Small-scale Variability Research Articles

Form-function relationships underlie rapid dietary changes in a lizard.

Macroevolutionary changes such as variation in habitat use or diet are often associated with convergent, adaptive changes in morphology. However, it is still unclear how small-scale morphological variation at the population level can drive shifts in ecology such as observed at a macroevolutionary scale. Here, we address this question by investigating how variation in cranial form and feeding mechanics relate to rapid changes in diet in an insular lizard (Podarcis siculus) after experimental introduction into a new environment. We first quantified differences in the skull shape and jaw muscle architecture between the source and introduced population using three-dimensional geometric morphometrics and dissections. Next, we tested the impact of the observed variation in morphology on the mechanical performance of the masticatory system using computer-based biomechanical simulation techniques. Our results show that small differences in shape, combined with variation in muscle architecture, can result in significant differences in performance allowing access to novel trophic resources. The confrontation of these data with the already described macroevolutionary relationships between cranial form and function in these insular lizards provides insights into how selection can, over relatively short time scales, drive major changes in ecology through its impact on mechanical performance.

Seasonal and interannual variability of Mediterranean Sea overturning circulation

The overturning streamfunction is a widely used metric to monitor ocean circulation changes in the North Atlantic Ocean. Analogously, it is known that an overturning circulation develops in the Mediterranean Sea, although substantially weaker and smaller than in the Atlantic. In this work we use monthly mean fields from a high-resolution ocean reanalysis (1/16°×1/16°) to explore seasonal and interannual variability of Mediterranean Sea overturning circulation in depth and water mass parameter spaces (potential density, potential temperature, salinity) from 1987 to 2018 (both included). Results show a clear single zonal clockwise transport cell in all three spaces that extends from the Strait of Gibraltar to the Levantine Basin, in which water masses tend to densify as they move eastward. This densification is mainly controlled by salinity in the Eastern Mediterranean basin (EMED), while in the Western Mediterranean (WMED) both salinity and potential temperature variations should be taken into account. In contrast, the meridional overturning transport, much more complex, is able to better capture smaller-scale variations since it responds faster to perturbations in the circulation.Seasonal variations in overturning transport are reflected in the size and strength of overturning cells in all spaces, being cells remarkably narrower in winter than in summer. Regarding interannual changes, we show that the meridional overturning transport in density space is significantly correlated with the North Atlantic Oscillation, the Mediterranean Oscillation Index and the Scandinavian pattern during wintertime in the WMED, and with the Eastern Atlantic and the East Atlantic–Western Russian in the EMED. The overturning metric in density space captures well the Eastern Mediterranean Transient, that occurred during years 1992–1996. Finally, we explore the usefulness of overturning metric to detect and track water mass changes associated to shorter scale events, using as example the strong deep convection events of years 2004–2006 in the Gulf of Lion, which marked the beginning of the Western Mediterranean Transition.

Hydrologic Assessment of IMERG Products Across Spatial Scales Over Iowa

Abstract IMERG provides state-of-the-art satellite-based precipitation estimates that combine observations from multiple satellite platforms. This study evaluates IMERG products by examining hydrologic simulations of streamflow at a range of spatial scales. The main objective of this study is to assess the predictive utility of the near-real-time product (IMERG-Early). The assessment also includes the IMERG-Final product that is not available in real time. The authors used MRMS precipitation estimates and USGS streamflow observation data as references for the precipitation and streamflow evaluations during a 5-yr period (2016–20). The precipitation evaluation results show that IMERG-Early yields significant overestimations, particularly during warm months, with higher variability in its conditional distributions, whereas the performance of IMERG-Final seems unbiased. The authors performed hydrologic simulations using the Iowa Flood Center’s Hillslope Link Model with three precipitation forcing products, i.e., MRMS, IMERG-Early, and IMERG-Final. The simulation results reveal that IMERG-Early leads to high hit and false alarm rates due to its overestimation in precipitation and has almost no skill, as measured by the overall performance metric Kling–Gupta efficiency (KGE), in streamflow prediction regarding basin scales ranging from 10 to 30 000 km2. This indicates that the product requires a bias correction before it is useful for real-time flood prediction. The streamflow prediction performance of IMERG-Final seems comparable to that of MRMS at spatial scales greater than 100 km2. This scale limitation is attributable to the IMERG’s product spatial resolution that is inadequate to capture the small-scale variability of precipitation.

