okotoks bioretention research - alidppurple coneflower. missouri goldenrod. smooth aster. black-eyed...
TRANSCRIPT
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
0
100
200
300
400
500
600
700
1 11 21 31 41 51 61 71 81 91 101
111
121
131
141
151
161
171
181
191
201
211
221
231
241
251
261
271
281
291
301
311
321
331
341
351
361
Prec
ipita
tion,
mm
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
0
10
20
30
40
50
60
70
80
% o
f tot
al n
umbe
r of e
vent
s in
May
-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%
-20%
-10%
0%
10%
20%
30%
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
olum
e re
tain
ed
70 40 CL
-30%
-20%
-10%
0%
10%
20%
30%
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
olum
e re
tain
ed
70 40 CL
Volume retention – seasonal variation
Volume retention – seasonal variation
-30%
-20%
-10%
0%
10%
20%
30%
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
olum
e re
tain
ed
70 40 CL
Volume retention – seasonal variation
-30%
-20%
-10%
0%
10%
20%
30%
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
olum
e re
tain
ed
70 40 CL
Volume retention – seasonal variation
-30%
-20%
-10%
0%
10%
20%
30%
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
olum
e re
tain
ed
70 40 CL
-5%
0%
5%
10%
15%
20%
25%
control herbaceous woody control herbaceous woody
% V
olum
e re
tain
ed, a
vera
ge fo
r Jun
e-Au
g
70 40 CL
Seasonal average volume retention
IP = 30IP = 15
Infiltration– seasonal variation
0.0
500.0
1000.0
1500.0
2000.0
2500.0
3000.0
3500.0
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
Infil
trat
ion
rate
, mm
/hr
70 40 CL
Seasonal average infiltration
0
200
400
600
800
1000
1200
c h w c h w
Infil
trat
ion,
mm
/hr
70 40 CL
IP = 30IP = 15
Reactive P – seasonal variation
-4500%
-4000%
-3500%
-3000%
-2500%
-2000%
-1500%
-1000%
-500%
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
0
0.5
1
1.5
2
2.5
c h w c h w
Reac
tive
Phos
phat
e Pe
rcol
ate
Conc
entr
atio
n,
mg/
L
70 40 CL Inflow – AVG of 0.0679 mg/L
IP = 30IP = 15
Nitrate – seasonal variation
-3000%
-2500%
-2000%
-1500%
-1000%
-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
0
0.5
1
1.5
2
2.5
3
3.5
4
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%
10%
20%
30%
c h w c h w% V
olum
e re
tain
ed
CL 4 mm 9 mm 14 mm 24 mm
-20%
-10%
0%
10%
20%
30%
c h w c h w% V
olum
e re
tain
ed
40 4 mm 9 mm 14 mm 24 mm
-20%
-10%
0%
10%
20%
30%
c h w c h w% V
olum
e re
tain
ed
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
CL 4 mm 9 mm 14 mm 24 mm
IP = 30IP = 15
IP = 30IP = 15
IP = 30IP = 15
0
500
1000
1500
2000
2500
4 mm 9 mm 14 mm 24 mm
Infil
trat
ion
rate
, mm
/hr
Simulated event magnitude
70 40 CL IP = 15
Reactive P – magnitude-specific
0
0.5
1
1.5
2
2.5
c h w c h w
Perc
olat
e co
ncen
trat
ion,
mg/
L
CL 4 mm 9 mm 14 mm 24 mm
0
0.5
1
1.5
2
2.5
c h w c h w
Perc
olat
e co
ncen
trat
ion,
mg/
L
40 4 mm 9 mm 14 mm 24 mm
0
0.5
1
1.5
2
2.5
c h w c h w
Perc
olat
e co
ncen
trat
ion,
mg/
L
70 4 mm 9 mm 14 mm 24 mm
0
0.2
0.4
0.6
0.8
1
1.2
4 mm 9 mm 14 mm 24 mm
Perc
olat
e co
ncen
trat
ion.
mg/
L
Simulated event magnitude
70 40 CL
IP = 30IP = 15 IP = 30IP = 15
IP = 15
IP = 30IP = 15
Nitrate – magnitude-specific
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
c h w c h w
Perc
olat
e co
ncen
trat
ion,
mg/
L
4 mm 9 mm 14 mm 24 mm
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
c h w c h w
Perc
olat
e co
ncen
trat
ion,
mg/
L
4 mm 9 mm 14 mm 24 mm
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
c h w c h w
Perc
olat
e co
ncen
trat
ion,
mg/
L
4 mm 9 mm 14 mm 24 mm
0
0.5
1
1.5
2
2.5
3
3.5
4 mm 9 mm 14 mm 24 mmPerc
olat
e co
ncen
trat
ion,
mg/
L
Simulated event magnitude
70 40 CL
IP = 30IP = 15
70
IP = 30IP = 15
40
IP = 30IP = 15
CL