Search

Understanding the complex interactions between land surface and atmosphere is essential to improve weather and climate predictions. Various numerical experiments have suggested that regions of strong coupling strength (hotspots) are located in the transitional climate zones. However, atmospheric processes in these hotspots are found to have different responses to the perturbation of surface properties. Here we establish analytical relationships to identify key role of soil moisture variances in controlling the coupling hotspots. Using the most recent numerical experiments, we find different signs of feedback in two such hotspots, suggesting the coupling can either reinforce or attenuate persistent extreme climates. We further uncover new coupling hotspots in regions where precipitation is highly sensitive to soil moisture perturbation. Our results highlight the importance of both signs and magnitudes of land-atmosphere interactions over extensive regions, where the ecosystems and communities are particularly vulnerable to the extreme climate events., Comment: 3 figures, 18 pages

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. The contact with the environment produces an interaction entropy, which controls the distribution of fluctuations and allows writing the generalized Gibbs ensembles for macrostates in potential form. This naturally yields exact nonlinear thermodynamic Langevin equations (TLEs) for such variables, with drift expressed in terms of entropic forces. The analysis of the canonical ensemble for an ideal monoatomic gas and the related TLEs show that introducing currents leads to nonequilibrium heat transfer conditions with interesting bounds on entropy production but with no obvious thermodynamic limit. For a colloidal particle under constant force, the TLEs for macroscopic variables are different from those for the microscopic position, typically used in the so-called stochastic thermodynamics; while TLEs are consistent with the fundamental equation obtained from the Hamiltonian, stochastic thermodynamics requires isothermal conditions and entropy proportional to position.

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 1D analytical solutions and 2D 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.

While the warming trends of the Earth's mean temperature are evident at climatological scales, the local temperature at shorter timescales are highly fluctuating. In this letter we show that the probabilities of such fluctuations are characterized by a special symmetry typical of systems out of equilibrium. Their nearly universal properties are linked to the fluctuation theorem and reveal that the progressive warming is accompanied by growing asymmetry of temperature distributions. These statistics allow us to project the global temperature variability in the near future, in line with predictions from climate models, providing valuable information about future extremes., Comment: 13 pages, 4 figures

For 1D Hamiltonian systems with periodic solutions, Helmholtz formalism provides a tantalizing interpretation of classical thermodynamics, based on time integrals of purely mechanical quantities and without need of statistical description. Here we extend this approach to include heat flux and pressure at the walls, thereby enabling it to describe actual thermodynamic transformations, such as isothermal compressions and expansions. The presence of hard walls, which gives rise to non zero pressure, is justified by means of the virial theorem, while the heat fluxes are introduced as quasi-static limits of suitably thermostatted Hamiltonians. Particular attention is paid to generalizing the minimalist cases of the harmonic oscillator and elastic bouncer, which afford clear physical interpretations. With such extensions, a complete picture of thermodynamics emerges, amenable to cyclic transformations capable of producing mechanical work from heat, like the Carnot cycle.

Mangrove canopy height is a key metric to assess tidal forests' resilience in the face of climate change. In terrestrial forests, tree height is primarily determined by water availability, plant hydraulic design, and disturbance regime. However, the role of water stress remains elusive in tidal environments, where saturated soils are prevalent, and salinity can substantially affect the soil water potential. Here, we use global observations of maximum canopy height, species richness, air temperature, and seawater salinity -- a proxy of soil water salt concentration -- to explain the causal link between salinity and Mangrove stature. Our findings suggest that salt stress affects Mangrove height both directly, by reducing primary productivity and increasing the risk of xylem cavitation, and indirectly favoring more salt-tolerant species, narrowing the spectrum of viable traits, and reducing biodiversity. Yet, salt tolerance comes to a price. Highly salt-tolerant mangroves are less productive and generally shorter than more sensitive species, suggesting a causal nexus between salinity, biodiversity, and tree height. As sea-level rise enhances coastal salinization, failure to account for these effects can lead to incorrect estimates of future carbon stocks in Tropical coastal ecosystems and endanger preservation efforts.

