Integrating sensor data and machine learning to advance the science and management of river carbon emissions
Estimates of greenhouse gas emissions from river networks remain highly uncertain in many parts of the world, leading to gaps in global inventories and preventing effective management. In-situ sensor technology advances, coupled with mobile sensors on robotic sensor-deployment platforms, will allow more effective data acquisition to monitor carbon cycle processes influencing river CO2 and CH4 emissions. However, if countries are to respond effectively to global climate change threats, sensors must be installed more strategically to ensure that they can be used to directly evaluate a range of management responses across river networks. We evaluate how sensors and analytical advances can be integrated into networks that are adaptable to monitor a range of catchment processes and human modifications. The most promising data analytics that provide processing, modeling, and visualizing approaches for high-resolution river system data are assessed, illustrating how multi-sensor data coupled with machine learning solutions can improve both proactive (e.g. forecasting) and reactive (e.g. alerts) strategies to better manage river catchment carbon emissions. Data measurement and integration can be used to advance assessments and management of river carbon dynamics and water quality.
Authors
- Shaked Stein (ORCID: https://orcid.org/0000-0001-5208-3475)
- Gideon Gal (ORCID: https://orcid.org/0000-0002-6962-4001)
- Richard Grayson (ORCID: https://orcid.org/0000-0003-3637-3987)
- Felicia Orah Rein Moshe (ORCID: https://orcid.org/0000-0002-9862-9012)
- Elad Levintal (ORCID: https://orcid.org/0000-0002-5029-0742)
- Shai Arnon (ORCID: https://orcid.org/0000-0002-7109-8979)
- Megan Klaar (ORCID: https://orcid.org/0000-0001-8920-4226)
- Lee E. Brown (ORCID: https://orcid.org/0000-0002-2420-0088)
- José Barquín (ORCID: https://orcid.org/0000-0003-1897-2636)
- Devanshi Pathak (ORCID: https://orcid.org/0000-0003-3290-5149)
- Taylor Maavara (ORCID: https://orcid.org/0000-0001-6677-9262)
- Hemma Philamore (ORCID: https://orcid.org/0000-0003-1457-9554)
- Misael Sebastián Gradilla‐Hernández (ORCID: https://orcid.org/0000-0002-8236-4400)
- Kieran Khamis (ORCID: https://orcid.org/0000-0002-5203-3221)
- Xiaohui Chen (ORCID: https://orcid.org/0000-0002-2053-2448)
- Jiangwei Zhang (ORCID: https://orcid.org/0000-0002-1221-3033)
- Laura Carter (ORCID: https://orcid.org/0000-0002-1146-7920)
- Pazit Ziv
- Frank Tarkowski
- Elad Ben-Zur
Institutions
- Ben-Gurion University of the Negev (IL)
- Natural Resources Conservation Service (US)
- University of Leeds (GB)
- Universidad de Cantabria (ES)
- Helmholtz Centre for Environmental Research (DE)
- University of Bristol (GB)
- Institute of Soil and Water Conservation (CN)
- Ministry of Forests and Soil Conservation (NP)
- Kelda Group (United Kingdom) (GB)
- Instituto de Física de Cantabria (ES)
- Israel Oceanographic and Limnological Research (IL)
- Ministry of Agriculture and Rural Development (IL)
- University of Birmingham (GB)
- Tecnológico de Monterrey (MX)
Publication Details
- Journal
- Critical Reviews in Environmental Science and Technology
- Published
- 2024-11-24
- DOI
- https://doi.org/10.1080/10643389.2024.2429912
- Citations
- 6
- Primary Topic
- Air Quality Monitoring and Forecasting
- Type
- article
- Field-Weighted Citation Impact
- 1.23
Funders
- UK Research and Innovation
- Sight Research UK
- British Council
- European Commission
- Natural Environment Research Council