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Peer-reviewed

Research Article

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

Abstract

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.

Citation: Moudi M, Gai Q, Yuan H, Guiqing L, Basirialmahjough M, Motamedi A, et al. (2023) A novel objective for improving the sustainability of water supply system regarding hydrological response. PLoS ONE 18(11): e0294578. https://doi.org/10.1371/journal.pone.0294578

Editor: Mohammed Magdy Hamed, Arab Academy for Science Technology and Maritime Transport, EGYPT

Received: January 16, 2023; Accepted: November 4, 2023; Published: November 30, 2023

Copyright: © 2023 Moudi et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the manuscript and its Supporting Information files.

Funding: 1. Sichuan Academy of Science and Research (Grant No. 22RKX0306) 2. Scientific research foundation of Chengdu University of Information Technology (Grant No.KYTZ2023001)." 3. Ministry of science and technology of China (Grant No. QN2023036001L). “The funders had role in decision to publish”.

Competing interests: The authors have declared that no competing interests exist.

Introduction

As a consequence of rapid population growth and the impact of climate change, the fulfillment of water demand in a regional water supply system is poised to emerge as a formidable challenge in the foreseeable future [1–3]. The scarcity of available water resources, coupled with inadequate water supply management, progressively jeopardizes the sustainability of the aforementioned water supply system [4]. As climate change reduces water resources, the equilibrium between water demand and supply will be disrupted to such an extent that an insufficient quantity of water will be available to meet the long-term requirements of system participants, thereby resulting in a failure of system performance [5, 6].

It is necessary to highlight that the capacity to uphold system performance amidst failure conditions is defined as a pivotal attribute of system sustainability [7]. In this context, the optimization of the supply-demand imbalance would serve to propel the system towards failure safety, enhance the adaptability and efficacy of performance, and consequently led to improved sustainability [8].

The analytical performance indicators utilized in the management of water resources on a large scale specifically in the context of water supply systems, have been recognized as a means of enhancing sustainability across diverse hydrological conditions [9, 10].

However, in recent times, there has been a growing emphasis on scientific research pertaining to the sustainability of water supply systems, primarily driven by the uncertainties associated with runoff and the profound influence of human activities in basins. For instance, Afshar et al. [11] devised an integrated framework, including both cyclic and non-cyclic components, to address various scenarios of uncertainty and thereby enhance the sustainability of water allocation in irrigation systems. Butler et al. [12] formulated a comprehensive framework, including reliability, resilience, and sustainability, to analyze the challenges faced by water supply systems and their consequential impacts on system performance, with the ultimate goal of improving sustainability. Abdi-Dehkordi et al. [13] proposed a spatial distribution framework, employing a system dynamics approach, to evaluate the behavior of water supply systems in terms of both quantitative and qualitative environmental aspects, thereby enhancing system sustainability. Kotir et al. [14] introduced an integrated dynamic tool to assess the extent of interaction between users and water resources in the agricultural sector of the Volta Basin in West Africa, with the aim of enhancing the sustainable management of water resources in the irrigation system. Chen et al. [15] developed a hierarchical water resources programming model based on a credit-based system, which effectively manages the sustainability of a regional water supply system.

In prior studies, the sustainability of water supply systems has been extensively analyzed as a paramount concern in the context of multisectoral participants, whose influence extends to various economic, social, and environmental factors. Previous studies have primarily put forth one or two indicators derived from hydrological time series, which consider sustainability without taking into account optimal performance and the projected climate patterns. Therefore, these specific measures implemented during periods of system failure often fail to meet the predefined threshold. Hence, the development of a scientific methodology grounded in long-term planning is necessary to bolster water supply strategies and enhance system sustainability. Moreover, the management of a water supply system is complicated due to the presence of multiple stakeholders [3]. In essence, a long-term strategic decision-making process that disregards the availability of water resources is destined for failure, as certain regions experience water surplus while others suffer from unmet water demands.

