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Because mangroves store greater amounts of carbon (C) per area than any other terrestrial ecosystem, conservation of mangrove forests on a global scale represents a potentially meaningful strategy for mitigating atmospheric greenhouse-gas (GHG) emissions. However, analyses of how coastal ecosystems influence the global C cycle also require the mapping of ecosystem area across the Earth’s surface to estimate C storage and flux (movement) in order to compare how different ecosystem types may mitigate GHG enrichment in the atmosphere. In this paper, we propose a new framework based on diverse coastal morphology (that is, different coastal environmental settings resulting from how rivers, tides, waves, and climate have shaped coastal landforms) to explain global variations in mangrove C storage, using soil organic carbon (SOC) as a model to more accurately determine mangrove contributions to global C dynamics. We present, to the best of our knowledge, the first global mangrove area estimate occupying distinct coastal environmental settings, comparing the role of terrigenous and carbonate settings as global “blue carbon” hotspots. C storage in deltaic settings has been overestimated, while SOC stocks in carbonate settings have been underestimated by up to 50%. We encourage the scientific community, which has largely focused on blue carbon estimates, to incorporate coastal environmental settings into their evaluations of C stocks, to obtain more robust estimates of global C stocks.

Assessing the spatial dynamics of soil organic carbon (SOC) is essential for carbon monitoring. Since variability of SOC is mainly attributed to biophysical land surface variables, integrating a compressive set of such indices may support the pursuit of an optimum set of predictor variables. Therefore, this study was aimed at predicting the spatial distribution of SOC in relation to remotely sensed variables and other covariates. Hence, the land surface variables were combined from remote sensing, topographic, and soil spectral sources. Moreover, the most influential variables for prediction were selected using the random forest (RF) and classification and regression tree (CART). The results indicated that the RF model has good prediction performance with corresponding R2 and root-mean-square error (RMSE) values of 0.96 and 0.91 mg·g−1, respectively. The distribution of SOC content showed variability across landforms (CV = 78.67%), land use (CV = 93%), and lithology (CV = 64.67%). Forestland had the highest SOC (13.60 mg·g−1) followed by agriculture (10.43 mg·g−1), urban (9.74 mg·g−1), and water body (4.55 mg·g−1) land uses. Furthermore, soils developed in bauxite and laterite lithology had the highest SOC content (14.69 mg·g−1). The SOC content was remarkably lower in soils developed in sandstones; however, the values obtained in soils from the rest of the lithologies could not be significantly differentiated. The mean SOC concentration was 11.70 mg·g−1, where the majority of soils in the study area were classified as highly humus and extremely humus. The soils with the highest SOC content (extremely humus) were distributed in the mountainous regions of the study area. The biophysical land surface indices, brightness removed vegetation indices, topographic indices, and soil spectral bands were the most influential predictors of SOC in the study area. The spatial variability of SOC may be influenced by landform, land use, and lithology of the study area. Remotely sensed predictors including land moisture, land surface temperature, and built-up indices added valuable information for the prediction of SOC. Hence, the land surface indices may provide new insights into SOC modeling in complex landscapes of warm subtropical urban regions.

(1) Background: The aim of this paper was to study landslide susceptibility mapping based on interpretable machine learning from the perspective of topography differentiation. (2) Methods: This paper selects three counties (Chengkou, Wushan and Wuxi counties) in northeastern Chongqing, delineated as the corrosion layered high and middle mountain region (Zone I), and three counties (Wulong, Pengshui and Shizhu counties) in southeastern Chongqing, delineated as the middle mountainous region of strong karst gorges (Zone II), as the study area. This study used a Bayesian optimization algorithm to optimize the parameters of the LightGBM and XGBoost models and construct evaluation models for each of the two regions. The model with high accuracy was selected according to the accuracy of the evaluation indicators in order to establish the landslide susceptibility mapping. The SHAP algorithm was then used to explore the landslide formation mechanisms of different landforms from both a global and local perspective. (3) Results: The AUC values for the test set in the LightGBM mode for Zones I and II are 0.8525 and 0.8859, respectively, and those for the test set in the XGBoost model are 0.8214 and 0.8375, respectively. This shows that LightGBM has a high prediction accuracy with regard to both landforms. Under the two different landform types, the elevation, land use, incision depth, distance from road and the average annual rainfall were the common dominant factors contributing most to decision making at both sites; the distance from a fault and the distance from the river have different degrees of influence under different landform types. (4) Conclusions: the optimized LightGBM-SHAP model is suitable for the analysis of landslide susceptibility in two types of landscapes, namely the corrosion layered high and middle mountain region, and the middle mountainous region of strong karst gorges, and can be used to explore the internal decision-making mechanism of the model at both the global and local levels, which makes the landslide susceptibility prediction results more realistic and transparent. This is beneficial to the selection of a landslide susceptibility index system and the early prevention and control of landslide hazards, and can provide a reference for the prediction of potential landslide hazard-prone areas and interpretable machine learning research.

