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3 results for “Budyko”
Catchments do not strictly follow Budyko curves over multiple decades but deviations are minor and predictable
<p>The supplementary data is provided in the form of an Excel file which consists of two sheets. ST-1 consists of data for individual catchments, and ST-2 consists of data for regional marginal distributions of deviations</p> <p>ST-1: The topographic characteristics for all study catchments (Latitude, Longitude, Area, Elevation), the length of the runoff data availability, mean precipitation for the period 1901-2015, mean 20-year evaporative indices for the five 20-year periods, median deviation values along with the skew normal distribution parameters and p-values of the Wilcoxon Signed Rank Test for each 20-year distribution deviation are provided. p-values of the Kolmogorov-Smirnov Test, performed on the two consecutive distribution of deviations, are provided in the next columns. Similarly, the long-term median deviation values, IQR of the deviations, fitted distribution parameters and temporal stability flag of aggregated marginal distribution of deviations along with associated changes in Q are provided. The last column shows the IQR of median deviation values for the 20 individual moving window aggregated marginal distribution of deviations.</p> <p>ST-2: Long-term median deviation values, IQR of deviations, and parameters of the fitted regional distributions of deviations along with the number of catchments in each category are provided.</p>
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> 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>
Global runoff partitioning based on Budyko-constrained machine learning
<p>Global 0.25° datasets of runoff partitioning are developed by Budyko-constrained machine learning. The datasets include runoff (<em>Q</em>, TFlow_0.25), runoff coefficient (<em>Q</em>/<em>P</em>, TFC_0.25), baseflow (<em>Q<sub>b</sub></em>, BFlow_0.25) and baseflow coefficient (<em>Q<sub>b</sub></em>/<em>P</em>, BFC_0.25).</p>
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