The Effects of Terrain Physiography on Surface Water Source-flow in the Headwaters of a Mountain Catchment
Abstract
Many mountainous regions around the world rely on snowmelt modelling to make predictions regarding water availability. As climate change reduces the reliability of snowpack in these regions, we must work to improve our understanding of how rain and melt water is stored in the terrain. Understanding the mechanistic link between terrain physiography and the movement/storage of water in the landscape is critical to our ability to make sound predictions of spatial and temporal variations in water supply. For this analysis, we are using water stable isotopes - a powerful tool that allows us to understand spatial and temporal variability in flow paths. We will contrast isotope ratios in surface water samples collected in four synoptic campaigns: two summers (2021 and 2022), one spring (2022), and one fall (2022). Samples are collected across high elevation headwater streams (600-1200m) in the HJ Andrews Experimental Forest, Western Oregon, USA, where we also have a dataset of the isotopic composition of precipitation dating back to 2014. The spatial/temporal trends in isotopic composition of these streams combined with LiDAR derived metrics of physiography will be used to infer differences in storage across the landscape. Preliminary results demonstrated that localized variations in isotopic composition can occur within a <1-km2 area between catchments underlain by similar geology, but with differences in slope and roughness. We have also observed that many of these headwater catchments show no strong relationship between elevation and isotopic composition, suggesting that the sources of baseflow are not directly controlled by seasonal precipitation. The volcanic bedrock that underlays the basin, specifically large sections of ash tuffs may be contributing to these localized differences as sections of welded versus unwelded tuffs lead to differences in catchment storage capacity. Further analysis will refine the temporal shifts in isotopic composition and evaluate LiDAR derived indices of physiography within a spatial statistical model to evaluate the relative importance of sources (e.g., precipitation), geology, and physiography to water availability in time and space.
- Publication:
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AGU Fall Meeting Abstracts
- Pub Date:
- December 2022
- Bibcode:
- 2022AGUFM.H45N1572P