Grantee Research Project Results
Final Report: Development and Evaluation of Aquatic Indicators
EPA Grant Number: R829095C003Subproject: this is subproject number 003 , established and managed by the Center Director under grant R829095
(EPA does not fund or establish subprojects; EPA awards and manages the overall grant for this center).
Center: Space-Time Aquatic Resources Modeling and Analysis Program (STARMAP)
Center Director: Urquhart, N. Scott
Title: Development and Evaluation of Aquatic Indicators
Investigators: Theobald, David M. , Urquhart, N. Scott
Institution: Colorado State University
EPA Project Officer: Packard, Benjamin H
Project Period: October 1, 2001 through September 30, 2006
RFA: Research Program on Statistical Survey Design and Analysis for Aquatic Resources (2001) RFA Text | Recipients Lists
Research Category: Ecological Indicators/Assessment/Restoration , Watersheds , Water , Aquatic Ecosystems
Objective:
The objective is to develop, test, and distribute GIS-based tools that would facilitate computation of useful watershed metrics for statistical analysis of aquatic response variables.
Summary/Accomplishments (Outputs/Outcomes):
Project 3 developed, tested, and distributed GIS-based tools that would facilitate computation of useful watershed metrics for statistical analysis of aquatic indicator variables. Project investigators met these objectives through three key accomplishments by:
- Developing ArcGIS-based toolsets called Functional Linkage of Waterbasins and Streams (FLoWS), Functional Connectivity Model (FunConn), and Reversed Randomized Quadrant-Recursive Raster (RRQRR);
- Conducting series of demonstrations and application of those tools to a range of key U.S. Environmental Protection Agency (EPA) constituents; and
- Collaborating with other scientists and training of students.
FLoWS is a set of tools that operate within ArcGIS v9 (written in Python). These tools allow users to rapidly generate a stream network, identify and correct topological errors in a network (fairly common in GIS data), extract watershed characteristics derived from other ancillary data such as topography, land cover, road density, etc. in a way that allows ecologically-relevant processes to be developed. For example, discharge volume (flow volume) is estimated as a function not only of waterbasin area but also of the precipitation regime and the watershed topographic characteristics, including solar insolation and slope. Project investigators also developed a novel approach to identify catchments around stream reaches by identifying water basin boundaries using a cost-weight method, rather than relying on strictly local conditions (slopes) identified in a digital elevation model. The goal was to ensure that these tools work with very large datasets (basins to nationwide) and in a variety of situations. For example, the traditional approach to identify watersheds failed in the Central Shortgrass Prairie that contains low relief, many intermittent streams, and inconsistent network topology, as many local ridges caused the algorithms to “stop short” and caused “holes” or portions of watersheds that would not converge or connect with the larger network. Not only is our new method robust and fast, preliminary analysis suggest that it is more accurate as well. For a small test study area in the Fraser River, Colorado, waterbasin (high topographic relief), we found a higher mean accuracy using our novel methods as compared to the traditional method (i.e., 85% vs. 78% using Jaccard’s coefficient as compared to expert-based delineation of watershed boundaries at 1:24,000 scale).
The goal of the functional connectivity model, whose GIS implementation is named FunConn, is to allow landscape connectivity to be examined from a functional perspective. Functional connectivity recognizes that individuals, species, or processes respond functionally (or behaviorally) to the physical structure of the landscape. From this perspective, landscape connectivity is specific to a landscape and species/individual/process under investigation.
Project investigators also strove to develop metrics and approaches that are more robust to possible data quality issues. For example, a well-known problem with “blue-line” hydrography is that the identification of streamlines can abruptly change at a topographic quadrangle boundary. Traditional metrics that rely on Strahler stream order, for example, are very sensitive to these issues, whereas waterbasin-area computations are more robust.
A key to the investigators’ accomplishment was close interaction and collaboration with a variety of constituents. Two major collaborations were with Oregon Department of Fish and Wildlife (through collaborations with Designs and Models for Aquatic Resource Surveys [DAMARS] personnel) and the Alaska Department of Fish & Game. Project investigators participated in a variety of workshops and provided technical assistance throughout the Space-Time Aquatic Resources Modeling and Analysis Program (STARMAP).
