utilization of the swat model and remote sensing to demonstrate the effects of shrub encroachment on...
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Utilization of the SWAT Model and Remote Sensing to
Demonstrate the Effects of Shrub Encroachment on a
Small Watershed
Jason Afinowicz
Department of Biological and Agricultural EngineeringTexas A&M University
Shrub Encroachment
• Replacement of herbaceous growth with woody species
• Active process over the past century
• Potentially caused by a number of human factors and climate change
• Shrub species such as Juniper and mesquite are assocaited with increased water consumption and transpiration
Brush Control
• Factored into many water quantity BMPs
• Reducing new-growth cedar in the Edwards recharge zone may lead to increased recharge
• Studies have been conducted to determine the effects of these techniques
• Methods include mechanical removal as well as more environmentally friendly manual methods
Overview of Project
Hydrologic simulation of a watershed with brush cover
Electronically remove the brush and determine the changes on the modeled
hydrology
The SWAT ModelThe Soil and Water Assessment Tool (SWAT) is a basin and watershed scale model for estimating the effects of management practices on water quantity and quality.
The SWAT 2000 model was integrated into the EPA’s Better Assessment Science Integrating point and Non-point Sources (BASINS) package.
SWAT can incorporate many factors into the simulation, including land cover, soil types, weather, and crop growth.
Honey Creek• Located in western Comal County
• Part of the Upper Guadalupe watershed (HUC 12100201) and is in the contributing region of the Edwards Aquifer
• Drains approximately 6000 acres
• Remains active throughout the year due to the activity of several springs
• Site of an in-progress brush control study utilizing two upstream branches of the creek
DataSurface Elevation
Hydrography
Soil Distribution
Land Cover
Weather Data
30-meter Resolution DEMs for Anhalt and Bergheim Quads
Provided by TNRIS
RF3 Reach Files for Upper Guadalupe Basin
Provided by EPA
SSURGO 2.0 Data for Comal and Hays Counties
Provided by USDA-NRCS
Landsat ETM+ Image: 20 October 1999 (LE7027039009929350)
Provided by TNRIS
Rainfall, Temperature, Solar Radiation, PET, and Windspeed
Provided by Texas ET Network, San Antonio Station (1/96–10/98)
DEM and Reach Data30-m DEMs of the Anhalt and Bergheim quads were mosaiced to produce an elevation grid which covered
the entire Honey Creek area
SSURGO DataSSURGO provides a
high-resolution alternative to
STATSGO soil data
Data for Comal County is provided in the new SSURGO 2 format
SWAT is built to read data found in STATSGO datasets
A User Soils table obtained from the Texas A&M Spatial Sciences Lab aided in the integration of this data
Landsat DataBand 1: Visible Blue
Band 2: Visible Green
Band 3: Visible Red
Band 4: Near Infrared
Band 5: Middle Infrared
Band 6: Thermal Infrared
Band 7: Middle Infrared
Band 8: Panchromatic
ClassificationScheme
Using ENVI
ClassificationScheme
Using ENVI
Land Cover Classifications
Landsat Data
Original DOQQ Parallel Piped Mahalanobis Distance
Maximum Likelihood Minimum Distance1-m DOQQ courtesy of TNRIS
Unclassified 0.69%
FRSE 43.06%
RNGB 31.83%
RNGE 24.42%
Climate Data
SWAT allows for the input of historical rainfall, temperature, solar radiation, and windspeed data, as well as the ability to name a user defined weather generator.
Potential ET can also be read into the simulation.
Climate data is entered in two separate dbf tables
Pre-Analysis with BASINS
Delineate the Honey Creek basin using the DEM and RF3 datasets
Assign spatial data pertaining to land cover and soil distribution
Control Simulation
Experimental Simulations
Experiment 1: Replace RNGB land cover with RNGE to demonstrate complete removal.
Experiment 2: Replace RNGB land cover with RNGE to demonstrate complete removal AND replace FRSE with RNGB to demonstrate partial clearing of dense areas.
Water YieldTotal Water Yield Results
Without October 1998 Data
0
200
400
600
800
1000
1200
1400
1600
1800
October-95 May-96 December-96 June-97 January-98 July-98 February-99
Month
Yie
ld (
Acr
e-F
eet)
Control Run
Experimental Run 1
Experimental Run 2
Change In Water YieldChange In Simulated Water Yield
-150
-100
-50
0
50
100
150
200
250
300
Jan-
96
Feb-96
Mar
-96
Apr-9
6
May
-96
Jun-
96
Jul-9
6
Aug-9
6
Sep-9
6
Oct-
96
Nov-9
6
Dec-9
6
Jan-
97
Feb-97
Mar
-97
Apr-9
7
May
-97
Jun-
97
Jul-9
7
Aug-9
7
Sep-9
7
Oct-
97
Nov-9
7
Dec-9
7
Jan-
98
Feb-98
Mar
-98
Apr-9
8
May
-98
Jun-
98
Jul-9
8
Aug-9
8
Sep-9
8
Oct-
98
Month
Yie
ld (
Acr
e-F
eet)
Experimental Run 1
Experimental Run 2
Groundwater RechargeAquifer Recharge ResultsWithout October 1998 Data
0
100
200
300
400
500
600
700
800
October-95 May-96 December-96 June-97 January-98 July-98 February-99
Month
Per
cola
tio
n (
Acr
e-F
eet)
Control Run
Experimental Run 1
Experimental Run 2
Change In RechargeChange In Simulated Groundwater Recharge
-350
-300
-250
-200
-150
-100
-50
0
50
100
150
Jan-
96
Feb-96
Mar
-96
Apr-9
6
May
-96
Jun-
96
Jul-9
6
Aug-9
6
Sep-9
6
Oct-
96
Nov-9
6
Dec-9
6
Jan-
97
Feb-97
Mar
-97
Apr-9
7
May
-97
Jun-
97
Jul-9
7
Aug-9
7
Sep-9
7
Oct-
97
Nov-9
7
Dec-9
7
Jan-
98
Feb-98
Mar
-98
Apr-9
8
May
-98
Jun-
98
Jul-9
8
Aug-9
8
Sep-9
8
Oct-
98
Month
Per
cola
tio
n (
Acr
e-F
eet)
Experimental Run 1
Experimental Run 2
ETEvaoration-Transpiration
0.0000
0.5000
1.0000
1.5000
2.0000
2.5000
3.0000
3.5000
4.0000
4.5000
October-95 May-96 December-96 June-97 January-98 July-98 February-99
Month
ET
(In
ches
of
Wat
er)
Control Run
Experimental Run 1
Experimental Run 2
Change in ETChange in Simulated Evaporation-Transpiration
-0.8
-0.6
-0.4
-0.2
0
0.2
0.4
0.6
0.8
1
Jan-
96
Feb-96
Mar
-96
Apr-9
6
May
-96
Jun-
96
Jul-9
6
Aug-9
6
Sep-9
6
Oct-
96
Nov-9
6
Dec-9
6
Jan-
97
Feb-97
Mar
-97
Apr-9
7
May
-97
Jun-
97
Jul-9
7
Aug-9
7
Sep-9
7
Oct-
97
Nov-9
7
Dec-9
7
Jan-
98
Feb-98
Mar
-98
Apr-9
8
May
-98
Jun-
98
Jul-9
8
Aug-9
8
Sep-9
8
Oct-
98
Month
ET
(In
ches
of
Wat
er)
Experimental Run 1
Experimental Run 2
Future Goal:
Creation of a GIS
system for targeting
brush removal
1. Calibration of the model with gauging data now being recorded2. Enhancement of satellite land cover techniques3. Increased integration of available data