TY - JOUR
T1 - Linking science with environmental decision making
T2 - Experiences from an integrated modeling approach to supporting sustainable water resources management
AU - Liu, Yuqiong
AU - Gupta, Hoshin
AU - Springer, Everett
AU - Wagener, Thorsten
N1 - Funding Information:
Support for this work reported here was provided by the US National Science Foundation Science and Technology Center for Sustainability of Semi-Arid Hydrology and Riparian Areas (SAHRA) under grant number EAR-9876800. The Los Alamos National Laboratory contribution was supported by Laboratory Directed Research and Development Project “High-Resolution Physically-Based Model of Semi-Arid River Basin Hydrology” which is in collaboration with SAHRA.
Funding Information:
This paper aims to address some of the critical issues involved in bridging environmental science and decision making by describing some aspects of what has been learned from an ongoing 10-year integrated modeling effort aimed at improving support for water resources decision making in the semi-arid southwestern United States. The project involves a multi-institutional, multi-disciplinary research team supported by the National Science Foundation (NSF) Science and Technology Center for Sustainability of Semi-Arid Hydrology and Riparian Areas (SAHRA, www.sahra.arizona.edu ). The modeling approach involves activities that facilitate the development of a good balance between credibility, legitimacy, and saliency of information produced through various communication, translation, and mediation strategies. The application of integrated modeling to a specific SAHRA context was not, however, straightforward and we encountered practical issues such as how to start and move forward with a complex integrated study which investigates multiple system components and dimensions, and involves researchers, experts, and stakeholders from a broad range of disciplines and institutions/agencies. Although research in support of environmental decision making is by nature context-dependent, we believe that the lessons and insights learned from the SAHRA integrated modeling effort can inform other similar scientific efforts elsewhere around the globe in the broader area of linking environmental science with decision making. While the full integration of science and decision making also entails favorable changes in science policy and institutional infrastructures (e.g., Browning-Aiken et al., 2004, 2006 ), this paper will focus on what scientists and researchers can do to generate knowledge in a form more “usable” to and supportive of decision making.
PY - 2008/7
Y1 - 2008/7
N2 - 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 produce 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.
AB - 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 produce 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.
KW - Decision support
KW - Integrated modeling
KW - Scenario analysis
KW - Sustainability
KW - Water resources management
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U2 - 10.1016/j.envsoft.2007.10.007
DO - 10.1016/j.envsoft.2007.10.007
M3 - Article
AN - SCOPUS:40849089386
SN - 1364-8152
VL - 23
SP - 846
EP - 858
JO - Environmental Modelling and Software
JF - Environmental Modelling and Software
IS - 7
ER -