Modified Lemaire Tenodesis Forces in Cadaveric Specimens Are Not Affected by Random Small-Scale Variations in the Femoral Insertion Point During Active Knee Joint Flexion-Extension

PurposeTo directly measure lateral extra-articular tenodesis (LET) forces supporting anterior cruciate ligament reconstruction (ACLR) during dynamic flexion-extension cycles induced by simulated active muscle forces, to investigate the influence of random surgical variation in the femoral LET insertion point around the target insertion position, and to determine potential changes to the extension behavior of the knee joint in a cadaveric model.MethodsAfter iatrogenic anterior cruciate ligament deficiency and simulated anterolateral rotatory instability, 7 fresh-frozen cadaveric knee joints were treated with isolated ACLR followed by combined ACLR-LET. The specimens were tested on a knee joint test bench during active dynamic flexion-extension with simulated muscle forces. LET forces and the degree of knee joint extension were measured. Random variation in the LET insertion point around the target insertion position was postoperatively quantified by computed tomography.ResultsIn extension, the median LET force increased to 39 ± 2 N (95% confidence interval [CI], 36 to 40 N). In flexion over 70°, the LET was offloaded (2 ± 1 N; 95% CI, 0 to 2 N). In this study, small-scale surgical variation in the femoral LET insertion point around the target position had a negligible effect on the graft forces measured. We detected no difference in the degree of knee joint extension after combined ACLR-LET (median, 1.0° ± 3.0°; 95% CI, −6.2° to 5.2°) in comparison with isolated ACLR (median, 1.1° ± 3.3°; 95% CI, −6.7° to 6.1°; P = .62).ConclusionsLET forces in combined ACLR-LET increased to a limited extent during active knee joint flexion-extension independent of small-scale variation around 1 specific target insertion point. Combined ACLR-LET did not change knee joint extension in comparison with isolated ACLR under the testing conditions used in this biomechanical study.Clinical RelevanceLow LET forces can be expected during flexion-extension of the knee joint. Small-scale deviations in the femoral LET insertion point around the target insertion position in the modified Lemaire technique might have a minor effect on graft forces during active flexion-extension. To directly measure lateral extra-articular tenodesis (LET) forces supporting anterior cruciate ligament reconstruction (ACLR) during dynamic flexion-extension cycles induced by simulated active muscle forces, to investigate the influence of random surgical variation in the femoral LET insertion point around the target insertion position, and to determine potential changes to the extension behavior of the knee joint in a cadaveric model. After iatrogenic anterior cruciate ligament deficiency and simulated anterolateral rotatory instability, 7 fresh-frozen cadaveric knee joints were treated with isolated ACLR followed by combined ACLR-LET. The specimens were tested on a knee joint test bench during active dynamic flexion-extension with simulated muscle forces. LET forces and the degree of knee joint extension were measured. Random variation in the LET insertion point around the target insertion position was postoperatively quantified by computed tomography. In extension, the median LET force increased to 39 ± 2 N (95% confidence interval [CI], 36 to 40 N). In flexion over 70°, the LET was offloaded (2 ± 1 N; 95% CI, 0 to 2 N). In this study, small-scale surgical variation in the femoral LET insertion point around the target position had a negligible effect on the graft forces measured. We detected no difference in the degree of knee joint extension after combined ACLR-LET (median, 1.0° ± 3.0°; 95% CI, −6.2° to 5.2°) in comparison with isolated ACLR (median, 1.1° ± 3.3°; 95% CI, −6.7° to 6.1°; P = .62). LET forces in combined ACLR-LET increased to a limited extent during active knee joint flexion-extension independent of small-scale variation around 1 specific target insertion point. Combined ACLR-LET did not change knee joint extension in comparison with isolated ACLR under the testing conditions used in this biomechanical study.

