Definition of efficient scarcity-based water pricing policies through stochastic programming

Predicting and allocating water resources have become important tasks in water resource management. System dynamics and optimal planning models are widely applied to solve individual problems, but are seldom combined in studies. In this work, we developed a framework involving a system dynamics-multiple objective optimization (SD-MOO) model, which integrated the functions of simulation, policy control, and water allocation, and applied it to a case study of water management in Jiaxing, China to demonstrate the modeling. The predicted results of the case study showed that water shortage would not occur at a high-inflow level during 2018–2035 but would appear at mid- and low-inflow levels in 2025 and 2022, respectively. After we made dynamic adjustments to water use efficiency, economic growth, population growth, and water resource utilization, the predicted water shortage rates decreased by approximately 69–70% at the mid- and low-inflow levels in 2025 and 2035 compared to the scenarios without any adjustment strategies. Water allocation schemes obtained from the "prediction + dynamic regulation + optimization" framework were competitive in terms of social, economic and environmental benefits and flexibly satisfied the water demands. The case study demonstrated that the SD-MOO model framework could be an effective tool in achieving sustainable water resource management.

Section: Discussionmentioning

confidence: 99%

System Dynamics-Multiple Objective Optimization Model for Water Resource Management: A Case Study in Jiaxing City, China

Zhou

Wang

Huang

et al. 2021

“…However, their water pricing strategies are not linked to the marginal benefit of water use. Macian-Sorribes et al [29] proposed an efficient annual pricing policy based on the marginal benefit of water resources at the basin scale. Its application in Mijares River basin showed that this pricing policy could generate an incentive for water conservation during the scarcity periods and increase the economic benefit in the basin.…”

Section: Literature Reviewmentioning

confidence: 99%

Pricing Strategy for Residential Water in Drought Years. Application to the City of Tianjin, China

Chu

et al. 2021

In drought years, most residents fail to improve water use efficiency due to residential water supply normally being prioritized in many regions, which makes other low-priority industrial water users suffer more from water shortage. This paper proposes a Pricing Strategy for Residential Water (PSRW), a water tariff that changes on annual time scale, based on the scarcity value of water resources, aiming to promote residential water conservation and reallocate water resources across the residential and industrial sectors during droughts. An optimization model to maximize the total benefit of residents and industrial sectors is introduced based on marginal benefit and price elasticity. The water shortage of industrial sectors is used to reflect the scarcity of water resources, and the lowest water supply standard for households and the maximum proportion of household water fee expenditure (HWFE) to household disposable income (HDI) are used to ensure the residents’ acceptability to price raising. It shows an “S-type” relationship between the optimal price raising coefficient and industrial water shortage, and two turning points are found in the curve, which are the starting and stopping points of price raising. The appearance of starting point depends on the non-negative net benefit, and the stopping point is affected by the factors that represent the residents’ acceptability to price raising. The application to Tianjin, a city in northern China with the rapid growth of population and economy but scarce water resources, shows PSRW is a potential means to improve water efficiency and optimize water resource allocation in water scarcity situations.

“…From this approach, this adds an uncertainty component to the prediction because this fraction does not depend on time. The recorded value of target water year to assess the predictive reliability, 28.4 Hm³ was supplied by the Jucar River Basin Authority (please, see Reference [41]). On the other hand, the reliability of the predictive model is shown in Table 6, both a general approach and a detailed one developed through a definition of TNCR intervals which is an uninfluenced fraction of the time.…”

Section: Management Scenariosmentioning

confidence: 99%

Section: Management Scenariosmentioning

confidence: 99%

“…This zone regularly suffers noteworthy drought conditions [40]. In particular, an unregulated stretch, characterized by a very high time-dependency behavior and by the existence of important water springs at its headwater was selected [41] (Figure 1a). This case study was defined by gauging station "El Terde", code number 8030 ( Figure 1b).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Causal Reasoning: Towards Dynamic Predictive Models for Runoff Temporal Behavior of High Dependence Rivers

Molina¹,

Zazo

Martín³

2019

Nowadays, a noteworthy temporal alteration of traditional hydrological patterns is being observed, producing a higher variability and more unpredictable extreme events worldwide. This is largely due to global warming, which is generating a growing uncertainty over water system behavior, especially river runoff. Understanding these modifications is a crucial and not trivial challenge that requires new analytical strategies like Causality, addressed by Causal Reasoning. Through Causality over runoff series, the hydrological memory and its logical time-dependency structure have been dynamically/stochastically discovered and characterized. This is done in terms of the runoff dependence strength over time. This has allowed determining and quantifying two opposite temporal-fractions within runoff: Temporally Conditioned/Non-conditioned Runoff (TCR/TNCR). Finally, a successful predictive model is proposed and applied to an unregulated stretch, Mijares river catchment (Jucar river basin, Spain), with a very high time-dependency behavior. This research may have important implications over the knowledge of historical rivers´ behavior and their adaptation. Furthermore, it lays the foundations for reaching an optimum reservoir dimensioning through the building of predictive models of runoff behavior. Regarding reservoir capacity, this research would imply substantial economic/environmental savings. Also, a more sustainable management of river basins through more reliable control reservoirs’ operation is expected to be achieved.