Shaping Lake Erie Agriculture Nutrient Management through a

Multi-Model Approach

Margaret Kalcic, Rebecca Logsdon Muenich, Donald Scavia, Noel Aloysius, Jeffrey Arnold, Jay Atwood, Chelsie Boles, Remegio Confesor, Joseph DePinto, Marie Gildow, Jay Martin, Todd Redder, Dale Robertson, Scott

Sowa, Michael White, Haw Yen

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Eutrophication of Lake Erie

2011 extreme algal bloom in the western basin of Lake Erie

Phosphorus loading from Lake Erie tributaries drives seasonal hypoxia and harmful algal blooms (HABs)

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A brief history…

• 1970s: target load of 11,000 MT of total phosphorus (TP) to Lake Erie (Great Lakes Water Quality Agreement)

• Point sources (e.g. waste treatment plants) met the first target goal in 1981, and annual TP loads have generally met the goal since

• Despite meeting the goal, algal blooms have returned with severity rivaling the 1970s

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Western Lake Erie watersheds

• The Maumee River is the main contributor of TP and DRP causing western Lake Erie HABs

• Intensively managed agriculture

• Extensive subsurface tile-drainage

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Why Lake Erie re-eutrophication?

• Most likely non-point source pollution, including farm practices, which are primarily unregulated

• Dissolved reactive phosphorus (DRP) loading has increased (Maumee loading from ~200 MT (1985) to > 600 MT)

• Invasion of quagga mussels (discovered 1989 in Erie)

• Climate changes and other drivers?

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• Setting new P load targets for “spring” (March-July) loading from the western Lake Erie watersheds

• 2012 Great Lakes Water Quality Agreement Protocol, Annex 4 draft recommendations

What can we do about it?

Maumee River

Watershed

Western Lake

Erie Basin

DRP 186 MT 40% of 2008

TP 860 MT 40% of 2008

Spring (March-July) targets:

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Project goal

• Use the Maumee Watershed as a surrogate for addressing non-point source control

• Select a suite of management scenarios that can be tested at watershed scales

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Working Group Members

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• Each model has strengths & weaknesses

– All models are wrong, some are useful…

• Internal modeling decisions can be subjective

– Even using the "same" model

• Builds confidence in advice

– To the extent the models roughly agree!

• Multiple models and setups increase our ability to capture range of outcomes

Why a multi-model approach?

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1. Identify previously-calibrated full-watershed models capable of testing scenarios

2. With common meteorology and point source loads, validate all models for the same baseline (2005-2014)

3. Run agricultural non-point source scenariosto explore what would be needed to meet targets

Approach

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1. Identify calibrated models

– Don Scavia

– Margaret Kalcic

– Rebecca Logsdon Muenich

– Jay Martin

– Noel Aloysius

– Marie Gildow

– Rem Confessor

UM SWAT

OSU SWAT

HU SWAT

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– Joe DePinto

– Todd Redder

– Chelsie Boles

– Jeff Arnold

– Scott Sowa

– Jay Atwood

– Mike White

– Haw Yen

– Dale Robertson

LT SWAT

ARS/TNC SWAT

USGS SPARROW

1. Identify calibrated models

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ARS/TNC UM OSU HU LT

FlowNS 0.91 0.89 0.91 0.88 0.90

PBIAS 1.23 5.58 9.97 -0.50 9.53R2 0.91 0.91 0.93 0.88 0.91

TPNS 0.72 0.70 0.73 0.57 0.82

PBIAS -17.67 6.94 -6.63 21.70 -5.56R2 0.78 0.70 0.75 0.69 0.82

DRPNS -0.12 0.46 0.51 -0.03 0.71

PBIAS 89.06 -12.76 16.11 76.69 1.48R2 0.78 0.51 0.54 0.59 0.71

sedimentNS 0.76 0.87 0.69 0.66 0.19

PBIAS 24.06 11.15 -4.59 -17.96 -68.44R2 0.83 0.88 0.69 0.68 0.83

TNNS 0.24 0.73 0.23 0.51 0.54

PBIAS 42.87 3.74 -52.45 -33.06 15.97R2 0.82 0.77 0.58 0.62 0.75

NitrateNS 0.14 0.39 0.42 0.47 0.21

PBIAS 20.72 5.79 -37.88 -9.95 21.98R2 0.65 0.62 0.57 0.61 0.65

2. Validate baseline models

• Common met. and point sources

• Monthly validation at Waterville (near outlet)