Drivers of soil temperature variation in alpine lichen heaths and shrub vegetation during the summer

ABSTRACT Lichen heaths are decreasing in abundance in alpine and Arctic areas because of an increased competition with shrubs. This shift in vegetation might have important consequences for the soil temperature. The aim of this study is to find the drivers of the variation in soil temperature below lichen heaths and shrubs. Moreover, we want to gain more insight in the variability of the soil temperature below lichen heaths. We measured the soil temperature in thirty lichen plots and fifteen shrub plots in an alpine area in southern Norway during July and August 2019. We applied several treatments to study the drivers behind the variation in soil temperature between lichen heaths and shrub vegetation. We found that the average soil temperature was 1.45°C higher below lichen heaths than below shrub vegetation. Moreover, we measured a difference in soil temperature of 1.66°C between north- and south-facing lichen heaths, which contributes to the small-scale spatial variability in soil temperature below lichen heaths. Based on our experiments, we conclude that the buffering capacity of the litter layer below shrubs and shading of the soil by the shrub canopy lead to a lower soil temperature below shrubs compared to lichen heaths during the summer.

Broad-scale spatial distribution patterns of soft-sediment macrobenthic communities in the Red Sea

In sub-tropical and tropical regions, the diversity patterns of soft-sediment macrobenthic communities are still poorly understood, particularly when compared to temperate shelf environments. The present study investigates spatial patterns of variability in macrobenthic distribution along the eastern Red Sea margin, and the role of sediment composition and other explanatory variables in determining such patterns. This study has two main objectives: (i) to produce a baseline characterization of the Saudi Arabian Red Sea macrobenthic infauna, particularly along the central and southern regions, (ii) to assess the relative contribution of two major processes driving macrobenthic communities (i.e., dispersal-based and environmental filtering), based on changes on beta-diversity components (turnover and nestedness) across multiple scales (up to ~600km), using relevant modeling methodologies. Shallow soft-sediment macrobenthic communities in the Red Sea showed extremely high small-scale variability, highlighted by the percentage of rare species (44% of species present at a single sample) and the dominance of turnover (species replacement) over nestedness. Our results also suggest a strong influence of broad- over fine-scale variation in the species composition. However, sedimentary characteristics, particularly grain-size, played a critical role governing the distribution patterns of soft-sediment macrobenthic communities in the Red Sea. Our findings highlight the importance of regional factors in shaping the macrofaunal community composition whilst also highlighting the role of high species diversity at local scales. The current results suggest the need for conservation measures from regional levels (to maintain genetic diversity) to local levels (to preserve the high occurrence of rare species). To our knowledge, no other study investigated the distribution of Red Sea marine species over large spatial scales combining modeling methods and the partitioning of beta-diversity. The current approach applied to soft-sediment macroinvertebrates can be extended to other marine communities since conservation strategies can be more effective when mechanisms governing species distribution are considered.

Palaeoenvironmental and palaeoclimatic inferences based on X-ray computer tomography: a case study of alkaline lake deposits in Hungary

Widely distributed freshwater carbonate sediments, i.e., limestone, dolomitic limestone and dolomite, developed in inter-dune alkaline ponds of the Danube-Tisza Interfluve in the centre of the Carpathian Basin during the Holocene. The key parameters that determine the formation of any given type of carbonate mineral (calcite, dolomite) are temperature, evaporation rate, pH and ion concentrations, in addition to CO2 absorption by aquatic plants. CT analysis is capable of recording small-scale density variations attributable to compositional differences of sedimentary rocks. As the type and proportion of rock-forming minerals and other components is an artifact of past environmental and climatic conditions, CT values may act as potential palaeoenvironmental proxies. The present study compares variations in rock-forming components obtained for freshwater carbonates utilizing the CT method with already available geochemical and palaeoecological proxy data. Variations in molluscan ecology and isotope geochemistry, sedimentation times and CTbased rock density values all indicate the relevance of millennial-scale, climate-driven changes in carbonate formation. As previously observed, the emergence of colder conditions in the North Atlantic, which resulted in increased cyclonic activity and heavier rainfall in western Europe and the Danube watershed area between 10.3 and 9.3 kyr cal BP, resulted in the emergence of humid conditions favouring a rise in the groundwater table at our site and precipitation of calcite from pore waters as opposed to high-magnesium calcite. This is clearly reflected in a negative shift in CT density values in our dated rock samples.

