Assessing and Addressing the re-Eutrophication of Lake Erie Don - - PowerPoint PPT Presentation

Assessing and Addressing the re-Eutrophication of Lake Erie

Don Scavia

Donald Scavia, J. David Allan, Kristin K. Arend, Steven Bartell, Dmitry Beletsky, Nate

• S. Bosch, Stephen B. Brandt, Ruth D. Briland, Irem Daloğlu, Joseph V. DePinto, David
• M. Dolan, Mary Anne Evans, Troy M. Farmer, Daisuke Goto, Haejin Han, Tomas O.

Höök, Roger Knight, Stuart A. Ludsin, Doran Mason, Anna M. Michalak, J.I. Nassauer,

• R. Peter Richards, James J. Roberts, Daniel K. Rucinski, Edward Rutherford, David J.

Schwab, Timothy Sesterhenn, Hongyan Zhang, Yuntao Zhou

University of Michigan, Purdue University, Grace College, Ohio State University, Heidelberg University, University of Wisconsin-Green Bay, University of Wisconsin-Madison, LimnoTech, Oregon State University, Korea Environment Institute, Carnegie Institute for Science, Ohio Department of Natural Resources, USGS, NOAA Physical Scientists, Ecologists and Chemists, Physical and Ecological Modellers, Engineers, Social Scientists, Practitioners

The Team

What is hypoxia?

(aka the Dead Zone)

W ind Upper w arm , w ell m ixed epilim nion Low er colder, poorly m ixed hypolim nion therm ocline Radiant energy Sedim entation of Organic Matter Decom posing organic m atter consum es O2

Oxygen Flux W ell Mixed Stratified Hypoxia = “Dead Zones”

Tem perature

Length: 241 miles Breadth: 57 miles Average Depth: 19 m M aximum Depth: 64 m Volume: 116 cubic miles Shoreline Length: 871 miles Water Surface Area: 9,910 square miles Watershed: 30,140 square miles Flushing Time: 2.6 years Population: 10.5 million U.S. 1.9 million Canada

Lake Erie:

Southern most, warmest, and most productive Great Lake

“Walleye Capital of the World”

Special Physical Characteristics

Central Basin anoxia over time

10 20 30 40 50 60 70 80 1955 1960 1965 1970 1975 1980 1985

% Anoxia

Central Basin Anoxia (no oxygen)

Increased through 1970s (phosphorus pollution) Decreased following GL WQA-based clean-up

Classic Success Story

Central Basin Hypoxia (DO< 2 mg/ l)

Downward trend continued through the mid-1990s Then a resurgence

Zhou et al. 2012

Scavia et al. (in review)

Western Basin Algal Booms

Similar trend through the mid-1990s

Then a resurgence

M assive 2011 Toxic Bloom

Thickness of Central Basin Bottom Layer

Air temperature, winds, length of season

Organic M atter Flux to the Bottom

Algal production and settling

– P supply – Length of season

What M atters to Hypoxia?

Air temperature, winds, length of season Algal production and settling

– P supply – Length of season

What M atters to Algal Blooms?

Thickness of Central Basin Bottom Layer

Air temperature, winds, length of season

Organic M atter Flux to the Bottom

Algal production and settling

– P supply – Length of season

What M atters?

Thinner Bottom Layer?

=> Less O2 Available

y = 0.0264x - 71.129 R² = 0.1223 y = -0.0109x + 16.977 R² = 0.0363

• 22
• 17
• 12
• 7
• 2

3

1970 1975 1980 1985 1990 1995 2000 2005 2010

Thermocline Depth and Stratification Strength

• D. Beletsky et al

No clear evidence through 2005

Rucinski et al. 2010

Water Column Oxygen Depletion Rate

Rucinski, et al 2010

Thickness of Central Basin Bottom Layer

Air temperature, winds, length of season

Organic M atter Flux to the Bottom

Algal production and settling

– P supply – Length of season

What M atters?

