Topographic Data Research Articles

Quantifying landscape change following catastrophic dam failures in Edenville and Sanford, Michigan, USA

AbstractDam failures due to changing hydroclimate and ageing infrastructure pose a significant threat to downstream river systems and communities. The detailed geomorphic effects of catastrophic dam failures are not well known because of a lack of high‐resolution topographic data before and after failures. On 19 May 2020, the 17‐m‐tall Edenville and 11‐m‐tall Sanford dams near Midland, Michigan, USA, failed as a result of significant rainfall over the preceding 2 days. We analysed the geomorphic impacts of these failures using a pre‐failure airborne lidar dataset and three uncrewed aerial vehicle (UAV)‐based lidar surveys collected 2 weeks, 3 months and 11 months after failure. Our survey following the dam failure revealed 47 100 ± 11 900 m3 of net floodplain erosion and 17 300 ± 4200 m3 of net deposition downstream of the Edenville and Sanford dam breaches, respectively. Over the year following failure, most geomorphic change was confined to new knickpoints migrating slowly through underlying glacial till substrate, with little change to riverbanks or surrounding floodplains. A lack of impounded reservoir sediment and antecedent downstream topography, including valley width and the location of the breaches relative to the river below the dams, contributed to relatively modest geomorphic changes despite the magnitude of water released. We provide insight into how landscapes are shaped by catastrophic floods, which are likely to become more common with ageing dams and a changing hydroclimate.

Monitoring Change and Recovery of an Embayed Beach in Response to Typhoon Storms Using UAV LiDAR

The monitoring of beach topographical changes and recovery processes under typhoon storm influence has primarily relied on traditional techniques that lack high spatial resolution. Therefore, we used an unmanned aerial vehicle light detection and ranging (UAV LiDAR) system to obtain the four time periods of topographic data from Tantou Beach, a sandy beach in Xiangshan County, Zhejiang Province, China, to explore beach topography and geomorphology in response to typhoon events. The UAV LiDAR data in four survey periods showed an overall vertical accuracy of approximately 5 cm. Based on the evaluated four time periods of the UAV LiDAR data, we created four corresponding DEMs for the beach. We calculated the DEM of difference (Dod), which showed that the erosion and siltation on Tantou Beach over different temporal scales had a significant alongshore zonal feature with a broad change range. The tidal level significantly impacted beach erosion and siltation changes. However, the storm surge did not affect the beach area above the spring high-tide level. After storms, siltation occurred above the spring high-tide zone. This study reveals the advantage of UAV LiDAR in monitoring beach changes and provides novel insights into the impacts of typhoon storms on coastal topographic and geomorphological change and recovery processes.

Spherical‐Harmonic Distribution Analysis of Coronae in Relation to Volcanic Features on Venus

AbstractVenus boasts an abundance of volcanoes and volcano‐like structures. Synthetic aperture radar images of the surface have revealed extensive evidence of volcanism, including lava flows and edifices. Volcanic activity is further supported by crater statistics, and analysis of topography and gravity data. Unique to Venus, coronae are quasi‐circular volcano‐tectonic features exhibiting diverse volcanic characteristics. Despite this, volcanism is often under‐represented in formation models. We identify a new subset of coronae that display topographic changes subsequent to the emplacement of lava flows within their fracture annuli, pointing to the critical role of volcanic and magmatic processes in the formation of these coronae. Through spherical‐harmonic distribution analysis, we find that this new subset is spatially related to the full coronae database, pointing to an intrinsic process of coronae formation. Furthermore, coronae exhibit strong correlations and similar spectral shapes at low spherical harmonic degrees with large volcanoes, suggesting a shared geodynamic origin. Our findings underscore the pivotal role of volcanism in coronae formation and highlight the need for future research to integrate magmatic and volcanic processes more comprehensively into geophysical models. Such models would better capture the complex interactions between volcanic emplacement, magmatic activity, and lithospheric dynamics on Venus.