Considering this objective, this study formulates a comprehensive framework for optimizing water supply with the aim of improving the sustainability of the water supply system under future scenarios. Factoring in the inherent uncertainty associated with climate, the initial data are extracted utilizing the SWAT tool, and the proposed model is subjected to analysis under diverse hydrological responses.

According to the above descriptions, the main contributions of this study are listed below:

  1. A novel optimization framework is introduced to tackle the long-term water supply challenge while simultaneously examining the level of system sustainability across various sectors.
  2. Considering the impact of climate patterns on water resources, the Sistan Basin, located in an arid region of Iran, is selected as a case study. The SWAT tool is employed to extract the initial data, and subsequently, the developed model is analyzed for the time periods [2025–2055&2056–2085].
  3. Due to the inherent uncertainty in the supply process, several adaptation measures, including demand shrinkage, are implemented to evaluate and scrutinize the periodic decisions.

Materials and methods

Projecting climate patterns

The first step in projecting climate patterns through SWAT involves the analysis of future climate impact postulation (General Circulation Models (GCMs)). GCMs provide a quantitative analysis of climate impacts at regional scales based on various emission scenarios (RCP4.5&RCP8.5) and hydrological measures. These models effectively incorporate complex interactions in response to greenhouse gas emissions, surface water levels, and climate change simulations [16]. However, due to their large spatial resolution, GCM results are not sufficiently reliable for smaller regional scale features. To address this limitation, a refined downscaling technique (statistical method) is employed [17]. In this regard, downscaled models such as the Hadley Center Coupled Model (HadCM) and the European Center of Medium-Range Weather Forecast (ECMWF), along with different SRES-scenario reports (A1,B,B1, and A1), have been developed to investigate climate patterns for future periods (2025–2055&2056–2085). The spatial variability of the basin is then classified into Hydrological Response Units (HRUs), and subsequently, the characteristics of each HRU are analyzed with respect to soil type, topography (DEM), slope classes, and land use.

The model calibration and validation process utilizes two time periods: 2004–2013 (10 years) and 2014–2019 (6 years). During this process, various statistical measures, including the coefficient of determination R2, Nash-Sutcliffe efficiency (NSErel), Nash-Sutcliffe coefficient (NSE), and root mean square error (RSR), are employed to assess the model’s performance in the region. In addition, sensitivity analysis of parameters is conducted using SWAT-CUP (95% PPU) to ensure that the simulated data aligns closely with the observed data. The output data generated by the SWAT model is subsequently utilized in the proposed sustainability framework (Fig 1).

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Fig 1. Investigation of hydrological response unit.

https://doi.org/10.1371/journal.pone.0294578.g001

Novel sustainability assessment framework

According to the aforementioned descriptions, the sustainability of the water supply system involves the integration of performance indicators to investigate adaptation measures from diverse perspectives in a specific timeframe [18, 19]. When evaluating the performance of the water supply system, three indicators are taken into account: resilience, vulnerability, and reliability [20, 21]. Resilience refers to the comprehensive understanding of the system’s status and attempt to restore the system during a crisis, thereby mitigating the rate of system failure [22, 23]. Reliability denotes the system’s capacity to meet consumer expectations and address failures by fulfilling water demand [24, 25]. Vulnerability serves as a tool to assess the failure of water crises and its impact on consumer dissatisfaction [26]. In essence, the primary criterion for these indicators is to strike a balance between water demand and supply intervals, thereby reducing the failure rate in the water supply system. As defined by Karamouz et al. [27], these three factors collectively constitute the definition of sustainability measure. Therefore, a model is developed to optimize water supply across various sectors, with the aim of enhancing the sustainability of the water supply system under future scenarios (Fig 2). The proposed model is solved utilizing R software version 4.3.1. Subsequently, the developed model is expounded upon in greater detail.

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Fig 2. The developed framework fluxogram for improving the sustainability of water supply system.

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The definition provided for the sustainability of the water supply system was determined as follows [27]: (1)

Where, refers to the sustainability index, is the reliability index, refers to the resilience indicator, and is vulnerability index during period t in sector j. Indeed, reliability, resilience, and vulnerability are considered as performance indices of water supply system [12].

