Observational knowledge of the epidemic intensity, defined as the number of deaths divided by global population and epidemic duration, and of the rate of emergence of infectious disease outbreaks is necessary to test theory and models and to inform public health risk assessment by quantifying the probability of extreme pandemics such as COVID-19. Despite its significance, assembling and analyzing a comprehensive global historical record spanning a variety of diseases remains an unexplored task. A global dataset of historical epidemics from 1600 to present is here compiled and examined using novel statistical methods to estimate the yearly probability of occurrence of extreme epidemics. Historical observations covering four orders of magnitude of epidemic intensity follow a common probability distribution with a slowly decaying power-law tail (generalized Pareto distribution, asymptotic exponent = −0.71). The yearly number of epidemics varies ninefold and shows systematic trends. Yearly occurrence probabilities of extreme epidemics, Py, vary widely: Py of an event with the intensity of the “Spanish influenza” (1918 to 1920) varies between 0.27 and 1.9% from 1600 to present, while its mean recurrence time today is 400 y (95% CI: 332 to 489 y). The slow decay of probability with epidemic intensity implies that extreme epidemics are relatively likely, a property previously undetected due to short observational records and stationary analysis methods. Using recent estimates of the rate of increase in disease emergence from zoonotic reservoirs associated with environmental change, we estimate that the yearly probability of occurrence of extreme epidemics can increase up to threefold in the coming decades.

Since its introduction in 1954, the Soil Conservation Service curve number (SCS‐CN) method has become the standard tool, in practice, for estimating an event‐based rainfall‐runoff response. However, because of its empirical origins, the SCS‐CN method is restricted to certain geographic regions and land use types. Moreover, it does not describe the spatial variability of runoff. To move beyond these limitations, we present a new theoretical framework for spatially lumped, event‐based rainfall‐runoff modeling. In this framework, we describe the spatially lumped runoff model as a point description of runoff that is upscaled to a watershed area based on probability distributions that are representative of watershed heterogeneities. The framework accommodates different runoff concepts and distributions of heterogeneities, and in doing so, it provides an implicit spatial description of runoff variability. Heterogeneity in storage capacity and soil moisture are the basis for upscaling a point runoff response and linking ecohydrological processes to runoff modeling. For the framework, we consider two different runoff responses for fractions of the watershed area: “prethreshold” and “threshold‐excess” runoff. These occur before and after infiltration exceeds a storage capacity threshold. Our application of the framework results in a new model (called SCS‐CNx) that extends the SCS‐CN method with the prethreshold and threshold‐excess runoff mechanisms and an implicit spatial description of runoff. We show proof of concept in four forested watersheds and further that the resulting model may better represent geographic regions and site types that previously have been beyond the scope of the traditional SCS‐CN method.

In addition to buffering plants from water stress during severe droughts, plant water storage (PWS) alters many features of the spatio-temporal dynamics of water movement in the soil-plant system. How PWS impacts water dynamics and drought resilience is explored using a multi-layer porous media model. The model numerically resolves soil-plant hydrodynamics by coupling them to leaf-level gas exchange and soil-root interfacial layers. Novel features of the model are the considerations of a coordinated relationship between stomatal aperture variation and whole-system hydraulics and of the effects of PWS and nocturnal transpiration (Fe,night) on hydraulic redistribution (HR) in the soil. The model results suggest that daytime PWS usage and Fe,night generate a residual water potential gradient (Δψp,night) along the plant vascular system overnight. This Δψp,night represents a non-negligible competing sink strength that diminishes the significance of HR. Considering the co-occurrence of PWS usage and HR during a single extended dry-down, a wide range of plant attributes and environmental/soil conditions selected to enhance or suppress plant drought resilience is discussed. When compared with HR, model calculations suggest that increased root water influx into plant conducting-tissues overnight maintains a more favorable water status at the leaf, thereby delaying the onset of drought stress.

