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Introduction to Decision Support Systems

DSS aim to the enhance quality of decision making through an easy-to-use interactive systems. DSS integrate diverse sets of knowledge, data, and information from multiple disciplines to account for the multiple facets and complexities associated with water resources management. Furthermore,  DSS are coupled with analytical tools and visual methods that support interpretation of data from different perspectives making it useable for decision makers. They may include data from hydrologic stations, meteorological stations, experts, and local knowledge. DSS provide a knowledge base for holistic responses for IWRM (Giupponi, 2013).

A DSS can help bridge the gap between researchers and policy makers and act as a catalyst of trans-disciplinary research and improved decision making (Cai, 2015) (Figure 1). It connects the scientific research and experience of practitioners to decision makers while also transferring knowledge and awareness amongst stakeholders. Having an improved understanding regarding water systems and its interlinkages with other systems, creates a common language among the stakeholders, promoting effective communication (Carmona, 2013). This also fosters multi-stakeholder partnerships (Tool B3.05) and allows decision making processes to explore different alternative scenarios of basin development, hydrology, water use, and management policies. 

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Figure 1. DSS in the context of IWRM process. Source: Georgakakos (2007) 

Generic Components of DSS

DSS provide a basis for decision making by considering key information which are tailored to the decision-making process and context. A typical DSS for IWRM includes five main components which are as follows (Georgakakos, 2007):

  • Data acquisition system: This consists of data collection systems such as rain-gages, stream-gages, satellites, radars, surveys and interviews.
  • User-data-model interface: The interface allows data to the database and provide easy access to data, analysis tools and models.
  • Database: A depository of diverse sets of data and information including that of analysis results, model results and raw data.
  • Data analysis tools: user-friendly means to visualise and analyse various data sets
  • Set of interlinked models: the set of interlinked models allows users to gain insights into different aspects of water management and its interconnections such as with land-use, ecosystems, socio-economics and energy systems.
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Figure 2. Generic components of a DSS. Source: Georgakakos (2007) 

Scientific Modeling and Data Analysis

There are few important notions to consider while approaching the topic of DSS:

  • Data analysis and presentation: With the vast diversity of data required for water resources planning and management, the data must be analysed and represented in a form that is understandable and useable for decision makers. Decision support systems analyse large amounts of data and summarises required information for decision making processes. Furthermore, the data and information is visually represented in an interactive formats to ensure ease of interpretation. For example, Geographic Information Systems (Tool C2.01) have been widely used to integrate and visualise different data on spatial and geographical scales. Furthermore appropriate graphs and figures can also be used based on the information. Some of the data is fed into models to simulate alternative future scenarios and consequences of different decisions allowing for comparative decision making (Ahmadi, 2020).
  • Modelling: A model is a simplified representation of a complex system to assist calculations and predictions. A model commonly describes isolated hydrological, hydro-economic, or water resource process while DSS is a framework that links database, data analysis, knowledge and information systems, modelling, and communications (de Kok, 2003). A model must be representative to ensure that it supports improved decision making. Hydrological models simulate river run-off, ground water, water quality and evapotranspiration while other models situate social, ecological, and economic systems. A DSS might have a range of integrated models to promote systems thinking and holistic decision making. For example socio-hydrological modelling (Tool C2.04) integrates social sciences and technical or conventional hydrological modelling to describe the interactions and feedback between social and hydrological systems.
  • Optimization: Models and decision making processes are further supported by optimization algorithms based on a range of parameters and user-defined objectives (GWP, 2013). These algorithms account for competing objectives such as flood protection, environmental flow requirements, and user water demands to present a range of optimal strategies such as reservoir operation or water allocation.
Data and Information for Decision Making

DSS make information available and accessible for decision making processes. The accessibility of DSS to decision makers and relevant stakeholders supports transparency and trust in the decision making process (Serrat-Capdevila, 2011). Web publishing has been a popular method to disseminate information and knowledge which may include simulation results. Web publishing offers an easy way to provide access control, allowing various organisations different access rights (GWP, 2013). Some of this data and information can be directly accessed through different assessment instruments (Tools C1), which provide essential information. 

Monitoring and evaluation (M&E) systems (Tool C2.05) feed into the decision making processes with the lessons learnt from past and ongoing experiences. An appropriate and comprehensive M&E system can provide insights into the efficiency of a process and its management and help to reformulate policies, programmes, strategies or plans, reallocate resources and guide processes in a more efficient and effective manner. Data availability and quality underpins the accuracy of any model or simulation and trickles down to affect the basis of decision making. Hence, operation and maintenance of hydrological and metrological systems as well as other data collection systems is increasingly important to improve decision making. This may also include expansion of data collection systems. 

The goal of the decision-making process is to reach an agreement that is acceptable to all the stakeholders considering social equity and environmental sustainability (Castelletti & Soncini-Sessa, 2007). Hence, it  must address preferences and priorities of stakeholders that go beyond environmental friendliness, economic efficiency, and technical merits. As such, a stakeholder analysis (Tool C1.03) is essential for participatory decision making. Including participation in DSS allows decision making processes to be transparent while also empowering stakeholders and marginalised communities by providing access to decision making spaces. In order to achieve these objectives, a DSS must be neutral and objective while acting as a conduit of comprehensive information. 

Section Overview

This sub-section introduces several DSS that can help policy makers and water resources planners take more informed, and hopefully more inclusive, decisions. Geographic information systems (Tool C2.01), one of the most commonly used DSS in water resrources management, allow for spatial visualisations of rich datasets, including social, economic, meteological, and hydrological information. The Shared Vision Planning and Collaborative Modelling Tool (C2.02) discusses ways to simulate different scenarios and to evaluate best options given certain assumptions in a multi-stakeholder setting. If used properly, this DSS unables diverse interest groups and people from different backgrounds to participate to the decision-making process. Serious Games (Tool C2.03)  is an alternative type of DSS, which uses role playing and sometimes include technical models in water management to tackle competing stakeholder objectives and develop consensus. This sub-section also introduces Socio-Hydrological models (C2.04) that are used to bring social and natural elements together into a unified system. This DSS is extremely powerful and becoming an increasingly popular due to its ability to capture and approximate complex interactions and feedback mechanisms of the real world. Finally, this section explores the importance of setting up robust Monitoring and Evaluation Systems (Tool C2.05) as means of understanding ongoing challenges and making corrective adjustments to improve the state of water resources management.