Matthew Miller
Matt Miller is a Research Hydrologist with the Earth Systems Modeling Branch of the Integrated Modeling and Prediction Division in Boulder, Colorado.
His current research focuses on developing integrated approaches for assessing water availability, including novel approaches for interpreting large data sets to quantify the relationships between water quality, hydrology, land use, and climate at watershed, regional, and national scales. A major theme of Matt’s research is improving process-level understanding of groundwater-surface water interaction and incorporating this understanding into water budget and water quality models. Matt is currently the Project Manager for an Integrated Water Availability Assessment (IWAAs) project in the Upper Colorado River Basin. This project aims to provide insight into how past, present, and future snow conditions – including amount, timing, melt, and transitions from snow- to rain-dominated systems – impact water supply (quantity and quality) and the ability to meet demand.
Education and Certifications
Ph.D., Civil and Environmental Engineering, University of Colorado, Boulder (2008)
M.S., Civil and Environmental Engineering, University of Colorado, Boulder (2004)
B.S., Zoology, University of Wisconsin, Madison (2000)
Science and Products
The role of baseflow in dissolved solids delivery to streams in the Upper Colorado River Basin The role of baseflow in dissolved solids delivery to streams in the Upper Colorado River Basin
A database of natural monthly streamflow estimates from 1950 to 2015 for the conterminous United States A database of natural monthly streamflow estimates from 1950 to 2015 for the conterminous United States
Stream‐centric methods for determining groundwater contributions in karst mountain watersheds Stream‐centric methods for determining groundwater contributions in karst mountain watersheds
Managing salinity in Upper Colorado River Basin streams: Selecting catchments for sediment control efforts using watershed characteristics and random forests models Managing salinity in Upper Colorado River Basin streams: Selecting catchments for sediment control efforts using watershed characteristics and random forests models
Estimating discharge and nonpoint source nitrate loading to streams from three end‐member pathways using high‐frequency water quality data Estimating discharge and nonpoint source nitrate loading to streams from three end‐member pathways using high‐frequency water quality data
Predicting redox-sensitive contaminant concentrations in groundwater using random forest classification Predicting redox-sensitive contaminant concentrations in groundwater using random forest classification
Non-USGS Publications**
**Disclaimer: The views expressed in Non-USGS publications are those of the author and do not represent the views of the USGS, Department of the Interior, or the U.S. Government.
Science and Products
The role of baseflow in dissolved solids delivery to streams in the Upper Colorado River Basin The role of baseflow in dissolved solids delivery to streams in the Upper Colorado River Basin
A database of natural monthly streamflow estimates from 1950 to 2015 for the conterminous United States A database of natural monthly streamflow estimates from 1950 to 2015 for the conterminous United States
Stream‐centric methods for determining groundwater contributions in karst mountain watersheds Stream‐centric methods for determining groundwater contributions in karst mountain watersheds
Managing salinity in Upper Colorado River Basin streams: Selecting catchments for sediment control efforts using watershed characteristics and random forests models Managing salinity in Upper Colorado River Basin streams: Selecting catchments for sediment control efforts using watershed characteristics and random forests models
Estimating discharge and nonpoint source nitrate loading to streams from three end‐member pathways using high‐frequency water quality data Estimating discharge and nonpoint source nitrate loading to streams from three end‐member pathways using high‐frequency water quality data
Predicting redox-sensitive contaminant concentrations in groundwater using random forest classification Predicting redox-sensitive contaminant concentrations in groundwater using random forest classification
Non-USGS Publications**
**Disclaimer: The views expressed in Non-USGS publications are those of the author and do not represent the views of the USGS, Department of the Interior, or the U.S. Government.