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Water is an increasingly precious resource in California as years of drought, climate change, pollution, as well as an expanding population have all stressed the state's drinking water supplies. Currently, there are increasing concerns about whether regulated and unregulated contaminants in drinking water are linked to a variety of human-health outcomes particularly in socially disadvantaged communities with a history of health risks. To begin to address this data gap by broadly assessing contaminant mixture exposures, the current study was designed to collect tapwater samples from communities in Gold Country, the San Francisco Bay Area, two regions of the Central Valley (Merced/Fresno and Kern counties), and southeast Los Angeles for 251 organic chemicals and 32 inorganic constituents. Sampling prioritized low-income areas with suspected water quality challenges and elevated breast cancer rates. Results indicated that mixtures of regulated and unregulated contaminants were observed frequently in tapwater throughout the areas studied and the types and concentrations of detected contaminants varied by region, drinking-water source, and size of the public water system. Multiple exceedances of enforceable maximum contaminant level(s) (MCL), non-enforceable MCL goal(s) (MCLG), and other health advisories combined with frequent exceedances of benchmark-based hazard indices were also observed in samples collected in all five of the study regions. Given the current focus on improving water quality in socially disadvantaged communities, our study highlights the importance of assessing mixed-contaminant exposures in drinking water at the point of consumption to adequately address human-health concerns (e.g., breast cancer risk). Data from this pilot study provide a foundation for future studies across a greater number of communities in California to assess potential linkages between breast cancer rates and tapwater contaminants.

Groundwater aquifers worldwide experience unsustainable depletion, compounded by population growth, economic development, and climate forcing. Managed aquifer recharge provides one tool to alleviate flood risk and replenish groundwater. However, concerns grow that intentional flooding of farmland for groundwater recharge, a practice known as Ag-MAR, may increase the leaching of pesticides and other chemicals into groundwater. This study employs a physically based unsaturated flow model to determine the fate and transport of residues of four pesticide in three vadose zone profiles characterized by differing fractions of sand (41 %, 61 %, and 84 %) in California's Central Valley. Here, we show that the complex heterogeneity of alternating coarse and fine-grain hydrogeologic units controls the transit times of pesticides and their adsorption and degradation rates. Unsaturated zones that contain a higher fraction of sand are more prone to support preferential flow, higher recharge rates (+8 %), and faster (42 %) water flow and pesticide transport, more flooding-induced pesticide leaching (about 22 %), as well as more salt leaching correlating with increased risks of groundwater contamination. Interestingly, considering preferential flow predicted higher degradation and retention rates despite shorter travel times, attributed to the trapping of pesticides in immobile zones where they degrade more effectively. The findings underscore the importance of considering soil texture and structure in Ag-MAR practices to minimize environmental risks while enhancing groundwater recharge. The study also highlights that selecting less mobile pesticides can reduce leaching risks in sandy areas.

Abstract. The hydrological cycle of sub-arctic areas is dominated by the snowmelt event. An understanding of the mechanisms that control water fluxes during high-volume infiltration events in sub-arctic till soils is needed to assess how future changes in the timing and magnitude of snowmelt can affect soil water storage dynamics. We conducted a tracer experiment in which deuterated water was used to irrigate a plot on a forested hilltop in Lapland, tracked water fluxes of different mobility and monitored how the later snowmelt modifies the labelled soil water storage. We used lysimeters and destructive soil coring for soil water sampling and monitored and sampled the groundwater. Large spatiotemporal variability between the waters of different mobility was observed in the subsurface, while surface water flow during the tracer experiment was largely controlled by a fill-and-spill mechanism. Extensive soil saturation induced the flow of labelled water into the roots of nearby trees. We found that labelled water remained in deeper soil layers over the winter, but the snowmelt event gradually displaced all deuterated water and fully homogenized all water fluxes at the soil–vegetation interface. The conditions required for the full displacement of the old soil water occur only during a snowmelt with a persistently high groundwater table. We propose a conceptual model where infiltration into the soil and eventual soil water replenishment occur in three stages. First, unsaturated macropore flow is initiated via the surface microtopography and is directed towards the groundwater storage. The second stage is characterized by groundwater rise through the macropore network, subsequent pore water saturation and increased horizontal connectivity of macropores. Shallow subsurface lateral fluxes develop in more permeable shallow soil layers. In the third stage, which materializes during a long period with a high groundwater table and high hydrological connectivity within the soil, the soil water is replenished via enhanced matrix flow and pore water exchange with the macropore network.

Abstract: Huang and Merwade (2023), https://doi.org/10.1029/2023wr034947, hereafter conveniently referred to as HM23, wrongly claim improvement of their method for postprocessing multi‐model water stage predictions using Bayesian Model Averaging (BMA). Their results show all signs of a flawed implementation of the Metropolis algorithm. In this comment I will point out the many mistakes and shortcomings of the BMA methodology of HM23. Their method is deficient, inefficient and ineffective and wrongly quantifies BMA model parameter and predictive uncertainty. Furthermore, HM23 misrepresent BMA literature, articulate a poor understanding of Markov chain Monte Carlo methods and misuse the autocorrelation function for monitoring convergence of the sampled Markov chains. A proper implementation of the random walk Metropolis algorithm would have led HM23 to substantially different results and findings about their ensemble of water stage predictions. The MODELAVG toolbox of Vrugt (2018) https://www.researchgate.net/publication/299458373_MODELAVG_A_MATLAB_Toolbox_for_Postprocessing_of_Model_Ensembles satisfies all requirements of HM23 and provides robust estimates of BMA model parameter and prediction uncertainty for symmetric, skewed and truncated conditional forecast distributions of the ensemble members.

