Topographic Index Research Articles

Urban Flood Risk Assessment and Mapping Using GIS-DEMATEL Method: Case of the Serafa River Watershed, Poland

This paper develops a method integrating Geographic Information Systems (GIS) and the Decision-Making Trials and Evaluation Laboratory (DEMATEL) for the analysis of factors influencing urban flood risk and the identification of flood-prone areas. The method is based on nine selected factors: land use/land cover (LULC: the ratio of built-up areas, the ratio of greenery areas), elevation, slope, population density, distance from the river, soil, Topographic Wetness Index (TWI), and Normalized Difference Vegetation Index (NDVI). The DEMATEL method is used to determine the cause–effect relationship between selected factors, allowing for key criteria and their weights to be determined. LULC and population density were identified as the most important risk factors for urban floods. The method was applied to a case study—the Serafa River watershed (Poland), an urbanized catchment covering housing estates of cities of Kraków and Wieliczka frequently affected by flooding. GIS analysis based on publicly available data using QGIS with weights obtained from DEMATEL identified the vulnerable areas. 45% of the total catchment area was classified as areas with a very high or high level of flood risk. The results match the actual data on inundation incidents that occurred in recent years in this area. The study shows the potential and possibility of using the DEMATEL-GIS method to determine the significance of factors and to designate flood-prone areas.

Spatial Distribution of Thaw Depth in Palsas Estimated From Optical Unoccupied Aerial Systems Data

ABSTRACTMaximum seasonal thaw depth, referred to as active layer thickness (ALT), is one of the key parameters used to monitor permafrost conditions. ALT maps based on interpolation of point measurements or derived from coarse or moderate spatial resolution satellite data often hide small‐scale spatial variations in thaw depth resulting from differences in surface characteristics and microtopography. To model and predict changes in hydrological and biogeochemical processes in permafrost areas accurately, high‐resolution remote sensing‐based estimations of ALT are needed. Therefore, we applied random forest (RF) regression on a set of topographical and spectral vegetation indices derived from optical unoccupied aerial systems data, Landsat 8 land surface temperature (LST) data, and field measurements to estimate thaw depths in palsas at three mires in north‐west Finland. We also analyzed differences in thaw depths between mires located at different elevations, between dome and plateau‐shaped palsas, and between different vegetation and surface cover classes. The RF models resulted in root mean square errors from 2.4 to 5.7 cm between predicted and observed thaw depths and the R2 values of 0.57–0.96. Height from the surrounding fen surface and LST were the most important variables in thaw depth models, although high‐accuracy results were also achieved without LST. The mean thaw depths did not differ between the sites with lowest and highest elevation, whereas the thaw depths were significantly deeper in dome‐shaped palsas compared to plateaus. The thaw depths were significantly different between vegetation cover classes only on plateau‐shaped palsas. The results indicate the high impact of the topography on the palsa thaw depth, thus highlighting the importance of accurate elevation models in spatial modeling of palsa ALT. The methodology presented in this study can be applied to other permafrost regions where field measurements of ALT are accompanied with high‐resolution topographical and multispectral data.

Exploring the role of microtopography in the spatial pattern of soil water at the hillslope scale based on high-resolution observation