We present an overview of tsunami occurrences based on an analysis of a global database of tsunamis for the period 1900–2020. We evaluate the geographic and statistical distribution of various tsunami source mechanisms, high-fatality tsunamis, maximum water heights (MWHs) of tsunamis, and possible biases in the observation and recording of tsunami events. We enhance a global statistical overview with case studies from Indonesia, where tsunamis are generated from a diverse range of sources, including subduction zones, crustal faults, landslides, and volcanic islands. While 80% of global recorded tsunamis during 1900–2020 have been attributed to earthquake sources, the median MWH of earthquake tsunamis is just 0.4 m. In contrast, the median water height of landslide tsunamis is 4 m. Landslides have caused or contributed to 24% of fatal tsunamis. During 1900–2020, more tsunamis with water heights > 1 m occurred in Indonesia than in any other country. In this region fatal tsunamis are caused by subduction zone earthquakes, landslides, volcanos, and intraplate crustal earthquakes. Landslide and volcano tsunami sources, as well as coastal landforms such as narrow embayments have caused high local maximum water heights and numerous fatalities in Indonesia. Tsunami hazards are increased in this region due to the densely populated and extensive coastal zones, as well as sea level rise from polar ice melt and local subsidence. Interrelated and often extreme natural hazards in this region present both an opportunity and a need to better understand a broader range of tsunami processes.

Strong earthquakes along subduction zones are often devastating events, but sparse records along some tectonic margins limit our understanding of seismic hazards. Constraining shaking intensities is critical, especially in subduction zones with infrequent but large‐magnitude earthquakes like the Cascadia Subduction Zone (CSZ), where the lack of recorded ground motions has led to uncertainty in the severity and potential impacts of future earthquakes. Here we fill this observational gap with a novel inventory of quantitative estimates of past shaking intensities from geotechnical modeling of coastal landforms. One hundred fifty‐four deep‐seated landslides and 65 fragile geologic features constrain minimum and maximum peak ground accelerations, respectively. These estimates are broadly consistent with model predictions of M9 ruptures, suggesting strong shaking of 0.4–0.8 g during past CSZ earthquakes. Local discrepancies between our geologic shaking constraints and earthquake simulations may inform past rupture behavior, leading to better predictions of shaking intensity for future earthquakes. Plain Language Summary Strong subduction zone earthquakes are a major hazard capable of generating damaging ground shaking and landslides across widespread regions. In subduction zones with few or no observations of recent events, such as the Cascadia Subduction Zone (CSZ) offshore the Pacific Northwest U.S. and Canada, the severity of these hazards are particularly uncertain. Here we develop a methodology for estimating shaking intensities from past earthquakes using coastal landslides. Modeling a range of representative coastal hillslopes allows us to identify landslides most likely triggered by past earthquakes, as well as an estimate of the minimum shaking intensity during those events in each location. Minimum shaking intensities from landslides are combined with a complementary set of maximum shaking intensity estimates from intact sea stacks to comprehensively constrain shaking intensities along much of the CSZ. Although these shaking estimates mostly agree with recent earthquake simulations in the region, local discrepancies may indicate variations in past earthquake rupture style. Key Points Modeling of coastal landslides and fragile geologic features constrain shaking intensity from past earthquakes Peak ground accelerations from past Cascadia Subduction Zone earthquakes range from ∼0.4 to 0.8 g across much of the margin Along the central Cascadia coastline, our results suggest stronger shaking has occurred than some earthquake simulations predict

Given their relatively small area, mangroves and their organic sediments are of disproportionate importance to global carbon sequestration and carbon storage. Peat deposition and preservation allows some mangroves to accrete vertically and keep pace with sea-level rise by growing on their own root remains. In this study we show that mangroves in desert inlets in the coasts of the Baja California have been accumulating root peat for nearly 2,000 y and harbor a belowground carbon content of 900–34,00 Mg C/ha, with an average value of 1,130 (± 128) Mg C/ha, and a belowground carbon accumulation similar to that found under some of the tallest tropical mangroves in the Mexican Pacific coast. The depth–age curve for the mangrove sediments of Baja California indicates that sea level in the peninsula has been rising at a mean rate of 0.70 mm/y (± 0.07) during the last 17 centuries, a value similar to the rates of sea-level rise estimated for the Caribbean during a comparable period. By accreting on their own accumulated peat, these desert mangroves store large amounts of carbon in their sediments. We estimate that mangroves and halophyte scrubs in Mexico’s arid northwest, with less than 1% of the terrestrial area, store in their belowground sediments around 28% of the total belowground carbon pool of the whole region.

