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By combining long-term ground-based data on water withdrawal with climate model projections, this study quantifies the compounding effects of human activities and climate change on surface water availability in Iran over the twenty-first century. Our findings show that increasing water withdrawal in Iran, due to population growth and increased agricultural activities, has been the main source of historical water stress. Increased levels of water stress across Iran are expected to continue or even worsen over the next decades due to projected variability and change in precipitation combined with heightened water withdrawals due to increasing population and socio-economic activities. The greatest rate of decreased water storage is expected in the Urmia Basin, northwest of Iran, (varying from ~ − 8.3 mm/year in 2010–2039 to ~ − 61.6 mm/year in 2070–2099 compared with an observed rate of 4 mm/year in 1976–2005). Human activities, however, strongly dominate the effects of precipitation variability and change. Major shifts toward sustainable land and water management are needed to reduce the impacts of water scarcity in the future, particularly in Iran's heavily stressed basins like Urmia Basin, which feeds the shrinking Lake Urmia. [ABSTRACT FROM AUTHOR]

A warming climate and land management intensification have altered water supply characteristics in many regions of the world. Incorporation of water supply uncertainties into long-term water resources planning and management is, therefore, significant from both a scientific and societal perspective. This study proposes a set of analyses for integrated water resources management under changing water supply and demand expansion based on a newly developed methodology for vulnerability assessment. The basin of interest for the proposed analysis is the interprovincial Saskatchewan River Basin (SaskRB) in Canada, which supports a wide range of water demands, from municipal and industrial use to irrigated agriculture and hydropower. Proposals for an increase in irrigated area are used as a context for exploring the joint effects of current and future water supply uncertainty and increasing irrigation demand conditions on the water resources system. Changing water supply conditions are represented by perturbing annual volumes and the seasonal timing of the hydrograph peak as input to an integrated water resources model. The analysis enables evaluation of the effects of economic development plans as well as variations in volume and peak timing of flows on water availability and economic productivity, including possibilities for failure to meet demands. Results for the SaskRB show that a large increase in irrigated agriculture raises average net revenues, but these are highly dependent on water supply conditions and loss of revenue may arise under drought conditions. Hydropower production is more sensitive to changes in annual inflow volume than to changes in either annual timing of the peak flow or the magnitude of irrigation expansion. Irrigation expansion can considerably affect the peak flows in the Saskatchewan River Delta, the largest inland delta in North America, during low-flow conditions. For example, a 400% increase in irrigated area under a 25% decrease in inflow volume and 4-week-earlier annual peak timing can reduce the frequency of peak flows in the delta by more than 50%, though potential effects on the riparian and aquatic ecosystems remain uncertain. This case study illustrates the practical utility of stochastic analysis of system vulnerability to feasible futures in such a way that socioeconomic trade-offs can be readily visualized and understood. Such performance assessments are useful for long-term water resources planning and management under water supply uncertainty. [ABSTRACT FROM AUTHOR]

• An algorithm for incorporating climate indices in streamflow generation is proposed • The algorithm is based on vine copulas, merged with a formal input selector • The algorithm enables representing dynamic impacts of climate indices on streamflow • The algorithm shows a better prediction skill, particularly in high flow seasons • The algorithm captures modes of streamflow variability better than existing schemes • The algorithm is generic and can be applied in single and multisite modes Despite the existence of several stochastic streamflow generators, not much attention has been given to representing the impacts of large-scale climate indices on seasonal to interannual streamflow variability. By merging a formal predictor selection scheme with vine copulas, we propose a generic approach to explicitly incorporate large-scale climate indices in ensemble streamflow generation at single and multiple sites and in both short-term prediction and long-term projection modes. The proposed framework is applied at three headwater streams in the Oldman River Basin in southern Alberta, Canada. The results demonstrate higher skills than existing models both in terms of representing intra- and inter-annual variability, as well as accuracy and predictability of streamflow, particularly during high flow seasons. The proposed algorithm presents a globally relevant scheme for the stochastic streamflow generation, where the impacts of large-scale climate indices on streamflow variability across time and space are significant. [ABSTRACT FROM AUTHOR]