While it is well known that water availability controls vegetation growth and soil microbial activity, how aridity affects ecosystem carbon patterns is not completely understood. Towards a more quantitative assessment of terrestrial carbon stocks, here we uncover a remarkable self-similar behavior of the global carbon stock. Using international survey and remote sensing data, we find that the key statistics (e.g., mean, quantiles, and standard deviation) of carbon stock tend to scale with the hydrological regime (i.e., aridity) via a universal exponent. As a result, when normalized by its averages in the corresponding hydrological regime, the carbon stock distributions collapse onto a single curve. Such a scaling reflects the strong coupling between hydrological cycle and biogeochemical process and enables robust predictions of carbon stocks as a function of aridity only., Comment: 18 pages, 10 figures, 1 table

Anthropogenic emissions of CO2 must soon approach net-zero to stabilize the global mean temperature. Although several international agreements have advocated for coordinated climate actions, their implementation has remained below expectations. One of the main challenges of international cooperation is the different degrees of socio-political acceptance of decarbonization. Here we interrogate a minimalistic model of the coupled natural-human system representing the impact of such socio-political acceptance on clean energy investments and the path to net-zero emissions. We show that incentives and carbon pricing are essential tools to achieve net-zero before critical CO2 concentrations are reached, and deep international coordination is necessary for a rapid and effective transition. Although a perfect coordination scenario remains unlikely, as investments in clean energy are ultimately limited by myopic economic strategies and a policy system that promotes free-riding, more realistic decentralized cooperation with partial efforts from each actor could still lead to significant emissions cuts.

The local extremes (i.e., peaks and pits) of the landscape-elevation field play a critical role in energy, water, and nutrient distribution over the region, but the statistical distributions of these points in relation to landscape advancing towards maturity have received limited research attention. In this work, we first explain how the spatial correlation structure of the elevation field affects the counts and frequency distributions of local extremes. We then analyze local extremes statistics for 8 mountainous landscapes worldwide with diverse hydroclimatic forcings and geologic histories using 24 Digital Elevation Models (DEMs) and compare them with complex terrain of the Erythraeum Chaos region on Mars. The results reveal that the spherical covariance structure captures the observed spatial correlation in these cases with the peak frequency distribution agreeing well with the elevation frequency distribution. The ratio of the pit-over-peak (POP) count is linked to the degree to which the pit and peak frequency distributions match, and carries the mark of landscape aging. The relationship between the geomorphic development stage (quantified by the reduced fatness of the slope-distribution tail) and the deviation of POP values from unity in old mountainous landscapes confirms that the evolution towards smoother topographies is atypically accompanied by reduced pit counts typical of organized valley and ridge patterns.

Interesting analogies between shallow water dynamics and astrophysical phenomena have offered valuable insight from both the theoretical and experimental point of view. To help organize these efforts, here we analyze systematically the hydrodynamic properties of backwater profiles of the shallow water equations with 2D radial symmetry. In contrast to the more familiar 1D case typical of hydraulics, %already for {even in} isentropic conditions, a solution with minimum-radius horizon for the flow emerges, similar to the black hole and white hole horizons, where the critical conditions of unitary Froude number provide a unidirectional barrier for surface waves. Beyond these time-reversible solutions, a greater variety of cases arises, when allowing for dissipation by turbulent friction and shock waves (i.e., hydraulic jumps) for both convergent and divergent flows. The resulting taxonomy of the base-flow cases may serve as a starting point for a more systematic analysis of higher-order effects linked, e.g., to wave propagation and instabilities, capillarity, variable bed slope, and rotation.

Dengue fever impacts populations across the tropics. Dengue is caused by a mosquito transmitted flavivirus and its burden is projected to increase under future climate and development scenarios. The transmission process of dengue virus is strongly moderated by hydro-climatic conditions that impact the vector's life cycle and behavior. Here, we study the impact of rainfall seasonality and moisture availability on the monthly distribution of reported dengue cases in Sri Lanka. Through cluster analysis, we find an association between seasonal peaks of rainfall and dengue incidence with a two-month lag. We show that a hydrologically driven epidemiological model (HYSIR), which takes into account hydrologic memory in addition to the nonlinear dynamics of the transmission process, captures the two-month lag between rainfall and dengue cases seasonal peaks. Our analysis reveals a non-monotonic dependence of dengue cases on moisture, whereby an increase of cases with increasing moisture is followed by a reduction for very high levels of water availability. Improvement in prediction of the seasonal peaks in dengue incidence results from a seasonally varying dependence of transmission rate on water availability.