Remote Quantification of Soil Organic Carbon: Role of Topography in the Intra-Field Distribution

Soil organic carbon (SOC) measurements are an indicator of soil health and an important parameter for the study of land-atmosphere carbon fluxes. Field sampling provides precise measurements at the sample location but entails high costs and cannot provide detailed maps unless the sampling density is very high. Remote sensing offers the possibility to quantify SOC over large areas in a cost-effective way. As a result, numerous studies have sought to quantify SOC using Earth observation data with a focus on inter-field or regional distributions. This study took a different angle and aimed to map the spatial distribution of SOC at the intra-field scale, since this distribution provides important insights into the biophysiochemical processes involved in the retention of SOC. Instead of solely using spectral measurements to quantify SOC, topographic and spectral features act as predictor variables. The necessary data on study fields in South-East England was acquired through a detailed SOC sampling campaign, including a LiDAR survey flight. Multi-spectral Sentinel-2 data of the study fields were acquired for the exact day of the sampling campaign, and for an interval of 18 months before and after this date. Random Forest (RF) and Support Vector Regression (SVR) models were trained and tested on the spectral and topographical data of the fields to predict the observed SOC values. Five different sets of model predictors were assessed, by using independently and in combination, single and multidate spectral data, and topographical features for the SOC sampling points. Both, RF and SVR models performed best when trained on multi-temporal Sentinel-2 data together with topographic features, achieving validation root-mean-square errors (RMSEs) of 0.29% and 0.23% SOC, respectively. These RMSEs are competitive when compared with those found in the literature for similar models. The topographic wetness index (TWI) exhibited the highest permutation importance for virtually all models. Given that farming practices within each field are the same, this result suggests an important role of soil moisture in SOC retention. Contrary to findings in dryer climates or in studies encompassing larger areas, TWI was negatively related to SOC levels in the study fields, suggesting a different role of soil wetness in the SOC storage in climates characterized by excess rainfall and poorly drained soils.

Leveraging CNNs-RNNs and Adam Optimization: A Revolutionary Deep Learning approach to landslide prediction in district of Than Uyen, Lao Cai province, Vietnam

This paper presents an innovative deep learning framework for predicting landslides in the District of Than Uyen, Lao Cai Province, Vietnam, harnessing the power of Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and the Adam Optimization algorithm. Given the multifaceted nature of landslide phenomena, influenced by a myriad of geographical and meteorological factors, there is a growing need for advanced computational strategies that can decipher complex patterns and temporal correlations. Our cutting-edge model employs CNNs to analyze and extract significant spatial attributes from topographical and geological data. Concurrently, RNNs -specifically Long Short-Term Memory (LSTM) networks - are deployed to manage time-series data, such as weather conditions and other temporal elements. The Adam Optimization algorithm, renowned for its superior efficiency and effective performance, is used to optimize the model parameters. The model was trained and validated using a comprehensive dataset from the Than Uyen district, comprising 114 landslide and 114 non-landslide locations, along with ten key influential factors: elevation, slope, curvature, aspect, relief amplitude, soil type, geology, and proximity to faults, roads, and rivers. The results demonstrate a noteworthy predictive accuracy, sensitivity, and specificity, with the model surpassing benchmarks in prediction power (PPV=93.3%, NPV=83.2%, Sen=82.3%, Spe=94.1%, Acc=88.2%, F-score=0.875, Kappa=0.765, and AUC=0.968). The study advances deep learning in landslide prediction, aiding proactive disaster mitigation and showing potential for application in global regions prone to geographical hazards.

Combining Multitemporal Optical and Radar Satellite Data for Mapping the Tatra Mountains Non-Forest Plant Communities

Climate change is significantly affecting mountain plant communities, causing dynamic alterations in species composition as well as spatial distribution. This raises the need for constant monitoring. The Tatra Mountains are the highest range of the Carpathians which are considered biodiversity hotspots in Central Europe. For this purpose, microwave Sentinel-1 and optical multi-temporal Sentinel-2 data, topographic derivatives, and iterative machine learning methods incorporating classifiers random forest (RF), support vector machines (SVMs), and XGBoost (XGB) were used for the identification of thirteen non-forest plant communities (various types of alpine grasslands, shrublands, herbaceous heaths, mountain hay meadows, rocks, and scree communities). Different scenarios were tested to identify the most important variables, retrieval periods, and spectral bands. The overall accuracy results for the individual algorithms reached RF (0.83–0.96), SVM (0.87–0.93), and lower results for XGBoost (0.69–0.82). The best combination, which included a fusion of Sentinel-1, Sentinel-2, and topographic data, achieved F1-scores for classes in the range of 0.73–0.97 (RF) and 0.66–0.95 (SVM). The inclusion of topographic variables resulted in an improvement in F1-scores for Sentinel-2 data by one–four percent points and Sentinel-1 data by 1%–9%. For spectral bands, the Sentinel-2 10 m resolution bands B4, B3, and B2 showed the highest mean decrease accuracy. The final result is the first comprehensive map of non-forest vegetation for the Tatra Mountains area.

