The Porting of Wargi-DSS to Grid Computing Environment for Drought Plans in Complex Water Systems

Abstract

The increasing developments in computer technology have motivated the concurrent development of Decision Support Systems (DSSs) aimed at facilitating the planning and management of complex water systems (Assaf et al., 2008). Simulation and optimization models within DSSs provide the main tool for researchers and practitioners to analyze the behavior and performance of any proposed water resource system design or management policy alternative before it is implemented in real systems. Various strategies have been proposed to combine the adherence and flexibility of simulation models with the efficient exploration of mathematical optimization models (Loucks and van Beek, 2005). AQUATOOL (Valencia Polytechnic University) (Andreu et al., 1996), MODSIM (Colorado State University) (Labadie et al., 2000), RIBASIM (DELTARES) (Delft Hydraulics, 2006), WARGI-SIM (University of Cagliari) (Sechi and Sulis, 2009a) and WEAP (Stockholm Environmental Institute) (SEI, 2005) are representative of DSSs used for preliminary analysis of alternative plans and policies. Those popular generic simulation models have been implemented world-wide in a large number of water systems and incorporate most of the desirable attributes of a simulation model. WARGI (WAter Resources Graphical Interface) is a generic DSS for planning and management complex water systems developed at the University of Cagliari (Italy). The DSS is specifically developed to meet the system management requirements to satisfy the growing demands in multi-reservoir systems under water scarcity conditions, as frequently, happen in the Mediterranean regions. Sechi and Sulis (2009a) have recently developed a full integration of the simulation module WARGISIM and the linear optimization module WARGI-OPT in the DSS. This mixed simulationoptimization approach was proposed with the aim of identifying and evaluating mitigation measures in a proactive approach that anticipates the trigger of these actions. The processes that govern the behavior of multireservoir water systems are affected by uncertainty that increases with time and space investigation scales (Simonovic, 2000). Uncertainty is mainly associated with the value of hydrological exogenous inflows and users demand patterns. A common disadvantage of the traditional modeling approach is the large number of system simulations required to achieve acceptable levels of confidence treating data uncertainties in the model. In fact, the proactive approach to drought