The role of vegetation optimality in the Budyko framework

<p>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.</p> <p>We test the following hypotheses:</p> <p>- Model simulations based on vegetation optimality lead to a better reproduction of the empirical Budyko curve than model simulations without self-optimized vegetation.<br> - The empirical parameter <em>n</em>&nbsp;stays constant as climate changes, as long as vegetation cover and rooting depths stay constant.<br> - Changes in <em>n</em> values are a result of slowly varying, long-term vegetation properties.</p> <p><strong>How to use this project</strong></p> <p>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.</p> <p>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<br> platform - just click on the `Environments` tab and start a new session. This will start an interactive environment right in your browser.</p> <p>To work with the project anywhere outside the Renku platform,<br> click the `Settings` tab where you will find the<br> 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`.</p> <p>Please consult the various documentation sources for renkulab(https://renkulab.io/).</p>