Stream power

Flooding is a major threat to urban areas, especially in developing countries facing rapid urbanization and weak infrastructure. This study addresses the limited flood risk assessment for Arba Minch City, Ethiopia, a fast-growing city in a flood-prone region. The research used an integrated approach of Remote Sensing, Geographic Information Systems (GIS), and the Analytical Hierarchy Process (AHP) to map flood hazard and risk zones. Biophysical and socioenvironmental factors were weighted using AHP, which identified slope (26.3%) and elevation (23.7%) as the most significant contributors to flood risk. The model's reliability was confirmed with a Consistency Ratio (CR) of 0.042. The resulting flood hazard map shows that, out of the total 32.15 km² area of the city, 63.51% falls within the high and very high hazard categories (41.11% high and 22.40% very high), particularly in low-lying and urbanized areas. A comprehensive risk map was created by combining these hazard zones with socio-economic data. This revealed that, out of the total 32.15 km² area, 40.50% is at high and very high risk (32.16% high and 8.33% very high). The model's accuracy was validated using historical flood data from 1985-2003 and 26 flood points, yielding an Area Under the Curve (AUC) score of 0.847. The flood risk map highlights the critical interplay between urban expansion, socio-economic vulnerability, and hydrometeorological dynamics, underscoring the need for integrated flood management strategies. These findings provide valuable support for urban planners and disaster managers in developing targeted mitigation strategies to enhance resilience and protect vulnerable communities.

Hyderabad's rapid real estate boom, especially in its western corridor, has led to unchecked construction over natural drainage paths and open lands. This paper focuses on the environmental implications of real estate-driven urban flooding and traffic congestion through an integrated geospatial analysis using ArcGIS tools Using high-resolution satellite imagery historical rainfall datasets, and GHMC zoning maps, spatial

Worldwide and especially in less developed regions, process-based evaluations and/or geomorphological information on large-scale rivers are still scarce. Such investigation become of urgent need due to the climate change and expected occurrence of extreme floods and drought which may threaten the safety of nearby and downstream cities, especially in regions that are highly sensitive and affected by climatic changes. The Tigris River, in Iraq, is one such river that has undergone significant alteration to its flow and morphologic aspects due to climate change and the construction of many dams. However, morphology and its change for many reaches of this river are still uninvestigated. To this end, field and satellite-based investigations into the morphology of a reach located between Makhool District and Tikrit City have been conducted. In addition to the cross-sectional survey-based determination of the reach geometrical aspects, a sinuosity indices-based evaluation of the reach planform was implemented, utilizing a satellite indices-based approach. Furthermore, the characteristics of bed material were identified through field sampling. Investigation results show that the reach has a steep bed slope and many islands of low altitude with an elongated shape. The reach has a mild sinuosity with alternating bars. The dominant particle sizes of the bed material are coarse and medium gravel with a dominant particle shape of disc particles. Moreover, the satellite-based change detection indicated the fading out and disappearance of some secondary channels, the growth of many islands, and the movement of some bends downstream. The percentage of changing parts for the period 1975-2021 is 14%. Most of this change, 11%, occurred after the construction of the Mosul Dam. This reveals the sensitivity of reach morphology to flow change due to the construction of dams. The conducted fieldwork and the applied methodology contribute to supporting efforts to add knowledge worldwide about uninvestigated rivers.

In this study, flood hazard areas were evaluated in the Lighvan catchment as a mountainous area. So after preparing seven criteria such as slope, flow accumulation, distance from drainage network, lithology, runoff depth, land use and vegetation cover, the relative importance of criteria and sub-criteria for occurrence of the flood were weighted using the Analytic Hierarchy Process (AHP) method and then flood hazard map was extracted by Flood Hazard Index in GIS. The results of weights showed that flow accumulation, distance from drainage network and slope are the most and vegetation cover and lithology are the less importance factors in flood risk of the Lighvan catchment. Flood hazard zonation map divided to five classes; very low, low, moderate, high and very high. According to this, 3.35% of the catchment area in the range of very high risk and 5.97% in the range of high risk. Also weak pasture, rainfed farming, barren land, garden and dense pasture have high distribution in high and very high flood hazard classes. Finally regarding to priority for management and mitigation of flood hazard, the sub-catchment 4, 13 and 19 of the study area is facing serious problem of flood hazard, spatially Lighvan village is located in sub-catchment 4 and Beyragh villages is located in sub-catchment 3. So flood hazard mitigation and flood control in these sub-catchments should be implemented immediately. The results of this study act as guidelines for managers and planners to determine rational management of catchment for mitigation of flood hazard.

Groundwater quality is typically measured through water sampling and lab analysis. The field-based measurements are costly and time-consuming when applied over a large domain. In this study, we developed a machine learning-based framework to map groundwater quality in an unconfined aquifer in the north of Iran. Groundwater samples were provided from 248 monitoring wells across the region. The groundwater quality index (GWQI) in each well was measured and classified into four classes: very poor, poor, good, and excellent, according to their cutoff values. Factors affecting groundwater quality, including distance to industrial centers, distance to residential areas, population density, aquifer transmissivity, precipitation, evaporation, geology, and elevation, were identified and prepared in the GIS environment. Six machine learning classifiers, including extreme gradient boosting (XGB), random forest (RF), support vector machine (SVM), artificial neural networks (ANN), k-nearest neighbor (KNN), and Gaussian classifier model (GCM), were used to establish relationships between GWQI and its controlling factors. The algorithms were evaluated using the receiver operating characteristic curve (ROC) and statistical efficiencies (overall accuracy, precision, recall, and F-1 score). Accuracy assessment showed that ML algorithms provided high accuracy in predicting groundwater quality. However, RF was selected as the optimum model given its higher accuracy (overall accuracy, precision, and recall = 0.92; ROC = 0.95). The trained RF model was used to map GWQI classes across the entire region. Results showed that the poor GWQI class is dominant in the study area (covering 66% of the study area), followed by good (19% of the area), very poor (14% of the area), and excellent (< 1% of the area) classes. An area of very poor GWQI was observed in the north. Feature analysis indicated that the distance to industrial locations is the main factor affecting groundwater quality in the region. The study provides a cost-effective methodology in groundwater quality modeling that can be duplicated in other regions with similar hydrological and geological settings.

