The water footprint of hydrogen production.

Hydrogen (H2) is the most promising energy carrier for reducing the carbon emissions of the energy sector, but the impact of its production on water resources remains unclear. Here, we quantify the water footprint (WF) of different H2 production pathways accounting for the WF of the primary energy used in the production process, as well as feedstock and infrastructure water requirements. Results suggest that green H2 obtained from water electrolysis powered by renewable energy has the lowest WF (65 ± 2 m3/TJ for wind and 204 ± 79 m3/TJ for solar) mostly due to the low WF of renewable energy. The WF of blue H2 derived from fossil fuels is significantly higher (369 ± 30 m3/TJ for natural gas and 564 ± 82 m3/TJ for coal) due to high WF of fossil fuels as well as the water required for carbon capture and storage (CCS). H2 produced from nuclear energy and biomass have extremely high WF (741 ± 277 m3/TJ for nuclear and > 50,000 m3/TJ for biomass). Considering global and country-based energy scenarios, where the main H2 colors (green and blue) individually account for 15 % of energy consumption, we find that the use of green H2 could reduce the water demand of the energy sector while blue H2 would generally increase it, except in countries already characterized by high water consumption due to reliance on water-intensive energy sources. At the global level, we find that for every 5 % of H2 energy adoption, the energy sector could have water savings between 1 and 4 % for green H2 and increase water consumption between 1 and 5 % for blue H2. These results highlight the potential and criticalities of H2 within the water-energy nexus.

Self-similarity and vanishing diffusion in fluvial landscapes.

Complex topographies exhibit universal properties when fluvial erosion dominates landscape evolution over other geomorphological processes. Similarly, we show that the solutions of a minimalist landscape evolution model display invariant behavior as the impact of soil diffusion diminishes compared to fluvial erosion at the landscape scale, yielding complete self-similarity with respect to a dimensionless channelization index. Approaching its zero limit, soil diffusion becomes confined to a region of vanishing area and large concavity or convexity, corresponding to the locus of the ridge and valley network. We demonstrate these results using one dimensional analytical solutions and two dimensional numerical simulations, supported by real-world topographic observations. Our findings on the landscape self-similarity and the localized diffusion resemble the self-similarity of turbulent flows and the role of viscous dissipation. Topographic singularities in the vanishing diffusion limit are suggestive of shock waves and singularities observed in nonlinear complex systems.

Minimizing the impacts of the ammonia economy on the nitrogen cycle and climate.

Ammonia (NH3) is an attractive low-carbon fuel and hydrogen carrier. However, losses and inefficiencies across the value chain could result in reactive nitrogen emissions (NH3, NOx, and N2O), negatively impacting air quality, the environment, human health, and climate. A relatively robust ammonia economy (30 EJ/y) could perturb the global nitrogen cycle by up to 65 Mt/y with a 5% nitrogen loss rate, equivalent to 50% of the current global perturbation caused by fertilizers. Moreover, the emission rate of nitrous oxide (N2O), a potent greenhouse gas and ozone-depleting molecule, determines whether ammonia combustion has a greenhouse footprint comparable to renewable energy sources or higher than coal (100 to 1,400 gCO2e/kWh). The success of the ammonia economy hence hinges on adopting optimal practices and technologies that minimize reactive nitrogen emissions. We discuss how this constraint should be included in the ongoing broad engineering research to reduce environmental concerns and prevent the lock-in of high-leakage practices.

Inertial and geometrical effects of self-propelled elliptical Brownian particles.

Active particles that self-propel by transforming energy into mechanical motion represent a growing area of research in mathematics, physics, and chemistry. Here we investigate the dynamics of nonspherical inertial active particles moving in a harmonic potential, introducing geometric parameters which take into account the role of eccentricity for nonspherical particles. A comparison between the overdamped and underdamped models for elliptical particles is performed. The model of overdamped active Brownian motion has been used to describe most of the basic aspects of micrometer-sized particles moving in a liquid ("microswimmers"). We consider active particles by extending the active Brownian motion model to incorporate translation and rotation inertia and account for the role of eccentricity. We show how the overdamped and the underdamped models behave in the same way for small values of activity (Brownian case) if eccentricity is equal to zero, but increasing eccentricity leads the two dynamics to substantially depart from each other-in particular, the action of a torque induced by external forces, induced a marked difference close to the walls of the domain if eccentricity is high. Effects induced by inertia include an inertial delay time of the self-propulsion direction from the particle velocity, and the differences between the overdamped and underdamped systems are particularly evident in the first and second moments of the particle velocities. Comparison with the experimental results of vibrated granular particles shows good agreement and corroborates the notion that self-propelling massive particles moving in gaseous media are dominated by inertial effects.

