Small-scale Variability Research Articles

Mapping soil moisture across the UK: assimilating cosmic-ray neutron sensors, remotely sensed indices, rainfall radar and catchment water balance data in a Bayesian hierarchical model

Abstract. Soil moisture is important in many hydrological and ecological processes. However, data sets which are currently available have issues with accuracy and resolution. To translate remotely sensed data to an absolute measure of soil moisture requires mapped estimates of soil hydrological properties and estimates of vegetation properties, and this introduces considerable uncertainty. We present an alternative methodology for producing daily maps of soil moisture over the UK at 2 km resolution (“SMUK”). The method is based on a simple linear statistical model, calibrated with 5 years of daily data from cosmic-ray neutron sensors at ∼ 40 sites across the country. The model is driven by precipitation, humidity, a remotely sensed “soil water index” satellite product and soil porosity. The spatial variation in the parameter describing the soil water retention (and thereby the response to precipitation) was estimated using daily water balance data from ∼ 1200 catchments with good coverage across the country. The model parameters were estimated by Bayesian calibration using a Markov chain–Monte Carlo method, so as to characterise the posterior uncertainty in the parameters and predictions. The approach reduces uncertainty by integrating multiple data sources, all of which have weaknesses but together act as a better constraint on the true soil moisture. The model explains around 70 % of the variance in the daily observations with a root-mean-square error of 0.05 m3 m−3, better than results from more complex process-based models. Given the high resolution of the inputs in time and space, the model can predict the very detailed variation in soil moisture which arises from the sporadic nature of precipitation events, including the small-scale and short-term variations associated with orographic and convective rainfall. Predictions over the period 2016 to 2023 demonstrated realistic patterns following the passage of weather fronts and prolonged droughts. The model has negligible computation time, and inputs and predictions are updated daily, lagging approximately 1 week behind real time.

Upscaling the contribution of crab burrows to mangrove ecosystem functioning in French Guiana (South America)

Burrow characteristics and bioturbation activities of benthic organisms play a key role in mangrove ecosystem biogeochemical and sedimentary functioning. In this study, we aimed to understand how small-scale topographic variations in a mud bank might influence burrow morphology and distribution in a French Guiana pioneer mangrove system (Sinnamary estuary), and to upscale spatial patterns using remote sensing. We used burrow resin casting and sediment conductivity measurements to depict subsurface 3D burrow structures. We found that the spatial heterogeneity of burrow sizes (small, medium, large) and morphologies (simple I- and J-shape, complex geometries) depended on the geomorphic units within mudflats (platform, channel, depression). The aperture areas of burrow casts were used to predict the volume and complexity of each burrow type, enabling us to use drone-derived burrow opening distribution maps to calculate burrow volumes and complexity at the mudflat scale. There are clear associations between tidal channels and depressions and voluminous, multi-aperture, complex and multi-species burrows. In contrast, simple I- and J-shaped burrows inhabited by single species were mainly found on platforms. These relationships lead to a comparatively large volume of tidal-irrigated and deeply aerated sediments connected to channels and depressions compared to platform areas. We suggest that, depending on their morphology and connectivity with the topography, burrows may exhibit differences in biogeochemical functioning depending on the geomorphic unit. We warn against generalizing the functioning of mangrove ecosystems across geomorphic units where impacts may differ. Further studies are needed to understand how different burrow morphologies and life cycles may alter hydrological, sedimentary and biogeochemical functioning.

