The SWAT Model Mauro Di Luzio, TAES-BREC Blackland Research and Extension Center, Temple, TX Jeff...
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Transcript of The SWAT Model Mauro Di Luzio, TAES-BREC Blackland Research and Extension Center, Temple, TX Jeff...
The SWAT ModelMauro Di Luzio, TAES-BREC
Blackland Research and Extension Center, Temple, TX
Jeff Arnold, USDA-ARSGrassland Research and Extension Center, Temple, TX
Jerry Whittaker, USDA-ARSNational Forage Seed Production Research Center, Corvallis,
OR
Rem Confesor, Oregon State UniversityNational Forage Seed Production Research Center, Corvallis,
OR
The Distributed Model Intercomparison Project (DMIP-2) Workshop
Hydrology Laboratory National Weather ServiceSeptember 10-12, 2007
Silver Spring, MD
ARS
Soil and Water Assessment ToolArnold et al. (1998)
SWAT is a product of over 30 years of USDA model development
History Time Line
CREAMS USLE (CLEAN WATER ACT) EPIC SWRRB SWAT
1960’s 1970’s 1980’s 1990’s
GLEAMS WEPP ANN AGNPS AGNPS
Partnership – Texas A&M, ARS, EPA, NRCSDeveloping models, GIS, databases, applications
Worldwide User Community
Widely used for water quality
TMDL Applications
Bosque River – Dairy Waste, Agriculture
Range, Treatment Plants Wisconsin – Nitrogen and Phosphorus Texas – Atrazine Missouri – Atrazine Oklahoma – Nutrients Nehalem River – OR Cannonsville Reservoir – NY ………….
ConservationEffects
AssessmentProject
C.E.A.P.—the acronym
SWAT River Basin ModelRiver Routing and Non-Cultivated Lands
CEAP National
• Channel routing: Muskingum routing method.
Di Luzio et al., 2004
Used Data• DEM 1 arc-second (30 m) USGS NED • LandUse/Land Cover NLCD 1992 (National Land Cover
Dataset) (30 m)21 classes
• Soil Map STATSGO (State Soil Geographic) 1:250,000-scale
• Hydrography NHD (National Hydrography Dataset)
• Precipitation NEXRAD DMIP2(Hourly Time Step)
• Temperature NCDC Cooperative Network (daily)
Elk River (119)
Baron Fork (41)
Illinois River (129, 78)
Blue River (55)
The Blue River near Blue, Oklahoma(1,233 Square Km)
DMIP1
• Single objective measure: sum of square of the residuals (SSQ).
• Optimization algorithm: Shuffled Complex Evolution Method (SCE)
(Duan, 1991; Sorooshian et al., 1993).
Automatic Calibration
DMIP1
Event 2, November 12–27, 1994
Event 6, September 17 – 24, 1995
Event 7, September 26–October 11, 1996
Event 9, November 6–21, 1996
DMIP1
Optimization of Multiple ObjectivesOptimization of Multiple Objectives
Objective 2
Objective 1
*
*
*
*
*
*
**
*
Objective 1: RMSE of driven flow
20 21 22 23 24 25 26 27 28
Obje
ctiv
e 2:
RM
SE
of non-d
rive
n flo
w
5
6
7
8
9
10
11
12
13
14
1st iteration
20th iteration50th iteration
500th iteration
AVSWAT2000default parameters
• 24 Pentium 4 processors (2.4 GHz) 1 GB of RAM,– 12 with hyperthreading technology
• 24 port, 1 gigabit/second ethernet switch
• Integrated INTEL 10/100/1000 Mbps network interface card
• 24 ports - KVM switches
• Linux, Fedora Core2, kernel version 2.6.5smp
NFSPRC Beowulf ClusterNFSPRC Beowulf Cluster
• Tahlequa 9,228 40 variables, 129 sub-basins, 419 HRU
• Blue 4,198
• Baron 3,422
• Elk 7,946
• Illinois 4,893
Number of calibration parametersNumber of calibration parameters
Thanks!
Questions?