Two unanticipated products of this Project were the result of synergistic activities. The key to each of these was an informal (initially) exchange of ideas, enthusiasm injected by graduate students involved (esp. Peterson), and the STARMAP Director’s support for risk taking. For example, the new geostatistical method for stream networks developed by ver Hoef, Peterson, and Theobald was the result of informal discussion at workshops, identification of an important research question, and eagerness of a key individual (Peterson) who provided a key trans-disciplinary role. A second example was the development of a robust, spatially-balanced sampling design algorithm implemented in ArcGIS, called the RRQRR tool. This is built fundamentally around Stevens and Olsen’s Generalized Random Tessellation Stratified (GRTS) algorithm, but development within a GIS framework provides the ability to develop a raster of sample locations and extends a tool to a different (and broader) user base.
This project’s investigators collaborated closely with investigators working on Projects 1 and 2 (R829095C001 and R829095C002, respectively). Specifically there was close cooperation between Erin Peterson and Andrew Merton, graduate students funded, respectively, under Projects 3 (R829095C003) and 1. Merton, under the guidance of Hoeting (Project 1) and Davis (Project 2), developed computer software used extensively by Peterson and adapted it to several of her special situations. This collaboration produced a jointly authored publication and presentations illustrating how the collaboration functioned. Peterson also collaborated with postdoctoral fellow Ranalli who conducted research under the auspices of Project 2. Another interaction involved Breidt (Project 2), Theobald (Project 3), and international visitors unfunded by STARMAP; this is described in more detail under Project 2. Further, Project 3 investigators developed or assisted in developing covariate data sets for at least five other graduate student projects.
Significance of Accomplishments
The next section gives a list of known adopters of the methodology developed under this Project. The diversity of adopters speaks eloquently to the current significance of the accomplishments of this Project. Adopters ranged from local environmental agencies to national environmental agencies, from governmental agencies to nonprofit agencies to academics, from across the United States to across the world. The acceptance of the products of this Project was due to two factors: the relevancy of the products developed and the scale of the outreach activities associated with them. The specific outreach activities are documented under Project 4 (R829095C004), but it needs to be noted here that this Project’s investigators made a major effort in this area.
Stakeholders and Users of Results
This project has written and made Web-available three sets of GIS tools oriented toward the design and statistical analysis of data resulting from studies in aquatic systems. The tools are programmed in Python and accessible as ArcGIS tools in v9. Each is further documented and available through this Web site: http://www.nrel.colostate.edu/projects/starmap/ Exit
- FLoWS v1: Functional Linkage of Watersheds and Streams tools for ArcGIS v9: The goal of the functional linkage of watersheds and streams tools is to allow aquatic and terrestrial landscapes to be hydrologically-linked. In this sense, relationships between sites can be represented through functional distance measures. For many hydrological processes (not all!), downstream flow direction is an important ecological process, so that distance is not symmetric. Also, including important landscape attributes that modify the degree to which nearby locations are connected is important. This would include topographic considerations such as stream gradient and slope, as well as features that might impede the movement of a species or process such as waterfalls, dams, or certain vegetation types. The agencies and organizations listed below have requested and received a copy of the FLoWS software (as of December 1, 2006):
- U.S. Environmental Protection Agency, National Exposure Research Laboratory, Athens, GA
- U.S. Forest Service, Rocky Mountain Research Station, Boise, ID
- U.S. Forest Service, Aquatic and Riparian Effectiveness Monitoring Program, Portland OR
- New Hampshire Geological Survey, Concord, NH.
