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.

Earth’s Critical Zone (CZ), the near-surface layer where rock is weathered and landscapes co-evolve with life, is profoundly influenced by the type of underlying bedrock. Previous studies employing the CZ framework have focused almost exclusively on landscapes dominated by silicate rocks. However, carbonate rocks crop out on approximately 15% of Earth’s ice-free continental surface and provide important water resources and ecosystem services to ~1.2 billion people. Unlike silicates, carbonate minerals weather congruently and have high solubilities and rapid dissolution kinetics, enabling the development of large, interconnected pore spaces and preferential flow paths that restructure the CZ. Here we review the state of knowledge of the carbonate CZ, exploring parameters that produce contrasts in the CZ in different carbonate settings and identifying important open questions about carbonate CZ processes. We introduce the concept of a carbonate-silicate CZ spectrum and examine whether current conceptual models of the CZ, such as the conveyor model, can be applied to carbonate landscapes.We argue that, to advance beyond site-specific understanding and develop a more general conceptual framework for the role of carbonates in the CZ, we need integrative studies spanning both the carbonate-silicate spectrum and a range of carbonate settings.

Rooting depth is an ecosystem trait that determines the extent of soil development and carbon cycling. Recent hypotheses propose that human-induced changes to Earth’s biogeochemical cycles propagate deeply due to rooting depth changes from agricultural and climate-induced land cover changes. Yet, the lack of a global-scale quantification of rooting depth responses to human activity limits knowledge of hydrosphere-atmosphere-lithosphere feedbacks in the Anthropocene. Here we use land cover datasets to demonstrate that global rooting depths have become shallower in the Anthropocene, and are likely to become yet shallower this century. Specifically, globally averaged depths above which 99% of root biomass occurs (D99) are 8.7%, or 16 cm, shallower relative to those for potential vegetation. This net shallowing results from agricultural expansion truncating D99 by 82 cm, and woody encroachment linked to anthropogenic climate change extending D99 by 65 cm. Projected land cover scenarios in 2100 suggest further D99 shallowing of 63 to 72 cm, exceeding that experienced to date and suggesting that the pace of root shallowing will quicken in the coming century. Losses of Earth’s deepest roots—soil-forming agents—suggest unanticipated changes in fluxes of water, solutes, and carbon. Our work constrains rooting depth distributions for global models, allowing the land modeling community to explore cascading effects of rooting depth changes on water, carbon, and energy dynamics, and can guide design of field-based efforts to quantify deep anthropogenic influences. Understanding human influence on biota’s reach into Earth’s subsurface will improve predictions of interactive functioning of the biosphere, lithosphere, and hydrosphere.

This study quantifies small-scale carbon dioxide (CO2) efflux and estimates annual CO2 emission from a headwater stream at the Konza Prairie Long-Term Ecological Research Site and Biological Station (Konza), in a terrain of horizontal, alternating limestones and shales. We characterized the CO2 efflux and stable carbon isotopes (δ13C-CO2) at point sources of groundwater discharge from small-scale karst features, identified by temperature, and downstream of those point sources, as well as in stream reaches without identifiable point sources. CO2 effluxes ranged from 2.2 to 214 g CO2 m-2 day-1 (mean was 20.9± 41.4 g CO2 m-2 day-1). Downstream of point groundwater discharge sources, CO2 efflux decreased, over 2 meters, to 3% to 40% of the point-source flux, while δ13C-CO2 increased, ranging from -9.8 ‰ to -23.2 ‰ V-PDB (mean was -14.9 ‰ ± 4.2 ‰ V-PDB). The δ13C-CO2 increase was not strictly proportional to the CO2 flux but related to the origin of vadose-zone CO2 (C3 versus C4 vegetation). Over the study period, ~7.0 metric tons of CO2 were emitted from the 1.1-km-long stream, comparable to other headwater streams. The high spatial and temporal variability of CO2 efflux from this headwater stream informs those doing similar measurements and those working on upscaling stream data, that local variability should be assessed to make the best estimate of the impact of headwater stream CO2 efflux on the global carbon cycle.

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.

Understanding relationships between stream chemistry and watershed factors: land use/land cover, climate, and lithology are crucial to improving our knowledge of critical zone processes that influence water quality. We compiled major ion data from more than 100 monitoring stations collected over 60 years (1958-2018) across the Colorado River Watershed in Texas (103,000 km2). We paired this river chemistry data with complementary lithology, land use, climate and stream discharge information. A combination of graphical geochemistry and machine learning techniques were used to produce new insights on controls of stream water chemical behavior. Studies on stream flow and chemistry in the American west and globally have shown strong relationships between major ion chemical composition and lithology, which hold true for the Colorado River basin in this study. Reactive minerals, including carbonates and evaporites, dominate major ion chemistry across the upper watershed. Upstream and central reaches of the Colorado River showed shifts from Na-Cl-SO4 dominated water from multiple sources including dissolution of gypsum and halite in shallow groundwater, agricultural activities, and oil and gas development, to Ca-HCO3 water types controlled by carbonate dissolution. In the lower portion of the watershed multiple analyses demonstrate that stream chemistry is more influenced by greater precipitation and the presence of relatively fewer reactive silicate minerals than middle and upstream reaches. This study demonstrates the power of applying machine learning approaches to publicly available long term water chemistry datasets to improve the understanding of water and nutrient cycling, salinity sources, and water use.

Rooting depth is an ecosystem trait that determines the extent of soil development and carbon (C) and water cycling. Recent hypotheses propose that human-induced changes to Earth’s biogeochemical cycles propagate deeply due to rooting depth changes from agricultural and climate-induced land cover changes. Yet, the lack of a global-scale quantification of rooting depth responses to human activity limits knowledge of hydrosphere-atmosphere-lithosphere feedbacks in the Anthropocene. Here we use land cover datasets to demonstrate that root depth distributions are changing globally as a consequence of agricultural expansion truncating depths above which 99% of root biomass occurs (D99) by ~60 cm, and woody encroachment linked to anthropogenic climate change extending D99 in other regions by ~38 cm. The net result of these two opposing drivers is a global reduction of D99 by 5%, or ~8 cm, representing a loss of ~11,600 km3 of rooted volume. Projected land cover scenarios in 2100 suggest additional future D99 shallowing of up to 30 cm, generating further losses of rooted volume of ~43,500 km3, values exceeding root losses experienced to date and suggesting that the pace of root shallowing will quicken in the coming century. Losses of Earth’s deepest roots — soil-forming agents — suggest unanticipated changes in fluxes of water, solutes, and C. Two important messages emerge from our analyses: dynamic, human-modified root distributions should be incorporated into earth systems models, and a significant gap in deep root research inhibits accurate projections of future root distributions and their biogeochemical consequences.