Urban landscapes homogenize our world at global scales, contributing to “extinction of experience”, a progressive decline in human interactions with native greenspace that can disconnect people from the services it provides. College age adults report feeling disconnected from nature more than other demographics, making universities a logical place to explore interventions intended to restore a connection with nature. This study surveyed 1088 students and staff across four university campus communities in Southern California, USA and used multicriteria decision analysis to explore their landscape preferences and the implications of those preferences for combatting extinction of experience. Our results suggest that perspectives of, and preferences for, different greenspace forms vary significantly (i.e., they are not perceived as substitutable). Support for native ecosystems, particularly coastal sage scrub (top ranked landscape) was generally high, suggesting that disaffection with wild nature is not particularly widespread. Programs for replacing turf grass lawns (lowest ranked landscape) with native plants were also well supported, but support for stormwater bioswales was more moderate (and variable). This may reflect their relative newness, both on university campuses and in urban spaces more generally. Not all members of campus communities preferred the same landscapes; preferences differed with degree of pro-environmentalism and university status (undergraduate student, graduate student, staff). Even so, all respondents exhibited landscape preferences consistent with at least one approach for combatting extinction of experience, suggesting that ecologists, engineers and urban planners have a viable set of generalizable tools for reconnecting people with nature.

Terrestrial evapotranspiration is the second-largest component of the land water cycle, linking the water, energy, and carbon cycles and influencing the productivity and health of ecosystems. The dynamics of ET across a spectrum of spatiotemporal scales and their controls remain an active focus of research across different science disciplines. Here, we provide an overview of the current state of ET science across in situ measurements, partitioning of ET, and remote sensing, and discuss how different approaches complement one another based on their advantages and shortcomings. We aim to facilitate collaboration among a cross-disciplinary group of ET scientists to overcome the challenges identified in this paper and ultimately advance our integrated understanding of ET.

The capture and separation of carbon dioxide (CO2) has been the focus of a plethora of research in order to mitigate its emissions and contribute to global development. Given that CO2 is commonly found in natural gas streams, there have been efforts to seek more efficient materials to separate gaseous mixtures such as CO2/CH4. However, there are only a few reports regarding adsorption processes within pressurized systems. In the offshore scenario, natural gas streams still exhibit high moisture content, necessitating a greater understanding of processes in moist systems. In this article, a metal-organic framework synthesis based on zirconium (MOF-808) was carried out through a conventional solvothermal method and autoclave for the adsorption of CO2 and CH4 under different temperatures (45–65 °C) and pressures up to 100 bar. Furthermore, the adsorption of humid CO2 was evaluated using thermal analyses. The MOF-808 synthesized in autoclave showed a high surface area (1502 m2/g), a high capacity for CO2 adsorption at 50 bar and 45 °C and had a low selectivity to capture CH4 molecules. It also exhibited a fine stability after five cycles of CO2 adsorption and desorption at 50 bar and 45 °C − as confirmed by structural post-adsorption analyses while maintaining its adsorption capacity and crystallinity. Furthermore, it can be observed that the adsorption capacity increased in a humid environment, and that the adsorbent remained stable after adsorption cycles in the presence of moisture. Finally, it was possible to confirm the occurrence of physisorption processes through nuclear magnetic resonance (NMR) analyses, thus validating the choice of mild temperatures for regeneration and contributing to the reduction of energy consumption in processing plants.

Results of previous research on chemigated imazamox for control of branched broomrape (Phelipanche ramosa) in processing tomatoes suggested potential soil-type differences in imazamox availability. Over two years, there were differences in crop-injury between two sites less than 30-km apart: imazamox-treated tomatoes in the Davis location had relatively minor early season injury while tomatoes at the Woodland location were severely injured or killed. The following study was conducted to investigate imazamox sorption in four California soils to determine if differences in herbicide adsorption played a role in variable crop-injury observed in the field trials. To determine the sorption capacity of imazamox of each soil, a batch-equilibrium study was conducted. There were significant differences in sorbed imazamox: the clay soil had the highest adsorption (Robert's Island: 742.5 pg µL-1 sorbed), followed by the sandy loam soil (Ripon: 723.9 pg µL-1 sorbed), while the loam soils from both trial sites (Davis: 704.2 pg µL-1 sorbed; Woodland: 699.9 pg µL-1 sorbed) had the lowest adsorption and were not significantly different from one another. Results from this study illustrate only minor differences in imazamox adsorption among the soils tested which suggests that soil type was likely not a major factor contributing to differences in crop-injury.

Anode-free batteries possess the optimal cell architecture due to their reduced weight, volume and cost. However, their implementation has been limited by unstable anode morphological changes and anode–liquid electrolyte interface reactions. Here we show that an electrochemically stable solid electrolyte and the application of stack pressure can solve these issues by enabling the deposition of dense sodium metal. Furthermore, an aluminium current collector is found to achieve intimate solid–solid contact with the solid electrolyte, which allows highly reversible sodium plating and stripping at both high areal capacities and current densities, previously unobtainable with conventional aluminium foil. A sodium anode-free all-solid-state battery full cell is demonstrated with stable cycling for several hundred cycles. This cell architecture serves as a future direction for other battery chemistries to enable low-cost, high-energy-density and fast-charging batteries.

Non-fused ring electron acceptors (NFREAs) potentially have lower synthetic costs than their fused counterparts. However, the low backbone planarity and the presence of bulky substituents adversely affect the crystallinity of NFREAs, impeding charge transport and the formation of bicontinuous morphology in organic solar cells. Here we show that a binary solvent system can individually control the crystallization and phase separation of the donor polymer (for example, D18) and the NFREA (for example, 2BTh-2F-C2). We select solvents such as chloroform and o-xylene that evaporate at different temperatures and rates and have different solubility for D18. Upon evaporation of chloroform, D18 starts to assemble into fibrils. Then, the evaporation of o-xylene induces the rapid formation of a fibril network that phase segregates 2BTh-2F-C2 into pure domains and leads to a bicontinuous morphology. The well-defined interpenetrating network morphology affords an efficiency of 19.02% on small-area cells and 17.28% on 1 cm2 devices.

Community solar, a business model where multiple customers buy output from shared solar systems, has expanded solar access among multifamily housing occupants, renters, and low-income households. Policies to enable community solar could be expanded and benefits of access augmented through targeted measures to support community solar adoption in underserved communities.

Correction to: Nature Energyhttps://doi.org/10.1038/s41560-024-01546-2, published online 3 June 2024 In the version of the article initially published, data in Fig. 5b–d were displayed incorrectly and have now been amended in the HTML and PDF versions of the article. The original and corrected figures can be seen below in Fig. 1. (Figure presented.)

