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

System dynamics modeling of lake water management under climate change

This is the final version. Available on open access from Nature Research via the DOI in this recordAvailability of data and materials: The data that support the findings of this study are available from the corresponding author upon reasonable request.Code availability: The codes that support the findings of this study are available from the corresponding author upon reasonable request.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.Iran National Science Foundation (INSF

Optimal virtual water flows for improved food security in water-scarce countries

This is the final version. Available on open access from Nature Research via the DOI in this recordThe worsening water scarcity has imposed a significant stress on food production in many parts of the world. This stress becomes more critical when countries seek self-sufficiency. A literature review shows that food self-sufficiency has not been assessed as the main factor in determining the optimal cultivation patterns. However, food self-sufficiency is one of the main policies of these countries and requires the most attention and concentration. Previous works have focused on the virtual water trade to meet regional food demand and to calculate trade flows. The potential of the trade network can be exploited to improve the cropping pattern to ensure food and water security. To this end, and based on the research gaps mentioned, this study develops a method to link intra-country trade networks, food security, and total water footprints (WFs) to improve food security. The method is applied in Iran, a water-scarce country. The study shows that 781 × 106 m3 of water could be saved by creating a trade network. Results of the balanced trade network are input to a multi-objective optimization model to improve cropping patterns based on the objectives of achieving food security and preventing water crises. The method provides 400 management scenarios to improve cropping patterns considering 51 main crops in Iran. Results show a range of improvements in food security (19–45%) and a decrease in WFs (2–3%). The selected scenario for Iran would reduce the blue water footprint by 1207 × 106 m3, and reduce the cropland area by 19 × 103 ha. This methodology allows decision makers to develop policies that achieve food security under limited water resources in arid and semi-arid regions.Iran National Science FoundationCenter for International Scientific Studies and Collaboration (CISSC), Ministry of Science, Research and Technolog

Forensic engineering analysis applied to flood control

This is the author accepted manuscript. The final version is available from Elsevier via the DOI in this recordFloods have various impacts, including loss of life and damage to property. Flood- management reservoirs can help mitigate floods, but their operation can also worsen flood impacts. This paper presents a novel forensic engineering approach to assess the role of reservoir operation on flood control. Fourteen criteria are employed for assessing forecast-based prereleases of water from reservoir storage to reduce the impact of flooding. The proposed approach is applied for forensic assessment of the system performance of reservoirs during the large flood of 2019 in southwestern Iran (the Great Karun Basin). The two main study areas are in the sub-basins of Karun and Dez. Results concerning two key performance criteria (the peak discharge reduction (PDR) and flood volume reduction (FVR)) show the PDR criterion in the Karun sub-basin multi-reservoir system reached about 79% (where 100% is the theoretically best performance) under historic operations (actual operating conditions in 2019), and improved from 8 to 19% if various prerelease operations were made. The FVR achieved about 33% in the historical situation and improved from 20 to 59% under prerelease operations scenarios, respectively. The PDR criterion achieved 26% under the historical scenario, but with better operation could exceed 55% in the Dez sub-basin multi-reservoir system, whereas FVR was as low as 11% in 2019 but could be raised to between 15 and 25% under prerelease operations. This forensic work's assessments establish that improved reservoir operation could be achieved by applying specialized operation approaches.Iran National Science Foundation (INSF

Towards a better understanding of the Oulmes hydrogeological system (Mid-Atlas, Morocco)

Located in the Mid-Atlas (Morocco), the Oulmes plateau is famous for its mineral water springs “Sidi Ali” and “Lalla Haya” commercialised by the company “Les Eaux minérales d’Oulmès S.A”. Additionally, groundwater of the Oulmes plateau is intensively exploited for irrigation. The objective of this study, essentially performed from data collected during isotopic (summer 2004) and piezometric and hydrogeochemical field campaigns (spring 2007), is to improve the understanding of the Oulmes hydrogeological system. Analyses and interpretation of these data lead to the statement that this system is constituted by a main deep aquifer of large extension and by minor aquifers in a perched position. However, these aquifers interact enough to be in total equilibrium during the cold and wet period. As highlighted by isotopes, the origin of groundwater is mainly infiltration water except a small part of old groundwater with dissolved gas rising up from the granite through the schists

Microbial diversity and impact on carbonate geochemistry across a changing geochemical gradient in a karst aquifer

Although microbes are known to influence karst (carbonate) aquifer ecosystem-level processes, comparatively little information is available regarding the diversity of microbial activities that could influence water quality and geological modification. To assess microbial diversity in the context of aquifer geochemistry, we coupled 16S rRNA Sanger sequencing and 454 tag pyrosequencing to in situ microcosm experiments from wells that cross the transition from fresh to saline and sulfidic water in the Edwards Aquifer of central Texas, one of the largest karst aquifers in the United States. The distribution of microbial groups across the transition zone correlated with dissolved oxygen and sulfide concentration, and significant variations in community composition were explained by local carbonate geochemistry, specifically calcium concentration and alkalinity. The waters were supersaturated with respect to prevalent aquifer minerals, calcite and dolomite, but in situ microcosm experiments containing these minerals revealed significant mass loss from dissolution when colonized by microbes. Despite differences in cell density on the experimental surfaces, carbonate loss was greater from freshwater wells than saline, sulfidic wells. However, as cell density increased, which was correlated to and controlled by local geochemistry, dissolution rates decreased. Surface colonization by metabolically active cells promotes dissolution by creating local disequilibria between bulk aquifer fluids and mineral surfaces, but this also controls rates of karst aquifer modification. These results expand our understanding of microbial diversity in karst aquifers and emphasize the importance of evaluating active microbial processes that could affect carbonate weathering in the subsurface