Mentored Employment Program (MEP)

River and Floodplain Ecohydrology

Faculty mentor/Supervisor
Skuyler Herzog
Email Address
Department Affiliation
Forest Ecosystems & Society
Project Location
OSU-Cascades (Bend, OR) and field work in the Whychus Creek (Sisters, OR), Crooked River (Post, OR), Thirtymile Creek (Condon, OR)
Project Description
This project monitors the hydrology of the Upper Crooked River, Whychus Creek, and Thirtymile Creek floodplains, which have suffered from historic degradation due to factors like overgrazing, drought, and river channelization. The goal is to gather the necessary data to guide practical floodplain reconnection/restoration efforts, which would help reduce erosion, improve water quality, and restore native fish habitats. By studying the river’s hydrology and ecology, the project will provide landowners and stakeholders with information they need to reconnect floodplains, recharge groundwater, and increase late-season river flows. Many private landowners, environmental nonprofits, and government agencies are participating and supporting this project. Thus, students will have the opportunity to learn trans-disciplinary research skills. Ultimately, this work will support both environmental restoration and future fish passage improvements in Central Oregon.
Can the tasks of this project be performed remotely by a Cascades or E-Campus student?
No
Describe the type of work and tasks you anticipate the student will perform
This position will include a combination of laboratory work (e.g., reading articles, processing data) and field work (e.g., surveying riparian zones, collecting water measurements).
Hourly rate of pay
17
Certification
Yes
What is the expected timeline of this project?
The broader floodplain project has been running since 2020. The student will begin with training and introduction to the project during Winter Term 2026. With the help of the faculty mentor Dr. Herzog, the student will choose an individual subproject to focus on during Spring 2026. This subproject will be simple and well-defined, so that they student can gain confidence in their abilities while also gaining appreciation for the inherent complexities of even simple questions. They will conduct their own research and present a poster at the OSU-Cascades Undergraduate Research Symposium in May 2026. The student will have the option of continuing to work with Dr. Herzog during Summer 2026 and beyond.
Are special skills or knowledge required to work on this project?
No
Will training be provided?
Yes
How many hours per week do you anticipate a student to work?
10
How many hours per week do you anticipate engaging in direct mentorship?
5
At OSU-Cascades I have formally mentored 20 undergraduates through paid research positions and fellowships. I hold a weekly research group meeting for all undergraduates to share about their projects (i.e., peer-to-peer learning) and coordinate for the week. I also have individual meetings with each student each week. Finally, the group of students is invited to attend ad hoc field work. My mentorship plan for each student centers on identifying their long-term career/research goals and then advancing them along their unique path. This involves developing their confidence, technical skills, and critical thinking. Many students are unsure of exact career goals, so I also work with them to research career options and network directly with relevant professionals. Past examples include connecting students with watershed councils, government agency scientists (e.g., USGS, USFS, BLM), and more. At the end of their research experience, my students have improved their resumes, gained clarity and confidence about their future careers, and developed basic research skills. As one example, I recently had an underrepresented student share how much her confidence has grown over the course of her fellowship, and how she now believes that she can be a scientist. Back in the lab, she triumphantly announced “It’s coming together!” when she realized how a set of quantitative statistics matched her qualitative understanding of a dataset.