A global streamflow indices time series dataset for large-sample hydrological analyses on streamflow regime (until 2022)

Abstract

Abstract. 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 a lack of 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 (i.e., magnitude, frequency, duration, changing rate, timing, variability, and recession) of 41 263 river reaches globally on yearly and multiyear scales. Streamflow indices values until 2022 are covered in the dataset. Time span of the time series dataset is from 1806 to 2022 with an average length of 36 years. Compared to existing global datasets, this global dataset covers more stations and 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. The dataset can be accessed at https://doi.org/10.57760/sciencedb.07227 (Chen et al., 2023a).