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Uncertainty in Forest Carbon and Nutrient Budgets Ruth D. Yanai State University of New York College of Environmental Science and Forestry Syracuse NY 13210, USA

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 Uncertainty in Forest Carbon and Nutrient Budgets

Ruth D. Yanai

State University of New YorkCollege of Environmental Science and Forestry

Syracuse NY 13210, USA

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Quantifying uncertainty in ecosystem budgetsPrecipitation (evaluating monitoring intensity)Streamflow (filling gaps with minimal uncertainty)Forest biomass (identifying the greatest sources of uncertainty)Soil stores (detectable differences)

QUANTIFYING UNCERTAINTY IN ECOSYSTEM STUDIES

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UNCERTAINTY

Natural Variability

Spatial Variability

Temporal Variability

Knowledge Uncertainty

Measurement Error

Model Error

Types of uncertainty commonly encountered in ecosystem studies

Adapted from Harmon et al. (2007)

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Bormann et al. (1977) Science

How can we assign confidence in ecosystem nutrient fluxes?

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Bormann et al. (1977) Science

The N budget for Hubbard Brook published in 1977 was “missing” 14.2 kg/ha/yr

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Net N gas exchange = sinks – sources = - precipitation N input+ hydrologic export+ N accretion in living biomass+ N accretion in the forest floor ± gain or loss in soil N stores- weathering N input

The N budget for Hubbard Brook published in 1977 was “missing” 14.2 kg/ha/yr

14.2 ± ?? kg/ha/yr

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The N budget for Hubbard Brook published in 1977 was “missing” 14.2 kg/ha/yr

14.2 ± ?? kg/ha/yr

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Measurement Uncertainty Sampling UncertaintySpatial and Temporal Variability

Model Uncertainty

Error within models Error between models

Volume = f(elevation, aspect): 3.4 mm

Undercatch: 3.5%Chemical analysis: 0-3%

Model selection: <1%

Across catchments:

3%

Across years:

14%

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We tested the effect of sampling intensity by sequentially omitting individual precipitation gauges.

Estimates of annual precipitation volume varied little until five or more of the eleven precipitation gauges were ignored.

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The N budget for Hubbard Brook published in 1977 was “missing” 14.2 kg/ha/yr

14.2 ± ?? kg/ha/yr

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The N budget for Hubbard Brook published in 1977 was “missing” 14.2 kg/ha/yr

14.2 ± ?? kg/ha/yr

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Yanai, Levine, Green, and Campbell (2012) Journal of Forestry

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Don Buso HBES

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Gaps in the discharge record are filled by comparison to other streams at the site, using linear regression.

S5

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Cross-validation: Create fake gaps and compare observed and predicted discharge

Gaps of 1-3 days: <0.5%Gaps of 1-2 weeks: ~1%

2-3 months: 7-8%Yanai et al. (2014) Hydrological Processes

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Net N gas exchange = sinks – sources = - precipitation N input (± 1.3)+ hydrologic export (± 0.5)+ N accretion in living biomass + N accretion in the forest floor± gain or loss in soil N stores

The N budget for Hubbard Brook published in 1977 was “missing” 14.2 kg/ha/yr

14.2 ± ?? kg/ha/yr

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Net N gas exchange = sinks – sources = - precipitation N input (± 1.3)+ hydrologic export (± 0.5)+ N accretion in living biomass + N accretion in the forest floor± gain or loss in soil N stores

The N budget for Hubbard Brook published in 1977 was “missing” 14.2 kg/ha/yr

14.2 ± ?? kg/ha/yr

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Tree Inventory

log(Height) = a + b*log(Diameter) ± errorlog (Mass) = a + b*log(1/2 r2 *Height) ± error

Nutrient content = Mass * (Concentration ± error)Sum all trees and all tissue types

Allometric Equations

and Nutrient Concentrations

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Monte Carlo

Simulation

Yanai, Battles, Richardson, Rastetter, Wood, and Blodgett (2010) Ecosystems

Monte Carlo simulations use random sampling of the distribution of the inputs to a calculation. After many iterations, the distribution of the output is analyzed.

