11th International Drainage Symposium August 30-September 2, 2022 Des Moines, Iowa

1. The Impact of Drainage Water Recycling
on Reduction of Nutrients and Sediment
Losses from a Drained Field in
Eastern North Carolina
Hossam Moursi, Mohamed Youssef, Chad Poole,
Celso Castro-Bolinaga, George Chescheir,
Robert Richardson
The 11th International Drainage Symposium
Des Moines, Iowa, August 31, 2022
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2.  Drainage is essential for crop
production on naturally poorly drained
soils with shallow water table.
 Drainage water carries nutrients and,
sediment from drained fields to
surface water.
 Excessive nutrients in surface water
cause major water quality
degradation and fish kills.
https://oceanservice.noaa.gov
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 Several best management practices (BMPs) have been proposed
to reduce nutrients and sediment losses from drained cropland to
surface water.
Introduction

3. Irrigated Field
Pump
On-farm reservoir
Drainage Water Recycling (DWR)
 Stores surface and
subsurface drainage water in
reservoirs during wet periods
to use for supplemental
irrigation during dry periods
of the growing season.
 DWR could potentially:
 Increase crop yield;
 Conserve water;
 Improve water quality.
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Introduction

4.  Limited studies investigated the performance of DWR.
 Performance of DWR systems varies depending on field
conditions, thus evaluating DWR across a wide range of
conditions is necessary.
 Concentration reduction caused by natural removal
processes, which occur in the reservoir of the DWR
systems has not been adequately investigated.
 DWR has never been investigated in the U.S. Southeast.
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Drainage water recycling (DWR)
Introduction

5. Research Objectives
Experimentally quantify the effect of DWR on
reducing losses of nutrients and sediment from
agricultural fields to surface water bodies; and
investigate the factors affecting removal
efficiency of the reservoir.
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6. Methods
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7. Experimental Site
 Located in Beaufort county, in eastern North Carolina.

8. Experimental Site
 Two treatments: Subirrigated from on-farm reservoir (DWR)
vs. non-irrigated (control treatment; CT)
 Area: DWR=11.48 ha, CT=11.23 ha, and reservoir =0.38 ha
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 Soil: AltaVista fine
sandy loam.
 Crops:
Corn (2019, 2021),
Soybeans (2020).

9. (1) Inflow from forested land (2) Outflow (3) Subsurface drainage, DWR
(4) Subirrigation (5) Surface runoff , CT (6) Surface runoff, DWR
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 Flow and water quality data were collected at the reservoir inlets and
outlet for two years (May 2019 – April 2021).
Field Measurements

10.  Flow measurements
 Water quality samples
 Nitrogen: NO3-N, NH4-N, TKN, TN
 Phosphorous: Ortho-P, TP
 Sediment: TSS
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Field Measurements
Flow and water quality measurements
at reservoir outlet
Flow and water quality measurements
of runoff from DWR field

11. Field Measurements
 Precipitation and air temperature
 Reservoir water level and water temperature
 Topographical survey of the storage reservoir
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Bathymetric survey using
ADCP
Reservoir water level
Automated and manual
rain gauges

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 Reservoir area-volume-depth relationship
Field Measurements
Reservoir elevation map

13. Water Management at DWR Treatment
A water level control structure (left) installed at the outlet of the main drain of
DWR treatment. A smart drainage system (right) is connected to the control
structure to automatically manage drainage and sub-irrigation.
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14. Reservoir Water Balance
 Water balance was conducted for the reservoir for two years
(May 2019-Apr. 2021).
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𝚫𝑽 = 𝑷 + 𝑸𝒊𝒏 + 𝑸𝑹𝑶_𝑫𝑾𝑹 + 𝑸𝑹𝑶_𝑪𝑻 + 𝑸𝑫𝑹𝑵 − 𝑸𝑰𝑹𝑹 − 𝑸𝒐𝒖𝒕 − 𝑺 − 𝑬
Δ𝑉 is change in reservoir water volume;
𝑃 is precipitation;
𝑄𝑖𝑛 is inflow water from upstream forested land;
𝑄𝑅𝑂_𝐷𝑊𝑅 and 𝑄𝑅𝑂_𝐶𝑇 are surface runoff from DWR and CT treatments;
𝑄𝐷𝑅𝑁 is subsurface drainage flow from DWR treatment;
𝑄𝐼𝑅𝑅 is irrigation water;
𝑄𝑜𝑢𝑡 is outflow released from the reservoir;
S is seepage losses from the reservoir;
E is evaporation.

15. Reservoir Nutrient and Sediment
Balances
 Nutrient and sediment balances were conducted for the reservoir for
two years (May 2019-Apr. 2021).
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𝜟𝑳 = 𝑳𝑸_𝒊𝒏 + 𝑳𝑹𝑶_𝑫𝑾𝑹 + 𝑳𝑹𝑶_𝑪𝑻 + 𝑳𝑫𝑹𝑵 − 𝑳𝑰𝑹𝑹 − 𝑳𝒐𝒖𝒕
Δ𝐿 is change in nutrients/sediment mass load in reservoir;
𝐿𝑄_𝑖𝑛 is nutrient/sediment mass load from upstream forested land;
𝐿𝑅𝑂_𝐷𝑊𝑅 and 𝐿𝑅𝑂_𝐶𝑇 are nutrient/sediment mass loads in surface runoff
from DWR and CT treatments, respectively;
𝐿𝐷𝑅𝑁 is nutrient/sediment mass load in the subsurface drainage flow from
DWR treatment;
𝐿𝐼𝑅𝑅 and 𝐿𝑜𝑢𝑡 are nutrient/sediment mass load leaving reservoir via
irrigation and outflow, respectively.

