Soil, climate, and landscape drivers of base cation concentrations in green stormwater infrastructure soils.

Deicing practices and infrastructure weathering can impact plants, soil, and water quality through the input and transport of base cations. Base cation accumulation in green stormwater infrastructure (GSI) soils has the potential to decrease soil infiltration rates and plant water uptake or to promote leaching of metals and nutrients. To understand base cation retention in GSI soils and its drivers, we sampled 14 GSI soils of different age, contributing areas, and infiltration areas, across 3 years. We hypothesized that soil, climate, and landscape drivers explain the spatial and temporal variability of GSI soil base cation concentrations. Sodium (Na), Calcium (Ca), and Magnesium (Mg) concentrations in GSI soils were higher than in reference soils, while Ca and Mg were similar to an urban floodplain soil. Neither the contributing area, contributing impervious area, nor their ratios to infiltration area predicted base cation concentrations. Age predicted the spatial variability of Potassium (K) concentrations. Ca and Mg were moderately predicted by sand and silt, while clay predicted Mg, and sand predicted K. However, no soil characteristics predicted Na concentrations. A subset of sites had elevated Na in Fall 2019, which followed a winter with many freezing events and higher-than-average deicer salt application. K in sites with elevated Na was lower than in non-elevated sites, suggesting that transient spikes of Na driven by deicer salt decreased the ability of GSI soils to accumulate K. These findings demonstrate the large variability of GSI soil base cation concentrations and the relative importance of soil, climate, and landscape drivers of base cation dynamics. High variability in GSI soil data is commonly observed and further research is needed to reduce uncertainties for modeling studies and design. Improved understanding of how GSI soil properties evolve over time, and their relation to GSI performance, will benefit GSI design and maintenance practices.

Effects of Regional Climate and BMP Type on Stormwater Nutrient Concentrations in BMPs: A Meta-Analysis.

Nutrient treatment performance of stormwater best management practices (BMPs) is highly variable. Improved nutrient management with BMPs requires a better understanding of factors that influence stormwater BMP treatment processes. We conducted a meta-analysis of vegetated BMPs in the International Stormwater BMP Database and compared influent and effluent nitrogen and phosphorus concentrations to quantify the BMP effect on nutrient management across climates. BMP effect on nutrient concentration change was compared between vegetated BMPs in wet and dry climates. We examined paired dissolved inorganic nitrogen (DIN), total nitrogen (TN), dissolved inorganic phosphorus (DIP), total phosphorus (TP), and combinations of these analytes as dissolved inorganic ratios and N:P ratios. Meta-analysis with subgroup analysis was used to determine differences between wet and dry climates and among vegetated BMP types. We found that across both wet and dry climates, BMPs leach DIP and TP, increase the fraction of dissolved inorganic P (DIP:TP), and decrease dissolved N:P ratios. Dry-climate BMPs leach DIP and TP more consistently and at a higher magnitude than wet-climate BMPs, and bioretention leaches more DIP than grass strips and swales. These findings generally align with biogeochemical cycling, differences in influent chemistry, and BMP design types and goals.

Intensity and frequency of extreme novel epidemics.

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.

K in an Urban World: New Contexts for Hydraulic Conductivity.

Hydraulic conductivity (K) is a key hydrologic parameter widely recognized to be difficult to estimate and constrain, with little consistent assessment in disturbed, urbanized soils. To estimate K, it is either measured, or simulated by pedotransfer functions, which relate K to easily measured soil properties. We measured K in urbanized soils by double-ring infiltrometer (K dring), near-saturated tension infiltrometry (K minidisk), and constant head borehole permeametry (K borehole), along with other soil properties across the major soil orders in 12 United States cities. We compared measured K with that predicted from the pedotransfer function, ROSETTA. We found that regardless of soil texture, K dring was consistently larger than K minidisk; with the latter having slightly less sample variance. K borehole was dependent upon specific subsurface conditions, and contrary to common expectations, did not always decrease with depth. Based on either soil textural class, or percent textural separates (sand, silt clay), ROSETTA did not accurately predict measured K for surface nor subsurface soils. We go on to discuss how K varies in urban landscapes, the role of measurement methods and artifacts in the perception of this metric, and implications for hydrologic modeling. Overall, we aim to inspire consistency and coherence when addressing K-related challenges in sustainable urban water management.