On-Board Small-Scale Object Detection for Unmanned Aerial Vehicles (UAVs)

Object detection is a critical task that becomes difficult when dealing with onboard detection using aerial images and computer vision technique. The main challenges with aerial images are small target sizes, low resolution, occlusion, attitude, and scale variations, which affect the performance of many object detectors. The accuracy of the detection and the efficiency of the inference are always trade-offs. We modified the architecture of CenterNet and used different CNN-based backbones of ResNet18, ResNet34, ResNet50, ResNet101, ResNet152, Res2Net50, Res2Net101, DLA-34, and hourglass14. A comparison of the modified CenterNet with nine CNN-based backbones is conducted and validated using three challenging datasets, i.e., VisDrone, Stanford Drone dataset (SSD), and AU-AIR. We also implemented well-known off-the-shelf object detectors, i.e., YoloV1 to YoloV7, SSD-MobileNet-V2, and Faster RCNN. The proposed approach and state-of-the-art object detectors are optimized and then implemented on cross-edge platforms, i.e., NVIDIA Jetson Xavier, NVIDIA Jetson Nano, and Neuro Compute Stick 2 (NCS2). A detailed comparison of performance between edge platforms is provided. Our modified CenterNet combination with hourglass as a backbone achieved 91.62%, 75.61%, and 34.82% mAP using the validation sets of AU-AIR, SSD, and VisDrone datasets, respectively. An FPS of 40.02 was achieved using the ResNet18 backbone. We also compared our approach with the latest cutting-edge research and found promising results for both discrete GPU and edge platforms.

Joint observation–model mixed-layer heat and salt budgets in the eastern tropical Atlantic

Abstract. In this study, we use a joint observation–model approach to investigate the mixed-layer heat and salt annual mean as well as seasonal budgets in the eastern tropical Atlantic. The regional PREFCLIM (PREFACE Climatology) observational climatology provides the budget terms with a relatively low spatial and temporal resolution compared to the online NEMO (Nucleus for European Modeling of the Ocean; Madec, G., 2014) model, and this is later resampled as in PREFCLIM climatology. In addition, advection terms are recomputed offline from the model as PREFCLIM gridded advection computation. In the Senegal, Angola, and Benguela regions, the seasonal cycle of mixed-layer temperature is mainly governed by surface heat fluxes; however, it is essentially driven by vertical heat diffusion in the equatorial region. The seasonal cycle of mixed-layer salinity is largely controlled by freshwater flux in the Senegal and Benguela regions; however, it follows the variability of zonal and meridional salt advection in the equatorial and Angola regions, respectively. Our results show that the time-averaged spatial distribution of NEMO offline heat and salt advection terms compares much better to PREFCLIM horizontal advection terms than the online heat and salt advection terms. However, the seasonal cycle of horizontal advection in selected regions shows that NEMO offline terms do not always compare well with PREFCLIM, sometimes less than online terms. Despite this difference, these results suggest the important role of small-scale variability in mixed-layer heat and salt budgets.

Spatiotemporal characteristics of upper-crust seasonal velocity changes in North–South Seismic Belt and South China block in China

Investigating mechanisms and characteristics of the non-tectonic deformation has crucial implications for evaluating the subsurface stress evolution. In this study, we continuously tracked the seasonal velocity changes of Rayleigh wave at 2–10 s with the ambient noise interferometry and discrete wavelet decomposition, in different regions along the actively deforming North–South Seismic Belt (NSSB) and the stable Guangdong region in the South China block in China. The seasonal velocity changes exhibit strong lateral variations across different regions, with much larger seasonality (annual periodicity) and amplitude along the NSSB than those in the stable Guangdong region. There are also small-scale lateral variations inside of each region and generally larger amplitudes of the seasonal velocity changes at greater depth, but the phases (peaks, mostly in winter or summer) at different depths are consistent. The mechanism of the seasonal changes is most likely the direct loading effect of the precipitation, as indicated by consistency in their phases. However, the amplitudes of the seasonal velocity changes seem not controlled by the amount of precipitation, but are more dependent on the tectonic deformation at the different scales, from the correlation between the amplitude of the velocity change and the absolute strain rate. We demonstrate that the crustal media in the regions with larger tectonic background stress are more sensitive to the seasonal loadings. The new finding also highlights the role of the external seasonal loadings in the modulation of seismic activities and risk evaluations. Keypoints•Seasonal seismic velocity variations are found in different regions in China, likely from direct hydrological loadings of precipitation.•The spatial pattern of the seasonal velocity changes is consistent with the distribution of tectonic deformation at multi-scales.•The amplitude of the seasonal velocity changes is more dependent on the magnitude of local deformation than the amount of precipitation.

