Increases in greenhouse gases caused by human activities have raised global temperature. Global warming affects water resources systems and the hydrologic cycle and may impact the performance of water resource systems. Water resources managers face challenges balancing conflicting goals in reservoir operation given the uncertainties introduced by climatic change. The HadCM3 climate model is used in this paper to estimate temperature and precipitation for early (2025–2039), middle (2055–2069) and late (2085–2099) periods of the 21st century under the A2 greenhouse gases emission scenario. The estimated temperature and precipitation from the climate model are input to a calibrated hydrologic model (IHACRES) to simulate inflow in a river basin draining to the Karoon-4 reservoir in Iran. A meta-heuristic multi-objective optimization algorithm (NSGA-II) is used in conjunction to predicted hydrologic variables to optimize dynamic operation rules in the Karoon-4 reservoir. The Karoon4 reservoir is operated non-adaptively and adaptively under climatic change. Our results show that adaptive reservoir management increases the reliability and reduces the vulnerability associated with hydropower generation in early, middle, and late simulation periods of the 21st century. These findings establish the importance of factoring in climatic change and considering adaptive strategies in future reservoir operations.

Species distribution models (SDMs) are valuable tools for assessing species' responses to environmental factors and identifying areas suitable for their survival. The careful selection of input variables is critical, as their interactions and correlations with other environmental factors can affect model performance. This study evaluates the influence of climate and soil variables on the performance of SDMs for 5033 Australian terrestrial vascular plant species, representing the largest phylogenetic diversity of native flora assessed in such an analysis. Using an ensemble of correlative models, we assessed the predictive performance of climate and soil variables, individually and in combination, across four distinct ecoregions: Desert (n = 640 species), Mediterranean (n = 1246), Temperate (n = 1936), and Tropical (n = 1211). Our results demonstrate that on a continental scale, climate variables have a greater influence on plant distributions than soil variables. Although incorporating soil and climate variables enhanced model performance in some ecoregions, our results indicate that relying solely on small-scale variables such as soil may increase the likelihood of underfitting. The most influential predictor variables in the models varied across ecoregions and between specialist and generalist species. Mean annual rainfall (bio1) was consistently a strong climate predictor variable across ecoregions, but other climate variables became more important when analyses were restricted to ecoregion-specific species (i.e., specialists). Soil organic carbon (SOC) was the most important soil variable in the Temperate and Tropical ecoregions across generalist and specialist species. In the Mediterranean ecoregion, clay content (CLY) became more important than SOC when analyses were restricted to ecoregion-specific species, whereas nitrogen total organic (NTO) was consistently the strongest predictor soil variable for plants in the Desert. Our findings have significant implications for understanding the interplay between climate, soil, and plant distribution within diverse ecoregions. This study serves as a foundation for developing more accurate SDM predictions.