Tropospheric NO2 retrieval algorithm for geostationary satellite instruments: applications to GEMS

Abstract. In this study, we develop an advanced retrieval algorithm for tropospheric nitrogen dioxide (NO2) from the geostationary satellite instruments and apply it to Geostationary Environment Monitoring Spectrometer (GEMS) observations. Overall, the algorithm follows previous heritage for the polar-orbiting satellites Global Ozone Monitoring Experiment-2 (GOME-2) and Tropospheric Monitoring Instrument (TROPOMI), but several improvements are implemented to account for specific features of geostationary satellites. The DLR GEMS NO2 retrieval employs an extended fitting window compared to the current fitting window used in GEMS operational v2.0 NO2 retrieval, which results in improved spectral fit quality and lower uncertainties. For the stratosphere–troposphere separation in GEMS measurements, two methods are developed and evaluated: (1) STRatospheric Estimation Algorithm from Mainz (STREAM) as used in the DLR TROPOMI NO2 retrieval and adapted to GEMS and (2) estimation of stratospheric NO2 columns from the Copernicus Atmosphere Monitoring Service (CAMS) Integrated Forecast System (IFS) cycle 48R1 model data, which introduce full stratospheric chemistry as it will be used in the operational Sentinel-4 NO2 retrieval. While STREAM provides hourly estimates of stratospheric NO2, it has limitations in describing small-scale variations and exhibits systematic biases near the boundary of the field of view. In this respect, the use of estimated stratospheric NO2 columns from the CAMS forecast model profile demonstrates better applicability by describing not only diurnal variation but also small-scale variations. For the improved air mass factor (AMF) calculation, sensitivity tests are performed using different input data. In our algorithm, cloud fractions retrieved from the Optical Cloud Recognition Algorithm (OCRA) adapted to GEMS level 1 data are applied instead of the GEMS v2.0 cloud fraction. OCRA is used operationally in TROPOMI and Sentinel-4. Compared to the GEMS level 2 cloud fraction which is typically set to around 0.1 for clear-sky scenes, OCRA sets cloud fractions close to or at 0. The OCRA-based cloud corrections result in increased tropospheric AMFs and decreased tropospheric NO2 vertical columns, leading to better agreement with results from existing TROPOMI observations. The effects of surface albedo on GEMS tropospheric NO2 retrievals are assessed by comparing the GEMS v2.0 background surface reflectance (BSR) and TROPOMI Lambertian-equivalent reflectivity (LER) climatology v2.0 product. The differences between the two surface albedo products and their impact on tropospheric AMF are particularly pronounced over snow/ice scenes during winter. A priori NO2 profiles from the CAMS forecast model, applied in the DLR GEMS algorithm, effectively capture variations in NO2 concentrations throughout the day with high spatial resolution and the advanced chemical mechanism, which demonstrates its suitability for geostationary satellite measurements. The retrieved DLR GEMS tropospheric NO2 columns show good capability for capturing hotspot signals at the scale of city clusters and describe spatial gradients from city centres to surrounding areas. Diurnal variations of tropospheric NO2 columns over Asia are well described through hourly sampling of GEMS. Evaluation of DLR GEMS tropospheric NO2 columns against TROPOMI v2.4 and GEMS v2.0 operational products shows overall good agreement. The uncertainty of DLR GEMS tropospheric NO2 vertical columns varies based on observation scenarios. In regions with low pollution levels such as open-ocean and remote rural areas, retrieval uncertainties typically range from 10 % to 50 %, primarily due to uncertainties in slant columns. For heavily polluted regions, uncertainties in tropospheric NO2 columns are mainly driven by errors in tropospheric AMF calculations. Notably, the total uncertainty in GEMS tropospheric NO2 columns is most significant in winter, particularly over heavily polluted regions with low-level clouds below or near the NO2 peak.

