Introduction

on water use in Spain were examined through coupling a physical hydrological model with a linear programming economic model for the Guadiana Basin (Varela-Ortega et al., 2011) and the Ebro Basin (Graveline et al., 2013). Both studies in Spain were able to determine optimal policies under varying climate projections. In the River Orb Basin (France), a least cost optimization model assessed agricultural and urban water reduction measures, considering uncertainty in future evapotranspiration and precipitation (Girard et al., 2015).

What can be clearly observed is that optimization-based IWRM models can effectively supply model users with a best alternative. Whether this best alternative is supplied through single or multiple objectives, or through stochastic means, it is a rigid choice. Water resources problems pose a unique challenge that cannot be solved through considering optimality (Reed and Kasprzyk, 2009). Although MCDM goes beyond a single optimum solution, it is still a process where specific objectives and model structure are assumed. Instead, one can utilize a simulation approach that allows for the modification of model structure. In reality systems are always changing, and the model user needs a means of investigating these changes. Also, the objectives assumed by the modeler may not necessarily be the objectives used by all stakeholders. What is needed is a modeling approach the clearly illustrates feedbacks, and gives the end user a flexible and adaptive means to assess any possible situation modeled. The next section will discuss such an approach to foster flexibility and clearly define feedbacks.

2.3 Simulation-based Water Integrated Water Resource Systems Models

Simulation models provide a different approach from that of optimization models. In many cases it may not be of interest to find an optimal solution, but rather to understand the implications of different scenarios. A simulation approach allows users to understand the behavior of a system.

From the behavior, users can test different options or scenarios - whether that is policy alteration,

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climate change, or structural upgrades. Several case studies were presented by Jakeman and Letcher (2003) to understand effects of deforestation in Thailand, and investments in water supply systems in Australia on farm productivity. Hydro-economic models were seen to have a strength, especially in the case of simulation models, to allow the user to understand how specific mechanisms of water allocation interact. Some extensions included the interaction between ground water storage and urban water use (Srinivasan et al., 2010), irrigation district growth and farm profits (Bennette et al., 2013), and how management of channel vegetation impacted flood damages (Kourgialas and Karatzas, 2013). Simulation models also simulated non-market values, such as recreation, greenhouse gases and habitat biodiversity (Grossman and Dietrich, 2012).

These models proved to be useful at examining the feasibility of the European Water Framework Directive (WFD) over current practices. Bateman et al., (2006) observed pre-WFD and post-WFD effects on agricultural productivity and non-market values of stream quality and habitat. Through water resource systems simulation models, effects of policies on reducing the dependence on groundwater, water licenses, irrigation limits and environmental flow requirements were understood in both short and long term time frames (Letcher et al., 2004).

Some studies have recognized the need to examine market mechanisms, and their effect on water allocation. The global trade in blue and green water, considering the relationship of trade-networks on water resources, was simulated by Konar et al. (2012). This simulation was achieved through utilizing a compartmental model linking hydrology, river routing, crop growth, reservoir operation and human consumption (Hanasaki et al.., 2010). Mahan et al. (2012) quantified welfare gains under different trading scenarios by linking a farm sub-model to a non-linear economic welfare model. Game Theory was shown to depict the motivations of various parties, as ulterior motives of different parties can lead to a sub-optimal allocation (Madani, 2010). An extensive list

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of case studies applying game theory to water management can be found in Madani (2010). Water transfers for controls on water quality and agricultural production were examined in Southern Iran using a simulation model (Mahjouri and Ardestani, 2010). A coalition analysis was applied to the South Saskatchewan River Basin, illustrating the effects of water transfers to promote higher value crops (Hipel et al., 2013). Both game theory case studies showed that reality can be better simulated by considering realistic strategies of different water users. A further development to the literature presented so far is the advancement towards decision support systems.

Loucks et al. (2005) discussed a decision support system (DSS) as a means to aid the decision making process. This was achieved through various levels of support (e.g. whether to allow the user or model to rank alternatives). This was useful in IWRM as it gives stakeholders a shared vision, allowing them to interact as they build the model (Loucks et al., 2005). One of the oldest DSSs to be used in IWRM is the AQUATOOL, currently being used by the River Basin Agency of Spain (Andreu et al., 1996). AQUATOOL addressed early initiatives of integrating hydrological characteristics with risk assessments in a user interface. The WaterWare was also developed at the same time, providing a GIS interface and a river-basin planning module (Fedrac, 1996a; Fedrac and Jamieson, 1996). Early studies found WaterWare competent in managing surface water-ground water interactions dealing with contaminant transport (Fedrac, 1996b). With the advent of the WFD, MULINO was developed to include socio-economic considerations of policy changes (Mysiak et al., 2005; Giupponi et al., 2004). This platform utilized a cause and effect relationship for stakeholders to assess consequences of different management strategies. The WFD also required volumetric water pricing to promote sustainability. Multi-attribute Utility Theory (MAUT) was applied to develop water demand functions to irrigation districts in northern Greece (Latinopoulos, 2008). These utility functions allowed stakeholders to analyze water management

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decisions while considering the farmer’s values. In Australia, the Water Accounting System (Turner et al., 2009) managed water through accounting. This allowed stakeholders to perform a design-based approach, minimizing ‘tensions’ in the system. Weng et al. (2010) developed the MEMO model, an integrated scenario-based multi criteria DSS. MEMO utilized fuzzy theory and multi-criteria decision analysis to present decision makers with preferred solutions. The strength of MEMO was that multi-criteria capabilities allow it to analyze many performance indicators.