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 co-evolving 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 2-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 3-dimensional case, the scalar field serves as the chemical signal strength, in 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.

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. Here, the energy cascade is approximated by a combined contribution of a forward drift and diffusion that recover accepted phenomenological theories of turbulence. The fluctuation theorem (FT) is then 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 may be used to formulate an extended turbulence thermodynamics.

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 of 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, here we characterize solar radiation intermittency to assess future photovoltaic reliability. We find that the relation between the future power supply and long-term trends of mean solar radiation is highly nonlinear, thus making power reliability more sensitive to the fluctuations of mean solar radiation in regions where insolation is the highest. 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., Comment: 19 pages, 10 figures, 1 table

From infiltration of water into the soil during rainstorms to seasonal plant growth and death, the ecohydrological processes that are thought to be relevant to the formation of banded vegetation patterns in drylands occur across multiple timescales. We propose a new fast-slow switching model in order to capture these processes on appropriate timescales within a conceptual modeling framework based on reaction-advection-diffusion equations. The fast system captures hydrological processes that occur on minute to hour timescales during and shortly after major rainstorms, assuming a fixed vegetation distribution. These include key feedbacks between vegetation biomass and downhill surface water transport, as well as between biomass and infiltration rate. The slow system acts between rain events, on a timescale of days to months, and evolves vegetation and soil moisture. Modeling processes at the appropriate timescales allows parameter values to be set by the actual processes they capture. This reduces the number of parameters that are chosen expressly to fit pattern characteristics, or to artificially slow down fast processes by the orders of magnitude required to align their timescales with the biomass dynamics. We explore the fast-slow switching model through numerical simulation on a one-dimensional hillslope, and find agreement with certain observations about the pattern formation phenomenon, including band spacing and upslope colonization rates. We also find that the predicted soil moisture dynamics are consistent with time series data that has been collected at a banded vegetation site. This fast-slow model framework introduces a tool for investigating the possible impact of changes to frequency and intensity of rain events in dryland ecosystems., Comment: 35 pages, 21 figures, 2 tables, 2 appendices

The Jarzynski equality (JE) is analyzed in regard to its validity for both quasi-static transformations in the thermodynamic limit and Hamiltonian evolutions of the work protocol. In the first case, we show that the JE holds for isothermal transformations only; in the second case, we show that the work done (linked to the final state Hamiltonian) depends on the temporal dynamics of the work protocol (including its speed), thus precluding the possibility of identifying it with the free energy or any other thermodynamic state function. Even in the case of thermodynamic limit and infinitesimally slow transformations of the Hamiltonian (adiabatic invariance) following states of thermodynamic equilibrium, the resulting work expression does not default to the JE, but to the work relation for adiabatic thermodynamic transformations, W=Delta U, where W is work and U is internal energy., Comment: We request to withdraw the manuscript because it contains a conceptual mistake that affects our conclusions. Thanks

Landscapes evolve toward surfaces with complex networks of channels and ridges in response to climatic and tectonic forcing. Here we analyze 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 specific drainage area in the erosion term, the critical surfaces are either minima (01), with m=1 corresponding to a saddle point. Our results establish a connection between Landscape Evolution Models (LEMs) and Optimal Channel Networks (OCNs) and elucidate the role of diffusion in the governing variational principles.

