) input parameters. The innovation lies in the combination of on-the-spot variables with additional characteristics of the watershed. The model performances were evaluated using coefficient of determination (R2), Nash-Sutcliffe efficiency coefficient (NSE), Root Mean Squared Error (RMSE) and Kling-Gupta Efficiency (KGE) coefficient. The robust five-fold cross-validation, along with hyperparameter tuning, achieved R2 values of 0.71, 0.66, and 0.69 for phosphate, nitrate, and BOD; NSE values of 0.67, 0.65, and 0.62, and KGE values of 0.64, 0.75, and 0.60, respectively. XGBoost yielded good results, showcasing superior performance when considering all analysis performed, but his performance was closely match by other algorithms. The overall modeling design and approach, which includes careful consideration of data preprocessing, dataset splitting, statistical evaluation metrics, feature analysis, and learning curve analysis, are just as important as algorithm selection.

Water quality has been a global concern, as evidenced by UN Sustainable Development Goals. The current paper has focused on the Piabanha River rehabilitation as a case study which can be generalized to other similar watersheds. A monitoring program during a hydrological year was carried out, and different databases were used to calibrate and validate the QUAL-UFMG water quality model. Sanitation is the major problem in the watershed, notably in its headwater catchments, which concentrate the most urbanized regions where water quality is worse in the dry season due to low river flows. Thus, simulations of the river water quality have been performed through computational modeling suggesting organic load reductions in some sub-basins. In conclusion, some strategies to improve water quality have been discussed: (i) The water quality rehabilitation must consider progressive goals of pollution reduction starting with an initial implementation in a reduced area. The monitoring should be based on a few parameters relevant and simple to monitor. (ii) Pollution reduction ought to be carried out strategically with deadlines and intermediate goals that must be agreed upon between the stakeholders in the watershed. (iii) Watershed committees should supervise projects to improve water quality in partnership with the State Prosecutor’s Office.

Cyprus experiences the highest level of water stress among European Union countries due to several interplaying factors such as rainfall variability and increasing water demand. These instigate the nation to build dams on almost all rivers of the island to satisfy the requirements for drinking water and irrigation. Many studies have been primarily conducted on assessing water availability for various uses, particularly for drinking water supply and irrigation. However, there is still a gap/less explored area in terms of a better understanding of changes in surface water over time. Thus, this study aims to evaluate the water surface area variation in Cyprus over the past four decades based on remote sensing products, timeseries analysis and trend detection. The result reveals a statistically significant increasing trend (p < 0.05) in water surface area between 1984–2021. However, following the completion of the final reservoir in 2010, a statistically significant decreasing trend (p < 0.05) was observed in the permanent water surface area. This decline is related to both climatic variability and increased water demands. We observed cycles of 6, 8, and 11 years in permanent water. These cycles indicate a recurring pattern of water scarcity, with severe implication already observed on both economic activity and agriculture. The recent decade has witnessed a decline in rainfall, and this is evident through the decrease in vegetation greenness in rainfed agricultural regions, highlighting its impact. Therefore, the findings of this study underscore not only the necessity for the development of infrastructure aimed at conserving water, but also reinforces the need to discuss water use priorities in Cyprus.

Water scarcity is one of the global challenges that threatens economic development and imposes constraints on societal growth. In the semi-arid expanse of West Texas, small communities are struggling with both growing populations and decreasing water resources in the regional aquifer. This study compares two nature-based methods that could solve this problem. The first approach uses ponds and wetlands to make natural processes work together to treat the wastewater that the community receives. We applied a novel Pond-in-Pond system, which offers advantages compared to conventional pond system configurations. This system unlocks strategic hydrodynamic advantages by introducing a deeper anaerobic pit surrounded by berms, which then outflows into a larger pond. The second approach consists of an alternative strategy which integrates waste stabilization ponds, a storage basin, and the reuse of wastewater for crop irrigation—a feat that not only treats water but also enriches soil fertility. Both approaches were analyzed in terms of economic potential and pollution control. The land application had a better return on investment and emphasized the importance of innovative solutions for sustainable water management in arid regions, offering economic and community benefits. The application conveys a clear message: where water is scarce, innovation can grow; where problems are big, solutions are available; and where nature’s processes are understood, they can be used.