Examining the Impacts of Disturbances on River Hydrology at Regional to Continental Scales
Authors
Charuleka Varadharajan1* (cvaradharajan@lbl.gov), Mohammed Ombadi1, Emily Nagamoto1,2, Fabio Ciulla1, Helen Weierbach1, Jared Willard1,3, Aranildo R. Lima4
Institutions
1Lawrence Berkeley National Laboratory, Berkeley, CA; 2Duke University, Durham, NC; 3University of Minnesota–Twin Cities, Minneapolis/St. Paul, MN; 4Environment and Climate Change Canada, Vancouver, Canada
URLs
Abstract
Hydrometeorological disturbances such as floods, droughts, and heatwaves are projected to increase due to climate change impacting river water availability and quality. This presentation examines the effects of disturbances, and particularly drought, on river flow, temperature, and salinity across different watersheds in the United States. An overarching goal is to determine how watersheds with different traits such as climate, topography, vegetation, and land use respond to disturbance. First, the team presents a conceptual framework to study the resistance of hydrologic catchments to droughts and demonstrates its utility by studying the California megadrought (2012 to 2015). Researchers find that human-impacted catchments were more resistant to meteorological drought compared to pristine catchments and that the prolonged drought period and cumulative impact of prior drought episodes impaired catchment resistance. The team then shows how periods of severe drought throughout the last 20 years resulted in annual streamflow decreases and increases in water temperature and specific conductance in the Upper Colorado River Basin. Natural (e.g., average summer rain and forest cover) and anthropogenic (e.g., distance to dams) traits are found to be correlated with the relative change of each variable as well as the variation within sites. Finally, the team presents a novel network-based method for multiscale catchment classification using watershed traits, where the relationships between catchments are represented by the edges of a network. The ability of networks is leveraged to capture collective behaviors to find clusters of catchments with similar traits at a particular scale. Researchers first build a network of 274 traits and determine a small number of interpretable trait categories based on their similarity. The network method is then applied using the trait categories to classify 9,067 river catchments across the contiguous United States. The resulting classification shows a remarkable geographical coherence across traits categories. Additionally, the team finds that different catchment clusters have distinct hydrologic behavior (e.g., streamflow statistics). This approach allows the establishment of a connection between catchment hydrological behavior, including their response to disturbance and physical traits.