This job has expired

Hydrologic Science Postdoctoral Fellowship

Employer
University of Michigan, School for Environment and Sustainability
Location
Ann Arbor, Michigan
Closing date
Jun 25, 2022

View more

Discipline
Hydrology
Career Level
Postdoctoral
Education Level
PhD
Job Type
Full-time
Relocation Cost
No Relocation
Sector Type
Academia
You need to sign in or create an account to save a job.

Summary

The University of Michigan’s School for Environmental and Sustainability (SEAS) seeks outstanding candidates for a postdoctoral fellow position. This candidate will collaborate with; the National Oceanic and Atmospheric Administration (NOAA), Great Lakes Environmental Research Laboratory (GLERL), and the Cooperative Institute for Great Lakes Research (CIGLR). The successful candidate will lead research in hydrology, coastal processes, and regional climate through the use of process-based numerical models in high-performance computing environments. Specific research foci include testing, calibration, and validation of numerical models, understanding water cycles in select spatial domains, and informing future projections of total water levels in coastal regions under potential future climate scenarios.

The selected scholar will work with Dr. Andrew Gronewold (based in SEAS, with adjunct appointments in the University’s Department of Civil and Environmental Engineering, and Department of Earth and Environmental Sciences) on development, testing, and application of state-of-the-art hydrological models in select spatial domains (e.g., large lakes and reservoirs). A specific goal of this research is to advance foundational hydrological science through development and deployment of models such as the Weather Research and Forecasting Hydrological model (WRF-Hydro) within the University’s high performance computing environment. It is also expected that the selected scholar, as part of this research, will identify and apply suitable forcing datasets to hydrological models that adequately represent large inland water bodies. Such forcing datasets can be from climate model outputs, such as those from the Coupled Model Intercomparison Project (CMIP).  The selected scholar will also be expected to collaborate with a diverse range of experts not only at GLERL and CIGLR, but also at the U.S. Army Corps of Engineers, on hydrodynamic, ice, and atmospheric modeling to advance research examining future coastal changes under climate change. The envisioned research will also leverage partnerships with scientists across additional branches at NOAA and scientists at the National Center for Atmospheric Research (NCAR).

The project has a duration of two years. The initial appointment for the position is one year with a possible renewal for the second year contingent on performance. Applications will be reviewed one a first-come first-served basis until the position is filled.  The position is expected to start before the end of the summer of 2022.

SEAS is committed to creating an inclusive and equitable environment that respects diverse experiences, promotes generous listening and communication, discourages and genuinely respond to acts of discrimination, harassment, or injustice. Our commitment to diversity, equity and inclusion is rooted in our values for a sustainable and just society.

Responsibilities

  • Evaluate hydrological forcing data for the Great Lakes watershed by comparing multiple products, including those from the Coupled Model Intercomparison Project Phase 6 (CMIP6).   Identify forcing datasets for basin-scale runoff and evaporation that are best suited for WRF-Hydro model simulations under climate change scenarios. Closely work with the other project sub-teams on Hydrodynamic-ice modeling (Fujisaki-Manome, Cannon at CIGLR) and the stochastic Weather Generator model (Steinschneider at Cornell University) to evaluate the consistency in terms of basin-wide averages and seasonal cycles.
  • Conduct hydrology simulations using the WRF-Hydro model for the Great Lakes region using the select hydrological forcing data product for the historical and future periods.  The simulation will be conducted at the University of Michigan’s High Performance Computing System.
  • Evaluate the hydrological simulation results by comparing with available observations and outputs from lumped conceptual models for the historical period. Examine the difference in the future projection results between WRF-Hydro and lumped conceptual models and interpret them with respect to the biases observed in the historical period, as well as the forcing inconsistency used in these models. Work with the hydrodynamic-ice modeling sub-team (Fujisaki-Manome, Cannon) to effectively evaluate the biases and forcing inconsistencies.
  • Lead at least one manuscript based on research findings for submission to a peer-reviewed journal, present results at a conference(s). A manuscript is expected to be initiated within the 6 months from the start date.
  • Attend regular project meetings at GLERL and CIGLR, as well as inter-agency group meetings to report progress.

Required Qualifications

  • A Ph.D. in hydrology, coastal engineering, or a similar field.
  • Experience with state-of-the-art hydrological models (e.g., WRF-Hydro) and application to simulating and forecasting water supplies.
  • Familiarity with data analysis and visualization in a scripting environment using R, Python, or similar software.
  • Experience with running simulations on a supercomputer or a cluster computing environment.
  • Strong communication skills and a demonstrated ability to work both as a team and independently.

Desired Qualifications

  • Knowledge of hydrological processes in large water bodies, including evaporation, seasonal cycles, and water level change.
  • Knowledge of climate change scenarios used in model projections, as well as pathways that hydrological processes can take under these scenarios. 
  • Experience with running the WRF-Hydro model. 
  • Experience managing, processing, and analyzing spatial data in a GIS environment.
  • A strong publication record in hydrologic science or a related field, particularly as lead author, in refereed journal publications.
You need to sign in or create an account to save a job.

Get job alerts

Create a job alert and receive personalized job recommendations straight to your inbox.

Create alert