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Repeated Calculations of N in Biomass

Hubbard Brook Watershed 6

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611 ± 54 kg N/ha

Nitrogen Content of Biomasswith Uncertainty

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***IMPORTANT***

Random selection of parameter values applies across all the trees and all the time periods in each iteration.

The uncertainty between two measurements can be less than in a single measurement!

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100 Simultaneous Calculations of N in Biomass in 1997 and 2002

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100 Simultaneous Calculations of N in Biomass in 1997 and 2002

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Accumulation Rate of N in Biomass

Distribution of Estimates

18 ± 5 kg N/ha over 5 yr

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C1 C2 C3 C4 C5 C6 HB-Mid JB-Mid C7 C8 C9 HB- Old JB-Old

Young Mid-Age Old

Biomass of thirteen standsof different ages

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C1 C2 C3 C4 C5 C6 HB-Mid JB-Mid C7 C8 C9 HB- Old JB-Old

3% 7% 3%

4% 4% 3% 3% 3%

3% 2% 4% 4% 5%

Coefficient of variation (standard deviation / mean)of error in allometric equations

Young Mid-Age Old

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C1 C2 C3 C4 C5 C6 HB-Mid JB-Mid C7 C8 C9 HB- Old JB-Old

Young Mid-Age Old

3% 7% 3%

4% 4% 3% 3% 3%

3% 2% 4% 4% 5%

CV across plots within stands (spatial variation)Is greater than the uncertainty in the equatsions

6% 15% 11%

12% 12% 18% 13% 14%

16% 10% 19% 3% 11%

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Yanai, Levine, Green, and Campbell (2012) Journal of Forestry

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Net N gas exchange = sinks – sources = - precipitation N input (± 1.3)+ hydrologic export (± 0.5)+ N accretion in living biomass (± 1)+ N accretion in the forest floor± gain or loss in soil N stores

The N budget for Hubbard Brook published in 1977 was “missing” 14.2 kg/ha/yr

14.2 ± ?? kg/ha/yr

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Net N gas exchange = sinks – sources = - precipitation N input (± 1.3)+ hydrologic export (± 0.5)+ N accretion in living biomass (± 1)+ N accretion in the forest floor± gain or loss in soil N stores

The N budget for Hubbard Brook published in 1977 was “missing” 14.2 kg/ha/yr

14.2 ± ?? kg/ha/yr

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Oi

Oe

Oa

E

Bh

Bs

ForestFloor

MineralSoil

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Nitrogen in the Forest FloorHubbard Brook Experimental Forest

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Nitrogen in the Forest FloorHubbard Brook Experimental Forest

The change is insignificant (P = 0.84).The uncertainty in the slope is ± 22 kg/ha/yr.

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Net N gas exchange = sinks – sources = - precipitation N input (± 1.3)+ hydrologic export (± 0.5)+ N accretion in living biomass (± 1)+ N accretion in the forest floor (± 22)± gain or loss in soil N stores

The N budget for Hubbard Brook published in 1977 was “missing” 14.2 kg/ha/yr

14.2 ± ?? kg/ha/yr

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Studies of soil change over time often fail to detect a difference.We should always report how large a difference is detectable.

Yanai et al. (2003) SSSAJ

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Power analysis can be used to determine the difference detectable with known confidence

Yanai et al. (2003) SSSAJ

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Sampling the same experimental units over time permits detection of smaller changes

Yanai et al. (2003) SSSAJ

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In this analysis of forest floor studies, few could detect small changes

Yanai et al. (2003) SSSAJ

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Net N gas exchange = sinks – sources = - precipitation N input (± 1.3)+ hydrologic export (± 0.5)+ N accretion in living biomass (± 1)+ N accretion in the forest floor (± 22)± gain or loss in soil N stores

The N budget for Hubbard Brook published in 1977 was “missing” 14.2 kg/ha/yr

14.2 ± ?? kg/ha/yr

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Nitrogen Pools (kg/ha)Hubbard Brook Experimental Forest

Forest Floor

Live Vegetation

Coarse Woody Debris

Mineral Soil10 cm-C

Dead Vegetation

Mineral Soil0-10 cm

Yanai et al. (2013) ES&T

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Quantitative Soil Pits0.5 m2 frame

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Excavate Forest Floor by horizonMineral Soil by depth increment

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Sieve and weigh in the fieldSubsample for laboratory analysis

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In some studies, we excavate in the C horizon!

