Hydrologic network metrics based on
functional distance and stream discharge
David Theobald &
Mary Kneeland
Natural Resource Ecology Lab
Dept of Recreation & Tourism
Colorado State University
Fort Collins, CO 80523 USA
May 16, 2003
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.
STARMAP Projects
1.
2.
3.
4.
5.
Combining environmental datasets
(Hoeting)
Local inferences (Briedt)
Development and evaluation of
landscape indicators (Theobald)
Extension and outreach (Urquhart)
Integration and coordination
(Urquhart)
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
Goals of indicator development
Develop and evaluate landscape-level
indicators suitable for spatial and
temporal analyses of EMAP data
Investigate limitations of currentlyavailable data and offer new, robust
methodologies
Overview of presentation
Link watershed and hydrologic
network: “…in every respect, the
valley rules the stream.” – Hynes
1975
 From surrogates to direct measures
 Towards network-based metrics

Indicators that measure
watershed characteristics and
aquatic ecology: Reviews
1. 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)
Key: measuring watershedstream 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?!
1. 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.
1. Lumped (cont.)

ArcINFO, Basinsoft (Harvey and
Eash 1996):
– Drainage area, shape, relief
– # O1 streams, main channel length,
stream density
2. Spatially-explicit,
Distance:

As the crow flies
(Euclidean)
3. Network-based
Distance:

As the seed floats
(downstream)
Distance:

As the fish swims
(down & up stream)
Distance:

Upstream length
- mainstem (2)
- arbolate (1+2+3+4)
Upstream
66 km
Mainstem
Upstream
37 km
Downstream
298 km
Network
16 km (down)
6 km (up)
RWTools ArcView v3 extension
Direct 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
Link watershed and network

1 to 1 relationship
between stream reach
and catchment
 Need robust method
of delineation for
large extents
Pilot area: Colorado, Yampa
“Smart bump” delineation
1. 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
Comparison of automated vs.
hand-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
Handdelineated
“truth”
watersheds

11% error
Reaches 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.)
Networking


Import into
ArcGIS Geometric
Network
Use networking
tools, e.g.
1. Set flag
2. Trace upstream
3. Trace
downstream
4. Direct metric: stream discharge
Physical-based model:
Q = Precipitation –
Evapotranspiration

Q is VMAD (Virgin Mean Annual
Discharge)
USGS Stream Gauges
R2=0.7282
P-value=3.407e-006
Surrogate  Direct metric
Order
 Area
 Discharge

Fragmentation 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
Alteration of natural flow regime
Accumulation
of dam
storage
Tributaries
below dams
mediating flow
modification?
Flow modification
How to measure relative modification
of hydrologic regime?
1. Degree of modification to flow =
cumulative annual flow – cum. dam max.
storage:
Q’ = Q-S
2. Proportion of modified to VMAD (
“natural”) flow:
F = Q’/Q
Reservoirs
Dam “shadow”
High
Or/CO
Table of output data
 Expand this to other factors: e.g.,
geology, vegetation, etc.
 Linked to rest of data

