Introduction
• PC-based GIS tool for watershed modeling
– KINEROS & SWAT (modular)
• Investigate the impacts of land cover
change on runoff, erosion, water quality
• Targeted for use by research scientists,
management specialists
– technology transfer
– widely applicable
Objectives of the Research
• Develop landscape assessment tool for land
managers using indicators
• Advance scientific understanding of principles
governing watershed response to change
land cover changes in the US and associated
impacts on runoff volume, water quality
• Investigate historical changes using repeat
imagery (San Pedro, Catskill/Delaware)
• Investigate spatially distributed hydrologic
processes using single scenes (Las Vegas)
• Forward looks with simulations
Introduction
• Hydrologic Modeling in GIS
– The Shape and characteristics of the earth’s surface is
useful for many fields of study.
– Understanding how changes in the composition of an
area will affect water flow is important!
What happens when residential development occurs?
How does this affect the watershed?
How can these affects be mitigated?
– Best Management Practices (BMPs)
Introduction
Introduction
Topographic Maps
Introduction
Traditional watershed delineation had been done manually using
Contours on a topographic map.
Outlet Point
A watershed
boundary can be
sketched by starting
at the outlet point
and following the
height of land defining
the drainage divides
using the contours on
a map.
Introduction: Terminology
• Drainage system - The area upon which
water falls and the network through
which it travels to an outlet.
• Drainage Basin - an area that drains
water and other substances to a common
outlet as concentrated flow (watersheds,
basins, catchments, contributing area).
• Subbasin - That upstream area flowing
to an outlet as overland flow
• Drainage Divide - The boundary
between two basins. This is an area of
divergent flow.
GIS Background
GIS Background
• Raster Data Structure
– Much of the data we will use in this class
will be “Raster” data.
– Raster formatted data is much more
suitable for many types of landscape
modeling, including hydrologic analysis.
– Inputs such as elevation can only be
processed as a raster data set
– Raster is Faster, Vector is Corrector
GIS Background
Raster
Vector
Real World
GIS Background: DEMs
• Digital Elevation Models (DEM)
– A DEM is a digital representation of the
elevation of a land surface.
– X,Y and Z value
– The USGS is the major producer of DEM’s
in the Nation
GIS Background: DEMs
• DEM’s consist of an array of data representing
elevation sampled at regularly spaced intervals
Y
ELEVATION
VALUES
X
GIS Background: DEMs
• Two Scales of DEMs Available
– 1:24,000 Scale
 Level 1 - 30 meter spacing
–
–
–
–
Errors up to 15 meters inherent in data
Developed using automated methods from air photos
Systematic errors evident as banding
Not appropriate for hydrologic modeling
 Level 2- 30 meter spacing
– Matches map accuracy of 1:24,000 scale quads
– Developed by scanning published quads
– Appropriate for hydrologic modeling
– 1 Degree (~250,000) scale - 93 meter spacing
 Appropriate for regional analysis (not for AGWA)
GIS Background: DEMs
• Preprocessing DEMs
– DEMs typically require some type of
preprocessing prior to hydrologic modeling
to remove errors inherent in the data. This
type of processing can greatly increase the
accuracy of a DEM..
– Primary error found in DEMs are “Sinks”
A sink is an erroneous depression created by
the DEM interpolation routine
Sinks are usually small and cause drainage
basins to be incorrectly delineated
GIS Background: DEMs
An example Sink
100
100
100
100
100
97
96
95
Stream
100-meter Elevation
contour
100
100
Sink Area
100
98
99
100
100
100
100
100
100
100
Cell Elevation
101
Cells containing the contour are assigned the value of the contour,
all other cells are interpolated. Sinks are always possible in areas
where contours converge near a stream.
GIS Background: Surface Parameters
• Generating Surface Parameters
– Flow across a surface will always be in the
steepest down-slope direction
– Known as “Flow Direction” this is the basis of all
further watershed modeling processes.
– Once the direction of flow is known it is possible to
determine which and how many cells flow into any
given cell!
– This information is used to determine watershed
boundaries and stream networks.
GIS Background: Flow Direction
• Generating Surface Parameters - Flow
Direction
– In ArcView Spatial Analyst, the output of a
Flow Direction is a grid whose values can
range from 1 to 255 based on the direction
water would flow from a particular cell. The
cells are assigned valued as shown below.
Target Cell
32 64 128
16
1
8 4 2
GIS Background: Flow Direction
• Generating Surface Parameters - Flow
Direction
– If a cell is lower than its eight neighbors, that cell
is given the value of its lowest neighbor and flow is
defined towards this cell.
