Local-scale landslide susceptibility mapping using the B-GeoSVC model

Analyzing the spatiotemporal characteristics of meteorological droughts (MD) and agricultural droughts (AD) and their propagation in different climate zones is important for effective drought management, climate adaptation, and food security. This study takes a unique approach by comparing irrigated and rainfed croplands. A comprehensive framework is developed using drought indices, statistical analysis, trend tests, and wavelet transforms. The spatiotemporal evolution patterns, trends, and correlations of MD and AD in Xinjiang and the Middle-lower Yangtze Plain (MYP) are investigated. The main results showed that severe MD events (e.g., Xinjiang 2005–2009 and MYP 2004–2009) significantly impacted rainfed agricultural systems, leading to a decline in vegetation condition. Long-term irrigation can substantially alleviate AD under MD conditions. From 2000 to 2019, AD on irrigated croplands in Xinjiang continuously improved, while rainfed croplands deteriorated significantly during MD events. In contrast, although overall AD in MYP was mitigated, the benefits of irrigation were only evident during severe AD periods and weakened after 2013. Correlation and wavelet analyses revealed different drought propagation mechanisms between irrigated and rainfed croplands, highlighting the key role of local climate conditions and spatial heterogeneity in determining irrigation efficiency. The findings provide important guidance for optimizing drought management strategies, agricultural planning, and sustainable water resource management.

This study examines the effect of oxygen concentration and sputtering power on the surface morphology of TiO2 thin films deposited by DC reactive magnetron sputtering. Surface roughness parameters were obtained using MountainsMap® software(10.2) from SEM images, while fractal dimensions and texture descriptors were extracted via Python-based image processing. Fractal dimension was calculated using the box-counting method applied to binarized images with multiple threshold levels, and texture analysis employed Gray-Level Co-occurrence Matrix (GLCM) statistics to capture local anisotropies and spatial heterogeneity. Four samples were analyzed, previously prepared with oxygen concentrations of 50% and 75%, and sputtering powers of 500 W and 1000 W. The results have shown that films deposited at higher oxygen levels and sputtering powers exhibited increased roughness, higher fractal dimensions, and stronger GLCM contrast, indicating more complex and heterogeneous surface structures. Conversely, films produced at lower oxygen and power settings showed smoother, more isotropic surfaces with lower complexity. This integrated analysis framework links deposition parameters with morphological characteristics, enhancing the understanding of surface evolution and enabling better control of TiO2 thin film properties.

Wetlands are one of the major contributors of methane (CH4) emissions to the atmosphere and the intensity of emissions is driven by local environmental variables and spatial heterogeneity. Peatlands are a major wetland class and there are numerous studies that provide estimates of methane emissions at chamber or eddy covariance scales, but these are not often aggregated to the site/ecosystem scale. This study provides a robust approach to map dominant vegetation communities and to use these areas to upscale methane fluxes from chamber to site scale using a simple weighted-area approach. The proposed methodology was tested at three peatlands in Ireland over a duration of 2 years. The annual vegetation maps showed an accuracy ranging from 83 to 99% for near-natural to degraded sites respectively. The upscaled fluxes were highest (2.25 and 3.80 gC m−2 y−1) at the near-natural site and the rehabilitation (0.17 and 0.31 gC m−2 y−1), degraded (0.15 and 0.27 gC m−2 y−1) site emissions were close to net-zero throughout the study duration. Overall, the easy to implement methodology proposed in this study can be applied across various landuse types to assess the impact of peatland rehabilitation on methane emissions by mapping ecological change.

Regionalisation of climate variability used for modelling the dispersal of genetically modified oil seed rape in Northern Germany

The kinetics of contact processes are determined by the interplay among local mass transfer mechanisms, spatial heterogeneity, and segregation. Determining the macroscopic behavior of a wide variety of phenomena across the disciplines requires linking reaction times to the statistical properties of spatially fluctuating quantities. We formulate the dynamics of advected agents interacting with segregated immobile components in terms of a chemical continuous-time random walk. The inter-reaction times result from the first-passage times of mobile species to and across reactive regions, and available immobile reactants undergo a restart procedure. Segregation leads to memory effects and enhances the role of concentration fluctuations in the large-scale dynamics.

