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USGS Mendenhall Postdoctoral Research Fellowship Program

17-22. Whole-network estimates and forecasts of stream temperature through data assimilation

Water temperature is an ecological master variable governing rates of biological activity, chemical reactions, and habitat suitability in streams and rivers. Although the U.S. Geological Survey (USGS) and many other institutions monitor water temperature widely around the country, and although models exist to predict water temperature even in unmonitored stream reaches, no data product exists to provide nowcasts or forecasts of water temperature across the nation. An essential step toward the development of such a product is the assimilation of water temperature observations from multiple data sources, including USGS monitoring data, estimates from remote sensing, and furnished datasets from state, local, and nonprofit groups. The inclusion of many data sources comes with challenges, such as varying levels of uncertainty and consistency across the datasets, but would greatly expand the coverage of temperature observations beyond what any one institution can provide, potentially improving our model accuracy and the value of a national water temperature data product to stakeholders.

Data assimilation (DA) has been shown to estimate the true state of a system better than models or observations alone by combining model predictions with observations according to their relative uncertainty and model-data agreement. This approach can also accommodate different observation sources with varying uncertainties, which makes DA a valuable tool in integrating many data sources into a single product. To apply DA to stream temperature forecasts and nowcasts, it will be necessary to (1) choose an appropriate model to represent temperatures within a stream network given the available inputs, (2) characterize the scope and expected quality of multiple observational data sources, and (3) implement a DA approach to modify the model's state according to observations.

We seek a Mendenhall Postdoctoral Fellow who will take a whole-network data assimilation approach to produce high-accuracy water temperature nowcasts and forecasts. Applicants are encouraged to consider the following elements as they develop their proposals:

Beyond the above recommendations, we encourage innovation and fundamental contributions to the sciences of water temperature prediction and data assimilation. Specific approaches for the candidate's proposal might include:

Candidates are expected to have experience with data assimilation and process modeling (e.g., of energy or hydrologic networks). Applicants should have experience with Python or R.

Applicants are highly encouraged to contact the advisors below early in the application process to discuss project ideas.


Cole, J.C., Maloney, K.O., Schmid, M. and McKenna Jr, J.E., 2014. Developing and testing temperature models for regulated systems: A case study on the Upper Delaware River. Journal of hydrology, 519, pp.588-598.

Dietze, M. C. 2017. Ecological forecasting. Princeton University Press.

Masutani, M., Schlatter, T.W., Errico, R.M., Stoffelen, A., Andersson, E., Lahoz, W., Woollen, J.S., Emmitt, G.D., Riishøjgaard, L.P. and Lord, S.J., 2010. Observing system simulation experiments. In Data Assimilation (pp. 647-679). Springer, Berlin, Heidelberg.

Proposed Duty Station: Middleton, WI; Reston, VA

Areas of Ph.D.: Hydrology, geophysics, oceanography, limnology, meteorology, computer science, geoinformatics, data science, engineering, environmental science, or other relevant discipline.

Qualifications: Applicants must meet one of the following qualifications: Research Hydrologist, Research Engineer, Research Computer Scientist. (This type of research is performed by those who have backgrounds for the occupations stated above. However, other titles may be applicable depending on the applicant's background, education, and research proposal. The final classification of the position will be made by the Human Resources specialist.)

Research Advisor(s): Jordan Read, (608) 821-3922,; Alison Appling, (814) 954-5735,; Brian Pellerin, (703) 648-6865,; Julie Kiang, (703) 648-5364,

Human Resources Office Contact: Nina Ngo,, 703-648-7431

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Last modified: 16:37:42 Mon 11 Mar 2019
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