Post on 23-Jan-2021
Rethinking Stormwater Pond Nutrient Removal Perry Forster, David Austin, Roger Scharf, Jason Carroll, Mark Enochs Minnesota Water Resources Conference. October 16, 2012
Project Location: Riley Purgatory Bluff Creek Watershed District
Nutrient management: • Reduce internal & external TP
contributions • Requires accurate
determination of stormwater pond TP contributions Modeled
versus Measured
Study genesis: Lake restoration projects
District Goals:
• Improve lake water quality
• Remove lakes from Impaired
Waters List
• Do ponds settle and keep particulate-P? → Models OK
• Does particulate-P solubilize enough to impair pond
nutrient removal performance? → Models problematic
• Findings: 2010-2012
• Conclusions
Stormwater pond study
Standard concept of stormwater pond performance
Stormwater Stormwater P0
P30 P10 P50 P80
Example1,2: P8 Model predicts nutrient performance by settling of particulate-P
Soluble Phosphorus
1) Walker, W.W., 1987. Phosphorus removal by urban runoff detention basins. Lake and Reservoir Management 3, 314-326. 2) Palstrom, N., Walker, W.W, 1990. The P8 urban catchment model for evaluating nonpoint source controls at the local level. Enhancing states’ lake management programs, US EPA, Washington, DC.
Problem statement: Does settled P stay in ponds?
Stormwater flush
Stormwater flush
TPS + P0
PO4 Algae TPS
sediments
• Flushing of high inter-storm TP concentration would impair performance
• Our study compares pond TP contributions: Modeled versus measured
• First a quick look at biogeochemistry … mechanisms are important
TP discharge = soluble-P + solubilized-P
The sulfur cycle in P-solubilization
H2S detected Strong H2S Hypolimnion of Lake
Ann, Chanhassen, MN
Apr-Oct 2010
No temperature correction. Add ~ 50 mV for standard conditions (25oC).
D.O. gone
SO42- loss associated with previous slide
H2S detected Strong H2S
Ingvorsen, K., J. G. Zeikus, et al. (1981). "Dynamics
of Bacterial Sulfate Reduction in a Eutrophic Lake."
Appl. Environ. Microbiol. 42(6): 1029-1036.
COOL, SHALLOW SEDIMENTS H2S
O2
• High redox above sediments • Sediment organic content → low influence
WARM, SHALLOW SEDIMENTS H2S
O2
• Low redox above sediments • Sediment organic content → high influence
Temperature dependence of sulfate reduction
Peak
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0.2
0.4
0.6
0.8
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1.2
1.4
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TP, m
g/L
Findings 2010-2011: “Good” &“Bad” Ponds
9 6 9 6 7 7 7 9 6 3 6 n = 6
Assumed soluble TP 150 µg/L
TP concentrations are from ponds sampled between rain events
0.0
5.0
10.0
15.0
20.0
25.0
30.0
35.0
6.17 0.73 2.64
P8, lbsMeasured, lbs
TP, l
bs
Precipitation, in
Pond 2.12 (2010) Woodbridge Pond (2011)
0.0
5.0
10.0
15.0
20.0
25.0
30.0
35.0
3.83 1.4 0.94 0.57
P8, lbsMeasured, lbs
TP, l
bs
Precipitation, in
•Measured TP is taken from concentrations sampled between rain events.
•Concentrations are then multiplied by P8 generated flows to obtain measured loads.
“Bad” ponds: P-solubilization important – TP released from sediments up to 1,500 μg/L between storms
– Downstream TP mass contributions high: 59 - 11,284% more than P8)
“Good” ponds: Settling predicts performance
How to tell the difference?
– Sample all ponds? - Idea is impractical – Findings suggested simpler method to find “bad” ponds
2010-2011 Overview
2010 bad ponds
Rapid Assessment Protocol Tool
Field AssessmentPond:
Staff:
Date: Time:
Current Weather (temp., wind, cloud cover, precip.):
Precipitation over previous 5 days:
Wet or Dry Year, in general:
Pond Silted In:
Total Phosphorous, surface concentration: None to date
Quick Depth Measurements:
complete 5 quick measurements (nearest 1/2 foot) up to 3 feet
1 2 3 4 5
Pond Max Depth:
Pond Mean Depth:
Place a "X" below the appropriate category for each element below:Dominated by Macrophytes: < 25% 25-50% 50-75% >75%
X
General Smell: none weak moderate strong
X
Water Color: clear brown light green dark green
X
Bubble Release from Sediments: no few lots
X
Sediment Color: brown dark brown black
X
Sediment Smell (H2S): none weak moderate strong
X
Vegetation: dediduous tree canopy prairie grasses mowed grasses impervious
X X
Likelihood for P-Mobilization:
no
previously wet, becoming dry
No
High
Mitchell Wetland
JC & RS
8/11/2011 11:30
75, sunny, calm winds
Conditional formatting calculates score and
likelihood of P-mobilization between storms.
