Okotoks Bioretention Research - ALIDPPurple coneflower. Missouri goldenrod. Smooth aster. Black-eyed Susan. Tall sunflower. Meadow blazingstar. Grasses: Foothills fescue . Green Needle

2018-09-21Anton Skorobogatov

Okotoks Bioretention Research:The Effects of Plants and Media on the Performance

Bioretention PerformanceTo date, successes and challenges have been observed anddocumented

Successes include reliable peak flow reductions, overallrunoff volume reductions, and consistently high (80-90 %)TSS removals

Challenges include highly variable nutrient removals, mediaclogging, vegetation failure, and unpredictable variationwith time

Plants + Soil Interactions

Complex biogeochemical interactions

Crucial impacts on soil structure,microbial communities, retention orbreakdown of water and contaminants

How would it impact bioretentionperformance?

This Project’s Objectives

1) Investigate the effect of three different soil media andplant communities on water and nutrient retention andto analyze the impact of accumulating sediment

2) Quantify the effect of plant roots on the media.

3) Quantify the effect of plant transpiration on thebioretention performance.

4) Develop an empirical tool/model to predict soil-plantimpacts on bioretention performance.

Construction-2017

• Construction of bioretention cells completed by June 10th

• All beds were lined

• 300 mm of drainage layer placed (drainage rock, pea gravel, sand)

• 600 mm of media placed in 200 mm lifts

Forbs:Blue flaxShowy milkweedPurple coneflowerMissouri goldenrodSmooth asterBlack-eyed SusanTall sunflowerMeadow blazingstar

Grasses:Foothills fescue Green Needle Grass Tufted HairgrassJune GrassFowl Manna GrassFowl BluegrassAwned SedgeRough Hair Grass

June 20, 2017

July 11, 2017

July 04, 2018

2017 Preliminary Experiments

• Leaching• 3 trial applications of raw water: Aug 22, Sep 06 and 29• ~700 L of water w/o additives applied to each bed

• Potential issues• nutrient leaching • rapid infiltration• poor retention

2018 Field Research

• Collaboration with the City of Calgary and the ALIDP

• 25 simulated events of varying magnitude

• Goal to match seasonal precipitation/run-off typical for Calgary area

Calgary Annual Ppt 1960-2016

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1 11 21 31 41 51 61 71 81 91 101

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Prec

ipita

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Days of Year

DRY YEARAVG YEARWET YEAR

DRY YEAR AVG YEAR WET YEAR

TOTAL 343.2 TOTAL 407.3 TOTAL 495.4

JAN 12.2 JAN 11.5 JAN 11.6

FEB 9.3 FEB 10.1 FEB 11.0

MAR 17.3 MAR 13.7 MAR 15.3

APR 23.2 APR 22.2 APR 34.0

MAY 44.6 MAY 53.7 MAY 69.4

JUN 64.0 JUN 89.1 JUN 115.5

JUL 51.5 JUL 71.1 JUL 81.7

AUG 49.7 AUG 49.2 AUG 60.4

SEP 31.0 SEP 45.6 SEP 50.2

OCT 16.3 OCT 15.5 OCT 18.1

NOV 10.4 NOV 14.4 NOV 14.2

DEC 13.6 DEC 11.1 DEC 14.2

Focus on May to Oct ( ~ growing season) of AVG years3490 days total, 2328 rain-free 1162 rain eventsHistogram is showing magnitude distribution within the 1162 rain events

67.6

16.9

6.83.4 2.5 1.5 0.5 0.2 0.3 0.0 0.1 0.0 0.1 0.0 0.0 0.1 0.0 0.0 0.1 0.0 0.0

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% o

f tot

al n

umbe

r of e

vent

s in

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-Oct

of A

VG y

ears

Bin - daily ppt magnitude (mm)

• Run majority as small events, find a combination that can achieve seasonal target

• Up to 30 total events• (at least 50% as small, e.g. 5 mm, events)• 15 small events• 5 medium events (e.g. 10 mm)• 5 water quality events (15 mm)• 3 large events (e.g. 25 mm)

• 28 events a season, total is 275 mm

• Another adjustment - 1 mm reduction to account for events that would not generate run-off

• Final application regime:

• 15 events at 4 mm• 5 events at 9 mm• 5 events at 14 mm• 3 events at 24 mm

• 28 events a season, total is 247 mm

Contributing Area

100% impervious

0% surface storage

I/P of 15 and 30

2018 June- Aug Preliminary Results

• Seasonal vs event-specific performance

• Hydrologic – volume retention, infiltration

• Water quality – RP, TP, Nitrate, TN, TOC

Volume retention – seasonal variation

-30%

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0%

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40%

50%4 mm 9 mm 4 mm 14 mm 4 mm 14 mm 4 mm 24 mm 4 mm 9 mm 4 mm 24 mm 4 mm 14 mm 4 mm 24 mm 4 mm 9 mm

01-Jun 05-Jun 10-Jun 15-Jun 20-Jun 25-Jun 03-Jul 08-Jul 13-Jul 18-Jul 23-Jul 28-Jul 01-Aug 07-Aug 10-Aug 15-Aug 20-Aug 27-Aug

