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The resilience of ecologically significant landscapes is often hindered by traditional approaches to natural resource management, which treat ecologic, hydrologic, and social systems as distinct entities. Although acknowledging interdependencies is a great first step in managing complex systems, challenges exist in predicting effects of intervention due to key features such as non-linearity and uncertainty. In order to project the impact of urban populations on riparian corridors in a semi-arid desert, we integrated several modeling approaches to simulate how policy decision-making will effect riparian vegetation along the Upper San Pedro River. Policy decision-making was characterized with a Bayesian Belief Network, allowing uncertainty in the decision-making process to be incorporated. Policy decisions ultimately effected population growth and water use. Urban water demand, calculated by multiplying urban population size with per capita water consumption, was used in conjunction with response functions, developed from MODFLOW, to simulate changes in depth-to-groundwater by well pumping in a spatially-explicit agent-based model. Depth-to-groundwater was then used as an indicator of unique vegetation guilds within the riparian corridor. The model was used to test the effects of policy decision-making on the spatial distribution of riparian vegetation along the Upper San Pedro River. By using the model as a tool, decision-makers may have the ability to make better-informed decisions to ensure the resilience of the Upper San Pedro Watershed.
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Arias, H. (2000). International groundwaters: The Upper San Pedro Basin case. Natural Resources Journal, 40, 199–222.
Arizona Water Company. (2007). Sierra Vista water plan.
Axelrod, R. (1976). Structure of decision: The cognitive maps of political elites. Chichester: Princeton University Press.
Berkes, F., Colding, J., & Folke, C. (2003). Navigating social-ecological systems. New York: Cambridge University Press.
Bierwagen, B., Thomas, J., Pyke, C., Theobald, D., Choate, A., Cohen, J., et al. (2009). Land-use scenarios: National-scale housing-density scenarios consistent with climate change storylines. Environmental Protection.
Bromely, J., Jackson, N., Giacomello, A.-M., & Bradford, R. J. (2002). Participatory development of Bayesian networks as an aid to integrated water resource planning. Proceedings of the International Conference on Policies and Tools for Sustainable Water Management in the European Union. Venice, November 2002.
Burns, I. S., Kepner, W. G., Sidman, G. S., Goodrich, D. C., & Guertin, D. P. (2013) Assessing hydrologic impacts of future land cover change scenarios in the San Pedro River (US/Mexico). US Environmental Protection Agency, Office of Research and Development.
Cain, J. (2001). Planning improvements in natural resources management. Guidelines for using Bayesian networks to manage development projects. Wallingford, UK: Institute of Hydrology.
Dellinger, E., Varady, R., & Browning-Aiken, A. (2006). Water policy research on the Upper San Pedro Basin: An annotated bibliography of contributions by the Udall Center for Studies in Public Policy, 1997–2006. Tucson, AZ: Udall Center for Studies in Public Policy, University of Arizona.
Eden, C., Ackermann, F., & Cropper, S. (1992). The analysis of cause maps. Journal of Management Studies, 29, 309–323. CrossRef
Elsawah, S., Guillaume, J. H. A., Filatova, T., Rook, J., & Jakeman, A. J. (2014). A methodology for eliciting, representing, and analyzing stakeholder knowledge for decision making on complex socio-ecological systems: From cognitive maps to agent-based models. Journal of Environmental Management, 151, 500–516. CrossRef
ESRI. (2011). ArcGIS Desktop: Release 10. Redlands, CA: Environmental Systems Research Institute.
Fulton, E. A., Smith, A. D. M., Smith, D. C., & van Putten, I. E. (2010). Human behavior: The key source of uncertainty in fisheries management. Fish and Fisheries, 12, 2–17. CrossRef
GeNIe. (2013). Decision Systems Laboratory, University of Pittsburgh. Retrieved from http://genie.sis.pitt.edu/.
Giordano, R., Preziosi, E., & Romano, E. (2010). An integration between cognitive map and Bayesian belief network for conflicts analysis in drought management. Proceedings of the International Congress on Environmental Modeling and Software. Ottawa, July 2010.
Goode, T. C., & Maddock, T., III. (2000). Simulation of groundwater conditions in the Upper San Pedro Basin for the evaluation of alternative futures. Tucson, AZ: Department of Hydrology and Water Resources (HWR-00-030), University of Arizona.
Grantham, C. (1997). Assessment of the ecological impacts of ground water overdraft on wetlands and riparian areas in the United States. NASA (19980000183).
Gunderson, L. H., Allen, C. R., & Holling, C. S. (Eds.). (2010). Foundations of ecological resilience. Washington, DC: Island Press.
Holling, C. S. (1973). Resilience and stability of ecological systems. Annual Review of Ecology and Systematics, 4, 1–23. CrossRef
Johnson, G. S., Cosgrov, D. M., & Spinazola, J. (1998). Use of MODFLOW for development of response function. Proceedings, MODFLOW 98 Conference. Golden, CO: Colorado School of Mines.
