This article investigates the influence of climatic and geographical characteristics in south-western region of Bangladesh on the temporal dynamics of post-cyclone impacts, with a critical focus on biophysical contexts. By quantitatively assessing the environmental consequences of cyclones Amphan (2020), Yaas (2021), Mocha (2023) and Remal (2024), the study offers a nuanced understanding of flood damage extent and vegetation health, measured through advanced remote sensing and geospatial techniques. Using Sentinel-1 (GRD) and Sentinel-2 (MSI) satellite imageries from 2020 to 2024, the study has examined post-cyclone changes of vegetation health and flood damage extent using available indices such as Normalized Difference Vegetation Index (NDVI) and Soil-Adjusted Vegetation Index (SAVI). The results exhibit substantial spatial disparities occurred due to the cyclone events, with NDVI variations ranging from - 0.124 to 0.546 (Amphan), - 0.033 to 0.498 (Mocha), - 0.086 to 0.458 (Yaas), and - 0.061 to 0.362 (Remal), indicating significant ecological stress. Corresponding SAVI changes ranged from - 0.001 to 0.396 (Amphan), - 0.029 to 0.338 (Mocha), - 0.002 to 0.345 (Yaas), and - 0.0524 to 0.269 (Remal). Negative indices underscore potential vegetation degradation, while positive values indicate resilience or post-cyclone recovery. Furthermore, flood damage analysis indicates to a more severe and unevenly distributed impact than previously recognized, particularly in areas with pre-existing vulnerabilities with the damage extent variations between - 35.918 to - 2.0093 (Amphan), - 35.334 to - 4.4059 (Mocha), - 34.806 to - 0.94921 (Yaas), and - 48.469 to 0.00255 (Remal). The Geographically Weighted Regression (GWR), model demonstrates a robust relationship, with r2 values of 0.894, 0.889, 0.899, and 0.95, indicating that approximately 85% of the ecological changes are driven by fluctuations of vegetation due to flood. The insight from this research provides a foundation of flood damage assessment technique occurred by cyclones in a short span of time to aid immediate policy recommendations to enhance resilience in remote areas of the coastal regions of Bangladesh.

The distribution of total soil nitrogen (TSN) and total soil phosphorus (TSP) plays a pivotal role in shaping soil quality, fertility, agricultural practices, and environmental balance, especially in ecologically sensitive regions like the North-Western Himalayas (NWH). The primary objectives of this study were to contribute to clarify the impact and the rationale of various land uses on the spatial variation of TSN and TSP in the corresponding soils. This study aimed to explore the relation of TSN and TSP distribution in NWH soils with various factors like landscape physiography and soil physical and chemical properties using random sampling and geostatistical analyses. Employing random sampling, 300 soil surface samples (at a depth of 0-20 cm) were collected across various 500 m x 500 m grids from agriculture, horticulture, forest and fallow lands in the NWH region. The spatial land heterogeneity of TSN and TSP were systematically analyzed using standard statistical and geostatistical approaches (Gaussian, spherical, exponential, and linear). Results revealed a decreasing order of TSN and TSP levels i.e., horticulture (0.410 and 0.723 mg/kg) > agriculture (0.314 and 0.597 mg/kg) > forest (0.236 and 0.572 mg/kg) > fallow (0.275 and 0.342 mg/kg). Stepwise multiple regression results demonstrated a correlation between TSN and soil organic carbon (SOC), while TSP was correlated with soil organic carbon (SOC) and fine-grained soil particles. Nugget % values indicated the following spatial variability for TSN: agricultural (1.4) > horticultural (3.2) > forest (3.9) > fallow land (4.8) > mixed land (5.8), whereas the spatial variability of TSP showed a similar trend for all land uses. The optimized conceptual framework and isotropy models varied for TSN and TSP on dependence on land use type. The results of this study revealed the spatial patterns and land userelated variations and improved the prediction of nutrient distribution, so contributing an optimized conceptual framework for future studies. Finally, this study provided crucial insights to enhance soil quality, fertility, agricultural sustainability, and environmental equilibrium in the ecologically fragile NWH region, contributing to solve a significant research gap in the global understanding of soil dynamics.