Global convergence of COVID-19 basic reproduction number and estimation from early-time SIR dynamics.

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 Ro inferred from SIR formulation. The work here shows that there is global convergence (i.e., across many nations) to an uncontrolled Ro = 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 Ro = 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 Ro are analyzed using idealized scenarios. Implications for evaluating potential control strategies from this uncontrolled Ro 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 Ro > 2.7 within 44 days after first infection, intervention is unlikely to be effective in general for COVID-19.

Rainfall manipulation experiments as simulated by terrestrial biosphere models: Where do we stand?

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) Inter-model 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.

Forecasting semi-arid biome shifts in the Anthropocene.

Shrub encroachment, forest decline and wildfires have caused large-scale changes in semi-arid vegetation over the past 50 years. Climate is a primary determinant of plant growth in semi-arid ecosystems, yet it remains difficult to forecast large-scale vegetation shifts (i.e. biome shifts) in response to climate change. We highlight recent advances from four conceptual perspectives that are improving forecasts of semi-arid biome shifts. Moving from small to large scales, first, tree-level models that simulate the carbon costs of drought-induced plant hydraulic failure are improving predictions of delayed-mortality responses to drought. Second, tracer-informed water flow models are improving predictions of species coexistence as a function of climate. Third, new applications of ecohydrological models are beginning to simulate small-scale water movement processes at large scales. Fourth, remotely-sensed measurements of plant traits such as relative canopy moisture are providing early-warning signals that predict forest mortality more than a year in advance. We suggest that a community of researchers using modeling approaches (e.g. machine learning) that can integrate these perspectives will rapidly improve forecasts of semi-arid biome shifts. Better forecasts can be expected to help prevent catastrophic changes in vegetation states by identifying improved monitoring approaches and by prioritizing high-risk areas for management.

An analytical approach to ascertain saturation-excess versus infiltration-excess overland flow in urban and reference landscapes.

Uncontrolled overland flow drives flooding, erosion, and contaminant transport, with the severity of these outcomes often amplified in urban areas. In pervious media such as urban soils, overland flow is initiated via either infiltration-excess (where precipitation rate exceeds infiltration capacity) or saturation-excess (when precipitation volume exceeds soil profile storage) mechanisms. These processes call for different management strategies, making it important for municipalities to discern between them. In this study, we derived a generalized one-dimensional model that distinguishes between infiltration-excess overland flow (IEOF) and saturation-excess overland flow (SEOF) using Green-Ampt infiltration concepts. Next, we applied this model to estimate overland flow generation from pervious areas in 11 U.S. cities. We used rainfall forcing that represented low- and high-intensity events and compared responses among measured urban versus predevelopment reference soil hydraulic properties. The derivation showed that the propensity for IEOF versus SEOF is related to the equivalence between two nondimensional ratios: (a) precipitation rate to depth-weighted hydraulic conductivity and (b) depth of soil profile restrictive layer to soil capillary potential. Across all cities, reference soil profiles were associated with greater IEOF for the high-intensity set of storms, and urbanized soil profiles tended towards production of SEOF during the lower intensity set of storms. Urban soils produced more cumulative overland flow as a fraction of cumulative precipitation than did reference soils, particularly under conditions associated with SEOF. These results will assist cities in identifying the type and extent of interventions needed to manage storm water produced from pervious areas.

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. Across a watershed in central Texas we modeled dynamic water stress for a dominant tree species, Juniperus ashei, and projected future dynamic water stress through the 21st century. Modeled dynamic water stress tracked spatial patterns of remotely sensed drought-induced canopy loss. Accuracy in predicting drought-impacted stands increased from 60%, accounting for spatially variable soil conditions, to 72% when also including lateral redistribution of water and radiation/temperature effects attributable to aspect. Our analysis also suggests that dynamic water stress will increase through the 21st century, with trees persisting at only selected microsites. Favorable microsites/refugia may exist across a landscape where trees can persist; however, if future droughts are too severe, the buffering capacity of an heterogeneous landscape could be overwhelmed. Incorporating spatial data will improve projections of future tree water stress and identification of potential resilient refugia.

Increasing atmospheric humidity and CO2 concentration alleviate forest mortality risk.

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 CO2 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 CO2 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 CO2 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.

The effect of plant water storage on water fluxes within the coupled soil-plant system.

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.