Spatial-Temporal Evolution Characteristics and Driving Force Analysis of NDVI in Hubei Province, China, from 2000 to 2022

Exploring the characteristics of vegetation dynamics and quantitatively analyzing the potential drivers and the strength of their interactions are of great significance to regional ecological environmental protection and sustainable development. Therefore, based on the 2000–2022 MODIS NDVI dataset, supplemented by climatic, topographic, surface cover, and anthropogenic data for the same period, the Sen+Mann–Kendall trend analysis, coefficient of variation, and Hurst exponent were employed to examine the spatial and temporal characteristics and trends of NDVI in Hubei Province, and a partial correlation analysis and geographical detector were used to explore the strength of the influence of driving factors on the spatial differentiation of NDVI in vegetation and the underlying mechanisms of interaction. The results showed that (1) the mean NDVI value of vegetation in Hubei Province was 0.762 over 23 years, with an overall increasing trend and fluctuating upward at a rate of 0.01/10a (p < 0.005); geospatially, there is a pattern of “low east and high west”; the spatial change in NDVI shows a trend of “large-scale improvement in the surrounding hills and mountains and small-scale degradation in the middle plains”; it also presents the spatial fluctuation characteristics of “uniform distribution in general, an obvious difference between urban and rural areas, and a high fluctuation of rivers and reservoirs”, (2) the future trend of NDVI in 70.76% of the region in Hubei Province is likely to maintain the same trend as that of the 2000–2022 period, with 70.78% of the future development being benign and dominated by sustained improvement, and (3) a combination of partial correlation analysis and geographical detector analysis of the drivers of vegetation NDVI change shows that land cover type and soil type are the main drivers; the interactions affecting the distribution and change characteristics of NDVI vegetation all showed two-factor enhancement or nonlinear enhancement relationships. This study contributes to a better understanding of the change mechanisms in vegetation NDVI in Hubei Province, providing support for differentiated ecological protection and project implementation.

Experimental and numerical investigation into the local scour of bridge cofferdam with anti-scour ribs

Cofferdam is widely employed in the construction of underwater bridge foundations. Its crucial attribute lies in providing a dedicated platform for construction activities and enhancing the water resistance dimensions in structural design, consequently amplifying local scour. However, previous research on local scour has seldom investigated the effect of construction facilities on the life cycle development of local scour on foundations. This gap has led to a misunderstanding of protective strategies against local scour throughout the construction period. In this paper, a scour experiment platform was implemented with a unidirectional flume. Physical model experiments were conducted to scrutinize the protective impact of anti-scour rib structures against local scour. The experimentally determined scour depth was compared to assess the performance of the anti-scour rib protection system. Oblique photogrammetry was subsequently used to capture the morphology of the equilibrium scour pit in the experiments. The associated topographical data were imported into Fluent commercial fluid software for in-depth flow field analysis. A numerical flume model was established to examine the hydraulic characteristics under two distinct topographical conditions: a smooth riverbed during the initial stage of scour and a scoured riverbed at the equilibrium stage of scour. To further determine the protective mechanism of anti-scour rib protection, the influence of anti-scour rib protection on shear stress was investigated numerically. Analyses revealed that incorporating scour protection ribs during cofferdam construction alters the flow field characteristics, hindering the downward movement of subsurface flow beneath the structure, reducing bed shear stress, and consequently mitigating scour effects. The instantaneous protective effect of scour protection ribs strengthens as the scour topography develops. The protective effectiveness of scour protection ribs was mainly influenced by rib length, spacing, and shape.

