agwa and integration
TRANSCRIPT
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Darius SemmensDarius Semmens****,, Scott N. Miller, David Goodrich,Scott N. Miller, David Goodrich,
Ryan Miller, Mariano HernandezRyan Miller, Mariano Hernandez
USDAUSDA Agricultural Research ServiceAgricultural Research Service
Southwest Watershed Research CenterSouthwest Watershed Research Center
Tucson, AZTucson, AZ
(current address for Daruis: EPA, Las Vegas(current address for Daruis: EPA, Las Vegas
[email protected])[email protected])
Bill Kepner, Don EbertBill Kepner, Don EbertUSUS EPAEPA
Landscape Ecology BranchLandscape Ecology Branch
Las Vegas, NVLas Vegas, NV
GISGIS--BASED HYDROLOGIC MODELING:BASED HYDROLOGIC MODELING:
THE AUTOMATED GEOSPATIALTHE AUTOMATED GEOSPATIAL
WATERSHED ASSESSMENT TOOLWATERSHED ASSESSMENT TOOL
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Project Background & Acknowledgements
Long-Term Research Project
Landscape Ecology Branch 4 years
Interdisciplinary Watershed management
Landscape ecology
Atmospheric modeling
Remote sensing GIS
Multi-Agency USDA ARS
US EPA
USGS
Universities of Arizona & Wyoming US Army
NWS
Primary Support 2 Post-Doc
2 Ph.D.
1 Masters 2 Full time
USDA-ARSDavid Goodrich
Mariano Hernandez
Averill Cate
Shea Burns
Casey Tifft
Soren ScottLainie Levick
US-EPA
Bill Kepner
Darius Semmens
Dan HeggemBruce Jones
Don Ebert
University of Arizona
Phil Guertin
University of Wyoming
Scott Miller
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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
Introduction
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Used with US-EPA Analytical Tool Interface forLandscape 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 & free w/ US coverage)
Useful for scenario development, alternativefutures simulation work.
Objectives of the Automated Geospatial
Watershed Assessment (AGWA) tool
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Impacts of scale addressed using 2 models
(KINEROS & SWAT)
Temporal & spatial effects
Focus on relative change to reduce
confounding effects of changing rainfall Interested in both volume and rate of runoff
Water supply & water quality
Applicable across range of landscape,precipitation regimes
Semi-arid San Pedro
Humid Catskills#
#
After Omernick
Modeling the Impacts of Land Cover Change
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Range of characteristic space time scales
Hydrology and Human Activities
Small WS Models(e.g. KINEROS2)
Large WS Models(e.g. SWAT)
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(SWAT)
Daily time step
Distributed: empirical and physically-based model
Hydrology, sediment, nutrient, and pesticide yields
Larger watersheds (> 1,000 km2)
Similar effort used by BASINS
71
73
Soil Water and Assessment Tool
71
73
pseudo-channel 71
channel 73
Abstract Routing Representation
to nextchannel
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(KINEROS2)
Event-based (< minute time steps)
Distributed: physically-based model with
dynamic routing
Hydrology, erosion, sediment transport Smaller watersheds (< 100 km2)
72
Kinematic Runoff and Erosion Model
73
71
Abstract Routing Representation
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Where KINEROS2 Works
http://ialcworld.org/soils/surveys/states.html
http://science.nasa.gov/headlines/y2000/ast15
nov_1.htm
Arid and Semi-Arid WatershedsHeavily Urbanized Watersheds
Watersheds characterized by predominantly overland flow
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Watershed Discretization(model elements)
++
Land
Cover
Soil
Rain
Results
Run modeland import
results
Intersect model
elements with
Digital
Elevation
Model (DEM)
Sediment yield (t/ha)Sediment discharge (kg/s)
Water yield (mm)Channel Scour (mm)
Transmission loss (mm)Peak flow (m3/s or mm/hr)
Surface runoff (mm)Sediment yield (kg)
Percolation (mm)Runoff (mm or m3)
ET (mm)Plane Infiltration (mm)
Precipitation (mm)Channel Infiltration (m3/km)
SWAT OutputsKINEROS Outputs
AGWA Inputs and Outputs
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AGWA ArcView Interface