In this paper, a comprehensive flood hazard map for the vicinity of King Talal Dam in Jordan, utilizing advanced remote sensing (RS) and GIS methodologies, is developed. Key geographical and environmental factors, encompassing terrain slope, elevation, aspect, proximity to water streams, drainage density, and land use/land cover, are integrated to highlight areas with increased flood risk. This study, by employing a novel theoretical approach, harnesses the synergistic capabilities of RS and GIS to collect and analyze geospatial data. The Analytic Hierarchy Process (AHP) is applied to assign weights to various flood-conditioning factors, quantifying their relative importance in flood risk assessment. Through the weighted sum overlay technique, the aforementioned factors are integrated to categorize flood risk levels from very low to very high. This study successfully maps flood hazards, identifying areas near main water channels, ravines, and lower-elevation areas prone to flooding. This research provides a robust framework for flood risk assessment, contributing valuable knowledge to the fields of environmental management and disaster mitigation. It underscores the importance of continuous monitoring and updating of flood hazard maps to accommodate changing land use, climate, and hydrological conditions. The innovative application offers crucial insights for urban planners and policymakers, emphasizing the need for proactive strategies in flood-prone areas and serving as a model for similar geographical regions.

Soil is the most important gift of nature formed in hundreds/thousands of years covering the land surface but soil cover is depleting day by day due to geological and/or anthropological activities. Erosion is one of the most important geological processes in reducing the soil cover. Hence, there is a requirement to conserve the soil cover and the first thing which can be done in order to conserve the soil is the identification of vulnerable areas. Identification of vulnerable areas is of foremost importance which could be done by scientific methods. Hydrological Parameters are one of them which can be calculated using Geographic Information System. In this study, Digital Elevation Models have been processed in geographic Information System to calculate three important hydrological indices i.e., Sediment Transport Index, Topographic Wetness Index and Stream Power Index along with other parameters like Slope. Maps showing variability in Slope, Sediment Transport Index, Topographic Wetness Index and Stream Power Index have been prepared to understand the variability and the importance of each parameter in marking the vulnerable areas to erosion. With this, an Erosion Hazard Vulnerability Map of the study area (in and Around Kharagpur Hills, Munger-Jamui, Bihar) has been prepared using Weighted method of Geographic Information System. It has been found that Soil cover over the hills with Steep Slopes are more prone to erosion whereas Soil cover over the very gentle slopes or in the flat areas are least prone to erosion. Along with this it has also been found that river sediments deposited in the point bars along the banks of the river Ganges are also moderately to highly prone to erosion. The importance of spatial analyst tool of geographic information system has been utilized in delineating the vulnerable areas.

Two sets of triangular hydrographs were generated in a 12-m-long laboratory flume for two sets of initial bed conditions: intact and water-worked gravel bed. Flowrate ranging from 0.0013 m 3 s -1 to 0.0456 m 3 s -1 , water level ranging from 0.02 m to 0.11 m, and cumulative mass of transported sediment ranging from 4.5 kg to 14.2 kg were measured. Then, bedload transport rate, water surface slope, bed shear stress, and stream power were evaluated. The results indicated the impact of initial bed conditions and flow unsteadiness on bedload transport rate and total sediment yield. Difference in ratio between the amount of supplied sediment and total sediment yield for tests with different initial conditions was observed. Bedload rate, bed shear stress, and stream power demonstrated clock-wise hysteretic relation with flowrate. The study revealed practical aspects of experimental design, performance, and data analysis. Water surface slope evaluation based on spatial water depth data was discussed. It was shown that for certain conditions stream power was more adequate for the analysis of sediment transport dynamics than the bed shear stress. The relations between bedload transport dynamics, and flow and sediment parameters obtained by dimensional and multiple regression analysis were presented.

Two sets of triangular hydrographs were generated in a 12-m-long laboratory flume for two sets of initial bed conditions: intact and water-worked gravel bed. Flowrate ranging from 0.0013 m3 s-1 to 0.0456 m3 s-1, water level ranging from 0.02 m to 0.11 m, and cumulative mass of transported sediment ranging from 4.5 kg to 14.2 kg were measured. Then, bedload transport rate, water surface slope, bed shear stress, and stream power were evaluated. The results indicated the impact of initial bed conditions and flow unsteadiness on bedload transport rate and total sediment yield. Difference in ratio between the amount of supplied sediment and total sediment yield for tests with different initial conditions was observed. Bedload rate, bed shear stress, and stream power demonstrated clock-wise hysteretic relation with flowrate. The study revealed practical aspects of experimental design, performance, and data analysis. Water surface slope evaluation based on spatial water depth data was disc...

The main objective of this study is to investigate potential application of frequency ratio (FR), weights of evidence (WoE), and statistical index (SI) models for landslide susceptibility mapping in a part of Mazandaran Province, Iran. First, a landslide inventory map was constructed from various sources. The landslide inventory map was then randomly divided in a ratio of 70/30 for training and validation of the models, respectively. Second, 13 landslide conditioning factors including slope degree, slope aspect, altitude, plan curvature, stream power index, topographic wetness index, sediment transport index, topographic roughness index, lithology, distance from streams, faults, roads, and land use type were prepared, and the relationships between these factors and the landslide inventory map were extracted by using the mentioned models. Subsequently, the multi-class weighted factors were used to generate landslide susceptibility maps. Finally, the susceptibility maps were verified and compared using several methods including receiver operating characteristic curve with the areas under the curve (AUC), landslide density, and spatially agreed area analyses. The success rate curve showed that the AUC for FR, WoE, and SI models was 81.51, 79.43, and 81.27, respectively. The prediction rate curve demonstrated that the AUC achieved by the three models was 80.44, 77.94, and 79.55, respectively. Although the sensitivity analysis using the FR model revealed that the modeling process was sensitive to input factors, the accuracy results suggest that the three models used in this study can be effective approaches for landslide susceptibility mapping in Mazandaran Province, and the resultant susceptibility maps are trustworthy for hazard mitigation strategies.

This study aimed at (i) developing, evaluating and comparing the performance of support vector machines (SVM), boosted regression trees (BRT), random forest (RF) and logistic regression (LR) models in mapping gully erosion susceptibility, and (ii) determining the important gully erosion conditioning factors (GECFs) in a Kenyan semi-arid landscape. A total of 431 geo-referenced gully erosion points were gathered through a field survey and visual interpretation of high-resolution satellite imagery on Google Earth, while 24 raster-based GECFs were retrieved from the existing geodatabases for spatial modeling and prediction. The resultant models exhibited excellent performance, although the machine learners outperformed the benchmark LR technique. Specifically, the RF and BRT models returned the highest area under the receiver operating characteristic curve (AUC = 0.89 each) and overall accuracy (OA = 80.2%; 79.7%, respectively), followed by the SVM and LR models (AUC = 0.86; 0.85 & OA ...