Figuring Out Gas & Galaxies in Enzo (FOGGIE). VI. The Circumgalactic Medium of L ∗ Galaxies Is Supported in an Emergent, Nonhydrostatic Equilibrium

The circumgalactic medium (CGM) is often assumed to exist in or near hydrostatic equilibrium, with the regulation of accretion and the effects of feedback treated as perturbations to a stable balance between gravity and thermal pressure. We investigate global hydrostatic equilibrium in the CGM using four highly resolved L * galaxies from the Figuring Out Gas & Galaxies in Enzo (FOGGIE) project. The FOGGIE simulations were specifically targeted at fine spatial and mass resolution in the CGM (Δx ≲ 1 kpc h −1 and M ≃ 200M ⊙). We develop a new analysis framework that calculates the forces provided by thermal pressure gradients, turbulent pressure gradients, ram pressure gradients of large-scale radial bulk flows, centrifugal rotation, and gravity acting on the gas in the CGM. Thermal and turbulent pressure gradients vary strongly on scales of ≲5 kpc throughout the CGM. Thermal pressure gradients provide the main supporting force only beyond ∼0.25R 200, or ∼50 kpc at z = 0. Within ∼0.25R 200, turbulent pressure gradients and rotational support provide stronger forces than thermal pressure. More generally, we find that global equilibrium models are neither appropriate nor predictive for the small scales probed by absorption line observations of the CGM. Local conditions generally cannot be derived by assuming a global equilibrium, but an emergent global equilibrium balancing radially inward and outward forces is obtained when averaging over the nonequilibrium local conditions on large scales in space and time. Approximate hydrostatic equilibrium holds only at large distances from galaxies, even when averaging out small-scale variations.

The Polytropic Behavior of Solar Wind Protons as Observed by the Ulysses Spacecraft during Solar Minimum

We analyze proton bulk parameters derived from Ulysses observations and investigate the polytropic behavior of solar wind protons over a wide range of heliocentric distances and latitudes. The large-scale variations of the proton density and temperature over heliocentric distance indicate that plasma protons are governed by subadiabatic processes (polytropic index γ < 5/3), if we assume protons with three effective kinetic degrees of freedom. From the correlation between the small-scale variations of the plasma density and temperature in selected subintervals, we derive a polytropic index γ ∼ 1.4 on average. Further examination shows that the polytropic index does not have an apparent dependence on the solar wind speed. This agrees with the results of previous analyses of solar wind protons at ∼1 au. We find that the polytropic index varies slightly over the range of the heliocentric distances and heliographic latitudes explored by Ulysses. We also show that the homogeneity of the plasma and the accuracy of the polytropic model applied to the data points vary over Ulysses' orbit. We compare our results with the results of previous studies that derive the polytropic index of solar wind ions within the heliosphere using observations from various spacecraft. We finally discuss the implications of our findings in terms of heating mechanisms and the effective degrees of freedom of the plasma protons.

Capturing Small-Scale Surface Temperature Variation across Diverse Urban Land Uses with a Small Unmanned Aerial Vehicle

Urbanization increases the urban land surface temperature (LST), challenging society and the environment. This study measured the LST of diverse land uses (LU) in Dallas–Fort Worth (DFW) using a high-resolution (8 cm) thermal infrared sensor onboard a small, unmanned aerial vehicle (UAV). LUs included park (PA), industrial (IA), residential low-cost (RLC), and residential high-cost (RHC) areas. LST was collected by the UAV at different times on eight nonconsecutive days. UAV-collected LST was compared with that from Landsat 8-9 and in situ measurements. RHC reported the highest mean LST, and PA showed the lowest mean LST. Dark-colored asphalt shingle roofs in RHC had the highest mean LST range at 35.67 °C. Lower LST was measured in shaded areas and under thick green cover, whereas areas with thin green cover occasionally reported higher LST than pavements. The micro-urban heat island (MUHI) was calculated between LUs and within land cover types (roof, pavement, green, and water). The MUHI varied from 4.83 °C to 15.85 °C between LUs and 0.2 °C to 23.5 °C within LUs for the less than 1 km2 study area. While the UAV thermal sensor and Landsat demonstrated a similar trend of LST variation, the UAV sensor reported more intense MUHI. An average percent bias (PBIAS) of 5.1% was calculated between the UAV sensor and in situ measurements. This study helps inform the urban design process by demonstrating how land use decisions impact LST locally and provides valuable insight for studies concerned with fine-scale urban LST variability.

