Sensitivity of non-conditional climatic variables to climate-change deep uncertainty using Markov Chain Monte Carlo simulation

This is the final version. Available on open access from Nature Research via the DOI in this recordData availability: The data that support the findings of this study are available from the corresponding author upon reasonable request.There is substantial evidence suggesting climate change is having an adverse impact on the world's water resources. One must remember, however, that climate change is beset by uncertainty. It is therefore meaningful for climate change impact assessments to be conducted with stochastic-based frameworks. The degree of uncertainty about the nature of a stochastic phenomenon may differ from one another. Deep uncertainty refers to a situation in which the parameters governing intervening probability distributions of the stochastic phenomenon are themselves subjected to some degree of uncertainty. In most climatic studies, however, the assessment of the role of deep-uncertain nature of climate change has been limited. This work contributes to fill this knowledge gap by developing a Markov Chain Monte Carlo (MCMC) analysis involving Bayes' theorem that merges the stochastic patterns of historical data (i.e., the prior distribution) and the regional climate models' (RCMs') generated climate scenarios (i.e., the likelihood function) to redefine the stochastic behavior of a non-conditional climatic variable under climate change conditions (i.e., the posterior distribution). This study accounts for the deep-uncertainty effect by evaluating the stochastic pattern of the central tendency measure of the posterior distributions through regenerating the MCMCs. The Karkheh River Basin, Iran, is chosen to evaluate the proposed method. The reason for selecting this case study was twofold. First, this basin has a central role in ensuring the region's water, food, and energy security. The other reason is the diverse topographic profile of the basin, which imposes predictive challenges for most RCMs. Our results indicate that, while in most seasons, with the notable exception of summer, one can expect a slight drop in the temperature in the near future, the average temperature would continue to rise until eventually surpassing the historically recorded values. The results also revealed that the 95% confidence interval of the central tendency measure of computed posterior probability distributions varies between 0.1 and 0.3 °C. The results suggest exercising caution when employing the RCMs' raw projections, especially in topographically diverse terrain.Iran National Science Foundation (INSF

A robust multiple-objective decision-making paradigm based on the water-energy-food security nexus under changing climate uncertainties

This is the final version. Available on open access from Nature Research via the DOI in this recordData availability; The data that support the findings of this study are available from the corresponding author upon reasonable request.From the perspective of the water-energy-food (WEF) security nexus, sustainable water-related infrastructure may hinge on multi-dimensional decision-making, which is subject to some level of uncertainties imposed by internal or external sources such as climate change. It is important to note that the impact of this phenomenon is not solely limited to the changing behavior patterns of hydro-climatic variables since it can also affect the other pillars of the WEF nexus both directly and indirectly. Failing to address these issues can be costly, especially for those projects with long-lasting economic lifetimes such as hydropower systems. Ideally, a robust plan can tolerate these projected changes in climatic behavior and their associated impacts on other sectors, while maintaining an acceptable performance concerning environmental, socio-economic, and technical factors. This study, thus, aims to develop a robust multiple-objective decision-support framework to address these concerns. In principle, while this framework is sensitive to the uncertainties associated with the climate change projections, it can account for the intricacies that are commonly associated with the WEF security network. To demonstrate the applicability of this new framework, the Karkheh River basin in Iran was selected as a case study due to its critical role in ensuring water, energy, and food security of the region. In addition to the status quo, a series of climate change projections (i.e., RCP 2.6, RCP 4.5, and RCP 8.5) were integrated into the proposed decision support framework as well. Resultantly, the mega decision matrix for this problem was composed of 56 evaluation criteria and 27 feasible alternatives. A TOPSIS/Entropy method was used to select the most robust renovation plan for a hydropower system in the basin by creating a robust and objective weighting mechanism to quantify the role of each sector in the decision-making process. Accordingly, in this case, the energy, food, and environment sectors are objectively more involved in the decision-making process. The results revealed that the role of the social aspect is practically negligible. The results also unveiled that while increasing the power plant capacity or the plant factor would be, seemingly, in favor of the energy sector, if all relevant factors are to be considered, the overall performance of the system might resultantly become sub-optimal, jeopardizing the security of other aspects of the water-energy-food nexus.Iran National Science Foundation (INSF