% V

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tain

ed

70 40 CL

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25%

control herbaceous woody control herbaceous woody

% V

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vera

ge fo

r Jun

e-Au

g

70 40 CL

Seasonal average volume retention

IP = 30IP = 15

Infiltration– seasonal variation

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4 mm 9 mm 4 mm 14 mm 4 mm 14 mm 4 mm 24 mm 4 mm 9 mm 4 mm 24 mm 4 mm 14 mm 4 mm 24 mm 4 mm 9 mm


Infil

trat

ion

rate

, mm

/hr

70 40 CL

Seasonal average infiltration

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c h w c h w

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trat

ion,

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IP = 30IP = 15

Reactive P – seasonal variation

-4500%

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0%

500%9 mm 14 mm 4 mm 14 mm 24 mm 9 mm 4 mm 24 mm 14 mm 24 mm 4 mm 9 mm

05-Jun 15-Jun 20-Jun 25-Jun 08-Jul 18-Jul 23-Jul 28-Jul 07-Aug 15-Aug 20-Aug 27-Aug

% re

mov

al

70 40 CL

Reactive P – seasonal average

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0.5

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c h w c h w

Reac

tive

Phos

phat

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rcol

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entr

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n,

mg/

L

70 40 CL Inflow – AVG of 0.0679 mg/L

IP = 30IP = 15

Nitrate – seasonal variation

-3000%

-2500%

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-500%

0%9 mm 14 mm 4 mm 14 mm 24 mm 9 mm 4 mm 24 mm 14 mm 24 mm 4 mm 9 mm

05-Jun 15-Jun 20-Jun 25-Jun 08-Jul 18-Jul 23-Jul 28-Jul 07-Aug 15-Aug 20-Aug 27-Aug

% re

mov

al

70 40 CL

Nitrate – seasonal average

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0.5

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c h w c h w

Perc

olat

e co

ncen

trat

ion,

mg/

L

70 40 CLInflow- AVG 0.317 mg/L

IP = 30IP = 15

Next steps

• Complete data collection for the 2018 growing season• Soil moisture data

• analyze if antecedent moisture variation can explain the performance

• analyze losses between events, estimate ET

• Weather station data• analyze how natural precipitation impacts the performance

• Investigate which factors have the greatest impact on the performance

Special Thanks:

• Richard Nadori

• Mike(Xing) Li

Thank you!

-20%

-10%

0%

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30%

c h w c h w% V

olum

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CL 4 mm 9 mm 14 mm 24 mm

-20%

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c h w c h w% V

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40 4 mm 9 mm 14 mm 24 mm

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30%

c h w c h w% V

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70 4 mm 9 mm 14 mm 24 mm

Magnitude-specific volume retention

IP = 30IP = 15

IP = 30IP = 15

IP = 30IP = 15

-15%

-10%

-5%

0%

5%

10%

15%

20%

25%

30%

4 mm 9 mm 14 mm 24 mm% v

olum

e re

tain

ed

Simulated event magnitude

70 40 CL IP = 15

Magnitude-specific infiltration

0200400600800

100012001400160018002000

c h w c h w

Infil

trat

ion,

mm

/hr

70 4 mm 9 mm 14 mm 24 mm

0200400600800

100012001400160018002000

c h w c h w

Infil

trat

ion,

mm

/hr

40 4 mm 9 mm 14 mm 24 mm

0200400600800

100012001400160018002000

c h w c h w

Infil

trat

ion,

mm

/hr


IP = 30IP = 15

IP = 30IP = 15

IP = 30IP = 15

0

500

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2500

4 mm 9 mm 14 mm 24 mm

Infil

trat

ion

rate

, mm

/hr


70 40 CL IP = 15

Reactive P – magnitude-specific

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c h w c h w

Perc

olat

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ncen

trat

ion,

mg/

L


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c h w c h w

Perc

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mg/

L

40 4 mm 9 mm 14 mm 24 mm

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Perc

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70 4 mm 9 mm 14 mm 24 mm

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4 mm 9 mm 14 mm 24 mm

Perc

olat

e co

ncen

trat

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mg/

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70 40 CL

IP = 30IP = 15 IP = 30IP = 15

IP = 15

IP = 30IP = 15

Nitrate – magnitude-specific

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c h w c h w

Perc

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4 mm 9 mm 14 mm 24 mm

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c h w c h w

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4 mm 9 mm 14 mm 24 mm

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c h w c h w

Perc

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L

4 mm 9 mm 14 mm 24 mm

0

0.5

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2.5

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3.5

4 mm 9 mm 14 mm 24 mmPerc

olat

e co

ncen

trat

ion,

mg/

L


70 40 CL

IP = 30IP = 15

70

IP = 30IP = 15

40

IP = 30IP = 15

CL

Documents

Okotoks Bioretention Research - ALIDPPurple coneflower. Missouri goldenrod. Smooth aster. Black-eyed Susan. Tall sunflower. Meadow blazingstar. Grasses: Foothills fescue . Green Needle