Relationships Between Watershed Scale Co-Variability of Traits and Watershed C-Q Function
Authors
Michelle E. Newcomer1* (mnewcomer@lbl.gov), Haruko Wainwright1,2* (hmwainwright@lbl.gov), Lijing Wang1, Lucien Stolze1, Evan King2, Zexuan Xu1, Baptiste Dafflon1, Bhavna Arora1, Dipankar Dwivedi1, Kristin Boye3, Markus Bill1, Carl Steefel1, Robin Thibaut1, Nicola Falco1, Craig Ulrich1, Nicholas Bouskill1, Curtis Beutler4, Rosemary Carroll5, Kenneth Hurst Williams1, Eoin Brodie1 (elbrodie@lbl.gov)
Institutions
1Lawrence Berkeley National Laboratory, Berkeley, CA; 2Massachusetts Institute of Technology, Boston, MA; 3SLAC National Accelerator Laboratory, Menlo Park, CA; 4Rocky Mountain Biological Laboratory Gothic, CO; 5Desert Research Institute, Reno, NV
URLs
Abstract
Predictions of stream hydrobiogeochemistry including chemistry and discharge (C-Q) in response to hydrological perturbations involve physically based models. Spatially distributed models rely on the availability of geospatial trait data layers, which are difficult to obtain at spatial-temporal scales compatible with model grid sizes. These data layers are often created from limited spatial data and statistical models to extrapolate/interpolate spatially. To overcome these inherent limitations, new approaches have been proposed to harness the remote sensing spatial covariates to predict other parameters such as soil hydraulic conductivity, soil geochemical properties, and bedrock properties. Additionally, characteristic and reproducible C-Q patterns emerge at different scales, and these C-Q patterns contain information on hydrological, ecological, and biogeochemical dynamics within a watershed, yet these patterns and associations between covariability of traits and C-Q as a watershed function are not well understood.
The watershed study leverages in-stream data as aggregated observations of C-Q watershed response to changing snow conditions to advance understanding of watershed function in response to trait covariability. Researchers investigate the >30 year record of historical C-Q data available for the East and Taylor River watersheds. Researchers have chosen to use watershed C-Q relationships because hydrologic connectivity, and ecological and hydrobiogeochemical processes vary in space and time, such that dynamic C-Q relationships, measured within and between watersheds, may be diagnostic of how watershed functional traits are interacting and changing.
Researchers extend the prior developed zonation approach and evaluate the co-variability of traits by applying clustering to multiple data layers. A particular focus is to explore the transferability of the zonation approach to different basins as well as to investigate the impact of spatial resolutions and scales. Using spatially resolved maps of traits, and field observations of C-Q collected since 2014 (Upper East) and since 1970 (larger East and Taylor), including export of nitrogen and other elements at multiple locations within the both watersheds, researchers explore C-Q response variability across elevations, the relationship with dynamic traits (vegetation, microbial), and more static traits (bedrock, topography). Trait importance to C-Q outcomes is explored using sensitivity analyses with trait-informed numerical models in a one-factor-at-a-time approach