The long-lived radiotoxicity of the high-level radioactive waste generated by nuclear power plants requires safe isolation from the biosphere for many hundreds of thousands of years. An international consensus has emerged that such isolation can best be provided by disposal in mined geologic repositories, a strategy that today is pursued by most countries dealing with radioactive waste. However, the need to predict the performance of such repositories over very long time periods generates large uncertainties that have to be accounted for in safety assessments. The findings from such safety assessments need to be conveyed to all stakeholders in a clear way, such that public confidence in geologic disposal solutions can be achieved. It is suggested here that close international collaboration on the technical aspects of geologic waste disposal has helped, and will continue to help, building trust and increasing confidence. This paper discusses a particular international collaboration initiative referred to as DECOVALEX, which brings together multiple teams and disciplines to collectively tackle complex experimental and modeling challenges related to geologic disposal. By describing how DECOVALEX works and by providing joint research examples, a case is made that such international collaboration contributes to knowledge transfer and confidence building in radioactive waste disposal science.

Rooftop and community solar are alternative product classes for residential solar in the United States. Community solar, where multiple households buy solar from shared systems, could make solar more accessible by reducing initial costs and removing adoption barriers for renters and multifamily building occupants. Here we test whether community solar has expanded solar access in the United States. On the basis of a sample of 11 states, we find that community solar adopters are about 6.1 times more likely to live in multifamily buildings than rooftop solar adopters, 4.4 times more likely to rent and earn 23% less annual income. We do not find that community solar expands access in terms of race. These differences are driven, roughly evenly, by inherent differences between the two solar products and by policies to promote low-income community solar adoption. The results suggest that alternative solar products can expand solar access and that policy could augment such benefits.

Complete representation of rainfall–runoff responses in complex, large watersheds using a single-objective parameterization approach in watershed models is often unachievable. In this study we present a calibration approach for the SWAT+ model that independently fits model parameters for different flow segments of the hydrograph. The approach is demonstrated for the Feather River, California, USA, using daily streamflow from the Lake Oroville Reservoir outlet gage. Results show that when model parameters were independently fitted for different flow segments the KGE, NSE, PBIAS, and RSR values improved to 0.96, 0.99, −3.3, and 0.10, respectively, compared to 0.72, 0.66, −9.30, and 0.53, respectively, achieved under a multiobjective and full hydrograph (average hydrograph) calibration. The results highlight that when considering the average hydrograph and flow duration curves, a more balanced representation of both poorly and well-performing segments is achieved, emphasizing the importance of segment-specific parameterization and multi-objective evaluation for accurately representing different flow conditions.

The constitutive relations of the Richardson-Richards equation encode the macroscopic properties of soil water retention and conductivity. These soil hydraulic functions are commonly represented by models with a handful of parameters. The limited degrees of freedom of such soil hydraulic models constrain our ability to extract soil hydraulic properties from soil moisture data via inverse modeling. We present a new free-form approach to learning the constitutive relations using physically constrained neural networks. We implemented the inverse modeling framework in a differentiable modeling framework, JAX, to ensure scalability and extensibility. For efficient gradient computations, we implemented implicit differentiation through a nonlinear solver for the Richardson-Richards equation. We tested the framework against synthetic noisy data and demonstrated its robustness against varying magnitudes of noise and degrees of freedom of the neural networks. We applied the framework to soil moisture data from an upward infiltration experiment and demonstrated that the neural network-based approach was better fitted to the experimental data than a parametric model and that the framework can learn the constitutive relations.

Terrestrial production and export of dissolved organic and inorganic carbon (DOC and DIC) to streams depends on water flow and biogeochemical processes in and beneath soils. Yet, understanding of these processes in a rapidly changing climate is limited. Using the watershed-scale reactive-transport model BioRT-HBV and stream data from a snow-dominated catchment in the Rockies, we show deeper groundwater flow averaged about 20% of annual discharge, rising to ∼35% in drier years. DOC and DIC production and export peaked during snowmelt and wet years, driven more by hydrology than temperature. DOC was primarily produced in shallow soils (1.94 ± 1.45 gC/m2/year), stored via sorption, and flushed out during snowmelt. Some DOC was recharged to and further consumed in the deeper subsurface via respiration (−0.27 ± 0.02 gC/m2/year), therefore reducing concentrations in deeper groundwater and stream DOC concentrations at low discharge. Consequently, DOC was primarily exported from the shallow zone (1.62 ± 0.96 gC/m2/year, compared to 0.12 ± 0.02 gC/m2/year from the deeper zone). DIC was produced in both zones but at higher rates in shallow soils (1.34 ± 1.00 gC/m2/year) than in the deep subsurface (0.36 ± 0.02 gC/m2/year). Deep respiration elevated DIC concentrations in the deep zone and stream DIC concentrations at low discharge. In other words, deep respiration is responsible for the commonly-observed increasing DOC concentrations (flushing) and decreasing DIC concentrations (dilution) with increasing discharge. DIC export from the shallow zone was ~66% of annual export but can drop to ∼53% in drier years. Numerical experiments suggest lower carbon production and export in a warmer, drier future, and a higher proportion from deeper flow and respiration processes. These results underscore the often-overlooked but growing importance of deeper processes in a warming climate.

Distribution forecasts P over future quantities or events are routinely made in hydrology but usually traded for a (likelihood-weighted) mean or median prediction to accommodate error measures or scoring functions such as the mean absolute error or mean squared error. Case in point is the so-called KG efficiency (KGE) of Gupta et al. (2009, https://doi.org/10.1016/j.jhydrol.2009.08.003) and improvements thereof (Lamontagne et al., 2020, https://doi.org/10.1029/2020wr027101), which have rapidly gained popularity among hydrologists as alternative scoring functions to the commonly used Nash and Sutcliffe (1970, https://doi.org/10.1016/0022-1694(70)90255-6) efficiency, but are equally exclusive in how they quantify model performance using only single-valued output of the quantities of interest. This point-valued mapping necessarily implies a loss of information about model performance. This paper advocates the use of probabilistic watershed model training, evaluation and diagnostics. Distribution evaluation opens a mature literature on scoring rules whose strong statistical underpinning provides, as we will demonstrate, the theory, context and guidelines necessary for the development of robust information-theoretically principled metrics for watershed signatures. These so-called hydrograph functionals are scalar-valued mappings of major behavioral watershed functions embodied in a strictly proper scoring rule. We discuss past developments that led to the current state-of-the-art of distribution evaluation in hydrology and review scoring rules for dichotomous and categorical events, quantiles (intervals) and density forecasts. We are particularly concerned with elicitable functionals and scoring rule propriety, discuss the decomposition of scoring rules into a sharpness, reliability and entropy term and present diagnostically appealing strictly proper divergence scores of hydrograph functionals for flood frequency analysis, flow duration and recession curves. The usefulness and power of distribution-based model evaluation and diagnostics by means of scoring rules is demonstrated on simple illustrative problems and discharge distributions simulated with watershed models using random sampling and Bayesian model averaging. The presented theory (a) enables a more complete evaluation of distribution forecasts, (b) offers a statistically principled means for watershed model training, evaluation, diagnostics and selection using hydrograph functionals and/or extreme events and (c) provides a universal framework for metric development of watershed signatures, promoting metric standardization and reproducibility.