– If a cell has the same slope in all directions the
flow direction is undefined (lakes)
– If a cell has the same slope in multiple directions
and is not part of a sink the flow direction is
calculated by summing the multiple directions
GIS Background: Flow Direction
Original Surface
100
Flow Direction Surface
100
100
100
97
96
95
100
98
99
100
100
100
100
100
100
101
100
94
2
2
2
1
1
1
128
128
128
128
64
32
128
64
64
32
80
1
100
128
64
GIS Background: Flow Accumulation
• Generating Surface Parameters - Flow
Accumulation
– If we know where the flow is going then we can
figure out what areas (cells) have more water
flowing through them than others.
– By tracing backwards up the flow direction grid we
can figure the number of cells flowing into all cells
in a study area
– Accumulated flow is calculated as the
accumulated number of all cells flowing into each
downslope cell.
GIS Background: Flow Accumulation
• Generating Surface Parameters - Flow
Accumulation
– For an accumulation surface the value of
each cell represents the total number of
cells that flow into an individual cell
– Cells that have high accumulation are
areas of concentrated flow and may be
used to identify stream channels.
GIS Background: Flow Accumulation
Flow Direction Surface
2
Flow Accumulation Surface
2
2
1
1
1
128
128
128
128
64
32
128
64
100
32
80
1
128
0
0
0
0
3
8
15
0
2
2
0
0
0
0
0
0
0
0
64
18
0
GIS Background: Flow Accumulation
DEM  Flow Direction
 Flow Accumulation
Flow Accumulation Surface
AGWA
Objectives of AGWA
• Integrated with US-EPA Analytical Tool Interface
for Landscape Assessment (ATtILA)
• Simple, direct method for model parameterization
• Provide accurate, repeatable results
• Require basic, attainable GIS data
– 30m USGS DEM (free, US coverage)
– STATSGO soil data (free, US coverage)
– US-EPA NALC & MRLC landscape data
(regional)
• Useful for scenario development, alternative
futures simulation work.
Land Cover & Hydrologic Response
Natural Condition
Land cover change
Degradation
Urbanization
Woody plant invasion
Decreased Vegetation
infiltration
interception
evapotranspiration
Increased Runoff
flood hazard
surface roughness
Increased Velocity
soil moisture
groundwater recharge
Increased Erosion
Decreased Water Quality
Navigating Through AGWA
Generate Watershed Outline
Subdivide Watershed Into Model Elements
SWAT
KINEROS
Intersect Soils & Land Cover
generate rainfall input files
Thiessen map from…
Gauge locations
Pre-defined continuous record
Storm Event from…
Pre-defined return-period / magnitude
“Create-your-own”
Navigating Through AGWA, Cont’d…
prepare input data
Subwatersheds & Channels
Continuous Rainfall Records
Channel & Plane Elements
Event (Return Period) Rainfall
Run The Hydrologic Model & Import Results
Display Results
For subwatershed elements:
•Precipitation (mm)
•Evapotranspiration (mm)
•Percolation (mm)
•Surface Runoff (mm)
•Transmission Losses (mm)
•Water Yield (mm)
•Sediment Yields (t/ha)
For Plane & Channel Elements:
•Runoff (mm, m3)
•Sediment Yield (kg)
•Infiltration (mm)
•Peak runoff (mm/hr, m3/sec)
•Peak sediment discharge (ks/sec)
Suggested File Structure for AGWA
AGWA directory –
primary tables, AV project
file, and model executables
Simulation input/output –
Separate directories for
each simulation
Spatial data –
primary coverages and grids
ArcView working directory –
secondary or temporary
coverages, grids, and tables
Hydrologic Modeling & AGWA
GIS Data
Rainfall
Assumptions
AGWA
Runoff
Erosion
Conceptual Design of AGWA
PROCESS
PRODUCTS
STATSGO
NALC, MRLC
USGS 7.5' DEM
Build GIS Database
Discretize Watershed
f (topography)
Contributing
Source Area
Characterize Model Elements
f (landcover, topography, soils)
Gravelly loam Soil
 Ks = 9.8 mm/hr
 G = 127 mm
 Por. = 0.453
intensity
Derive Secondary Parameters
look-up tables
View Model Results
link model to GIS
runoff
Build Model Input Files
10-year, 30-minute event
time
time
runoff, sediment hydrograph
GIS Data Layers for AGWA
 Upper San Pedro Basin, SE Arizona
Land Cover
Forest
Oak Woodlands
Mesquite Woodlands
Grasslands
Desertscrub
Riparian
Agriculture
Urban
Water
Barren / Clouds
N
0
1992 NALC
5 10 km
Hillshade DEM
STATSGO
Automated Watershed Characterization
the influence of CSA on watershed complexity
CSA: 2.5% (6.9 km2)
44 watershed elements
29 channel elements
CSA: 5% (13.8 km2)
23 watershed elements
15 channel elements
Note channel initiation
Point changing with CSA
km2)
CSA: 10% (27.5
11 watershed elements
7 channel elements
CSA: 20% (55 km2)
8 watershed elements
5 channel elements
N
0
5
10 km
Watershed Configuration for SWAT
 channel and subwatershed hydrology
Abstract Routing Representation
14
64
to channel 64
11
14
11
21
channel 14
74
84
24
31
pseudochannel 11
94
44
34
N
41
54
51
0
10
20 km
Watershed Configuration for KINEROS
 upland, lateral and channel elements in cascade
73
72
Abstract Routing Representation
71
74
0
5 km
N
Characterizing the Watershed
complex topography
land cover
high spatial variability
complex watershed response
soils
Characterizing the Watershed
• Homogeneous planes
• Hydrologic parameters represent
intersections of topo., cover, soil
• Information loss as f (geometric
complexity)
• Scaling issues
Watershed modeling relies on condensing spatial
data into appropriate units for representing processes
leaves plenty of room for error!