Geologic Provinces Beneath the Greenland Ice Sheet Constrained by Geophysical Data Synthesis

AbstractPresent understanding of Greenland's subglacial geology is derived mostly from interpolation of geologic mapping of its ice‐free margins and unconstrained by geophysical data. Here we refine the extent of its geologic provinces by synthesizing geophysical constraints on subglacial geology from seismic, gravity, magnetic and topographic data. North of 72°N, no province clearly extends across the whole island, leaving three distinct subglacial regions yet to be reconciled with margin geology. Geophysically coherent anomalies and apparent province boundaries are adjacent to the onset of faster ice flow at both Petermann Glacier and the Northeast Greenland Ice Stream. Separately, based on their subaerial expression, dozens of unusually long, straight and sub‐parallel subglacial valleys cross Greenland's interior and are not yet resolved by current syntheses of its subglacial topography.

Refraction and topographic risk factors for early myopic regression after small-incision lenticule extraction surgery.

We sought to evaluate the topographic risk factors for early myopic regression after small-incision lenticule extraction (SMILE). A retrospective case‒control study was conducted, and individuals who underwent SMILE surgery were enrolled. Among them, 406 and 14 eyes were categorized into the nonregression and regression groups, respectively. The preoperative and postoperative parameters in the two groups were collected, including spherical refraction (SE), axial length (AXL) and topographic data. A generalized linear model was adopted to analyze the difference in each parameter between the two groups. After 6months, UCVA decreased in the regression group, and SE increased in the regression group (both P < 0.05). The increase in the CCT at the thinnest point (P = 0.044), flat corneal curvature (P = 0.012) and TCRP (P = 0.001) were significantly greater in the regression group. Regarding the risk factors for myopic regression, preoperative SE, preoperative sphere power, preoperative AXL, preoperative flat corneal curvature, preoperative SA, early postoperative SE, early postoperative sphere power, early postoperative AXL and early postoperative CCT difference were significantly greater in the regression group (all P < 0.05). The SE, sphere power, AXL, preoperative flat corneal curvature, preoperative SA, and postoperative CCT difference correlate with early myopic regression after SMILE.

Spatial Downscaling of ESA CCI Soil Moisture Data Based on Deep Learning with an Attention Mechanism

Soil moisture (SM) is a critical variable affecting ecosystem carbon and water cycles and their feedback to climate change. In this study, we proposed a convolutional neural network (CNN) model embedded with a residual block and attention module, named SMNet, to spatially downscale the European Space Agency (ESA) Climate Change Initiative (CCI) SM product. In the SMNet model, a lightweight Convolutional Block Attention Module (CBAM) dual-attention mechanism was integrated to comprehensively extract the spatial and channel information from the high-resolution input remote sensing products, the reanalysis meteorological dataset, and the topographic data. The model was employed to downscale the ESA CCI SM from its original spatial resolution of 25 km to 1 km in California, USA, in the annual growing season (1 May to 30 September) from 2003 to 2021. The original ESA CCI SM data and in situ SM measurements (0–5 cm depth) from the International Soil Moisture Network were used to validate the model’s performance. The results show that compared with the original ESA CCI SM data, the downscaled SM data have comparable accuracy with a mean correlation (R) and root mean square error (RMSE) of 0.82 and 0.052 m3/m3, respectively. Moreover, the model generates reasonable spatiotemporal SM patterns with higher accuracy in the western region and relatively lower accuracy in the eastern Nevada mountainous area. In situ site validation results in the SCAN, the SNOTEL network, and the USCRN reveal that the R and RMSE are 0.62, 0.63, and 0.77, and 0.077 m3/m3, 0.093 m3/m3, and 0.078 m3/m3, respectively. The results are slightly lower than the validation results from the original ESA CCI SM data. Overall, the validation results suggest that the SMNet downscaling model proposed in this study has satisfactory performance in handling the task of soil moisture downscaling. The downscaled SM model not only preserves a high level of spatial consistency with the original ESA CCI SM model but also offers more intricate spatial variations in SM depending on the spatial resolution of model input data.

