Improved forecasting of river flow
Laurel Larsen, Professor
Geography
Closed. This professor is continuing with Fall 2024 apprentices on this project; no new apprentices needed for Spring 2025.
River flow forecasting is essential for planning reservoir operations, defense strategies against flooding, and fluvial ecosystems management plans. However, flow forecasting is a highly uncertain science. One of the biggest uncertainties lies in resolving the timescales over which water is stored in the subsurface and time lags between perturbations in hydrometeorological variables and perturbations in streamflow.
To reduce this uncertainty, we are synthesizing data from highly instrumented watersheds throughout the world into a common database. With the organized and cleaned data, we will be applying statistical techniques from information theory to identify the critical timescales over which predictors of streamflow are relevant to streamflow forecasting. We are recruiting students to help in the process of database compilation and analysis of results.
Role: The recruited student will be working with a large and multidisciplinary team of researchers at Berkeley and external institutions. Specific tasks will include implementing a workflow to ensure that datasets downloaded from instrumented watersheds are comparable and operational. This may involve running code to fill gaps in the datasets or aggregate different datapoints in time. Once the complete dataset from a particular site has been compiled, the student will interpret the output of the code that derives time lags between precipitation, other hydroclimatic variables, and discharge, drawing conclusions and syntheses.
Learning outcomes will be improved experience with scientific computing and programming, and exposure to a central challenge in hydrology. Students will also gain familiarity with cutting-edge tools emerging from the discipline of Information Theory and will gain insight into different modeling approaches for forecasting river flow.
Qualifications: The student should have programming (Python is preferred, though strong programming skills in another language, such as Matlab, will also be viewed favorably) and computing (Github experience is preferred) skills. The student ideally has also had some exposure to statistics and is comfortable interpreting scientific graphs.
This would be an ideal opportunity for a statistics, CS, or civil engineering student who would like to gain experience with environmental applications. It could also be a good fit for an earth science or geography student with strong programming skills.
Hours: to be negotiated
Related website: http://esdlberkeley.com
Mathematical and Physical Sciences