Linking science with environmental decision making: Experiences from an integrated modeling approach to supporting sustainable water resources management Yuqiong Liu a, * , Hoshin Gupta a , Everett Springer b , Thorsten Wagener c a SAHRA (Sustainability of Semi-Arid Hydrology and Riparian Areas), Department of Hydrology and Water Resources, University of Arizona, Tucson, AZ 85721, USA b Atmospheric, Climate, and Environmental Dynamics Group, Los Alamos National Laboratories, MS J495, Los Alamos, NM 87545, USA c Department of Civil and Environmental Engineering, Pennsylvania State University, University Park, PA 16802, USA Received 27 April 2007; received in revised form 17 October 2007; accepted 21 October 2007 Available online 21 February 2008 Abstract The call for more effective integration of science and decision making is ubiquitous in environmental management. While scientists often complain that their input is ignored by decision makers, the latter have also expressed dissatisfaction that critical information for their decision making is often not readily available or accessible to them, or not presented in a usable form. It has been suggested that scientists need to pro- duce more ‘‘usable’’ information with enhanced credibility, legitimacy, and saliency to ensure the adoption of research results. In basin-scale management of coupled human-water systems, water resources managers, like other decision makers, are frequently confronted with the need to make major decisions in the face of high system complexity and uncertainty. The integration of useful and relevant scientific information is necessary and critical to enable informed decision-making. This paper describes the main aspects of what has been learned in the process of supporting sustainable water resources planning and management in the semi-arid southwestern United States by means of integrated modeling. Our experience indicates that particular attention must be paid to the proper definition of focus questions, explicit conceptual modeling, a suitable modeling strategy, and a formal scenario analysis approach in order to facilitate the development of ‘‘usable’’scientific information. We believe that these lessons and insights can be useful to other scientific efforts in the broader area of linking environmental science with decision making. Ó 2007 Elsevier Ltd. All rights reserved. Keywords: Integrated modeling; Decision support; Scenario analysis; Water resources management; Sustainability 1. Introduction Science is increasingly being called upon to provide infor- mation for complex environmental decision making (e.g., Browning-Aiken et al., 2004, 2006; NRC, 1999; Matthies et al., 2007). However, despite recent remarkable advances in environmental science with growing availability of relevant knowledge, data, and information, how science can best sup- port environmental decision making remains an outstanding question (Cash et al., 2003; Lee, 1993; Reichert et al., 2007; van der Sluijs, 2007). For example, the results of scientific re- search may not always be made available in the form required by decision makers (e.g., Jacobs, 2002); and lack of uncer- tainty estimates may render the results from scientific research not directly applicable to decision-making (e.g., Refsgaard et al., 2007; Xu et al., 2007). Since science and policy serve different purposes (Lee, 1993), scientists and decision makers typically maintain different values, interests, concerns, and perspectives and, more importantly, tend to lack a mutual un- derstanding of each other’s knowledge systems (Jacobs, 2002; McNie, 2007; Sarewitz and Pielke, 2007). This has led to huge knowledge gaps between science and decision making, and has hampered the effective flow of information across the boundary between knowledge and practice (Acreman, 2005; * Corresponding author. Tel.: þ1 520 626 8799; fax: þ1 520 626 7770. E-mail address: yqliu@hwr.arizona.edu (Y. Liu). 1364-8152/$ - see front matter Ó 2007 Elsevier Ltd. All rights reserved. doi:10.1016/j.envsoft.2007.10.007 Available online at www.sciencedirect.com Environmental Modelling & Software 23 (2008) 846e858 www.elsevier.com/locate/envsoft