Daily flow prediction in Ungauged watersheds: SWAT-ANN Dr. Navideh Noori Post-Doc, Odum School of Ecology, University of Georgia & Dr. Latif Kalin Prof., School of Forestry and Wildlife Sciences, Auburn University Assoc. Director, Center for Environmental Studies at the Urban-Rural Interface
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Daily flow prediction in Ungauged watersheds:SWAT-ANN
Dr. Navideh NooriPost-Doc, Odum School of Ecology, University of Georgia
&
Dr. Latif KalinProf., School of Forestry and Wildlife Sciences, Auburn University
Assoc. Director, Center for Environmental Studies at the Urban-Rural Interface
Background
2
β’ Streamflow prediction:
Operation and optimization of water resources
Flood control and water resource management
It is complicated: Climate, topology, topography, soil, geology, landuse/cover
β’ Accuracy of different flow prediction models:
Empirical methods are simplistic and are constrained to a functionalform between variables prior to the analysis.
Process-based models take into account various processes of thehydrological cycle via mathematical formulation.
Background
3
Predictions in ungauged watersheds are more challenging
No data for calibration
Regionalization: Transfer of parameters from neighboring gauged watersheds (donor) to an ungauged (target) watershed.
Regression based Physical similarity based Proximity based
SWAT - ANN
4
β’ Soil Water Assessment Tool (SWAT)
a large amount of spatial and temporal data needed.
Calibration and validation processes are time consuming, requires goodexpertise and could be challenging.
β’ Artificial Neural Network (ANN)
Select the best combination of the input variables for a parsimoniousmodel.
If an event is beyond their training data range, the predictive model wouldperform poorly with high uncertainty.
Cobb
DeKalb
Gwinnett
Fulton
Study Area
5
Auburn
Lafayette
Study Area
Topographic map Hydrologic soil group map
6
LULC 2006
Coupling SWAT with ANN
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πππ¬πππ₯π¨π°
πππ¨π«π¦ππ₯π¨π°Outflow
DEM
Climate
LULC
Soil
SWAT
Warm Season (May to Oct)
Cool Season (Nov to Apr)
ANN
ANN
Model Performance Rate (Kalin et al. 2010):
β’ Very good: ENASH β₯ 0.70; π π΅πΌπ΄π β€ 0.25
β’ Good: 0.50 β€ ENASH < 0.70; 0.25 < π π΅πΌπ΄π β€ 0.50
β’ Satisfactory: 0.30 β€ ENASH < 0.50; 0.50 < π π΅πΌπ΄π β€ 0.70
β’ Unsatisfactory: ENASH < 0.30; π π΅πΌπ΄π > 0.70
Warm Season (May to Oct)
Run 1
Run 2
Run 3
Run 29
29 USGS Stations
Leave-One-Out Jackknifing Technique
ANN Output
Prediction 1 Prediction 29Prediction 2
Coupling SWAT with ANN
8
Results, Warm Season
9
10
Results, Cool Season
11
Flow PredictionUsing SWAT
SWAT-CUP
Calibration:
- 3 iteration
- Each iteration
was 500 run
Validation:
- Parameters
transferred
from nearest
donor with
ENASH>0.5 to a
target watersh.
12
Conclusions
β’ Performance rates of coupled models:
62% of the runs for the cool season Good to Very good
83% of the runs for the warm season Good to Very good
β’ Performance rate of SWAT models:
34% of the runs Good to Very good
β’ As the percent forest cover or the size of test watershed increased, the coupledmodel performances gradually decreased during both cool and warm.
β’ Coupled models work better in urbanized watersheds with size <200 km2.
β’ Combining ANN and SWAT could enrich the modeling environment by:
Excluding the calibration and sensitivity analysis to adjust the SWAT modelparameters
Narrowing down the number of inputs to ANN.
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Thank you for your attention!For more information please contact: Latif Kalin, [email protected]
May 2015, Texas, USA
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