Hydrology induces intraspecific variation in freshwater fish morphology under contemporary and future climate scenarios

<p>Central Java plays an important role in Indonesia’s food security, ranking second nationally in the 2023 Food Security Index (FSI). However, nearly 45% of its districts/cities fall below the provincial average, reflecting spatial disparities. This study applies the Mixed Geographically Weighted Regression (MGWR) method to model the factors influencing FSI in Central Java by considering global and local spatial heterogeneity. Six clusters were formed based on similar characteristics. The MGWR model identifies that the factor of households not having access to clean water has a global negative effect which contributes 0.1710 points in decreasing the FSI, while population density is the dominant local factor that has a significant negative effect on the FSI in 21 districts/cities, covering approximately 60% of the area in Central Java. The MGWR model using a fixed Gaussian kernel outperforms global regression and GWR, with the lowest AIC, highest (93.11%), and a MAPE of 1.00838%. </p>

Resumo: A adoção crescente de abordagens territoriais no processo de elaboração de políticas públicas no Brasil tem estimulado uma série de estudos sobre o tema. A heterogeneidade espacial local e a complexidade inerente dos sistemas sociais vinculados ao território sugerem o uso de métodos quantitativos multivariados como ferramentas de auxílio ao planejamento dessas políticas públicas. Este trabalho avaliou características habitacionais de domicílios rurais sergipanos a partir de dados agregados por setor censitário rural de forma a subsidiar os formuladores de políticas públicas em suas decisões sobre a distribuição espacial de recursos direcionados à habitação. Foram analisados diferentes recortes territoriais: municípios, Territórios de Planejamento do Estado e Territórios da Cidadania, utilizando dados do Censo Demográfico 2010. O estudo foi conduzido através da análise fatorial e análise de agrupamentos. Foram identificados três agrupamentos de setores rurais representativos do conjunto de dados estudados. Esses grupos estão presentes nos três recortes territoriais e diferenciaram-se, principalmente, com relação às características de domicílios associadas ao cômodo banheiro e ao destino do lixo.

Structural equation models are highly suited for evaluating ecosystem-level hypotheses, but to be effective, structural equation models need to be able to accommodate spatial and temporal data. Here, the importance of different abiotic and biotic drivers on wet heathland vegetation is investigated using a spatio-temporal structural equation model in a hierarchical Bayesian framework. Ecological data from 39 Danish sites, each with several wet heathland plots, were sampled in the period 2007–2014. Including resampling over the years, 1322 plots were sampled. Plant cover was measured using the pin-point method and the joint distribution of the key plant species in the wet heathland ecosystem, Erica tetralix, Calluna vulgaris, Molinia caerulea, and an aggregate class of other higher plants was estimated assuming a Dirichlet-multinomial mixture distribution. The investigated drivers of wet heathland vegetation include nitrogen deposition, soil type, pH, precipitation and grazing. Generally, the two dwarf shrubs, E. tetralix and C. vulgaris, responded in qualitatively similar ways to the abiotic variables and qualitatively oppositely to the way the grass M. caerulea and the aggregate class of other higher plants. Furthermore, the spatial effects were qualitatively similar to the temporal effects. The two dwarf shrub species were most likely positively affected by nitrogen deposition, soil pH, sandy soils, low precipitation, and the absence of grazing. The study demonstrated that important insight on ecosystem dynamics and regulation can be obtained by spatial and temporal structural equation modeling in a hierarchical Bayesian framework and that the proper statistical modeling of the joint species abundance is a key feature of such models. Furthermore, the advantages of partitioning different types of uncertainties become clear when the fitted structural equation model is used for predictive purposes at a specific site.

Hierarchical Bayesian modelling for spatial analysis of the number of avalanche occurrences at the scale of the township

Understanding the relationship between mixed land use and urban vibrancy is vital in advanced urban planning applications. This study presents a Bayesian spatially varying coefficient (SVC) model to explore the spatially nonstationary relationship between mixed land use and urban vibrancy after controlling for other factors. We first use the convolutional conditional autoregressive prior to accommodate the ecological bias resulting from unobserved confounders. Then we develop our approach in the case of a single predictor to allow the spatially varying coefficient process. We further introduce a type of the Bayesian SVC model that considers the stratified heterogeneity of the outcome, allowing the coefficients to simultaneously vary at the local and subregion level. We illustrate the proposed model by conducting a case study in Shenzhen using mobile phone data, an officially registered point-of-interest (POI) dataset, and several supplementary datasets. The model evaluation results show that including spatially unstructured and structured component combinations can improve the model's fitness and predictive ability; additionally, considering spatial stratified heterogeneity can further enhance the model's performance. Our findings provide an alternative for measuring the variable local-scale association between mixed-use and urban vibrancy and offer new insights that broaden the fields of environmental science and spatial statistics.