Geochemically driven alterations of fractures in multi-mineral media can create altered layers (ALs) at the fracture-matrix interface. Spatial variations in the AL significantly influence mass transfer across the interface, and the hydraulic and mechanical properties of the fractured medium. A real-rock based microfluidic experiment reported spatial variations in AL thickness despite the initially smooth fracture surface, suggesting potential effects of matrix heterogeneity on AL development. However, the respective contribution of structural and mineralogical characteristics is still poorly understood. Using the microfluidic experimental data and a micro-continuum reactive transport model, we systematically evaluated how micro-porosity and initial mineral texture impact AL development and thus the overall reactive transport behaviors. Our simulation results confirmed that the extent of AL spatial variations, mainly controlled by mineralogical texture, influences the evolution of reaction and permeability in different ways. Accounting for spatial heterogeneity in mineral distribution produces “channeling” structures in ALs and lower overall reaction (by up to 35.6%), but larger permeability increase (by up to 9.8%). The characteristic length of the reactive mineral cluster was observed to dominate the internal texture of ALs. Whereas the presence of micro-porosity can enhance mineral accessibility via improving connectivity for flow and transport, and lead to both higher bulk reaction, that is, thicker ALs, and permeability enhancement. Considerations of surface roughness with characteristic length on the same order of magnitude as mineral texture did not change the overall development of AL, which further highlights the importance of accounting for rock matrix properties in predicting long-term evolution of fractured media. The resulting spatial variations of ALs and their impacts on bulk properties, however, are expected to be further complicated by the coupling of chemical and mechanical processes, and may trigger matrix disaggregation, erosion and other mechanisms of fractured media alteration.

We present a new pore-scale model for multicomponent advective-diffusive transport with coupled mineral dissolution and precipitation. Both dissolution and precipitation are captured simultaneously by introducing a phase transformation vector field representing the direction and magnitude of the overall phase change. An effective viscosity model is adopted in simulating fluid flow during mineral dissolution-precipitation that can accurately capture the velocity field without introducing any empirical parameters. The proposed approach is validated against analytical solutions and interface tracking simulations in simplified structures. After validation, the proposed approach is employed in modeling realistic rocks where mineral dissolution and precipitation are dominant at different locations. We have identified three regimes for mineral dissolution-precipitation coupling: (a) compact dissolution-precipitation where dissolution is dominant near the inlet and precipitation is dominant near the outlet, (b) wormhole dissolution with clustered precipitation where dissolution generates wormholes in the main flow paths and precipitation clogs the secondary flow paths, and (c) dissolution dominant where all solid grains are gradually dissolved. In the three regimes, the proposed approach provides reliable porosity-permeability relationships that cannot be described well by traditional macroscale models. We find that the permeability can increase while the overall porosity decreases when the main flow paths are expanded by dissolution and adjacent pore spaces are clogged by precipitation.

Knowledge of the recurrence intervals of precipitation extremes is vital for infrastructure design, risk assessment, and insurance planning. However, trends and shifts in rainfall patterns globally pose challenges to the application of extreme value analysis (EVA) which relies critically on the assumption of stationarity. In this paper, we explore: (1) the suitability of nonstationary (NS) models in the presence of statistically significant trends, and (2) their potential in modeling out-of-sample data to improve frequency analysis of extreme precipitation. We analyze the benefits of using a nonstationary Generalized Extreme Value (GEV) model for annual extreme precipitation records from Southern Brazil. The location of the GEV distribution is allowed to change with time. The unknown GEV model parameters are estimated using Bayesian techniques coupled with Markov chain Monte Carlo simulation. Next, we use GAME sampling to compute the evidence (and their ratios, the so-called Bayes factors) for stationary and nonstationary models of annual maximum precipitation. Our results show that the presence of a statistically significant trend in annual maximum precipitation alone does not justify the use of a NS model. The location parameter of the GEV distribution must also be well defined, otherwise, stationary models of annual maximum precipitation receive more support by the data. These findings reiterate the importance of accounting for GEV model parameters and predictive uncertainty in frequency analysis and hypothesis testing of annual maximum precipitation data records. Furthermore, a meaningful EVA demands detailed knowledge about the origin and persistence of observed changes.

The intensity of global groundwater use rose from 124 m3 per capita in 1950 to 152 m3 in 2021, for a 22.6% rise in the annual per capita use. This rise in global per capita water use reflects rising consumption patterns. The global use of groundwater, which provides between 21% and 30% of the total freshwater annual consumption, will continue to expand due to the sustained population growth projected through most of the 21st century and the important role that groundwater plays in the water-food-energy nexus. The rise in groundwater use, on the other hand, has inflicted adverse impacts in many aquifers, such as land subsidence, sea water intrusion, stream depletion, and deterioration of groundwater-dependent ecosystems, groundwater-quality degradation, and aridification. This paper projects global groundwater use between 2025 and 2050. The projected global annual groundwater withdrawal in 2050 is 1535 km3 (1 km3 = 109 m3 = 810,713 acre-feet). The projected global groundwater depletion, that is, the excess of withdrawal over recharge, in 2050 equals 887 km3, which is about 61% larger than in 2021. This projection signals probable exacerbation of adverse groundwater-withdrawal impacts, which are worsened by climatic trends and the environmental requirement of groundwater flow unless concerted national and international efforts achieve groundwater sustainability.

