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Abstract As drought and wildfire frequency increase across the western United States, our ability to predict how water resources will respond to these disturbances depends on our understanding of the feedbacks that maintain watershed function and streamflow. Previous studies of non‐perennial headwater streams have ranked drivers of low‐flow conditions; however, there is a limited understanding of the interactions between these drivers and the processes through which these interactions affect streamflow. Here, we use stream water level, soil moisture, sap flow, and vapor pressure deficit data to investigate ecohydrological interactions along a mountainous headwater stream. Correlation and cross‐correlation analyses of these variables show that ecohydrological interactions are (a) nonlinear and (b) interconnected, suggesting that analyses assuming linearity and independence of each driver are inadequate for quantifying these interactions. To account for these issues and investigate causal linkages, we use convergent cross‐mapping (CCM) to characterize the feedbacks that influence non‐perennial streamflow. CCM is a nonlinear, dynamic method that has only recently been applied to hydrologic systems. CCM results reveal that atmospheric losses associated with local sap flow and vapor pressure deficit are driving changes in soil moisture and streamflow (p < 0.01) and that atmospheric losses influence stream water more directly than shallow soil moisture. These results also demonstrate that riparian processes continue to affect subsurface flows in the channel corridor even after stream drying. This study proposes a nonlinear framework for quantifying the ecohydrologic interactions that may determine how headwater streams respond to disturbance.
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Complex Connections: How Information Flow Networks Can Quantify Ecohydrological Interactions
Ecohydrological systems comprised of soil, water and vegetation are intricately connected, and changes in one component can trigger feedback mechanisms throughout the network. Understanding how these complex interactions occur and propagate is challenging. To address this, Dr Allison Goodwell and Professor Praveen Kumar from the University of Illinois Urbana-Champaign have characterised information flow — a mathematical concept initially developed for communication systems — to better quantify and understand these interactions. Their research offers new insights into ecosystem responses, evolving precipitation patterns, and ecohydrological models, advancing our understanding of environmental dynamics.
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- Award ID(s):
- 2012850
- PAR ID:
- 10630773
- Editor(s):
- NA
- Publisher / Repository:
- Scientia
- Date Published:
- Journal Name:
- Scientia
- Volume:
- NA
- ISSN:
- 2317-5869
- Subject(s) / Keyword(s):
- Complexity, Information Flow
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.33548/SCIENTIA1137
