Comment on essd-2023-49

Abstract

<strong class="journal-contentHeaderColor">Abstract.</strong> With the booming big data techniques, large-sample hydrological analysis on streamflow regime is becoming feasible, which could derive robust conclusions on hydrological processes from a big-picture perspective. However, there is not a comprehensive global large-sample dataset for components of the streamflow regime yet. This paper presents a new time series dataset on global streamflow indices calculated from daily streamflow records after data quality control. The dataset contains 79 indices over seven major components of streamflow regime (<em>i.e.</em>, magnitude, frequency, duration, changing rate, timing, variability, and recession) of 5548 river reaches globally. The indices time series in the dataset are available until 2021, the lengths of which vary from 30 to 215 years with an average of around 66 years. Restricted-access streamflow data of typical river basins in China are included in the dataset. Compared to existing global datasets, this global dataset covers more indices, especially those characterizing the frequency, duration, changing rate, and recession of streamflow regime. With the dataset, research on streamflow regime will become easier without spending time handling raw streamflow records. This comprehensive dataset will be a valuable resource to the hydrology community to facilitate a wide range of studies, such as studies of hydrological behaviour of a catchment, streamflow regime prediction in data-scarce regions, as well as variations in streamflow regime from a global perspective.