STRATEGI MITIGASI BANJIR BERDASARKAN KAPASITAS SALURAN DRAINASE DI KELURAHAN JAGALAN

&lt;p&gt;&lt;em&gt;Flood mitigation is an important step in maintaining the sustainability and safety of urban environments. One key factor that affects the capacity of drainage channels is the flow rate of water through the channels. Therefore, this research aims to assess flood mitigation strategies based on the flow rate in drainage channels using the HSS SCS method. The study was conducted in Jagalan Sub-District, which frequently experiences flooding due to inadequate drainage channel capacity. The HSS SCS method was used to estimate the maximum flow rate that can be accommodated by the drainage channels. Hydrological and topographic data of the study area were collected and analyzed to identify relevant hydrological characteristics and physical parameters.&lt;/em&gt;&lt;/p&gt;&lt;p&gt;&lt;em&gt;The analysis results showed the planned flood discharge values in Jagalan Sub-District through the Kali Boro Watershed, which were 3.1154 mm3/dt for a 2-year return period, 3.8297 mm3/dt for a 5-year return period, 4.3025 mm3/dt for a 10-year return period, and 4.7501 mm3/dt for a 20-year return period. The existing discharge values were 14.5243 mm3/dt for Surya Road, 19.1823 mm3/dt for Rejosari Road, 8.7376 mm3/dt for Kali Kepunton Road, 66.0338 mm3/dt for Kali Simpang Road, 21.7882 mm3/dt for Bororejo Road, and 42.2077 mm3/dt for Ir. Juanda Road. From these values, it can be concluded that the drainage channels in Jagalan Sub-District have sufficient capacity to accommodate the maximum flow rate during high-intensity rainfall. &lt;/em&gt;&lt;/p&gt;

High-Resolution Canopy Height Mapping: Integrating NASA’s Global Ecosystem Dynamics Investigation (GEDI) with Multi-Source Remote Sensing Data

Accurate structural information about forests, including canopy heights and diameters, is crucial for quantifying tree volume, biomass, and carbon stocks, enabling effective forest ecosystem management, particularly in response to changing environmental conditions. Since late 2018, NASA’s Global Ecosystem Dynamics Investigation (GEDI) mission has monitored global canopy structure using a satellite Light Detection and Ranging (LiDAR) instrument. While GEDI has collected billions of LiDAR shots across a near-global range (between 51.6°N and &gt;51.6°S), their spatial distribution remains dispersed, posing challenges for achieving complete forest coverage. This study proposes and evaluates an approach that generates high-resolution canopy height maps by integrating GEDI data with Sentinel-1, Sentinel-2, and topographical ancillary data through three machine learning (ML) algorithms: random forests (RF), gradient tree boost (GB), and classification and regression trees (CART). To achieve this, the secondary aims included the following: (1) to assess the performance of three ML algorithms, RF, GB, and CART, in predicting canopy heights, (2) to evaluate the performance of our canopy height maps using reference canopy height from canopy height models (CHMs), and (3) to compare our canopy height maps with other two existing canopy height maps. RF and GB were the top-performing algorithms, achieving the best 13.32% and 16% root mean squared error for broadleaf and coniferous forests, respectively. Validation of the proposed approach revealed that the 100th and 98th percentile, followed by the average of the 75th, 90th, 95th, and 100th percentiles (AVG), were the most accurate GEDI metrics for predicting real canopy heights. Comparisons between predicted and reference CHMs demonstrated accurate predictions for coniferous stands (R-squared = 0.45, RMSE = 29.16%).

Mapping of Forest Structural Parameters in Tianshan Mountain Using Bayesian-Random Forest Model, Synthetic Aperture Radar Sentinel-1A, and Sentinel-2 Imagery

The assessment of forest structural parameters is crucial for understanding carbon storage, habitat suitability, and timber stock. However, the labor-intensive and expensive nature of field measurements, coupled with inadequate sample sizes for large-scale modeling, poses challenges. To address the forest structure parameters in the Western Tianshan Mountains, this study used UAV-LiDAR to gather extensive sample data. This approach was enhanced by integrating Sentinel satellite and topographic data and using a Bayesian-Random Forest model to estimate forest canopy height, average height, density, and aboveground biomass (AGB). Validation against independent LiDAR-derived samples confirmed the model’s high accuracy, with coefficients of determination (R2) and root mean square errors (RMSE) indicating strong predictive performance (R2 = 0.63, RMSE = 5.06 m for canopy height; R2 = 0.64, RMSE = 2.88 m for average height; R2 = 0.68, RMSE = 62.84 for density; and R2 = 0.59, RMSE = 29.71 Mg/ha for AGB). Notably, the crucial factors include DEM, Sentinel-1 (VH and VV backscatter in dB), and Sentinel-2 (B6, B8A, and B11 bands). These factors contribute significantly to the modeling of forest structure. This technology aims to expedite and economize forest surveys while augmenting the range of forest parameters, especially in remote and rugged terrains. Using a wealth of UAV-LiDAR data, this outcome surpasses its counterparts’ by providing essential insights for exploring climate change effects on Central Asian forests, facilitating precise carbon stock quantification, and enhancing knowledge of forest ecosystems.

