Dataset for 'Satellite based Budyko framework reveals the human imprint on long-term surface water partitioning across India'

The folder 'Fitting_Budyko_Parameter' contains codes and data for obtaining calibrated values of Tixeront-Fu's omega from long-term mean annual precipitation, potential evapotranspiration, and actual evapotranspiration. Codes for performing a split sample test to check the performance of the omega calibration are also in the same folder. Data for the remaining portion of the analysis is extracted from 'ProcessedData.xlsx'. The file contains all physio-climatic and socio-economic characteristics used for analysis. 'CorrelationAnalysis.r' is used to generate correlograms for identifying correlated characteristics. Intra-category correlations are identified from generated correlograms to shortlist characteristics. Further correlations are eliminated by examining a combined correlogram having factors from all categories. 'Main_Code.r' calls the functions 'CART_func.r' and 'RF_func.r' iteratively for each omega threshold defined in 'CART_RF_Model_Thresholds.xlsx'. Pruned and unpruned trees for class prediction are extracted as outputs from 'CART_func.r'. Pruning is done using the 1-SE rule (Breiman et al., 1984) Pruned trees are used to develop a circos plot using the online tool on ‘http://mkweb.bcgsc.ca/tableviewer/visualize/’. Factor predictive importance computed using permutation of variable importance quantification is obtained from 'RF_func.r'. The function also generates confusion matrices indicating the categorization performance of each model trained. Outputs are written to the 'Outputs' folder in accordingly named sub-folders名为`Fitting_Budyko_Parameter`的文件夹存储了用于从长期年均降水量、潜在蒸散量与实际蒸散量中获取Tixeront-Fu模型的ω参数（omega）校准值的代码与数据集。该文件夹同时包含用于执行拆分样本检验（split sample test）以验证ω参数校准效果的代码。 本分析其余部分所需的数据均提取自`ProcessedData.xlsx`文件，该文件包含了本次分析所用的全部生理气候与社会经济特征指标。 `CorrelationAnalysis.r`脚本用于生成相关图（correlogram）以识别存在相关性的特征指标。通过对生成的相关图进行分析，可识别类别内相关性，以此完成特征指标的初步筛选；随后通过整合所有类别因子的组合相关图，进一步剔除冗余的相关特征。 `Main_Code.r`脚本会针对`CART_RF_Model_Thresholds.xlsx`中定义的每一组ω参数阈值，迭代调用`CART_func.r`与`RF_func.r`两个函数。 `CART_func.r`的输出结果包含用于分类预测的剪枝与未剪枝决策树。剪枝过程采用1个标准误法则（1-SE rule，Breiman等，1984）。 利用`http://mkweb.bcgsc.ca/tableviewer/visualize/`提供的在线工具，可基于剪枝后的决策树生成弦图（circos plot）。 `RF_func.r`可输出基于变量重要性置换量化法计算得到的因子预测重要性得分，同时会生成混淆矩阵，用于展示各训练完成模型的分类性能表现。 所有输出结果均存储至`Outputs`文件夹下的对应命名子文件夹中。