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Navigating Through AGWA
Subdivide Watershed Into Model Elements
SWAT KINEROS
Generate rainfall input files
Daily Rainfall from
Gauge locations
Thiessen map
Pre-defined continuous record
Storm Event from
NOAAAtlas-II
Pre-defined return-period / magnitude
Create-your-own
Intersect Soils & Land Cover
Generate Watershed Outline grid
polygon
Choose the modelto run
look-up tables
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Navigating Through AGWA, Contd
Subwatersheds & ChannelsContinuous Rainfall Records
Prepare inputdata
Run The Hydrologic Model & Import Results
Display Results
SWAT output:Runoff, water yield (mm)Evapotranspiration (mm)
Percolation (mm)
Transmission Losses (mm)
Sediment Yields (mm)
Channel & Plane ElementsEvent (Return Period) Rainfall
KINEROS output:Runoff (mm,m3)
Sediment Yield (kg/ha)
Infiltration (mm, in)
Transmission losses (m3/km)
Peak runoff rate (m3/s)
Peak sediment discharge (kg/s)
external to
AGWA
Visualization for
each model
element
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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
Forest (41) 55 75 80 50
Shrubland (51) 63 77 85 88 25
Grasslands/herbaceous (71) 80 87 93 70
Small grains (83) 65 76 84 88 80
CURVE NUMBER
Hydrologic Soil Group
SWAT Parameter Estimation
- Example: Curve Number from MRLC land cover
Higher numbers result in higher runoff
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Texture Ksat Suction Porosity Smax CV Sand Silt Clay Dist KffClay 0.6 407.0 0.475 0.81 0.50 27 23 50 0.16 0.34
Fractured Bedrock 0.6 407.0 0.475 0.81 0.50 27 23 50 0.16 0.05
Clay Loam 2.3 259.0 0.464 0.84 0.94 32 34 34 0.24 0.39
Sandy Clay Loam 4.3 263.0 0.398 0.83 0.60 59 11 30 0.40 0.36
Silt 6.8 203.0 0.501 0.97 0.50 23 61 16 0.23 0.49
Loam 13.0 108.0 0.463 0.94 0.40 42 39 19 0.25 0.42
Sandy Loam 26.0 127.0 0.453 0.91 1.90 65 23 12 0.38 0.32
Gravel 210.0 46.0 0.437 0.95 0.69 27 23 50 0.16 0.15
KINEROS Parameter Estimation
Parameters based on soil texture
Parameters based on land cover classification (NALC)
Land Cover Type Interception (mm/hr) Canopy (%) Manning's n
Forest 1.15 30 0.070Oak Woodland 1.15 20 0.040Mesquite Woodland 1.15 20 0.040Grassland 2.0 25 0.050Desertscrub 3.0 10 0.055Riparian 1.15 70 0.060
Agriculture 0.75 50 0.040Urban 0.0 0.0 0.010
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AZ061
Component 1
20%
Component 2
45% Component 3
35%
9 inches
Layer 1
Layer 2
Layer 3
2
2
5
Layers for component 3
Components for MUID AZ061
Intersection of model
element with soils map
AGWA Soil Weighting (KINEROS)
Area and depth weighting of soil
parameters
Area weighting of averaged
MUID values for each watershed
element
AZ076
AZ067
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Parameter Manipulation (optional)
Ksat
Can manually
change parameters
for each channel
and plane element
Stream channel attributes
Upland plane attributes Ksat
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Automated tracking of
simulation inputs
Calculate and viewdifferences between
model runs
Multiple simulation runs
for a given watershed
Color-ramping of
results for each
element to show
spatial variability
Visualization of Results
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Spatial and Temporal Scaling of Results
High urban growth
1973-1997Upper San Pedro
River Basin
#
#
ARIZONA
SONORA
Phoenix
Tucson
WY
Water yield change
between 1973 and 1997
SWAT Results
Sierra Vista Subwatershed
KINEROS Results
N
Forest
Oak Woodland
Mesquite
Desertscrub
Grassland
Urban1997 Land Cover
Concentrated urbanization
Using SWAT and KINEROS for integrated watershed assessment
Land cover change analysis and impact on hydrologic response
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Urbanization Effects (KINEROS2)
Pre-urbanization
1973 Land Cover
Post-urbanization
1997 Land Cover
Results from pre- and post-urbanization simulations using
the 10-year, 1-hour design storm event
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Limitations of GIS - Model Linkage
Model Parameters are based on look-up tables
- need for local calibration for accuracy- FIELD WORK!
