Large-scale Spatial Variability Research Articles

The sensitivity of Southern Ocean atmospheric dimethyl sulfide (DMS) to modeled oceanic DMS concentrations and emissions

Abstract. The biogeochemical formation of dimethyl sulfide (DMS) from the Southern Ocean is complex, dynamic, and driven by physical, chemical, and biological processes. Such processes, produced by marine biogenic activity, are the dominant source of sulfate aerosol over the Southern Ocean. Using an atmosphere-only configuration of the United Kingdom Earth System Model (UKESM1-AMIP), we performed eight 10-year simulations for the recent past (2009–2018) during austral summer. We tested the sensitivity of atmospheric DMS to four oceanic DMS datasets and three DMS transfer velocity parameterizations. One oceanic DMS dataset was developed here from satellite chlorophyll a. We find that the choice of oceanic DMS dataset has a larger influence on atmospheric DMS than the choice of DMS transfer velocity. Simulations with linear transfer velocity parameterizations show a more accurate representation of atmospheric DMS concentration than those using quadratic relationships. This work highlights that the oceanic DMS and DMS transfer velocity parameterizations currently used in climate models are poorly constrained for the Southern Ocean region. Simulations using oceanic DMS derived from satellite chlorophyll a data, and when combined with a recently developed linear transfer velocity parameterization for DMS, show better spatial variability than the UKESM1 configuration. We also demonstrate that capturing large-scale spatial variability can be more important than large-scale interannual variability. We recommend that models use a DMS transfer velocity parameterization that was developed specifically for DMS and improvements to oceanic DMS spatial variability. Such improvements may provide a more accurate process-based representation of oceanic and atmospheric DMS, and therefore sulfate aerosol, in the Southern Ocean region.

Geotechnical variability: in the ground and throughout a career

This paper presents three aspects of geotechnical engineering research that have been conducted throughout the author’s career and concludes with a brief treatment of the use of physical models in teaching. The first research topic deals with quantifying the large-scale spatial variability of the Keswick Clay in Adelaide by means of undrained shear strength data acquired from several private consulting companies and government departments, incorporating a large number of site investigations. The mathematical technique of geostatistics is used, and it is observed that kriging with a spherical model, with a range of influence of 1000 m, a nugget of 1500 kPa2, and a sill of 2500 kPa2, is able to generate good estimates of the undrained shear strength of the Keswick Clay that can be used for preliminary design purposes. Secondly, the ground improvement technique of rolling dynamic compaction (RDC) is examined in the field and in the laboratory, and numerically by means of artificial neural networks (ANNs). It is observed that RDC is able to improve the ground to depths in excess of 3 m, and the use of transparent soils in the laboratory provides useful insights regarding the influence of RDC on the subsurface profile. In addition, ANNs facilitate the development of reliable models for the prediction of the level of ground improvement due to RDC. The third and final research topic presented involves ground improvement on the Moon. It is a work-in-progress, and early results are presented in this fascinating and exciting endeavour. The paper concludes with a brief treatment of the use of three different physical models used in teaching. It is observed that incorporating demonstrations involving physical models in teaching is helpful for enhancing student learning and engagement.

Testing macroecological hypotheses in sandy beach populations: the wedge clam Donax hanleyanus in South America

Large-scale spatial and temporal variability in environmental conditions may result in differences in life-history traits, population demography, and abundance of sandy-beach species. We analyzed the effects of salinity, chlorophyll a (chl a), and sea surface temperature (SST) on population parameters of the wedge clam Donax hanleyanus from 75 South American sandy beaches covering a 15° latitudinal range. Generalized modeling results showed that between-beach differences in abundance, population structure, growth performance, productivity, mortality, and individual shell mass were mainly explained by salinity fluctuations, with chl a and SST as secondary contributors, overriding, in most cases, local habitat features (Dean’s parameter, grain size, slope). Our results provide valuable insights into macroscale ecological processes, setting a basis to delineate conservation guidelines at large spatial scales that respond to the potential effects of climate variability and change on sandy beach populations.

Large-scale spatial variability in urban tolerance of birds.