Soil erosion is one of the most important and critical processes occurring in Türkiye, as in all parts of the world. It is of great importance to understand the processes that occur as soil erosion continues. The aim of this study is to determine the erosion susceptibility occurring in the Çapakçur Stream basin, one of the important erosion areas of Türkiye. In the study, erosion susceptibility analysis was carried out using 4 different methods Shannon Entropy (SE), Logistic Regression (LR), Frequency Ratio (FR) and Weight of Evidence (WoE) that are effectively used today in erosion susceptibility analysis and determination of critical areas in terms of erosion, and 19 conditioning factors based on these methods. Analysis Results Model performances were evaluated using Receiver Operating Characteristic (ROC) and Area under the Curve (AUC) values based on a dataset consisting of 840 training (70%) and 360 testing (30%) points. According to result of the AUC values show that Logistic regression seems to perform well on both training (AUC= 94.7%) and validating datasets (AUC=93.5%). On the other hand, Weight of Evidence training (AUC= 93.5%) and testing datasets (AUC= 91.4%), Frequency Ratio training (AUC= 93.5%) and testing datasets (AUC=92.4%) of the Weight of Evidence result show that AUC and ROC values similar to Logistic Regression result, but slightly lower than Logistic Regression. Additionally, Shannon Entropy shows that it performs lower than other methods on both training (AUC= 55.7%) and testing datasets (AUC= 56.3%). Conducting analyses based on these methods, especially in erosion susceptibility studies, will facilitate both planning and the accuracy of the results obtained.

The study area is a tropical environment located in the Batanghari watershed, Sumatra, Indonesia. The existence of the environmental problems and damages in the study area can be identified based on land degradation. It can be interpreted as a complex process and is influenced by human activities, climate change, and natural events. This study proposes the latest Geospatial Artificial Intelligence (Geo-AI) model using multi-sources geospatial data that is specifically used to address challenges and phenomena related to the identification of land degradation in the study area. The novelty of this study is that it is the first time to integrate the 6 (six) main variables of multi-source geospatial data - Topographical, Biophysical, Bioclimatic, Geo-environmental, Global human modification, and Accessibility - in predicting potential land degradation in the tropical environment, such as Indonesia. Machine learning-based prediction Support Vector Machine (SVM), Minimum Distance (MD), Cl...

Climate change, urbanization, land use and land cover changes, and political and economic problems, caused
increasing impervious layers and floods in Erbil Province in recent years. Due to economic growth and immigration from
the middle and south of Iraq, the demand for residential areas has increased leading to more urban and impervious
surface areas. The research aims to study the factors behind the increasing impervious areas and floods in Erbil City.
Data were collected through several site visits and from related governmental sectors. Results illustrated that after 2017,
urbanization noticeably increased. From 2017 to 2021, the construction zones and shrub areas were markedly increased.
On the other hand, the agriculture area and green zone were significantly reduced. Additionally, the impervious layers
increased and infiltration areas decreased only in four years. Moreover, some factors, such as geological, topographical,
demographic, political, economic, and transportation, have affected the increase of urbanization and impervious areas,
which increased surface runoff and floods in Erbil City

Landslides are a widespread occurrence in various regions across
the globe, and Ethiopia serves as a typical example. The objective
of this research was to develop and evaluate a landslide susceptibility
map (LSM) using two different approaches: the frequency
ratio (FR) and analytical hierarchy process (AHP). The suitability
and effectiveness of both approaches were assessed for the specific
study area. In the initial phase, a total of 175 past landslides,
covering an area of 13.09 km2, were identified and mapped for
further analysis. Eight causative factors, including elevation, slope
steepness, slope aspect, lithology, distance from fault and stream,
precipitation, and land use/land cover, were selected and mapped
accordingly. Subsequently, a landslide susceptibility index (LSI)
was calculated based on the relative influence of these factors on
the recorded past landslides. The resulting LSMs were then categorized
into five zones representing varying degrees of susceptibility:
very low, low, moderate, high, and very high. For the LSM
derived from the FR approach, the very high and high susceptibility
zones accounted for 16.65 km2 (7.58%) and 79.96 km2 (36.4%)
of the total area, respectively. Conversely, for the LSM derived
from the AHP approach, these values were 21.9km2 (9.99%) and
68.9km2 (31.37%). Generally, both the FR and AHP approaches
yielded comparable results. The LSMs were validated using the
area under the curve (AUC) method. The FR model exhibited
higher accuracy and prediction rates, with values of 0.807 and
80.7%, respectively, compared to the AHP model with values of
0.756 and 75.6%, respectively. These generated LSMs offer valuable
insights for identifying and addressing the requirements of
landslide hazard mitigation and adaptation.

Convergent orogens exhibit high elevations and relief, features characteristic of active rock uplift, the latter influencing normalized channel steepness (K_sn). In systems with significant horizontal displacement, K_sn values and interfluves are elevated over a region of tens of kilometers and gradually decline in the direction of rock advection. To evaluate potential relationships between elevated K sn , a gradual decline in interfluve elevation (i.e., tapered topography), and lateral advection, we integrated kinematic models that simulate advection over a midcrustal ramp with a 2-D surface processes model. Varying convergence rate, bedrock erodibility, and ramp angle, we tracked topographic evolution over time. The process of advection through the region of active rock uplift above a midcrustal ramp is preserved in the geomorphic record through transient legacy landscapes characterized by (i) high-relief, advection-parallel interfluves, (ii) tapered topography, (iii) elevated and gradually declining K sn values, and (iv) higher K_sn in trunk relative to tributary streams likely reflecting the influence of increased sediment flux, elevated interfluves, and changes in drainage area. The width of legacy landscapes provides a minimum constraint on the total lateral displacement, controlled by the duration of ramp activity and the rates of advection and erosion. The development of legacy landscapes is facilitated by spatial variations in flow convergence that occur in a 2-D setting but are not captured in idealized 1-D approaches. The presence of elevated K_sn and high relief, advection-parallel interfluves beyond the region of active rock uplift likely reflects the horizontal advection component inherent to convergent orogenic systems.