Dolan and Chapra 2012

GL WQA led to successful reduction in P loads

Reached the 11,000 M T target quickly M ostly point source reductions Remaining loads dominated by non-point sources

• D. Baker, Heidelberg

The Trends Particulate Phosphorus

Particulate Phosphorus, Maum ee 0.1 0.2 0.3 0.4 0.5 0.6 1970 1975 1980 1985 1990 1995 2000 2005 2010 Flow -w eighted Mean Concentration ( m g/ L) Particulate Phosphorus, Sandusky 0.1 0.2 0.3 0.4 0.5 0.6 1970 1975 1980 1985 1990 1995 2000 2005 2010 Flow -w eighted Mean Concentration ( m g/ L)

P . Richards, Heidelberg

M aumee River Sandusky River

The Trends in Dissolved Reactive P

SRP

Dissolved Reactive Phosphorus, Maum ee 0.02 0.04 0.06 0.08 0.1 0.12 1970 1975 1980 1985 1990 1995 2000 2005 2010 Flow - w eighted Mean Concentration ( m g/ L) Dissolved Reactive Phosphorus, Sandusky 0.02 0.04 0.06 0.08 0.1 0.12 0.14 1970 1975 1980 1985 1990 1995 2000 2005 2010 Flow - w eighted Mean Concentration ( m g/ L)

M aumee River Sandusky River

P . Richards, Heidelberg

Rucinski et al 2010 DRP Load O2 depletion rate

Water Column Oxygen Depletion Rate

Richards 2012

High-resolution SWAT model the Sandusky Watershed

• I. Daloglu

Observed DRP Load

100 200 300 400 500 600 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 SRP (kg/ year)

P . Richards 4-year M oving Average

Baseline

Representative :

• Tillage practices
• Fertilizer inputs
• Crop choices
• Fertilizer timing
• Soil P accumulation

in topsoil

100 200 300 400 500 600 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 4 per. M ov. Avg. (Baseline-KD8) 4 per. M ov. Avg. (Observed)

Baseline Observed

Calibrated and validated with

• bserved Sandusky DRP loads

Han and Allan 2010

How about fertilizer use trends?

0.0 50.0 100.0 150.0 200.0 250.0 300.0 350.0 1974-1981 1982-1986 1987-1991 1992-1996 1997-2001 2002-2006 2007-2010 Fertilizer inputs kg/ ha 11-52-00 11-52-00 00-15-00

Fertilizer application rate scenario: Little impact on trend

100 200 300 400 500 600 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 4 per. M ov. Avg. (Baseline-KD8) 4 per. M ov. Avg. (constantfertKD8)

Baseline Constant fertilizer

Tillage practices scenario: Increased conservation tillage

20000 40000 60000 80000 100000 Acres 1989 1991 1993 1995 1997 1999 2001 2003 2005

Sandusky County Conservation Tillage

MTWH NTWH MTSB NTSB MTCN NTCN

Tillage practices scenario: Appears to have some impact

100 200 300 400 500 600 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 4 per. M ov. Avg. (Baseline-KD8) 4 per. M ov. Avg. (convKD8) 4 per. M ov. Avg. (notillKD8)

100% conventional Baseline 100% notill

100 200 300 400 500 600 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 4 per. M ov. Avg. (Baseline-KD8) 4 per. M ov. Avg. (Observed)

Observed Baseline

Is this because of the P accumulation at topsoil?

Surface application of P fertilizer and manure Fertilizer application exceeding crop needs Adoption of conservation tillage Soil stratification

Is this because of the P accumulation at topsoil?

100 200 300 400 500 600 1970 1975 1980 1985 1990 1995 2000 2005 2010

Higher values allow phosphorus to accumulate at topsoil Lower values allow more P runoff/ vulnerability

Introduce No-Till

M odified topsoil SRP: runoff concentration ratio in the SWAT model

100 200 300 400 500 600 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 4 per. M ov. Avg. (Baseline-KD8) 4 per. M ov. Avg. (baseline)

Baseline Constant PHOSKD

Simulated SRP Load Appears to be a significant factor

But …

2 4 6 8 10 12

1965 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 Number of storm events

Lake Erie Extreme Precipitation

Sandusky Watershed

Random weather scenario

50 100 150 200 250 300 350 400 450 500 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015

Actual Weather Random Weather

100 200 300 400 500 600 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 4 per. M ov. Avg. (Baseline-KD8)

Actual Weather

Reversed weather scenario

100 200 300 400 500 600 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 4 per. M ov. Avg. (Reversed weather) 4 per. M ov. Avg. (Baseline-KD8)

Actual Weather Reversed Weather

Reversed weather scenario

Weather matters, but interacts with land-based conditions

Simulated SRP Load

Watershed appears more vulnerable to weather impacts in recent years. Soil P accumulation and tillage and fertilizing practices appear to underlie the weather driver. Change in overall fertilizer rates shift load but do not seem to drive the pattern.

100 200 300 400 500 600 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 4 per. M ov. Avg. (Baseline-KD8) 4 per. M ov. Avg. (Observed)

Observed Baseline

But, if weather matters …

… where are we heading?

Hayhoe et al. 2010 Predicted Storm Intensity

Do Phosphorus Loads Matter?

