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22-5. Holistic process-guided approaches for predicting hydrologic extremes

We seek a Mendenhall Postdoctoral Scholar to investigate the integration of process knowledge – including but not limited to climatic, land use, groundwater interactions, and/or human-induced flow alteration – across several components of a workflow that predicts hydrologic extremes for hindcasts, forecasts, and/or projections.  

Description of the Research Opportunity

The generation of extreme events occurs from unique combinations of physical processes, which may change differently over time and act at different spatial and temporal scales. Process-guided modeling approaches are becoming more common in data-driven hydrologic prediction (e.g., Jia et al., 2019; Varadharajan et al., 2022). These approaches typically integrate known physical processes, like mass and energy balance, as constraints or objectives within model architectures, or use predictor data as proxies for processes. The use of process guidance in hydrology has been shown to improve water quantity and quality models’ predictive performance and functional performance (i.e., how well a model matches physical relationships [Ruddell et al., 2019]). However, taking a holistic process-guided ‘view’ over the entire modeling workflow instead of a single component of it is largely unexplored and could further improve model performance. We seek proposals that address: How can we develop better predictions of hydrologic extremes by integrating process knowledge across key modeling components in the workflow? 

Research proposals should address multiple components of a modeling workflow that evaluates the impact of process integration on hindcasts, forecasts and/or projections of floods, droughts, and/or ecological flow regimes. Because of the focus on process guidance, proposals must describe how the candidate will evaluate a model’s functional performance as well as the predictive performance. Uncertainty quantification approaches should also be detailed. The expected workflow will be modular and utilize best practices for software prototyping and engineering.

Modeling components could include, but are not limited to: 

  • Informing model spatial domains and subsequent modeling approaches with process-guided hydrologic regionalization, 

  • Using process information to bridge models across spatiotemporal scales, 

  • Creating hybrid process-based and data-driven (machine learning, statistical) models, 

  • Handling multiple climatic processes that generate extreme high or low flows, 

  • Integrating unique seasonal processes, such as ice jams and snowmelt, 

  • Considering groundwater interactions to extreme surface flows, and 

  • Accounting for flow alteration processes in predictive models. 

Candidates will have opportunities to discuss their research with their Research Advisors and several teams, such as ongoing hydrologic extremes research in collaboration with the Federal Highway Administration (FHWA) and the Federal Emergency Management Administration (FEMA), hydroecologists in the EcoFlows Program, and the Water Mission Area (WMA) Hydrologic Extremes Working Group (HEWG) in the Modeling Community of Practice (CoP).

Interested applicants are strongly encouraged to contact the Research Advisors early in the application process to discuss project ideas.


Jia, X., Zwart, J., Sadler, J., Appling, A., Oliver, S., Markstrom, S., Willard, J., Xu, S., Steinbach, M., Read, J., and Kumar, V. Physics-guided recurrent graph model for predicting flow and temperature in river networks. Proceedings of the 2021 SIAM International Conference on Data Mining (SDM). 2021, 612-620.

Ruddell, B. L., Drewry, D. T., & Nearing, G. S. (2019). Information theory for model diagnostics: Structural error is indicated by trade-off between functional and predictive performance. Water Resources Research, 55, 6534– 6554.

Varadharajan, C., Appling, A. P., Arora, B., Christianson, D. S., Hendrix, V. C., Kumar, V., Lima, A. R., Müller, J., Oliver, S., Ombadi, M., Perciano, T., Sadler, J. M., Weierbach, H., Willard, J. D., Xu, Z., & Zwart, J. (2022). Can machine learning accelerate process understanding and decision-relevant predictions of river water quality? Hydrological Processes, 36(4), e14565.


Proposed Duty Station(s)

Reston, Virginia

Denver, Colorado

Menlo Park, California 


Areas of PhD

Hydrology, climatology, ecology, geology, civil or environmental engineering, statistics, data science, or related fields (candidates holding a Ph.D. in other disciplines, but with extensive knowledge and skills relevant to the Research Opportunity may be considered).



Applicants must meet one of the following qualifications: Research Hydrologist, Research Physical Scientist, Research EcologistResearch Computer Scientist, Research Civil Engineer, Research Environmental Engineer, Research Statistician, or Research Economist

(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.)