Quantifying intraspecific and interspecific trait variability is critical to our understanding of biogeography, ecology and conservation. But quantifying such variability and understanding the importance of intraspecific and interspecific variability remain challenging. This is especially true of large geographic scales as this is where the differences between intraspecific and interspecific variability are likely to be greatest. Our goal is to address this research gap using broad-scale citizen science data to quantify intraspecific variability and compare it with interspecific variability, using the example of bird responses to urbanization across the continental United States. Using more than 100 million observations, we quantified urban tolerance for 338 species within randomly sampled spatial regions and then calculated the standard deviation of each species' urban tolerance. We found that species' spatial variability in urban tolerance (i.e. standard deviation) was largely explained by the variability of urban cover throughout a species' range (R2 =0.70). Variability in urban tolerance was greater in species that were more tolerant of urban cover (i.e. the average urban tolerance throughout their range), suggesting that generalist life histories are better suited to adapt to novel anthropogenic environments. Overall, species differences explained most of the variability in urban tolerance across spatial regions. Together, our results indicate that (1) intraspecific variability is largely predicted by local environmental variability in urban cover at a large spatial scale and (2) interspecific variability is greater than intraspecific variability, supporting the common use of mean values (i.e. collapsing observations across a species' range) when assessing species-environment relationships. Further studies, across different taxa, traits and species-environment relationships are needed to test the role of intraspecific variability, but nevertheless, we recommend that when possible, ecologists should avoid using discrete categories to classify species in how they respond to the environment.

Health-Related Disparities in the Metropolitan Region Ruhr: Large-Scale Spatial Model of Local Asthma Prevalence, Accessibility of Health Facilities, and Socioeconomic and Environmental Factors

This paper investigates the area of the Metropole Ruhr in terms of spatial distributions of environmental factors that can prevent or cause a significantly lower or higher rate of respiratory diseases such as asthma. Environmental factors can have negative impact, like air pollution, and positive, like the access to urban green areas. In the second part of the analysis, the accessibility of pharmacies, hospitals, and medical facilities that offer a special treatment for people with respiratory diseases will be spatially analysed and associated to those detected urban areas of higher and lower prevalence. The results of both approaches are spatially blended with socioeconomic and socio-demographic values of the respective residents. With this it is possible to point out whether accessibility of health facilities is a suitable and equitable for all people diagnosed with asthma regardless of their educational or migration background, their employment rate, salary or age. Consequently, all values will be disaggregated from large spatial units, such as city districts municipalities or neighbourhoods, to small city blocks, to assess large-scale spatial variability. This provides the opportunity of a point-by-point investigation and statistical analysis with a high level of detail that significantly exceeds previous study results. In the sociological context of environmental justice this highly interdisciplinary study contributes to the assessment of fair health conditions for people in densely populated conurbations.

Large-scale spatial variability in loess landforms and their evolution, Luohe River Basin, Chinese Loess Plateau

Loess landform variability across large spatial extents needs to be analyzed to understand the formation and evolution of loess landscapes. This is becoming increasingly possible via the automated analysis of remotely-sensed data. Here, we quantify loess landforms using an object-based image analysis (OBIA) method and use this classification to describe the spatial variability of loess landforms. Quantitative indicators are used to drive the spatial variability analysis of loess landforms and explain their spatio-temporal evolution. Moreover, the hypsometric integral (HI) and topographic interpolation are employed to investigate soil erosion and development patterns of loess landscape. Results show that the OBIA method classified loess landforms to an accuracy of 88.7 %. The derived metrics in terms of the area, slope and complexity of landform shape allow the determination of the spatial structure of the loess landscapes. The HI value of the entire basin is 0.486, representing the mature stage of landform development, with relatively severe surface erosion. Correlation analysis of HI values and related indicators in the sub-basins shows that HI is poorly correlated with the area proportion of loess landform types and the total erosion volume in the basin but shows a relatively strong correlation with the volume of erosion per unit area.