Terraces have consistently been regarded as crucial measures for water conservation and vegetation restoration, particularly in mountainous environments. However, the hydrological effects of terraces in artificial forests have been rarely studied. In this study, detailed topography, soil properties and high-resolution (30 sites) soil water data (0–70 cm) observed from March 2015 to March 2016 were collected at hillslope scale (0.2 ha). The effects of static (soil properties and topography) and dynamic factors (precipitation) on the dynamics and variability of soil water content (SWC) were studied based on statistical methods, temporal stability and variance decomposition methods. The results showed that slope, topographic wetness index (TWI) and microtopography were the main control factors of different soil layers (shallow, middle and deep layer), which explained 37.2 %, 48.6 % and 47.3 % of the total variance of corresponding soil layers, respectively. Microtopography primarily affects deep layer soil water (40–70 cm). Level terraces show significantly higher soil water content (SWC) (30.89 ± 5.28 %) compared to gentle slopes (25.40 ± 5.33 %), steep slopes (27.05 ± 5.74 %), and scarps (19.71 ± 4.43 %) (P<0.05). Temporal stability of soil water varied across different depths, with shallow layer soil water exhibiting significantly weaker stability compared to other depths, the most representative point of different soil layers was located on relatively flat terraces. Decomposition of spatial variance revealed that the time-invariant component was the primary contributor to the total spatial variance, with a mean value over 70 %. These results highlight that microtopography could weaken the influence of soil-terrain factors on soil water, and the implementation of level terraces can enhance hillslope hydrological conditions in the Rocky Mountain area.

Landscape positioning of Neolithic mustatil stone structures along the margins of the Nefud Desert, Saudi Arabia

Monumental rectangular stone structures called mustatils are an important emerging feature of the Holocene archaeological record of northwestern Arabia. To date, few have been excavated, with available radiocarbon dates suggesting an age range of ca. 5400–4200 BC. Here we present a rigorous spatial analysis to identify the patterning and landscape context of 169 mustatils in the southern and western margins of the Nefud Desert. This included: (1) a systematic survey of satellite imagery to identify mustatils; (2) viewshed analysis to examine location and landscape visibility; (3) a point process model to understand how diverse environmental and landscape variables affect mustatil locations; (4) mark correlation function to assess spatial patterning of mustatils based on their size. Results indicate that mustatil locations are determined most by proximity to water (likely locations of enhanced surface water occurrence under the enhanced humidity of the Mid-Holocene), on east facing slopes, close to rocky areas, at elevations between 880 and 950 masl, and on or near topographic ridges (positive topographic position index). Viewshed analysis showed that mustatils are preferentially located in areas that have good views, but not the best that are available, indicating complex landscape positioning that balances a range of topographic and behavioural factors. Using a rank permutation method with size (length) of mustatils as a proxy for labour mobilization we show that mustatils within clusters are not arranged hierarchically based on size, and were likely built by non-stratified groups of people. Our analyses show that people were choosing multiple factors when deciding where to build mustatils, and that the distribution of mustatils may relate to different groups of people resulting in the construction of complex ritual landscapes. As one of the earliest examples of large-scale monumental stone structure construction in global prehistory, understanding mustatils can enlighten us on human-environment interaction during the Neolithic.

Groundwater potential mapping in arid and semi-arid regions of kurdistan region of Iraq: A geoinformatics-based machine learning approach

Groundwater (GW) is a crucial and increasingly scarce natural resource, that is affected by climate change and mismanagement. To manage GW resources effectively, it is crucial to accurately identify GW potential zones (GWPZs) using modern techniques. This study aimed to employ and assess geoinformatics-based machine learning (ML) models to delineate GWPZs in the Akre district, Kurdistan region of Iraq. Six nonparametric ML models were used: a support vector machine (SVM), k-nearest neighbours (KNN), decision tree (DT), random forest (RF), gradient boost DT (GBDT), and extreme gradient boosting (XGBoost). These models were trained on diverse GWPZ-favourable influencing factors, encompassing topographic, hydrological, geological, and environmental aspects. The findings of this study revealed that the XGBoost model outperformed the other nonparametric models in terms of best-fit performance and accuracy in generating a GW potential map (GWPM), achieving a R2 of 0.88, a root mean square error (RMSE) of 11.348, and a mean absolute error (MAE) of 6.623. Notably, over half of the study area (53%) was categorised as having high or very high GWPZs, primarily in the low-lying Rovia Plain. The study identified rainfall, elevation, lineament density (LD), drainage density (Dd), topographic wetness index (TWI), and slope as the most significant factors influencing GWPZ modelling. This study provides a comprehensive framework for GW resource management, ecological conservation, and urban development planning. These insights are crucial for stakeholders, policymakers, and local authorities in strategic resource planning and environmental stewardship.