KINEROS Parameter
Look-Up Table
BEGIN PLANE
ID = 71, LEN = 1303.0, AREA = 10783378.3
SL = 0.029, MAN = 0.052, X = 593519.0, Y = 3505173.5
CV = 0.92, PRINT = 1
KS = 7.94, G = 118.14, DIST = 0.3, POR = 0.459, ROCK = 0.43
FR = 0.49, 0.33, 0.17, SPLASH = 24.42, COH = 0.006, SMAX = 0.93
INTER = 2.56, CANOPY = 0.133, PAVE = 0.00
END PLANE
BEGIN PLANE
ID = 72, LEN = 765.0, AREA = 4357163.9
SL = 0.043, MAN = 0.054, X = 591637.8, Y = 3507025.3
CV = 0.93, PRINT = 1
KS = 7.77, G = 116.95, DIST = 0.3, POR = 0.459, ROCK = 0.43
FR = 0.49, 0.33, 0.16, SPLASH = 24.61, COH = 0.006, SMAX = 0.93
INTER = 2.85, CANOPY = 0.112, PAVE = 0.00
END PLANE
BEGIN PLANE
ID = 73, LEN = 945.0, AREA = 7405044.9
SL = 0.038, MAN = 0.052, X = 593864.3, Y = 3507560.5
CV = 0.95, PRINT = 1
KS = 8.19, G = 114.97, DIST = 0.3, POR = 0.459, ROCK = 0.43
FR = 0.5, 0.33, 0.16, SPLASH = 24.91, COH = 0.006, SMAX = 0.93
INTER = 2.6, CANOPY = 0.137, PAVE = 0.00
END PLANE
74
Curve Number From MRLC
 Higher numbers result in higher runoff
CURVE NUMBER
Hydrologic Soil Group
NLCD
Land cover
A
B
C
D
Cover
High intensity residential (22)
81
88
91
93
15
Bare rock/sand/clay (31)
96
96
96
96
2
55
75
80
50
77
85
88
25
80
87
93
70
76
84
88
80
Forest (41)
Shrubland (51)
63
Grasslands/herbaceous (71)
Small grains (83)
65
Sample Watershed Configuration - SWAT
N
Contributing Source Area: 2000 acres
- ~5% of total watershed area
20 subwatershed elements
19 channels
STATSGO ID: AZ061
Grassland & desertscrub
Moderate relief
Watershed ID: 7
Area: 11.8 km2
Slope: 3.7 %
Cover: 12.8 %
Ks: 18.1 mm/hr
CN: 71.8
Soil Hyd. Group: B
Multiple Soil Horizons
Sample Watershed Configuration - KINEROS
N
STATSGO ID: AZ061
Grassland & desertscrub
Moderate relief
Contributing Source Area: 2000 acres
- ~5% of total watershed area
33 planes
- 7 upland elements
- 25 lateral element
19 channels
Watershed ID: 73
Area: 7.45 km2
Slope: 3.53 %
Width: 945 m
Length: 7876 m
Interception: 2.60 mm
Cover: 13.70 %
Manning's n: 0.052
Pavement: 0.00 %
Splash: 24.91
Rock: 0.43
Ks: 6.67 mm/hr
Suction: 115 mm
Porosity: 0.459
Max saturation: 0.93
Cv of Ks: 0.95
Sand: 50 %
Silt: 33 %
Clay: 17 %
Distribution: 0.30
Cohesion: 0.006
Rainfall Characteristics in SE Arizona
Summer convective storm
August 11, 2000
high spatial variability
high temporal variability
difficult to characterize
flashy runoff response
short duration (45 min)
Winter frontal storm
January 13, 2001
low spatial variability
low temporal variability
lead to little or no runoff
long duration (3 hours)
0
0
1
2
rainfall depth (mm)
3
5
5 km
N
What Could Possibly Go Wrong??