According to the aforementioned definition, the reliability of the system denoted the probability that the system would persist in a satisfactory state over a specified duration. Therefore, the reliability of the water supply system was assessed based on its adherence to a predetermined criterion of demand fulfillment [21]: (2)

Where, T is the total period, treats as a binary variable (0 and 1), hence if the requested demand is met in period t, the system is satisfied and minimum = 0, otherwise, minimum = 1.

Resilience, on the other hand, refers to the system ability to swiftly recover from a state of failure to a state of non-failure. Therefore, the resilience of a system was quantified as the probability of it not encountering any failure-related issues in a certain timeframe [20]: (3)

Where is defined as the transition between failure and non-failure modes in the given period whose value is 1 or 0: (4)

It must be mentioned that is referred to a given time series of a parameter of interest.

The vulnerability of a system, as defined, represented the probability of failures occurring in a specified period [9]. In the context of the water supply system, the vulnerability indicated the severity of the failure state [26].

(5)

Where, and indices represent water demand and water scarcity, respectively. In addition, is the total number of shortage periods as follows: (6)

However, the degree of water scarcity in period t is as follows: (7)

However, the rate of water shortage in sector j was contingent upon the disparity between water demand and water supply (as a decision variable) in period t.

Drawing upon the proposed long-term assessment framework, the primary objective of this study was to maximize the sustainability of the water supply system in the following manner: (8)

The constraints are listed below:

The dynamic process of determining the volume of water in the reservoir relied upon the volume of available water in the preceding period and the rate of runoff, as depicted in Eq (9). In addition, the allocation of water to different sectors necessitated that the allocated volume be less than the reservoir’s total capacity, as illustrated in Eq (10). Additionally, the volume of water transferred to the sub-areas had to be less than the available water in the reservoirs, as exemplified in Eq (11).

(9)(10)(11)

Where, lt−1 is the volume of available water in the reservoir in period t−1, It is the rate of runoff during the period t, is referred to the maximum capacity of the reservoir.

However, the global framework of this study is as follows:

Case study and data collection

The Sistan Basin (30°–31.5° N to 61°–66° E), also referred to as the Hamoon watershed, is situated in the Sistan and Baluchestan province of Iran, in close proximity to the Iran-Afghanistan border [28]. In recent years, the region has experienced an exceedingly severe drought, primarily attributable to the prevailing climatic conditions and significant decline in the average annual precipitation, which predominantly occurs during the winter season [29]. In addition, the Afghan government has undertaken the construction of several dams along the Helmand River (as a transboundary river) with the intention of impounding runoff. However, this development has exacerbated the drought situation [30, 31]. Thus, in order to meet the water requirements of two sub-areas, namely Zabol and Zahedan, decision-makers have redirected the runoff originating from the Afghanistan side to the Helmand River, utilizing four large reservoirs collectively known as Chahnimeh [32–34]. As the drought intensifies and water resources continue to dwindle, decision-makers have encountered significant challenges in maintaining a balance between water demand and supply across various sectors. Therefore, the Sistan Basin has been identified as the focal area of study (Fig 3).

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Fig 3. Sistan Basin location, Iran, map created using ESRI software.

https://doi.org/10.1371/journal.pone.0294578.g003

The basin delineation and DEM of the study area, with a resolution of 30 meters, were derived from the National Aeronautics and Space Administration (NASA). The soil map data were obtained from the site (soilgrids.org). The land use data associated with the study area were extracted from GLC30 (globallandcover.com) dataset, provided by globallandcover.com, with a resolution of 300 meters. The Sistan Basin was further divided into 39 sub-basins based on detailed Hydrologic Response Units (HRUs), which took into account topography, soil type, slope characteristics, and land use. In addition, the observed streamflow data used in this study were obtained from the Regional Water Organization (sbrw.ir) and the Meteorological Organization of Sistan and Baluchestan (sbmeteo.ir). To forecast water demand, two criteria were considered: the correlation rate between Gross Domestic Product (GDP) and population growth. These criteria were used to analyze water demand in the agricultural, industrial, and domestic sectors [35, 36]. The water demand in these sectors, as assessed by the Iranian Water Research Center, (Table 1).