Most of the available data on diffusion in natural clayey rocks consider tracer diffusion in the absence of a salinity gradient despite the fact that such gradients are frequently found in natural and engineered subsurface environments. To assess the role of such gradients on the diffusion properties of clayey materials, through-diffusion experiments were carried out in the presence and absence of a salinity gradient using salt-diffusion and radioisotope tracer techniques. The experiments were carried out with vermiculite samples that contained equal proportions of interparticle and interlayer porosities so as to assess also the role played by the two types of porosities on the diffusion of water and ions. Data were interpreted using both a classical Fickian diffusion model and with a reactive transport code, CrunchClay that can handle multi-porosity diffusion processes in the presence of charged surfaces. By combining experimental and simulated data, we demonstrated that (i) the flux of water diffusing through vermiculite interlayer porosity was minor compared to that diffusing through the interparticle porosity, and (ii) a model considering at least three types of porous volumes (interlayer, interparticle diffuse layer, and bulk interparticle) was necessary to reproduce consistently the variations of neutral and charged species diffusion as a function of salinity gradient conditions.

Geologic features (e.g., fractures and alluvial fans) can play an important role in the locations and volumes of groundwater discharge and degree of groundwater-surface water (GW-SW) interactions. However, the role of these features in controlling GW-SW dynamics and streamflow generation processes are not well constrained. GW-SW interactions and streamflow generation processes are further complicated by variability in precipitation inputs from summer and fall monsoon rains, as well as declines in snowpack and changing melt dynamics driven by warming temperatures. Using high spatial and temporal resolution radon and water stable isotope sampling and a 1D groundwater flux model, we evaluated how groundwater contributions and GW-SW interactions varied along a stream reach impacted by fractures (fractured-zone) and downstream of the fractured hillslope (non-fractured zone) in Coal Creek, a Colorado River headwater stream affected by summer monsoons. During early summer, groundwater contributions from the fractured zone were high, but declined throughout the summer. Groundwater contributions from the non-fractured zone were constant throughout the summer and became proportionally more important later in the summer. We hypothesize that groundwater in the non-fractured zone is dominantly sourced from a high-storage alluvial fan at the base of a tributary that is connected to Coal Creek throughout the summer and provides consistent groundwater influx. Water isotope data revealed that Coal Creek responds quickly to incoming precipitation early in the summer, and summer precipitation becomes more important for streamflow generation later in the summer. We quantified the change in catchment dynamic storage and found it negatively related to stream water isotope values, and positively related to modeled groundwater discharge and the ratio of fractured zone to non-fractured zone groundwater. We interpret these relationships as declining hydrologic connectivity throughout the summer leading to late summer streamflow supported predominantly by shallow flow paths, with variable response to drying from geologic features based on their storage. As groundwater becomes more important for sustaining summer flows, quantifying local geologic controls on groundwater inputs and their response to variable moisture conditions may become critical for accurate predictions of streamflow.

Understanding connectivity between the soil and deeper bedrock groundwater is needed to accurately predict a watershed's response to perturbation, such as drought. Yet, the bedrock groundwater dynamics in mountainous environments are typically under-constrained and excluded from watershed hydrologic models. Here, we investigate the role of groundwater characterized with decadal and longer water ages on the hydrologic and mass-transport processes within a steep snow-dominated mountain hillslope in the Central Rocky Mountains (USA). We quantify subsurface and surface water mass-balance, groundwater flowpaths, and age distributions using the ParFlow-CLM integrated hydrologic and EcoSLIM particle tracking models, which are compared to hydrometric and environmental tracer observations. An ensemble of models with varied soil and hydrogeologic parameters reproduces observed groundwater levels and century-scale mean ages inferred from environmental tracers. The numerical models suggest soil water near the toe of the hillslope contains considerable (>60 % of the mass-flux) contributions from bedrock flowpaths characterized with water ages >10 years. Flowpath connectivity between the deeper bedrock and soil systems is present throughout the year, highlighting the potentially critical role of groundwater with old ages on processes such as evapotranspiration and streamflow generation. The coupled numerical model and groundwater age observations show the bedrock groundwater system influences the hillslope hydrodynamics and should be considered in mountain watershed conceptual and numerical models.

The sources and sinks of nitrous oxide, as control emissions to the atmosphere, are generally poorly constrained for most environmental systems. Initial depth-resolved analysis of nitrous oxide flux from observation wells and the proximal surface within a nitrate contaminated aquifer system revealed high subsurface production but little escape from the surface. To better understand the environmental controls of production and emission at this site, we used a combination of isotopic, geochemical, and molecular analyses to show that chemodenitrification and bacterial denitrification are major sources of nitrous oxide in this subsurface, where low DO, low pH, and high nitrate are correlated with significant nitrous oxide production. Depth-resolved metagenomes showed that consumption of nitrous oxide near the surface was correlated with an enrichment of Clade II nitrous oxide reducers, consistent with a growing appreciation of their importance in controlling release of nitrous oxide to the atmosphere. Our work also provides evidence for the reduction of nitrous oxide at a pH of 4, well below the generally accepted limit of pH 5.

Natural conversion of ferrihydrite (Fh), a widespread Fe(iii)-oxyhydroxide mineral at the Earth's surface, to thermodynamically more stable iron oxides such as goethite (Gt) and hematite (Hm) is a slow process that spans months to years. Here we examined the effects of synthesis and storage conditions on the hydration, the ratio of tetrahedral to octahedral iron sites, and the transformation of naturally aged 2-line Fh at room temperature and mildly acidic pH over an ageing period of 5 years. Fh samples synthesized and aged in either aerobic or anaerobic conditions were characterized over time by XRD, SEM, thermogravimetric analysis - mass spectroscopy (TGA-MS), and X-ray absorption spectroscopies (XANES and XMCD). The findings show that the ratio of tetrahedral to octahedral Fe(iii) sites in Fh is correlated to its extent of hydration, with fresher Fh samples exhibiting a higher ratio and more bound water. Fresh Fh aged in aerobic conditions has similar bound inorganic carbon, is more hydrated, and has less tetrahedral Fe(iii) than that aged in anaerobic conditions. Hence, for relatively fresh Fh there is a link between Fh properties and storage conditions. However, the long-term ageing characteristics, such as the transformation rate and relative phase fraction of Gt and Hm products, are not noticeably impacted by storage conditions. TGA-MS measurements coupled with O K-edge XANES spectra confirm that Fh tends to lose its hydration as it ages, as expected. Corresponding Fe L2,3-edge XMCD spectra reveal that this dehydration is coupled to a steady decrease in the ratio of tetrahedral to octahedral Fe(iii) sites. In addition to the obvious constraints these findings place on making comparisons across Fh samples of different age and environmental settings, they also highlight that Fh structure, and consequently magnetism, are linked to its bound water content.