EMAP sites
SITE_ID
WCON99-0003
WCON99-0007
WCON99-0022
WCON99-0027
WCON99-0043
WCON99-0050
WCON99-0056
WCON99-0057
WCON99-0072
WCON99-0081
WCON99-0085
WCON99-0087
WCON99-0088
WCON99-0100
WWYN99-0024
WWYP99-0589
WCOP99-0578
WCOP99-0512
WCOP99-0565
WCOP99-0601
WCOP99-0596
WCOP99-0571
WCOP99-0595
WCOP99-0517
WCOP99-0570
UTM_X
286761
327707
343400
299456
167974
241385
384823
219593
261920
251563
236471
313770
294571
229070
233606
304096
330168
306389
307627
253215
379359
337832
267481
248712
210402
UTM_Y
4459213
4257031
4324332
4472729
4475774
4357041
4369339
4317281
4504833
4506939
4250961
4326421
4319625
4540158
4565619
4550484
4537347
4503840
4499698
4480887
4450211
4415185
4391449
4390205
4371194
Perennial
no
no
no
no
no
no
no
no
no
no
no
no
no
no
no
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
VMAD (acft) may_flow oct_flow Basin Area (m2)
dam_accum
1049
88
84
2181600
0
61145
4329
4979
174219300
0
2411
173
163
3762000
0
2901
253
267
7587000
0
1395
147
170
4833900
0
2441
224
232
6010200
0
105055
8593
6186
200349900
2160
110797
12869
10499
156833100
637
29455
2800
2562
75493800
0
19761
1918
1754
51792300
0
5561
405
555
17300700
0
106587
6298
6434
119459700
0
149910
12227
15358
243615600
17589
13088
1411
1210
42529500
0
41630
5581
3716
232304400
0
27982
2552
1950
55316700
0
186077
16419
10856
267651000
0
160393
13697
10608
239858100
0
186182
15957
12556
291218400
0
4340038
379626 331549 8267067900
519759
194238
15918
13611
379411200
3865
4899263
450024 319170 10753651800
16681192
61407
5532
4571
113334300
0
1215
110
105
2119500
0
49830
4651
4371
110944800
0
Oregon
SITE_ID
WORN99-0016
WORN99-0025
WORN99-0088
WORN99-0096
WORP99-0516
WORP99-0666
WORP99-0597
WORP99-0597
WORP99-0501
WORP99-0735
WORP99-0657
WORP99-0519
WORP99-0507
WORP99-0669
WORP99-0669
WORP99-0659
WORP99-0503
perennial Albers X Albers Y
VMAD (acft) Catch Area (m2)
no
-2094094
1095330
12873
13126500
no
-2119705
1035322
24151
23542200
no
-2124416
1118661
3450
3390300
no
-2064091
1187865
2785
3070800
yes
-2061698
1191106
5609
5822100
yes
-2091159
1176117
15139
14463000
yes
-2089295
1166791
34608465 26957114154
yes
-2089295
1166791
34608465 26957114154
yes
-2025974
1121503
696628
402086700
yes
-2083158
1130921
80251
65678400
yes
-2047240
1108615
2272
1138500
yes
-2130764
1089984
16583292 13528536554
yes
-2134772
1081048
970218
916844404
yes
-2129081
1025428
2162883
1829772000
yes
-2129081
1025428
2162883
1829772000
yes
-2126543
996469
13135
10544400
yes
-2081208
966596
14100
9510300
+ dam accumulation
+ overlap of catchment area
Within catchment hydrologic
distance

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
Challenges
Data
NHD 1:100K
Dams – NID
Processes
natural flow
diversions
ET
Data: attribute errors
Irrigation canals and pipelines incorrectly attributed as river/stream
Data:
positional
error
?
?
Spatial location of dam locations is imprecise
Data:
duplicates
Stagecoach reservoir is duplicated –
Challenges of understanding diverse
datasets
Data:
missing
data?
?
Scale
Dam on tributary that is not in 1:100K network
NID dams (red) > 50’ high, many other dams (in yellow) and other structures!
Dam data
Western Water Assessment, Figure 7
Network 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
EMAP
sites
Euclidean distance
2
• Use x,y to create
distance matrix
• Reasonable for
broad-scale
processes
1
Hydrologic distance
2
• Follows stream
network
4
3
1
Spatial weights
W=
11
3
2
4
5
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
Functional distance
• Reflect distance
weighted by:
-
Stream gradient
Geology
Land use
Etc.
A
1.7
1.2
B
1.0
1.9
C
Functional weighting
W=
1
1
2
3
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
0.2 0.8 0
4
5
0
0.1 0.2 1.0 0.1 0.2 1.0 0
6
7
E.g., downstream hydrology
Connectivity matrix
To/
from
1
2
3
4
5
6
7
1 2
3
1 1
1 1
1
1 1
1
4
5
6
1
1
1
7
Functional spatial weights
Station
5
6
2
7
Station
4
3
Discharge
(kacft)
Order
Area overlap
(%, km2)
13
55
98%=2900/2952
1 14
55
17
Length Discharge
(m)
4532
99.00%
11%=2900/25316
42568
15.00%
55
11%=2900/26001
58389
15.00%
23
15
0.4%=14/2952
23121
0.20%
1
1501
24
15
0.05%=14/25316
59715
0.04%
2
4
27
15
11%=14/26001
75536
0.04%
3
1515
34
55
11%=2952/25316
38105
15.00%
4
9651
37
55
11%=2952/26001
53925
15.00%
5
84
47
55
97%=25316/26001
15820
96.00%
6
82
57
25
0.5%=145/26001
54964
0.80%
7
9972
67
45
0.5%=140/26001
30933
0.80%
Incorporate watershed conditions?
W=0
1
1
2
3
4
5
6
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
7
E.g., macroinvertebrates
Challenges

Generating spatial weights matrix
– O(n2)  O(n)?

Functional (cost-weighted) spatial
weights table
Products
Watershed-reach network database
 GIS-based tool to develop
functional spatial weights matrix
 ArcGIS extension for hydrologic
network metrics

Thanks!

Comments? Questions?


Work funded by: US-EPA STAR Cooperative agreement CR829095 awarded
to CSU
STARMAP: www.stat.colostate.edu/~nsu/starmap

RWTools: email davet@nrel.colostate.edu