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MACH: A Multi-Attribute Catchment Hydrometeorological dataset
Katharine Sink1, Tom Brikowski2
1Sustainable Earth Systems Sciences, University of Texas at Dallas, Richardson, USA. katharine.sink@utdallas.edu.
Scientific Data
|April 4, 2026
Summary
MACH is a new hydrometeorological dataset for the US, offering 75 years of streamflow and climate data for 1,014 watersheds. This resource aids in understanding climate-runoff interactions and long-term watershed changes.
Area of Science:
- Hydrology
- Climate Science
- Environmental Science
Background:
- Existing large-sample hydrology datasets lack comprehensive, long-term meteorological and streamflow data.
- Understanding climate-runoff interactions requires integrated datasets across diverse watersheds.
- Long-term hydrologic analyses are crucial for assessing watershed change and climate impacts.
Purpose of the Study:
- To introduce the MACH dataset, a unified and enhanced hydrometeorological resource for the United States.
- To provide daily meteorological forcings and streamflow observations for 1,014 watersheds over an extended period.
- To support large-sample hydrology research, hydroclimatic assessment, and data-driven water science.
Main Methods:
- Integrated daily meteorological forcings and streamflow observations for 1,014 US watersheds.
- Extended data records to 75 years for 395 catchments (dating back to 1948).
- Included comprehensive watershed attributes: topography, soils, geology, land cover, and anthropogenic influences.
Main Results:
- Developed MACH, a high-resolution dataset spanning 1980-2023 (with extended records to 1948 for some catchments).
- The dataset covers 1,014 watersheds across the United States.
- Provides a rich set of watershed attributes and hydroclimatic indices.
Conclusions:
- MACH is a flexible, high-resolution dataset advancing large-sample hydrology and hydroclimatic assessment.
- The dataset supports investigations of climate-runoff interactions, hydrologic sensitivity, and long-term watershed change.
- MACH facilitates data-driven water science and improved hydrologic model calibration.
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