TY  - Data
T1  - A global streamflow indices time series dataset for large-sample hydrological analyses on streamflow regime (until 2022)
A1  - Jiang Liguang
DO  - 10.57760/sciencedb.07227
PY  - 2024
DA  - 2024-03-26
PB  - National Cryosphere Desert Data Center
AB  - With the vigorous development of big data technology, it is becoming feasible to conduct large-scale hydrological analysis of river flow patterns, which can draw reliable conclusions about hydrological processes from a global perspective. However, there is currently a lack of a comprehensive global large sample dataset for analyzing the components of stream states. This article introduces a new time series dataset that calculates global river indices based on daily river records after data quality control. This dataset contains 79 indices from 41263 river sections worldwide, covering the seven main components of flow regime (i.e. magnitude, frequency, duration, rate of change, time series, variability, and decline) on an annual and multi-year scale. The dataset covers river flow index values before 2022. The time series dataset spans from 1806 to 2022, with an average length of 36 years. Compared with existing global datasets, this global dataset covers more sites and indices, especially those that characterize the frequency, duration, rate of change, and decline of river mechanisms. With this dataset, it is easier to conduct river hydrological research without spending time processing raw river flow records. This comprehensive dataset will become a valuable resource for the hydrological community, facilitating extensive research such as hydrological behavior studies in catchment areas, prediction of water flow status in data scarce regions, and studying changes in water flow status from a global perspective.
DB  - NCDC
UR  - http://www.ncdc.ac.cn/portal/metadata/82cb45b4-d0d2-4f65-8c8f-a2eff815326c
ER  -