Transition from endorheic alluvial fan environments to well-channelized fluvial systems in natural conditions may occur in response to base-level fluctuations. However, human-induced changes in semi-arid regions can also be responsible for similar unforeseen modifications. Our results confirm that in-channel gravel mining and aquifer overexploitation over the last 50 years in the case study area have changed the natural stability of the Nogalte stream and, as a result, its geomorphic parameters including channel depth and longitudinal profile have begun to adapt to the new situation. Using interferometric synthetic aperture radar (InSAR) data we obtain maximum values for ground subsidence in the Upper Guadalentín basin of ~ 10 cm yr -1 for the period 2003-2010. In this context of a lowered base level, the river is changing its natural flood model to a more energetic one. A comparison of the 1973 flood event, the most dramatic ever recorded in the area, with the 2012 event, where there was a similar discharge but a sediment load deficit, reveals greater changes and a new flooding pattern and extension. In-channel gravel mining may be responsible for significant local changes in channel incision and profile. This, together with the collateral effects of aquifer overexploitation, can favor increased river velocity and stream power, which intensify the consequences of the flooding. The results obtained here clearly demonstrate an existing transition from the former alluvial pattern to a confined fluvial trend, which may become more pronounced in the future due to the time-lag between the drop in aquifer level and ground subsidence, and introduce a new scenario to be taken into consideration in future natural hazard planning in this area.

Flood events in recent years have increased as a major catastrophic event and India is listed among top nations in the globe. The Gandak river basin in Indian Territory puts heavy flood situations every year and the present study was done to classify it into flood susceptible zones using analytical hierarchical process (AHP). The AHP was implemented through a series of steps, including the multicollinearity test, creation of a pairwise comparison matrix, the assignment of weights, consistency check, sensitivity analysis and weighted overlay analysis using ArcGIS V.10.5 platform. The Consistency Ratio (CR) values showed perfect consistency when calculated for each thematic parameter as the values were below 0.1 or 10% showing high acceptance for AHP model to run. To attain dependable precision, the study incorporated a substantial number of thematic layers (N = 12 for FSZ) and conducted multicollinearity analysis of these variables to address issues related to high correlation among them using SPSS. For the multicollinearity check, the tolerance value should be greater than 0.1, and the percentage of variance inflation factor (VIF) should be less than 10 for the accuracy evaluation of this test. The AHP was successfully employed in the present area and flood susceptibility zone (FSZ) map generated revealed elevation and slope were dominant factors affecting FSZ map. The map removal sensitivity analysis and single parameter sensitivity analysis described the importance of elevation and slope in the final FSZ map. The high flood susceptibility region mostly lies in the southern part of the study area and percent cover under high and very high is around 44.19% reflecting regions suffering extreme conditions of flood during monsoon season. Very low and low flood zones are situated in northern part of the basin which clearly indicates that slope and elevation are the predominant factors controlling the flood zones. The AUC/ ROC curve value was 83.60%. The current study finds its greater utilization for environmentalists, farmers, agricultural engineers and researchers to carry out sustainable development, and most importantly would help government bodies to regulate remedial measures.

The purpose of this study is to prepare landslide susceptibility map using a spatial multi criteria evaluation approach (SMCE) in a landslide-prone area (Haraz) in Iran. In the first stage, landslide locations were identified in the study area from interpretation of aerial photographs, and field surveys. In the second stage, twelve data layers were used as landslide conditioning factors for susceptibility mapping. These factors are slope, aspect, altitude, lithology, land use, distance from rivers, distance from roads, distance from faults, topographic wetness index, stream power index, stream transport index, and plan curvature. Next, landslide-susceptible areas were analyzed using the SMCE approach and mapped using landslide conditioning factors. For verification, the results of the analyses was compared with the field-verified landslide locations. Additionally, the receiver operating characteristics (ROC) curves for all landslide susceptibility models were drawn and the area under curve values was calculated. Landslide locations were used to validate results of the landslide susceptibility map generated using the SMCE approach and the verification results showed a 76.84% accuracy. According to the results of the AUC evaluation, the produced map has exhibited good performance.

This study explores the spatiotemporal patterns and drivers of deforestation and forest degradation along the politically sensitive Iraq-Turkey border within the Duhok Governorate between 2015 and 2024. Utilizing paired remote sensing (RS) and highend machine learning (ML) methods, forest dynamics were simulated from Sentinel-2 imagery, climate datasets, and topographic variables. Seven ML models were evaluated, and XGBoost consistently outperformed the others, yielding predictive accuracies (R 2) of 0.903 (2015), 0.910 (2019), and 0.950 (2024), and a low RMSE (≤0.035). Model interpretability was further improved through the application of SHapley Additive exPlanations (SHAP) to estimate variable contributions and a Generalized Additive Model (GAM) to elucidate complex nonlinear interactions. The results showed distinct temporal shifts; climatic factors (rainfall and temperature) primarily influenced vegetation cover in 2015, whereas anthropogenic drivers such as forest fires (NBR), road construction (RI), and soil exposure (BSI) intensified by 2024, accounting for up to 12% of the observed forest loss. Forest canopy cover decreased significantly, from approximately 630 km 2 in 2015 to 577 km 2 in 2024, mainly due to illegal deforestation, road network expansion, and conflict-induced fires. This study highlights the effectiveness of an ML-driven RS analysis for geoinformation needs in geopolitically complex and data-scarce regions. These findings underscore the urgent need for robust, evidence-based conservation policies and demonstrate the utility of interpretable ML techniques for forest management policy optimization, providing a reproducible methodological blueprint for future ecological assessment.

In this study through field observations, 9 debris flows were investigated and their morphometric characteristics were determined in Roodbarak sub-basin. The most important morphometric characteristics of debris flows in this region are dip of debris flow’s head, dip of debris flow’s slope, length of crossing part, debris flow’s height and size of debris flow’s base. In order to analyze is these morphometric parameters acquired from debris flows of Roodbarak sub-basin, linear regression has been used for a meaningful analysis between parameters. According to the results of linear regression, maximum coefficient of determination of the size of debris flow’s base is computed with the independent variable of debris flow’s height (0.89) and after that with the length of crossing part (0.55) that shows more justification and determination of variation in the size of debris flow’s base with these two parameters. After these two parameters, the coefficient of determination for slope’s dip ...