Hypoxia duration (days)

40 50 60 70 80 90 100

Smelt relative abundance (number / trawl min)

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5

1987-2005 R2 = 0.46

Ludsin, Pangle et al.

Hypoxia duration (days)

40 50 60 70 80 90 100

Smelt commercial harvest (metic tonnes)

2000 4000 6000 8000 10000

1987-2005 R2 = 0.64 Smelt: Commercial Fisheries Hypoxia: Water Quality M odel

Vertical Distributions under Strong Hypoxia

Daily M ax. Density 75% 50% 25% 0% Depth (m)

Rainbow Smelt Y ellow Perch Hypoxia OFF

10 5 15 20

M ulti-species, 1-D individual-based model

Höök et al

Oxy-thermal Squeeze

T emperature Oxygen

10 5 15 20 Daily M ax. Density 75% 50% 25% 0%

Depth (m)

Jun Jul Aug Sep Oct Nov

Rainbow Smelt, Strong Hypoxia, Baseline

M ulti-species, 1-D individual-based model

Höök et al

So, can we reset P load targets?

Great Lakes Water Quality Agreement 2012 Protocol

Build M ixing M odel

Hypolimnion Epilimnion

Diffusion WCOD SOD

M odel Data

2005

Rucinski et al. 2010

Rucinski et al. 2013

Eutrophication M odel

M odel Calibration

Observations M odel Rucinski et al. 2013

M odel-derived Response Curve

Envelop encompasses interannual weather variability

46% reduction Scavia et al. in review

M odel-derived Response Curve

Based on Dissolved Reactive Phosphorus (DRP)

78% reduction Scavia et al. in review

Fish Response Curves Rapid improvement when approaching same 4200 M T load Scavia et al. in review

Setting targets is “easy” M eeting them is a …

How can we reduce the TP load by 46% (or 3600 M T) ?

Very Hard Very Expensive Scavia et al. in review

Rondeau Ashtabula- Chagrin Ottawa-Stony

W here is that P com ing from ?

Han et al. 2012

M ost significant sources:

Detroit, M aumee, Sandusky, and Cuyahoga Rivers

Scavia et al. in review

Relative importance of individual P sources

Fertilizer is the largest P input to both U.S. and Canada agricultural watersheds Anim al m anure is the largest P input to the agricultural watersheds of Ontario Hum an loading is the largest P input for urbanized watersheds

Han et al. 2012

SWAT models for all M ajor Watersheds

Bosch et al 2013

SWAT model identified sub-watershed “hot spots” of high TP and DRP yields.

Scavia et al. in review

• Ca. 35% of DRP and

40% of TP come from 25% of the M aumee and Sandusky watersheds.

M aumee River Watershed

“Feasible”

The new Farm Bill!?

Bosch et al. 2013

The “tough nut”

Environmental conservation in U.S. Agriculture is voluntary and incentive-based So, we need to find ways to get more adoption

• f those BM P practices

What drives those decisions (besides $$)? What are those BM Ps?

Conservation practices Non-structural

62

Conservation practices Structural

63

Conservation practices Land retirement programs

64

Conservation practices Nutrient M anagement Plans

65

A Social-Ecological-System M odel

Social M odel

– Farmer adoption of conservation practices – M odeling tool: ABM

Water quality modeling

– Sandusky watershed, OH – Phosphorus modeling – M odeling tool: SWAT

Daloğlu et al. in review

The Framework

1. Build farmer typology of conservation practice adoption 2. M odel adoption of conservation practices by farmers 3. M odel water quality (P forms) in Sandusky to understand impacts of adoption on water quality 4. Evaluate and modify policies to improve water quality metrics 1 2 3 4

1 2 3 4

Daloğlu et al. in review

Farmer Typology

Policy-relevant

farmer characteristics explaining adoption

• f conservation

practices

1

1

68

Agent-based modeling, ABM

Typology:

• Traditional farmers
• Supplementary farmers
• Business-oriented farmers
• Non-operator landowners: Absentee landowners, investors

Policy relevant variables:

• Land tenure arrangements
• Source of income
• Farm size
• Information network

Daloğlu et al. in review

Characteristics Traditional Supplementary Business-

• riented

Non-operator

• wners

Farm size Small Small Large N/A Land tenure Full owner Full / Part owner Part owner Non-operator

• wner

Source of income On-farm Off-farm On-farm Off-farm Information network M oderately connected M oderately connected M ost connected Least connected Preferred Conservation Practices Non-structural Land retirement Non-structural Structural Land retirement Non-structural Structural Structural Land retirement