Preliminary Results: 2010 and 2011 TP vs. RAP Score
0.0000.1000.2000.3000.4000.5000.6000.7000.8000.900
0 1 2 3 4 5 6 7 8 9 10
Mea
n To
tal P
, mg/
L
RAP Score
2011 2010 TP Reference
2012 Study
• 2010-2011 results statistically weak
• Needed more ponds to test RAP and overall hypothesis
• 2012 about 60 ponds sampled in Eden Prairie, Chanhassen,
Minnetonka, Bloomington, Shorewood – District funded 4 samples June-September each pond
– District trained cities to take RAP score and TP samples.
– Cities: dedicated, hard work that made the study possible!
– District QA/QC 2012
– District statistical analysis planned for 2013
2012 Raw Data – Inter-storm pond TP concentrations
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
0 1 2 3 4 5 6 7 8 9 10
TP, m
g/L
RAP Score 150 μg/L NURP soluble TP mean
280 μg/L NURP soluble TP 90th percentile
Preliminary statistical analysis of 2012 results
RAP *Athayde, D.N., Shelly, P.E., Driscoll, E.D., Gaboury, D.,
G., B., 1983. Results of the Nationwide Urban Runoff
Program: Volume I - Final Report. In: Water Planning
Division., U.S.E.P.A., Washington, DC.
NURP* Soluble P
concentrations (µg/L):
Median: 130-170
90th percentile: 230-300
Next actions
• Statistical analyses:
– Refine RAP: ANOVA, factor weighting, significance
– Define pond populations: Are there meaningful pond categories?
• Pond improvement pilot program
Study conclusions to date
• Inter-storm phosphorus solubilization is substantial in some ponds
• RAP-P method appears to identify worst ponds
• TMDL implications?
• Method may assist evaluation of pond improvements
– SAFL iron filter: very promising technology
– Alum microfloc: Dick Osgood’s work at Regions Walk Pond (Shorewood)
– Other candidate methods
– Consider shifting from BMP to BAT view of stormwater ponds
Questions?
Further reading
Exploring the Limits of Asian Carp eDNA as
a Genetic Surveillance Tool
Lorin K. Hatch1, Kelly Baerwaldt2, Richard Lance2, Duane Chapman3, Jon J. Amberg4, Edward Perkins2, Meredith Bartron5,
Edmond Russo2
Minnesota Water Resources Conference 16-17 October 2012
1HDR Engineering, Inc., 2US Army Corps of Engineers, 3USGS-CERC, 4USGS-UMESC,
5USFWS-NEFC
eDNA: What is it?
Environmental DNA (eDNA): DNA from an organism detected in non-biological samples soil, air, and water
GOOD (Asian carp): sensitive, powerful, quick to collect samples, has been audited by EPA, easily modified, simple concept
BAD (Asian carp): Origin: unknown vectors, unknown shedding rates, unknown degradation
rates
Monitoring: depth to collect water, best location for collecting, best sampling scheme unknown
Processing: requires very good lab practices, time consuming, poor extraction efficiency, contamination issues, effects of inhibitors, cannot infer population size in sample
Literature on threatened/endangered/rare aquatic populations Bullfrogs, whales
eDNA is a recognized tool for monitoring populations in a variety of fields including monitoring for invasive species Zebra mussels, Asian carp
Though we don't have all the answers, it does provide useful information to guide implementation of other monitoring tools and to inform management decisions
So if we don’t know what eDNA means, why are we using it?