Investigation of the Relationship between Topographic and Forest Stand Characteristics Using Aerial Laser Scanning and Field Survey Data

The article thoroughly investigates the relationships between terrain features and tree measurements derived from aerial laser scanning (ALS) data and field surveys in a 1067-hectare forested area. A digital elevation model (DEM) was generated from ALS data, which was then used to derive additional layers such as slope, aspect, topographic position index (TPI), and landforms. The authors developed a mathematical procedure to determine the radii for the topographic position index. The canopy height model was created, and individual trees were segmented using a novel voxel aggregation method, allowing for the calculation of tree height and crown size. Accuracy assessments were conducted between ALS-derived data and field-collected data. Terrain variability within each forest unit was evaluated using characteristics such as standard deviation, entropy, and frequency. The relationships between tree height and the derived topographic features within forest subcompartments, as well as the correlation between the height yield map for the entire area and the TPI layer, were analysed. The authors found strong correlation between the topographic position index and tree heights in both cases. The presented remote-sensing-based methodology and the results can be effectively used in digital forest site mapping, complemented by field sampling and laboratory soil analyses, and, as final goal, in carbon stock assessment.

Comparing the Postoperative Outcomes of Intrastromal Corneal Ring Segments in Three Different Topographic Patterns of Eccentric Keratoconus.

To compare the postoperative outcomes following implantation of KERATACx ring segments (Imperial Medical Technologies Europe GmbH) in patients having eccentric keratoconus with three different topographic patterns. This retrospective cohort study was conducted at Maadi Eye Subspeciality Center, Cairo, Egypt. The study included patients with keratoconus who had implantation of KERATACx ring segments. Three groups were segregated based on topographic keratoconus patterns using the Sirius CSO Topographer (CSO Italia): type I ectasia where the cone coincides with the corneal flat axis, type II ectasia in which the cone coincides with neither the steep nor the flat axis and lies between the two axes, and type II ectasia for cones coinciding with the corneal steep axis. The visual and topographic outcomes were compared preoperatively and postoperatively for the three enrolled groups. This study enrolled 92 eyes of 92 patients and had a mean ± standard deviation follow-up of 16.9 ± 9.2 months. The patients' medical records revealed that night vision complaints and halos around the light were experienced the most by the type III ectasia group (31.25%). For the type III ectasia group, four topographic indices and one visual parameter did not show statistically significant differences between the preoperative and postoperative data (inferior-superior difference at 2- and 4-mm diameter, coma aberration, higher order aberrations, and uncorrected distance visual acuity), contrary to the type I and II ectasia groups, which showed significant improvements in all evaluated parameters. The type III morphological pattern of ectasia is the least likely to benefit from KERATACx ring segments implantation. [J Refract Surg. 2024;40(9):e625-e634.].

Application of GIS-Based Multi-Criteria Decision Analysis of Hydro-Geomorphological Factors for Flash Flood Susceptibility Mapping in Bangladesh

Flash floods are one of the most prevalent natural disasters, triggering deadly damage to homesteads, crops, infrastructure, road networks, communications, and the natural environment in the Haor (Wetland) region of Bangladesh. The purpose of the study aims to identify eleven (11) hydro-geomorphological driving factors, namely elevation, slope, aspect, rainfall, land use and land cover (LULC), lithology, soil type, topographic wetness index (TWI), NDVI, distance from the river, and drainage density, which are being explored for mapping flood-prone areas. This research has produced a flash flood susceptibility map using the Analytical Hierarchy Process (AHP) and Analytical Network Process (ANP), which are interactive decision-making approaches under multi-criteria decision analysis (MCDA) in ArcGIS 10.8. The findings of this study showed that the susceptibility to flood hazards differs significantly among the seven Haor districts. As a result of the ANP and AHP, a more significant proportion of the Haor region is moderately susceptible to flooding (8685.09 to 9275.15 sq. km.), whereas 35.34% to 38.32% (7069.70 to 7668.67 sq. km.) accounts for high susceptible to flooding. Furthermore, 200 flood locations were identified in the northeast Haor region, where 140 (70%) randomly selected floods were used for training, and the remaining 60 (30%) were employed for validation purposes. The validation results showed that the AHP model had greater prediction accuracy (AUROC = 92.1%) than the ANP (AUROC = 88.5%) model. Therefore, the study findings can be helpful for researchers, academics, policymakers, and planners for sustainable flood mitigation strategies, particularly in Haor areas.

