yanai quest asm 2015 part 1
TRANSCRIPT
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Quantifying Uncertainty in Ecosystem Studies
QUEST is a NSF-funded Research Coordination Network with the goal of improving understanding and facilitating the use of uncertainty analyses in ecosystem studies.
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First, a quick survey…• Which of you make measurements of
• Precipitation• Streamflow• Vegetation (biomass)• Soils• Other
• Which of you reports• Measurement Uncertainty • Natural Variation• Model Error• Model Selection Error
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First, a quick survey…• Which of you deals with:
• Deciding when data are unusable• Filling gaps in datasets• Analytical values at or below detection limit
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About this Working Group
Brief Presentations, each followed by discussion• Ruth Yanai: intro to QUEST: uncertainty in ecosystem budgets• Craig See: Streamflow Gaps: Why they occur and what we can do about it• John Campbell: Uncertainty in net hydrologic flux of calcium • Melissa Slater: Spatial patterns of precipitation in complex terrain• Josh Roberti: Connecting uncertainty estimates and QA/QC methods• Carrie Levine: Bayesian hierarchical analysis of demographic processes
Discussion of more general issues
Take our survey! Tweeted at www.quantifyinguncertainty.org
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A Brief History of QUEST1983 Yanai started at HBR: Ecosystem Budgets have no Error
2008 Yanai commits to addressing error in a symposium. Ed Rastetter helps.
2009 Battles and Richardson join in publication of “Error for Dummies” paper in Ecosystems (Yanai et al. 2010)
2010 Hubbard Brook Committee of Scientists addresses uncertainty. Mark Green and John Campbell join.
2011 John Campbell names QUEST. Carrie Levine designs the logo
2011, 2012 LTER Working Groups address streamflow, precipitation
2013 QUEST funded by NSF as an RCN for 5 years
2012 - Sessions at LTER ASM, ESA, AGU, ESA, IUFRO, ASM…
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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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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%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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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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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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0
50
100
150
200
250
300
350
400
LeavesBranchesBarkWood
Biom
ass
(Mg/
ha)
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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0
50
100
150
200
250
300
350
LeavesBranchesBarkWood
Biom
ass
(Mg/
ha)
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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0
50
100
150
200
250
300
350
LeavesBranchesBarkWood
Biom
ass
(Mg/
ha)
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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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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Excavation of a forest floor block (10
x 10 cm)
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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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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 Pits0.5 m2 frame
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Excavate Forest Floor by horizonMineral Soil by depth increment
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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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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