Water Quality Management And Water Resources Allocation Under Multiple Uncertainties

Population growth, urbanization, industrialization and climate change impacts have generated increasing demands for freshwater resources worldwide. Problems with water resources security resulting from water scarcity and water quality deterioration are getting more and more serious. It is of great importance to make out sound strategies for water resources allocation management and water quality management, which aims at obtaining certain goals such as the maximization of return from economic activites under the limitation of water resources and environmental requirements.Optimization techniques are effective tools to analyze the relevant information,evaluate pollutant mitigation and water resources limitation, assess the resulting impact,and generate desired decision alternative. In fact, in water quality management and water resources managemant problems, various uncertainties exist in a number of system components as well as their interrelationships. The uncertainties are often associated with various complexities in terms of information quality. This paper focuses on the identifications of sound water resources and water quality management strategies under multiple uncertainties. A series of optimization methods would be developed and applied to water quality management and water resources allocation. These methods include: (1)an interval fuzzy credibility-constrained programming (IFCP) method. 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. It is applied to plan water quality management system of the Xiangxihe watershed. Interval solutions of the production amount of economic activities and pollution discharges are generated. (2) a fuzzy-boundary interval programming (FBIP) method. FBIP can deal with dual uncertainties expressed as intervals and fuzzy-boundary intervals. It is applied to plan water quality management system of the Xiangxihe watershed. An interactive algorithm and a vertex analysis approach are proposed for solving the FBIP model and solutions with ?-cut levels have been generated. Results show that a lower a-cut level(i.e.,a lower degree of plausibility) would correspond to a wider interval; conversely, a higher a-cut level would lead to a narrower interval. Results reveal that the uncertainties projected in parameters and different combinations of parameters would lead to changed system benefits. (3) a multi-level-factorial risk-inference-based possibilistic-probabilistic programming (MRPP) method. The MRPP method can handle uncertainties expressed as fuzzy-random-boundary intervals, probability distributions and interval numbers, and analyze the effects of uncertainties as well as their interactions on modeling outputs. It is applied to plan water quality management system of the Xiangxihe watershed. Results reveal that a lower probability of satisfying the objective function as well as a higher probability of violating environmental constraints would correspond to a higher system benefit with an increased risk of violating system feasibility. Results of factorial analysis reveal that optimistic decision makers should pay more attention to the interactions between chemical plant and water supply, while decision makers who possess a risk-averse attitude would focus on the interactive effect of probability of violating environmental constraints and benefit of water supply. (4) an interval-stochastic-based risk analysis (RSRA) method. The RSRA method is effective in risk management and policy analysis, particularly when the inputs are expressed as probability distributions and interval values. Decision makers' attitudes towards system risk can be reflected using a restricted resource measure by controlling the variability of the recourse cost. The RSRA method is then applied to a real case of water quality management in the Heshui River Basin. Results reveal that uncertainties and risk-attitudes have significant effects on both pollutant discharge and system benefit. Decisions at a lower & level would lead to an increased reliability in fulfilling the system requirements but with a lower expected benefit; in comparison, a strong desire to acquire higher expected benefits would entail a raised risk of violating the constraints. Results also disclose that agriculture is the dominant contributor to soil loss, TN, and TP loads as well as livestock husbandry is the main COD discharger. (5) a Monte Carlo simulation based dual-interval stochastic programming (MC-DSP) method is developed for assessment of uncertainty effects on crop planning and irrigation water supply associated with multiple uncertainties expressed as dual intervals and probability distributions. The developed method is applied to crop planning and water allocation for the Zhangweinan River Basin in China.Results reveal that water availabilities associated with different probability distributions can lead to changed system benefits and irrigation shortages. A probability distribution with higher mean value and lower standard deviation would correspond to a higher system benefit (with lower irrigation shortages); in comparison, a probability distribution with lower mean value and higher standard deviation would lead to a lower system benefit (with higher irrigation shortages).In this paper, the integrated optimization methods are capable of assisting decision makers tackle the system multiple uncertainties. Results cover all aspects of water resources management and/or water quality management systems. including water supply and demand, production generation, pollutant discharge control and maximization of system benefit. The findings can facilitate the local authority in identifying desired pollution control strategies and water resources allocation polices with the tradeoff between socio-economic development and environmental sustainability.