Modelling landscape-scale occurrences of common grassland species in a topographically complex mountainous environment

Mountainous regions typically harbour high plant diversity but are also characterised by low sampling intensity. Coarse-scale species distribution models can provide insights into the distribution of poorly sampled species, but the required bioclimatic data are often limited in these landscapes. In comparison, several environmental factors that vary over relatively fine scales in mountain environments (e.g. measures of topography) can be quantified from remotely-sensed data, and can potentially provide direct and indirect measures of biologically-relevant habitat characteristics in mountains. Therefore, in this study, we combine field-sampled floristic data with environmental predictors derived from remotely-sensed data, to model the ecological niches of 19 montane plant species in the Maloti-Drakensberg mountains, South Africa. The resulting models varied considerably in their performance, and species showed generally inconsistent responses to environmental predictors, with altitude and distance to watershed being most frequently included in models. These results highlight the species-specificity of the forb species’ environmental tolerances and requirements, suggesting that environmental change may result in re-shuffling of community composition, instead of intact communities shifting along gradients. Furthermore, while the relatively high importance of altitude (a proxy for temperature) and topographic wetness index (a proxy for soil moisture) suggest that the flora of this region will be sensitive to shifts in temperature and rainfall patterns, several non-climatic environmental variables were also influential. Our findings indicate that local response to climate change in mountains might be especially constrained by soil type and topographic variables, supporting the important influence of non-climatic factors in microclimatic refugia dynamics.

Landslide susceptibility zonation mapping using geospatial technologies and multi criteria evaluation techniques in the upper Didessa sub-basin, Southwest Ethiopia

ABSTRACT Landslides have a profound impact on landscape geology, resulting in extensive devastation and loss of human lives. Mapping landslide susceptibility is crucial for effective land use planning in mountainous country like Ethiopia. This study was conducted in the upper Didessa sub-basin, southwestern parts of Ethiopia using Geographic Information System (GIS) and multi criteria evaluation (MCE) technique. This study employed a blend of primary data, encompassing field surveys and interviews with experts, as well as secondary data derived from diverse source, such as remote sensing data, digital soil maps, and geological maps. A total of eleven critical factors were employed to assess the triggers of landslides. These factors include slope, aspect, drainage density, topographic wetness index (TWI), stream power index (SPI), topographic ruggedness index (TRI), hypsometric integral, lithology, land use land cover (LULC), soil texture, and distance from roads. The analytical hierarchy process (AHP) method was used to determine the significance of each indicator through pairwise comparison matrix. The study area was categorized into different zones based on the susceptibility to landslides, namely very high, high, moderate, low, and very low. Results revealed that cultivated land had the highest likelihood of experiencing landslides, with a total of nine incidents out of 25, followed by built-up areas with seven landslides. Conversely, dense forests, sparse forests, and grazing land experienced a lower likelihood of landslides. Out of the 11 factors contributing to landslides, 24% of the surveyed region was deemed to have a moderate susceptibility, with 12% and 6% falling into the categories of high and very high susceptibility to landslides, respectively. The findings of this research provide important information for policymakers to develop efficient measures for preventing and reducing the risks of landslides.