Beach-face slope dataset for Australia

Abstract. Sandy beaches are unique environments composed of unconsolidated sediments that are constantly reshaped by the action of waves, tides, currents, and winds. The most seaward region of the dry beach, referred to as the beach face, is the primary interface between land and ocean and is of fundamental importance to coastal processes, including the dissipation and reflection of wave energy at the coast and the exchange of sediment between the land and sea. The slope of the beach face is a critical parameter in coastal geomorphology and coastal engineering, as it is needed to calculate the total elevation and excursion of wave run-up at the shoreline. However, datasets of the beach-face slopes along most of the world's coastlines remain unavailable. This study presents a new dataset of beach-face slopes for the Australian coastline derived from a novel remote sensing technique. The dataset covers 13 200 km of sandy coast and provides an estimate of the beach-face slope every 100 m alongshore accompanied by an easy-to-apply measure of the confidence of each slope estimate. The dataset offers a unique view of large-scale spatial variability in the beach-face slope and addresses the growing need for this information to predict coastal hazards around Australia. The beach-face slope dataset and relevant metadata are available at https://doi.org/10.5281/zenodo.5606216 (Vos et al., 2021).

Spatial and Subannual Variability of the Antarctic Slope Current in an Eddying Ocean–Sea Ice Model

Abstract The Antarctic Slope Current (ASC) circumnavigates the Antarctic continent following the continental slope and separating the waters on the continental shelf from the deeper offshore Southern Ocean. Water mass exchanges across the continental slope are critical for the global climate as they impact the global overturning circulation and the mass balance of the Antarctic ice sheet via basal melting. Despite the ASC’s global importance, little is known about its spatial and subannual variability, as direct measurements of the velocity field are sparse. Here, we describe the ASC in a global eddying ocean–sea ice model and reveal its large-scale spatial variability by characterizing the continental slope using three regimes: the surface-intensified ASC, the bottom-intensified ASC, and the reversed ASC. Each ASC regime corresponds to a distinct classification of the density field as previously introduced in the literature, suggesting that the velocity and density fields are governed by the same leading-order dynamics around the Antarctic continental slope. Only the surface-intensified ASC regime has a strong seasonality. However, large temporal variability at a range of other time scales occurs across all regimes, including frequent reversals of the current. We anticipate our description of the ASC’s spatial and subannual variability will be helpful to guide future studies of the ASC aiming to advance our understanding of the region’s response to a changing climate.

Large-Scale Spatial Variability in Loess Landforms and Their Evolution, Luohe River Basin, Chinese Loess Plateau

Large-Scale Spatial Variability in Loess Landforms and Their Evolution, Luohe River Basin, Chinese Loess Plateau

Beach-face slope dataset for Australia

Abstract. Sandy beaches are unique environments composed of unconsolidated sediments that are constantly reshaped by the action of waves, tides, currents, and winds. The most seaward region of the dry beach, referred to as the beach face, is the primary interface between land and ocean and is of fundamental importance to coastal processes, including the dissipation and reflection of wave energy at the coast, and the exchange of sediment between the land and sea. The slope of the beach-face is a critical parameter in coastal geomorphology and coastal engineering, necessary to calculate the total elevation and excursion of wave run-up at the shoreline. However, datasets of the beach-face slope remain unavailable along most of the world’s coastlines. This study presents a new dataset of beach-face slopes for the Australian coastline derived from a novel remote sensing technique. The dataset covers 13,200 km of sandy coast and provides an estimate of the beach-face slope at every 100 m alongshore, accompanied by an easy to apply measure of the confidence of each slope estimate. The dataset offers a unique view of large-scale spatial variability in beach-face slope and addresses the growing need for this information to predict coastal hazards around Australia. The beach-face slope dataset and relevant metadata are available at https://doi.org/10.5281/zenodo.5606217 (Vos et al., 2021)

Assessment of Interannual Variability of Moistening of Siberian Territory According to Observational Data

