Summary
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 ann
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