Classifying a given landscape on the basis of its susceptibility to surface processes is a standard procedure in low to mid-latitudes. Conversely, these procedures have hardly been explored in periglacial regions. However, global warming is radically changing this situation and will change it even more in the future. For this reason, un-derstanding the spatial and temporal dynamics of geomorphological processes in peri-arctic environments can be crucial to make informed decisions in such unstable environments and shed light on what changes may follow at lower latitudes. For this reason, here we explored the use of data-driven models capable of recognizing locations prone to develop retrogressive thaw slumps (RTSs) and/or active layer detachments (ALDs). These are cryo-spheric hazards induced by permafrost degradation, and their development can negatively affect human set-tlements or infrastructure, change the sediment budget and release greenhouse gases. Specifically, we test a binomial Generalized Additive Modeling structure to estimate the probability of RST and ALD occurrences in the North sector of the Alaskan territory. The results we obtain show that our binary classifiers can accurately recognize locations prone to RTS and ALD, in a number of goodness-of-fit (AUCRTS = 0.83; AUCALD = 0.86), random cross-validation (mean AUCRTS = 0.82; mean AUCALD = 0.86), and spatial cross-validation (mean AUCRTS = 0.74; mean AUCALD = 0.80) routines. Overall, our analytical protocol has been implemented to build an open-source tool scripted in Python where all the operational steps are automatized for anyone to replicate the same experiment. Our protocol allows one to access cloud-stored information, pre-process it, and download it locally to be integrated for spatial predictive purposes.

Soil moisture has a pronounced effect on earth surface processes. Global soil moisture is strongly driven by climate, whereas at finer scales, the role of non-climatic drivers becomes more important. We provide insights into the significance of soil and land surface properties in landscape-scale soil moisture variation by utilizing high-resolution light detection and ranging (LiDAR) data and extensive field investigations. The data consist of 1200 study plots located in a high-latitude landscape of mountain tundra in north-western Finland. We measured the plots three times during growing season 2016 with a hand-held time-domain reflectometry sensor. To model soil moisture and its temporal variation, we used four statistical modelling methods: generalized linear models, generalized additive models, boosted regression trees, and random forests. The model fit of the soil moisture models were R-2 = 0.60 and root mean square error (RMSE) 8.04 VWC% on average, while the temporal variation models showed a lower fit of R-2 = 0.25 and RMSE 13.11 CV%. The predictive performances for the former were R-2 = 0.47 and RMSE 9.34 VWC%, and for the latter R-2 = 0.01 and RMSE 15.29 CV%. Results were similar across the modelling methods, demonstrating a consistent pattern. Soil moisture and its temporal variation showed strong heterogeneity over short distances; therefore, soil moisture modelling benefits from high-resolution predictors, such as LiDAR based variables. In the soil moisture models, the strongest predictor was SAGA (System for Automated Geoscientific Analyses) wetness index (SWI), based on a 1m(2) digital terrain model derived from LiDAR data, which outperformed soil predictors. Thus, our study supports the use of LiDAR based SWI in explaining fine-scale soil moisture variation. In the temporal variation models, the strongest predictor was the field-quantified organic layer depth variable. Our results show that spatial soil moisture predictions can be based on soil and land surface properties, yet the temporal models require further investigation. Copyright (c) 2017 John Wiley & Sons, Ltd.

The possible influence of permafrost degradation on the formation of debris flows in an area of the South Tyrolean Alps, Italy, was examined by comparing debris flow activity since 1983 with the modelled contemporary permafrost distribution. The study focused on the spatial congruence of new initiation zones and potentially marginal permafrost, which should be especially sensitive to climatic change and is presumed to be currently degrading. The results show that distinct changes in the spatial position of debris flow initiation areas mainly occurred at elevations above this marginal zone. Consequently, the changes detected in debris flow activity do not appear to have been influenced by atmospheric warming-induced degradation of permafrost. However, a link may exist to the thickening of the active layer caused by the melting of a glacier. Copyright (C) 2011 John Wiley & Sons, Ltd.

This paper provides a review of permafrost modelling advances, primarily since the 2003 permafrost conference in Zurich, Switzerland, with an emphasis on spatial permafrost models, in both arctic and high mountain environments. Models are categorised according to temporal, thermal and spatial criteria, and their approach to defining the relationship between climate, site surface conditions and permafrost status. The most significant recent advances include the expanding application of permafrost thermal models within spatial models, application of transient numerical thermal models within spatial models and incorporation of permafrost directly within global circulation model (GCM) land surface schemes. Future challenges for permafrost modelling will include establishing the appropriate level of integration required for accurate simulation of permafrost-climate interaction within GCMs, the integration of environmental change such as treeline migration into permafrost response to climate change projections, and parameterising the effects of sub-grid scale variability in surface processes and properties on small-scale (large area) spatial models. Copyright (c) 2008 John Wiley & Sons, Ltd.