Scale-dependent inflation algorithms for ensemble Kalman filters

Abstract Ensemble-based data assimilation methods often suffer sampling errors due to limited ensemble sizes and model errors, which can result in filter divergence. One method to avoid filter divergence is inflation, which inflates ensemble perturbations to increase ensemble spread and account for model errors. The commonly applied inflation methods, including the multiplicative inflation, relax to prior spread (RTPS), and additive inflation, often use a constant inflation parameter. To capture different error growths at different scales, a scale-dependent inflation is proposed here, which applies different inflation magnitudes for variables associated with different scales. Results from the two-scale Lorenz05 model III show that for the ensemble square-root filter (EnSRF) and integrated hybrid ensemble Kalman filter with ensemble mean updated by hybrid background error covariances (IHEnKF-Mean), scale-dependent inflation is superior to constant inflation. Constant inflation overinflates small-scale variables and results in increased small-scale errors, which then propagate to large-scale variables through the coupling between large- and small-scale variables and lead to increased large-scale errors. Scale-dependent inflation applies larger inflation for large-scale variables and imposes no inflation for small-scale variables, since large-scale errors have larger magnitudes than small-scale ones, and small-scale errors grow faster than large-scale ones. But IHEnKF-Ensemble that updates both the ensemble mean and perturbations with hybrid background error covariances is much less sensitive to scale-dependent inflation, compared to EnSRF and IHEnKF-Mean, because updating ensemble perturbations with hybrid background error covariances can play a role similar to the scale-dependent inflation.

Combining observations and simulations to investigate the small-scale variability of surface solar irradiance under continental cumulus clouds

Abstract. The amount of solar radiation reaching the Earth surface (surface solar irradiance, SSI) is critical for a variety of applications, ranging from surface–atmosphere interactions to solar energy. SSI is characterized by a large spatiotemporal variability, in particular in the presence of cumulus clouds. This results in complex spatial patterns of shadows and sunlight directly related to clouds' geometry and physical properties. Although key in many respects, the instantaneous spatial distribution of SSI remains largely unexplored. Here, we use unique observations from a dense network of pyranometers deployed during the HOPE field campaign to investigate the SSI spatial distribution. For cumulus scenes, bimodal distributions are found, with one mode corresponding to cloud shadows and the other to sunlit areas with enhanced SSI exceeding clear-sky values. Combining large-eddy simulations of cumulus clouds with Monte Carlo ray tracing, we demonstrate the capability of advanced numerical tools to reproduce the observed distributions and quantify the impact of cloud geometrical and physical properties on both modes. In particular, cloud cover strongly modulates their amplitudes, in addition to their position and width, which are also sensitive to cloud height, geometrical depth, and liquid water content. Combining observations and simulations, we also explore sampling strategies to estimate the SSI spatial distribution with a limited number of sensors, suggesting that 10 pyranometers integrated over 10 min can capture most details of the full distribution. Such a strategy could be used for future campaigns to further investigate SSI distributions and their impact on land–atmosphere exchanges or photovoltaic farm management.

An ecological and evolutionary approach to arboreal communities under different environmental stress conditions

Biodiversity distribution patterns and the ecological processes underlying them reflect how organisms respond to abiotic and biotic factors that interact across both space and time. In this study, we aimed to test the hypothesis that small-scale environmental variations shape ecological and phylogenetic patterns in arboreal communities through environmental filters created by the specific conditions of each environment examined. We investigated a mosaic of Atlantic forest environments, analyzing species composition, community dynamics, and phylogenetic relationships among the various environments. This approach provided valuable insight into how environmental factors influence the ecological differentiation of these communities. Variations in conditions and resources produced environmental filters that resulted in significant small-scale heterogeneity in species composition, community dynamics, and evolutionary relationships among the environments. By considering the dynamic interations between biotic and abiotic factors, we conclude that the prevailing environmental conditions at these sites play a crucial role in shaping the behavior and distribution of the species that inhabit them.

