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The role of vegetation optimality in the Budyko framework

Authors: Nijzink, Remko; Schymanski, Stanislaus;

The role of vegetation optimality in the Budyko framework

Abstract

In this study, we test how vegetation optimality explains convergence on the Budyko-curve. This is done for five study sites along the North Australian Tropical Transect, five catchments in Australia and a selection of catchments of the CAMELS-dataset. We test the following 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 climate changes, as long as vegetation cover and rooting depths stay constant. - Changes in n values are a result of slowly varying, long-term vegetation properties. How to use this project This project contains all pre- and post-processing scripts for the model runs to test our hypotheses. All final figures are in the notebooks, as well as the supplementary analyses. These scripts can be re-used for other applications. This is also a Renku project. Hence, to use this repository for your own analysis, create a free login for renku, then fork the project and modify it to your liking. The simplest way to start this project is right from the Renku platform - just click on the `Environments` tab and start a new session. This will start an interactive environment right in your browser. To work with the project anywhere outside the Renku platform, click the `Settings` tab where you will find the git repo URLs - use `git` to clone the project on whichever machine you want. Python and conda packages are in `requirements.txt` and `environment.yml`. Please consult the various documentation sources for renkulab(https://renkulab.io/).

Funded by the Luxembourg National Research Fund, ATTRACT programme (A16/SR/11254288)

Keywords

Budyko, Vegetation, Optimality, Modelling

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selected citations
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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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