Farmer typology

Agent-based modeling, ABM

• Decentralized,

bottom-up decision

making and

interactions

• Agents have

– Characteristics – Preferences – M emory of recent

events

– Social connections

2

2

Daloğlu et al. in review

Plausible Scenarios

1. Changes in land tenure dynamics increasing involvement

• f non-operator owners

2. Changes in agricultural policy replacing commodity payments with crop revenue insurance

NON-OPERATOR INVOL VEM ENT CROP REVENUE INSURANCE NO YES NO YES 1 Baseline Simplified existing land tenure and policy context 2 Non-operator owner involvement Increased non-operator owner involvement in decisions 3 Crop revenue insurance Only operators are decision makers; crop revenue insurance is available 4 Crop revenue insurance with non-

• perator owner involvement

Both operators and non-operators owners are decision makers’; crop revenue insurance is available

Then Irem “did stuff”

ABM Results

Scenario 1: Baseline

Traditional, supplementary and business-oriented farmers are the decision makers Compared to observed adoption rates

74

0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1970 1980 1990 2000 2010

Envelopes from M onte Carlo simulations based on preference distributions “no-till” Land Retirement

• Non-operator owners -- absentee

landowners and investors are active decision makers

ABM Results

Scenario 2: Non-operator involvement

75

Baseline

0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1970 1980 1990 2000 2010 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1970 1980 1990 2000 2010 No practice Non-structural Structural Land Retirement Nutrient M gt

• favor structural

and land retirement practices

• Potential to increase

water quality

• Decreases structural practices with owner
• perator (Scenario 3)
• Somewhat mitigated with non-operator owner

involvement (Scenarios 4); but decreased land retirement

Scenario 3: Only operators

• Homogenous conservation landscape, in the

absence of conservation compliance

ABM Results

Scenarios 3 & 4: Crop insurance

76

0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1970 1980 1990 2000 2010 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1970 1980 1990 2000 2010

Scenario 4: Both operators and non-operator owners

0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1970 1980 1990 2000 2010

20-year avg adoption rate: 9.8 % 20-year avg adoption rate: 16.5 %

Baseline

Implementation Results

Non-operator involvement – increases structural and land retirement

adoption

Crop revenue insurance – homogenous conservation landscape with

production focus

77

Water quality modeling - SWAT

River basin scale model

Inputs: – Land-use and

management practices from the ABM

– Digital elevation

model, climate data, soil profiles

Outputs: – Loads of N, P

, and sediment to Lake Erie

3

3

Daloglu et al., 2012

Changes in Water Quality M etrics

Comparison to the baseline (Scenario 1)

Non-operator involvement Crop revenue insurance Only operators Non-operator Involvement

Daloglu et al., in review

Tie Crop Insurance to Conservation Compliance?

– Current focus on non-

structural practices (conservation tillage and no-till)

– Expand and strengthen

compliance definition to include structural practices

80

Framework – with Compliance

Operators are decision makers Operators and non-operators are decision makers

Crop revenue insurance is tied to:

Non- structural Non- structural

Structural Structural

Daloglu et al., in review

Policy Implications

Nutrient and sediment loads reduction is highest when

non-operator owners had active role

Subsidized crop insurance without conservation compliance increased nutrient and sediment loads But, requiring conservation compliance and expanding it

to include structural practices reduces nutrient runoff

Wrap Up

• Original GLWQA “solved” Lake Erie hypoxia problem
• Algae blooms and hypoxia returned in mid-1990s

Serious issues, putting people and fisheries at risk

• Driven by increased in DRP load (TP load stable)
• DRP Increase likely from farm practices and storms

Storms not as important under previous practices

• Need to increase BM Ps to reduce loads

Probably need new BM Ps to address DRP

• Need new incentives to encourage BM P adoption

Potential new focus on non-operator owners

• Farm Bill insurance program can help or hurt

Need cross-compliance with conservation programs

Tomas Hook, Purdue Dave Schwab, NOAA Ed Rutherford, NOAA Doran M ason, NOAA J

• e DePinto, LimnoTech

Stuart Ludsin, Ohio State Dave Baker, Heidelberg Dave Allan, Univ of M ichigan Pete Richards, Heidelberg Tom Bridgeman, Univ of Toledo Dima Beletsky, Univ of M ichigan Dave Dolan, Univ of Wisconsin

M ost of the Team

Funding: NOAA-Ecofore, NSF Water Sustainability and Climate, U-M Graham Institute

Dan Rucinski Yuntao Zhou Irem Daloglu

Students who actually do my work!

M yriam Wright Kyung Hwa Cho

Assessing and Addressing the re-Eutrophication of Lake Erie

Thanks!