Changes in rainfall amounts and patterns have been observed and are expected to continue in the near future with potentially significant ecological and societal consequences. Modelling vegetation responses to changes in rainfall is thus crucial to project water and carbon cycles in the future. In this study, we present the results of a new model-data intercomparison project, where we tested the ability of 10 terrestrial biosphere models to reproduce the observed sensitivity of ecosystem productivity to rainfall changes at 10 sites across the globe, in nine of which, rainfall exclusion and/or irrigation experiments had been performed. The key results are as follows: (a) Intermodel variation is generally large and model agreement varies with timescales. In severely water-limited sites, models only agree on the interannual variability of evapotranspiration and to a smaller extent on gross primary productivity. In more mesic sites, model agreement for both water and carbon fluxes is typically higher on fine (daily-monthly) timescales and reduces on longer (seasonal-annual) scales. (b) Models on average overestimate the relationship between ecosystem productivity and mean rainfall amounts across sites (in space) and have a low capacity in reproducing the temporal (interannual) sensitivity of vegetation productivity to annual rainfall at a given site, even though observation uncertainty is comparable to inter-model variability. (c) Most models reproduced the sign of the observed patterns in productivity changes in rainfall manipulation experiments but had a low capacity in reproducing the observed magnitude of productivity changes. Models better reproduced the observed productivity responses due to rainfall exclusion than addition. (d) All models attribute ecosystem productivity changes to the intensity of vegetation stress and peak leaf area, whereas the impact of the change in growing season length is negligible. The relative contribution of the peak leaf area and vegetation stress intensity was highly variable among models. K E Y W O R D S drought, irrigation, rainfall manipulation experiment, terrestrial biosphere models

The SIR ('susceptible-infectious-recovered') formulation is used to uncover the generic spread mechanisms observed by COVID-19 dynamics globally, especially in the early phases of infectious spread. During this early period, potential controls were not effectively put in place or enforced in many countries. Hence, the early phases of COVID-19 spread in countries where controls were weak offer a unique perspective on the ensemble-behavior of COVID-19 basic reproduction number R o inferred from SIR formulation. The work here shows that there is global convergence (i.e., across many nations) to an uncontrolled R o = 4.5 that describes the early time spread of COVID-19. This value is in agreement with independent estimates from other sources reviewed here and adds to the growing consensus that the early estimate of R o = 2.2 adopted by the World Health Organization is low. A reconciliation between power-law and exponential growth predictions is also featured within the confines of the SIR formulation. The effects of testing ramp-up and the role of 'super-spreaders' on the inference of R o are analyzed using idealized scenarios. Implications for evaluating potential control strategies from this uncontrolled R o are briefly discussed in the context of the maximum possible infected fraction of the population (needed to assess health care capacity) and mortality (especially in the USA given diverging projections). Model results indicate that if intervention measures still result in R o > 2.7 within 44 days after first infection, intervention is unlikely to be effective in general for COVID-19.

<p>Human-natural processes that generate extreme events with large financial, social, and health consequences,  are inherently non-stationary due to ever-changing anthropogenic pressures and societal exposure. The issues posed by non-stationarity are recognized and addressed in Earth system science.  However, extensive epidemiological information remains fragmented and virtually unexplored from this perspective due to the lack of approaches to leverage observations of a heterogeneous past. To address this gap, we assembled a long historical record (1600-present) of infectious disease epidemics from the literature.  This new record enabled the development and applications of methods to quantify the time-varying probability of occurrence of extreme epidemic events. We define the intensity of epidemic events, the number of deaths/time/global population, and find that observations from several hundred years, covering almost four orders of magnitude of epidemic intensity, follow a probability distribution  with a slowly-decaying power-law tail (Generalized Pareto Distribution, asymptotic exponent = -0.705). To the contrary, the yearly number of epidemics is non-stationary, implying that conventional extreme value analyses are inappropriate.  We find that the rate of occurrence of extreme epidemics varies nine-fold over centennial time scales, from about 0.4 to 3.6 epidemics/year. As a result, yearly occurrence probabilities of extreme epidemics are far from constant:  The intensity computed for the most extreme event on record – the “Spanish Influenza” of 1918-1920 – has a probability of occurrence varying from 0.27 to 1.75 %/year in the time frame from 1600 to present. When optimistically assuming that 1 year is required to develop, produce, and begin distributing a vaccine/treatment for a new disease (e.g. the recent COVID-19 case), we estimate that the average recurrence time of a pandemic killing most of the global population is now less than 12,000 years.</p>

