While some rivers have been modified and regulated for centuries requiring recovery efforts and restoration, other rivers are currently under development and facing the challenges of environmental degradation and loss of ecological functionality. As countries, such as China, expand current infrastructure to develop and construct flood reduction, hydropower, and water storage facilities along their rivers, the threat of detrimental ecological impacts expands too. To address these pressing challenges, this data set was used in the referenced article's analysis. Climatic, hydrogeomorphic, ecological, and anthropogenic characteristics summarized in this dataset were analyzed to determine the key principal components relevant to decision-making for environmental flows in the river basin. This resulting dataset includes a summary of the applied variables by subbasin cluster as discussed in detail in the article.

Data was collected from global data sets compiled within HydroATLAS and MODIS supported datasets in other publications. Variables with monthly information were aggregated to quarterly for analysis.

Variables were evaluated for their relevent influence to e-flows with a pricipal component analysis (PCA) applied to the dataset. With the results from the PCA, the list of variables was reduced to the most important or pricipal components. K-means clustering facilitated the grouping of subbasins according to the characteristics and variable values. 

This table includes those values used in clustering.

This data set includes annual, quarterly, monthly, and/or coefficient of variance means, medians, minimum values, and maximum values which were derived from monthly data within the original data set as cited in (Linke et al. 2019; Friedl and Sulla-Menashe 2019). Refer to the original HydroATLAS data (Linke et al. 2019) and other information found in the article's references for the full list of available variables and the complete dataset for all months and variables.