Salars (basins of internal drainage) in the "Lithium Triangle" countries (Argentina, Bolivia, Chile) hold >50 % of the global lithium resources within lithium-rich brines. Given the imperative for lithium production to enable the energy transition and that salars by their very nature are highly variable, so a framework to both characterise their differences as well as identifying their similarities would be beneficial to understanding their provenance and potential for exploitation. In this study, data for 29 salars based on environmental factors: rainfall, evaporation as well as their physical characteristic: pan size and basin size have been used to characterise them along with those describing their setting land-use/cover and geological outcrop. These parameters have been normalised by creating a ratio of the lithium concentration divided by the factor for each salar. Cross-correlation has been used to develop relationships between these normalised factors, combined with principal component analysis to identify clustering and to further characterise groupings of behaviours. Two such relationships emerge out of this process: regional and local. Regional covers factors such as elevation, precipitation, and evaporation; local includes size of watershed, salar nucleus, land cover and geological outcrop in the watershed. However, Salar de Atacama is identified as an outlier and so the transferability of the understanding of its provenance and operation must be treated with caution. Other salars could be added to the framework as more information becomes available. The methodology presented here could help exploration by characterising salars into categories as their smaller size may not necessarily mean lower lithium mass. Further, such a framework can inform policy decisions and instruments by recognising the complexity of salars combined with the need to understand the environmental impacts of brine extraction.

A robust decision-making tool is needed to meet sustainability challenges and to manage water resources that are under development pressure, water scarcity, and climate change impact. To tackle such challenges, optimization modelling can be employed to explore regional sustainable management scenarios of groundwater exploitation. Multi-objective management modelling of various alternatives was developed for the Diyala River Basin in Iraq using Borg multi-objectives evolutionary algorithm (MOEA) and ε-DSEA algorithms. In almost all modelled cases, the upper aquifer storage is predicted to be depleted after 40 years due to large water demands and regional recharge scarcity. Hence, there is a need to develop a strategy to reduce water stresses by 45% to achieve sustainability within the next 25 years. Optimization modelling successfully generated future predictions that can be used by decision makers to manage the predicted groundwater shortages in the future.

Competitive optimization techniques have been developed to address the complexity of integrated water resources management (IWRM) modelling; however, model adaptation due to changing environments is still a challenge. In this paper we employ multi-variable techniques to increase confidence in model-driven decision-making scenarios. Here, water reservoir management was assessed using two evolutionary algorithm (EA) techniques, the epsilon-dominance-driven self-adaptive evolutionary algorithm (ε-DSEA) and the Borg multi-objective evolutionary algorithm (MOEA). Many objective scenarios were evaluated to manage flood risk, hydropower generation, water supply, and release sequences over three decades. Computationally, the ε-DSEA’s results are generally reliable, robust, effective and efficient when compared directly with the Borg MOEA but both provide decision support model outputs of value.