A multi-spatial scale assessment of land-use stress on water quality in headwater streams in the Platinum Belt, South Africa

River water quality is affected by various stressors (land-uses) operating at different hydrological spatial scales. Few studies have employed a multi-scaled analyses to differentiate effects of natural grasslands and woodlands, agriculture, impoundments, urban and mining stressors on headwater streams. Using a multi-scaled modeling approach, this study disentangled the distinct spatial signatures and mechanistic effects of specific stressors and topographic drivers on individual water quality parameters in tributaries of the Gwathle River Catchment in the Platinum Belt of South Africa. Water samples were collected on six occasions from 15 sites on three rivers over 12-months. Physio-chemical parameters as well as major anions, cations and metals were measured. Five key water quality parameters were identified using principal components analysis: sulfate, ammonium, copper, turbidity, and pH to characterise catchment water quality conditions. Using class-level composition (PLAND) and connectedness (COHESION) metrics together with topographic data, generalized linear mixed models were developed at multiple scales (sub-basin, cumulative catchment, riparian buffers) to identify the most parsimonious model with the dominant drivers of each water quality parameter. Ammonium concentrations were best explained by urban stress, Cu increased with mining and agriculture, turbidity increased with elevation heterogeneity, agriculture, urbanisation and fallow lands all at the sub-basin scale. River pH was positively predicted by slope heterogeneity, mining cover and impoundment connectivity at the catchment scale. Sulfate increased with mining and agriculture composition in the 100 m riparian buffer. Hierarchical cluster analysis of water quality and scale-dependent parsimonious drivers separated the river sites into three distinct groups distinguishing pristine, moderately impacted, and heavily mined sites. By demonstrating stressor- and scale-dependent water quality responses, this multi-scale nested modeling approach reveals the importance of developing adaptive, targeted management plans at hydrologically meaningful scales to sustain water quality amid intensifying land use.

Studying Patterns of Rainfall and Topographical Clustering for Kingdom of Bahrain: An Application of Big Data

Rainfall is an important aspect for urban planners, especially for professionals related to highway design. This study was aimed at studying the patterns of rainfall in Kingdom of Bahrain using cluster analysis with topographical data. The rainfall data was collected from the website meteorological directorate of ministry of transportation and telecommunication of Bahrain, which consisted of approximately 14000 days, thus referred to as big data. K-means clustering was used to identify the patterns. Moreover, Geographic Information System (GIS) was used for topographical clustering of elevation map of Bahrain. This was done to identify the areas which should receive more attention with regards to drainage due to the topography of Bahrain. The results of this study showed that the time periods which receive comparatively heavier rainfall are around January while those with significantly lower rainfall are during May and October. The topographical analysis showed that the northern parts of the country are supposed to be paid more attention to avoid flooding and, consequently, damage to infrastructure. Moreover, results of the study could be used by the urban and transportation planners in Bahrain for design of efficient drainage resulting in economic benefits to the society.

Cause Analysis of Salinity Intrusion by Environmental Changes Considering Water Intake and Sand Mining on Seomjin River Estuary Using Model for Maintaining Corbicula Habitats

Anthropogenic development can strongly influence natural river processes, leading to environmental changes that negatively affect important habitats and biodiversity and consequently reduce economically important natural resources. This study investigated the effects of salinity intrusion on the habitat of the clam Corbicula japonica in the Seomjin River estuarine zone. We employed the Environmental Fluid Dynamics Code (EFDC) model, which incorporates topographic data and hydrological changes, to simulate salinity. Two salinity measurement facilities were installed in Seomjin River estuarine and operated to optimize the EFDC model. The results show that reduced flow rates due to intake have a negligible impact on the increased salinity. Maintaining optimal salinity (15–20 psu) during neap tides at the Seomjin River Bridge requires constant high flow rates, which poses significant challenges. Saltwater stratification is identified as the primary cause of pronounced salinity stratification, particularly during neap tides. Addressing this issue through river discharge and intake facility operation is challenging. Structural measures, including riverbed restoration and underwater barriers, are recommended to improve resistance to seawater intrusion. Future research should aim to develop scenarios to reduce salinity, quantify the reduction efficiency, and propose region-specific measures.