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Table 1. Parameter of water demand in sub-areas.

https://doi.org/10.1371/journal.pone.0294578.t001

Results and discussion

Downscaling procedure to simulate climate data

For model calibration, the SCS-CN method was employed to estimate monthly precipitation, as it is the most sensitive parameter. To accomplish this, 16 sensitive parameters varying ranks were developed to analyze the inflow rate (Table 2).

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Table 2. Parameters of sensitivity response to flow rate.

https://doi.org/10.1371/journal.pone.0294578.t002

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Fig 4. Calibrated and validated monthly flow rate in Sistan basin.

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Table 3. Statistical analysis for inflow at gauging stations for calibration and validation periods.

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Meanwhile, the application of frequency adaptation was employed to rectify the disparity in frequency between the simulated and observed data [30]. Indeed, frequency adaptation is regarded as an investigative tool to explore the empirical frequency discrepancy between projected and observed drought occurrences [37]. Fig 5 illustrates the prognostication of rectified rainfall and temperature under the RCP4.5&RCP8.5 scenarios for the period (2025–2085). In this context, both rainfall and temperature factors are practically overestimated, leading to the negative correction terms for both scenarios.

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Fig 5. Cumulative distribution functions response to observed and corrected data (2025–2085) under RCP4.5&RCP8.5 scenarios.

https://doi.org/10.1371/journal.pone.0294578.g005

Following the calibration and validation of the data, the projection of rainfall and temperature was conducted under the RCP4.5&RCP8.5 scenarios for the time periods (2025–2055&2056–2085). Based on the acquired results (Fig 6), the escalation in temperature and the decline in average rainfall are evident under the proposed scenarios. For instance, the difference in average rainfall between the RCP8.5(2025–2055) scenario and the RCP8.5(2056–2085) scenario exceeds -5%, thereby indicating a worsening water crisis in the region.

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Fig 6. Rainfall and temperature adjustments under different scenarios.

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Adopted optimal water supply to sub-sectors

Table 4 presents the optimal water allocation outputs across sectors for four scenarios: A1 = RCP4.5(2025–2055), A2 = RCP4.5(2056–2085), B1 = RCP8.5(2025–2055), and B2 = RCP8.5(2056–2085). Based on the findings, agricultural sector in Zabol received a greater share of water compared to the other sectors. Conversely, in Zahedan city, the industrial sector emerged as the primary beneficiary, receiving a substantial volume exceeding 40.00*106m3. In addition, the domestic sector in Zahedan received nearly four times the amount of water allocated to Zabol. Notably, under scenario B2, this sector obtained the largest water allocation, amounting to 22.14*106m3.

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Table 4. Optimal water allocated to sectors (106m3).

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Upon analyzing the system sustainability (Table 5), it becomes evident that the domestic sector attained the highest value in both sub-areas (0.491, 0.389). This signifies that the status of the domestic sector surpasses that of the other two sectors across all three factors: reliability, resilience (which remained unchanged as the system did not transition from failure to non-failure), and vulnerability.

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Table 5. Analysis of sustainability measure.

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In addition, the magnitude of deviation between water demand and water supply is depicted in Fig 7. The highest deviation was observed for Zahedan city under scenario B1, wherein the total supply amounted to 81.48*106m3 and the total demand reached 88.32*106m3. Similarly, under scenario A2, Zabol city exhibited a deviation with a total supply of 63.25*106m3 and a total demand of 69.98*106m3. Despite the reduction in the deviation between water demand and water supply achieved through the proposed optimal framework, the unsustainability of the water supply system remains evident due to the decline in the runoff rate.

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Fig 7. Maintaining the deviation between water demand and water supply.