Risk of the hydrogen economy for atmospheric methane.

Hydrogen (H2) is expected to play a crucial role in reducing greenhouse gas emissions. However, hydrogen losses to the atmosphere impact atmospheric chemistry, including positive feedback on methane (CH4), the second most important greenhouse gas. Here we investigate through a minimalist model the response of atmospheric methane to fossil fuel displacement by hydrogen. We find that CH4 concentration may increase or decrease depending on the amount of hydrogen lost to the atmosphere and the methane emissions associated with hydrogen production. Green H2 can mitigate atmospheric methane if hydrogen losses throughout the value chain are below 9 ± 3%. Blue H2 can reduce methane emissions only if methane losses are below 1%. We address and discuss the main uncertainties in our results and the implications for the decarbonization of the energy sector.

Nano- to Global-Scale Uncertainties in Terrestrial Enhanced Weathering.

Enhanced weathering (EW) is one of the most promising negative emissions technologies urgently needed to limit global warming to at least below 2 °C, a goal recently reaffirmed at the UN Global Climate Change conference (i.e., COP26). EW relies on the accelerated dissolution of crushed silicate rocks applied to soils and is considered a sustainable solution requiring limited technology. While EW has a high theoretical potential of sequestering CO2, research is still needed to provide accurate estimates of carbon (C) sequestration when applying different silicate materials across distinct climates and major soil types in combination with a variety of plants. Here we elaborate on fundamental advances that must be addressed before EW can be extensively adopted. These include identifying the most suitable environmental conditions, improving estimates of field dissolution rates and efficacy of CO2 removal, and identifying alternative sources of silicate materials to meet future EW demands. We conclude with considerations on the necessity of integrated modeling-experimental approaches to better coordinate future field experiments and measurements of CO2 removal, as well as on the importance of seamlessly coordinating EW with cropland and forest management.

The Carbon-Capture Efficiency of Natural Water Alkalinization: Implications For Enhanced weathering.

Enhanced weathering (EW) is a promising negative-emission technology that artificially accelerates the dissolution of natural minerals, promotes biomass growth, and alleviates the acidification of soils and natural waters. EW aims to increase the alkalinity of natural waters (alkalinization) to promote a transfer of CO2 from the atmosphere to the water. Here we provide a quantification of the alkalinization carbon-capture efficiency (ACE) as a function of the water chemistry. ACE can be used for any alkaline mineral in various natural waters. We show that ACE strongly depends on the water pH, with a sharp transition from minimum to maximum in a narrow interval of pH values. We also quantify ACE in three compartments of the land-to-ocean aquatic continuum: the world topsoils, the lakes of an acid-sensitive area, and the global surface ocean. The results reveal that the efficiency of terrestrial EW varies markedly, from 0 to 100 %, with a significant trade-off in acidic conditions between carbon-capture efficiency and enhanced chemical dissolution. The efficiency is more stable in the ocean, with a typical value of around 80 % and a latitudinal pattern driven by differences in seawater temperature and salinity. Our results point to the importance of an integrated hydrological and biogeochemical theory to assess the fate of the weathering products across the aquatic continuum from land to ocean.

Cloud cooling effects of afforestation and reforestation at midlatitudes.

Because of the large carbon sequestration potential, reforestation and afforestation (R&A) are among the most prominent natural climate solutions. However, while their effectiveness is well established for wet tropics, it is often argued that R&A are less advantageous or even detrimental at higher latitudes, where the reduction of forest albedo (the amount of reflected solar radiation by a surface) tends to nullify or even overcome the carbon benefits. Here, we carefully analyze the situation for R&A at midlatitudes, where the warming effects due to vegetation albedo are regarded to be almost balanced by the cooling effects from an increased carbon storage. Using both satellite data and atmospheric boundary-layer models, we show that by including cloud-albedo effects due to land-atmosphere interactions, the R&A cooling at midlatitudes becomes prevalent. This points to a much greater potential of R&A for wet temperate regions than previously considered.

A minimalist model for coevolving supply and drainage networks.