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We can’t detect a difference of 730 kg N/ha in the mineral soil.

From 1983 to 1998, 15 years post-harvest, there was an insignificant decline of 54 ± 53 kg N ha-1 y-1

Huntington et al. (1988)

Yanai et al. (2013) ES&T

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Net N gas exchange = sinks – sources = - precipitation N input (± 1.3)+ hydrologic export (± 0.5)+ N accretion in living biomass (± 1)+ N accretion in the forest floor (± 22)± gain or loss in soil N stores (± 53)

The N budget for Hubbard Brook published in 1977 was “missing” 14.2 kg/ha/yr

14.2 ± ?? kg/ha/yr

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Net N gas exchange = sinks – sources = - precipitation N input (± 1.3)+ hydrologic export (± 0.5)+ N accretion in living biomass (± 1)+ N accretion in the forest floor (± 22)± gain or loss in soil N stores (± 53)

The N budget for Hubbard Brook published in 1977 was “missing” 14.2 kg/ha/yr

14.2 ± 57 kg/ha/yr

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Net N gas exchange = sinks – sources = - precipitation N input (± 1.3)+ hydrologic export (± 0.5)+ N accretion in living biomass (± 1)

The N budget for Hubbard Brook published in 1977 was “missing” 14.2 kg/ha/yr

14.2 ± 2.6 kg/ha/yr

Draw your budget boundaries to ask questions that can be answered with confidence!

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The Value of Uncertainty Analysis

Quantify uncertainty in our resultsUncertainty in regressionMonte Carlo samplingDetectable differences

Identify ways to reduce uncertaintyDevote effort to the greatest unknowns

Improve efficiency of monitoring efforts

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Be a part of QUEST!• Find more information at: www.quantifyinguncertainty.org

• Read papers, share sample code, stay updated with QUEST News• Email us at [email protected]• Follow us on LinkedIn and Twitter: @QUEST_RCN

QUANTIFYING UNCERTAINTY IN ECOSYSTEM STUDIES

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ReferencesYanai, R.D.,  N. Tokuchi, J.L. Campbell, M.B. Green, E. Matsuzaki, S.N. Laseter, C.L. Brown, A.S. Bailey, P. Lyons, C.R. Levine, D.C. Buso,  G.E. Likens, J. Knoepp, K. Fukushima. 2014. Sources of uncertainty in estimating stream solute export from headwater catchments at three sites.   Hydrological Processes. DOI: 10.1002/hyp.10265

Yanai, R.D., M.A. Vadeboncoeur, S.P. Hamburg, M.A. Arthur, M.A. Fuss, P.M.Groffman, T.G. Siccama, and C.T. Driscoll.  2013.  From Missing Source to Missing Sink: Long-Term Changes in a Forest Nitrogen Budget.  Environmental Science & Technology. 47(20):11440-11448.  

Yanai, R.D., C.R. Levine, M.B. Green, and J.L. Campbell. 2012. Quantifying uncertainty in forest nutrient budgets,  J. For.  110:  448-456

Yanai, R.D., J.J. Battles, A.D. Richardson, E.B. Rastetter, D.M. Wood, and C. Blodgett. 2010. Estimating uncertainty in ecosystem budget calculations. Ecosystems 13: 239-248

Wielopolski, L, R.D. Yanai, C.R. Levine, S. Mitra, and M.A Vadeboncoeur. 2010. Rapid, non-destructive carbon analysis of forest soils using neutron-induced gamma-ray spectroscopy. For. Ecol. Manag. 260: 1132-1137

Yanai, R.D., S.V. Stehman, M.A. Arthur, C.E. Prescott, A.J. Friedland, T.G. Siccama, and D. Binkley. 2003. Detecting change in forest floor carbon. Soil Sci. Soc. Am. J. 67:1583-1593

My web site: www.esf.edu/faculty/yanai (Download any papers)

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Alternative spatial models for precipitation in the Hubbard Brook Valley

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Alternative spatial models for precipitation in the Hubbard Brook Valley

0.36%

0.58%

0.24%

0.77%

0.83%