The article discusses the features of large-scale spatial and temporal variability of moistening (potential evapotranspiration, precipitation, potential evapotranspiration coefficient) in the Siberian part of Russia for the period 1981–2015. The All-Russian Research Institute of Hydrometeorological Information—World Data Center (RIHMI-WDC) archive has served as a source of initial information. Due to the rare network of stationary meteorological stations in most of Siberia, only 32 stations located mainly in the valleys of large rivers have been used for calculations. To estimate potential evapotranspiration, the modified method of M.I. Budyko has been used. A comprehensive delimitation of Siberia has been carried out by the interannual fluctuations of characteristics of moistening, being well divided into four regions, three of which encompass the basins of the largest rivers: the Ob, the Yenisei, the Lena and the fourth region represents the Baikal region. Analysis of the trends shows that the evapotranspiration in Siberia is growing only in the Ob basin and the Baikal region. Precipitation, excluding the Baikal region, is also increasing in the Yenisei and Lena basins. As for the potential evapotranspiration coefficient, a significant trend refers only to the Baikal region due to the rapid increase in evaporation. The modeling of the annual values of the characteristics of moistening for the selected regions has been carried out using the decision trees method. For 4-branch trees, the coefficient of determination R2 describes about two-thirds of the variance of the original variable (0.57–0.73). In the models of annual evapotranspiration values, the main predictor is the air temperature. In precipitation models, the contribution of local and external circulation factors to interannual precipitation fluctuations is equal.

Storms, Variability, and Multiple Equilibria on Hot Jupiters

Abstract Observations of hot-Jupiter atmospheres show large variations in the location of the “hot spot” and the amplitude of spectral features. Atmospheric flow simulations using the commonly employed forcing and initialization have generally produced a large, monolithic patch of nearly stationary hot area located eastward of the substellar point at high altitude. Here we perform high-resolution (T682) pseudospectral simulations that accurately capture small-scale eddies and waves inherent in hot-Jupiter atmospheres due to ageostrophy. The atmospheres contain a large number of intense storms over a wide range of scales, including the planetary scale. The latter sized storms dictate the large-scale spatial distribution and temporal variability of hot, as well as cold, regions over the planet. In addition, they exhibit quasi-periodic life cycles within multiple equilibrium states—all identifiable in the disk-integrated time series of the temperature flux.

A downscaling approach for constructing high-resolution precipitation dataset over the Tibetan Plateau from ERA5 reanalysis

Current gridded precipitation datasets are hard to meet the requirements of hydrological and meteorological applications in complex-terrain areas due to their coarse spatial resolution and large uncertainties. High-resolution atmospheric simulations are capable of describing the influence of topography on precipitation but are difficult to be used to obtain long-term precipitation datasets because they are computationally expensive, while reanalysis data has a long-term coverage and can provide reasonable large-scale spatial and temporal variability of precipitation. This study presents an approach to obtain long-term high-resolution precipitation datasets over complex-terrain areas by combining the ERA5 reanalysis with short-term high-resolution atmospheric simulation. The approach consists of two main steps: first, the ERA5 precipitation is corrected by the high-resolution simulation at the coarse spatial resolution; second, the corrected data is downscaled using a convolution neural network (CNN) based model at daily scale. The proposed approach is applied to the Tibetan Plateau (TP). The downscaled results from ERA5 have a finer spatial structure than ERA5 and can reproduce the spatial patterns of precipitation revealed by the high-resolution simulation. An evaluation based on rain gauge data shows that the downscaled ERA5 has remarkably lower biases than the original ERA5 which overestimates precipitation a lot, and even higher accuracy than the high-resolution simulation data over the TP. The downscaled ERA5 preserves the temporal characteristics of ERA5 which are more consistent with the rain gauge data than that of high-resolution simulation. Since this approach is much less computing resources consuming than the high-resolution simulation, it is an effective method to obtain long-term high-resolution precipitation datasets in complex-terrain areas and is expected to have extensive applications.

Distribution, characteristics and short-term variability of microplastics in beach sediment of Fernando de Noronha Archipelago, Brazil

Fernando de Noronha Archipelago is highly vulnerable to microplastic pollution, which has been previously reported with snapshot samplings on the site. The present study has performed daily beach sediment samplings on the archipelago, aiming to assess the distribution, characteristics and short-term variability of microplastics (1–5 mm), expressing concentrations in three different units. The concentrations ranged from 0.6 ± 2.5 particles/m2 to 1059.3 ± 1385.6 particles/m2 and showed a large spatial and temporal small-scale variability. The results indicate that microplastic contamination is recurrent in Fernando de Noronha and the distribution of these particles is associated with a combination of various physical processes. A wider comparison with results obtained in beaches worldwide was possible using different units of concentration, but standard methods for sampling and analysis of microplastics is needed to better understanding of large-scale spatial and temporal variability.

