A multi-stakeholder framework for urban runoff quality management: Application of social choice and bargaining techniques
Graphical abstract
Introduction
Population growth, increasing rate of development and climate variability are driving forces threatening the quality and quantity of water resources in urban areas. Shifting scale to rainfall–runoff process in urban watersheds; when runoff volume is more than the drainage system capacity, it can be managed and used for recreational and outdoor water uses. Low Impact Development (LID) measures are economically and environmentally efficient tools for runoff quality and quantity management and control in urban watersheds (Zahmatkesh et al., 2014).
Speaking of urban runoff, selection of appropriate management scenarios depends on more than one objective, such as increasing runoff quality and quantity while decreasing the implementation costs of the LIDs. Therefore, to find the most preferred scenario for urban runoff management, multi-objective optimization tools are preferably used. The NSGA-II (Deb et al., 2002), for instance, has been proved to be an effective and efficient multi-objective search technique in various urban management applications. In the current paper, the NSGA-II algorithm is used for optimizing areas and locations of different LID measures. Details of this algorithm and its applications in water resources management can be found in Nazemi et al. (2006), Muschalla (2008), Nikoo et al. (2014), and Rafipour-Langeroudi et al. (2014).
Usually, in decision-making problems in urban watersheds, more than two stakeholders with different interests are involved. The non-dominated solutions obtained from the optimization problem, are considered as management scenarios of the main stakeholders in the bargaining model. Conflict is known as the disagreement among stakeholders that differ in attitudes, beliefs, values or needs (Karamouz et al., 2006). When conflict exists among stakeholders, the preferred action is to reach an agreement mostly through bargaining. Bargaining can be performed using cooperative and non-cooperative game theoretic approaches. Game theory is defined as the study of the interactions among different decision makers with conflicting objectives. It provides systematic pattern to plan strategies for providing resolutions of conflicting situations. To deal with conflicts among different stakeholders, Nash cooperative bargaining theory is also an alternative modeling approach. This theory is one of the most effective methods in bargaining problems which incorporates the utility functions of the decision makers and the stakeholders, and their relative authorities (Nash, 1950). Bargaining methods and game theory can be used to find the stable solution for the decision making problems (Madani et al., 2014). Modeling bargaining processes have been performed by different researchers such as: Dinar et al. (1992) for regional cooperation in the use of irrigation water; Shahidehpour et al. (2001) to consider the problem of optimizing hydropower generation; Kerachian and Karamouz, 2006, Kerachian and Karamouz, 2007 for water quality management in river–reservoir systems; Kerachian et al. (2010) for surface and groundwater quality management in urban areas; Skardi et al. (2013) for best management practice (BMP) cost allocation among landowners in a watershed; Rafipour-Langeroudi et al. (2014) for conjunctive use of surface and groundwater resources; Safari et al. (2014) to resolve conflicts among different water users and water suppliers while considering environmental requirements and the system's constraints and Parsapour-Moghaddam et al. (2015) to determine evolutionary stable equilibrium strategies for conjunctive surface and groundwater allocation to stakeholders with conflicting benefits.
Similar to the game theory and Nash bargaining model, social choice theory is concerned with relationships between individuals' preferences (Fishburn, 1973). Social choice procedures are also applicable to reach some level of cooperation in multi-criteria decision making problems. Social choice procedures can produce outputs which fall between non-cooperative game theory/bargaining approaches and multi-criteria decision making approaches (Madani et al., 2014). The basic objective of social choice is to combine individual preferences into a collective choice. When individual utility functions are combined, the aggregation could be interpreted as a social welfare function (Martin et al., 1996). Examples to use social choice theory in hydro-environmental management problems are as follows. d'Angelo et al. (1998) applied social choice procedures to solve a water resources management problem in Northern Arizona. They used social choice procedures of plurality voting, pairwise voting and dictatorship. These procedures can be applied when several alternatives are simultaneously ranked by different decision makers, with conflicting utilities. Srdjevic (2007) investigated different contexts in modeling decentralized decision problems in water management. He used analytic hierarchy process and social choice methods to support group decision making. Madani et al. (2014) applied several commonly used voting methods to solve a multi-stakeholder water management problem. Comprehensive examples of social choice procedures can be found in Taylor (1995), McNutt (1996), d'Angelo et al. (1998), Kelly (2013), Laukkanen et al. (2002), Mahjouri and Bijani-Manzar (2013) and Mahjouri and Abbasi (2015).
This paper discusses the importance of incorporating conflicting utilities of stakeholders in integrated runoff quality and quantity management. The proposed framework is used for a real world case study, Velenjak urban watershed in Tehran, Iran, which is simulated utilizing the SWMM (Storm Water Management Model) rainfall–runoff model.
Surface runoff in Tehran is an important source for watering landscapes and garden plants as well as irrigation of agricultural lands. High population, industrial activities and climatic attributes negatively impact the runoff quality. Different agencies are involved in the decision making process for controlling runoff quality and quantity, each suggesting different and sometimes diverse management scenarios. Therefore, studies are required to be conducted in order to provide an integrated scheme for runoff quality and quantity management. Application of the proposed methodology helps incorporate the main factors affecting successful modeling of runoff quality and quantity in urban areas.
Section snippets
Methodology
A framework is proposed for improving runoff quality and quantity considering existing conflict of interests among different stakeholders. In this framework, a set of simulation, optimization and bargaining models are incorporated. The SWMM rainfall–runoff model is used for runoff simulation. Main stakeholders are identified and the corresponding utility functions are formulated. An optimization algorithm is developed using the NSGA-II and linked with the SWMM to find the Pareto optimal
Case study
The study area is Velenjak watershed, northeast part of Tehran mega city, Iran. Tehran located at 35°31′ to 35°57′ north and 51°4′ to 51°47′ east, with an area of 730 km2 is the capital of Iran (Fig. 2). In this urban area, surface runoff carries pollutant because streets are significant sources for contaminants such as debris and floatables as well as, oils and greases. Besides, first-flushes are significantly polluted due to air pollution in the populated cities.
Velenjak watershed includes 27
Objective functions in the optimization problem
With the aim of runoff quality and quantity management considering the costs of LIDs implementation, the multi-objective problem is defined. Considering 10 selected sub-catchments for the LIDs implementation and 4 LIDs, 40 decision variables are included in the optimization problem:subject to the constraints defined in Table 2 with the general form of lower bound ≤ variable ≤ upper bound. In Eq. (15), G1(x), G2(x) and G3(x) are
Summary and conclusion
In this paper, a framework was proposed for controlling runoff quality and quantity in urban areas. Different simulation, optimization, bargaining and social choice models were developed and used for this purpose. The Velenjak watershed in Tehran was selected as the study area and was simulated using the SWMM rainfall-runoff simulation model. A multi-objective optimization algorithm (i.e. NSGA-II) was used to find 700 non-dominated LID scenarios for urban runoff management.
Three main
Acknowledgments
Technical contributions of Dr. M. R. Nikoo, Mr. S. M. Estalaki and Ms. M. Soltani are hereby acknowledged. Also, the authors would like to acknowledge the financial support of the University of Tehran for this research under grant number 8102060/1/08.
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