hydrologic network metrics based on functional distance and stream discharge david theobald &...
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![Page 1: Hydrologic network metrics based on functional distance and stream discharge David Theobald & Mary Kneeland Natural Resource Ecology Lab Dept of Recreation](https://reader030.vdocuments.site/reader030/viewer/2022032522/56649d635503460f94a45cb5/html5/thumbnails/1.jpg)
Hydrologic network metrics based on Hydrologic network metrics based on functional distance and stream dischargefunctional distance and stream discharge
David Theobald & Mary Kneeland
Natural Resource Ecology Lab
Dept of Recreation & Tourism
Colorado State University
Fort Collins, CO 80523 USAMay 16, 2003
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Goal: develop approaches for spatio-temporal design and modeling in order to further our understanding of aquatic resources
Objectives, to develop:1. spatio-temporal models for a continuous
response, 2. spatio-temporal models for count and/or
categorical data, 3. design and analysis methods for data collected
at different scales.
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STARMAP ProjectsSTARMAP Projects
1. Combining environmental datasets (Hoeting)
2. Local inferences (Briedt)
3. Development and evaluation of landscape indicators (Theobald)
4. Extension and outreach (Urquhart)
5. Integration and coordination (Urquhart)
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Big questions:Big questions: Broad-scale processes (e.g., acid deposition in
Mid-Atlantic region) to watershed processes Probability-based sampling for state compliance
to CWA Sampling perennial/intermittent streams (I.e.
flow all year for most years)– What is perennial and shouldn’t be? (~24%)– What is not included and should be? (~18%)
Fragmentation of hydrologic regime on biodiversity
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Goals of indicator developmentGoals of indicator development
Develop and evaluate landscape-level indicators suitable for spatial and temporal analyses of EMAP data
Investigate limitations of currently-available data and offer new, robust methodologies
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Overview of presentationOverview of presentation
Link watershed and hydrologic network: “…in every respect, the valley rules the stream.” – Hynes 1975
From surrogates to direct measuresTowards network-based metrics
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Indicators that measure Indicators that measure watershed characteristics and watershed characteristics and aquatic ecology: Reviewsaquatic ecology: Reviews1. Land use in entire watershed vs. riparian buffer
(IBI):- watershed better: Richards et al. 1996- buffer better: Arya (1999); Lammert and Allan (1999)
2. Other indicators:- road density (Bolstad and Swank)- dam density (Moyle and Randall 1998)- amount of roads near streams (Moyle & Randall) and Arya (1999)
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Key: measuring watershed-Key: measuring watershed-stream linkage?stream linkage?1. Lumped measures
- %, #, density
2. Spatially-explicit- Euclidean distance
3. Network-based (directional, cumulative)- Strahler stream order- Length of stream line- Watershed area
4. Direct network-based- Discharge?!
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1. Lumped1. Lumped % agricultural, % urban Ave road density Dam density (Moyle and Randall 1998)
# mines Road length w/in riparian zone
EPA. 1997. An ecological assessment of the US Mid-Atlantic Region: A landscape atlas.
Southern Rockies Ecosystem Project. 2000.
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1. Lumped (cont.)1. Lumped (cont.)
ArcINFO, Basinsoft (Harvey and Eash 1996):– Drainage area, shape, relief– # O1 streams, main channel length,
stream density
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2. Spatially-explicit, 2. Spatially-explicit, Distance:Distance:
As the crow flies (Euclidean)
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3. Network-based3. Network-basedDistance:Distance:
As the seed floats (downstream)
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Distance:Distance: As the fish swims
(down & up stream)
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Distance:Distance: Upstream length
- mainstem (2)- arbolate (1+2+3+4)
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Upstream66 km
Downstream298 km
MainstemUpstream37 km
Network16 km (down)6 km (up)
RWTools ArcView v3 extension
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Direct measuresDirect measures
Surrogate, e.g. Strahler order:
The usefulness of stream order assumes, with a sufficiently large sample, that order is proportional to stream discharge – Strahler 1957
Ordinal data
Not robust to data artifacts
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Link watershed and networkLink watershed and network 1 to 1 relationship
between stream reach and catchment
Need robust method of delineation for
large extents
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Pilot area: Colorado, YampaPilot area: Colorado, Yampa
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““Smart bump” delineationSmart bump” delineation1. Reach catchment
- flowdirection 30 m DEM- watershed from buffered hydrology (USGS NHD 1:100K)
2. Differentiate local ridges (artifacts) from true catchment boundary - “smart bump” using ZONALMIN
3. Remove conversion slivers at shared boundaries- regiongroup- if <10 cells, NIBBLE
Currently, 1-2 days processing time per basin
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Comparison of automated vs. Comparison of automated vs. hand-delineatedhand-delineated
1. Randomly selected 111 (out of 2151 watersheds)
2. Computed area of automated vs. hand-delineated (“truth”)
3. RMSE = 204.39 (in ha)4. Mean error 2.4%5. Challenges in defining
commensurate watersheds
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Hand-Hand-delineated delineated “truth” “truth” watershedswatersheds11% error
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Reaches are linked to catchmentsReaches are linked to catchments
1 to 1 relationship Properties of the
watershed can be linked to network for accumulation and networking operations
Ordinal value (order) to real value (length, area, etc.)
