USGS water observational networks are used for emergency operations during floods and to support water management and planning activities. They also serve as the backbone of scientific research to understand and predict water hazards, water quality, and water availability. Efficient and effective use of limited resources is critical for future operation of these networks. The focus of research under this Opportunity is on how to optimize a hydrologic monitoring network as part of the development of a Next Generation Water Observing System (NGWOS) in support of water quantity and quality prediction activities. Outcomes from this research will inform the selection of new observing locations and prioritization of data collection across a network to maximize information gained from observing systems.
The NGWOS is a new USGS initiative to develop new monitoring methods and to demonstrate the benefits of intelligent design of monitoring networks. The initial pilot area is the Delaware River Basin, where the observational network has been expanded recently by deploying new technologies for monitoring, which would allow the postdoctoral researcher to focus immediately on significant science questions.
We seek a postdoctoral fellow to build a solid understanding of data gaps within hydrologic monitoring systems so that any changes to observing networks result in better support of water management, hazards awareness, and modeling activities. A successful candidate will have the discretion to focus on any of a number of issues for improving observational networks. Analysis of streamflow, groundwater, or water quality networks are possible avenues. Previous work by USGS and others could be useful for identifying critical knowledge gaps for the design of observational networks. USGS has in the last several years undertaken limited analysis of its observing networks, primarily in the streamflow network (for example, Kiang and others, 2013). USGS has also conducted a series of studies to examine the effects of individual streamgages on regional predictions of streamflow statistics using GLSNET (for example, Olson 2003) and to examine the cost-effectiveness of the network (Thomas and Wahl, 1993). Another example of an analysis of streamgage networks is included in DeWeber and others (2014), who identified landscape biases in USGS gage locations.
The researcher may also choose to build on approaches that have been described in the literature (for example, Mishra and Coulibaly, 2009; Chacon-Hurtado, Alfonso, and Solomatine, 2017), or develop other new ideas for optimizing observational networks. Of particular interest is adapting Observing System Simulation Experiments (OSSE, see Masutani et al., 2010), often used in numerical weather prediction, for use in hydrologic system modeling. These experiments are designed to understand the impact of specific observations on simulation results, particularly in the context of data assimilation. The USGS is partnering with the National Weather Service and National Oceanic and Atmospheric Administration on development of the National Water Model (NWM). The NWM as well as other USGS models may be used as testbeds for incorporating OSSE concepts.
Chacon-Hurtado, J. C., Alfonso, L., and Solomatine, D. P. (2017) Rainfall and streamflow sensor network design: a review of applications, classification, and a proposed framework, Hydrol. Earth Syst. Sci., 21, 3071-3091, https://doi.org/10.5194/hess-21-3071-2017.
DeWeber, JT, Tsang, Y-P, Krueger, D.M., Whittier, J.B., Wagner, T., Infante, D.M., and Whelan, G. (2014) Importance of Understanding Landscape Biases in USGS Gage Locations: Implications and Solutions for Managers, Fisheries, 39(4), 155-163.
Kiang, J.E., Stewart, D.W., Archfield, S.A., Osborne, E.B., and Eng, Ken, 2013, A national streamflow network gap analysis: U.S. Geological Survey Scientific Investigations Report 2013–5013, 79 p. plus one appendix as a separate file, http://pubs.usgs.gov/sir/2013/5013/.
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.
Mishra, A. K., and P. Coulibaly (2009), Developments in hydrometric network design: A review, Rev. Geophys., 47, RG2001, doi:10.1029/2007RG000243.
Olson, Scott (2003) Effectiveness of the New Hampshire stream-gaging network in providing regional streamflow information, Water Resources Investigations Report 2003-4041, https://pubs.er.usgs.gov/publication/wri034041.
Thomas, W.O. and Wahl, K.L. (1993) Summary of the Nationwide Analysis of the Cost Effectiveness of the U.S. Geological Survey Stream-Gaging Program (1983-1988), Water Resources Investigations Report 93-4168, https://pubs.usgs.gov/wri/1993/4168/report.pdf.
Proposed Duty Station: Reston, VA; Middleton, WI; Lakewood, CO; Tacoma, WA; Richmond, VA; Lubbock, TX.
Areas of PhD: Hydrology, Water resources, Atmospheric Sciences, Civil and Environmental Engineering, Computer Science, Operations Research, Systems Engineering, Statistics, Data Science, Econometrics (candidates holding a Ph.D. in other disciplines, but with extensive knowledge and skills relevant to the Research Opportunity may be considered).
Qualifications: Applicants must meet one of the following qualifications: Research Physical Scientist, Research Hydrologist, Research Statistician, Operations Research Analyst, Research Engineer, Research Computer Scientist, 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 USGS Human Resources specialist.)
Research Advisors: Julie Kiang, (703) 648-5364, firstname.lastname@example.org; Brian Pellerin, (703) 648-6865, email@example.com; Jordan Read, (608) 821-3922, firstname.lastname@example.org; Christopher Konrad, (253) 552 1634, email@example.com.
Human Resources Office Contact: Nina Ngo, firstname.lastname@example.org, 703-648-7431
|Summary of Opportunities|