Intensity and frequency of extreme novel epidemics Marani, Marco; Katul, Gabriel G; Pan, William K ...
Proceedings of the National Academy of Sciences - PNAS,
08/2021, Volume:
118, Issue:
35
Journal Article
Peer reviewed
Open access
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, P
, vary widely: P
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.
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.
Monitoring water quality at high frequency is challenging and costly. Compressed sensing (CS) offers an approach to reconstruct high‐frequency water quality data from limited measurements, given that ...water quality signals are commonly “sparse” in the frequency domain. In this study, we investigated the sparsity of stream flow and concentration time‐series and tested reconstruction with CS. All stream signals were sparse using 15‐min discrete time‐series transformed to the Fourier domain. Stream temperature, conductance, dissolved oxygen, and nitrate plus nitrite (NOx‐N) concentration were sparser than discharge, turbidity, and total phosphorus (TP) concentration. CS effectively reconstructed these signals with only 5%–10% of measurements needed. Stream NOx‐N and TP loads were well estimated with errors of −6.6% ± 3.8% and −9.0% ± 2.9% with effective sampling frequencies of 10 and 0.4 days, respectively. For broader applications in environmental geosciences and engineering domains, CS can be integrated with dimensionality reduction and optimization techniques for more efficient sampling schemes.
Plain Language Summary
Most natural environmental signals can be represented by less numbers or compressed. Signals that can be compressed can be accurately monitored with many fewer measurements than currently used in practice. This study reports and compares the ability of streamflow and water quality signals to be compressed. The results demonstrate that stream flow and several water quality metrics can be compressed when expressed in a specific mathematical format, called the frequency space. Electrical conductivity, dissolved oxygen, and nitrate plus nitrite concentration were found to be more compressible than stream flow, turbidity, and total phosphorus concentration. These variables can be effectively estimated with only 5%–10% of the number of measurements currently used in practice. Therefore, the compressed sensing method has potential to achieve large reductions in sampling effort when collecting data for environmental science and engineering projects, with associated time and cost savings.
Key Points
Streamflow and concentration signals are characterized as sparse in Fourier frequency domain
Compressed sensing (CS) effectively reconstructs 15‐min‐scale stream flow and concentration signals with only 5%–10% of measurements needed
CS well estimates stream NOx‐N and total phosphorus loads with an effective sampling frequency of 10 and 0.4 days, respectively
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.
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•Analyzed soil, climate, and landscape drivers of base cation variability in green stormwater infrastructure (GSI).•Base cation concentrations in GSI soils were distinct from reference soils with lower K, but higher Na, Ca, and Mg.•GSI soil composition moderately predicted the spatial variability of K, Ca, and Mg, but not Na.•Some GSI soils had elevated Na after a relatively cold winter, and these sites were also lower in K.
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 (F
e;night) on hydraulic redistribution (HR) in the soil.
The model results suggest that daytime PWS usage and F
e;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.
Many rivers and streams are ungauged or poorly gauged and predicting streamflow in such watersheds is challenging. Although streamflow signals result from processes with different frequencies, they ...can be “sparse” or have a “lower‐dimensional” representation in a transformed feature space. In such cases, if this appropriate feature space can be identified from streamflow data in gauged watersheds by dimensionality reduction, streamflow in poorly gauged watersheds can be predicted with a few measurements taken. This study utilized this framework, named data‐driven sparse sensing (DSS), to predict daily‐scale streamflow in 543 watersheds across the contiguous United States. A tailored library of features was extracted from streamflow training data in watersheds within the same climatic region, and this feature space was used to reconstruct streamflow in poorly gauged watersheds and identify the optimal timings for measurement. Among different regions, streamflow in snowmelt‐dominated and baseflow‐dominated watersheds (e.g., Rocky Mountains) was more effectively predicted with fewer streamflow measurements taken. The prediction efficiency in some rainfall‐dominated regions, for example, New England and the Pacific coast, increased significantly with an increasing number of measurements. The spatial variability of prediction efficiency can be attributed to the process‐driven mechanisms and the dimensionality of watershed dynamics. Storage‐dominated systems are lower‐dimensional and more predictable than rainfall‐dominated systems. Measurements taken during periods with large streamflow magnitudes and/or variances are more informative and lead to better predictions. This study demonstrates that DSS can be an especially useful technique to integrate ground‐based measurements with remotely sensed data for streamflow prediction, sensor placement, and watershed classification.