The SimBat DSS is a flow network model that considers municipal, irrigation, and environment users (Preziosi, 2013). Preziosi et al. (2013) applied this model to assess impacts of hydrological droughts, considering different alterations in streamflow. The DSS was found effective as it quantified impacts of various management decisions by using vulnerability indices.

Further advancements in hydro-economic modelling allowed for a systems thinking approach. System Dynamics (SD) is a novel approach to systems thinking, pioneered by Jay Forrester- initially used as a tool to understand industrial systems (Forrester, 1961). When compared to other modelling approaches for integrated assessment, SD was found to best characterize feedback loops (Ford, 1999). These strong feedbacks are useful in allowing the user to easily identify leverage points in the system (Hjorth and Bagheri, 2006). This modeling approach is highly applicable to water resources modeling as it provides a user-friendly simulation environment with capabilities to analyze complex system interactions (Mirchi et al., 2012). SD also provides a strong environment for stakeholder participation (Stave, 2002; Winz et al., 2009;

Mirchi et al. 2012) and system validation (Barlas, 1996; Peterson and Eberlein, 1994).

SD was widely used in water resources modeling and management (Elshorbagy and Ormsbee, 2006; Hassanzadeh et al., 2014; Elshorbagy et al. 2007; Simonovic et al., 1997). SD was shown in many case studies to provide management alternatives to complex, multi-objective

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problems. The CanadaWater model integrated the economy, freshwater, groundwater, agriculture, and population to holistically examine different management policies (Simonovic and Rajasekaram, 2004). Wei et al. (2012) were able to determine the optimal environmental flow in a river basin in China by examining the feedback between environmental flow demand and socio-economic development. Benefits of agricultural productivity under different water transfer schemes between Mexico and US were examined by Gastelum et al. (2009). By using an SD model, Qin et al. (2011) determined that economic growth in Shenzhen, China, is strongly linked to population and pollution. Often, water resources systems have complex feedbacks between resource consumption and socio-economic development, and SD is a valuable approach in examining carrying capacities of such systems under different policies by understanding these feedbacks (Song et al. 2011, Yang et al. 2014, Yang et al. 2015). Similar complexities linking ground water extraction to net present value in an irrigation district was analyzed by Karamouz et al. (2013). These studies serve to illustrate the use of SD to understand the cause and effect mechanisms within a resource system.

A further advancement was the integration of SD into a DSS framework. As discussed previously, DSS’s strength is in aiding model-users in the decision process. This, combined with SD, can provide a system that fosters integration and feedback between multiple components of the model. Many studies have applied SD in compartmental models to handle complexity within a DSS framework. The NHREYS DSS (Kazeli and Keravnou, 2003) linked together four compartments (Data, Manager, Display, and Decision Maker) for an intelligent water management DSS. A stochastic model (Data) accounted for uncertainty in the inputs to the SD-based water allocation model and results from the SD model are saved in the Decision Maker compartment.

This constituted an intelligent system as it was able to determine recommended resource

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allocations at each time step of the model based on previously stored information. Studies by Chen et al. (2005) and Nikolic et al. (2012) linked SD with a GIS tool to account for spatial variability in the decision process. The first study employed the Driving-force State Response (DSR) framework, and the second used intelligent agents, to incorporate policy in decision making. A compartmental DSS, known as TAI WAP (Liu et al., 2009), integrated a weather generator and the physical hydrological model, GWLF, to produce inputs to the SD model. To stream-line the decision making process regarding infrastructure upgrades, Xi and Poh (2014) evaluated the outputs of their SD model using Analytic Hierarchy Process. This process allowed three subjective attributes (adequacy, self-sufficiency, and cost) to be weighted for each scenario and provide users with the optimal solution. These five modeling studies show the potential of using SD within a DSS framework.

The advantages discussed above make SD a suitable method for the study considered and reported in this thesis. SD was shown to illustrate feedbacks, as well as promoting flexibility and adaptability inherent of all simulation models. Further, SD was shown many times to serve as a DSS. The SD-based model developed in this study, SWAMPB, aims at producing a self-contained DSS model, not requiring any external modules. This means that the entire modeling process can be modified within one platform.