Aircraft observations of gravity wave activity and turbulence in the tropical tropopause layer: prevalence, influence on cirrus clouds, and comparison with global storm-resolving models

Abstract. The tropical tropopause layer (TTL) is a sea of vertical motions. Convectively generated gravity waves create vertical winds on scales of a few to thousands of kilometers as they propagate in a stable atmosphere. Turbulence from gravity wave breaking, radiatively driven convection, and Kelvin–Helmholtz instabilities stirs up the TTL on the kilometer scale. TTL cirrus clouds, which moderate the water vapor concentration in the TTL and stratosphere, form in the cold phases of large-scale (&gt; 100 km) wave activity. It has been proposed in several modeling studies that small-scale (&lt; 100 km) vertical motions control the ice crystal number concentration and the dehydration efficiency of TTL cirrus clouds. Here, we present the first observational evidence for this. High-rate vertical winds measured by aircraft are a valuable and underutilized tool for constraining small-scale TTL vertical wind variability, examining its impacts on TTL cirrus clouds, and evaluating atmospheric models. We use 20 Hz data from five National Aeronautics and Space Administration (NASA) campaigns to quantify small-scale vertical wind variability in the TTL and to see how it varies with ice water content, distance from deep convective cores, and height in the TTL. We find that 1 Hz vertical winds are well represented by a normal distribution, with a standard deviation of 0.2–0.4 m s−1. Consistent with a previous observational study that analyzed two out of the five aircraft campaigns that we analyze here, we find that turbulence is enhanced over the tropical west Pacific and within 100 km of convection and is most common in the lower TTL (14–15.5 km), closer to deep convection, and in the upper TTL (15.5–17 km), further from deep convection. An algorithm to classify turbulence and long-wavelength (5 km &lt; λ &lt; 100 km) and short-wavelength (λ &lt; 5 km) gravity wave activity during level flight legs is applied to data from the Airborne Tropical TRopopause EXperiment (ATTREX). The most commonly sampled conditions are (1) a quiescent atmosphere with negligible small-scale vertical wind variability, (2) long-wavelength gravity wave activity (LW GWA), and (3) LW GWA with turbulence. Turbulence rarely occurs in the absence of gravity wave activity. Cirrus clouds with ice crystal number concentrations exceeding 20 L−1 and ice water content exceeding 1 mg m−3 are rare in a quiescent atmosphere but about 20 times more likely when there is gravity wave activity and 50 times more likely when there is also turbulence, confirming the results of the aforementioned modeling studies. Our observational analysis shows that small-scale gravity waves strongly influence the ice crystal number concentration and ice water content within TTL cirrus clouds. Global storm-resolving models have recently been run with horizontal grid spacing between 1 and 10 km, which is sufficient to resolve some small-scale gravity wave activity. We evaluate simulated vertical wind spectra (10–100 km) from four global storm-resolving simulations that have horizontal grid spacing of 3–5 km with aircraft observations from ATTREX. We find that all four models have too little resolved vertical wind at horizontal wavelengths between 10 and 100 km and thus too little small-scale gravity wave activity, although the bias is much less pronounced in global SAM than in the other models. We expect that deficient small-scale gravity wave activity significantly limits the realism of simulated ice microphysics in these models and that improved representation requires moving to finer horizontal and vertical grid spacing.

Soil information on a regional scale: Two machine learning based approaches for predicting saturated hydraulic conductivity

