Date of Award:


Document Type:


Degree Name:

Master of Science (MS)


Civil and Environmental Engineering

Committee Chair(s)

Belize Lane


Belize Lane


David Tarboton


Patrick Singleton


River ecosystems are controlled in part by natural variation in the flow regime and streamflow alterations that impair these natural variations often have negative impacts for aquatic species. Flow metrics describing attributes of the natural flow regime can inform environmental water management goals to maintain and restore river ecosystems by mirroring critical aspects of the natural flow regime. However, unimpaired daily streamflow data needed to calculate these flow metrics is not always readily available. Statistical scaling approaches present an opportunity to estimate unimpaired flow metrics at ungauged locations to better address environmental water management objectives. This study evaluates a suite of scaling approaches for their ability to estimate ecologically significant daily unimpaired flow metrics applied across the State of California. Results demonstrate the utility of stratification by hydrologic and water-year-type to improve statistical scaling methods and indicate that different scaling approaches are better suited to estimate certain flow metrics. Aggregated dimensionless reference hydrographs accounted for spatial and inter-annual variability better than a single index site for improved representation across large regions. This is the first known example of combining hydrologic classifications and stream class stratified reference hydrographs to refine scaling streamflow relationships and capture natural streamflow magnitude and timing patterns across a large heterogeneous region. Results are intended to inform selection of appropriate streamflow scaling approaches for a given study region based on the specific flow metrics of interest, stream classes present, and reference gauge density and distribution. Better prediction of unimpaired daily flow metrics will lead to more accurate streamflow regime characterization and better flow management decisions.