Download - Great Environmental Indicators (GLEI) Lakes
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Great
EnvironmentalIndicators
(GLEI)
Lakes
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http://glei.nrri.umn.edu/default/Reports.htm
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Objectives
Quantify stressor-response relationships for novel and existing indicators;
Develop predictive models to infer ecological status;
Develop integrative metrics among sub-components
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SOLEC Indicator Classes
SOLEC differentiates between indicator types:
• Pressure (= stressor) indicators (e.g. contaminants)
• State (= response) indicators (e.g. fish populations)
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Multimetric&
MultivariateApproach
LANDSCAPEsegment-sheds
& watersheds
LOCAL-Habitat quality,-Habitat quantity
STATE INDICATORSGeology ElevationHydrography ClimateLand Use/Cover
Habitat patch HydrologyFood web SubstrateNutrient dynamics
DiatomsVegetationMacrobenthosFishAmphibiansBirdsWater QualityContaminants
INTEGRATEDINDICATORS
-Habitat-Chemical-Biotic-Physical-Hydrologic
REFERENCEDEGRADED
PRESSURE INDICATORS
Shoreline UnitsHigh energy shore
EmbaymentCoastal marsh
River-influenced wetlandProtected wetland
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Spatial Scale
Temporal Scale
meters 10km
days
year
10yr
month
100yr
vegetation
Contaminants, diatoms(cores)
fish
Spatial & Temporal Scales
WQ
invertebrates
birds
amphibians
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3 types of wetlands
Protected wetland(Barrier beach)open shorelineriverine influenced wetland
2 types of shoreline
High energylow energy
(embayment)
Stratified Random Sample
REFERENCEDEGRADED REFERENCEDEGRADED
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Site Selection vs Site Characterization
• Need to move quickly into the field.
• No complete inventory of geomorphic types or anthropogenic stressors.
• Data for site selection can be coarse, but across the Great Lakes Basin.
• Site characterization data should be high resolution, but only needed for sampled sites.
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Site Selecton -
• Segment Sheds as Summary units– Watersheds for lengths of shoreline beginning and
ending ½ way between 2nd order and higher streams (n = 762).
– Data summarized across US side of Great Lakes
2nd orderSegments
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Lake Ontario 90Lake Erie 102Lake Huron 148Lake St. Clair 12Lake Michigan 157Lake Superior 236Connecting Channels 17
TOTAL 762
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Stressor Gradient (cont.)
DATA SOURCE
Agriculture fertilizer and herbicide use (NRCS)
Ag Runoff (erosion, pesticides, and nitrogen; NRCS)
Distance to nearest AOC (Areas Of Concern; EPA)
National Atmospheric Deposition Program (NADP)
Population density (US Census Bureau)
Land use by cropland type (NRCS)
Erosion from agricultural land (NRCS)
Fertilizer use on agricultural land (NRCS)
Confined animal facility waste treatment (NRCS)
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DATA SOURCE
Shoreline alteration (MRV; ACOE)
Land use, general (USGS-NLCD)
N, P runoff potential (USGS-NAWQA SPARROW)
NPDES categories (EPA)
Urbanization amount/rate (NRCS)
Wetland amount (total; NRCS)
Wetland types, hydric soils, and erosion (NRCS-NRI)
Road area, 4 types (US CENSUS TIGER)
Soil properties (NRCS STATSGO)
Toxic Release Inventory points (EPA from BASINS)
Stressor Gradient (cont.)
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Classify Stressors
Purpose: to reduce overlap in the types of information from different stressor source
Seven ‘natural’ categories• Agricultural / Ag-chemical (n = 21)• Atmospheric Deposition (n = 11)• Human Population / Development (n = 14)• Landcover (n = 23)• Point-source / Pollution (n = 79)• Shoreline (n = 6)• Soils (n = 53)
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Define Segment-sheds
Compile Stressor Data
Evaluate & Categorize Stressors
Data organization
Select Segment-sheds
Select Sampling Locations within Segment-sheds
Site selection
Cluster Analysis
Second-round PCAs
Ordering sites in stressor-space (multivariate statistics)
PCAs of Individual Stressor Cat.
1 2 3 4 5 6 7
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•Anthropogenic Stressor GradientAnthropogenic Stressor GradientSummarized 217 variables from 19 Summarized 217 variables from 19 different sources to identify a multi-different sources to identify a multi-deminsional stressor gradient deminsional stressor gradient (Represented using PC’s of 7 natural (Represented using PC’s of 7 natural categories)categories)
Site Selecton (cont.)Site Selecton (cont.)
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Site Characterization
• Identified specific watershed for sampled sites– GPS from the field– Locale polygons created
for each sub-component team.
– Complex polygons for each sampled area
– Watersheds delineated for complexes
– Stressors summarized.
GPS points from fieldLocale Polygons from GPSComplex polygons from locale polygonsWatersheds for Complexes
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High Energy Site
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Scalable Watersheds – Arc Hydro
• ArcHydro data model – developed to “pre-process” elevation data to more efficiently delineate watersheds.– AGREE drainage enforcement using NHD
line- work– Fill sinks, flow direction, flow accumulation,
stream identification, sub-catchment delineation.
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ArcHydro – Cont.
• Catchments are delineated for each stream confluence and river mouth along coast. – Catchments for river
systems are dissolved together.
Streams
Catchments
Catchment
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Grand River Watersheds
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Extending Watersheds to the Coast
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ArcHydro – Cont.
• Along the coast, areas between river mouths, but outside of watersheds remain. We refer to these mostly small coastal watersheds draining directly to the coast but without significant streams as “Coastal Interfluves”.
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Extending Watersheds to the Coast
Coastal Interfluves
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ArcHydro – Cont.
• Both stream and interfluve sheds are then ordered and numbered along the coast from west to east.
• This provides a framework for scaling stressor summaries up and down the coast.
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Additional Efforts
• Lake Erie Integrated Habitat Map– Both US and Canadian sides of Lake Erie basin.
• Canadian Great Lakes Anthropogenic Stressor Gradient.– Currently summarizing stressors in the same way as
we have done for GLEI for Canadian side of Great Lakes Basin
• Full ArcHydro implementation for Saint Louis River (Lake Superior), Maumee and Grand River watersheds (Lake Erie).– Provides for “accumulated stress” or landscape
characterization down the drainage network.
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LAND COVER – Great Lakes
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N = 9,860
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Accumulated SumRel stressor score for the Saint Louis River Watershed
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