The Drying of Dry Creek: Examining the influence of springs on headwater stream intermittency through in-field mapping
Abstract
The presence of springs strongly influences flow intermittency in headwater streams because reliable groundwater sources can often sustain flow even during droughts and in arid or semi-arid climates. Therefore, knowledge about the distribution of springs and their relationships to flow permanence is important for managing headwater catchments. However, springs are not always accurately mapped by the National Hydrography Dataset (NHD) and stream drying predictions in spring-dominated catchments are a known limitation of the USGS Probability of Streamflow Permanence (PROSPER) model. In this study, we sought to improve the understanding of stream drying in spring-dominated vs. non-spring-dominated catchments. To do so, we examined relationships between streamflow permanence and the active flow network length, annual hydrograph characteristics, and geomorphologic properties of two adjacent catchments: one dominated by springs and the other largely without springs in the Dry Creek Experimental Watershed in southwestern Idaho. We mapped the active channel network in both catchments from wet to dry conditions and quantified the relationship between length and outlet flow rate. The spring-dominated catchment exhibited more stability in stream network length across a range of discharge measurements, whereas the non-spring-dominated catchment exhibited more contraction and disconnection throughout its stream network. However, in the non-spring-dominated catchment that dried seasonally at the outlet, the length-discharge relationship did not represent catchment network dynamics well because the streams continued to contract and expand even after the outlet dried. This work emphasizes the importance of in-field stream network mapping for intermittent streams because it reveals both areas of much more and less reliable flows than otherwise predicted, and highlights the need for an accurate large-scale spring database to improve prediction models and water management decisions in a changing climate.
- Publication:
-
AGU Fall Meeting Abstracts
- Pub Date:
- December 2022
- Bibcode:
- 2022AGUFM.H55L0737C