Subdivision of the watershed is based on topography
- prefer it be 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
Mostuseful for relative assessmentunless calibrated
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Improvements in AGWA 1.4 and BASINS-AGWA
Run SWAT on a daily time step & visualize animated results
SSURGO soil parameterization for SWAT
Enhanced ground-water parameterization dialog for SWAT
Elevation bands for SWAT
FAO Soils international usage
Multiple hydraulic-geometry relations for channelcharacterization
Land-Cover Modification Tool
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Land-Cover Modification Tool Overview
Allows user to specify type and location of land-cover alterations by
either drawing a polygon on the display, or specifying a polygon map
Types of Land Cover Changes
Change entire user defined area to new land cover
e.g. to grassland Change one land cover type to another in user defined area
e.g. to simulate road restoration, change from barren to desert
scrub
Change land cover type within user supplied polygon map
e.g. to simulate a prescribed burn, change map of burnarea to barren
Create a random land cover pattern
e.g. to simulate burn pattern, change to 64% barren, 31%
desert scrub, and 5% mesquite woodland
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Integrating a Landscape/Hydrologic Analysis
for Watershed Assessment
Mariano Hernandez, William G. Kepner, Darius J. Semmens,
Donald W. Ebert, David C. Goodrich, Scott N. Miller
U.S. Department of Agriculture
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OBJECTIVES
Demonstrate the coordinated application of theAnalytical Tools Interface for Landscape
Assessments (ATtILA) and the Automated
Geospatial Watershed Assessment (AGWA) tool to:
Assess the contribution of different land-cover types
to surface runoff and sediment yield for the period 1993
to 1997
Identify subwatersheds with high sediment loadings
as a result of land-cover management
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BACKGROUND
Land use decisions can exacerbate:
Natural hazards and soil erosion
Alter hydrologic balance
Pollute surface and ground water
Destroy wildlife habitats
Increase air pollution
Diminish community quality life
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Upper San Pedro Watershed
(Arizona/Sonora)
7,600 km2
5,800 km2
Arizona/ 1,800 km2
Sonora
Elevation 900 2,900 m
Annual ppt. 30 75 cm
Sonoran/Chihuahuan Transition Zone
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METHODOLOGY
The general approach used in this study
was carried out as follows:
1) Discretization of the San Pedro River Basin into
reporting units or subwatersheds using AGWA
2) Computation of landscape metrics with ATtILA
a) Land use proportions
b) Number of patches
c) Patch density
d) Largest patch index
e) Average patch size
3) Characterization of Hydrologic Response Units
(HRUs) based on land use proportions for SWAT
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METHODOLOGY
4) Application of the AGWA tool to parameterize theSWAT model
5) Identification of subwatersheds with high sediment
yield based on land-cover type, slope steepness,
and average patch size
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2.88 - 84.21
1.20 - 2.88
0.55 - 1.20
0.00 - 0.55
Average Patch Size (ha)Percentage (%)
33.62 - 93.60
14.10 - 33.62
4.95 - 14.10
0.00 - 4.95
RESULTS
Percentage of agriculture and average patch size on
each individual subwatershed
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RESULTS
Spatially distributed average surface runoff and
average sediment yield for the period 1993 - 1997
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RESULTS
Sediment yield and mean annual surface runoff relationship
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RESULTS
Watershed Assessment
HRUs were ranked according to high contributing
sediment yield areas using the relationship between
sediment yield to mean annual surface runoff as a
function of four land cover types
The average slope (9%) and the average sediment
yield (0.8 t/ha) of all HRUs were used as cutoff
criteria
The selection process yielded eight HRUs; six are
classified as agriculture (Ag) and two as
desertscrub (Ds)
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RESULTS
Areas with high sediment yields for 1993 - 1997
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Highest contributions to sediment yield is produced
in areas with agriculture and desertscrub land cover
types
Average slope steepness, average annual sediment
yield, and average patch size were used to identify and
rank sensitive subwatersheds
CONCLUSIONS
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Simulating the Impact of Landscape Change
on Channel Geomorphology in Semi-Arid
Watersheds
Darius J. Semmens
U of AZ, USDA-ARS, U.S. EPA-LEBApril 2, 2004
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Introduction
To understand how an individual stream reach responds toexternal stresses it is necessary to study the channel network asa whole
Watershed-based models are thus necessary to evaluategeomorphic impacts of landscape change
Development of watershed-based geomorphic models is alsothe first step towards linking landscape and ecologicalindicators with surficial processes and response
Event-based watershed models simulate erosion and depositionbased on assumption that channel geometry is static during the
course of an event Prevents simulation of cumulative impacts from multiple events
No event-based watershed models for arid and semi-aridregions that can track cumulative adjustment of the channelnetwork in terms of channel width, depth, and slope.
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Approach
Implement channel-geometry adjustments inKINEROS2 based on total stream power minimization
Develop a GIS-based interface to facilitate modelparameterization, multiple-event simulations, andresults visualization
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KINEROS2 Geomorphic Model (K2G)
Width and depth adjusted to minimize total
stream power at end of each time step
Depth adjustments
Maximum erodible depth
Bank failure
Width adjustments Compound channels
Depth
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AGWA-G
GIS-based interface for K2G, customized
version of AGWA
Watershed delineation and discretization
Land cover and soils parameterization
Coordinates multiple consecutive simulations
and tracks cumulative outputs
Results visualization
Differencing results from two simulations
relative assessment
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Results
Simulations based on
Hydraulic-geometry
channels
1997 land cover
Wet (top), intermediate
(middle), and dry (bottom)
year simulation results
Erosion during wet year,
and deposition during
dry year
DecreasingPrecipitation
1964
1977
1978
Depth Changes Width Changes
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Results Relative Assessment
Significant differencesconcentrated on urbanizedtributary
Erosion increases withinurbanized area more
pronounced for wet year Reduced erosion or increased
deposition begins furtherupstream during drier year
Aggradation downstreamcharacterized by depthdecreases and width increases
DecreasingPrecipitation
1964
1977
Difference in
Depth Changes
Difference in
Width Changes
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Conclusions
Geomorphic response varies with rainfall record able to
resolve changing spatial patterns of sediment movement
Relative assessment useful for highlighting the relative
magnitude of geomorphic impacts associated with land-cover change
Assessment of channel stability, or vulnerability to
degradation will require simulations for a range of
rainfall records and durations more research needed
Linkages to riparian condition not yet established
Arid-region model at present