Thermodynamic Langevin equations.

The physical significance of the stochastic processes associated to the generalized Gibbs ensembles is scrutinized here with special attention to the thermodynamic fluctuations of small systems. Differently from the so-called stochastic thermodynamics, which starts from stochastic versions of the first and second law of thermodynamics and associates thermodynamic quantities to microscopic variables, here we consider stochastic variability directly in the macroscopic variables. By recognizing the potential structure of the Gibbs ensembles, when expressed as a function of the potential entropy generation, we obtain exact nonlinear thermodynamic Langevin equations (TLEs) for macroscopic variables, with drift expressed in terms of entropic forces.

Multiple time scale optimization explains functional trait responses to leaf water potential.

Plant response to water stress involves multiple timescales. In the short term, stomatal adjustments optimize some fitness function commonly related to carbon uptake, while in the long term, traits including xylem resilience are adjusted. These optimizations are usually considered independently, the former involving stomatal aperture and the latter carbon allocation.

The water footprint of hydrogen production.

Hydrogen (H) 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 H 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 H obtained from water electrolysis powered by renewable energy has the lowest WF (65 ± 2 m/TJ for wind and 204 ± 79 m/TJ for solar) mostly due to the low WF of renewable energy.

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.

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

Ammonia (NH) is an attractive low-carbon fuel and hydrogen carrier. However, losses and inefficiencies across the value chain could result in reactive nitrogen emissions (NH, NO, and NO), 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.

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.

Risk of the hydrogen economy for atmospheric methane.

Hydrogen (H) 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 (CH), the second most important greenhouse gas. Here we investigate through a minimalist model the response of atmospheric methane to fossil fuel displacement by hydrogen.

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).

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 CO 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.

The delusive accuracy of global irrigation water withdrawal estimates.

Miscalculating the volumes of water withdrawn for irrigation, the largest consumer of freshwater in the world, jeopardizes sustainable water management. Hydrological models quantify water withdrawals, but their estimates are unduly precise. Model imperfections need to be appreciated to avoid policy misjudgements.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Accounting for landscape heterogeneity improves spatial predictions of tree vulnerability to drought.

As climate change continues, forest vulnerability to droughts and heatwaves is increasing, but vulnerability varies regionally and locally through landscape position. Also, most models used in forecasting forest responses to heat and drought do not incorporate relevant spatial processes. In order to improve spatial predictions of tree vulnerability, we employed a nonlinear stochastic model of soil moisture dynamics accounting for landscape differences in aspect, topography and soils.

Similarities in the evolution of plants and cars.

While one system is animate and the other inanimate, both plants and cars are powered by a highly successful process which has evolved in a changing environment. Each process (the photosynthetic pathway and the car engine, respectively) originated from a basic scheme and evolved greater efficiency by adding components to the existing structure, which has remained largely unchanged. Here we present a comparative analysis of two variants on the original C3 photosynthetic pathway (C4 and CAM) and two variants on the internal combustion engine (the turbocharger and the hybrid electric vehicle).

Diurnal cloud cycle biases in climate models.

Clouds' efficiency at reflecting solar radiation and trapping the terrestrial radiation is strongly modulated by the diurnal cycle of clouds (DCC). Much attention has been paid to mean cloud properties due to their critical role in climate projections; however, less research has been devoted to the DCC. Here we quantify the mean, amplitude, and phase of the DCC in climate models and compare them with satellite observations and reanalysis data.