Ecosystem processes at the watershed scale: Hydrologic vegetation
Multisite- Multivariate Calibration of Watershed Models · 2009-08-17 · Watershed Models •...
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Multisite- Multivariate Calibration of Watershed Models Mahdi Ahmadi Mazdak Arabi August 06, 2009 5 th International SWAT Conference
Transcript of Multisite- Multivariate Calibration of Watershed Models · 2009-08-17 · Watershed Models •...
Multisite- Multivariate Calibration of Watershed Models
Mahdi AhmadiMazdak Arabi
August 06, 20095th International SWAT Conference
Objective• Evaluate the
“Efficiency” and “Applicability”of
“Optimization Techniques”for
“Parameter Estimation”in
“Multiple Locations” for ”Multiple Variables“
of a Comprehensive Watershed model (i.e., Soil and Water Assessment Tool; SWAT) in a watersheds in Indiana
Watershed Models• Computer-based hydrologic models have become
popular for– performing hydrologic forecasts– managing watersheds
• Models should be calibrated before being applied in decision-making process
Model Calibration• To specify values for model "parameters" in such a
way that the model's behavior (Simulation Results) closely matches that of the real system it represents (Historical Data)
• Most of the parameters are conceptual representations of abstract watershed characteristicsand should be determined through a trial-and-errorprocess not by direct measurements
Model Calibration• Manual vs. Automatic Calibration
– Manual• Tedious and time consuming• Subject to experience of the user
– Automatic(use of an optimization algorithm to determine best-fit parameters)
• Faster and less experience• Less subjective
Auto-calibration• Optimization
– Single objectiveor/ to lump all different objectives into one• Shuffled Complex Evolution (SCE-UA)• Single-objective Genetic Algorithm (GA)
– Multi-objective• Multi-objective GA (MOGA)
to choose the best trade-offs among all the defined and conflicting objectives
• Water resources systems analysis is often confronted with multiple conflicting decision objectives that should be optimized
… outline• Auto-calibration Methods• Auto-calibration Tool• Case Study• Results/ Discussion• Conclusion
Auto-calibration• Shuffled Complex Evolution (SCE, Duan 1992)
– Combination of random and deterministic approaches– Includes concept of clustering
• Sampling using Random or/ Prior Distribution• Partitions results in several “Complexes”• Evolving sequences
– Used in the auto-calibration of ArcSWAT
Auto-calibration• Multi-Objective GA (MOGA: NSGA-II, Deb 2001)
– Ranking of individuals– New individuals
• Selection• Mating• Crossover• Mutation
Auto-calibration Tool
Auto-calibration Tool
108 parameters included
Auto-calibration Tool
(MO)GA Settings
SCE settings
Case StudyWildcat Creek, Kokomo Region, Indiana (588 km2)
Reservoir
Case Study• Objective functions
• Group 1– outlet # 1: RMSE of Total pesticide (Tpest)– outlet # 4: RMSE of Streamflow (SF) in
• Group 2– outlet # 1: (1-NS) of Tpest– outlet # 4: (1-NS) of SF
(18 parameters of Streamflow, Reservoir, and pesticide included)
• Group 3– outlet # 4: (1-NS) of SF– outlet #11: (1-NS) of SF– outlet # 1: (1-NS) of Tpest– outlet # 1: (1-NS) of Total Nitrogen (TN)
(29 parameters of Streamflow, Reservoir, Pesticide, and Nitrogen included)
Case Study
Local Sensitivity Analysis Global Sensitivity Analysis
0
2
4
6
8
10
0 20 40 60 80 100 120 140 160 180 200
Screening
Prioritize Parameters
5 Streamflow
2 Pesticide
11 reservoir
11 Nitrogen
Group 1 $ 2
Group 3
• Parameters Selection
Results• Group 1 (2000 Runs- 7 years)
Corresponding NS= 0.67
Corresponding NS= 0.39
Results• Group 2 (3500 Runs- 7 years)
Results• Group 3 (4500 Runs- 7 years)
Conclusion• Flow processes and pesticide can be reasonably
modeled, however representation of pesticide processes is more challenging
• Examined auto-calibration algorithms:– provide a systematic approach for parameter estimation– reduce the subjectivity and time requirements of the
manual calibration exercises
• Based on the same number of runs, Multi-Objective Optimization method gave better results
Conclusion• A wider range of alternatives is usually identified
when a multi-objective methodology is employed
• More realistic results are available when many objectives are considered
• Multiple objectives promotes more appropriate roles for the participants in the planning (analyst) and decision-making processes
Thank you!
Mahdi Ahmadi Mazdak Arabi
