What question did this study set out to answer?

The aim is to create a comprehensive dataset to facilitate hydrologic analyses across varied watersheds.

April 6, 2026Scientific Data0 citationsOpen Access

MACH: A Multi-Attribute Catchment Hydrometeorological dataset

KSKatharine SinkThe University of Texas at Dallas TBTom BrikowskiThe University of Texas at Dallas

Key Points

The aim is to create a comprehensive dataset to facilitate hydrologic analyses across varied watersheds.
Integrated daily meteorological and streamflow data over 44 years
Covered 1,014 watersheds in the United States
Extended records for 395 catchments back to 1948
Included diverse watershed attributes like topography, soils, and land cover
Enhanced understanding of climate-runoff interactions
Supported long-term assessments of watershed changes
Provided resources for model calibration and hydrologic sensitivity analyses

Abstract

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.

问 AI

Bookmark

View Full Paper