Saturated hydraulic conductivity (Ksat) and other soil (hydraulic) properties are fundamental for applications that depend on modeling hydrological processes, such as the quantification of future groundwater recharge rates. Yet, for most areas in the world, local soil information is lacking. Additionally, access to local soil surveys is often restricted or costly. Available global and regional digital soil mapping (DSM) products differ in scale and degree of data aggregation, as well as in spatial coverage. Ksat – and soil properties in general – are also characterized by a high spatial variability at all scales. Most often, there is no single data product available that covers the whole study area and still displays the variability of local soil observations. Thus, it is often a challenge to combine and predict soil data from different sources and resolutions while preserving the characteristically high spatial variability of soil properties.This study develops and compares two approaches for producing spatially distributed Ksat maps. First, an indirect approach based on two machine learning (ML) models – eXtreme Gradient Boosting (XGBoost) and feed-forward neural network (FNN) – that are trained with available local soil data sources and environmental raster datasets to predict the soil parameters sand, silt, clay, and organic matter content. Ksat is then determined by applying existing pedotransfer-functions (PTFs) on these regionalized soil parameters. Second, a direct approach in which ML models are directly trained with available soil hydraulic datasets to predict Ksat.Both approaches are applied to predict Ksat for Austria. While the resulting soil property maps of the indirect approach are able to largely reproduce the original data variability, the prediction of Ksat includes high levels of uncertainties and the predicted vertical distribution of Ksat is not plausible. The spatial distribution of Ksat in the direct approach resembles available global Ksat maps. In the existing global Ksat maps as well as in the results of the direct approach the small-scale variability of Ksat is reduced. In both approaches XGBoost outperforms FNN. The derived soil property maps help to reduce current gaps in soil data availability for Austria, but also highlight the need for additional Ksat field data acquisition.

Explorative Assessment of the Temperature-Mortality Association to Support Health-Based Heat-Warning Thresholds: A National Case-Crossover Study in Switzerland.

Defining health-based thresholds for effective heat warnings is crucial for climate change adaptation strategies. Translating the non-linear function between heat and health effects into an effective threshold for heat warnings to protect the population is a challenge. We present a systematic analysis of heat indicators in relation to mortality. We applied distributed lag non-linear models in an individual-level case-crossover design to assess the effects of heat on mortality in Switzerland during the warm season from 2003 to 2016 for three temperature metrics (daily mean, maximum, and minimum temperature), and various threshold temperatures and heatwave definitions. Individual death records with information on residential address from the Swiss National Cohort were linked to high-resolution temperature estimates from 100 m resolution maps. Moderate (90th percentile) to extreme thresholds (99.5th percentile) of the three temperature metrics implied a significant increase in mortality (5 to 38%) in respect of the median warm-season temperature. Effects of the threshold temperatures on mortality were similar across the seven major regions in Switzerland. Heatwave duration did not modify the effect when considering delayed effects up to 7 days. This nationally representative study, accounting for small-scale exposure variability, suggests that the national heat-warning system should focus on heatwave intensity rather than duration. While a different heat-warning indicator may be appropriate in other countries, our evaluation framework is transferable to any country.

Linear and Nonlinear Land Use Regression Approach for Modelling PM2.5 Concentration in Ulaanbaatar, Mongolia during Peak Hours

In recent decades, air pollution in Ulaanbaatar has become a challenge regarding the health of the citizens of Ulaanbaatar, due to coal combustion in the ger area. Households burn fuel for cooking and to warm their houses in the morning and evening. This creates a difference between daytime and nighttime air pollution levels. The accurate mapping of air pollution and assessment of exposure to air pollution have thus become important study objects for researchers. The city center is where most air quality monitoring stations are located, but they are unable to monitor every residential region, particularly the ger area, which is where most particulate matter pollution originates. Due to this circumstance, it is difficult to construct an LUR model for the entire capital city’s residential region. This study aims to map peak PM2.5 dispersion during the day using the Linear and Nonlinear Land Use Regression (LUR) model (Multi-Linear Regression Model (MLRM) and Generalized Additive Model (GAM)) for Ulaanbaatar, with monitoring station measurements and mobile device (DUST TRUK II) measurements. LUR models are frequently used to map small-scale spatial variations in element levels for various types of air pollution, based on measurements and geographical predictors. PM2.5 measurement data were collected and analyzed in the R statistical software and ArcGIS. The results showed the dispersion map MLRM R2 = 0.84, adjusted R2 = 0.83, RMSE = 53.25 µg/m3 and GAM R2 = 0.89, and adjusted R2 = 0.87, RMSE = 44 µg/m3. In order to validate the models, the LOOCV technique was run on both the MLRM and GAM. Their performance was also high, with LOOCV R2 = 0.83, RMSE = 55.6 µg/m3, MAE = 38.7 µg/m3, and GAM LOOCV R2 = 0.77, RMSE = 65.5 µg/m3, MAE = 47.7 µg/m3. From these results, the LUR model’s performance is high, especially the GAM model, which works better than MRLM.

