The role of vegetation optimality in the Budyko framework

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/).本研究旨在验证植被最优性（vegetation optimality）如何解释布迪科曲线（Budyko-curve）上的收敛现象。本次研究选取北澳大利亚热带样带（North Australian Tropical Transect）上的5个研究站点、澳大利亚境内的5个水文流域，以及CAMELS数据集（CAMELS-dataset）中的部分流域开展相关实验。我们将对以下三项假说进行检验： 1. 相较于未包含自主优化植被的模型模拟，基于植被最优性的模型模拟可更精准地复现经验布迪科曲线； 2. 只要植被覆盖度与根系深度保持恒定，经验参数n将随气候变化保持稳定； 3. 参数n的变化源于缓慢变化的长期植被特性。 项目使用方法 本项目包含用于模型运行以验证上述假说的全部预处理与后处理脚本。所有最终图表与补充分析均存储于Notebook中，本项目的脚本可复用至其他研究应用场景。 本项目同时属于Renku项目，因此若要将该代码库用于自有分析，需先免费注册Renku账号，随后复刻该项目并按需修改。 通过Renku平台启动本项目的最简方式为：点击「Environments（环境）」标签页并新建会话，即可直接在浏览器中启动交互式运行环境。 若需在Renku平台之外使用本项目，请点击「Settings（设置）」标签页获取Git仓库地址，随后通过`git`命令在任意目标机器上克隆该项目。Python与Conda依赖包的相关信息存放在`requirements.txt`与`environment.yml`文件中。有关Renkulab的详细使用说明，请参考其官方文档：https://renkulab.io/