A synthesis of Global Streamflow Characteristics, Hydrometeorology, and Catchment Attributes (GSHA) for large sample river-centric studies

作者：Ziyun Yin, Peirong Lin, Ryan Riggs, George H. Allen, Xiangyong Lei, Ziyan Zheng, Siyu Cai · 发表于：Earth system science data · 年份：2024 · DOI：10.5194/essd-16-1559-2024 · 被引用次数：13 · 研究领域：Hydrology and Watershed Management Studies、Flood Risk Assessment and Management、Cryospheric studies and observations

Abstract. Our understanding and predictive capability of streamflow processes largely rely on high-quality datasets that depict a river's upstream basin characteristics. Recent proliferation of large sample hydrology (LSH) datasets has promoted model parameter estimation and data-driven analyses of hydrological processes worldwide, yet existing LSH is still insufficient in terms of sample coverage, uncertainty estimates, and dynamic descriptions of anthropogenic activities. To bridge the gap, we contribute the synthesis of Global Streamflow characteristics, Hydrometeorology, and catchment Attributes (GSHA) to complement existing LSH datasets, which covers 21 568 watersheds from 13 agencies for as long as 43 years based on discharge observations scraped from the internet. In addition to annual and monthly streamflow indices, each basin's daily meteorological variables (i.e., precipitation, 2 m air temperature, longwave/shortwave radiation, wind speed, actual and potential evapotranspiration), daily–weekly water storage terms (i.e., snow water equivalence, soil moisture, groundwater percentage), and yearly dynamic descriptors of the land surface characteristics (i.e., urban/cropland/forest fractions, leaf area index, reservoir storage and degree of regulation) are also provided by combining openly available remote sensing and reanalysis datasets. The uncertainties in all meteorological variables are estimated with independent data sources. Our analyses reveal the following insi...