Several theoretical frameworks have been proposed to explain observed biodiversity patterns, ranging from the classical niche-based theories, mainly employing a continuous formalism, to neutral theories, based on statistical mechanics of discrete communities. Differences in the descriptions of biodiversity can arise due to the discrete or continuous nature of the underlying models and the way internal or external perturbations appear in their formulations. Here, we trace the effects of stochastic population dynamics on biodiversity, from the scale of the individuals to the community and based on both discrete and continuous representations of the system, by consistently using measures of community diversity like the species abundance distribution and the rank abundance curve and applying them to both discrete and continuous populations. A novel measure, the community abundance distribution, is introduced to facilitate the comparison across different levels of description, from microscopic to macroscopic. Using a simple birth and death process and an interacting population model, we highlight discrepancies in their discrete and continuous distributions and discuss relevant implications for the analysis of rare species and extinction dynamics. Quantitative consideration of these issues is useful for better understanding of the contributions of non-neutral processes and the mathematical approximations to various measures of biodiversity.

The logarithmic mean-velocity profile is a key experimental and theoretical result in wall-bounded turbulence. Similarly, here we show that the topographic surface emerging between parallel zero-elevation boundaries presents an intermediate region with a logarithmic mean-elevation profile. We use model simulations, which account for growth, erosion, and smoothing processes and give rise to complex topography with channel branching and fractal river networks, as well as data from a physical landscape-evolution experiment. Dimensional and self-similarity arguments are used to corroborate this finding. Our results suggest a universality of the logarithmic scaling in bounded complex systems out of equilibrium, of which landscape topography and turbulence are quintessential examples.

A few decades after Hill's work on nano-thermodynamics, the development of a thermodynamic framework, to account consistently for the fluctuations of small systems due to their interactions with the surrounding environment, is still underway. Here we discuss how, in a small system, the interaction energy with the environment may be described through a conjugate pair of intensive and extensive variables, giving rise to a Gibbs thermodynamics with an additional thermodynamic degree of freedom. The relevant thermodynamic potentials help describe the equilibrium conditions and the material properties that measure the susceptibility of the system to the interaction with the environment. The resulting generalized ensembles, which describe the non-negligible, small system fluctuations, are shown to be equivalent. Away from the average thermodynamic state, the availability describes the distance between system and the environment, in terms of the maximum work extractable from the fluctuation, and a proper definition of entropy extends the thermodynamics to the generic fluctuating state. From the latter state, entropic forces arise to restore the average thermodynamic state. Our framework unifies and extends the ensemble thermodynamics by Hill and the recent advances in statistical mechanics under strong coupling and reduces to classical macroscopic thermodynamics when the system is large. The particular case of a small ideal gas system is discussed in detail and the example of a single particle immersed in a bath is revisited in the light of the new formalism.

We present an algorithm that is well suited to find the linear layout of the multiple flow-direction network (directed acyclic graph) for an efficient implicit computation of the erosion term in landscape evolution models. The time complexity of the algorithm varies linearly with the number of nodes in the domain, making it very efficient. The resulting numerical scheme allows us to achieve accurate steady-state solutions in conditions of high erosion rates leading to heavily dissected landscapes. We also establish that contrary to single flow-direction methods such as D8, D$\infty$ multiple flow-direction method follows the theoretical prediction of the linear stability analysis and correctly captures the transition from smooth to the channelized regimes. We finally show that the obtained numerical solutions follow the theoretical temporal variation of mean elevation.

We consider the dynamics of a 1D system evolving according to a deterministic drift and randomly forced by two types of jumps 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 probabilistically crop-failure risk, a problem of interest for environmental geophysics., Comment: 12 pages, 4 figures

It is well known that evaporative cooling of Earth's surface water reduces the amount of radiation that goes into sensible heat, namely the portion of radiation that produces higher temperatures. However, a rigorous use of long-term hydrologic measurements and the related theories of hydrologic partitioning have not yet been fully exploited to quantify these effects on climate. Here, we show that the Budyko's curve, a well-known and efficient framework for water balance estimation, can be effectively utilized to partition the surface energy fluxes by expressing the long-term evaporative fraction as a function of the dryness index. The combination of this energy partitioning method with hydrological observations allows us to estimate the surface energy components at watershed and continental scales. Analyzing climate model outputs through this new lens reveals energy biases due to inaccurate parameterization of hydrological and atmospheric processes, offering insight into model parameterization and providing useful information for improving climate projections., Comment: 20 pages and 8 figures

The hierarchy of channel networks in landscapes displays features that are characteristic of non-equilibrium 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.