Global patterns of water storage in the rooting zones of vegetation

The rooting-zone water-storage capacity—the amount of water accessible to plants—controls the sensitivity of land–atmosphere exchange of water and carbon during dry periods. How the rooting-zone water-storage capacity varies spatially is largely unknown and not directly observable. Here we estimate rooting-zone water-storage capacity globally from the relationship between remotely sensed vegetation activity, measured by combining evapotranspiration, sun-induced fluorescence and radiation estimates, and the cumulative water deficit calculated from daily time series of precipitation and evapotranspiration. Our findings indicate plant-available water stores that exceed the storage capacity of 2-m-deep soils across 37% of Earth’s vegetated surface. We find that biome-level variations of rooting-zone water-storage capacities correlate with observed rooting-zone depth distributions and reflect the influence of hydroclimate, as measured by the magnitude of annual cumulative water-deficit extremes. Smaller-scale variations are linked to topography and land use. Our findings document large spatial variations in the effective root-zone water-storage capacity and illustrate a tight link among the climatology of water deficits, rooting depth of vegetation and its sensitivity to water stress.

A random batch method for efficient ensemble forecasts of multiscale turbulent systems.

A new efficient ensemble prediction strategy is developed for a multiscale turbulent model framework with emphasis on the nonlinear interactions between large and small-scale variables. The high computational cost in running large ensemble simulations of high-dimensional equations is effectively avoided by adopting a random batch decomposition of the wide spectrum of the fluctuation states, which is a characteristic feature of the multiscale turbulent systems. The time update of each ensemble sample is then only subject to a small portion of the small-scale fluctuation modes in one batch, while the true model dynamics with multiscale coupling is respected by frequent random resampling of the batches at each time updating step. We investigate both theoretical and numerical properties of the proposed method. First, the convergence of statistical errors in the random batch model approximation is shown rigorously independent of the sample size and full dimension of the system. Next, the forecast skill of the computational algorithm is tested on two representative models of turbulent flows exhibiting many key statistical phenomena with a direct link to realistic turbulent systems. The random batch method displays robust performance in capturing a series of crucial statistical features with general interests, including highly non-Gaussian fat-tailed probability distributions and intermittent bursts of instability, while requires a much lower computational cost than the direct ensemble approach. The efficient random batch method also facilitates the development of new strategies in uncertainty quantification and data assimilation for a wide variety of general complex turbulent systems in science and engineering.

Combining a population balance approach with detailed chemistry to model the condensation of oxide smoke during aluminum combustion in spatially homogeneous reactors

At high temperatures, aluminum particles can be burned exothermally in oxidizing atmospheres in a similar way to carbon-based particulate fuels. The main reaction product, aluminum oxide (Al2O3, alumina), is solid under ambient conditions and may form through two distinct chemical pathways. On the one hand, vapourized aluminum burns in the gas phase, leading to the condensation of aluminum oxide into very fine smoke droplets that are thought to be nano-sized. On the other hand, heterogeneous reactions at the particle surface cause a direct conversion of aluminum into aluminum oxide and result in the transformation into a bi-phasic aluminum/alumina particle. As aluminum powder is being investigated as a potential carbon-free, recyclable energy carrier, we face unanswered questions about the droplet sizes within the oxide smoke and the techniques required for extraction from flue gases. This article constitutes a first step towards a detailed modelling framework for predicting alumina smoke size distributions.Physically, the oxide droplet size distribution is influenced by the ambient gas phase composition and shaped by the mutual competition of nucleation, condensational surface growth, evaporation, dissociation and coagulation. The heat release and dispersion temperature, on the other hand, are affected by chemical reactions, the gas-to-liquid phase change and radiation. In this article, we present a complete set of droplet formation and interaction kinetics for alumina droplets and analyze the interaction, competition and mutual reinforcement of the relevant physical processes in a perfectly stirred reactor and a partially stirred reactor. These simplified model formulations are representative of the dynamics that characterize the changes in gas phase composition and droplet size distribution in a single grid cell during the reaction and droplet formation fractional steps of a spatially inhomogeneous laminar reactive flow solver or a one-point, one-time probability density function (PDF) description. The partially stirred reactor model is extended to account for the presence of a reactive surface with small-scale variability in terms of surface composition. Detailed chemical and heterogeneous surface kinetics, also including NOx formation, are taken into account. Finally, we point out open challenges and incomplete kinetic or constitutive information.