Predicting soil thickness on soil mantled hillslopes

A mechanistic approach to include climate change and unplanned urban sprawl in landslide susceptibility maps

Empirical evidence shows that climate, deforestation and informal housing (i.e. unregulated construction practices typical of fast-growing developing countries) can increase landslide occurrence. However, these environmental changes have not been considered jointly and in a dynamic way in regional or national landslide susceptibility assessments. This gap might be due to a lack of models that can represent large areas (>100km²) in a computationally efficient way, while simultaneously considering the effect of rainfall infiltration, vegetation and housing. We therefore suggest a new method that uses a hillslope-scale mechanistic model to generate regional susceptibility maps under changing climate and informal urbanisation, which also accounts for existing uncertainties. An application in the Caribbean shows that the landslide susceptibility estimated with the new method and associated with a past rainfall-intensive hurricane identifies ~67.5 % of the landslides observed after that event. We subsequently demonstrate that the hypothetical expansion of informal housing (including deforestation) increases landslide susceptibility more (+20 %) than intensified rainstorms due to climate change (+6 %). However, their combined effect leads to a much greater landslide occurrence (up to +40 %) than if the two drivers were considered independently. Results demonstrate the importance of including both land cover and climate change in landslide susceptibility assessments. Furthermore, by modelling mechanistically the overlooked dynamics between urban growth and climate change, our methodology can provide quantitative information of the main landslide drivers (e.g. quantifying the relative impact of deforestation vs informal urbanisation) and locations where these drivers are or might become most detrimental for slope stability. Such information is often missing in data-scarce developing countries but is key for supporting national long-term environmental planning, for targeting financial efforts, as well as for fostering national or international investments for landslide mitigation.

Optimizing process-based models to predict current and future soil organic carbon stocks at high-resolution

From hillslope to small catchment scales (< 50 km²), soil carbon management and mitigation policies rely on estimates and projections of soil organic carbon (SOC) stocks. Here we apply a process-based modeling approach that parameterizes the MIcrobial-MIneral Carbon Stabilization (MIMICS) model with SOC measurements and remotely sensed environmental data from the Reynolds Creek Experimental Watershed in SW Idaho, USA. Calibrating model parameters reduced error between simulated and observed SOC stocks by 25%, relative to the initial parameter estimates and better captured local gradients in climate and productivity. The calibrated parameter ensemble was used to produce spatially continuous, high-resolution (10 m²) estimates of stocks and associated uncertainties of litter, microbial biomass, particulate, and protected SOC pools across the complex landscape. Subsequent projections of SOC response to idealized environmental disturbances illustrate the spatial complexity of potential SOC vulnerabilities across the watershed. Parametric uncertainty generated physicochemically protected soil C stocks that varied by a mean factor of 4.4 × across individual locations in the watershed and a − 14.9 to + 20.4% range in potential SOC stock response to idealized disturbances, illustrating the need for additional measurements of soil carbon fractions and their turnover time to improve confidence in the MIMICS simulations of SOC dynamics.

GIS and Remote Sensing-Based Multi-Criteria Analysis for Delineation of Groundwater Potential Zones: A Case Study for Industrial Zones in Bangladesh

Groundwater is a crucial natural resource that varies in quality and quantity across Bangladesh. Increased population and urbanization place enormous demands on groundwater supplies, reducing both their quality and quantity. This research aimed to delineate the groundwater potential zone in the Gazipur district, Bangladesh, by integrating eleven thematic layers. Data and information were gathered from Landsat 8, the digital elevation model, the google earth engine, and several ancillary sources. A multi-criterion decision-making (MCDM) based analytical hierarchy process (AHP) was used in a GIS platform to estimate the groundwater potential index. The potential index values were finally classified into five sub-groups: very low, low, moderate, high, and very high to generate a groundwater water potential zone (GWPZ) map. The results show that groundwater potential in about 0.002% (0.026 km2) of the area is very low, 3.83% (63.18 km2) of the area is low, 56.2% (927.05 km2) of the area is medium, 39.25% (647.46 km2) of the area is high, and the rest 0.72% (11.82 km2) of the area is very high. The validation of GWPZ maps based on the groundwater level data at 20 observation wells showed an overall accuracy of 80%. In addition, the ROC curve showed 84% accuracy of GWPZ maps when validated with water inventory points across the study region. Overall, this study presents an easy and practical approach for identifying groundwater potential zones, which may help improve planning and sustainable groundwater resource management.

