This document summarizes a study that improved ecohydrological simulations in central Iowa watersheds by incorporating more accurate tile drainage and fertilizer application rate data into the SWAT model. The updated models showed better representation of streamflows, crop yields, and water balances compared to default models. Refining inputs like tile drainage maps and fertilizer rates improved model performance at over half of monitoring stations. The results highlight the importance of selecting accurate management data to realistically simulate baseline hydrologic and water quality conditions.

1. 1
Improving Ecohydrological Simulations by
Incorporating Accurate Tile Drainage and
Fertilizer Application Rate: A Case Study of
Central Iowa
Authors: Philip W. Gassman; Tássia Mattos Brighenti; Jan R. Thompson

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Introduction Methods Results Conclusion
Iowa Urban FEWS – OVERVIEW
The project is focused on developing sustainable food
production systems in the Des Moines–West Des Moines, IA
Metropolitan Statistical Area (DMMSA). Multiple models are
being integrated (co-simulation approach) to evaluate the
impact of converting cropland, peri-urban and/or urban
landscapes to table food production, in DMMSA
transboundary and urban subareas.

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Introduction Methods Results Conclusion
• Quantify crop growth.
• Hydrological cycling.
• Nutrient and sediment cycling and transport for
cropping systems and associated management
practices.
• Simulate future scenarios for climate and land
use change; and characterize streamflow,
nutrient, sediment, and yields production.
SWAT model within the Iowa UrbanFEWS:

4. • Why? The needed to address food insecurity and environmental impacts is still a challenge in the
21st century.
• How? Ecohydrological modeling is a key component of the modeling system, which provides
comparisons of hydrologic and water quality impacts between row crop and table food cropping
systems.
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Introduction Methods Results Conclusion

5. • Why? The needed to address food insecurity and environmental impacts is still a challenge in the
21st century.
• How? Ecohydrological modeling is a key component of the modeling system, which provides
comparisons of hydrologic and water quality impacts between row crop and table food cropping
systems.
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Introduction Methods Results Conclusion
Determining optimal configuration strategies for such models is important to evaluate the hydrologic
and water quality impacts of changes in land use.

6. 6
Introduction Methods Results Conclusion
Determining optimal configuration strategies for such models is important to evaluate the hydrologic
and water quality impacts of changes in land use.
Goal
The Soil and Water Assessment Tool (SWAT) ecohydrological model is being used in Iowa
UrbanFEWS to simulate three basins that intersect the project domain: Des Moines River Basin
(DMRB), South Skunk River Basin (SSRB), and North Skunk River Basin (NSRB).
These basins are dominated by agriculture (up to 85% of land sue) and are characterized by
dense tile drained networks and intensive use of fertilizers on cropland landscapes.
The SWAT model was applied using a framework consisting of the Hydrologic and Water Quality
System (HAWQS) online platform, multi-site streamflow evaluation, accurate tile drainage
location, and fertilizer management data.

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Introduction Methods Results Conclusion
STUDY AREA
Des Moines (DMRB) 31,892 km²
South Skunk (SSRB) 4,593 km²
North Skunk (NSRB) 2,259 km²
• Land use: soybean and corn fields
representing together 70%, 71%, and
61% of the DMRB, SSRB, and NSRB.
• Soil type: Loamy Wisconsin Glacial Till
(tile drainage represent 54%, 51% and
44% of the DMRB, SSRB and NSRB)
HAWQS calibration outlet
40 stations

8. Introduction Methods Results Conclusion

9. Introduction Methods Results Conclusion

10. Introduction Methods Results Conclusion

11. Introduction Methods Results Conclusion
Tile drainage map Subsurface tile drainage is one of the most widely-used agriculture management practices to
increase crop yield in regions with high water tables or poorly drained soils.
Tile drainage causes significant changes in watershed hydrology, water quality, local and
regional climate.
Valayamkunnath, et al. 2020 method
Inputs:
• County tile drainage statistics
• National land cover database (NLCD) 2016.
• Slope generated from Shuttle Radar Topography Mission
(SRTM) derived DEM.
• Soil Survey Geographic (SSURGO) data from USDA NRCS.
Results validation:
16000 tile drainage ground-truth points from aerial imagery
basemap. The accuracy ranges from 82.7% to 93.6%.

