What is Optimality Theory? Introduction to Optimality Theory
The role of vegetation optimality in the Budyko-framework
Transcript of The role of vegetation optimality in the Budyko-framework
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The role of vegetation optimality in the Budyko-frameworkR.C. Nijzink1, S. Schymanski1
Hypotheses
Conc lusi ons
Budyko-framework
Vegetation Optimality
Study sites
Vegetation OptimalityModel
Results
Convergence to the curveby optimality
Modifying precipitation
Sensitivity n-values
Supported by the Luxembourg National Research Fund (FNR) ATTRACT programme (A16/SR/11254288)
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1 Luxembourg Institute of Science and Technology, Belvaux, Luxembourg,
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• Catchments around the world plot close to water and energy limits:
• Ea/P < 1
• Ea/P < Ep/P• Empirical curve by Budyko (1974)
THE BUDYKO FRAMEWORK
Water limit
Ene
rgy
limit
• Why do catchments converge to the curve?
• What happens under changing climate?
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• Different formulations of the curve• Parametric formulation:
THE BUDYKO FRAMEWORK
Water limit
Ene
rgy
limit
Budyko-parameter n• Widely assumed as a catchment
property• Changes with changes in
catchment properties?• Changes with climate?
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VEGETATION OPTIMALITY
Net Carbon Profit :Total difference of carbon uptake by photosynthesis and carbon costs of the system
AssimilationEvaporation
Carb
on cos ts
Root uptake
Vegetation Optimality Model: Optimizes vegetation properties to maximize NCP More info
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Available resources:• Water• Light
• CO2
Natural selection:• Optimally adapted vegetation• Uses resources in the best
possible way
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RESEARCH QUESTIONS
Assimilation
Evaporation
Carb
on cos ts
Root uptake
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• Does optimality explain convergence on the Budyko-curve?
• Does climate change move a catchment along its individual curve?
• Does a change in vegetation properties result in shifting between curves?
Climate change?
Vegetation change?
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HYPOTHESES
• Model simulations based on vegetation optimality lead to a better reproduction of the empirical Budyko-curve than model simulations without self-optimized vegetation.
• The empirical parameter n stays constant as precipitation changes, as long as vegetation and other meteorological forcing variables stay constant.
• Changes in n-values are a result of slowly varying, long-term vegetation properties.
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North Australian Tropical Transect • Mean annual rainfall: 500-1800 mm• Pronounced wet season: Nov-Feb• Evergreen trees + seasonal grass• Sites:
• Five flux tower sites
• Six catchments
• 36 additional locations
CAMELS-data• Catchments around the contiguous
United States• Selection of 357 catchments
STUDY SITES
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Tre
e r
oo
tin
g d
epth
Tree coverGrass cov.
Gra
ss r
oo
tin
gd
ep
th
Root distributions
VEGETATION OPTIMALITY MODEL
Optimized constants• Tree cover fraction• Tree rooting depth• Grass rooting depth• Water use strategies
Dynamically optimized variables:• Grass cover fraction• Photosynthetic capacity• Stomatal conductances• Fine root surface area
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FLEX
CONCEPTUAL HYDROLOGICAL MODELS
GR4J
Perrin, Michel, and Andréassian. “Improvement of a Parsimonious Model for Streamflow Simulation.” JoH 279, no. 1–4 (2003): 275–89. https://doi.org/10.1016/S0022-1694(03)00225-7.
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TUW (HBV)
• Simple bucket-models• Calibrated• Applied to:
– Australian catchments
– CAMELS-catchments
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EXPERIMENTS
Unmodified situation• Optimize VOM to maximize the Net Carbon Profit• Calibrate hydrological models to observed streamflow
Increase/decrease precipitation• Run VOM:
➔ Vegetation from unmodified situation• Run hydrological models:
➔ Parameters from unmodified situation
Let vegetation adjust…• Re-optimize VOM for new precipitation
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CONVERGENGE BY VEGETATION OPTIMALITY
Flux tower sites
Australian catchments
Extra locations NATT
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• VOM with full optimization of vegetation properties• VOM without vegetation → bare soil
Optimizing vegetation leads to a higher curve!
Higher and more realistic n-values for optimized vegetation!
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MODIFYING PRECIPITATION• Rainfall multiplied: 0.2 - 2.0, steps of 0.2• VOM with constant long-term vegetation• VOM with re-optimized vegetation for new precipitation
Howard Springs
Optimizing vegetation leads to a lower standard deviation!
Non-optimal vegetation deviates from curve!
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See also:
Adelaide River
Daly Uncleared
Dry River
Sturt Plains
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MODIFYING PRECIPITATION
Curve moves down for increased precipitation...
…but moves back if vegetation re-optimizes!