3
Monitoring and Rapid Response Plan eDNA Sampling Strategy
eDNA: genetic surveillance tool identified in 2012 ACRCC MRRP
MRRWG views positive eDNA results as an indicator of the possible presence of live Asian carp
2012 Monitoring Sites (60 samples/site):
Chicago Lock
North Shore Channel
Little Calumet River
Lake Calumet
2012 Results to date (as of 11 September)
Lake Calumet: 37 positive for silver carp
Little Calumet River: 3 positive for silver carp
North Shore Channel: 17 positive for silver carp
All negative for bighead carp
North Shore Channel 0 of 171 positive: bighead carp 17 of 171 positive: silver carp
2012 sampling period: Total Samples Collected and Processed: 825 Above Barrier Positive for Bighead Carp: 0 Above Barrier Positive for Silver Carp: 80
Lake Calumet 0 of 312 positive: bighead carp 37 of 312 positive: silver carp
Little Calumet River 0 of 171 positive: bighead carp 3 of 171 positive: silver carp
Justification for how we have designed ECALS Improve our understanding of factors contributing to positive or negative eDNA
results
Assessing alternate vectors for eDNA transport
Improving laboratory methods
Learning about impacts to DNA persistence
Calibrating tool: understanding of different AC loading levels
Ensuring that the laboratory methods are repeatable and consistent
Outcomes will improve our response to Asian carp eDNA results to be more efficient and effective
ECALS is designed to address many of the unknowns associated with this tool to increase its application for management
Resulting methods and products will be integrated into the MRRP
Environmental DNA Calibration Study (ECALS)
5
USACE-USGS-USFWS
10/30/2012 6
INTERAGENCY PARTICIPATION: USACE-ERDC: Vicksburg, MS USACE-LRD: Rock Island, IL & Cincinnati, OH USGS-UMESC: La Crosse, WI USGS-CERC: Columbia, MO USFWS-NEFC: Lamar, PA USFWS-Region 3: Bloomington, MN
Grass carp in Chicago Chinatown
Project Goal/Objectives ECALS
ECALS Goal: Improve understanding of eDNA results
Three Main Objectives:
Vectors: Investigate viable alternate sources of eDNA, assign probability to sources
Markers: qPCR, improve efficiency of sampling and processing, population estimate
Calibration: influence of environmental variables, shedding/loading rates, degradation rates, model eDNA transport in CAWS
How did it get there? Vectors (likelihood of each source)
How long has it been there? Loading Studies (shedding rate according to AC size, population number,
diet, spawning)
Degradation Studies (influence of temperature, light, etc.)
Hydromodel (water transport of eDNA from origin)
How can we improve eDNA as a tool? Markers (improved specificity, improved sensitivity, infer population size)
qPCR and sampling/processing methods (faster and cheaper, QAPP)
Consider a positive hit for silver carp eDNA in Lake Calumet…
8
Assess potential vectors of DNA distribution
Conceptual and Probabilistic model
Storm sewers
Carcass deposition
Sediment
Vessels (barges, recreational, agency)
Hull transport
Gear contamination
Piscivorous birds
Cormorant telemetry study (ERDC)
Cormorant /Pelican/Eagle lab study (USGS)
Vectors
10/30/2012 9
Results to Date: Vectors
Storm sewer experiments NOV 2011, JUN 2012 Chinatown experiments
YES, DNA can be transported via storm sewer and detected in eDNA samples
Results indicate signal at least one week later
Birds: feeding trials USGS working with Brookfield Zoo (Chicago -
pelicans, cormorants) and National Eagle Center (Wabasha - bald eagles)
Birds are fed silver carp diet then feces processed to detect presence of silver carp DNA
Eagles fed silver carp on June 25: eDNA found in feces more than 5 days after eating carp
Silver carp DNA detectable in feces 18 days post-deposit (with up to 160 F temperatures)
Double-crested Cormorants tagged with satellite transmitters; will be tracked through October Baker’s Lake (Barrington, IL): 15 Birds
TNC Emiquon Preserve (south of Peoria, IL): 15 Birds
Vectors: Piscivorous Birds Telemetry Study
11
6 Birds have remained close to the Lake Some have had large movements [>40 mi]
2 Birds moved to Canada
3 Birds moved east and spend time along Lake Michigan coast
One bird moved south to Lake Renwick
Baker’s Lake
10/30/2012 12
TNC Emiquon Preserve
10/30/2012 13
8 Birds have remained close to the Lake Some have had large movements [>40 mi]
2 Birds moved east close to Davenport, IA; another close to Springfield, IL
1 Bird moved east to Minneapolis/St. Paul, MN
2 Birds moved south to MO; one to St. Louis, the other to Jefferson City, MO
Large movements through the region
1 Bird moved far south and is now in MS About 65 mi north of Vicksburg!
Birds exhibited wide range of movements
Local movements to over 800 miles
Cloacal swabs also procured from each bird
Several positive for eDNA at Baker’s Lake
Summary: Cormorant Telemetry
10/30/2012 14
Vectors: What’s next?
A Probabilistic Model will facilitate Inference from eDNA Monitoring Data
Multiple potential sources and vectors of Asian carp eDNA in the CAWS confound inferences from eDNA monitoring results.