Numerous complex systems, both natural and artificial, are characterized by the presence of intertwined supply and/or drainage networks. Here, we present a minimalist model of such coevolving networks in a spatially continuous domain, where the obtained networks can be interpreted as a part of either the counter-flowing drainage or co-flowing supply and drainage mechanisms. The model consists of three coupled, nonlinear partial differential equations that describe spatial density patterns of input and output materials by modifying a mediating scalar field, on which supply and drainage networks are carved. In the two-dimensional case, the scalar field can be viewed as the elevation of a hypothetical landscape, of which supply and drainage networks are ridges and valleys, respectively. In the three-dimensional case, the scalar field serves the role of a chemical signal, according to which vascularization of the supply and drainage networks occurs above a critical 'erosion' strength. The steady-state solutions are presented as a function of non-dimensional channelization indices for both materials. The spatial patterns of the emerging networks are classified within the branched and congested extreme regimes, within which the resulting networks are characterized based on the absolute as well as the relative values of two non-dimensional indices.

Modelling nonlinear dynamics of Crassulacean acid metabolism productivity and water use for global predictions.

Crassulacean acid metabolism (CAM) crops are important agricultural commodities in water-limited environments across the globe, yet modelling of CAM productivity lacks the sophistication of widely used C3 and C4 crop models, in part due to the complex responses of the CAM cycle to environmental conditions. This work builds on recent advances in CAM modelling to provide a framework for estimating CAM biomass yield and water use efficiency from basic principles. These advances, which integrate the CAM circadian rhythm with established models of carbon fixation, stomatal conductance and the soil-plant-atmosphere continuum, are coupled to models of light attenuation, plant respiration and biomass partitioning. Resulting biomass yield and transpiration for Opuntia ficus-indica and Agave tequilana are validated against field data and compared with predictions of CAM productivity obtained using the empirically based environmental productivity index. By representing regulation of the circadian state as a nonlinear oscillator, the modelling approach captures the diurnal dynamics of CAM stomatal conductance, allowing the prediction of CAM transpiration and water use efficiency for the first time at the plot scale. This approach may improve estimates of CAM productivity under light-limiting conditions when compared with previous methods.

Fluctuation theorem and extended thermodynamics of turbulence.

Turbulent flows are out-of-equilibrium because the energy supply at large scales and its dissipation by viscosity at small scales create a net transfer of energy among all scales. This energy cascade is modelled by approximating the spectral energy balance with a nonlinear Fokker-Planck equation consistent with accepted phenomenological theories of turbulence. The steady-state contributions of the drift and diffusion in the corresponding Langevin equation, combined with the killing term associated with the dissipation, induce a stochastic energy transfer across wavenumbers. The fluctuation theorem is shown to describe the scale-wise statistics of forward and backward energy transfer and their connection to irreversibility and entropy production. The ensuing turbulence entropy is used to formulate an extended turbulence thermodynamics.

Cyclic epidemics and extreme outbreaks induced by hydro-climatic variability and memory.

A minimalist model of ecohydrologic dynamics is coupled to the well-known susceptible-infected-recovered epidemiological model to explore hydro-climatic controls on infection dynamics and extreme outbreaks. The resulting HYSIR model reveals the existence of a noise-induced bifurcation producing oscillations in infection dynamics. Linearization of the governing equations allows for an analytic expression for the periodicity of infections in terms of both epidemiological (e.g. transmission and recovery rate) and hydrologic (i.e. soil moisture decay rate or memory) parameters. Numerical simulations of the full stochastic, nonlinear system show extreme outbreaks in response to particular combinations of hydro-climatic conditions, neither of which is extreme per se, rather than a single major climatic event. These combinations depend on the assumed functional relationship between the hydrologic variables and the transmission rate. Our results emphasize the importance of hydro-climatic history and system memory in evaluating the risk of severe outbreaks.

From turbulence to landscapes: Logarithmic mean profiles in bounded complex systems.

We show that similarly to the logarithmic mean-velocity profile in wall-bounded turbulence, the landscape topography presents an intermediate region with a logarithmic mean-elevation profile. Such profiles are present in complex topographies with channel branching and fractal river networks resulting from model simulation, controlled laboratory experiments, and natural landscapes. Dimensional and self-similarity arguments are used to corroborate this finding. We also tested the presence of logarithmic profiles in discrete, minimalist models of networks obtained from optimality principles (optimal channel networks) and directed percolation. The emergence of self-similar scaling appears as a robust outcome in dynamically different, but spatially bounded, complex systems, as a dimensional consequence of length-scale independence.

Impacts of solar intermittency on future photovoltaic reliability.

As photovoltaic power is expanding rapidly worldwide, it is imperative to assess its promise under future climate scenarios. While a great deal of research has been devoted to trends in mean solar radiation, less attention has been paid to its intermittent character, a key challenge when compounded with uncertainties related to climate variability. Using both satellite data and climate model outputs, we characterize solar radiation intermittency to assess future photovoltaic reliability. We find that the relation between the future power supply and long-term mean solar radiation trends is spatially heterogeneous, showing power reliability is more sensitive to the fluctuations of mean solar radiation in hot arid regions. Our results highlight how reliability analysis must account simultaneously for the mean and intermittency of solar inputs when assessing the impacts of climate change on photovoltaics.

