Climate change, driven by rising atmospheric concentrations of greenhouse gases (GHGs) like carbon dioxide (CO₂), poses one of the most pressing environmental challenges today. Soil carbon sequestration emerges as a crucial strategy to mitigate this issue by capturing atmospheric CO₂ and storing it in soil organic carbon (SOC), thereby reducing GHG levels and enhancing soil health. Although soil is the largest terrestrial carbon sink, capable of storing between 1500 to 2400 petagrams (Pg) of carbon, the practical potential for SOC sequestration through regenerative practices is still widely debated. This review examines the biotic, abiotic, structural, physical, and chemical limitations that constrain soil carbon sequestration, along with the human dimensions that influence these processes. It explores the role of plant physiology, root architecture, microbial interactions, and environmental factors such as temperature and moisture in determining the efficacy of SOC sequestration. Furthermore, it discusses the potential of innovative strategies, including photosynthetic modifications, root system engineering, microbial bioengineering, and the application of advanced materials like C-capturing minerals, poly-carboxylic compounds, and nanomaterials, to enhance carbon capture and storage in soils. By providing a comprehensive understanding of these factors, this review aims to inform future research and policy development, offering pathways to optimize soil carbon sequestration as a viable tool for climate change mitigation.

This article comprises three independent commentaries about the state of ICON principles in hydrology and discusses the opportunities and challenges of adopting them. Each commentary focuses on a different perspective as follows: (i) field, experimental, remote sensing, and real-time data research and application (Section 1); (ii) Inclusive, equitable, and accessible science: Involvement, challenges, and support of early career, marginalized racial groups, women, LGBTQ+, and/or disabled researchers (Section 2); and (iii) an ICON perspective on machine learning for multiscale hydrological modeling (Section 3). Hydrologists depend on data monitoring, analyses, and simulations from these diverse scientific disciplines to ensure safe, sufficient, and equal water distribution. These hydrologic data come from but are not limited to primary (in-situ: lab, plots, and field experiments) and secondary sources (ex-situ: remote sensing, UAVs, hydrologic models) that are typically openly available and discoverable. Hydrology-oriented organizations have pushed our community to increase coordination of the protocols for generating data and sharing model platforms. In addition, networking at all levels has emerged with an invigorated effort to activate community science efforts that complement conventional data collection methods. With increasing amounts of data, it has become difficult to decipher various complex hydrologic processes. However, machine learning, a branch of artificial intelligence, provides accurate and faster alternatives to understand different biogeochemical and hydrological processes better. Diversity, equity, and inclusivity are essential in terms of outreach and integration of peoples with historically marginalized identities into this professional discipline and respecting and supporting the local environmental knowledge of water users.

Hydrologic sciences depend on data monitoring, analyses, and simulations of hydrologic processes to ensure safe, sufficient, and equal water distribution. These hydrologic data come from but are not limited to primary (lab, plot, and field experiments) and secondary sources (remote sensing, UAVs, hydrologic models) that typically follow FAIR Principles (FAIR Principles - GO FAIR (go-fair.org)). Easy availability of FAIR data has become possible because the hydrology-oriented organizations have pushed the community to increase coordination of the protocols for generating data and sharing model platforms. In addition, networking at all levels has emerged with an invigorated effort to activate community science efforts that complement conventional data collection methods. However, it has become difficult to decipher various complex hydrologic processes with increasing data. Machine learning, a branch of artificial intelligence, provides more accurate and faster alternatives to better understand different hydrological processes. The Integrated, Coordinated, Open, Networked (ICONs) framework provides a pathway for water users to include and respect diversity, equity, and inclusivity. In addition, ICONs support the integration of peoples with historically marginalized identities into this professional discipline of water sciences. This article comprises three independent commentaries about the state of ICON principles in hydrology and discusses the opportunities and challenges of adopting them.