Mapping Benggang Erosion Susceptibility: An Analysis of Environmental Influencing Factors Based on the Maxent Model

Benggang erosion is one of the most severe geomorphological hazards occurring on deeply weathered crusts in the hilly regions of southern China. Unraveling the susceptibility and pinpointing the risk areas of Benggang erosion are essential for developing effective prevention and management strategies. This study introduced the Maxent model to investigate Benggang erosion susceptibility (BES) and compared the evaluation results with the widely used Random Forest (RF) model. The findings are as follows: (1) the incidence of Benggang erosion is rising initially with an increase in elevation, slope, topographic wetness index, rainfall erosivity, and fractional vegetation cover, followed by a subsequent decline, highlighting its distinct characteristics compared to typical types of gully erosion; (2) the AUC values from the ROC curves for the Maxent and RF models are 0.885 and 0.927, respectively. Both models converge on elevation, fractional vegetation cover, rainfall erosivity, Lithology, and topographic wetness index as the most impactful variables; (3) both models adeptly identified regions prone to potential Benggang erosion. However, the Maxent model demonstrated superior spatial correlation in its susceptibility assessment, contrasting with the RF model, which tended to overestimate the BES in certain regions; (4) the Maxent model’s advantages include no need for absence samples, direct handling of categorical data, and more convincing results, suggesting its potential for widespread application in the BES assessment. This research contributes empirical evidence to study the Benggang erosion developing conditions in the hilly regions of southern China and provides an important consideration for the sustainability of the regional ecological environment and human society.

Unveiling Wheat’s Future Amidst Climate Change in the Central Ethiopia Region

Quantifying how climatic change affects wheat production, and accurately predicting its potential distributions in the face of future climate, are highly important for ensuring food security in Ethiopia. This study leverages advanced machine learning algorithms including Random Forest, Maxent, Boosted Regression Tree, and Generalised Linear Model alongside an ensemble approach to accurately predict shifts in wheat habitat suitability in the Central Ethiopia Region over the upcoming decades. An extensive dataset consisting of 19 bioclimatic variables (Bio1–Bio19), elevation, solar radiation, and topographic positioning index was refined by excluding collinear predictors to increase model accuracy. The analysis revealed that the precipitation of the wettest month, minimum temperature of the coldest month, temperature seasonality, and precipitation of the coldest quarter are the most influential factors, which collectively account for a significant proportion of habitat suitability changes. The future projections revealed that up to 100% of the regions currently classified as moderately or highly suitable for wheat could become unsuitable by 2050, 2070, and 2090, illustrating a dramatic potential decline in wheat production. Generally, the future of wheat cultivation will depend heavily on developing varieties that can thrive under altered conditions; thus, immediate and informed action is needed to safeguard the food security of the region.

Using algorithmic game theory to improve supervised machine learning: A novel applicability approach in flood susceptibility mapping.

This study was carried out with the aim of applying Condorcet and Borda scoring algorithms based on Game Theory (GT) to determine flood points and Flood Susceptibility Mapping (FSM) based on Machine Learning Algorithms (MLA) including Random Forest (RF), Support Vector Regression (SVR), Support Vector Machine (SVM), and K-Nearest Neighbors (KNN) in the Cheshmeh-Kileh watershed, Iran. Therefore, first, FS conditioning factors including Aspect (As), Elevation (El), Euclidean distance (Euc), Forest (F), NDVI, Precipitation (P), Plan Curvature (PlC), Profile Curvature (PrC), Residential (Re), Rangeland (Rl), Slope (Sl), Stream Power Index (SPI), Topographic Position Index (TPI), and Topographic Wetness Index (TWI) were quantified in each Sub-Watershed (SW). Based on this, flood and non-flood points were identified based on both GT algorithms. In the following, MLAs including Random Forest (RF), Support Vector Regression (SVR), Support Vector Machines (SVM), and K-Nearest Neighbors (KNN) were used for the distributional mapping of FS. Finally, based on optimal conjunct approaches, FS maps were presented in the study watershed. Based on the results, among the conjunct algorithms in FS classification, RF-Condorcet and RF-Borda models were selected as the most optimal MLA-GT hybrid models. The upstream SWs were highly susceptible. Also, the effectiveness of NDVI and forest conditioning factors in each classification approach was high. The similarity of SW prioritization based on Condorcet algorithm with RF-Condorcet algorithm was about 86.70%. Meanwhile, the degree of similarity in RF-Borda conjunct algorithm was around 73.33%. These results showed that Condorcet algorithm had an optimal classification compared to Borda scoring algorithm.