Climate-induced forest mortality is being increasingly observed throughout the globe. Alarmingly, it is expected to exacerbate under climate change due to shifting precipitation patterns and rising air temperature. However, the impact of concomitant changes in atmospheric humidity and CO 2 concentration through their influence on stomatal kinetics remains a subject of debate and inquiry. By using a dynamic soil-plant-atmosphere model, mortality risks associated with hydraulic failure and stomatal closure for 13 temperate and tropical forest biomes across the globe are analyzed. The mortality risk is evaluated in response to both individual and combined changes in precipitation amounts and their seasonal distribution, mean air temperature, specific humidity, and atmospheric CO 2 concentration. Model results show that the risk is predicted to significantly increase due to changes in precipitation and air temperature regime for the period 2050-2069. However, this increase may largely get alleviated by concurrent increases in atmospheric specific humidity and CO 2 concentration. The increase in mortality risk is expected to be higher for needleleaf forests than for broadleaf forests, as a result of disparity in hydraulic traits. These findings will facilitate decisions about intervention and management of different forest types under changing climate.forest mortality | drought | climate change | hydraulic failure | stomatal closure F orest mortality can lead to irreversible change in vegetation cover, thereby affecting many processes pertinent to water, carbon, and nutrient budgets (1). Multiple studies (2-10) have noted close association between forest mortality and water and heat stress, owing to shifting precipitation patterns and rising air temperature. However, the influence of concurrent changes in specific humidity (SH) and CO2 concentration, which affect plant response to stress by altering stomatal kinetics (11), have not received similar attention. Although elevated CO2 concentration is expected to promote future forest productivity (12), the extent to which it affects forest mortality in the context of water and heat stress remains a subject of inquiry. Short-term records (3, 4) and long-term manipulative field studies in forests such as the Free Air CO2 Enrichment experiments (13-15) have tried to fill the knowledge gap; however, they do not cover the entire manifold of projected climate conditions. The goals of this study are to evaluate the individual and combined influence of projected changes in precipitation, temperature, SH, and CO2 concentration on forest mortality risk and to investigate whether the response of mortality risk differs among plant functional types (PFTs).Tree mortality may occur through several mechanisms, including hydraulic failure, carbon starvation, phloem transport limitation, and biotic attack (16,17). Hydraulic failure is characterized as the malfunction of xylem water transport associated with cavitation, which is induced by low xylem water potential under limited soil wate...

The objective of stormwater detention basins is to capture stormwater runoff to reduce and delay peak flow and to improve the water quality. These objectives can be improved upon by actively controlling the outflow of the basins rather than traditional passive outflow structures. There are studies demonstrating the performance of the active controls that respond in real-time to basin hydraulics, detention time, and rainfall forecasts. We hypothesize that the performance of these active controls can be improved upon by incorporating real-time water quality data streams into the control algorithm. Furthermore, we hypothesize that performance of these active controls also depends on hydrologic variability, perturbing the highly dynamic rainfall-runoff process. Here, these hypotheses are tested using a numerical modeling framework evaluating the systemslevel reliability of passive and active control of stormwater basin outflow using a Monte Carlo method. The numerical modeling is performed in EPA-SWMM urban hydrologic model driven by stochastic rainfall time-series generated from the Modified Bartlett-Lewis Rectangular Pulses Model. Water quality-informed real-time active control algorithms are developed, tested, and demonstrated to result in a clear improvement over the traditional passive (no control) systems and other storage-based active controls for water and suspended sediment capture. Duration curve analysis showed that both water level-and water quality-informed control performance varied for different storm return periods and this variability could partly be attributed to the fraction of time the valve is closed. In addition, control performance was sensitive to rainfall variability, generally decreasing as storms become less frequent and more intense. Therefore, control system performance may depend on seasonal and longer timescale variability in climate and rainfall-runoff processes. We anticipate this study to be a starting point to incorporate theories of reliability to assess detention basin and conveyance network performance under more complex real-time control algorithms and failure modes.