Core Ideas HYDRUS‐1D code was adapted to solve the diffusion wave equation for overland flow. The modified code can simulate transport and fate of many different kinds of solute. The model can simulate physically nonequilibrium overland flow and transport processes. Surface runoff is commonly described in numerical models using either the diffusion wave or kinematic wave equations, which assume that surface runoff occurs as sheet flow with a uniform depth and velocity across the slope. In reality, overland water flow and transport processes are rarely uniform. Local soil topography, vegetation, and spatial soil heterogeneity control directions and magnitudes of water fluxes. These spatially varying surface characteristics can generate deviations from sheet flow such as physical nonequilibrium flow and transport processes that occur only on a limited fraction of the soil surface. In this study, we first adapted the HYDRUS‐1D model to solve the diffusion wave equation for overland flow at the soil surface. The numerical results obtained by the new model produced an excellent agreement with an analytical solution for the kinematic wave equation. Additional model tests further demonstrated the applicability of the adapted model to simulate the transport and fate of many different solutes (non‐adsorbing tracers, nutrients, pesticides, and microbes) that undergo equilibrium and/or kinetic sorption and desorption and first‐ or zero‐order reactions. HYDRUS‐1D includes a hierarchical series of models of increasing complexity to account for both uniform and physical nonequilibrium flow and transport, e.g., dual‐porosity and dual‐permeability models, up to a dual‐permeability model with immobile water. This same conceptualization was adapted to simulate physical nonequilibrium overland flow and transport at the soil surface. The developed model improves our ability to describe nonequilibrium overland flow and transport processes and our understanding of factors that cause this behavior.

Soil datasets with outliers lead to inaccurate farm-level digital soil mapping (DSM) results. Existing methods identify potential outliers in soil datasets based on expert experience or simple statistics that neglect the geographical characteristics of soil. In this paper, a novel potential outlier recognition method was developed from the perspective of geographical context. First, spatial search distance was automatically determined by the spatial distance among soil samples. Second, similarities of adjacent soil samples and the local spatial variation level were comprehensively considered to calculate outlier scores. Finally, a frequency histogram of outlier scores was generated to determine a suitable threshold for recognizing potential abnormal samples. To validate the proposed method, it was compared to Lambda and Box-Plot methods, and the ordinary kriging method was used to map five soil properties, including pH, soil organic matter, total nitrogen, available phosphorus and available potassium, in an agricultural region. Then, a synthetic study using artificially contaminated DEM data was also conducted. The comparative experiment shows that the proposed method is better able to recognize potential outliers by mining the local spatial structure, as indicated by lower mean absolute error (MAE) and root mean square error (RMSE) values. It can be concluded that consideration of local spatial autocorrelation and heterogeneity is helpful in recognizing potential outliers.

Transport affordability defined as the share of household income devoted to transport expenditure is a key dimension of urban equity and social inclusion, particularly in contexts characterised by spatial inequality and income disparities. This study examines provincial variation in public transport affordability across South Africa using a hierarchical Bayesian regression framework applied to province–year data from 2015 to 2022 (n = 72). Affordability is operationalised as a transport cost burden, with higher values indicating a greater proportion of household income spent on transport, and is modelled as a function of household income, trip frequency, household population, and total provincial employment, with province-level random intercepts capturing unobserved regional heterogeneity. The results indicate that household income is negatively associated with transport cost burden, suggesting that provinces with higher average income devote a smaller share of income to transport and therefore experience better affordability. In contrast, household population and aggregate provincial employment are positively associated with transport cost burden, reflecting higher overall mobility and commuting demands in larger and more economically active provinces rather than improved affordability. Trip frequency shows no statistically meaningful association with affordability once household composition and income capacity are accounted for. After accounting for observed characteristics, between-province variation is limited, indicating that affordability dynamics are broadly similar across provinces over the study period. Methodologically, the hierarchical Bayesian framework enables partial pooling across provinces and supports probabilistic inference through credible intervals, thereby improving the stability of estimates in a small-sample multilevel context. While the analysis is associational rather than causal, the findings provide policy-relevant evidence for monitoring transport affordability, including benchmarking the prevalence of affordability burdens relative to the commonly used 10% threshold.

Landslide susceptibility map delineates the potential zones for landslides occurrence. The paper presents a statistical approach through spatial data analysis in GIS for landslide susceptibility mapping in parts of Sikkim Himalaya. Six important causative factors for landslide occurrences were selected and corresponding thematic data layers were prepared in GIS. Topographic maps, satellite image, field data and published maps constitute the input data for thematic layer preparation. Numerical weights for different categories of these factors were determined based on a statistical approach and the weighted thematic layers were integrated in GIS environment to generate the landslide susceptibility map of the area. The landslide susceptibility map classifies the area into five different landslide susceptible zones i.e., very high, high, moderate, low and very low. This map was validated using the existing landslide distribution in the area.