Revisiting the tension water storage capacity distribution in conceptual rainfall-runoff modeling: A large-sample approach

Accurately characterizing the spatial variability of tension water storage capacity (TWC) within a catchment is challenging due to limited in-situ hydrologic data availability. Conventional conceptual rainfall-runoff models typically rely on an empirically specified TWC distribution. However, this empirical distribution lacks a physical foundation and fails to effectively redistribute critical hydrologic components, such as local capacity and contributing area, to real-world contexts. To overcome this limitation, the topographic wetness index (TWI) and its generalized form (GTWI) are introduced to bridge local topographic information with hydrologic components. Four TWC distribution curves are contrived based on the empirical parabolic distribution, empirical linear distribution, TWI, and GTWI, respectively. The effects of these alternate TWC distributions on streamflow are investigated within the framework of the HYdrologic MODel (HYMOD) across 460 Australian catchments. The results illustrate that the GTWI-based HYMOD (GTHYMOD) outperforms other models in terms of daily streamflow, with high Kling-Gupta Efficiency (KGE) attained in 74.8% of the study catchments during the validation period. The eastern coast of Australian catchments presents a superior streamflow performance compared to that in the western coast. GTHYMOD demonstrates its superiority in characterizing spatial variability, an aspect HYMOD lacked. This study has the potential to refine the empirical TWC distribution from a physical perspective and advance our comprehension of underlying hydrologic behaviors.

A national-scale high-resolution runoff risk and channel network mapping workflow for diffuse pollution management

Managing diffuse pollution from agricultural land requires a spatially explicit risk assessment that can be applied over large areas. Major components of such assessments are the precise definition of both channel networks that often originate as small channels and streams, and Hydrologically Sensitive Areas (HSAs) of storm runoff that occur on land surfaces. Challenges relate to regions of complex topography and land use patterns, particularly those which have been heavily modified by arterial drainage. In this study, a national scale, transferrable workflow and analysis were developed using a specifically commissioned LiDAR survey. Research on the first half of Northern Ireland (6927 km2) is reported where field-edge drain to major river channels were mapped from 1 m (16 points per metre) digital terrain models, and in-field HSAs were defined across over 400,000 fields with a median field size of 0.86 ha. Manual drainage mapping supplemented with a novel automated drainage channel correction process resulted in an unparalleled high-resolution national drainage network with 37,320 km of channels, increasing mapped channel density from 0.9 km km−2 to 5.5 km km−2. The HSAs were based on a Soil Topographic Index (STI) system using hillslope and contributing area models combined with soil hydraulic characteristics. In all, 249 km2 of runoff risk HSAs were identified by extracting the top 95th percentile of the modelled STI as the areas with the highest propensity to generate in-field runoff. At field and individual farm scale these targeted risk maps of diffuse pollution were delivered to over 13,000 farmers and form part of the nationwide Soil Nutrient Health Scheme programme.