- Alaska Department of Fish & Game, Juneau, AK
- Oregon Department of Fish and Wildlife, Salem, OR
- Rensselaer Polytechnic Institute, New Orleans, LA
- Missouri Resource Assessment Partnership (MoRAP) at the University of Missouri, Columbia, MO
- University of Kansas, Department of Geography, Lawrence, KS
- University of Iowa, Iowa City, IA
- Montana State University, Land Resources and Environmental Sciences, Bozeman, MT
- Nicholas School of the Environment, Duke University, NC
- Colorado State University, College of Natural Resources, Fort Collins, CO
- Department of Forest Science, Oregon State University, Corvallis, OR
- The Nature Conservancy: Tucson, AZ; Seattle, WA; Madison, WI; Cuddebackville, NY; Beijing, China; San Jose, Costa Rica
- GreenInfo Network, San Francisco, CA
- Colorado Natural Heritage Program, Fort Collins, CO
- TST, Inc. Consulting Engineers, Fort Collins, CO
- TSH = Engineers, Architects, and Planners, ON, Canada
- Ontario Ministry of Natural Resources, ON, Canada
- BEACONs Project, University of Alberta, AB, Canada
- Environnement Canada/Environment Canada, QC, Canada
- Center for Northern Forest Ecosystem Research, ON, Canada
- Watershed Science Center, ON, Canada
- Instituto Internacional en Conservación, Costa Rica
- Centro Agronomo Tropical de Investigation y Ense anza, Turrialba, Costa Rica
- Water Services, Glasgow, Scotland
- Dipartimento di Ingegneria e Fisica dell’Ambiente, Università degli Studi della Basilicata, Italy
- Academy of Sciences of the Czech Republic
- CSIRO Mathematical and Information Sciences, Brisbane, Australia
- NAARM (National Academy of Agricultural Research Management), Hyderabad, India
- ROLTA (software/information technology based engineering and geospatial solutions), Mumbai, India
- Functional Connectivity Tools (FunConn): There is a large and critical difference between simple hydrological datasets that “look” correct on a map but must have correct topology and attribution to run network-based algorithms correctly. The goal of the functional connectivity model is to allow landscape connectivity to be examined from a functional perspective. Functional connectivity recognizes that individuals, species, or processes respond functionally (or behaviorally) to the physical structure of the landscape. From this perspective, landscape connectivity is specific to a landscape and species/individual/process under investigation. The agencies and organizations listed below have requested and received a copy of the FunConn software (as of December 1, 2006):
- U.S. Environmental Protection Agency, National Exposure Research Laboratory, Cincinnati, OH
- U.S. Environmental Protection Agency, Washington DC
- U.S. Forest Service, Rocky Mountain Research Station, Fire Sciences Laboratory, LANDFIRE, Missoula, MT
- U.S. Forest Service, Pacific Northwest Research Station, Olympia Forestry Sciences Laboratory
- U.S. National Oceanic and Atmospheric Administration, Boise, ID
- U.S. Geological Survey, Annapolis, MD
- U.S. Fish and Wildlife Service
- University of Montana, Missoula, MT
- Columbia University, New York, NY
- University of Idaho, Remote Sensing and GIS, Moscow, ID
- University of Idaho, Rangeland Ecology and Management, Moscow, ID
- University of Pennsylvania, Anthropology, Philadelphia, PA
- Department of Ecology, University of California, Davis, CA
- Nicholas School of the Environment, Duke University, North Carolina
- Hopland Research and Extension Center, University of California, Hopland, CA
- Northern Arizona University, Flagstaff, AZ
- Department of Fisheries and Wildlife, Michigan State University
- World Wildlife Fund, Washington DC
- The Nature Conservancy - many locations
- Wildlife Conservation Society, New York, NY
- CDM Engineers & Constructors Inc., Denver, CO
- ESRI, the corporation which develops and distributes the Arc family of Geographic Information System products, widely used by the environmental community
- Queen’s University, Department of Geography, Kingston, Ontario, Canada
- O2 Planning & Design Inc., Calgary, AB, Canada
- Parks Canada
- University of Alberta, Edmonton, AB, Canada
- National University, Heredia, Costa Rica
- Centro Agronomo Tropical de Investigation y Ense anza, Turrialba, Costa Rica
- Escuela de Biologia. Univsidad de Costa Rica, San José, Costa Rica
- Conservation International-Brazil
- Universidad Distrital, Bogotá, Colombia
- University of Reading, Reading, UK
- University of Girona, Spain
- University of Lleida, Lleida, Spain
- Lisbon University, Departamento de Biologia Animal, Lisbon, Portugal
- Department of Land and Water Resources Engineering, Royal Institute of Technology, Stockholm, Sweden
- Lund University, GIS Specialty, Lund, Sweden
- Charles University, Prague, Czech Republic
- Viterbo University, Viterbo, Italy
- Department of Vegetable Science, University of Bari, Barium, Italy
- Sohag, Egypt