Tectonic displacement of drainage divides and the consequent deformation of river networks during crustal shortening have been proposed for a number of mountain ranges, but never tested. In order to preserve crustal strain in surface topography, surface displacements across thrust faults must be retained without being recovered by consequent erosion. Quantification of these competing processes and the implications for catchment topography have not previously been demonstrated. Here, we use structural mapping combined with dating of terrace sediments to measure Quaternary shortening across the Indus River valley in Ladakh, NW Himalaya. We demonstrate ~0.21 m k.y. -1 of horizontal displacement since ca. 45 ka on the Stok thrust in Ladakh, which defines the southwestern margin of the Indus Valley catchment and is the major back thrust to the Tethyan Himalaya in this region. We use normalized river channel gradients of the tributaries that drain into the Indus River to show that the lateral continuation of the Stok thrust was active for at least 70 km along strike. Shortening rates combined with fault geometries yield vertical displacement rates that are compared to time-equivalent erosion rates in the hanging wall derived from published detrital 10 Be analyses. The results demonstrate that vertical displacement rates across the Stok thrust were approximately twice that of the time-equivalent erosion rates, implying a net horizontal displacement of the surface topography, and hence narrowing of the Indus Valley at ~0.1 m k.y. -1 . A fill terrace records debris-flow emplacement linked to thrust activity, resulting in damming of the valley and extensive lake development. Conglomerates beneath some of the modern alluvial fans indicate a northeastward shift of the Indus River channel since ca. 45 ka to its present course against the oppo site side of the valley from the Stok thrust. The integration of structural, topographic, erosional, and sedimentological data provides the first demonstration of the tectonic convergence of drainage divides in a mountain range and yields a model of the surface processes involved.

Understanding how tectonics, climate and lithology interact to control fluvial erosion is complicated because these factors are spatially-variable and they may not be well-represented by mean values. We address these complications using eight new and 54 published 10 Be catchment-wide fluvial erosion rates from the south-central Andes. We assess how tectonics, climate, lithology, and topography control erosion through bivariate and multivariate Bayesian regression analysis. We first compare catchment-wide mean values of independent variables compared to other summary statistics and find that metrics that capture extreme values (e.g., 90th percentile) and spatial variability (e.g., 90th minus 10th percentile) produce stronger correlations. This suggests that catchment-wide means may oversimplify the roles of tectonics, climate, and lithology in influencing erosion rates. We find that the overall variability of erosion rates in the south-central Andes is best explained by a combination of lithologic resistance and spatial variability in both vegetation (using the normalized difference vegetation index, NDVI) and topography (using specific stream power). Despite poor bivariate correlations, both lithologic resistance and spatial variability of specific stream power are significant regressors in our multivariate modeling. Lithology influences the relationship (i.e., linearity) between topography and erosion rates. Spatial variability of NDVI produces the strongest correlation with erosion rates of any of the variables we consider. Hence, spatial variability of NDVI both accounts for potential non-uniform vegetation responses to climate and also incorporates the role of both humid climates (high 90th percentile) and large bare regions (low 10th percentile) within a single catchment.

Malaysia, particularly Pahang, experiences devastating floods annually, causing significant damage. The objective of the research was to create a flood susceptibility map for the designated area by employing an Ensemble Machine Learning (EML) algorithm based on geographic information system (GIS). By analyzing nine key factors from a geospatial database, flood susceptibility map was created with the ArcGIS software (ESRI ArcGIS Pro v3.0.1 x64). The Random Forest (RF) model was employed in this study to categorize the study area into distinct flood susceptibility classes. The Feature selection (FS) method was used to ranking the flood influencing factors. To validate the flood susceptibility models, standard statistical measures and the Area Under the Curve (AUC) were employed. The FS ranking demonstrated that the primary attributes to flooding in the study region are rainfall and elevation, with slope, geology, curvature, flow accumulation, flow direction, distance from the river, and land use/land cover (LULC) patterns ranking subsequently. The categories of 'very high' and 'high' class collectively made up 37.1% and 26.3% of the total area, respectively. The flood vulnerability assessment of Pahang found that the Eastern, Southern, and central regions were at high risk of flooding due to intense precipitation, low-lying topography with steep inclines, proximity to the shoreline and rivers, and abundant flooded vegetation, crops, urban areas, bare ground, and rangeland. Conversely, areas with dense tree canopies or forests were less susceptible to flooding in this research area. The ROC analysis demonstrated strong performance on the validation datasets, with an AUC value of >0.73 and accuracy scores exceeding 0.71. Research on flood susceptibility mapping can enhance risk reduction strategies and improve flood management in vulnerable areas. Technological advancements and expertise provide opportunities for more sophisticated methods, leading to better prepared and resilient communities.

Flooding is one of the most devastating natural disasters in worldwide, with significant socioeconomic and environmental impacts. In such a scenario, flood susceptibility analysis is essential for successful risk management and disaster preparedness. The Larkana district located in densely populated area of Sindh province, approximately 1.8 million population with 0.32 million households, is highly susceptible to floods due to its geographical location, river systems, and hot climatic patterns with extremely high temperature in summer and mild winter. Larkana district often faces significant flood risks mainly due to monsoon rains and the overflow of the Indus River which often lead to widespread flooding. The research aims to identify and quantify key flood risk factors, including rainfall pattern, distance to river, topography, land use/land cover, soil texture, vegetation index, and drainage infrastructure. AHP was applied to prioritize these risk factors based on expert opinions and their relative significance in contributing to flood vulnerability. GIS employed for spatial analysis and mapping of risk zones, allowing for detailed visualization of high-risk areas across the district. For this study, the various data sources such as topographic data, land use and landcover information, rainfall and infrastructure data, were used to develop a comprehensive flood susceptibility model. The AHP method was employed to determine the relative weights with consistency ratio (CR) and GIS techniques to generate flood susceptibility maps by considering all nine flood risk factors. Flood risk levels were further classified into five different classes as very low, low, moderate, high and very high. By using AHP, the weights of each parameter were calculated as a percentage, and it was determined that four out of nine parameters had 79% impact on flood hazard. These factors are ranked as the most influential factors in flood hazards for study area, with rainfall, distance to river, slope, and elevation having the greatest influences, while LULC, TWI, NDVI, soil type, and curvature had 21% impacts, much less impact as compared to top ranked parameters. After that, each flood risk parameter maps were reclassified. These maps were superimposed by weighted overlay maps in order to show that 7.65% of the entire area is at very high flood risk, 63.89% is at high risk, 28.38% is at moderate risk, and 0.12% is at low risk. The findings and established complete flood susceptibility model will facilitate policymakers and disaster management authorities by identifying high-risk locations and prioritizing mitigation measures, ultimately reducing the impact of floods on local communities and infrastructure in Larkana district.

