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. One of the watersheds that we will be treating as a case-study is the East River, CO, a site that has been intensively monitored by scientists at Lawrence Berkeley Lab. We are recruiting students to help in the process of database compilation and analysis of results.
This project was originally launched as part of UC Berkeley's Undergraduate Research Apprentice Program (URAP) in 2018-2019. It is currently full and no new students are being accepted for the 2019-2020 academic year.