SYSTEMIC ERRORS
PROCESSING ERRORS
These are “hidden” & include:
These are “visible” & include:
• Poor conceptual model
• Programming errors
 AGWA, SWAT, KINEROS
•Errors in GIS data
 DEM
•Lack of input data
 GIS, rainfall
• Poor process representation
•AGWA fails to characterize watershed
• Errors in GIS data
 Land cover, soils
• Assumptions in the look-up tables
Rainfall-Runoff Process in SE Arizona
Spatial Distribution of Rain Gauges
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High Density Watershed
Walnut Gulch Exp. WS
148 km2
89 rain gauges
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Typical Distribution
Upper San Pedro Basin
6100 km2
15 rain gauges
Sample Design Rainfall Events for KINEROS
 after Osborne et al., 1985**
** Data reduced “on the fly” for
watershed area
160
Design Storms
stored in AGWA
Intensity (mm/hr)
100-year, 60-minute
120
80
5 yr
10 yr
100 yr
5 yr
10 yr
100 yr
10-year, 60-minute
30
30
30
60
60
60
min
min
min
min
min
min
“create your own”
40
5-year, 30-minute
0
0
20
Time (min)
40
60
Limitations of GIS - Model Linkage
• Model Parameters are based on look-up tables
- need for local calibration for accuracy
• Subdivision of the watershed is based on topography
- prefer it based on intersection of soil, lc, topography
• No sub-pixel variability in source (GIS) data
- condition, temporal (seasonal, annual) variability
- MRLC created over multi-year data capture
• No model element variability in model input
-averaging due to upscaling
• Most useful for relative assessment unless calibrated
AGWA Gone Awry
 when good software goes bad
Problems Running AGWA
- DEM pre-processing
 sinks
 indeterminate boundary
 converging flow
 lack of defined flow path (big flat area)
- User error
 clicking on hill slope
- Data coverage
 no overlap in GIS data
 suitability of GIS data f (scale, model)
Watersheds Generated From Different DEMs
30m DEM – Level I USGS
- unfiltered, unfilled
- contains many sinks
- has poor drainage
- watershed delineation fails
10m DEM from Air Photos
- still contains some sinks
- exhibits better drainage
- boundary is correct
- there are still internal failures
10m DEM improved
- filtered using high filter
smoothing
- filled to remove 222 sinks
- no sinks at the end
- good drainage pattern
- watershed succeeds; good boundary
Streams Generated From Different DEMs
Same points, tolerances, settings
note differences in results
30m DEM
Sinks interrupt flow
Parallel channels affect drainage
10m DEM
Example of DEM Processing To Avoid Problems
Problem: 30m DEM contains sinks, poor flow direction,
and cannot create correct watershed
Solution: Filter and fill the DEM before analysis
smoothing can be good & bad
Positives:
• better definition of channels
• no sinks
• hydrologic connectivity
Negative:
• streams running across the
watershed divide
• boundary extends beyond the
correct position
Get the Best Available Data!
inaccurate flow
minimum CSA
lower for Level II
USGS level 1
USGS level 2
DEM error…
banding
better boundary
bottom line
junk in = junk out
User Error
User selected a watershed outlet
missed the channel and grabbed a separate basin
could also fail to generate a watershed entirely
should have selected over here
user selected here
user selects
here
AGWA
uses here
AGWA Helps With This Problem
- AGWA uses a search radius to
find maximum flow accumulation
- can move the point downstream
- use a point coverage to specify outlet
Lack of GIS Data Coverage
Commensurate Land Cover, DEM, STATSGO
- simple to determine
- AGWA can handle small errors through averaging
Watershed boundary
Land cover does not extend fully
Relative vs. Absolute Change
• Availability of repeat classified imagery for change detection
- NALC
• Calibration data set for absolute change analysis
- USGS runoff gauging station
- Internal validation preferable to calibrating solely on outlet
• Plenty of rainfall data for the time periods
- NWS gauges
- Potentially NEXRAD radar data
• Confounding effects of land cover change & rainfall data
- Uniform vs. distributed rainfall
Extra Slides Follow
AGWA Processing Time
 benchmarks on a PIII, 866 MHz, 256 Mb RAM
Discretization level
Watershed
Area (km2)
Boundary
Delineation
CSA 20%
CSA 10%
CSA 2.5%
150 *
0:03
0:22
0:25
0:37
150
0:56
0:28
0:35
0:43
750
1:18
0:48
1:13
1:30
1940
2:03
2:50
2:45
3:20
3370
3:03
5:37
5:43
6:13
7550
6:50
9:05
9:30
10:36
* Data was clipped to a small buffer around watershed
Curve Number Modeling
Rainfall
+
Determining Curve Numbers