Nutrient Cycling in River Corridors

A special issue of Geosciences (ISSN 2076-3263). This special issue belongs to the section "Biogeosciences".

Deadline for manuscript submissions: closed (30 April 2021) | Viewed by 229

Special Issue Editors


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Guest Editor
Pacific Northwest National Laboratory, Richland, WA, USA
Interests: uncertainty quantification and data assimilation; subsurface flow and transport modeling; watershed hydrologic and biogeochemical modeling; spatial-temporal data analysis using machine learning; ecohydrology

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Guest Editor
Key Laboratory of Surficial Geochemistry (Ministry of Education), School of Earth Sciences and Engineering, Nanjing University, Nanjing 210023, China
Interests: hydro-biogeochemical processes; reactive transport modeling; fate and movement of heavy metals

Special Issue Information

Dear Colleagues,

River corridors play a major role in watershed nutrient cycling by mixing waters of different composition and stimulating biogeochemical reactions. Hydrological exchange flows and the associated biogeochemical processes show significant spatiotemporal variability as river corridor systems are subject to dynamic forcing over a wide range of timescales. Understanding and predicting nutrient cycling in river corridors is crucial for sustainable management of water resources under a changing environment. We invite contributions that advance our process understanding and mechanistic representation of nutrient cycling in river corridors, including but not limited to (1) coupling between hydrologic processes and nutrient cycling, (2) role of microbial functions in nutrient cycling, (3) process-based and data-driven methods to improve predictions of nutrient cycling, (4) bridging scales from reaction mechanisms to emergent watershed- and basin-scale system responses, (5) methods to integration observations, experiments, and models to improve predictive models, and (6) impacts of environmental changes, including extreme events.

Dr. Xingyuan Chen
Assoc. Prof. Yuanyuan Liu
Guest Editors

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Keywords

  • Nutrient cycling
  • Numerical modeling
  • Scaling
  • Uncertainty quantification
  • Machine learning
  • Environmental change

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Published Papers

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