Here, a geomorphological map of Horseshoe Island, which is one of the most ice-free islands in Marguerite Bay of the Antarctic Peninsula, is provided. The landforms on the island were mapped by using Google Earth images. Field reconnaissance of the landforms was carried out in March 2018. The island is subdivided into three major geomorphologically different sectors. The northern sector is mostly covered by a remnant of a non-erosive ice cap and has limited glacial landforms and deposits. The central sector is rich in terms of glacial and periglacial landforms and deposits. Glaciers are still sculpting the southern sector and it has extensive features of glacial erosion and deposition. The most common landforms on the island are talus cones, moraines, patterned ground, and raised beaches. The geomorphological map of the island will be a useful base for further geomorphic and/or glaciologic research in this climate-sensitive region.

The semi-automated extraction of flat surface landforms was carried out, merging a GIS tools application and a geomorphic analysis. The study focuses on seven areas in southern Italy, characterized by different physical landscapes, using a 5 m resolution digital elevation model (DEM). The GIS application allowed the selection of polygonal areas of flat surfaces from diverse arrays of landforms and was implemented using a filtering process to minimize noises. Subsequently, the mean elevation and mean slope of the detected surfaces were extracted and visualized using scatter plots, which helped in determining the elevation ranges and average slope angles for various flat-floored and terraced surfaces. To enhance the reliability of the results, a detailed geomorphic analysis and field survey were conducted to differentiate between fluvial and marine flat surfaces across different physical landscapes. This comprehensive approach included statistical analyses and comparisons with the existing literature to validate the identified flat surfaces, ensuring the accuracy and reliability of the semi-automated extraction procedure. The integration of GIS technology with field investigations not only streamlines the detection of flat landforms but also contributes to a deeper understanding of their geomorphic characteristics, ultimately enhancing geomorphic analysis efficiency.

The advent of geomatic techniques and novel sensors has opened the road to new approaches in mapping, including morphological ones. The evolution of a land portion and its graphical representation constitutes a fundamental aspect for scientific and land planning purposes. In this context, new paradigms for geomorphological mapping, which are useful for modernizing traditional, geomorphological mapping, become necessary for the creation of scalable digital representation of processes and landforms. A fully remote mapping approach, based on multi-source and multi-sensor applications, was implemented for the recognition of landforms and processes. This methodology was applied to a study site located in central Italy, characterized by the presence of ‘calanchi’ (i.e., badlands). Considering primarily the increasing availability of regional LiDAR products, an automated landform classification, i.e., Geomorphons, was adopted to map landforms at the slope scale. Simultaneously, by collecting and digitizing a time-series of historical orthoimages, a multi-temporal analysis was performed. Finally, surveying the area with an unmanned aerial vehicle, exploiting the high-resolution digital terrain model and orthoimage, a local-scale geomorphological map was produced. The proposed approach has proven to be well capable of identifying the variety of processes acting on the pilot area, identifying various genetic types of geomorphic processes with a nested hierarchy, where runoff-associated landforms coexist with gravitational ones. Large ancient mass movement characterizes the upper part of the basin, forming deep-seated gravity deformation, highly remodeled by a set of widespread runoff features forming rills, gullies, and secondary shallow landslides. The extended badlands areas imposed on Plio-Pleistocene clays are typically affected by sheet wash and rill and gully erosion causing high potential of sediment loss and the occurrence of earth- and mudflows, often interfering and affecting agricultural areas and anthropic elements. This approach guarantees a multi-scale and multi-temporal cartographic model for a full-coverage representation of landforms, representing a useful tool for land planning purposes.

Solar energy development is a significant driver of land‐use change worldwide, and desert ecosystems are particularly well suited to energy production because of their high insolation rates. Deserts are also characterized by uncertain rainfall, high species endemism, and distinct landforms that vary in geophysical properties. Weather and physical features that differ across landforms interact with shade and water runoff regimes imposed by solar panels, creating novel microhabitats that influence biotic communities. Endemic species may be particularly affected because they often have limited distributions, narrow climatic envelopes, or specialized life histories. We used experimental panels to simulate the effects of solar development on microhabitats and annual plant communities present on gravelly bajada and caliche pan habitat, two common habitat types in California's Mojave Desert. We evaluated soils and microclimatic conditions and measured community response under panels and in the open for seven years (2012–2018). We found that differences in site characteristics and weather affected the ecological impact of panels on the annual plant community. Panel shade tended to increase species richness on the more stressful caliche pan habitat, and this effect was strongest in dry years. Shade effects on diversity and abundance also tended to be positive or neutral on caliche pan habitat. On gravelly bajada habitat, panel shade did not significantly affect richness or diversity and tended to decrease plant abundance. Panel runoff rarely affected richness or diversity on either habitat type, but effects on abundance tended to be negative—suggesting that panel rain shadows were more important than runoff from low‐volume rain events. These results demonstrate that the ecological consequences of solar development can vary over space and time, and suggest that a nuanced approach will be needed to predict impacts across desert landforms differing in physical characteristics.