The presence of toxic metals in tobacco smoke is well documented. However, few studies have quantified trace metals in secondhand smoke (SHS) and thirdhand smoke (THS). Their presence in indoor air can contribute to nonsmokers’ exposures and health effects. In this study, emission and deposition rates of toxic trace metals were determined, and their airborne concentration in typical indoor scenarios was predicted. PM2.5 was collected on Teflon-coated filters at various times following a smoking event in a room-sized chamber over a 43 h period. The concentration of 28 trace metals was determined by extraction and analysis using inductively coupled plasma-triple quadrupole-mass spectrometry (ICP-QQQ-MS). Emission and indoor deposition rates of cadmium, arsenic, chromium, manganese, beryllium and selenium were determined, and used to predict concentrations expected in a smokers’ home and a smoking bar. In most of the considered scenarios, average indoor concentrations of Cd, As, and Cr exceeded their corresponding cancer risk thresholds and, in some cases, also noncancer reference exposure levels, more than 3 h after smoking ended. The fraction of cadmium that remained airborne was significantly higher than those of other metal traces and that of PM2.5, suggesting an association of Cd traces with small particles.

In this study we construct and compare three different deep learning (DL) models for estimating daily snow water equivalent (SWE) from high-resolution gridded meteorological fields over the Rocky Mountain region. To train the DL models, Snow Telemetry (SNOTEL) station-based SWE observations are used as the prediction target. All DL models produce higher median Nash-Sutcliffe Efficiency (NSE) values than a conceptual SWE model and interpolated gridded data sets, although mean squared errors also tend to be higher. Sensitivity of the SWE prediction to the model's input variables is analyzed using an explainable artificial intelligence (XAI) method, yielding insight into the physical relationships learned by the models. This method reveals the dominant role precipitation and temperature play in snowpack dynamics. In applying our models to estimate SWE throughout the Rocky Mountains, an extrapolation problem arises since the statistical properties of SWE (e.g., annual maximum) and geographical properties of individual grid points (e.g., elevation) differ from the training data. This problem is solved by normalizing the SWE with its historical maximum value to alleviate extrapolation for all tested DL models. Our work shows that the DL models are promising tools for estimating SWE, and sufficiently capture relevant physical relationships to make them useful for spatial and temporal extrapolation of SWE values.

Existing simplified procedures for evaluating soil liquefaction potential or for estimating excess pore pressures during earthquakes are typically based on undrained cyclic tests performed on saturated soil samples under controlled loading and boundary conditions. Under such conditions, the effect of excess pore pressure (ue) dissipation and redistribution to neighboring soil layers cannot be accounted for. Existing simplified procedures treat liquefiable layers as isolated soil layers without any boundary conditions even if dense and loose layers are very thin, permeable, and adjacent to each other. However, redistribution is likely to increase and decrease ue in the neighboring dense and loose layers respectively. Until now, no procedure short of fully coupled numerical analysis is available to estimate the importance of redistribution. This paper presents an approximate analytical procedure for assessing the effects of ue redistribution in (1) soil layers that would have liquefied if they were undrained, and (2) soil layers that would have not liquefied even if undrained. It is found that a layer that is initially assumed liquefied under undrained conditions might not even liquefy accounting for the ue redistribution to neighboring layers. On the other hand, a layer initially assumed to not liquefy can develop significant ue and can even liquefy due to pore pressure migration from the neighboring layers. Thus, accounting for redistributed ue is important for liquefaction consequence assessment quantification, particularly in systems that span the depth of these effects like deep foundations. Migration of u toward the tip of a pile can reduce its capacity, even if the tip is embedded in a dense sand layer. On the other hand, if redistribution can result in the reduction of ue in initially assumed liquefied layers, risks associated with liquefaction might be avoided. A criterion is also developed to evaluate the thicknesses of a layer below which redistribution could prevent liquefaction even if the layer is deemed liquefied according to the existing liquefaction-triggering procedures. Finally, the proposed procedure is illustrated by application to selected shaking events of centrifuge tests involving liquefaction of layered soil profiles. The predictions from the procedure matched the centrifuge test results reasonably.

Abstract: Climate change and the increase of population pose challenges to ensuring suitable water supply in water-scarce regions. This work presents a comparative analysis of the water-supply approaches adopted in Los Angeles, Southeastern Spain, and Sydney. Results show a decrease in per-capita water use in the period 2000–2020, which reflects an improvement in water conservation. Social factors in the domain of hydropolitics and economic efficiency explain the divergence of water policies adopted. The adaptation to water scarcity and growing population in three regions of developed countries located in different continents sheds light on challenges facing the achievement of water security worldwide.

Residual trapping is an important process that affects the efficiency of cyclic storage and withdrawal and in-situ production of hydrogen in geological media. In this study, we have conducted pore-scale modeling to investigate the effects of pore geometry and injection rate on the occurrence and efficiency of residual trapping via dead-end bypassing. We begin our theoretical and numerical analyses using a single rectangular pore to understand the key controls in bypassing. We further investigated two factors affecting bypassing: (a) a continuous cycle of injection-extraction of H2, and (b) variable pore geometry. Based on our pore-scale simulations, we found that: (a) a higher pore height/width ratio (h/w) and a higher injection rate cause more residual trapping, which is unfavorable for withdrawal of H2; (b) the trapping percentage increases with the h/w first and then decreases after h/w reaches 0.5; (c) and a converging-shaped pore can result in less trapping volume. Based on a theoretical comparison of the residual trapping behavior of H2 and CO2, we discuss the mechanisms that are applicable to CO2 residual trapping and the possibility of developing engineering controls of H2 storage and production.