Channel characterization and instream wood surveys demonstrate the spatial complexity of an ungauged, tropical headwater stream (HWS) in the Tilarán Mountains of Costa Rica. Throughout the stream course, we characterized 29 individual reaches based on their morphological organization and instream wood occurrences. Bifurcated reaches were the most numerous and consistently distributed. They also had the highest values of wood abundance and loading and contained the majority of unattached instream wood. We selected two bifurcated reaches for ultrahigh resolution terrestrial laser scanning (TLS) surveys. Orthographic TLS produced millimeter-resolution point cloud data sets from which 2 cm, planimetric-resolution digital elevation models (DEMs) of the channel surface were derived for both reaches. Cross-sectional surveys of these DEMs, using the River Bathymetry Toolkit (RBT), produced hydraulic geometries at two flow levels (high and low) for every meter-length of channel reach. We input these geometries into a one-dimensional, analytical model to demonstrate the downstream evolution of mean stream power through varying morphology elements. Mean stream power variation displays a regular pattern through the vertical modulation of step-pool sequences, but this regular pattern is lost through channel widening and bifurcation. Reach geometric data indicates that increasing width-to-depth ratio is the most notable feature of channel bifurcation and corresponds to a reduction in slope and roughness variation. This primary understanding of channel dynamics denotes the complex organization of a tropical HWS and promotes further research into tropical HWS systems.

Flooding is one of the most destructive natural disasters, capable of causing significant damage to infrastructure, ecosystems, and human lives. It is triggered by a variety of factors such as persistent rainfall, rapid snowmelt, rising sea levels, cloudbursts and anthropogenic activities. This study focuses on mapping flood-prone areas in the Alappuzha district of Kerala using the Weighted Overlay Method for flood risk mapping, the research integrates geospatial techniques such as Remote Sensing (RS) and Geographic Information Systems (GIS) to create a flood inundation map. Key parameters include physical factors like slope, drainage density, rainfall, geomorphology, soil, land use, NDVI, NDBI, MNDWI, and WRI, along with social factors such as population, households, building conditions, and access to information. The analysis reveals that low-lying areas near water bodies, including Cherthala, Ambalapuzha, and Kuttanad, are highly susceptible to flooding, intensified by poor drainage infrastructure. Kuttanad situated below sea level, faces frequent floods, severely impacting agriculture and livelihoods. Southern taluks like Karthikappally and Mavelikkara, although at higher altitudes, still face moderate flood risks due to proximity to minor rivers. This study underscores the importance of integrating geospatial data and hydrological models for effective flood risk assessment and sustainable flood management practices.

Identifying areas prone to ooding is a key step in ood hazard management. The purpose of this study is to develop and present a novel ood susceptibility model based on Bayesian Additive Regression Tree (BART) methodology. The predictive performance of new model is assessed via comparison with the Naïve Bayes (NB) and Random Forest (RF) based methods that were previously published in the literature. All models were tested on a real case study based in the Kan watershed in Iran. The following fteen climatic and geo-environmental variables were used as inputs into all ood susceptibility models: altitude, aspect, slope, plan curvature, pro le curvature, drainage density, distance from river distance from road, stream power index (SPI), topographic wetness index (TPI), topographic position index (TPI), curve number (CN), land use, lithology and rainfall. Based on the existing ood eld survey and other information available for the analyzed area, a total of 118 ood locations were identi ed as potentially prone to ooding. The data available were divided into two groups with 70% used for training and 30% for validation of all models. The receiver operating characteristic (ROC) curve parameters were used to evaluate the predictive accuracy of the new and existing models. Based on the area under curve (AUC) the new BART (86%) model outperformed the NB (80%) and RF (85%) models. Regarding the importance of input variables, the results obtained showed that the altitude and distance from the river are the most important variables for assessing ooding susceptibility.

Identifying areas prone to ooding is a key step in ood hazard management. The purpose of this study is to develop and present a novel ood susceptibility model based on Bayesian Additive Regression Tree (BART) methodology. The predictive performance of new model is assessed via comparison with the Naïve Bayes (NB) and Random Forest (RF) based methods that were previously published in the literature. All models were tested on a real case study based in the Kan watershed in Iran. The following fteen climatic and geo-environmental variables were used as inputs into all ood susceptibility models: altitude, aspect, slope, plan curvature, pro le curvature, drainage density, distance from river distance from road, stream power index (SPI), topographic wetness index (TPI), topographic position index (TPI), curve number (CN), land use, lithology and rainfall. Based on the existing ood eld survey and other information available for the analyzed area, a total of 118 ood locations were identi ed as potentially prone to ooding. The data available were divided into two groups with 70% used for training and 30% for validation of all models. The receiver operating characteristic (ROC) curve parameters were used to evaluate the predictive accuracy of the new and existing models. Based on the area under curve (AUC) the new BART (86%) model outperformed the NB (80%) and RF (85%) models. Regarding the importance of input variables, the results obtained showed that the altitude and distance from the river are the most important variables for assessing ooding susceptibility.

There is a growing evidence-based concerns pointing to global increase of weather and climate extreme events (WCEE). What decides whether these hazards become a disaster is how the society and humanity prepare and responds to it. Although various researchers have step up efforts through scholarly contributions in response to WCEE. However, the task of utilizing the needed tools, selecting the most suitable approach, and choosing appropriate techniques for WCEE studies can be daunting for researchers. Moreover, the technicality and complexity associated with the wide array of models and methods present additional challenges when it comes to their practical implementationF in disaster applications. This review comprehensively explores four approaches in WCEE studies: statistical analysis, geospatial modeling, MCDA, and AI-based techniques. It evaluates their effectiveness in assessing impacts within WCEE and highlights AI's (ML and DL's) superior performance compared to other conventional methods in disaster-related studies.

Construction of dams for hydroelectric power requires significant quantities of soil and rock, which are often extracted in borrow pits from adjacent regions. Although the effects of dams on stream processes has received significant attention, the effects of borrow pits has not. The main objective of this study was to analyze the geomorphological and sedimentological aspects of two second-order streams, one of which was directly affected by the borrow pit located upstream of its source (Pedra Branca stream). Flow rates were measured and cross-sections of 600m stretches in both streams were monitored over a hydrological year. At the same time, sediments from the bed of the channels and soils on their banks had their physical and chemical characteristics evaluated. Streams sediments differed in their chemical and organic matter composition. The mean particle size of the sediment particles was different between the reference and degraded streams. The water flow was very similar to both...