Kemmerer, B., Mishra, S., & Shenoy, P. (2002). Bayesian causal maps as decision aids in venture capital decision making: Methods and applications. Proceedings of the Academy of Management Conference. August 2002.
Keshvari, S., van den Berg, R., & Ma, W. (2012). Probablistic computation in human perception under variability in encoding precision. PLoS One, 7, 1–9. CrossRef
Lite, S. J., & Stromberg, J. C. (2005). Surface water and ground-water thresholds for maintaining Populus–Salix forests, San Pedro River, Arizona. Biological Conservation, 125(2), 153–167. CrossRef
Marcot, B., Steventon, J., Sutherland, G., & McCann, R. (2006). Guidelines for developing and updating Bayesian belief networks applied to ecological modeling and conservation. Canadian Journal of Forest Resources, 36, 3063–3074. CrossRef
Milly, P. C. D., Betancourt, J., Falkenmark, M., Hirsch, R. M., Kundzewicz, Z. W., Lettenmaier, D. P., et al. (2008). Stationarity is dead. Whither water management? Science, 319, 573–574. CrossRef
Pahl-Wostl, C. (2007). Transitions towards adaptive management of water facing climate and global change. Journal Water Resources Management, 21(1), 49–62. CrossRef
Pahl-Wostl, C., Jeffrey, P., Isendahl, N., & Brugnach, M. (2011). Maturing the new water management paradigm: Progressing from aspiration to practice. Water Resource Management, 25, 837–856. CrossRef
Pahl-Wostl, C., Kabat, P., & Moltgen, J. (2008). Adaptive and integrated water management. Berlin, Heidelberg: Springer. CrossRef
Pool, D. R., & Dickinson, J. E. (2007). Ground-water flow model of the Sierra Vista subwatershed and Sonoran portions of the upper San Pedro basin, southeastern Arizona, United States, and northern Sonora, Mexico (No. 2006-5228). Geological Survey (US).
Rijke, J., Brown, R., Zevenbergen, C., Ashley, R., Farrelly, M., Morison, P., et al. (2012). Fit-for-purpose governance: A framework to make adaptive governance operational. Environmental Science and Policy, 22, 73–84. CrossRef
Scheffer, M., & Westley, F. R. (2007). The evolutionary basis of rigidity: Locks in cells, minds, and society. Ecology and Society, 12(2), 36. CrossRef
Scott, C. A., Meza, F. J., Varady, R. G., Holm, T., Jamie, M. E., Garfin, G. M., et al. (2013). Water security and adaptive management in the arid Americas. Annals of the Association of American Geographers, 103, 280–289. CrossRef
Sedki, K., & de Beaufort, L. (2012). Cognitive maps and Bayesian networks for knowledge representation and reasoning. 24th International Conference on Tools with Artificial Intelligence. Greece.
Sheppard, P. R., Comrie, A. C., Packin, G. D., Angersbach, K., & Hughes, M. K. (2002). The climate of the US Southwest. Climate Research, 21, 219–238. CrossRef
Sierra Vista Department of Public Works. (2014). Area water use. Environmental Services Division.
Steinitz, C., Rojo, H., Basset, S., Flazman, M., Goode, T., Maddock, T., III, et al. (2003). Alternative futures for changing landscapes: The Upper San Pedro River Basin in Arizona and Sonora. Washington, DC: Island Press.
Tolman, E. (1948). Cognitive maps in rats and men. Psychological Review, 55, 189e208.
Tuan, Y. F. (1962). Structure, climate, and basin land forms in Arizona and New Mexico. Annals of the Association of American Geographers, 52(1), 51–68. CrossRef
U.S. Census Bureau. (2010, April 1). State and county Quickfacts: Arizona. Retrieved December 10, 2013, from http://quickfacts.census.gov.
U.S. Environmental Protection Agency (EPA). (2009). Land-use scenarios: National-scale housing-density scenarios consistent with climate change storylines (Final Report). Washington, DC: Global Change Research Program, National Center for Environmental Assessment. EPA/600/R-08/076F. Retrieved from National Technical Information Service, Springfield, VA, and http://www.epa/gov/ncea.
Varady, R. G., Scott, C. A., Wilder, M., Morehouse, B., Pineda Pablos, N., & Garfin, G. M. (2013). Transboundary adaptive management to reduce climate change vulnerability in the western U.S.–Mexico border region. Environmental Science and Policy, 26, 102–112. CrossRef
Walker, B., Carpenter, S., Anderies, J., Abel, N., Cumming, G. S., Janssen, M., et al. (2002). Resilience management in social-ecological systems. A working hypothesis for a participatory approach. Conservation Ecology, 6, 14. CrossRef
Wilensky, U. (1999). NetLogo. Evanston, IL: Center for Connected Learning and Computer-Based Modeling, Northwestern University. Retrieved from http://ccl.northwestern.edu/netlogo/.
- Effects of Policy Decision-Making on Riparian Corridors in a Semi-arid Desert: A Modeling Approach
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