The Headwaters Hydrology Project (HHP) is a machine learning (ML)-based streamflow model that provides daily streamflow estimates at the HUC-10 scale across the contiguous United States. Trained on high-quality observed streamflow data (USGS, MT DNRC), hydroclimatic variables, and basin characteristics, HHP delivers seamless, natural streamflow simulations—excluding the effects of dams, diversions, and other human water management influences.

Unlike traditional process-based hydrologic models, HHP leverages ML techniques to improve streamflow predictions in ungaged basins and headwater watersheds, where existing operational models often struggle. Benchmarking results show that HHP consistently outperforms process-based model benchmarks in accuracy, achieving a median Nash-Sutcliffe Efficiency (NSE) of 0.75, demonstrating its reliability for streamflow estimation.

HHP is updated daily, publicly available, and supports real-time hydrology, drought assessment, and ecological applications via an open-access API. This dataset advances water resource management and drought monitoring by providing high-resolution, data-driven streamflow predictions for the scientific and operational communities.

• Data Record Length: 1982–Present (updated daily, ~2-day latency)
• Percentile Period of Record: 1995-2024 (most recent 30 years)

Relevant Citation:

• Period of Record that Accounts for Climate Non-Stationarity

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