This title appears in the Scientific Report :
2023
Using a network of networks for high-frequency multi-depth soil moisture observations to infer spatial and temporal drivers of subsurface preferential flow
Using a network of networks for high-frequency multi-depth soil moisture observations to infer spatial and temporal drivers of subsurface preferential flow
Preferential flow (PF) is defined as rapid subsurface bypass flow in the unsaturated soil and bedrock, and is a critical process that influences soil water availability and quality. Unfortunately, the lack of a mechanistic understanding of the controls on PF limits the ability to ensure ecological h...
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Personal Name(s): | Groh, Jannis (Corresponding author) |
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Ajami, Hoori / Araki, Ryoko / Crompton, Octavia / Gimenez, Daniel / Hirmas, Daniel / Li, Bonan / Nimmo, John R. / Singh, Nitin / Sprenger, Matthias / Sullivan, Pamela / Wiekenkamp, Inge / Wyatt, Briana M. / Xu, Tianfang | |
Contributing Institute: |
Agrosphäre; IBG-3 |
Imprint: |
2023
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Conference: | 2nd TERENO-OZCAR Conference 2023, Bonn (Germany), 2023-09-25 - 2023-09-28 |
Document Type: |
Poster |
Research Program: |
Agro-biogeosystems: controls, feedbacks and impact |
Publikationsportal JuSER |
Preferential flow (PF) is defined as rapid subsurface bypass flow in the unsaturated soil and bedrock, and is a critical process that influences soil water availability and quality. Unfortunately, the lack of a mechanistic understanding of the controls on PF limits the ability to ensure ecological health and proper management of water supply and quality. However, recent developments in the availability of high-frequency and multi-depth soil moisture data from global monitoring networks across diverse landscapes (e.g., meteorology, ecology, and geology), as well as advances in data analysis methods (e.g., artificial intelligence and machine learning) make it possible to find answers to the fundamental questions of where and when PF occurs and what factors control PF occurrence. Outcomes of this synthesis work may be utilized to develop models capable of detecting and predicting PF events based on non-sequential wetting patterns, water flow velocity estimates, and other methods. In this presentation, we will describe the general approach and present initial results of a synthesis project and will interactively explore possibilities of additional data sets from conference participants. |