Publication Date
Summer 2024
Degree Type
Thesis
Degree Name
Master of Science (MS)
Department
Geology
Advisor
Nathaniel Bogie; Matthias Sprenger; June Oberdorfer; Katherine Cushing
Abstract
Climate models forecast that headwater catchments in the western U.S. will undergo a reduction in snowpack, early season snowmelt, and increases in evapotranspiration. The resulting extended dry season will stress vegetation in mountainous watersheds throughout the Upper Colorado River Basin. We investigate infiltration patterns and root water uptake in response to dry season disturbances within the East River watershed in Colorado. To do this, we collected soil cores, measured matric potential and sap flow, and monitored tree xylem and soil for stable isotopes of water (2H, 18O) in two soil profiles to 90 cm depth (with three Engelmann spruce and three aspen trees instrumented, respectively). Sub-daily stable water isotope dynamics were analyzed between mid-June and late October of WY-22 using a cavity ring-down spectrometer. HYDRUS-1D was calibrated with both field measurements of matric potential and δ2H to simulate the ecosystem response to late summer dry spells and monsoonal rainfall for the 128-day time domain. Simulations show both aspen and spruce trees relying heavily on headwater snowmelt. Water-use analysis reveals a reduction in transpiration during the late summer and early autumn months, particularly in September, the driest month of WY-22. Spruce trees appear more tolerant of dry soil conditions than aspen trees. As a result, it is likely that spruce will outcompete aspen in the future, given forecasted shifts in climate and changes in the timing of snowmelt. This study highlights the benefits of coupling high frequency stable water isotope data and more commonly used matric potential field measurements for parameter optimization in numerical modeling.
Recommended Citation
Hess, Raymond J., "Optimizing Field-Linked Simulations of Dry Season Uptake and Monsoon Infiltration within an Aspen-Mixed Conifer Forest" (2024). Master's Theses. 5538.
DOI: https://doi.org/10.31979/etd.mvws-cqj4
https://scholarworks.sjsu.edu/etd_theses/5538