16. Reservoir Hydraulic Retention Time
(HRT)
 The average time that inflow water remains in the
reservoir before it is released.
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𝑉𝑚𝑎𝑥 is reservoir storage capacity
𝑯𝑹𝑻 =
𝑽𝒎𝒂𝒙
𝑷 + 𝑸𝒊𝒏 + 𝑸𝑹𝑶_𝑫𝑾𝑹 + 𝑸𝑹𝑶_𝑪𝑻 + 𝑸𝑫𝑹𝑵

17. Statistical Analysis
 Non-parametric two-sample Wilcoxon-rank test was
used to compare concentrations and loadings.
 Multivariate linear correlation analysis was conducted to
investigate the factors that could affect nutrients and
sediment removal efficiency.
 The factors included in this analysis were water
temperature, hydraulic retention time (HRT), water
volume, and reservoir inflow.
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18. Results and Discussion
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19. Reservoir Water Balance
 Relatively dry (2019-2020), relatively wet (2020-2021).
Year May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr Total
2019-2020 37 65 77 124 162 89 86 64 49 154 92 86 1086
2020-2021 166 112 126 150 105 55 208 162 129 175 76 24 1489
30-year normal 99 121 133 131 104 60 72 78 103 76 106 81 1163
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 Surface runoff was the main source of water for the reservoir (82%)
Precipitation (mm)

20. Reservoir Water Balance
 The reservoir remained nearly full most of the time except during
the growing season.
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21. Reservoir Water Balance (Last slide edited)
 The reservoir received 2.5 times greater inflow in the wetter year.
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22. Hydraulic Retention Time (HRT)
 HRT for 2019-2020 (33.8 days) was almost three times longer than
the HRT for 2020-2021 (12.4 days).
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23. Nitrogen Concentration Reduction (CR)
 Total nitrogen concentration in outflow (Cout) was significantly lower
than that in inflow (Cin) by 40%.
Year
Cin Cout CR
(mg L-1) (mg L-1) (mg L-1) (%)
NO3-N
2019 5.05 2.04 3.01* 60*
2020 2.00 1.09 0.91* 45*
Total 2.80 1.32 1.48* 53*
NH4-N
2019 0.45 0.14 0.31* 70*
2020 0.09 0.08 0.01 14
Total 0.19 0.09 0.10* 51*
ON
2019 0.66 0.82 -0.16 -25
2020 0.72 0.79 -0.08 -11
Total 0.69 0.80 -0.11 -16
TN
2019 6.16 3.00 3.16* 51*
2020 2.81 1.96 0.85* 30*
Total 3.68 2.21 1.47* 40*
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24. Nitrogen Loads
 TN load was significantly reduced by 47%.
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25. Phosphorous Concentration Reduction
 Ortho-phosphate concentration in outflow was significantly lower than
that in inflow.
 Particulate P concentration in outflow was significantly higher than
that in inflow.
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Year
Cin Cout CR
(mg L-1) (mg L-1) (mg L-1) (%)
Ortho-P
2019 0.28 0.13 0.16 56
2020 0.11 0.07 0.04* 35*
Total 0.16 0.08 0.07* 46*
PP
2019 0.07 0.11 -0.03* -42*
2020 0.08 0.10 -0.02 -21
Total 0.08 0.10 -0.02* -29*
TP
2019 0.36 0.23 0.12 35
2020 0.19 0.17 0.02 11
Total 0.23 0.19 0.05 21

26. Phosphorous Loads
 TP load was significantly reduced by 30%.
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27. Sediment Concentration and Loads
 TSS concentration was significantly reduced by 86%.
Year
Cin Cout CR
(mg L-1) (mg L-1) (mg L-1) (%)
TSS
2019 391 50 341* 87*
2020 300 45 255* 85*
Total 319 46 273* 86*
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 TSS Load was significantly reduced by 87%.

28. Factors Affecting Nutrient and Sediment
Dynamics in the Reservoir
 Load reductions were positively correlated with HRT and water
temperature, negatively correlated with reservoir inflow and water
volume.
 Water temperature was significantly correlated with the load reduction
of NO3-N and NH4-N.
Total inflow Water volume Water temperature HRT
NO3-N -0.90* -0.74 0.85* 0.95*
ON -0.25 -0.86* 0.70 0.63
NH4-N -0.70 -0.44 0.83* 0.80
TN -0.85* -0.72 0.76 0.99*
OP -0.52 -0.49 0.56 0.46
PP -0.38 -0.89* 0.75 0.59
TP -0.59 -0.73 0.71 0.61
TSS -0.45 -0.33 0.50 0.58 28

29. Conclusions
 The DWR reservoir retained 14% of total inflow.
 The reservoir reduced TN, TP, and TSS concentrations by
40%, 21%, and 86%, respectively.
 The reservoir reduced TN, TP, and TSS loadings by 47% 30%,
and 87%, respectively.
 The HRT was the major factor affecting the removal efficiency
of N and sediment in the reservoir, while water volume was the
most significant factor affecting the removal efficiency of organic
N and P species.
 The removal efficiency of the reservoir would be highest during
the summer and early fall months.
 The expected water quality benefits from the DWR systems
would be highly correlated with the crop water requirements.
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30. Thank you!
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