- Istanbul Engineering and Consultancy Services Cooperation, Turkey
- University of Cape Town, Rondebosch, South Africa
- Leslie Hill Institute for Plant Conservation, Rondebosch, South Africa
- TPF, University of Antananarivo, Antananarivo, Madagascar
- Department of Geography, University of Queensland, Australia
- Kunming Institute of Zoology, Kunming, Yunnan, China
- Spatially-Balanced Sampling Using RRQRR: The goal of the RRQRR algorithm is to provide environmental managers a practical, useful GIS tool to generate simple, efficient, and robust survey designs for natural resource applications. RRQRR generates a rigorous probability-based survey design that is spatially-balanced and allows surfaces to be used to specify the inclusion probability. The agencies and organizations listed below have requested and received a copy of the RRQRR software (as of December 1, 2006):
- U.S. National Park Service, Southeast Coast inventory and Monitoring Network, Cumberland Island National Seashore, St. Marys, GA
- U.S. Forest Service, Northern Research Station, Forest Inventory and Analysis Unit, Newtown Square, PA
- U.S. National Park Service, Inventory and Monitoring Program, Fort Collins, CO
- U.S. Department of Defense, San Clemente Island—for designing a monitoring plan for kit fox
- Backcountry campsite monitoring, Yosemite National Park
- Department of Fish and Wildlife, University of Idaho, Moscow, ID
- Laramie Foothills Fire Learning Network, Larimer County, CO
- Soils mapping, Frasier Experimental Forest, Frasier, CO
- Environmental Systems Research Institute (ESRI, maker of ArcGIS software)—is currently implementing the RRQRR algorithm into its core software package
- In addition to building tools for statistical analysis of hydrology, this project generated a database (called the FLoWS database) that builds on U.S. Geological Survey (USGS) National Elevation Data and National Hydrography Dataset (1:100K). This effort provided two important benefits:
- A nationwide, pre-processed and pre-packaged dataset that will support many types of hydrological analysis and provides a significant “head-start” for EPA clients; and
- Nationally-consistent, hierarchical, and high-resolution catchment boundaries at a variety of scales—from basins (hydrologic unit code [HUC] 2s) to roughly the HUC 14 level.
How Products Will Further Science/Management of Resources
These products will support the analysis of aquatic responses in diverse contexts but will be especially useful in developing landscape indicators associated with specific aquatic sample points. These tools and demonstrations will support the more accurate and defensible analysis of diverse environmental variables.
Listing of Specific Communications Related to Indicator Development
The complete list of outputs from STARMAP, including those originating from Project 3, is available on the Web at http://www.stat.colostate.edu/starmap Exit .
Journal Articles:
No journal articles submitted with this report: View all 36 publications for this subprojectSupplemental Keywords:
GIS, tessellation stratified sampling, water quality, land cover, land use, accuracy, precision,, RFA, Scientific Discipline, Air, Ecosystem Protection/Environmental Exposure & Risk, Aquatic Ecosystems & Estuarine Research, climate change, Air Pollution Effects, Aquatic Ecosystem, Environmental Monitoring, Atmosphere, EMAP, ecosystem monitoring, spatial and temporal modeling, aquatic ecosystems, water quality, Environmental Monitoring and Assessment Program, modeling ecosystems, STARMAPRelevant Websites:
http://www.stat.colostate.edu/starmap Exit
http://www.nrel.colostate.edu/projects/starmap/flows_index.htm Exit
http://www.nrel.colostate.edu/projects/starmap/funconn_index.htm Exit
http://www.nrel.colostate.edu/projects/starmap/rrqrr_index.htm Exit
Progress and Final Reports:
Original AbstractMain Center Abstract and Reports:
R829095 Space-Time Aquatic Resources Modeling and Analysis Program (STARMAP) Subprojects under this Center: (EPA does not fund or establish subprojects; EPA awards and manages the overall grant for this center).
R829095C001 Combining Environmental Data Sets
R829095C002 Local Inferences from Aquatic Studies
R829095C003 Development and Evaluation of Aquatic Indicators
R829095C004 Extension of Expertise on Design and Analysis to States and Tribes
R829095C005 Integration and Coordination for STARMAP
The perspectives, information and conclusions conveyed in research project abstracts, progress reports, final reports, journal abstracts and journal publications convey the viewpoints of the principal investigator and may not represent the views and policies of ORD and EPA. Conclusions drawn by the principal investigators have not been reviewed by the Agency.
Project Research Results
4 journal articles for this subproject
Main Center: R829095
291 publications for this center
43 journal articles for this center