12. Introduction Methods Results Conclusion
Implementation of tile drain map into SWAT database
Step 2: Counting pixels (0 – no tile, 1 –
tile drained).
The method used in this study was parametrized for: tile drain depth (DDRAIN; mm), the time required to drain the soil to field capacity
(TDRAIN; h), tile drain lag time (GDRAIN; h) and an impervious layer depth (DEP_IMP, mm).
The parameters were set in the three SWAT models (Des Moines, North and South Skunk Rivers) with the values of DDRAIN=1200 mm,
GDRAIN=48 h and TDRAIN=24 h, based on Gassman et al. (2017).
Step 3: Percentage distribution between
the agricultural land use.
Step 1: overlapping the tile drain raster file and
the 12-digit subbasin shapefile.

13. Introduction Methods Results Conclusion
Kg/ha of elemental nitrogen applied
% of Area
DMRB SSRB NSRB
64-79 15.0 7.1 20.9
80-99 27.1 39.2 37.0
100-159 13.5 2.6 11.4
160-176 44.4 51.1 30.8
1
Time of the Year Crop Rotation Application Rate (Kg/ha)
Fall Corn-soybean 183
Spring Corn-soybean 172
Spring Continuous corn 196
Updated fertilizer – Elemental nitrogen Default HAWQS data
Updated fertilizer
The model update was focused on nitrate application since it is a crucial factor in corn fields. The
operation management update was based on ensuring that all cornfields has the proper fertilizer
application.

14. Introduction Methods Results Conclusion
Baseline simulations

15. Introduction Methods Results Conclusion
Crop yields
Basin Annual average HAWQS Default Updated USDA Survey
Des Moines River
Biomass (t/ha) 22.9 26.5 x
Grain Yield (t/ha) 10.8 12.5 12.6
N stress days 20.9 16.8 x
South Skunk River
Biomass (t/ha) 26.0 28.1 x
Grain Yield (t/ha) 12.2 13.4 12.8
N stress days 16.2 11.6 x
North Skunk River
Biomass (t/ha) 25.4 24.6 x
Grain Yield (t/ha) 12.1 11.7 13.5
N stress days 15.8 11.42 x
1
• DMRB: updated N rates result in values closer to USDA survey-based, with a 0.1 t/ha difference.
• SSRB: same absolute difference (0.6 t/ha).
• NSRB: both setup underestimate the total yields with the non-updated models resulting in values
closer to the survey.
• The updated nitrogen application rates resulted in fewer nitrate stress days for all three basins.
Baseline 1 Baseline 2

16. Introduction Methods Results Conclusion
Spatial representation of streamflows
Provide valuable spatial
information about model
dynamics for the different
baselines:
• Tile drain information and
proper fertilizer application
improved SWAT model
performance in 55% (22 out
of 40) of the monitoring
gauges analyzed.
Baseline 1 Baseline 2

17. Introduction Methods Results Conclusion
Representation of streamflows

18. Introduction Methods Results Conclusion
Representation of streamflows

19. Introduction Methods Results Conclusion
Water balance
Baseline
1
Baseline
2
Baseline
1
Baseline
2
Baseline
1
Baseline
2
DMRB NSRB SSRB
• DMRB: baseline 1 better ratio baseflow/surface runoff; baseline 2 better surface runoff volume estimation.
• NSRB and SSRB: baseline 2 resulted in realistic baseflow/surface runoff ratio.

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Introduction Methods Results Conclusion
Conclusion & Hightlights
• The results revealed that careful selection of management data is essential for
obtaining the most accurate representation of baseline conditions for the
simulated stream systems (realistic ratios between baseflow/surface runoff).
• The addition of refined tile drain information and proper fertilizer application
improved SWAT model performance in 55% (22 out of 40) of the monitoring
gauges analyzed.
• Automatic model calibration can mask the use of inaccurate inputs on
management data.

21. Introduction Methods Results Conclusion
•DOI: 10.1016/j.scitotenv.2022.156302

22. Introduction Methods Results Conclusion
Thank you!
Improving Ecohydrological Simulations by
Incorporating Accurate Tile Drainage and
Fertilizer Application Rate Data: A Case Study
of Central Iowa.
Authors: Philip W. Gassman; Tássia Mattos Brighenti; Jan R. Thompson