A review of limitations and potentials of desalination as a sustainable source of water.

For centuries, desalination, in one way or another, has helped alleviate water scarcity. Over time, desalination has gone through an evolutionary process influenced largely by available contemporary technology. This improvement, for the most part, was reflected in the energy efficiency and, in turn, in terms of the cost-effectiveness of this practice. Thanks to such advancements, by the 1960s, the desalination industry experienced notable exponential growth, becoming a formidable option to supplement conventional water resources with a reliable non-conventional resource. That said, often, there are pressing associated issues, most notably environmental, socioeconomic, health, and relatively recently, agronomic concerns. Such reservations raise the question of whether desalination is indeed a sustainable solution to current water supply problems. This is exceptionally important to understand in light of the looming water and food crises. This paper, thus, tends to review these potential issues from the sustainability perspective. It is concluded that the aforementioned issues are indeed major concerns, but they can be mitigated by actions that consider the local context. These may be either prophylactic, proactive measures that require careful planning to tailor the situation to best fit a given region or reactive measures such as incorporating pre- (e.g., removing particles, debris, microorganisms, suspended solids, and silt from the intake water prior to the desalination process) and post-treatments (e.g., reintroducing calcium and magnesium ions to water to enhance its quality for irrigation purposes) to target specific shortcomings of desalination.

No-free-lunch-theorem: a page taken from the computational intelligence for water resources planning and management.

The long pursuit to alleviate the global water crisis has been riddled with revolutionary decision-making paradigms, forward-thinking theoretical concepts, and even ground-breaking technologies. This journey, however, is centered around the expectation of discovering what could be seen as the ultimate solution to all water-related problems. These nuances, revolutionary ideas, and cutting-edge technologies raise an ostensibly simple but fundamentally crucial question: Is there or can there ever be a singular universal ideal solution to address the water resources crisis that can potentially ensure the ideas of the sustainable development paradigm? This paper tends to take inspiration from the well-established no-free-lunch theorem (NFLT) to refute the possibility of such a solution in the context of water resources management. Such an interpretation also emphasizes that any remedy intended to address water resources issues must be tailored to the particular circumstances of each case. However, it should be noted that these findings are not intended to undermine the importance of current approaches but rather to emphasize how these concepts or technologies should be used as an inspiration to curate an ad hoc version of the said solution that can reflect local requirements or constraints.

System dynamics modeling of lake water management under climate change.

Lake Urmia, the twentieth largest lake in the world, is the most valuable aquatic ecosystem in Iran. The lake water level has decreased in recent years due to human activities and climate change. Several studies have highlighted the significant roles of climatic and anthropogenic factors on the shrinkage of the lake. Management policies for water resources harvesting must be adopted to adapt to climate change and avoid the consequent problems stemming from the drought affecting Lake Urmia, and rationing must be applied to the upstream water demands. This study analyzes strategies and evaluates their effectiveness in overcoming the Urmia Lake crisis. Specifically, system dynamics analysis was performed for simulating the water volume of Lake Urmia, and the Hadley Centre coupled model was applied to project surface temperature and precipitation for two future periods: 2021-2050 and 2051-2080. Six management scenarios were considered for decreasing the allocation of agricultural water demand corresponding to two options: (1) one-reservoir option (Bukan reservoir only), and (2) six-reservoir option. The net inflow of Urmia Lake was simulated for the two future periods with the IHACRES model and with artificial neural network models under the six management scenarios. The annual average volumes of Lake Urmia would be 30 × 109 and 12 × 109 m3 over the first and second future periods, respectively, without considering the management scenarios. The lake volumes would rise by about 50% and 75% for the first and second periods, respectively under the management scenarios that involve strict protective measures and elimination of the effect of all dams and their reservoirs. Implementing strict measures would increase the annual average lake volume to 21 × 109 m3 in the second period; yet, this volume would be less than the long-term average and strategic volume. The human water use would be completely eliminated under Scenario 6. Nevertheless, Lake Urmia would experience a considerable loss of storage because of drought.

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

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.

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

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.