Impact of socio-economic environment and its interaction on the initial spread of COVID-19 in mainland China

Coronavirus disease 2019 (COVID-19) has strongly impacted society since it was first reported in mainland China in December 2020. Understanding its spread and consequence is crucial to pandemic control, yet difficult to achieve because we deal with a complex context of social environment and variable human behaviour. However, few efforts have been made to comprehensively analyse the socio-economic influences on viral spread and how it promotes the infection numbers in a region. Here we investigated the effect of socio-economic factors and found a strong linear relationship between the gross domestic product (GDP) and the cumulative number of confirmed COVID-19 cases with a high value of R2 (between 0.57 and 0.88). Structural equation models were constructed to further analyse the social-economic interaction mechanism of the spread of COVID-19. The results show that the total effect of GDP (0.87) on viral spread exceeds that of population influx (0.58) in the central cities of mainland China and that the spread mainly occurred through its interplay with other factors, such as socio-economic development. This evidence can be generalized as socio-economic factors can accelerate the spread of any infectious disease in a megacity environment. Thus, the world is in urgent need of a new plan to prepare for current and future pandemics.

Quantification of Cartilage Surface Degeneration by Curvature Analysis Using 3D Scanning in a Rabbit Model

OBJECTIVE

Accurate analysis to quantify cartilage morphology is critical for evaluating degenerative conditions in osteoarthritis (OA). Three-dimensional (3D) optical scanning provides 3D data for the entire cartilage surface; however, there is no consensus on how to quantify it. Our purpose was to validate a 3D method for evaluating spatiotemporal alterations in degenerative cartilages in a rabbit OA model by analyzing their curvatures at various stages of progression.

DESIGN

Twelve rabbits underwent anterior cruciate ligament transection (ACLT) unilaterally and were divided into 4 groups: 4 weeks control, 4 weeks OA, 8 weeks control, and 8 weeks OA. 3D scanning, India ink staining, and histological assessments were performed in all groups. In 3D curvature visualization, the surfaces of the condyles were divided into 8 areas. The standard deviations (SD) of mean curvatures from all vertices of condylar surfaces and subareas were calculated.

RESULTS

Regarding the site of OA change, curvature analysis was consistent with India ink scoring. The SD of mean curvature correlated strongly with the India ink Osteoarthritis Research Society International (OARSI) score. In curvature histograms, the curvature distribution in OA was more scattered than in control. Of the 8 areas, significant OA progression in the posterolateral part of the lateral condyle (L-PL) was observed at 4 weeks. The histology result was consistent with the 3D evaluation in terms of representative section.

CONCLUSIONS

This study demonstrated that 3D scanning with curvature analysis can quantify the severity of cartilage degeneration objectively. Furthermore, the L-PL was found to be the initial area where OA degeneration occurred in the rabbit ACLT model.

A High-Resolution, Random Forest Approach to Mapping Depth-to-Bedrock across Shallow Overburden and Post-Glacial Terrain

Regolith, or unconsolidated materials overlying bedrock, exists as an active zone for many geological, geomorphological, hydrological and ecological processes. This zone and its processes are foundational to wide-ranging human needs and activities such as water supply, mineral exploration, forest harvesting, agriculture, and engineered structures. Regolith thickness, or depth-to-bedrock (DTB), is typically unavailable or restricted to finer scale assessments because of the technical and cost limitations of traditional drilling, seismic, and ground-penetrating radar surveys. The objective of this study was to derive a high-resolution (10 m2) DTB model for the province of New Brunswick, Canada as a case study. This was accomplished by developing a DTB database from publicly available soil profiles, boreholes, drill holes, well logs, and outcrop transects (n = 203,238). A Random Forest model was produced by modeling the relationships between DTB measurements in the database to gridded datasets derived from both a LiDAR-derived digital elevation model and photo-interpreted surficial geology delineations. In developing the Random Forest model, DTB measurements were split 70:30 for model development and validation, respectively. The DTB model produced an R2 = 92.8%, MAE = 0.18 m, and RMSE = 0.61 m for the training, and an R2 = 80.3%, MAE = 0.18 m, and RMSE = 0.66 m for the validation data. This model provides an unprecedented resolution of DTB variance at a landscape scale. Additionally, the presented framework provides a fundamental understanding of regolith thickness across a post-glacial terrain, with potential application at the global scale.