The cliff landslide process occurs due to a continuous scouring process at the bottom of the channel. The landslide pattern that occurs is strongly influenced by flow rate, channel slope, grain diameter, water level and time. The longer the flow, the longer the scouring occurs and in severe circumstances this will cause landslides. The purpose of this study was to observe changes in the water level at the turn starting from points P1 (STA 80), P2 (STA 110), P3 (STA 140), P4 (STA 170), P5 (STA 200). This research was carried out by testing experimental models in the laboratory using artificial channels with a scale model for 30 minutes of flow time to study the pattern of cliff landslides in river bends with the resulting discharge with 2 experiments, namely (Q1 = 0.15cm3 / second), (Q2 = 3.75cm3 / second). The results of this study indicate that there is considerable erosion on the outside of the channel bends. While the deposition is clearly visible on the inside of the channel bend and the narrowing of the channel on the inside of the curve with the first experiment the maximum results of scouring (2.1cm) of sediment (3cm), the second experiment getting the maximum results of scouring (2.5cm) and (3.1cm

Addressing the combined hazards of flooding and water scarcity in the same region requires a large-scale, evidence-based approach to disaster risk reduction. This investigation presents the evaluation of flood hazard area on a large scale for floodprone Barzan region as a multi-criteria index, located 80 km northeast of Erbil City-Iraqi Kurdistan Region. The Flood Hazard Index (FHI) is delimited, and GIS spatial analysis is utilized to predict its value. The methodology, integrates information from ten parameters: Topographic of Wetness Index (TWI), Elevation (E), Slope (S), Precipitation (P), Land Use Land Cover (LULC), Normalized Difference Vegetation Index (NDVI), Distance from River (D1), Distance from Road (D2), Density of Drainage (D3), with type of Soil (S). The importance of each characterizes in flood amount and severity is reflected through weight values calculated utilizing the Analyzing Hierarchy Process (AHP). Flood hazard mapping is generated by superimposing information from the parameters based on their weight values. The generated flood hazard map for the area under study, classifying flood vulnerability into five categories: very low, low, moderate, high, and very high. The generated flood hazard map for the study area indicates that regions with very high and high flood susceptibility are predominantly located in the central, southeastern, and southwestern parts, with a smaller area in the northwest. The distribution of the area across different flood susceptibility levels is as follows: 14% falls into the very high category, 24% into the high category, 23% into the moderate category, 25% into the low category, and 14% into the very low category. Areas with very high flood vulnerability are primarily situated near rivers, particularly the Greater Zab and Rezan Rivers. The method's accuracy is validated through sensitivity analysis, exploring various weight values and alternative scenarios. The sensitivity analysis leads to a revised index FHIS and flood mapping, reaffirming the methodology's robustness. Comparison with historical flood actions confirms the validity of the proposed approach. The methodology therefore suggests itself as a basis for large-scale predictive flood hazard mapping. The parameters of this approach also suggest it as a potential basis for planning Naturebased Solutions (NABs) on a large geographic scale.

On 24 July 2014, an exceptionally large flood (recurrence interval ca. 150 years) caused large-scale inundations, severe overbank sedimentation, and damage to infrastructure and buildings along the Emme River (central Switzerland). Widespread lateral bank erosion occurred along the river, thereby entraining sediment and large wood (LW) from alluvial forest stands. This work analyzes the catchment response to the flood in terms of channel widening and LW recruitment and deposition, but also identifies the factors controlling these processes. We found that hydraulic forces (e.g., stream power index) or geomorphic variables (e.g., channel width, gradient, valley confinement), if considered alone, are not sufficient to explain the flood response. Instead, the spatial variability of channel widening was first driven by precipitation and secondly by geomorphic variables (e.g., channel width, gradient, confinement, and forest length). LW recruitment was mainly caused by channel widening (lateral bank erosion) and thus indirectly driven by precipitation. In contrast, LW deposition was controlled by channel morphology (mainly channel gradient and width). However, we also observed that extending the analysis to the whole upper catchment of the Emme River by including all the tributaries and not only to the most affected zones resulted in a different set of significant explanatory or correlated variables. Our findings highlight the need to continue documenting and analyzing channel widening after floods at different locations and scales for a better process understanding. The identification of controlling factors can also contribute to the identification of critical reaches, which in turn is crucial for the forecasting and design of sound river basin management strategies.

On 24 July 2014, an exceptionally large flood (recurrence interval ca. 150 years) caused large-scale inundations, severe overbank sedimentation, and damage to infrastructure and buildings along the Emme River (central Switzerland). Widespread lateral bank erosion occurred along the river, thereby entraining sediment and large wood (LW) from alluvial forest stands. This work analyzes the catchment response to the flood in terms of channel widening and LW recruitment and deposition, but also identifies the factors controlling these processes. We found that hydraulic forces (e.g., stream power index) or geomorphic variables (e.g., channel width, gradient, valley confinement), if considered alone, are not sufficient to explain the flood response. Instead, the spatial variability of channel widening was first driven by precipitation and secondly by geomorphic variables (e.g., channel width, gradient, confinement, and forest length). LW recruitment was mainly caused by channel widening (lateral bank erosion) and thus indirectly driven by precipitation. In contrast, LW deposition was controlled by channel morphology (mainly channel gradient and width). However, we also observed that extending the analysis to the whole upper catchment of the Emme River by including all the tributaries and not only to the most affected zones resulted in a different set of significant explanatory or correlated variables. Our findings highlight the need to continue documenting and analyzing channel widening after floods at different locations and scales for a better process understanding. The identification of controlling factors can also contribute to the identification of critical reaches, which in turn is crucial for the forecasting and design of sound river basin management strategies.

On 24 July 2014, an exceptionally large flood (recurrence interval ca. 150 years) caused large-scale inundations, severe overbank sedimentation, and damage to infrastructure and buildings along the Emme River (central Switzerland). Widespread lateral bank erosion occurred along the river, thereby entraining sediment and large wood (LW) from alluvial forest stands. This work analyzes the catchment response to the flood in terms of channel widening and LW recruitment and deposition, but also identifies the factors controlling these processes. We found that hydraulic forces (e.g., stream power index) or geomorphic variables (e.g., channel width, gradient, valley confinement), if considered alone, are not sufficient to explain the flood response. Instead, the spatial variability of channel widening was first driven by precipitation and secondly by geomorphic variables (e.g., channel width, gradient, confinement, and forest length). LW recruitment was mainly caused by channel widening (lateral bank erosion) and thus indirectly driven by precipitation. In contrast, LW deposition was controlled by channel morphology (mainly channel gradient and width). However, we also observed that extending the analysis to the whole upper catchment of the Emme River by including all the tributaries and not only to the most affected zones resulted in a different set of significant explanatory or correlated variables. Our findings highlight the need to continue documenting and analyzing channel widening after floods at different locations and scales for a better process understanding. The identification of controlling factors can also contribute to the identification of critical reaches, which in turn is crucial for the forecasting and design of sound river basin management strategies.

