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Application of a coupled SWAT-BATHTUB model to evaluate phosphorus critical source areas and land management alternatives on the water quality of Lake Prespa, Macedonia
24 June 2015
Michael Winchell, Naresh Pai Stone Environmental, Inc.
Dave Dilks LimnoTech
Nikola Zdraveski United Nations Development Programme (UNDP)
Presented at the 2015 international SWAT conference, Sardinia, Italy
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Presentation Outline
Study area
Study objectives
SWAT model:• Development• Calibration and Validation
BATHTUB model • Background and development• Simulation results
Phosphorus critical source area (CSA) analysis
Alternative management practices (preliminary results)
Conclusions and next steps
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Study Area: Lake Prespa
You are
here!
International:• Macedonia• Albania• Greece
Watershed area:• Land: 1,054 km2
• Lake: 305 km2
Elevation:• Min: 823 m• Max: 2,420 m
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Study Area: Lake Water Quality Status
Based on 2014 monitoring data, Prespa lake can be classified as:
• eutrophic based on TP
• mesotrophic based on secchi depth and Chl-A concentrations
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Study Objectives
Develop a coupled SWAT/BATHTUB model
Identify phosphorus CSAs
Develop and evaluate alternative management practices
Determine impact of alternatives on lake water quality
Compile knowledge gained into a management tool for UNDP to guide future activities in the watershed
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SWAT Development: Subbasin/HRU Delineation Data
UNDP (20 m) and ASTER (30 m) stitched to
produce 20 m DEM
DEM (m)825 - 1,0411,042 - 1,3011,302 - 1,5731,574 - 1,8651,866 - 2,442
AGRL AGRR APPL BARR FRSD FRST ORCD PAST RNGB RNGE UCOM URLD URML WATR WETL
CORINE 2000/2006 (100 m) and vector orchard boundaries stitched and resampled to produce 20 m land use
ESDB (1 km), FAO (10 km) and 32 UNDP field samples integrated to produce soils data
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SWAT Development: Subbasin/HRU Delineation Results
Land Use:• 80% undeveloped• 19% agriculture• 1% developed
Soils: 48 soils classes, hydro group C dominant
Slope: 5 classes• 0 – 3%: 10%• 3 – 15%: 21%• 15 – 25%: 17%• 25 – 50%: 38%• > 50%: 13%
HRU Delineation: No thresholds applied• 126 subbasins• 5,061 HRUs total
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SWAT Model Development: Weather and Agronomy
Daily precipitation and temperature :• NCEP CFSR, 1979-2013, 4 stations • UNDP 2006-2014, 2 stations
Lapse rates generated from long term isohyetal and temperature maps
Agronomic management schedules:
• Apples, wheat, potato (Macedonia)
• Corn, lima bean (Greece, Albania)
• Timing and frequency of planting, harvesting, tillage, irrigation, and fertilizer applications derived by local agronomist.
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SWAT Calibration: Streamflow data
Monthly streamflow: 1983 – 2010 • Brajcinska River at Brajcino• Golema River near Resen
(estimated)
Model warmup: 1979-1982
Calibration: 1997-2010
Validation: 1983 – 1996
Model performance evaluated based on guidelines by Moriasi et al. (2007)
Golema River near Resen
Brajcinska River at Brajcino
Moriasi, D. N., J. G. Arnold, M. W. Van Liew, R. L. Bingner, R. D. Harmel and T. L. Veith. 2007. Model evaluation guidelines for systematic quantification of accuracy in watershed simulations. Trans. ASABE 50(3):885-900.
