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<p>MACH is a comprehensive hydrometeorological dataset spanning 1,014 watersheds across the United States. It unifies, extends, and enhances existing large-sample hydrology resources by integrating daily meteorological forcings and streamflow observations at the basin scale over a 44-year period (1980–2023). For 395 catchments, MACH also includes records dating back to 1948, extending the hydrologic analyses period to 75-years. MACH provides a rich set of watershed attributes, encompassing topography, soils, geology, land cover, anthropogenic influences, and a wide range of hydroclimatic indices. This comprehensive structure supports investigations of climate-runoff interactions, hydrologic sensitivity, model calibration, and long-term watershed change across diverse spatial and temporal gradients. MACH serves as a flexible, high-resolution dataset for advancing large-sample hydrology, hydroclimatic assessment, and data-driven water science.</p>

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MACH: A Multi-Attribute Catchment Hydrometeorological dataset

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年4月4日

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  • Katharine Sink,
  • Tom Brikowski

摘要

MACH is a comprehensive hydrometeorological dataset spanning 1,014 watersheds across the United States. It unifies, extends, and enhances existing large-sample hydrology resources by integrating daily meteorological forcings and streamflow observations at the basin scale over a 44-year period (1980–2023). For 395 catchments, MACH also includes records dating back to 1948, extending the hydrologic analyses period to 75-years. MACH provides a rich set of watershed attributes, encompassing topography, soils, geology, land cover, anthropogenic influences, and a wide range of hydroclimatic indices. This comprehensive structure supports investigations of climate-runoff interactions, hydrologic sensitivity, model calibration, and long-term watershed change across diverse spatial and temporal gradients. MACH serves as a flexible, high-resolution dataset for advancing large-sample hydrology, hydroclimatic assessment, and data-driven water science.