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NetworkingNetworking
Import into ArcGIS Geometric Network
Use networking tools, e.g.
1. Set flag
2. Trace upstream
3. Trace downstream
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4. Direct metric: stream discharge4. Direct metric: stream discharge
Physical-based model:
Q = Precipitation –Evapotranspiration
Q is VMAD (Virgin Mean Annual Discharge)
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USGS Stream Gauges
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R2=0.7282
P-value=3.407e-006
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Surrogate Surrogate Direct metric Direct metric
OrderAreaDischarge
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Fragmentation and flow regulationFragmentation and flow regulation
Deynesius and Nilsson, Science (1994) – 77% of upper 1/3 of northern hemisphere rivers are strongly or moderately affected- F = regulated/total channel length- R = % of VMAD (cumulative reservoir live, gross capacity)
RCL
TCL
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Alteration of natural flow regimeAlteration of natural flow regime
Accumulation of dam storage
Tributaries below dams mediating flow modification?
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Flow modificationFlow modification
How to measure relative modification of hydrologic regime?1. Degree of modification to flow = cumulative annual flow – cum. dam max. storage:
Q’ = Q-S2. Proportion of modified to VMAD (“natural”) flow:
F = Q’/Q
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High
Dam “shadow”
Reservoirs
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Or/COOr/CO
Table of output dataExpand this to other factors: e.g.,
geology, vegetation, etc.Linked to rest of data
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EMAP sitesEMAP sitesSITE_ID UTM_X UTM_Y Perennial VMAD (acft) may_flow oct_flow Basin Area (m2)dam_accumWCON99-0003 286761 4459213 no 1049 88 84 2181600 0WCON99-0007 327707 4257031 no 61145 4329 4979 174219300 0WCON99-0022 343400 4324332 no 2411 173 163 3762000 0WCON99-0027 299456 4472729 no 2901 253 267 7587000 0WCON99-0043 167974 4475774 no 1395 147 170 4833900 0WCON99-0050 241385 4357041 no 2441 224 232 6010200 0WCON99-0056 384823 4369339 no 105055 8593 6186 200349900 2160WCON99-0057 219593 4317281 no 110797 12869 10499 156833100 637WCON99-0072 261920 4504833 no 29455 2800 2562 75493800 0WCON99-0081 251563 4506939 no 19761 1918 1754 51792300 0WCON99-0085 236471 4250961 no 5561 405 555 17300700 0WCON99-0087 313770 4326421 no 106587 6298 6434 119459700 0WCON99-0088 294571 4319625 no 149910 12227 15358 243615600 17589WCON99-0100 229070 4540158 no 13088 1411 1210 42529500 0WWYN99-0024 233606 4565619 no 41630 5581 3716 232304400 0WWYP99-0589 304096 4550484 yes 27982 2552 1950 55316700 0WCOP99-0578 330168 4537347 yes 186077 16419 10856 267651000 0WCOP99-0512 306389 4503840 yes 160393 13697 10608 239858100 0WCOP99-0565 307627 4499698 yes 186182 15957 12556 291218400 0WCOP99-0601 253215 4480887 yes 4340038 379626 331549 8267067900 519759WCOP99-0596 379359 4450211 yes 194238 15918 13611 379411200 3865WCOP99-0571 337832 4415185 yes 4899263 450024 319170 10753651800 16681192WCOP99-0595 267481 4391449 yes 61407 5532 4571 113334300 0WCOP99-0517 248712 4390205 yes 1215 110 105 2119500 0WCOP99-0570 210402 4371194 yes 49830 4651 4371 110944800 0WCOP99-0505 291346 4345579 yes 4018 296 390 5058000 0WCOP99-0594 160492 4344097 yes 26186703 2114426 2185258 64065845872 18371090WCOP99-0503 364245 4335445 yes 17514 1347 1128 25980300 0WCOP99-0501 255370 4295958 yes 1503776 115988 142188 2908662300 125267WCOP99-0501 255388 4295958 yes 1503776 115988 142188 2908662300 125267WCOP99-0597 286917 4274224 yes 13965 987 915 23783400 0WCOP99-0567 204620 4268705 yes 19670 999 2327 31345200 0WCOP99-0561 354155 4250546 yes 53912 3739 4235 147976200 920WCOP99-0574 290214 4210647 yes 102709 7073 8079 159713100 0
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OregonOregonSITE_ID perennial Albers X Albers Y VMAD (acft) Catch Area (m2)WORN99-0016 no -2094094 1095330 12873 13126500WORN99-0025 no -2119705 1035322 24151 23542200WORN99-0088 no -2124416 1118661 3450 3390300WORN99-0096 no -2064091 1187865 2785 3070800WORP99-0516 yes -2061698 1191106 5609 5822100WORP99-0666 yes -2091159 1176117 15139 14463000WORP99-0597 yes -2089295 1166791 34608465 26957114154WORP99-0597 yes -2089295 1166791 34608465 26957114154WORP99-0501 yes -2025974 1121503 696628 402086700WORP99-0735 yes -2083158 1130921 80251 65678400WORP99-0657 yes -2047240 1108615 2272 1138500WORP99-0519 yes -2130764 1089984 16583292 13528536554WORP99-0507 yes -2134772 1081048 970218 916844404WORP99-0669 yes -2129081 1025428 2162883 1829772000WORP99-0669 yes -2129081 1025428 2162883 1829772000WORP99-0659 yes -2126543 996469 13135 10544400WORP99-0503 yes -2081208 966596 14100 9510300
+ dam accumulation+ overlap of catchment area
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Within catchment hydrologic Within catchment hydrologic distancedistance
Moved from basins, HUCs and watersheds to stream reach catchments
Within catchment:– Distance along hydro network
distance (distance along the network upstream of pour point)
– Allocation (using flat weight surface)
1
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ChallengesChallenges
DataNHD 1:100KDams – NID
Processesnatural flowdiversionsET
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Data: attribute errorsData: attribute errors
Irrigation canals and pipelines incorrectly attributed as river/stream
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Data: Data: positional positional errorerror
Spatial location of dam locations is imprecise
??
