Storage and Transit Time Data and Code

Author: Andrew J. FeltonDate: 11/15/2024 This R project contains the primary code and data (following pre-processing in python) used for data production, manipulation, visualization, and analysis, and figure production for the study entitled: "Global estimates of the storage and transit time of water through vegetation" Please note that 'turnover' and 'transit' are used interchangeably. Also please note that this R project has been updated multiple times as the analysis has updated throughout the peer review process. #Data information: The data folder contains key data sets used for analysis. In particular: "data/turnover_from_python/updated/august_2024_lc/" contains the core datasets used in this study including global arrays summarizing five year (2016-2020) averages of mean (annual) and minimum (monthly) transit time, storage, canopy transpiration, and number of months of data able as both an array (.nc) or data table (.csv). These data were produced in python using the python scripts found in the "supporting_code" folder. The remaining files in the "data" and "data/supporting_data" folder primarily contain ground-based estimates of storage and transit found in public databases or through a literature search, but have been extensively processed and filtered here. The "supporting_data"" folder also contains annual (2016-2020) MODIS land cover data used in the analysis and contains separate filters containing the original data (.hdf) and then the final process (filtered) data in .nc format. The resulting annual land cover distributions were used in the pre-processing of data in python. #Code information Python scripts can be found in the "supporting_code" folder. Each R script in this project has a role: "01_start.R": This script sets the working directory, loads in the tidyverse package (the remaining packages in this project are called using the `::` operator), and can run two other scripts: one that loads the customized functions (02_functions.R) and one for importing and processing the key dataset for this analysis (03_import_data.R). "02_functions.R": This script contains custom functions. Load this using the `source()` function in the 01_start.R script. "03_import_data.R": This script imports and processes the .csv transit data. It joins the mean (annual) transit time data with the minimum (monthly) transit data to generate one dataset for analysis: annual_turnover_2. Load this using the `source()` function in the 01_start.R script. "04_figures_tables.R": This is the main workhouse for figure/table production and supporting analyses. This script generates the key figures and summary statistics used in the study that then get saved in the "manuscript_figures" folder. Note that all maps were produced using Python code found in the "supporting_code"" folder. Also note that within the "manuscript_figures" folder there is an "extended_data" folder, which contains tables of the summary statistics (e.g., quartiles and sample sizes) behind figures containing box plots or depicting regression coefficients. "supporting_generate_data.R": This script processes supporting data used in the analysis, primarily the varying ground-based datasets of leaf water content. "supporting_process_land_cover.R": This takes annual MODIS land cover distributions and processes them through a multi-step filtering process so that they can be used in preprocessing of datasets in python.作者：安德鲁·J·费尔顿（Andrew J. Felton），日期：2024年11月15日 本R项目包含本研究的数据制作、处理、可视化、分析以及图表绘制所用的核心代码与经Python预处理后的数据，本研究标题为《全球植被储水与输水时间估算》。 请注意，本研究中“周转（turnover）”与“输水（transit）”为通用术语。此外，本R项目已伴随同行评议过程中的分析更新多次迭代。 # 数据说明 本项目的数据文件夹包含分析所用的关键数据集。具体而言： `data/turnover_from_python/updated/august_2024_lc/` 路径下存放本研究的核心数据集，包含汇总了2016-2020年五年平均的年均与月均最小输水时间、储水量、冠层蒸腾量，以及可用数据月数的全球数组，数据格式可为网格数据文件（.nc）或数据表（.csv）。上述数据由`supporting_code`文件夹下的Python脚本生成。 `data`与`data/supporting_data`文件夹中的其余文件，主要包含来自公共数据库或文献检索得到的地面实测储水与输水估算值，本项目中已对其进行了深度处理与筛选。 `supporting_data`文件夹还包含分析所用的2016-2020年MODIS（中分辨率成像光谱仪，Moderate Resolution Imaging Spectroradiometer）土地覆盖数据，其中同时存放了原始数据（.hdf）与最终处理（筛选后）的网格数据（.nc格式）。本研究预处理Python数据集时用到了经处理后的年度土地覆盖分布数据。 # 代码说明 Python脚本存放于`supporting_code`文件夹中。 本项目中的各R脚本分工明确： 1. `01_start.R`：该脚本用于设置工作目录，加载tidyverse包（本项目其余包均通过`::`运算符调用），同时可运行另外两个脚本：一个用于加载自定义函数（`02_functions.R`），另一个用于导入并处理本研究的核心数据集（`03_import_data.R`）。 2. `02_functions.R`：该脚本包含自定义函数，需在`01_start.R`中通过`source()`函数加载。 3. `03_import_data.R`：该脚本用于导入并处理.csv格式的输水数据，将年均输水时间数据与月均最小输水数据进行拼接，生成用于分析的单一数据集`annual_turnover_2`，需在`01_start.R`中通过`source()`函数加载。 4. `04_figures_tables.R`：本项目图表与表格制作及辅助分析的核心脚本，可生成研究所需的核心图表与统计量，并将结果保存至`manuscript_figures`文件夹。请注意，所有地图均通过`supporting_code`文件夹下的Python代码绘制。此外，`manuscript_figures`文件夹内包含`extended_data`子文件夹，其中存放了支撑箱线图与回归系数图的统计量（如四分位数与样本量）表格。 5. `supporting_generate_data.R`：该脚本用于处理分析所用的辅助数据，主要为各类地面实测叶含水量数据集。 6. `supporting_process_land_cover.R`：该脚本对年度MODIS土地覆盖分布数据进行多步筛选处理，使其可用于Python端的数据集预处理流程。