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SWAT Calibration: Example Streamflow Results
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Mon
thly
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eam
flow
(m3 /s
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Observed Simulated
Brajcinska River at BrajcinoPBIAS -2%, NSE 0.65, RSR 0.59
Based on performance evaluation criteria, PBIAS is “very good”, NSE is “satisfactory”, and RSR is “good”
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SWAT Validation: Example Streamflow Results
Based on performance evaluation criteria, PBIAS is “very good”, NSE and RSR are “satisfactory”
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Mon
thly
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eam
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(m3 /s
) Brajcinska River at BrajcinoPBIAS -8%, NSE 0.59, RSR 0.64
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SWAT Calibration: Water Quality Data
Monitoring data: 8 sites, 12/2013-12/2014 (continuing in 2015)
Between 6 and 11 samples per site
Majority of samples during low flows
Calibration Strategy:• Qualitative comparison of
observed concentration distributions with simulated distributions (same flow rate range)
• Watershed-level calibration, uniform parameter adjustments
Water quality stations
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SWAT Model Calibration: Example Water Quality Results, 3 of 8 Sites
Sediment and TP simulations very close to observed data. Over predictions in the highest percentiles likely reflect lack of monitoring data during high flows
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L) Observed
Simulated
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SWAT Model Calibration: Landscape Analysis, Plant Growth
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harvestSep 30
pruningMar 30
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Deciduous Forest
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Mixed Forest
Seasonal biomass growth pattern considered reasonable by a local agronomist
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plantingNov 1
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SWAT Model Calibration: Landscape Analysis, Total P by Soil Hydrologic Group
A B C DApple 1.24 1.33 1.53 1.48Barren 0.34 NA NA NAUrban 0.13 0.15 0.28 0.57Corn NA NA 10.31 NAForest Deciduous 0.09 0.09 0.11 0.06Forest Generic 0.09 0.09 0.13 NALima NA NA 8.21 NAPasture 0.09 0.10 0.13 0.06Potatoes 2.01 4.27 6.26 1.57Range Brush 0.11 0.10 0.16 NARange Grass 0.11 0.12 0.18 NAWetlands 0.09 0.10 0.13 0.11Winter Wheat 0.79 1.79 2.66 0.80
Soil Hydrologic GroupForest and wetlands generate lowest total P
Loads from urban areas higher than undeveloped
Highest loads from heavily cultivated agriculture (corn, lima, potato)
P load generally increases from A to C soils
D soils, a very small fraction of the watershed (0.5%) did not follow expected trend.
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SWAT-BATHTUB Coupling: BATHTUB Background
Steady-state model
A combination of mechanistic and empirical sub-models
Empirical equations based on 2.5 million observations from 271 lakes
Model outputs include:• Total phosphorus• Total nitrogen• Chl-A• Transparency• Hypolimnetic oxygen demand
0.0010.010.1
110
1001000
0.01 0.1 1 10 100 1000
Total Phosphorus
Chl
orop
hyll
a
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SWAT-BATHTUB Coupling: BATHTUB Results
BATHTUB simulation for Lake Prespa run with 2013 -2014 SWAT P load as input
Model calibrated to 2014 monitoring data
Results showed Total P, Chl-A, and Secchidepth observations within range of simulation
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CSA Analysis: Total P Load by Land Use
Corn and lima beans contribute highest total loads, followed by winter wheat potato, and apples
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Tota
l P (k
g/yr
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00
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CSA Analysis: Cumulative Watershed P Load
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Alternative Management Practices
Alternative management practices currently being considered include:
• Reduced irrigation in orchards
• Fertilizer best practices (placement and rate)
• Apple orchard waste management
• Wetland restoration (point source and non point source P)
• Erosion control from agricultural land
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Alternative Management Practices: Irrigation Code Modification
Current SWAT Code: Irrigation depth capped to field capacity when using outside source, resulting in lower than intended irrigation amount
Revised SWAT Code: Set to use allow user-specified depth (regardless of field capacity), resulting in intended irrigation amount
Code change impacted overall nutrient flux
Surface runoff from irrigation events currently a user-defined fraction of irrigation, not based on a mechanistic or empirical model
irrsub.f
revision
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Alternative Management Practices: Orchard Irrigation Reduction
Current Irrigation Practice: 864 mm/year, simulated as manual irrigation in SWAT
Based on a UNDP soil sampling, recommendation is to apply either 318 mm (drip) to 454 mm (surface) of irrigation, simulated as auto-irrigation in SWAT
Based on a 30-year simulation under alternative practice:
• Average irrigation of 369 mm/yr
• Water use reduced by 57%
• Total annual P reduced by 26%
• Simulated biomass growth increased, potentially due to greater nutrient availability in the soil profile
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Conclusions and Next StepsA coupled SWAT-BATHTUB model for the Prespa Lake watershed was able to simulate the hydrology and water quality of tributary rivers and the lake
Preliminary results suggest that 10% of the landscape contributes 64% of the total P, indicating high potential for meaningful P reduction with targeted alternative practices
Initial evaluation of improved irrigation practices showed benefits in terms of water use savings and lower pollutant losses, with no effect on crop yield
Early estimates suggest that a reduction of total P load of ~40% would bring lake conditions solidly into the mesotrophic range
Additional management alternatives will be assessed in the coming months
Through the support of the UNDP, water quality monitoring will continue throughout the watershed, providing data for future model refinements
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Thank you.Acknowledgements:Dimitar Sekovski, UNDPMary Watzin, North Carolina State UniversityOrdan Cukaliev and Cvetanka Popovska, University of Ss. Cyril & Methodius
For more information, contact:[email protected]