Unraveling the drivers of intensified landslide regimes in Western Ghats, India

The Western Ghats (WG) mountain range in the Indian sub-continent is a biodiversity hotspot, now faces a severe threat to the valley population and ecosystem because of changing rainfall patterns and land-use changes. Here, we use the 2018-2019 landslide inventory data together with various geo-environmental factors and show that the landslide activity in the WG region is amplified by anthropogenic disturbances. We applied a generalized feature selection algorithm and a random forest susceptibility model to demonstrate the major topographic controls of landslides and the risk associated with them in the WG region. Our results show that road cutting and slopes modified to plantations are the strongest environmental variable (50% - 73% within 300 m buffer distance) related to the landslide patterns, whereas short-duration intense precipitation in the high elevated terrain, profile concavity, and stream power contributed to the initiation of landslides. The susceptibility models made for the present, and Global Climate Models (GCM) under the representative concentration pathway (RCP) 8.5 scenario predicts the vulnerable nature of WG for future climate extremes. Our results highlight the impacts of Anthropocene hazards and sensitivity of the WG ecosystem, and a greater focus therefore should be placed to reduce the vulnerability and increase preparedness for future events.

Deep Nitrate Accumulation in a Highly Weathered Subtropical Critical Zone Depends on the Regolith Structure and Planting Year

Nitrate accumulated deep (>100 cm) in the regolith (soil and saprolite) threatens groundwater quality, but most studies focus only on nitrate nearer the surface (<100 cm). Surface soil management versus regolith interactions affect deep nitrate leaching, but their combined impact remains unclear. This study measured how deep nitrate accumulation was affected by crop practices including orchard/cropland planting years, regolith structure, and soil properties in highly weathered subtropical red soils. Deep nitrate storage varied from 43.6 to 1116.3 kg ha^-1. Regolith thickness was positively correlated with nitrate storage (R² = 0.43, p < 0.05). Reticulated red clay (110-838 cm) had 81% of the accumulated nitrate and overlapped with 79% of the nitrate accumulation layer. All of the nitrate accumulation parameters (except for peak depth (PD)) generally increased with the planting years. The difference in peak nitrate concentration (9.0-20.0 mg kg^-1) with planting year gradient (3-58 years) varied by 2.2 times, and the difference in nitrate storage (43.6-425.7 kg ha^-1) varied by 9.8 times. Texture and pH explain 41.6% of the variation in nitrate concentration. As soil management practices interact with deeper regolith to control the spatial pattern of nitrate accumulation, vulnerable regions could be identified to avoid high accumulation.

Topographic controls of soil organic carbon on soil-mantled landscapes

Large uncertainties in global carbon (C) budgets stem from soil carbon estimates and associated challenges in distributing soil organic carbon (SOC) at local to landscape scales owing to lack of information on soil thickness and controls on SOC storage. Here we show that 94% of the fine-scale variation in total profile SOC within a 1.8 km² semi-arid catchment in Idaho, U.S.A. can be explained as a function of aspect and hillslope curvature when the entire vertical dimension of SOC is measured and fine-resolution (3 m) digital elevation models are utilized. Catchment SOC stocks below 0.3 m depth based on our SOC-curvature model account for >50% of the total SOC indicating substantial underestimation of stocks if sampled at shallower depths. A rapid assessment method introduced here also allows for accurate catchment-wide total SOC inventory estimation with a minimum of one soil pit and topographic data if spatial distribution of total profile SOC is not required. Comparison of multiple datasets shows generality in linear SOC-curvature and -soil thickness relationships at multiple scales. We conclude that mechanisms driving variations in carbon storage in hillslope catchment soils vary spatially at relatively small scales and can be described in a deterministic fashion given adequate topographic data.