The probabilistic model will take the form of a Bayesian network that integrates various lines of evidence, including recent and ongoing ACRCC supported research efforts, to estimate:
The probability that each of the potential sources and vectors of eDNA in the CAWS is, in fact, an actual source of eDNA.
The probability that Asian carp are present in the CAWS given the evidence from eDNA monitoring studies and other lines of evidence.
GOAL: develop a model that is transferable to other locations along the Asian carp invasion front.
Vectors: What’s next?
Commercial fishing gear experiments
eDNA-contaminated nets may transfer eDNA from Asian carp-infested waters to the CAWS
Barge movements
Barges/boats from Asian carp-infested waters may transfer eDNA to the CAWS
Sediments
Sediments may act as a sink or source of eDNA (e.g. resuspension events)
Mud to Parks program
Dredging of Asian carp-infested waters and transport of sediments to CAWS may transfer eDNA
Markers
17
Sequence mitochondrial DNA genomes
First mitogenomic sequencing attempt looks good -- so far
10 silver carp and 4 bighead carp initial sequencing partially complete
Goal: 30 per species, samples from multiple locales
Develop qPCR marker
First two new markers being tested for accuracy and sensitivity
Look promising
Goal – suite of markers providing additional detection power, added info on sample age and population structure
Markers: Extraction methods
10/30/2012 18
Preliminary results (USGS): • Both markers were similarly sensitive • Qiagen kit appears to have improved DNA extraction efficiency (also cheaper)
After round-robin testing, may result in next change to QAPP
Optimized Sampling Protocol Experiment
Conducted in November and June 40 comparative samples for 3 methods
Validated surface sampling protocol Mid-column, lower column samples not as likely
to produce positive hits
Promising sieve-based field filtering results, tested centrifugation of samples (vs. filtering)
Extraction protocols
Field still using QAPP filtering protocol
Lab studies using centrifuging protocol
Results to Date: Calibration
10/30/2012 19
Alternative Extraction Protocols
20
USGS Centrifuging: - yields more DNA per volume sample - reduces sample processing time - reduces sample storage space
Conclusion: centrifuging used for lab samples. Still validating for field samples.
ERDC: Tested sieve filtering at different depths vs. surface grab samples (UND method). Conclusions: Surface sampling validated, but sieve sampling collected more eDNA (with more water). Study repeated in June with centrifuging added.
eDNA Methodology
eDNA Quality Assurance Project Protocol (QAPP) updated: MAY 2012 Incorporate processing improvements from ECALS
Decrease processing time by 2 days, save $4K/batch
Vessel protocols (vectors) Objective: no AC moving upstream over barrier on
vessels to eliminate potential to distort eDNA results
USACE protocol for lock staff implemented
Guidelines for commercial and recreational vessels
QAPP and Vessel Guidelines available on www.asiancarp.us
2012 Incidents of Asian Carp transported on upstream-bound barges
12 April Silver carp on barge at mouth of Calumet River Reported by City of Chicago
10 April Two silver carp on deck of tow at Lockport Lock and Dam Reported by Pat Wharry, Lockmaster
8 June Silver carp on TWO barges locking upstream at Brandon Road Lock and Dam Reported by Steve Koenig, Lockmaster
USFWS Transition in 2013
La Crosse Fish Health Center (WI)
Capacity: process at least 120 samples per week, emphasis on samples from the CAWS and other potential Great Lakes hotspots
July 2012: FWS hired lead geneticist
Nov 2012: FWS completes construction of the eDNA lab
August 2012 - April 2013: Transition of methodology
May 2013: USFWS lab up and running and ready to process eDNA samples, USACE remains on team for support and consulting
GOOD: sensitive, powerful, quick to collect samples, has been audited by EPA, easily modified, simple concept, faster,
efficient, reliable, known vectors and background signals,
known shedding, infer population size, degradation rate and
method of degradation (infer recency)
BAD:
Origin: unknown vectors
Monitoring: best sampling scheme unknown
Processing: requires very good lab practices, effects of inhibitors
Impact of ECALS
THANK YOU
Jon Amberg Duane Chapman Mark Gaikowski
Katy Klymus Sunnie McCalla Cathy Richter
Meredith Bartron Shannon Jilian Kurt Schilling
Kelly Baerwaldt Matthew Carr
Mike Channell Christine Edwards Heather Farrington
Richard Fischer Xin Guan
USACE
For more information please visit www.asiancarp.us/ecals
Michael Guilfoyle Jack Killgore
Richard Lance Edward Perkins Edmund Russo
David Schulenberg Martin Schultz David Smith
USGS USFWS
Lorin Hatch Lorin.Hatch@hdrinc.com