Spatial prioritization of groundwater resources in mountain region of Nepal: a GIS and AHP integrated framework

ABSTRACT Climate change and anthropogenic activities pose major challenges to water resource management in water-scarce mountain regions. Managing and monitoring groundwater effectively is crucial in mitigating these challenges. Understanding groundwater resources and their distribution is necessary for effective management. The present study covers Doramba rural municipality of Nepal. It employed the analytic hierarchy process (AHP) as a multi-criteria decision-making (MCDM) tool integrated with remote sensing (RS) data and geospatial techniques to identify groundwater potential zones (GPZs). The parameters for analysis include drainage density, slope, rainfall, lineament density, land use/land cover (LULC), soil, geology, curvature, roughness, topographic wetness index, and topographic position index. These 11 thematic layers of the study area were prioritized using Satty's AHP method for the delineation of GPZs. The results classified the study area into very high, high, moderate, low, and very low groundwater potential classes, covering 26.71, 27.98, 46.61, 34.71, and 4.89 km2, respectively. The delineated map accuracy was confirmed with a 70.7% prediction rate based on the spatial distribution of 56 springs in the study area. The study's results can be applied to the sustainable management of groundwater resources in the study region and other areas with similar hydrogeological conditions.

Outcomes of manual arcuate keratotomy with compression sutures for high regular postkeratoplasty astigmatism.

To report functional and refractive outcomes of manual arcuate keratotomy (AK) with compression sutures for high regular postkeratoplasty astigmatism.Setting: not specified (review). Retrospective analysis. This study included 90 eyes with high regular postkeratoplasty astigmatism (>4 diopters, D) who received AK with compression sutures between 2010 and 2022. Functional and refractive outcomes were assessed by evaluating topographic indices and by performing vector astigmatism and Fourier analysis. At last follow-up (mean=13.7±16.6 months), BCDVA improved from 0.59±0.28 logMAR to 0.34±0.25 logMAR. Cylinder magnitude decreased from 9.91±2.88 D to 5.42±3.35 D. Surface Asymmetry Index, Irregular Astigmatism Index, Corneal Eccentricity Index were equal to preoperative values, whereas Surface Regularity Index approached normal values at last follow-up. Fourier analysis indicated a decrease in the regular astigmatic component, whereas non-regular components (asymmetry and higher-order irregularity) remained stable. In vector astigmatism analysis, Target Induced Astigmatism magnitude was 9.92±2.86 D and Surgically Induced Astigmatism magnitude was 10.16±4.86 D (Correction Index of 0.91±0.48) with a Difference Vector of 5.42±3.35 D at last follow-up. Correction of astigmatism magnitude was adequate in 40% of the eyes, under-corrected in 30% and over-corrected in 30%. Angle of Error was <|22.5°| in 88% resulting in a low risk of off-axis treatment. AK with compression sutures is a simple, relatively effective and safe surgical procedure for astigmatism reduction after keratoplasty. In case of regular astigmatism, the procedure does not increase corneal irregularities. The remaining refractive error might be further corrected by spectacles, contact lenses or toric IOL implantation (in-the-bag / add-on), thus reducing the need for repeat keratoplasty.

Improving the identification of pollution source areas with catchment-resolution sensitivity analysis

The significant impacts of total nitrogen (TN) and total phosphorus (TP) on riverine ecosystems underscores the critical need to identify the primary nutrient source areas in watersheds. This study aims to unravel the influences of terrain and land use types on mean monthly TN (TNM) and mean monthly TP (TPM) export across varying catchment resolutions in the Qiantang River Watershed of China. The findings of this study illuminated the critical role of topography in understanding nutrient dynamics, wielding a profound influence over water flow patterns and nutrient dispersion. Both land slope and Stream Power Index (SPI) displayed substantial negative correlations (r < −0.6) with TNM and TPM concentrations, whereas the Topographic Wetness Index (TWI) showed positive correlations with the nutrient indexes. In addition to terrain characteristics, impervious land surfaces had a positive correlation with nutrient concentrations, while grassland and forest areas exhibited negative correlations. Results further underscored the substantial influence of catchment resolution on correlations between watershed properties and riverine nutrient concentrations. It was imperative to choose an effective catchment resolution in watershed delineation – not too coarse, nor too fine – to accurately capture the topographic and land use impacts on nutrient dynamics. With the most appropriate catchment size (Catchment 700 km2), the critical pollution source areas for TN and TP pollution were identified, and thus could be used to guide future pollution reduction efforts. The study not only highlights the importance of identifying an appropriate catchment size for water pollution, but also emphasizes the necessity of effectively extracting critical pollution source areas to mitigate water nutrient pollution and increase the ecological integrity of the Qiantang River Watershed.

