Nicholas KotlinskiKU Aquatic Ecology Laboratory, Kansas Biological Survey
GIS in the Rockies Conference: September 2016
Automated River Classification Using GIS-Delineated Functional Process
Zones
► The MACRO project focuses on the macrosystem ecology of river basins in temperate steppe regions of the United States and Mongolia
► The project received NSF support to sample rivers in 3 different ecoregions in each of 2 continents (18 rivers total) over 3 field seasons
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Riverine Ecosystem Synthesis (RES)► Theory of aquatic bio-complexity (Thorp et al. 2008)
► Ecosystem structure and function vary by hydrogeomorphic “patches”
► Hydrogeomorphic patches are repeatable and non-continuous
► At the reach-to-valley scale, these patches are referred to as Functional Process Zones (FPZs)
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Goal:
To provide a consistent and efficient way to extract hydrogeomorphic variables and define FPZs across different scales and river systemsWhy?
A standard, self emerging classification method has the potential to enhance compatibility between analyses of river systems and improve communication among river scientists and managersApplications: ► Characterizing riverbed sediment patterns by FPZs (Collins et al., 2015)► Classification framework used for selecting sampling sites for the
Macroecological Riverine Synthesis (MACRO) project
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Applying GIS to RES
RESonate Tool
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► A custom 6-step toolbox designed for ESRI ArcGIS that facilitates rapid, low cost riverine landscape characterization and FPZ classification
► RESonate automatically extracts ~13 hydrogeomorphic variables from readily available geospatial datasets and datasets derived from modeling procedures
► Tool overview and development process outlined in Williams et al., 2013
What are the inputs?
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Layer Type Dataset (U.S. Examples)
Hydrography NHD streamlines + ArcHydro segmentsDigital Elevation Model 10-m DEM (USGS)Precipitation PRISM (30-year mean annual
precipitation)Geology USGS National Geology MapFloodplain Arc Hydro + Valley Floor Mapper outputMicroshed (Valley Tops) Arc Hydro catchment delineation outputAerial Photography USDA NAIP imagery / satellite imagery
What are the outputs?
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► 11 variables can be extracted using automated analysis
► 2 variable must be extracted manually
Processing: Arc Hydro & Floodplain Mapping
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Arc Hydro - Standard Hydrologic Processing
Sink Filled DEM Flow Direction Flow Accumulation Stream Channel
Valley Floor Mapper (FLDPLN) - Models valley floor morphologyDeveloped by Jude Kastens at the Kansas Applied Remote Sensing Program (KARS)
Processing: Microsheds & Channel Belt
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► Microsheds ► Dense catchment lines created with Arc Hydro► Edited using the valley floor polygon► Correspond to valley ridge tops, providing a
valley width measurement
► Channel Belt► A channel belt, or meander belt, is created by
manually tracing lines connecting peaks in the meander bends of the river channel
RESonate: 1. Start Tool
► Input all prepared datasets
► Generates sampling points (SPs) based on user defined distance (e.g., 5km)
► Automatically creates master table and geodatabase for data storage
► SPs collect attribute information on:1. Precipitation2. Elevation3. Geology4. Down valley slope
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RESonate: 2. Transects & 3. Calculate Widths
► Transects are generated at each sample point
► Transect length and streamline simplification inputs are defined by user
► Variables calculated using transects:1. Whole valley width2. Valley floor (floodplain) width3. VW:VFW ratio
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RESonate: 4. Side Slope, 5. Sinuosity & 6. Channel Belt
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RESonate: Master Table
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► The primary product of RESonate is a data table containing the calculated hydrogeomorphic variables of each sample segment
► Values can then be exported into statistical software packages for further analysis
RESonate: Software & Workflow► ESRI ArcMap 10.x
► ESRI Arc Hydro Plugin
► Valley Flood Mapper v1.0 (FLDPLN)
► Statistical Software (i.e., R, Minitab)
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Estimated processing time from start (no data) to complete master table for a study basin of ~23,000 km2
Case Study: Statistical Analysis
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► Cluster analysis is used to identify groups of sample segments with similar hydrogeomorphic characteristics (i.e., FPZs)
► 6 Group solution used for Carson River (Great Basin, NV/CA)► 5 FPZ “classes”► 1 Reservoir/Water class (outlier)
Case Study: FPZ Mapping
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Case Study: FPZ Typing & Fieldwork
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High Energy Upland
Open Upland High Energy Upland
Constricted Lowland
Continued Research & Future Applications
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Applications:
Watershed Management► Rapid and cost-effective assessment► Scalable and question specific► Ability to add other continuous datasets to code (i.e., land cover)► Characterize difficult to study river basins (i.e., Mongolia)
Reach-level Analysis► LiDAR► Fine scale metrics and clustering
Continued Research:
► GIS analysis and sampling of selected Mongolian rivers (2017-2019)► Site selection for Yellowstone/Bighorn river basin (2017)► Riparian vegetation / land use variables► Automating bankfull width and river complexity measurements (i.e., island/sand bar morphology)
User manual, downloads and more information: macrorivers.org
Contact: [email protected]
Nicholas KotlinskiGIS Technician & Senior Assistant ResearcherAquatic Ecology LaboratoryKansas Biological SurveyUniversity of KansasLawrence, KS
Other Contributors:Bradley WilliamsJude Kastens (KARS, KBS)Ellen D’Amico (EPA)Jacob Goering (KBS)
Thank you.
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REFERENCES
Collins, S. E., Thoms, M. C., & Flotemersch, J. E. (2015). Hydrogeomorphic zones characterize riverbed sediment patterns within a river network. River Systems, 21(4), 203-213.
Thorp, J. H., Thoms, M. C., & Delong, M. D. (2010). The riverine ecosystem synthesis: toward conceptual cohesiveness in river science. Elsevier.
Williams, B. S., D’Amico, E., Kastens, J. H., Thorp, J. H., Flotemersch, J. E., & Thoms, M. C. (2013). Automated riverine landscape characterization: GIS-based tools for watershed-scale research, assessment, and management. Environmental monitoring and assessment, 185(9), 7485-7499.