Lightweight algorithm based on you only look once version 5 for multiple class defect detection on wind turbine blade surfaces

A lightweight wind turbine blade surface multiple class defect detection algorithm based on You Only Look Once version 5 (YOLOv5) is proposed to address the issues of low accuracy, poor efficiency, and high power consumption associated with manual and traditional automation algorithms used for detecting surface defects on wind turbine blades. Firstly, by replacing the Concentrated-Comprehensive Convolution Block module in YOLOv5 with the GhostBottleneckv2 module, the overall parameter count of the model is significantly reduced. Additionally, advocating the inclusion of the Convolutional Block Attention Module (CBAM) into the YOLOv5 backbone network to address the issue of small targets and significant scale variations in surface defects found on wind turbine blades. The YOLOv5 feature fusion module replaces the Feature Pyramid Network (FPN) structure with the Bidirectional Feature Pyramid Network (BiFPN) structure to enhance the fusion capability of multi-scale weighted features. Finally, the optimized model files underwent structured pruning through the application of pruning strategies, retraining fine-tuning, and knowledge distillation techniques. The experimental results on the self-made dataset of surface defects on wind turbine blades indicate that the optimized model has a parameter count that is only 48.79% of the small version of the YOLOv5 model. However, its mean Average Precision (mAP) and frame rate have increased by 5.05% and 8.1 frames/s, respectively, reaching 94.56% and 43.7 frames/s. The lightweight algorithm proposed in this study meets the accuracy and real-time requirements for surface defect detection on wind turbine blades.

Interannual differences vs small-scale spatiotemporal variability of spring zooplankton in the Crimean marine hypersaline lake

Ecosystems in the shallow marine hypersaline environment, such as marine lakes, are among the most changeable and have significantly lower species richness than freshwater and marine. It could be assumed that the intricate interconnections in hypersaline water ecosystems are easier to understand than in other aquatic ecosystems. Interannual differences vs small-scale spatiotemporal variabilities were studied in the spring zooplankton of hypersaline Lake Moynaki from 2019 to 2023. Salinity, water temperature, wind speed and direction were measured along with zooplankton sampling. The average spring wind speed and water temperature varied significantly from year to year, seemingly with a certain cyclicity. In total, 17 species were noted in zooplankton. The copepods Acanthocyclops vernalis and Arctodiaptomus salinus were not previously recorded in the lake. Bottom species were found in the plankton, among which the oligochaete Nais pseudobtusa, the gastropod Bittium reticulatum, and dragonfly larvae Gomphus sp. had not previously been found in the lake. Only four species, Gammarus aequicauda, Eucypris mareotica, Moina salina, and Cletocamptus retrogressus were regularly observed en masse. On average, the total zooplankton abundance near the windward shore was 2 times higher than that near the leeward shore and the abundance of individual species was from 2 to 5 times higher. If the values near only one coast were used, then the range of interannual differences would be underestimated or overestimated by approximately 2–4 times. Based on the zooplankton similarity in different years, all years except 2019, are grouped into a single cluster. Various changes are occurring in the lake, which are difficult to present as a complete picture of the cause-and-effect relationships that determine them.

Comparing modeled predictions of coral reef diversity along a latitudinal gradient in Mozambique

IntroductionPredictive models based on environmental proxy data are being used to predict biodiversity on large and even global scales. Yet, some of the underlying assumptions about the relationship between proxy variables and predictions require investigations and testing the consequences of using model alternatives, data sources, variables choices, and scales, extent, and overlap among the predictions. Mozambican coral reefs provide a good case study to test these assumptions given the paucity of field data, its long coastline, and transitions from tropical to temperate environments.MethodsThree modelling formulations and 5 specific models were made using satellite and shipboard measurements and extensive fish and corals field data to test their performance in predicting numbers of fish species and coral taxa from field data. Model predictions were mapped for the 1180 ~6.25 km2 Mozambican coral reef cells. Predictions were made and mapped 1) based on ~1000 field sites in the Western Indian Ocean (WIO) faunal province model, 2) using environmental variable selected in the WIO model (WIOMOD) but trained only with Mozambican field data (<113 sites), and 3) using only Mozambican environmental and field data and standard variable redundancy and selection procedures.Results and discussionTraining and testing cross validation of models indicated modest predictive ability (R2~0.42-0.56%) and reasonable transferability. Consequently, there was unexplained variation likely due to small-scale environmental variability finer than the mapped cell scale. Differences between model predictions were caused by different variable rankings and response relationship. For example, the Mozambique-only model predicted more fish but fewer coral taxa, a larger role of water quality and sediments, habitats, and temperature variation, and a lesser role of human influence than the WIOMOD. Therefore, differences between models indicate that large scale models (i.e. provincial or global) can contribute to understanding gross patterns but miss important local environmental and human drivers in transitional environments. Nevertheless, 79% of the fish and 88% of coral taxa cell-level predictions of taxonomic diversity had standardized coefficients of variations of <10%.

