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: kotlinic@ku.edu

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.