Y. P. Li

This study developed a fuzzy-stochastic programming with Green Z-score criterion (FSGZ) method for water resources allocation and water quality management with a trading-mechanism (WAQT) under uncertainties. FSGZ can handle uncertainties expressed as probability distributions, and it can also quantify objective/subjective fuzziness in the decision-making process. Risk-averse attitudes and robustness coefficient are joined to express the relationship between the expected target and outcome under various risk preferences of decision makers and systemic robustness. The developed method is applied to a real-world case of WAQT in the Kaidu-Kongque River Basin in northwest China, where an effective mechanism (e.g., market trading) to simultaneously confront severely diminished water availability and degraded water quality is required. Results of water transaction amounts, water allocation patterns, pollution mitigation schemes, and system benefits under various scenarios are analyzed, which indicate that a trading-mechanism is a more sustainable method to manage water-environment crisis in the study region. Additionally, consideration of anthropogenic (e.g., a risk-averse attitude) and systemic factors (e.g., the robustness coefficient) can support the generation of a robust plan associated with risk control for WAQT when uncertainty is present. These findings assist local policy and decision makers to gain insights into water-environment capacity planning to balance the basin’s social and economic growth with protecting the region’s ecosystems.

In this study, a hybrid factorial stepwise-cluster analysis (HFSA) method is developed for modelling hydrological processes. The HFSA method employs a cluster tree to represent the complex nonlinear relationship between inputs (predictors) and outputs (predictands) in hydrological processes. A real case of streamflow simulation for the Kaidu River basin is applied to demonstrate the efficiency of the HFSA method. After training a total of 24 108 calibration samples, the cluster tree for daily streamflow is generated based on a stepwise-cluster analysis (SCA) approach and is then used to reproduce the daily streamflows for calibration (1995–2005) and validation (2008–2010) periods. The Nash-Sutcliffe coefficients for calibration and validation are 0.68 and 0.65, respectively, and the deviations of volume are 1.68% and 4.11%, respectively. Results show that: (i) the HFSA method can formulate a SCA-based hydrological modelling system for streamflow simulation with a satisfactory fitting; (ii) the variability and peak value of streamflow in the Kaidu River basin can be effectively captured by the SCA-based hydrological modelling system; (iii) results from 2⁶ factorial experiments indicate that not only are minimum temperature and precipitation key drivers of system performance, but also the interaction between precipitation and minimum temperature significantly impacts on the streamflow. The findings are useful in indicating that the streamflow of the study basin is a mixture of snowmelt and rainfall water.

In this study, a joint-probabilistic interval multistage programming (JIMP) method is developed for planning water resources management under uncertainty. The JIMP method can tackle uncertainties presented in terms of interval parameters in objective function and constraints, in addition to random variables in the left and right-hand sides of constraints. It can also reflect the dynamics in terms of decisions for water resources allocation through transactions at discrete points of a complete scenario set over a multistage context. Moreover, the JIMP method can be used for analyzing various policy scenarios that are associated with different levels of economic consequences when the promised water-allocation targets are violated. The JIMP method is applied to a real case of planning water trading for supporting the regional sustainable development of the Kaidu-Qongque River basin, which is one of the most arid regions of China. Monte Carlo simulation is introduced into the JIMP framework for evalua...

In this study, a two-stage interval-stochastic water trading (TIWT) model is developed for reallocating water resources under uncertainty, which integrates techniques of interval-parameter programing and two-stage stochastic programing into a general framework. The TIWT model can provide an effective linkage between system benefit and the associated economic penalty attributed to the violation of the pre-regulated water permit under uncertainties expressed as probabilistic distributions and interval values. The trading scheme is introduced to optimize water allocation of Kaidu-Kongque River in northwestern China. Results obtained suggest that trading program can effectively allocate limited water resources to competitive users by market approach in such an arid area, which improves economic efficiency in the mass (e.g., maximizing system benefits) and remedies water deficiency. A number of policies for water permits are analyzed and reveal that different water permits lead to different water shortages, system benefits, and system-failure risks. Tradeoffs between economic benefit and system-failure risk are also examined under different policies, which support generating an increased robustness in risk control for water resources allocation under uncertainty. The results are helpful for local decision-makers in adjusting the current water allocation pattern optimally.