Strainy: phasing and assembly of strain haplotypes from long-read metagenome sequencing.

Bacterial species in microbial communities are often represented by mixtures of strains, distinguished by small variations in their genomes. Short-read approaches can be used to detect small-scale variation between strains but fail to phase these variants into contiguous haplotypes. Long-read metagenome assemblers can generate contiguous bacterial chromosomes but often suppress strain-level variation in favor of species-level consensus. Here we present Strainy, an algorithm for strain-level metagenome assembly and phasing from Nanopore and PacBio reads. Strainy takes a de novo metagenomic assembly as input and identifies strain variants, which are then phased and assembled into contiguous haplotypes. Using simulated and mock Nanopore and PacBio metagenome data, we show that Strainy assembles accurate and complete strain haplotypes, outperforming current Nanopore-based methods and comparable with PacBio-based algorithms in completeness and accuracy. We then use Strainy to assemble strain haplotypes of a complex environmental metagenome, revealing distinct strain distribution and mutational patterns in bacterial species.

The Korean infrasound catalogue (1999–2022)

SUMMARY The Korean infrasound catalogue (KIC) covers 1999–2022 and characterizes a rich variety of source types as well as document the effects of the time-varying atmosphere on event detection and location across the Korean Peninsula. The KIC is produced using data from six South Korean infrasound arrays that are cooperatively operated by Southern Methodist University and Korea Institute of Geoscience and Mineral Resources. Signal detection relies on an Adaptive F-Detector that estimates arrival time and backazimuth, which draws a distinction between detection and parameter estimation. Detections and associated parameters are input into a Bayesian Infrasonic Source Location procedure. The resulting KIC contains 38 455 infrasound events and documents repeated events from several locations. The catalogue includes many anthropogenic sources such as an industrial chemical explosion, explosions at limestone open-pit mines and quarries, North Korean underground nuclear explosions and other atmospheric or underwater events of unknown origin. Most events in the KIC occur during working hours and days, suggesting a dominance of human-related signals. The expansion of infrasound arrays over the years in South Korea and the inclusion of data from the International Monitoring System infrasound stations in Russia and Japan increase the number of infrasound events and improve location accuracy because of the increase in azimuthal station coverage. A review of selected events and associated signals at multiple arrays provides a location quality assessment. We quantify infrasound events that have accompanying seismic arrivals (seismoacoustic events) to support the source type assessment. Ray tracing using the Ground-to-Space (G2S) atmospheric model generally predicts observed arrivals when strong stratospheric winds exist, although the predicted arrival times have significant discrepancies. In some cases, local atmospheric data better captures small-scale variations in the wind velocity of the shallow atmosphere and can improve arrival time predictions that are not well matched by the G2S model. The analysis of selected events also illustrates the importance of topographic effects on tropospheric infrasound propagation at local distances. The KIC is the first infrasound catalogue compiled in this region, and it can serve as a valuable data set in developing more robust infrasound source localization and characterization methods.