Predictive high-fidelity modeling of wind turbines with computational fluid dynamics, wherein turbine geometry is resolved in an atmospheric boundary layer, is important to understanding complex flow accounting for design strategies and operational phenomena such as blade erosion, pitch-control, stall/vortex-induced vibrations, and aftermarket add-ons. The biggest challenge with high-fidelity modeling is the realization of numerical algorithms that can capture the relevant physics in detail through effective use of high-performance computing. For modern supercomputers, that means relying on GPUs for acceleration. In this paper, we present ExaWind, a GPU-enabled open-source incompressible-flow hybrid-computational fluid dynamics framework, comprising the near-body unstructured grid solver Nalu-Wind, and the off-body block-structured-grid solver AMR-Wind, which are coupled using the Topology Independent Overset Grid Assembler. Turbine simulations employ either a pure Reynolds-averaged Navier–Stokes turbulence model or hybrid turbulence modeling wherein Reynolds-averaged Navier–Stokes is used for near-body flow and large eddy simulation is used for off-body flow. Being two-way coupled through overset grids, the two solvers enable simulation of flows across a huge range of length scales, for example, 10 orders of magnitude going from O(μm) boundary layers along the blades to O(10 km) across a wind farm. In this paper, we describe the numerical algorithms for geometry-resolved turbine simulations in atmospheric boundary layers using ExaWind. We present verification studies using canonical flow problems. Validation studies are presented using megawatt-scale turbines established in literature. Additionally presented are demonstration simulations of a small wind farm under atmospheric inflow with different stability states.

Correction to: Transport in Porous Media (2024) 151:55–84https://doi.org/10.1007/s11242-023-02033-0. There are three numbers in Table 2 of the original paper that were incorrect. Specfically, the value of the density of hydrogen (H2) for the DB model and the values of density and viscosity of H2 for the TOUGH2 model listed in Table 2 of the original paper were incorrect. (Table presented.) Properties of the H2-water upconing system for comparison against the DB model. Property DB model Used for TOUGH2 Gas cap thickness, total reservoir thickness, and radial extent (outer radius) of the reservoir Infinite, infinite, infinite 50 m, 100 m (with open boundary at bottom), 100 m (open boundary condition) Porosity (ϕ) 0.10 0.10 Permeability (kH) 1.0 × 10−12 m2 1.0 × 10−12 m2 Permeability (kV) 1.0 × 10−12 m2 1.0 × 10−12 m2 Relative permeability (krel) Not applicable Linear with Slr = 0.99 Distance from well to H2-water interface (d) 10 m 10 m Extraction rate of rate of H2 (Qm) − 5.5 kg s−1 − 5.5 kg s−1 Density of water 996 kg m−3 996 kg m−3 Density of H2 7.32 kg m−3 7.87 kg m−3 Viscosity of water 6.54 × 10−4 Pa s 5.11 × 10−4 Pa s Viscosity of H2 9.31 × 10−6 Pa s 9.53 × 10−6 Pa s A corrected Table 2 is shown below. The erroneous values in Table 2 were not used in any of the modeling and simulation. Accurate values for density and viscosity in the modeling and simulation come from CoolProp for the DB model and from EOS7CH for the TOUGH2 simulations.

In this Reply, we address the concerns of Knoben and Clark (2023, https://doi.org/10.1029/2022WR034294) or KC23 that “the assumptions needed to effectively use difference-based variance estimation methods are not always met by hourly streamflow records.” There should be little doubt that the assumptions of our difference-based estimator will sometimes be violated in hourly streamflow records but the results from de Oliveira and Vrugt (2022, https://doi.org/10.1029/2022wr032263) and confirmed by the findings in our Reply show that such violations are sporadic enough not to affect much the error variance estimates. Snowmelt as pointed out by KC23 (https://doi.org/10.1029/2022WR034294) may not have received sufficient attention in our paper, yet their 365-day record is simply not long enough to demonstrate bias of our discharge error variance estimates (and their dependence on flow level). This would require analysis of a much longer, multi-year, streamflow record of a snowmelt-driven watershed. The snowmelt catchment analyzed in dOV22 (https://doi.org/10.1029/2022wr032263) did not demonstrate bias in the discharge error variance estimates. We also provide additional clarification on the interpretation of the variance estimates obtained with the nonparametric estimator, and discuss the main issues in the test case presented in Knoben and Clark (2023, https://doi.org/10.1029/2022WR034294)).

Coupling of power-to-gas processes with underground gas storage could effectively allow surplus electricity to be stored for later use. Depleted hydrocarbon reservoirs could be used as stores, but practical experience of hydrogen storage in such sites is limited. Here we present data from a field trial that stored 119,353 m3 of hydrogen admixed to natural gas in a depleted hydrocarbon reservoir. After 285 days, hydrogen recovery was 84.3%, indicating the process’s technical feasibility. Additionally, we report that microbes mediated hydrogen conversion to methane. In laboratory experiments studying mesocosms that mimic real reservoirs, hydrogen and carbon dioxide were converted to methane (0.26 mmol l−1 h−1 evolution rate) reproducibly over 14 cycles in 357 days. This rate theoretically allows 114,648 m3 of methane per year to be produced in the test reservoir (equivalent to ~1.08 GWh). Our research demonstrates the efficiency of hydrogen storage and the importance of geo-methanation in depleted hydrocarbon reservoirs.

Buried water reservoirs are increasingly being built to replace open aboveground municipal water supply reservoirs in urban areas to enhance water quality and utilize their surface footprint for other purposes such as public parks or placement of solar arrays. Many of these lifeline structures are in seismically active regions and, as such, need to be designed to remain operational after severe earthquake shaking. However, evaluating their seismic response is challenging and involves accounting for the interaction of the structure with the stored fluid and the retained soil; in other words, accounting for fluid-structure-soil interaction (FSSI). This paper presents a combined experimental-numerical study on the seismic behavior of buried water reservoirs while considering FSSI. Two series of centrifuge model tests were performed at different reservoir orientations to investigate one-dimensional (1D) and two-dimensional (2D) motion effects under full, half-full, and empty reservoir conditions. Corresponding numerical models were developed whereby the structure and the soil were represented by continuum Lagrangian finite elements, while the fluid was modeled via Arbitrary Lagrangian Eulerian formulation. Soil-structure and fluid-structure interface parameters were calibrated using the experimental measurements. The simulations successfully captured the measured reservoir responses in terms of accelerations, bending moment increments, and water pressures. The study found that the common assumption of plane strain is not applicable for reservoirs because their behavior was found to be truly three-dimensional (3D) whereby stresses accumulated at the corners. Furthermore, the full reservoir resulted in the highest seismic demands in the reservoir walls and roof while the empty reservoir yielded the highest base slippage. The study demonstrates that the complex reservoir seismic response is best captured by carrying out a 3D FSSI numerical simulation.