Topography plays a critical role in controlling water dispersion and soil movement in hydrologic modeling for water resources management with raster-based digital elevation model (DEM). This study aims to model effects of DEM resolution on runoff simulation through coupling fuzzy analysis technique with a topography based rainfall–runoff model (TOPMODEL). Different levels of DEM grid sizes between 30 m and 200 m are examined, and the results indicate that 30 m DEM resolution is the best for all catchments. Results demonstrate that the DEM resolution could have significant influence on the TOPMODEL rainfall–runoff simulation. Fuzzy analysis technique is used to further examine the uncertain DEM resolution based on considering Nash, sum of squared error, and sum of absolute error values of TOPMODEL. The developed model is calibrated and validated against observed flow during the period 2010–2012, and generally performed acceptably for model Nash–Sutcliffe value. The proposed method is useful for studying hydrological processes of watershed associated with topography uncertainty and providing support for identifying proper water resources management strategy.

This study proposes an interval semiinfinite De Novo programming (ISIDP) method for the planning of water resource management systems under uncertainty. The ISIDP problem is settled by dividing it into two interactive linear programming subproblems and solving it by using a conventional simplex method. To evaluate the applicability of the proposed model, the method is applied to a case study of the Yuecheng Reservoir in Zhangweinan River Basin, China. The results indicate that the strategies generated through ISIDP would not increase the complexity in decision-making processes. Compared with the conventional optimization method, ISIDP has the advantages of (1) better reflecting the association of the system benefits with water price, (2) generating more reliable solutions with a lower risk of system failure as a result of the possible violation of constraints, and (3) providing more flexible management planning because the availability of budgets can be adjusted with the variations in water price.

In this study, an interval fuzzy credibility-constrained programming (IFCP) method is developed for river water quality management. IFCP is derived from incorporating techniques of fuzzy credibility-constrained programming (FCP) and interval-parameter programming (IPP) within a general optimization framework. IFCP is capable of tackling uncertainties presented as interval numbers and possibility distributions as well as analyzing the reliability of satisfying (or the risk of violating) system’s constraints. A real-world case for water quality management planning of the Xiangxi River in the Three Gorges Reservoir Region (which faces severe water quality problems due to pollution from point and nonpoint sources) is then conducted for demonstrating the applicability of the developed method. The results demonstrate that high biological oxygen demand (BOD) discharge is observed at the Baishahe chemical plant and Gufu wastewater treatment plant. For nonpoint sources, crop farming generates large amounts of total phosphorus (TP) and total nitrogen (TN). The results are helpful for managers in not only making decisions of effluent discharges from point and nonpoint sources but also gaining insight into the tradeoff between system benefit and environmental requirement.

In this study, a robust simulation–optimization modeling system (RSOMS) is developed for supporting agricultural nonpoint source (NPS) effluent trading planning. The RSOMS can enhance effluent trading through incorporation of a distributed simulation model and an optimization model within its framework. The modeling system not only can handle uncertainties expressed as probability density functions and interval values but also deal with the variability of the second-stage costs that are above the expected level as well as capture the notion of risk under high-variability situations. A case study is conducted for mitigating agricultural NPS pollution with an effluent trading program in Xiangxi watershed. Compared with non-trading policy, trading scheme can successfully mitigate agricultural NPS pollution with an increased system benefit. Through trading scheme, [213.7, 288.8] × 10³ kg of TN and [11.8, 30.2] × 10³ kg of TP emissions from cropped area can be cut down during the planning horizon. The results can help identify desired effluent trading schemes for water quality management with the tradeoff between the system benefit and reliability being balanced and risk aversion being considered.