Airborne bacterial communities over the Tibetan and Mongolian Plateaus: variations and their possible sources

The transportation of atmospheric bacteria is increasingly recognized as an important factor that affects bacterial diversity in natural environments as well as human health. However, few studies have investigated airborne bacterial biodiversity in extremely remote, arid, and cold environments, such as desert and alpine areas, which may contain ambient bacteria transported by air masses. In this study, atmospheric aerosol samples were collected from the southern slope of the Himalayas, the southern Tibetan Plateau and the Mongolian Plateau to analyze the diversity of airborne bacteria and its relationship to environmental factors. The results show that the bacterial communities in all samples were predominantly composed of Proteobacteria, Actinobacteria, Firmicutes, and Bacteroidetes. Furthermore, the community structures of airborne bacteria exhibited significant differences among the collected sample, these differences mainly depended on meteorological parameters (P values < 0.01). In particular, the aerosol samples over the Tibetan Plateau showed the lowest diversity among the three regions. This suggests that the variability in airborne bacterial communities is determined by meteorological parameters and long-range transportation. Our study provides new information about airborne bacterial community composition in extreme environments and its large-scale spatial variability.

Modelling the spatial variability of soil micronutrients for site specific nutrient management in a semi-arid tropical environment

Deficiency of micronutrients in the soils of semi-arid tropical (SAT) environments is a major limiting factor for crop production. This study aims to model the spatial variability of soil micronutrients in croplands of Thimmajipet block, Mahabubnagar, Telangana, India. We collected 1508 georeferenced surface (0–15 cm) soil samples at a grid interval of 325 × 325 m and analysed them for DTPA extractable fractions of iron (Fe), manganese (Mn), copper (Cu), zinc (Zn) and boron (B). Landform and topographic variables were derived from the digital elevation model of the study area. Results showed that all the micronutrients varied highly (CV > 35%) in the order of Zn > Fe > Cu > B > Mn. The Q–Q plots indicated non-normal frequency distribution for all the micronutrients, and log transformation improved them. We compared spherical, circular, exponential, and Gaussian models of ordinary kriging interpolation to define the spatial variability structure. The spherical model with the lowest RMSE and better r2 values was selected to map the spatial distribution of the micronutrients. The nugget to sill ratio (N:S > 0.75) indicated that the micronutrients were weakly spatial dependent. Multiple regression analysis (p < 0.05) indicated that the micronutrient concentration and spatial distribution were controlled by (1) parent material; (2) CaCO3; (3) OC; and (4) antagonistic nutrient interaction. Further, critical deficiency zones of Zn, Cu, B and Fe were delineated by integrating cadastral map on the spatial distribution maps. Our study demonstrates that the large scale spatial variability mapping of soil micronutrients is a prerequisite for implementing site-specific nutrient management in the SAT regions.

Spatial variability of macrofaunal traits across the Greater North Sea

Large-scale spatial variability of macrofaunal traits has rarely been studied. This paucity of information hinders progress to derive indicators of seabed integrity under current policy drivers. Based on data from 327 stations across the Greater North Sea, a sub-region of the EU Marine Strategy Framework Directive (MSFD), we describe variability in the numerical dominance of ten important macrofaunal traits and their relationships with the physical environment.For six traits (longevity, larval development position, egg development position, sediment position and bioturbation method), macrofaunal assemblages were dominated by one or two trait categories; these traits showed more pronounced spatial patterns than traits with a wider (and more even) trait category expression. Similar spatial distributions of some dominant categories were observed. For example, egg-brooding, surface-dwelling and suspension feeding traits were dominant in the central English Channel and off the Humber Estuary. Traits generally showed weak spatial relationships with environmental variables, although sediment position and feeding type were related to variations in bed shear stress and gravel content. We discuss the implications of these findings in relation to monitoring seabed integrity under the MSFD.

