A novel objective for improving the sustainability of water supply system regarding hydrological response.

In general, the sustainability of the water supply system is indicative of an adaptive operational approach, wherein the decision-maker adjusts the system's performance based on the availability of water resources in a given time frame. In light of this, a novel framework is proposed in this study to evaluate sustainability, including three key indicators: resilience, reliability, and vulnerability. To address stressors that may lead to system failure, a multisectoral water allocation optimization is undertaken. In order to account for the future implications of climate change on the hydrological cycle, a simulation step, is incorporated, utilizing the Soil and Water Assessment Tool (SWAT) under various emission scenarios (RCP4.5 and RCP8.5), prior to integrating the streamflow data into our proposed optimal framework. To calibrate and validate historical data (2014-2019) and simulate future streamflow patterns (2025-2085), the Sistan Basin, located in an arid region of Iran, is analyzed as a case study. In light of the anticipated adverse impacts on the water supply system, certain adaptation measures, such as demand shrinkage scenarios, are considered to further appraise the proposed framework. Based on the final output, it is evident that the agricultural and industrial sectors, being the primary water consumers, are more susceptible to negative impacts resulting from the reduction in system sustainability. This susceptibility is primarily attributed to their highest vulnerability and comparatively lower reliability.

Prediction of water quality under the impacts of fine dust and sand storm events using an experimental model and multivariate regression analysis.

Many regions of the world, especially arid and semiarid areas, occasionally experience fine dust and sandstorms, known environmental problems that make normal life difficult. Since the intrusion of large amounts of dust into treatment plants may significantly change the water quality indices, the main goal of this study was to estimate these indices during the events, which can help decision-makers to improve water quality. To achieve relationships using nonlinear multivariate regression analysis, a long-term (three years: April 2017-February 2020) experimental study of water quality parameters including total dissolved solids (TDS), hydrogen content (pH), electrical conductivity (EC), chlorine (Cl), total hardness, sodium (Na), and magnesium (Mg) for water samples from wastewater treatment plants in Sistan region (Iran) was conducted where is one of the most popular regions in the world with high amount of annual fine dust level. Analysis of ANOVA showed that of all the independent parameters considered in this study, water quality parameters strongly correlated with monthly mean sand and dust storm index (SDSI), wind speed, temperature, and the number of monthly windy days. For the regression analysis, 25 months of data were used for the simulation process and 10 months for validation. The final results showed that the relationships obtained from the nonlinear multivariate regression analysis could predict the water quality indices very well (with R2 more than 0.75) except for Mg with R2 equal to 0.55. In addition, the maximum mean relative error belongs to Mg (10.8%) and then Na (9.9%) whereas the minimum mean relative error belongs to pH (2.6%) and then EC (2.9%).

Dynamic multi-objective programming model for improving consumer satisfaction within water supply system under uncertain environment.

Water scarcity poses a real crisis for decision makers of water supply system because satisfying growing demand and, as a result, achieving full consumer satisfaction in different sectors of the system remains a major problem. Therefore, this study develops a dynamic multi-objective model of water supply optimization under different scenarios to improve multisectoral consumer satisfaction. To diminish the negative effects of the water crisis on long-term consumer satisfaction, the performance of the dynamic water supply system is evaluated and optimized, which can change the situation from a state of dissatisfaction to satisfaction. In this regard, to analyze the developed model, a real case study of the Hamoun wetland in southeastern Iran is considered. According to the proposed model, various strategies are performed along with the analysis of two scenarios related to runoff uncertainty in order to investigate the consumer satisfaction status in detail. However, given to the final results, which show the greater impact of the two sensitive factors of reliability and vulnerability on consumer satisfaction, the highest level of dissatisfaction is related to the agricultural sector because it has less reliability and higher vulnerability compared to other sectors. In this regard, by proposing policies such as weight scenarios and demand reduction scheme, the situation of consumer satisfaction has improved much more desirable.