MODIS fPAR products do not reflect in-situ conditions in a tropical dry forest based on wavelet and cross-wavelet transforms

The fraction of Photosynthetically Active Radiation (fPAR) plays a pivotal role in determining the carbon flux in ecosystems. Although the MODIS fPAR product has demonstrated effectiveness in the Northern Hemisphere, its validity still needs to be verified in the context of Tropical Dry Forests (TDFs), which constitute 40% of all tropical forests. This study utilized a Wireless Sensor Network (WSN) to generate an in-situ Green fPAR dataset at the Santa Rosa National Park Environmental Monitoring Supersite, aiming to validate MODIS fPAR products from 2013 to 2017. This study employs a 2-flux fPAR estimation approach for the in-situ dataset, followed by Savitzky–Golay derivative-based smoothing, univariate-wavelet transforms, and cross-wavelet analysis to compare phenological variables between the in-situ and MODIS fPAR datasets. Our findings reveal a significant temporal disparity between the MODIS fPAR products and ground-based data, with MODIS consistently lagging in detecting the onset of green-up or senescence in TDFs by 18–55 days. However, the annual and inter-seasonal patterns were statistically significant (p < 0.05) and replicated in the MODIS and in-situ datasets. Notably, these patterns deviate during extreme water conditions (droughts and hurricanes), with MODIS underestimating the effects of drought and failing to represent hurricane impact. Furthermore, MODIS fPAR products do not effectively capture small-scale fPAR variations and intra-seasonal differences. Therefore, this study underscores the limited accuracy of MODIS fPAR observations in the context of TDFs. Consequently, caution is warranted when relying on MODIS fPAR products to monitor rapid phenological changes in Tropical Dry Forests.

Methodology for evaluating soil carbon stock changes based on regional geochemical survey data: A case study in Huangpi, China

Soil carbon pools play a crucial role in the Earth's ecosystem, acting as significant carbon sinks and sources. Through a detailed analysis of soil carbon content using data from the Huangpi District in Wuhan, China, this research employs geochemical survey data, field replicates, and spatial autocorrelation information to establish an assessment model for soil carbon stocks. The model addresses the sources of errors and their effects on carbon pool changes, using both traditional statistical theories and geostatistical models to detect changes in carbon density and estimate carbon sources and sinks with minimized error ranges. Key findings indicate that sampling errors, influenced by small-scale spatial variability, are the primary source of observational inaccuracies in assessing total soil carbon and organic carbon, accounting for over 90% of the variation. Meanwhile, analytical errors are more significant when quantifying soil inorganic carbon content due to its lower concentrations. From 2001 to 2022, no significant changes were observed in the soil organic carbon stock in Huangpi District, while a modest increase in inorganic carbon was noted. The study highlights that increasing sample density beyond a certain threshold does not significantly affect carbon stock estimates or their error ranges, emphasizing the stability of the block kriging method in estimating regional carbon stocks.

Unsupervised Characterization of Water Composition with UAV-Based Hyperspectral Imaging and Generative Topographic Mapping

Unmanned aerial vehicles equipped with hyperspectral imagers have emerged as an essential technology for the characterization of inland water bodies. The high spectral and spatial resolutions of these systems enable the retrieval of a plethora of optically active water quality parameters via band ratio algorithms and machine learning methods. However, fitting and validating these models requires access to sufficient quantities of in situ reference data which are time-consuming and expensive to obtain. In this study, we demonstrate how Generative Topographic Mapping (GTM), a probabilistic realization of the self-organizing map, can be used to visualize high-dimensional hyperspectral imagery and extract spectral signatures corresponding to unique endmembers present in the water. Using data collected across a North Texas pond, we first apply GTM to visualize the distribution of captured reflectance spectra, revealing the small-scale spatial variability of the water composition. Next, we demonstrate how the nodes of the fitted GTM can be interpreted as unique spectral endmembers. Using extracted endmembers together with the normalized spectral similarity score, we are able to efficiently map the abundance of nearshore algae, as well as the evolution of a rhodamine tracer dye used to simulate water contamination by a localized source.

Pine trees structure plant biodiversity patterns in savannas.