Modeling of the Vertical Movements of the Earth’s Crust in Poland with the Co-Kriging Method Based on Various Sources of Data

The main aim of this study was to evaluate the applicability of the co-kriging method for modeling the vertical movements of the Earth’s crust based on data acquired with the use of precision leveling techniques and measurements conducted by permanent Global Navigation Satellite System (GNSS) stations. Data were processed with the use of empirical, theoretical, and directional variograms (semivariograms), as well as variogram maps. Large-scale spatial variability was determined using polynomial regression. The relationships between the length of the semi-major and semi-minor axes vs. the root mean square (RMS) and the standard error of the estimate were analyzed. The relationships between the anisotropic direction and the number of lags were determined, and other parameters were calculated. Preliminary data fitting produced non-stationary surfaces. The leveling data were anisotropic, and the GNSS data were isotropic. Nugget effects were observed in both datasets, in particular in the GNSS data. The size of the ellipse was strongly correlated with the RMS and σ (average standard deviation of prediction). The anisotropy angle was determined using the number of lags. Co-kriging was found to not be a suitable method for modeling the vertical movements of the Earth’s crust based on data from various sources. The final result was strongly influenced by the initial dataset. The obtained results show how the method of combining data sets (interpolation, network adjustment) affected the final cartographic model.

Integration of small-scale surface properties in a new high resolution global wind speed model

A new model for mapping the near-surface wind speed L-moments on a high spatial resolution scale (250 m × 250 m) is introduced (GloWiSMo). The target variables are the first five L-moments of 6146 globally distributed wind speed time series. ERA5 reanalysis wind speed available on a 0.25° × 0.25° grid was used as predictor representing the large-scale wind field. Eleven predictors derived from a land cover model and a digital elevation model were applied to integrate the influence of small-scale surface properties on the wind field. The model is based on a least-squares boosting approach which is a machine learning algorithm. The parameters of the Kappa and Wakeby distribution were estimated based on the modeled L-moments. By applying the power law, the near-surface wind speed distribution can be extrapolated to any hub height. Here, we selected a typical wind turbine hub height of 120 m to demonstrate the potential of GloWiSMo. It was found that the relevance of a predictor on the spatial variability of the wind resource changes with the size of the investigation area. While the roughness length is a decisive factor for the large-scale spatial variability of the wind resource, the relative elevation is an important factor for the small-scale spatial variability. Rigorous model evaluation was performed using a validation dataset containing 598 globally distributed wind speed time series. The coefficient of determination calculated for the first L-moment was found to be 0.83. Based on the evaluation results, we argue that the developed model enables accurate and spatially explicit wind resource estimates at a very high spatial resolution.

Large-scale spatial variability of eight soil chemical properties within paddy fields

Rice is one of the most important crops in the world. It provides food for 40% of the world’s population and 60% of China’s population. Soil nutrients have a significant impact on both agriculture and the environment, particularly with regards to soil fertility, soil quality, and rice production. However, minimal research has been conducted to address spatial patterns of soil nutrients at a large scale within paddy fields. This information is crucial for improving not only soil nutrient management, but also rice yields. Soil surface samples (0–20 cm of plough depth, totalling 8890) were collected from paddy fields to determine the spatial variability of eight soil chemical properties (soil organic carbon (SOC), total nitrogen (TN), available N (AN), total phosphorus (TP), available P (AP), total potassium (TK), rapidly available K (RAK), and slowly available K (SAK)). Inverse distance weighting (IDW) and ordinary kriging methods were applied to produce a continuous soil nutrient surface. Results indicated that soil chemical properties vary substantially. Paddy fields were characterized by high mean concentrations of SOC (19.7 g kg−1), TN (1.91 g kg−1), and AN (164.7 mg kg−1), implying that no additional C and N fertilizer was needed in regions with high SOC and N. SAK and AP demonstrated moderate (36.9%) and weak (96.2%) spatial dependence, respectively, while strong spatial dependence (10.1–13.5%) was observed across the remaining six soil chemical properties. Regional distribution maps of soil chemical properties were produced and ordinary kriging methods interpolated more accurately than the IDW method.