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Hydrology (mm)

ARS UM OSU HU LTPrecipitation 966 975 973 975 976

Snow Fall 103 61 106 67 106Surface Runoff 224 166 191 292 195

Tile Flow 89 135 110 60 139ET 630 598 567 612 571

PET 1046 1092 1045 1428 1009

Nutrients (kg/ha)

ARS UM OSU HU LTSol P Tile 1.055 0.083 0.157 0.287 0.068

N Fert App 62 82 63 56 59P Fert App 10 13 21 18 12Initial MinP 2276 894 7810 3936 3708FinalMinP 2164 793 7764 4032 3623Initial OrgP 1846 1455 33 412 1676FinalOrgP 1872 1526 89 368 1705

OrgP in Fert 0.00 1.46 0.00 0.00 0.16Δ MinP in soil 112 101 46 -96 85Δ OrgP in soil -26 -71 -56 44 -29

Crop Yields (t/ha)

ARS UM OSU HU LTCorn 9.1 9.5 8.3 6.7 7.5

Soybean 2.3 2.5 2.5 1.3 2.6Wheat 3.9 5.0 4.2 4.1 2.4

2. Validate baseline models

• Other comparisons:

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2. Validate baseline models

0

0.5

1

1.5

2

2.5

3

3.5

4x 10

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Data or Model

Comparison of Spring TP Loads Across Models

Spri

ng T

P L

oad

(kg)

Observed ARS/TNC UM OSU HU LT

• Spring TP loading near outlet (2005-2014)

Annex 4 target load

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2. Validate baseline models

0

1

2

3

4

5

6

7

8

9x 10

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Data or Model

Comparison of Spring DRP Loads Across Models

Spri

ng D

RP

Loa

d (

kg

)

Observed ARS/TNC UM OSU HU LT

• Spring DRP loading near outlet (2005-2014)

Annex 4 target load

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2. Validate baseline models

0

1

2

3

4

5

6

7x 10

6

Data or Model

Comparison of TP Loads Across Models

Ann

ual

TP

Lo

ad

(kg

)

Observed ARS/TNC UM OSU HU LT USGS

• Annual TP loading near outlet (2004-2015)

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2. Validate baseline models

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ARS/TNC HU

TP (kg / ha)

0.00 – 0.73

0.73 – 1.04

1.04 – 1.49

1.49 – 2.16

2.16 – 7.65

SPARROW

• Annual TP hotspot maps: delivery to the lake

OSUUM LT

2. Validate baseline models

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• Annual DRP hotspot maps: delivery to the lake

UM OSU LTDRP (kg / ha)

0.00 – 0.13

0.13 – 0.18

0.18 – 0.23

0.23 – 0.30

0.30 – 2.08

DRP (kg / ha)

0.00 – 0.21

0.21 – 0.35

0.35 – 0.57

0.57 – 0.98

0.98 – 2.08

HU DRP (kg / ha)

0.08 – 0.38

0.38 – 0.49

0.49 – 0.58

0.58 – 0.71

0.71 – 1.57

ARS/TNC

3. Run scenarios: Spring TP results

Annex 4 target load

Mo

del

bia

s

Percent improvement in P loading

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3. Run scenarios: Spring DRP results

Annex 4 target load

Mo

del

bia

s

Percent improvement in P loading

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3. Run scenarios: Annual TP results

Mo

del

bia

s

Percent improvement in P loading

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Main findings so far

• Nutrient management effective (place and rate)

• Perennial cover crops are likely to reduce TP and DRP

• Drainage water management and no-tillage, as simulated, were not effective across models

• Filter strips (with optimum functionality) and wetlands were quite effective

• Point source treatment cannot reach targets

• Year-to-year climate variability also drives P loading; unlikely to reach targets every year 23

Potential next steps

• Bundling of scenarios

• Incorporating climate change

• Targeting practices to hotspots

• Optimization of practices

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