Variational analysis of landscape elevation and drainage networks.

Landscapes evolve towards surfaces with complex networks of channels and ridges in response to climatic and tectonic forcing. Here, we analyse variational principles giving rise to minimalist models of landscape evolution as a system of partial differential equations that capture the essential dynamics of sediment and water balances. Our results show that in the absence of diffusive soil transport the steady-state surface extremizes the average domain elevation. Depending on the exponent m of the specific drainage area in the erosion term, the critical surfaces are either minima (0 < m < 1) or maxima (m > 1), with m = 1 corresponding to a saddle point. We establish a connection between landscape evolution models and optimal channel networks and elucidate the role of diffusion in the governing variational principles.

Climatological, virological and sociological drivers of current and projected dengue fever outbreak dynamics in Sri Lanka.

The largest ever Sri Lankan dengue outbreak of 2017 provides an opportunity for investigating the relative contributions of climatological, epidemiological and sociological drivers on the epidemic patterns of this clinically important vector-borne disease. To do so, we develop a climatologically driven disease transmission framework for dengue virus using spatially resolved temperature and precipitation data as well as the time-series susceptible-infected-recovered (SIR) model. From this framework, we first demonstrate that the distinct climatological patterns encountered across the island play an important role in establishing the typical yearly temporal dynamics of dengue, but alone are unable to account for the epidemic case numbers observed in Sri Lanka during 2017. Using a simplified two-strain SIR model, we demonstrate that the re-introduction of a dengue virus serotype that had been largely absent from the island in previous years may have played an important role in driving the epidemic, and provide a discussion of the possible roles for extreme weather events and human mobility patterns on the outbreak dynamics. Lastly, we provide estimates for the future burden of dengue across Sri Lanka using the Coupled Model Intercomparison Phase 5 climate projections. Critically, we demonstrate that climatological and serological factors can act synergistically to yield greater projected case numbers than would be expected from the presence of a single driver alone. Altogether, this work provides a holistic framework for teasing apart and analysing the various complex drivers of vector-borne disease outbreak dynamics.

Channelization cascade in landscape evolution.

The hierarchy of channel networks in landscapes displays features that are characteristic of nonequilibrium complex systems. Here we show that a sequence of increasingly complex ridge and valley networks is produced by a system of partial differential equations coupling landscape evolution dynamics with a specific catchment area equation. By means of a linear stability analysis we identify the critical conditions triggering channel formation and the emergence of characteristic valley spacing. The ensuing channelization cascade, described by a dimensionless number accounting for diffusive soil creep, runoff erosion, and tectonic uplift, is reminiscent of the subsequent instabilities in fluid turbulence, while the structure of the simulated patterns is indicative of a tendency to evolve toward optimal configurations, with anomalies similar to dislocation defects observed in pattern-forming systems. The choice of specific geomorphic transport laws and boundary conditions strongly influences the channelization cascade, underlying the nonlocal and nonlinear character of its dynamics.

Jump processes with deterministic and stochastic controls.

We consider the dynamics of a one-dimensional system evolving according to a deterministic drift and randomly forced by two types of jump processes, one representing an external, uncontrolled forcing and the other one a control that instantaneously resets the system according to specified protocols (either deterministic or stochastic). We develop a general theory, which includes a different formulation of the master equation using antecedent and posterior jump states, and obtain an analytical solution for steady state. The relevance of the theory is illustrated with reference to stochastic irrigation to assess crop-failure risk, a problem of interest for environmental geophysics.

Thermodynamic Relations among Isotropic Material Properties in Conditions of Plane Shear Stress.

We present new general relationships among the material properties of an isotropic material kept in homogeneous stress conditions with hydrostatic pressure and plane shear. The derivation is not limited to the proximity of the zero shear-stress and -strain condition, which allows us to identify the relationship between adiabatic and isothermal shear compliances (inverse of the moduli of rigidity) along with new links, among others, between isobaric and isochoric shear thermal expansion coefficients and heat capacities at constant stress and constant shear strain. Such relationships are important for a variety of applications, including the determination of constitutive equations, the characterization of nanomaterials, and the identification of properties related to earthquakes precursors and complex media (e.g., soil) behavior. The results may be useful to investigate the behavior of materials during phase transitions involving shear or in non-homogeneous conditions within a local thermodynamic equilibrium framework.