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Adjusted strategies for analysis of sustainability measure under demand shrinkage scenarios

Recent studies [30, 33] have indicated that the Sistan Basin will encounter a decrease in rainfall and an increase in temperature, thereby exacerbating the scarcity of water resources. In addition, the growth in population contributes to the widening gap between water demand and water supply. However, prevailing adaptation measures for water supply primarily concentrate on proposing strategies for water allocation across various sectors, while neglecting the development of water demand management options. In light of this, it is necessary to put forth practical managerial insights that align with the current circumstances. These insights may involve the implementation of leverage policies and water tariffs, the utilization of advanced technologies such as drip irrigation systems, and the treatment of wastewater. Such measures can serve as viable alternatives for conserving water resources. Therefore, this study employed demand shrinkage scenarios (10% and 20% lower than the demand parameter specified in Table 1) to assess the sustainability of the water supply system.

According to the findings (presented in Table 6), the reliability and resilience of all sectors exhibited improvement when compared to their current status (Table 5). However, the vulnerability values for three sectors did not undergo significant changes. Overall, the system’s sustainability has been enhanced through the implementation of the demand shrinkage strategy.

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Table 6. The impact of demand shrinkage scenarios on sustainability measure.

https://doi.org/10.1371/journal.pone.0294578.t006

Discussion

To enhance the sustainability of system, it is necessary to develop novel adaptation measures that prioritize water supply among key sectors, considering the varying water requirements of intersectoral users based on their respective priorities. Nevertheless, in light of the preceding analysis, several key considerations should be considered:

Highlight 1: The distribution patterns of population growth in the coming years should be analyzed to facilitate demand management.

Considering that the level of water consumption is contingent upon the rate of population growth [35], the primary driver behind increased water consumption in the irrigation system, is the demand for additional food production. In practical terms, accurately assessing the potential gap between water demand and supply in the future necessitates the prediction of trends based on population growth rates and other influential variables. In light of the timing and magnitude of this gap, it is incumbent upon local and national authorities to devise appropriate measures aimed at minimizing the gap and meeting the projected demand.

Highlight 2: The challenge of water supply in the irrigation system requires adaptation measures.

The agricultural sector, being the largest consumer of water, has been directly affected by the drought, which has disrupted its supply chain. In addition, the inefficient and suboptimal utilization of water in the irrigation system presents significant operational hurdles for other businesses in a region that heavily rely on surface waters.

However, conducting a comprehensive assessment of the potential impacts of climate change on the agricultural sector at a regional scale proves to be highly effective. Additionally, the implementation of novel strategies, such as smart irrigation technologies, will greatly contribute to the conservation of water resources.

Based on the aforementioned description, the proposed model exhibits the following advantages:

  1. The proposed framework serves as a practical mechanism for optimizing water supply at the regional level, thereby enhancing sustainability across various scenarios and offering long-term perspectives for governing bodies.
  2. To minimize the impacts of uncertain runoff on the optimization of water supply, it is crucial to implement suitable adaptation measures that ensure a minimal deviation between water supply and demand. Merely concentrating on water resource management alone does not yield swift resolutions to the prevailing challenges. Instead, the exploration of innovative strategies, such as the implementation of demand shrinkage scenarios, can foster enhanced long-term sustainability in the system.

Conclusions

However, the challenge of meeting multi-sectoral water demand has emerged as a formidable task for authorities seeking to minimize failure and enhance the sustainability of the system. In light of this, a novel optimal framework is proposed in this study, which puts forth a refined definition for the sustainability of the water supply system. The primary objective of this framework is to reduce system failure and thereby enhance sustainability. Considering the influence of climate uncertainty on the supply process, the SWAT mechanism is employed as a supplementary tool to project climate data. In addition, due to the mismatch between water supply and water demand parameters, caused by both increased demand and the stress of climate change, an adaptive measure in the form of a demand shrinkage scenario is assumed. This scenario is analyzed to assess the impact of demand management on the sustainability of the system. The final output reveals that the domestic sector, characterized by the largest consumer base, exhibits the highest sensitivity to reliability and a lower vulnerability. Therefore, it exerts a significant influence on the improvement of system sustainability. The findings of this study highlight the importance of adopting a long-term perspective that focuses on optimizing the three key indicators of reliability, resilience, and vulnerability, as this approach leads to enhanced sustainability.

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