Plain Language Summary
Many rivers and stream reaches are ungauged or poorly gauged because streamflow measurement is costly and resource intensive. Predicting the streamflow time‐series in these ungauged or poorly gauged watersheds is still challenging. Here, we use a signal processing technique called data‐driven sparse sensing on a national‐scale streamflow data set across the contiguous United States. We predict streamflow time‐series in each watershed based on existing streamflow data in watersheds nearby, and explore the best times during the year for measuring streamflow. Our analysis shows that data‐driven sparse sensing is an effective tool to predict streamflow time‐series in poorly gauged watersheds based on very few streamflow measurements. The streamflow in watersheds with high snowmelt and high baseflow can be more easily predicted than in other watersheds. Our analysis also shows that the streamflow measurements taken during periods with large streamflow peaks and variances contain more information and are beneficial for making predictions. We conclude that data‐driven sparse sensing can be further used to classify watersheds and to identify the best locations for streamflow gauging.
Key Points
We utilize data‐driven sparse sensing to predict daily streamflow and identify the optimal times for streamflow measurement across the contiguous United States
Streamflow was more effectively predicted in watersheds dominated by snowmelt and baseflow than those dominated by rainfall and quickflow
The optimal sampling times for streamflow prediction by data‐driven sparse sensing are periods with large flow magnitudes and variances
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.
•A new stochastic water balance model captures the behavior of real-time controlled stormwater basins.•The model provides analytical PDFs for water level, detention time, and outflow.•Active control ...of stormwater flows allows land use and climate change adaptation.
Urbanization and changing rainfall intensities affect the performance of urban stormwater infrastructure, creating the necessity to design resilient stormwater systems. One proposed method to increase the resilience of stormwater infrastructure is the active control of system flows. To improve the understanding of actively-controlled urban water infrastructure function under variable hydro-climate, we develop a stochastic water balance model for stormwater retention and detention basins with both passive and actively-controlled outflow structures. Under active outflow control, the outflow valve is closed until the water level in the basin reaches a specified maximum at which point the valve opens and the basin empties. Using the stochastic water balance model, we develop analytical expressions for the steady-state probability density functions (PDFs) of water level and valve closure time, as well as the joint PDF of water level and valve closure time. These PDFs then are used to define water level and flow duration curves that provide a probabilistic description of the full range of basin performance. The model accurately predicts the water level PDF estimated from data collected at a retention basin with a passive outflow structure. The model provides a basis for evaluating how changes in the rainfall-runoff process, affected by land use and climate change, will impact the variability of stormwater basin water storage and pollutant removal function. We find that this variability can be managed through the adaptive updating of the active control rule for the outflow structure.
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.
The recent dust storm in the Middle East (Sepember 2015) was publicized in the media as a sign of an impending 'Dust Bowl.' Its severity, demonstrated by extreme aerosol optical depth in the ...atmosphere in the 99th percentile compared to historical data, was attributed to the ongoing regional conflict. However, surface meteorological and remote sensing data, as well as regional climate model simulations, support an alternative hypothesis: the historically unprecedented aridity played a more prominent role, as evidenced by unusual climatic and meteorological conditions prior to and during the storm. Remotely sensed normalized difference vegetation index demonstrates that vegetation cover was high in 2015 relative to the prior drought and conflict periods, suggesting that agricultural activity was not diminished during that year, thus negating the media narrative. Instead, meteorological simulations using the Weather Research and Forecasting (WRF) model show that the storm was associated with a cyclone and 'Shamal' winds, typical for dust storm generation in this region, that were immediately followed by an unusual wind reversal at low levels that spread dust west to the Mediterranean Coast. These unusual meteorological conditions were aided by a significant reduction in the critical shear stress due to extreme dry and hot conditions, thereby enhancing dust availability for erosion during this storm. Concluding, unusual aridity, combined with unique synoptic weather patterns, enhanced dust emission and westward long-range transport across the region, thus generating the extreme storm.
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