• 36 additional locations, precipitation +20%• VOM with constant vegetation• VOM with re-optimized vegetation
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MODIFYING PRECIPITATION• 36 additional locations, precipitation +20%• VOM with constant vegetation• VOM with re-optimized vegetation
Water use strategy parameters
Perennial vegetation cover
Perennial vegetation rooting depth
Seasonal vegetation rooting depth
Biggest changes in perennial vegetation properties
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MODIFYING PRECIPITATION• Australian catchments • Prec. multiplied: 0.2 - 2.0, steps of 0.2 • VOM with constant vegetation• VOM with re-optimized vegetation• Hydrological models with constant model parameters
Self-optimized vegetation has the best fit!
Adelaide River
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See also:
Dry River
Fergusson River
Magela Creek
Seventeen Mile Creek
South AlligatorCreek
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SENSITIVITY N-VALUES: VOM• Prec. multiplied: 0.2 - 2.0, steps of 0.2 • Budyko-parameter determined for each case, each site• VOM with constant vegetation• VOM with re-optimized vegetation
Re-optimizing vegetation results in constant n
Factors for m
ultip
licati on o
f precipitatio
n
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SENSITIVITY N-VALUES: ALL MODELS• Prec. multiplied:
• 0.2 - 2.0, steps of 0.2 • n-value:
• each case, each site• VOM:
• constant vegetation
• optimized vegetation• Hydrological models
• constant parameters
Optimized VOM always around one value!
Factors for m
ultiplica
ti on
of
precipitatio n
VOM optimized
VOM not optimized
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TUW
GR4J
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SENSITIVITY N-VALUES: MORE LOCATIONS VOM• 36 additional locations • VOM runs:
• Optimized for unmodified precipitation.
• Constant vegetation and increased prec. +20%
• Re-optimized vegetation and increased prec. +20%
• n-values for each case:
• Difference with optimized VOM and unmodified prec.
Re-optimized VOM for increased precipitation returns to the initial n-value!
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SENSITIVITY N-VALUES: CAMELS-DATA• CAMELS-data• Prec. +20%• Hydrological models with constant parameters• n-value for each catchment
Increasing precipitation results in lower in n-values: happens for all models!
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• Model simulations based on vegetation optimality lead to a better reproduction of the empirical Budyko-curve than model simulations without self-optimized vegetation.Accepted
• The empirical parameter n stays constant as precipitation changes, as long as vegetation and other meteorological forcing variables stay constant.Rejected
• Changes in n-values are a result of slowly varying, long-term vegetation properties.Rejected
CONCLUSIONS
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APPENDIX
AssimilationEvaporation
Carbo
n cos ts
Root uptake
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MODIFYING PRECIPITATION• Rainfall multiplied: 0.2 - 2.0, steps of 0.2• VOM with constant vegetation• VOM with re-optimized vegetation
Adelaide RiverNext
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Non-optimal vegetation deviates from curve!
Optimizing vegetation leads to a lower standard deviation!
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MODIFYING PRECIPITATION• Rainfall multiplied: 0.2 - 2.0, steps of 0.2• VOM with constant vegetation• VOM with re-optimized vegetation
Daly RiverNext
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Non-optimal vegetation deviates from curve!
Optimizing vegetation leads to a lower standard deviation!
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MODIFYING PRECIPITATION• Rainfall multiplied: 0.2 - 2.0, steps of 0.2• VOM with constant vegetation• VOM with re-optimized vegetation
Dry River
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Non-optimal vegetation deviates from curve!
Optimizing vegetation leads to a lower standard deviation!
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MODIFYING PRECIPITATION• Rainfall multiplied: 0.2 - 2.0, steps of 0.2• VOM with constant vegetation• VOM with re-optimized vegetation
Sturt Plains
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Optimizing vegetation leads to a lower standard deviation!
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MODIFYING PRECIPITATION• Australian catchments • Prec. multiplied: 0.2 - 2.0, steps of 0.2 • VOM with constant vegetation• VOM with re-optimized vegetation• Hydrological models with constant model parameters
Optimized VOM has the best fit!
Dry River
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MODIFYING PRECIPITATION• Australian catchments • Prec. multiplied: 0.2 - 2.0, steps of 0.2 • VOM with constant vegetation• VOM with re-optimized vegetation• Hydrological models with constant model parameters
Optimized VOM has the best fit!
Fergusson River
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MODIFYING PRECIPITATION• Australian catchments • Prec. multiplied: 0.2 - 2.0, steps of 0.2 • VOM with constant vegetation• VOM with re-optimized vegetation• Hydrological models with constant model parameters
Optimized VOM has the best fit!
Magela Creek
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MODIFYING PRECIPITATION• Australian catchments • Prec. multiplied: 0.2 - 2.0, steps of 0.2 • VOM with constant vegetation• VOM with re-optimized vegetation• Hydrological models with constant model parameters
Optimized VOM has the best fit!
Seventeen Mile Creek
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MODIFYING PRECIPITATION• Australian catchments • Prec. multiplied: 0.2 - 2.0, steps of 0.2 • VOM with constant vegetation• VOM with re-optimized vegetation• Hydrological models with constant model parameters
Optimized VOM has the best fit!
South Alligator River
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