Thermal energy storage (TES) solutions offer opportunities to reduce energy consumption, greenhouse gas emissions, and cost. Specifically, they can help reduce the peak load and address the intermittency of renewable energy sources by time shifting the load, which are critical toward zero energy buildings. Thermochemical materials (TCMs) as a class of TES undergo a solid–gas reversible chemical reaction with water vapor to store and release energy with high storage capacities (600 kWh m−3) and negligible self-discharge that makes them uniquely suited as compact, stand-alone units for daily or seasonal storage. However, TCMs suffer from instabilities at the material (salt particles) and reactor level (packed beds of salt), resulting in poor multi-cycle efficiency and high-levelized cost of storage. In this study, a model is developed to predict the pulverization limit or Rcrit of various salt hydrates during thermal cycling. This is critical as it provides design rules to make mechanically stable TCM composites as well as enables the use of more energy-efficient manufacturing process (solid-state mixing) to make the composites. The model is experimentally validated on multiple TCM salt hydrates with different water content, and effect of Rcrit on hydration and dehydration kinetics is also investigated.

Historical policies have been shown to underpin environmental quality. In the 1930s, the federal Home Owners' Loan Corporation (HOLC) developed the most comprehensive archive of neighborhoods that would have been redlined by local lenders and the Federal Housing Administration, often applying racist criteria. Our study explored how redlining is associated with environmental quality across eight California cities. We integrated HOLC's graded maps [grades A (i.e., "best" and "greenlined"), B, C, and D (i.e., "hazardous" and "redlined")] with 10 environmental hazards using data from 2018 to 2021 to quantify the spatial overlap among redlined neighborhoods and environmental hazards. We found that formerly redlined neighborhoods have poorer environmental quality relative to those of other HOLC grades via higher pollution, more noise, less vegetation, and elevated temperatures. Additionally, we found that intraurban disparities were consistently worse for formerly redlined neighborhoods across environmental hazards, with redlined neighborhoods having higher pollution burdens (77% of redlined neighborhoods vs 18% of greenlined neighborhoods), more noise (72% vs 18%), less vegetation (86% vs 12%), and elevated temperature (72% vs 20%), than their respective city's average. Our findings highlight that redlining, a policy abolished in 1968, remains an environmental justice concern by shaping the environmental quality of Californian urban neighborhoods.

Predicting the hydrological response of watersheds to climate disturbances requires a detailed understanding of the processes connecting hillslopes and streams. Using a network of soil moisture and temperature sensors, electrical resistivity tomography monitoring, and a weather station we assess the above and below-ground processes driving the hydrological response of a hillslope during snowmelt and summer monsoon. The transect covers bedrock and vegetation gradients, with a steep upper part characterized by shallow bedrock, and gentle lower part underlain by colluvium. The main vegetation cover is conifers on the upper, and grass and veratrum on the lower part. Combined with a simplified hydrological model, we show that the thin soil layer of the steep slope acts as a preferential flow path, leading to mostly shallow lateral flow, interrupted by vertical flow, mostly at tree locations, and likely facilitated by flow along fractures and roots. Vertical flow and upstream-driven groundwater dynamics are prevailing at the colluvium, presenting a very different hydrological behavior compared to the upper part. These results show that subsurface structure and features have a strong control on the hydrological response of a hillslope and that those can create considerably varying hydrological dynamics across small spatial scales.

Abstract: Floodplain lakes are widespread and ecologically important throughout tropical river systems, however data are rare that describe how temporal variations in hydrological, meteorological and optical conditions moderate stratification and mixing in these shallow lakes. Using time series measurements of meteorology and water‐column temperatures from 17 several day campaigns spanning two hydrological years in a representative Amazon floodplain lake, we calculated surface energy fluxes and thermal stratification, and applied and evaluated a 3‐dimensional hydrodynamic model. The model successfully simulated diel cycles in thermal structure characterized by buoyancy frequency, depth of the actively mixing layer, and other terms associated with the surface energy budget. Diurnal heating with strong stratification and nocturnal mixing were common; despite considerable heat loss at night, the strong stratification during the day meant that mixing only infrequently extended to the bottom at night. Simulations indicated that the diurnal thermocline up and downwelled creating lake‐wide differences in near‐surface temperatures and mixing depths. Infrequent full mixing creates conditions conducive to anoxia in these shallow lakes given their warm temperatures.

Abstract: Toxic cyanobacterial blooms (CBs) are becoming more frequent globally, posing a threat to freshwater ecosystems. While making long‐range forecasts is overly challenging, predicting imminent CBs is possible from precise monitoring data of the underlying covariates. It is, however, infeasibly costly to conduct precise monitoring on a large scale, leaving most lakes unmonitored or only partially monitored. The challenge is hence to build a predictive model that can use the incomplete, partially‐monitored data to make near‐future CB predictions. By using 30 years of monitoring data for 78 water bodies in Alberta, Canada, combined with data of watershed characteristics (including natural land cover and anthropogenic land use) and meteorological conditions, we train a Bayesian network that predicts future 2‐week CB with an area under the curve (AUC) of 0.83. The only monitoring data that the model needs to reach this level of accuracy are whether the cell count and Secchi depth are low, medium, or high, which can be estimated by advanced high‐resolution imaging technology or trained local citizens. The model is robust against missing values as in the absence of any single covariate, it performs with an AUC of at least 0.78. While taking a major step toward reduced‐cost, less data‐intensive CB forecasting, our results identify those key covariates that are worth the monitoring investment for highly accurate predictions.