HR: 1340h
AN: H33D-0501    [Abstracts]
TI: Taking the Next Step: Using Water Quality Data in a Decision Support System for County, State, and Federal Land Managers
AU: * Raby, K S
EM: kim.raby@colorado.edu
AF: Institute of Arctic and Alpine Research, 1560 30th Street UCB 450, Boulder, CO 80309
AU: * Raby, K S
EM: kim.raby@colorado.edu
AF: Department of Environmental Studies, University of Colorado at Boulder, Boulder, CO 80309
AU: Williams, M W
EM: markw@snobear.colorado.edu
AF: Institute of Arctic and Alpine Research, 1560 30th Street UCB 450, Boulder, CO 80309
AU: Williams, M W
EM: markw@snobear.colorado.edu
AF: Department of Geography, University of Colorado at Boulder, Boulder, CO 80309
AB: Each passing year amplifies the demands placed on communities across the US in terms of population growth, increased tourism, and stresses resulting from escalated use. The conflicting concerns of recreational users, local citizens, environmentalists, and traditional economic interests cause land managers to contend with controversial decisions regarding development and protection of watersheds. Local history and culture, politics, economic goals, and science are all influential factors in land use decision making. Here we report on a scientific study to determine the sensitivity of alpine areas, and the adaptation of this study into a decision support framework. We use water quality data as an indicator of ecosystem health across a variety of alpine and subalpine landscapes, and input this information into a spatially-based decision support tool that planners can use to make informed land use decisions. We develop this tool in a case study in San Juan County, Colorado, a site chosen because its largest town, Silverton, is a small mountain community experiencing a recent surge in tourism and development, and its fragile high elevation locale makes it more sensitive to environmental changes. Extensive field surveys were conducted in priority drainages throughout the county to map the spatial distribution and aerial extent of landscape types during the summers of 2003 and 2004. Surface water samples were collected and analyzed for inorganic and organic solutes, and water quality values were associated with different land covers to enable sensitivity analysis at the landscape scale. Water quality results for each watershed were entered into a module linked to a geographic information system (GIS), which displays maps of sensitive areas based on criteria selected by the user. The decision support system initially incorporates two major water quality parameters: acid neutralizing capacity (ANC) and nitrate (NO3-) concentration, and several categories of sensitivity were created based on ANC and NO3- levels (e.g., pristine, slightly sensitive, moderately sensitive, highly sensitive, sensitive but unimpacted, disturbance impacted). We based threshold concentrations for these water quality parameters on first principles developed at the Niwot Ridge LTER site. Additional parameters such as specific conductance, base cation concentration, sulfate concentration, and dissolved organic carbon concentration may be added for a particular landscape type. Superimposed on this categorization, federal, state, and county planners are able to make decisions about the degree of potential impairment or enhancement produced by a particular project, or the maximum level of acceptable impairment to a particular area. Because water quality parameters are correlated with landscape types, the model returns a map of the watershed, partitioned by landscape type, presenting the sensitivity level of each area. This format provides land use managers with spatial criteria for project implementation.
DE: 6309 Decision making under uncertainty
DE: 1871 Surface water quality
SC: Hydrology [H]
MN: 2004 AGU Fall Meeting