Spatiotemporal variability of water transit time distributions along an alpine stream network

This repository hosts the data and the codes to reproduce the analysis and the results presented in the manuscript:"Spatiotemporal variability of water transit time distributions along an alpine stream network"by Giulia Grandi et al. File "analyze_results.m": run the code to reproduce results of the manuscript and some useful Figures. Folder "input" contains the input structures to run the analyze_results.m script:- "Results.m" contains the results obtained from the MCMC sampling performed via the DREAM_ZS sampler.- "Variables.m" embeds the input variables as detailed in the script "analyze_results.m". Folder "auxiliary_func" contains the auxiliary functions to perform the posterior calculation and produce some plots:- "ComputeGaussianLogL.m" : performs the calculation of the gaussian log-Likelihood.- "ComputeLogPrior.m" : performs the calculation of the log-Prior for each parameter of the model based on the setting embedded in V.SP.- "subaxis.m" and "parseArgs.m" : functions to obtain nice subplot proposed by Aslak Grinsted (2026). "Subaxis - Subplot (https://ch.mathworks.com/matlabcentral/fileexchange/3696-subaxis-subplot), MATLAB Central File Exchange. Retrieved March 9, 2026."- "PlotPrior.m" : fuction to plot nicely the prior distribution (also in case of pooling). To use the model for a different case study, prepare the "V" data structure as detailed in the description of the analyze_results.m file and use the function "modelSASfunction", which return the log of the posterior density, with any sampler of choice. To use the DREAM_ZS sampler (as in the present study), please refer to "https://github.com/Zaijab/DREAM" for download. Language: MatLab (tested on Version R2024b)本仓库存储了用于复现Giulia Grandi等人发表的论文《阿尔卑斯河网水流停留时间分布的时空变异性》(Spatiotemporal variability of water transit time distributions along an alpine stream network)中所述分析过程与实验结果的代码与数据集。 文件`analyze_results.m`可用于运行代码，复现论文结果并生成实用图表。 文件夹`input`包含运行`analyze_results.m`脚本所需的输入结构体： - `Results.m`：存储了通过DREAM_ZS采样器（DREAM_ZS sampler）执行马尔可夫链蒙特卡洛（Markov Chain Monte Carlo, MCMC）采样得到的结果。 - `Variables.m`：嵌入了`analyze_results.m`脚本中详述的输入变量。 文件夹`auxiliary_func`包含用于执行后验计算与生成绘图的辅助函数： - `ComputeGaussianLogL.m`：用于计算高斯对数似然值。 - `ComputeLogPrior.m`：基于V.SP中嵌入的配置，为模型各参数计算对数先验值。 - `subaxis.m`与`parseArgs.m`：由Aslak Grinsted于2026年提出的优化子图绘制函数，相关资源引用：*Subaxis - Subplot* (https://ch.mathworks.com/matlabcentral/fileexchange/3696-subaxis-subplot), MATLAB Central 文件交换中心，2026年3月9日获取。 - `PlotPrior.m`：用于美观绘制先验分布的函数（支持多分布合并场景）。 若需将本模型应用于其他案例研究，请按照`analyze_results.m`文件的说明准备`V`数据结构体，再选用任意采样器调用`modelSASfunction`函数——该函数将返回后验密度的对数值。若需使用本研究采用的DREAM_ZS采样器，请访问`https://github.com/Zaijab/DREAM`下载获取。 本代码基于MatLab语言开发，已在R2024b版本中完成测试。