Shoreline Changes and Sediment Transport along Nhat Le Coast, Vietnam

One of the most beautiful beaches in Northern Vietnam, Nhat Le, has recently experienced severe erosion as a result of the ensemble interaction of natural factors, such as tropical cyclones, extreme weather events, and human activities. Consequently, negative impacts on tourism and social and economic development have been recorded. This paper aims to provide a deep understanding of the changes in shoreline and longshore sediment transport at Nhat Le estuary based on two modules of LITDRIFT and LITLINE of the LITPACK software package combined with geospatial analysis. The rate of change statistics is calculated using the Digital Shoreline Analysis System (DSAS) from 30-year multi-temporal satellite data (1989-2019) for multiple historical shoreline positions. The Module of LITDRIFT is employed to estimate sediment transport and the shoreline position calculated from the LITLINE module. These data are then compared with measured topographic data and satellite images. Wave climate conditions are incorporated into the LITDRIFT module to identify the volume of sediment transport along the coast on seasonal and annual bases. The results illustrate that a mean erosion rate of about 2 m per year was observed in the southern sandspit of Nhat Le from 1989 to 2019. This rate reaches 4.5 m per year during 2009-2019.

Evaluation of the results of contact lens assisted corneal cross-linking treatment in keratoconus patients with thin corneas.

We aimed to compare the efficacy and safety of accelerated contact lens-assisted cross-linking (CA-CXL) with Lotrafilcon B and Comfilcon A lenses in keratoconus (KC) patients with thin corneas. Retrospective, single-center study. We retrospectively included 51 eyes of 39 KC patients with corneal thickness <400µm after epithelial scraping (Epi-off), who underwent accelerated CA-CXL treatment with Lotrafilcon B (n=20) and Comfilcon A (n=31). Uncorrected and corrected distance visual acuity (UDVA and CDVA), manifest refraction values, corneal topographic data and endothelial cell density were recorded at preoperative and postoperative 1st, 3rd and 6th month controls. CDVA in the Comfilcon A group was higher than CDVA before surgery at 6 months postoperatively (p<0.001). When the two lenses were compared, CDVA was found to be significantly higher in the Lotrafilcon B group in the preoperative, postoperative 1st month and 3rd month values, but there was no significant difference between the postoperative 6th month values (p=0.028, p=0.018, p=0.044, p=0.181, respectively). The maximum keratometry (Kmax) value at the 6th month after surgery in the Comfilcon A group was significantly lower than in the Lotrafilcon B group (p=0,009). There was no significant difference between the endothelial cell density values between the groups (p=0.623, p=0.609, p=0.794, p=0.458, respectively). There was no significant difference between the progression, regression, and stability rates of the two groups (p=0.714). Accelerated CA-CXL with Lotrafilcon B and Comfilcon A silicone hydrogel lenses is a safe and effective method to stop progression in patients with thin corneas.

Improving Global Barotropic Tides With Sub‐Grid Scale Topography

AbstractIn recent years, efforts have been made to include tides in both operational ocean models as well as climate and earth system models. The accuracy of the barotropic tides is often limited by the model topography, which is in turn limited by model horizontal resolution. In this work, we explore the reduction of barotropic tidal errors in an ocean general circulation model (Modular Ocean Model version 6; MOM6) using sub‐grid scale topography representation. We follow the methodology from Adcroft (2013, https://doi.org/10.1016/j.ocemod.2013.03.002), which utilizes statistics from finer resolution topographic data sets to represent sub‐grid scale features with a light computational cost in a structured finite volume formulation. The geometric effect from sub‐grid scale topography can be introduced to the model with only a few parameters at each grid cell. The porous barriers, which are implemented at the walls of the grid cells, are used to modify transport between grid cells. Our results show that the globally averaged tidal error in lower‐resolution simulations is significantly reduced with the use of porous barriers. We argue this method is a potentially useful tool to improve simulations of tides (and other flows) in low‐resolution simulations.