Structural control on the landscape evolution and avulsive behavior of rivers at mountain exits: The example of the Kosi River in eastern Nepal Himalaya

The Kosi River flows from the eastern Nepal Himalaya into the state of Bihar (India) and has experienced frequent avulsions, causing extensive flood-related damage. Because of this avulsive behavior, the Kosi is called the “Sorrow of Bihar.” The avulsion of 2008 was the most catastrophic avulsion event recorded for the Kosi and has been attributed primarily to hydrological and sedimentological processes that formed a super-elevated river channel and caused avulsion. Detailed topographic analysis of the region near the Kosi exit from the Himalaya, using mean-corrected and resampled 1-arc, Shuttle Radar Topography Mission (SRTM) and Real Time Kinematic Global Positioning System (RTK-GPS) datasets, reveals that the Kosi channel is super-elevated only relative to its eastern floodplain. The western floodplain elevation is similar to or higher than the Kosi channel in the region between the Kosi River exit from the main eastern Nepal Himalaya and the Kosi barrage at the Indo-Nepal border. Structurally, the Kosi exits the Himalaya in the transition zone between the closed Trijuga dun to the west and the Dharan salient to the east. The Trijuga dun is closed by the Main Frontal thrust (MFT)-related frontal topography or the Outer Churia Hills. The eastern slopes of these hills induce west-to-east topographic slope in the channel, such that topographic avulsion indices are highest only in the Outer Churia Hills affected parts of the Kosi Channel and the 2008 avulsion region. Therefore, our preferred model for the primary control on the channel's asymmetric, metastable, super-elevation is the influence of the tectonically controlled MFT-related Outer Churia Hills on the Kosi River channel. Geomorphological processes have operated in the Kosi channel in this backdrop. This study emphasizes that detailed structural and topographic analysis of river exits from mountain belts like the Himalaya can provide better insights into river channel metastability and avulsion worldwide.

Futuristic flood risks assessment, in the Upper Vellar Basin, integrating AHP and bivariate analysis

Flood susceptibility maps provide invaluable information for assessing and managing flood-prone areas, aiding in proactive planning, risk reduction strategies, and safeguarding vulnerable communities. The current research concentrates on advancing sustainable development practices by undertaking a comprehensive assessment of flood susceptibility in the Upper Vellar basin, with a projection for 2050. Employing an integrative methodology, this study utilizes an Analytical Hierarchy Process (AHP) and Bivariate Analysis. Nine critical parameters were used: elevation, distance from the river, distance from the road, drainage density, predicted LULC, projected precipitation, slope, soil type, and Topographic Wetness Index (TWI). The Modules of Land Use Change Evaluation (MOLUSE) plugin, which uses Cellular Automata-Artificial Neural Network (CA-ANN), was employed to predict the LULC map for the year 2050. Furthermore, bias-corrected Coupled Model Intercomparison Project 6 (CMIP 6) EC EARTH 3 Model (GCM) RCP 4.5 and 8.5 projected precipitation data were employed. The resulting flood susceptibility zones are classified into three categories: low, moderate, and high, with proportions of 32.64%, 55.52%, and 11.84% for RCP 4.5, and 34.63%, 53.46%, and 11.91% for RCP 8.5, respectively, concerning the total area. In both scenarios, nearly 38% of the settlement area is at high flood risk. This study provides essential insights for policymakers and stakeholders, facilitating the formulation of sustainable strategies to address projected changes in land use, precipitation patterns, and flood susceptibility in the Upper Vellar region up to 2050.