quantifying uncertainty in belowground carbon turnover ruth d. yanai state university of new york...
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![Page 1: Quantifying Uncertainty in Belowground Carbon Turnover Ruth D. Yanai State University of New York College of Environmental Science and Forestry Syracuse](https://reader037.vdocuments.site/reader037/viewer/2022110116/551c3fff5503467b488b4a94/html5/thumbnails/1.jpg)
Quantifying Uncertainty in Belowground Carbon Turnover
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 budgets
Precipitation (evaluating monitoring intensity)
Streamflow (filling gaps with minimal uncertainty)
Forest biomass (identifying the greatest sources of uncertainty)
Soil stores, belowground carbon turnover (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
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+ hydrologic export+ 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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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%
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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Net N gas exchange = sinks – sources = - precipitation N input (± 1.3)+ hydrologic export+ 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+ 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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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.
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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%
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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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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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A Monte-Carlo approach could be implemented using specialized software or almost any programming language.
Here we used a spreadsheet model.
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Height Parameters
Height = 10^(a + b*log(Diameter) + log(E))
LookupLookup
Lookup
***IMPORTANT***Random selection of parameter values happens HERE, not separately for each tree
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If the errors were sampled individually for each tree, they would average out to zero by the time you added up a few thousand trees
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Biomass Parameters
Biomass = 10^(a + b*log(PV) + log(E))
LookupLookup
Lookup
PV = 1/2 r2 * Height
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Biomass Parameters
Biomass = 10^(a + b*log(PV) + log(E))
Lookup
Lookup Lookup
PV = 1/2 r2 * Height
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Biomass Parameters
Biomass = 10^(a + b*log(PV) + log(E))
Lookup
Lookup Lookup
PV = 1/2 r2 * Height
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Concentration Parameters
Concentration = constant + error
LookupLookup
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COPY THIS ROW-->
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After enough interations, analyze
your results
Paste Values button
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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
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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 equations
6% 15% 11%
12% 12% 18% 13% 14%
16% 10% 19% 3% 11%
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“What is the greatest source of uncertainty in my answer?”
Better than the sensitivity estimates that vary everything by the same amount--they don’t all vary by the same amount!
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Better than the uncertainty in the parameter estimates--we can tolerate a large uncertainty in an unimportant parameter.
“What is the greatest source of uncertainty to my answer?”
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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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10 points are sampled along each of 5 transects in 13 stands.
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Excavation of a forest floor block (10
x 10 cm)
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• Pin block is trimmed to size. Horizons are easy to see.
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• Horizon depths are measured on four faces• Oe, Oi, Oa and A (if present) horizons are bagged separately• In the lab, samples are dried, sieved, and a subsample oven-
dried for mass and chemical analysis.
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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
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Sampling the same experimental units over time permits detection of smaller changes
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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
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Quantitative Soil Pits
0.5 m2 frame
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Excavate Forest Floor by horizon
Mineral Soil by depth increment
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Sieve and weigh in the field
Subsample 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)
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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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Measurement Uncertainty Sampling UncertaintySpatial Variability
Model Uncertainty y Error within models Error between models
Excludes areas not sampled: rock area 5%, stem area: 1%
Measurement uncertainty and spatial variation make it difficult to estimate soil carbon and nutrient contents precisely
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Non-Destructive Evaluation of Soils
Neutrons generated by nuclear fusion of 2H and 3H interact with nuclei in the soil via inelastic neutron scattering and thermal neutron capture.
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Agreement with soil pits: 4.2 vs. 5.4 kg C m-2.
Detectable difference: 5%
Time for collection: 1 hour
Improvements are needed in portability and sampling geometry.
INSTNC
Wielopolski et al. (2010) FEM
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Minirhizotron Estimates of Root Production and Turnover
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Measurement Uncertainty Sampling UncertaintySpatial Variability
Model Uncertainty
Root Production vs. Root Lifespan: 45%
Sequential Coring, mean vs. max: 30%
?
Park et al. (2003) Ecosystems
Brunner al. (2013) Plant Soil
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Subjectivity in image analysis could be assessed by multiple observers analyzing the same images
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Sources of Uncertainty in Ecosystem Studies
Model selectionModel uncertaintySpatial Variation
Biomass
Spatial Variation
Precip
Spatial Variation
Soils
MeasurementTemporal Variation
Streams
Measurement
Root Turnover
Model selection
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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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ReferencesYanai, 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
Park, B.B., R.D. Yanai, T.J. Fahey, T.G. Siccama, S.W. Bailey, J.B. Shanley, and N.L. Cleavitt. 2008. Fine root dynamics and forest production across a calcium gradient in northern hardwood and conifer ecosystems. Ecosystems 11:325-341
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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QUANTIFYING UNCERTAINTY IN ECOSYSTEM STUDIES