Overstory trees serve multiple functions in grassy savannas. Past research has shown that understory species can vary along gradients of canopy cover and basal area in savannas. This variation is frequently associated with light availability but could also be related to other mechanisms, such as heterogeneity in soil and litter depth and fire intensity. Several savanna studies have found differences in understory plant functional groups within the local environment near trees versus away from them in canopy openings. Although small-scale variation is known to be high in southeastern U.S. pine savannas, patterns in understory species diversity have not been examined at the scale of individual overstory pine trees in this system. We conducted an observational study of the relationship between understory plant communities and proximity to individual pine trees in xeric and mesic pine savannas in frequently burned sites (1-3 year intervals). We recorded the plant community composition in plots adjacent to tree boles (basal) or outside crown driplines (open). Within each environment, raw species richness was significantly greater in open locations, where light transmittance was greater. In contrast, rarified species richness did not differ. Multivariate analyses showed that community composition differed significantly between basal and open plots. One native, woody species in each environment, Serenoa repens (W. Bartram) Small in mesic and Diospyros virginiana L. in xeric, was more abundant in basal plots. In mesic environments, eight species had greater occurrence in open plots. In xeric environments, four understory forbs were more abundant in open plots. Our results support previous research indicating that individual pine trees are associated with significant variation in understory vegetation in pine savannas.

Onsite measurements of pedestrian-level wind and preliminary assessment of effects of turbulence characteristics on human body convective heat transfer

Wind is an important factor affecting outdoor thermal comfort in urban environment, but its turbulence characteristics at the pedestrian level and effects on convective heat transfer (hc) from a human body remain poorly understood. Previous studies were only conducted in wind tunnels with low turbulence intensity, small turbulence integral length scale, and fixed prevailing wind direction. To address this knowledge gap, this field study was conceived to monitor the pedestrian level wind in actual outdoor settings and to measure the hc of a thermal manikin at the same time. The results show that both longitudinal and lateral turbulence intensity significantly enhance whole body hc. It remains to be examined whether the small-scale wind direction variation can be included in the lateral turbulence intensity or vice versa. The integral length scale found at the two sites were larger than a typical manikin dimension, and the whole body hc peaked when these two scales were comparable. As the first major field study to quantify convection heat loss of a thermal manikin exposed to real-life urban boundary layer wind, it is demonstrated crucial to consider realistic turbulence characteristics in the field when evaluating hc over a human body for urban microclimate design.

A physics-based model of thermodynamically varying fuel moisture content for fire behavior prediction

Fuel moisture content (FMC) is a critical parameter in fire and plume behaviors, showing diurnal and spatial variations influenced by local meteorological conditions, soil characteristics, and fuel properties. In low-intensity fires, small-scale FMC variations intensify, leading to an amplification of their effects on fire physics. In an effort to capture these variations, this paper presents the development of a physics-based model that couples a thermodynamic-based FMC prediction model for dead fuels with the Fire Dynamics Simulator of the National Institute of Standards and Technology. The model accuracy is validated against several existing experimental data, showing improvements over the baseline model which uses the kinetic-based Arrhenius drying approach. A case study of flame propagation in a small fuel bed is also presented, indicating the improved performance of the new model and its novel capabilities in capturing complex processes of fuel drying and moisture flux exchanges between the fuel and ambient atmosphere.

Angle-dependent quantum efficiency correction for improved signal accuracy in small-scale Scheimpflug lidar systems

Wide-incidence-angle detection in Scheimpflug lidar causes pixel-wise variations in signal intensity due to differences in quantum efficiency across the detector. This study proposes an angular response correction factor and a correction method to address the difference caused by a wide incidence angle. A Scheimpflug lidar system was developed with a 405 nm laser and an array image sensor; it achieved a detection angle of 11.4°. Experiments with uniform atmosphere and smoke aerosols demonstrated the effectiveness of the correction method. For uniform atmosphere, it ensured consistent signal intensity. In smoke aerosol detection, the Klett method revealed the correction factor’s role in counteracting small-scale variations in the extinction coefficient. The method improved the extinction coefficient accuracy within 4 m by 4%–75%.

