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David J Holtschlag, PStat

Starting in 1976, I have had a successful and enjoyable career as a hydrologist with the USGS. During more than 40 years, I participated in numerous field and research studies to characterize water resources in Michigan and parts of the Great Lakes. I am grateful and indebted to my colleagues for their professionalism, inspiration, and helpfulness.

David actively serves as an USGS Scientist Emeritus. 

Professional Studies/Experience

Served as the Surface-Water Specialist for the USGS Michigan Water Science Center.  Developed both 1- and 2-dimensional hydrodynamic models of surface-water bodies on the Great Lakes Waterway, Ohio River, and numerous rivers in Michigan.  An unsteady 1-dimensional model of St. Clair River was developed to quantify changes in conveyance properties along individual reaches of the river using water-level gaging station data distributed throughout St. Clair River. A 2-dimensional model of St. Clair-Detroit River was developed to identify source areas to public-water intakes.  A two-dimension model of the Ohio River was used to characterize flow and the dispersion of point discharge constituents based on a study using dye injections. Carried out flood frequency analysis and flood hydraulic analysis using standard models. Developed an Bayesian network model of multivariate water-quality constituents on the White River in Indiana to estimate an unmeasured subset of constituents based on an arbitrarily selected subset of measured constituents.  Developed mixed effects statistical models to characterize the effects of irrigation, land use, precipitation, and temperature on selected basins in southwestern Michigan. 

Mentorship/Outreach

Assisted early career hydrologists in the application of advanced statistical models including the mixed effects models and spatial models of hydrologic phenomena. 

Current Interest

Development of state-space models to sequentially update parameter estimates of spline functions describing stage-discharge relations based on individual discrete-flow measurements. Apply Kalman filtering and smoothing to provide real-time and finalized estimates of the magnitudes and uncertainties of unit flow data based on unit stage data.

Education and Certifications

  • University of Missouri-Columbia, B.S.. in Forestry, 1974

  • University of Minnesota, M'S. Forest Hydrology, 1976

  • Michigan State University, M'S. System Science, 1990

Affiliations and Memberships*

  • American Statistical Association, Accredited Professional Statistician

Science and Products

*Disclaimer: Listing outside positions with professional scientific organizations on this Staff Profile are for informational purposes only and do not constitute an endorsement of those professional scientific organizations or their activities by the USGS, Department of the Interior, or U.S. Government