The role of vegetation optimality in the Budyko-framework

MethodsIntroduction

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,

Hydrological Models

Experiments

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ConclusionsConclusions

IntroductionIntroduction

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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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AppendixAppendix

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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

https://agupubs.onlinelibrary.wiley.com/doi/epdf/10.1029/2008WR006841

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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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AppendixAppendix

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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

https://doi.org/10.1016/S0022-1694(03)00225-7

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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

FLEX

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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Daly RiverNext

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Dry River

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Sturt Plains

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Optimized VOM has the best fit!

Dry River

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Fergusson River

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Magela Creek

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Seventeen Mile Creek

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South Alligator River

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The role of vegetation optimality in the Budyko-framework

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