The response of epibenthic molluscan assemblages associated with Mytilus galloprovincialis beds to natural- and human-induced changes: Structure and small-scale spatiotemporal variability

Mussel beds are species-rich and diversified biogenic habitats for associated assemblages, playing important ecological roles and contributing significantly to local littoral invertebrate biodiversity. The community structure and seasonal patterns of molluscan assemblages associated with rocky-intertidal mussel Mytilus galloprovincialis beds along NW Atlantic coast of Morocco were investigated. Sampling was conducted at reference and contaminated stations in three distant coastal areas over a 4-season sampling period (between August 2018 and May 2019). We tested the hypothesis that the biogenic mussel habitats in areas experiencing different environmental conditions along the shore would support different associated molluscan assemblages under human-induced and natural changes at small spatial and temporal scales. A total of 6371 specimens comprising 28 taxa were identified and encompassing four trophic groups (carnivores, detritivores/deposivores, micrograzers and suspensivores). Micrograzers predominated, followed by carnivores, with gastropods being the numerically dominant taxa accounting for 79 % of total abundance. Steromphala pennanti emerged as the most abundant and frequent species, across almost all seasons and stations contributing to 31 % of total abundance. Significant seasonal variations were observed in species richness and Shannon-Wiener’s index (ANOVA, p<0.05), with the highest values recorded at the most polluted stations during cold/wet periods, and the lowest at the cleanest stations during hot/warm periods. Furthermore, the ANOVA analyses unveiled a significant spatiotemporal variation in abundance, emphasizing the dynamic nature of molluscan assemblages in response to environmental conditions and seasonal changes. The multivariate structure of associated molluscan assemblages (PERMANOVA analysis) differed significantly among stations and seasons, highlighting clear geographical and seasonal effects. Gastropod species, including Acanthochitona fascicularis, Lepidochitona cinerea, Nucella lapillus, Patella ulyssiponensis, P. vulgata, Phorcus lineatus, P. sauciatus, and Steromphala pennanti, were identified as the most contributive species to the dissimilarity of molluscan assemblage structure among stations throughout seasons (SIMPER analysis). Based on the canonical correspondence analysis results, salinity, water temperature, nutrient concentrations, sediment granulometry and total organic matter were identified as the key factors driving changes in the molluscan community structure. This study underscores the ecological importance of habitat-forming mussels as ecosystem engineers and biodiversity enhancers in shallow coastal waters along the Moroccan Atlantic coast.

Along‐Track Resolution and Uncertainty of Altimeter‐Derived Wave Height and Sea Level: Re‐Defining the Significant Wave Height in Extreme Storms

AbstractSatellite altimeters are the most common source of wave measurement in phenomenal sea states, with significant wave heights exceeding 14 m. Unfortunately their data is still considered with skepticism, because there is usually no other data to verify the accuracy of the largest values. Here we investigate the self‐consistency of the measurement, and their small scale variability, in order to define an estimate of satellite altimeter precision. Using numerical simulations of ocean surfaces and the processing involved in satellite retracking, we find that wave groups are responsible for a variance in estimated altimeter wave heights that is proportional to the square of the spectral peakedness parameter and the significant wave height. Additional variance induced by speckle noise is proportional to the wave height. The effect of wave groups generally dominates in the most severe storms. This variability requires a relatively large scale smoothing or filtering to yield accurate wave height estimates. For example, the largest ever reported 1 s average significant wave height from altimeters sampled by Jason‐2 in the North Atlantic in 2011, at m, is now interpreted to correspond to a true wave height Hs = 18.5 ± 0.3 m. The difference between 20.1 and 18.5 m is mostly due to wave group contributions to the raw measurement. We argue that wave group effects should not be included in the definition of the significant wave height, just like the maximum wave height differs from the significant wave height.