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Data: Data: duplicatesduplicates
Stagecoach reservoir is duplicated – Challenges of understanding diverse datasets
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Data: Data: missing missing data?data?
?
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ScaleScale
Dam on tributary that is not in 1:100K network
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NID dams (red) > 50’ high, many other dams (in yellow) and other structures!
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Dam dataDam data
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Western Water Assessment, Figure 7
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Network metricsNetwork metrics
Have foundation – direct measure Build on/refine existing metrics:
– # first order streams– Main-channel length– Total stream length– Drainage density = stream length /
catchment area Examine location within network and
make available to statistical models
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EMAP EMAP sitessites
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Euclidean distanceEuclidean distance
1
2• Use x,y to create
distance matrix
• Reasonable for broad-scale processes
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Hydrologic distanceHydrologic distance
1
2• Follows stream
network
3
4
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Spatial weightsSpatial weights
11 2
3 54
0 0 0 0 0
0 0 0 0 0
0 0 0 0 0
1 0 1 0 0
0 1 0 1 0
W =
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Functional distanceFunctional distance
• Reflect distance
weighted by:- Stream gradient
- Geology
- Land use
- Etc.
1.7
1.2
1.9
1.0
A
B
C
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Functional weighting Functional weighting
1
1 2 3
54
0 0 0 0 0 0 0
0.7 0 0 0 0 0 0
0 0 0 0 0 0 0
0 0 0 0 0 0 0
0 0 0 0.7 0 0 0
0.2 0.8 0 0.2 0.8 0 0
0.1 0.2 1.0 0.1 0.2 1.0 0
W =
6 7
E.g., downstream hydrology
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Connectivity matrixConnectivity matrix
To/ from 1 2 3 4 5 6 7
1
2
3 1 1
4 1 1 1
5
6
7 1 1 1 1 1 1
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Functional spatial weightsFunctional spatial weights
Station Discharge (kacft)
1 1501
2 4
3 1515
4 9651
5 84
6 82
7 9972
1
2
34
56
7
Station Order Area overlap
(%, km2)
Length (m)
Discharge
1 3 5 5 98%=2900/2952 4532 99.00%
1 4 5 5 11%=2900/25316 42568 15.00%
1 7 5 5 11%=2900/26001 58389 15.00%
2 3 1 5 0.4%=14/2952 23121 0.20%
2 4 1 5 0.05%=14/25316 59715 0.04%
2 7 1 5 11%=14/26001 75536 0.04%
3 4 5 5 11%=2952/25316 38105 15.00%
3 7 5 5 11%=2952/26001 53925 15.00%
4 7 5 5 97%=25316/26001 15820 96.00%
5 7 2 5 0.5%=145/26001 54964 0.80%
6 7 4 5 0.5%=140/26001 30933 0.80%
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Incorporate watershed conditions? Incorporate watershed conditions?
1
1 2 3
54
0 1 0 0 0 0 0
1 0 0 0 1 1 0
0 0 0 0 0 0 0
0 0 0 0 1 0 0
0 1 0 1 0 1 0
0 1 0 0 1 0 1
0 0 0 0 0 1 0
W =
6 7
E.g., macroinvertebrates
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ChallengesChallenges
Generating spatial weights matrix– O(n2) O(n)?
Functional (cost-weighted) spatial weights table
![Page 59: Hydrologic network metrics based on functional distance and stream discharge David Theobald & Mary Kneeland Natural Resource Ecology Lab Dept of Recreation](https://reader030.vdocuments.site/reader030/viewer/2022032522/56649d635503460f94a45cb5/html5/thumbnails/59.jpg)
ProductsProducts
Watershed-reach network databaseGIS-based tool to develop
functional spatial weights matrixArcGIS extension for hydrologic
network metrics
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Thanks! Comments? Questions? Work funded by: US-EPA STAR Cooperative agreement CR829095 awarded
to CSU STARMAP: www.stat.colostate.edu/~nsu/starmap RWTools: email [email protected]