Recent geographers in fluvial geomorphology include hydrological characteristics has become an important aspect to study the riverine regime. The present work signifies the hydraulic characters like velocity, discharge, water head calculations etc. of Karatoya river in Jalpaiguri District, West Bengal. Hydrological problems in the field of geomorphology are more concerned with identification of relationships and the impact of fluvial processes of a river basin. As the water flowing over the surface of the earth is a dominant agent of landscape sculpturing, a qualitative and quantitative study of stream discharges, stream velocity, and other hydraulic parameters also with the bank characters etc. will be necessary in understanding the nature of fluvial processes. II. HISTORICAL SIGNIFICANCE OF KARATOYA

River channel sediment dynamics are important in integrated catchment management because changes in channel morphology resulting from sediment transfer have important implications for many river functions. However, application of existing approaches that account for catchment-scale sediment dynamics has been limited, largely due to the difficulty in obtaining data necessary to support them. It is within this context that this study develops a new, reach-based, stream power balance approach for predicting river channel adjustment. The new approach, named ST:REAM (Sediment Transport: Reach Equilibrium Assessment Method), is based upon calculations of unit bed area stream power (ω) derived from remotely sensed slope, width and discharge datasets. ST:REAM applies a zonation algorithm to values of ω that are spaced every 50m along the catchment network in order to divide the branches of the network up into relatively homogenous reaches. ST:REAM then compares each reach's ω value with the ω of its upstream neighbour in order to predict whether or not the reach is likely to be either erosion dominated or deposition dominated. The paper describes the application of ST:REAM to the River Taff in South Wales, UK. This test study demonstrated that ST:REAM can be rapidly applied using remotely sensed data that are available across many river catchments and that ST:REAM correctly predicted the status of 87.5% of sites within the Taff catchment that field observations had defined as being either erosion or deposition dominated. However, there are currently a number of factors that limit the usefulness of ST:REAM, including inconsistent performance and the need for additional, resource intensive, data to be collected to both calibrate the model and aid interpretation of its results.

This paper examines the feasibility of a basin-scale scheme for characterising and quantifying river reaches in terms of their geomorphological stability status and potential for morphological adjustment based on auditing stream energy. A River Energy Audit Scheme (REAS) is explored, which involves integrating stream power with flow duration to investigate the downstream distribution of Annual Geomorphic Energy (AGE). This measure represents the average annual energy available with which to perform geomorphological work in reshaping the channel boundary. Changes in AGE between successive reaches might indicate whether adjustments are likely to be led by erosion or deposition at the channel perimeter. A case study of the River Kent in Cumbria, UK, demonstrates that basin-wide application is achievable without excessive field work and data processing. However, in addressing the basin scale, the research found that this is inevitably at the cost of a number of assumptions and limitations, which are discussed herein. Technological advances in remotely sensed data capture, developments in image processing and emerging GIS tools provide the near-term prospect of fully quantifying river channel stability at the basin scale, although as yet not fully realized. Potential applications of this type of approach include system-wide assessment of river channel stability and sensitivity to land-use or climate change, and informing strategic planning for river channel and flood risk management.

Previous studies of alluvial rivers have shown that channel patterns form a continuum controlled by interactions among factors such as gradient, discharge, sediment size, and bank strength. Data from channels in the permanent wetlands of the Okavango Delta add to these findings by focusing on pattern transitions in channels with banks formed by sedges and grasses that are rooted in peat and underlain by unconsolidated sediment. Channels are well defined, and transport fine-medium sand as bedload between the vertical, vegetation-lined banks. Water depths, velocities, grain sizes, and bankline vegetation do not vary significantly or systematically downstream, but the permeable banks allow water to leak from the channels, contributing to an overall downstream decrease in discharge and width. In addition, as the Okavango River flows from the <12 km wide 'Panhandle' and splits into distributaries in the broader 'Fan', valley gradient steepens by c. 60 per cent. These downstream changes result in channel pattern adjustments. In the Panhandle, the Okavango River is a relatively wide (c. 30-100 m), actively meandering, sinuous channel (P > 2•0), but further downstream in the Fan, the narrower (<40 m) distributaries follow laterally stable, less sinuous ('straight') courses (P < 1•75). Some channel pattern discrimination diagrams based on simple indices of gradient, discharge, sediment size or stream power are inadequate for analysing the meandering-straight transition in the Okavango but Parker's (1976) approach, based on ratios of depth-width and slope-Froude number, accurately characterizes the transition. Our field observations, combined with the results from previous experimental studies, suggest that in relatively wide channels (w/d > 10), thalweg meandering results in scour of the unconsolidated sediment at the bank base, leading to undermining and collapse of the vegetation, and to slow meander migration. However, as channels narrow downstream (w/d < 10) with discharge losses, proportionally increasing sidewall drag exerted by bankline vegetation suppresses thalweg meandering and bank scour, and channels follow stable, less sinuous courses.

Single-thread, gravel-bed streams of moderate slope in the northern Negev are characterized by three channel units: bars exhibit steeper than average slopes and poorly sorted mixtures of small-medium cobbles and coarse-very coarse pebbles; flats are associated with more gentle slopes and well-sorted medium-fine pebbles and granules; and transitional units have intermediate slopes and grain size. In general, all three units are planar, span the full channel width and have well-defined boundaries. Bars and flats are more common than the transitional units and alternate downstream for distances of several hundred metres, forming sequences that are reminiscent of the riffle-pool structure commonly observed in humid-temperate gravel-bed rivers. A notable contrast is the absence of significant bed relief: bars lack crests and flats lack depressions. The relative lack of bed relief in bar-flat sequences is attributed to the high rate of sediment supply from the sparsely vegetated hillslopes which promotes the infilling of depressions and to the erosion of crests under conditions of intense transport. This reduction of bed relief lowers channel roughness, which in turn increases flow velocity and, therefore, the ability of the channel to transmit the large sediment loads it receives. Although our analyses pertain to a semi-arid river system, the results have wider implications for understanding the adjustment of channel bedform to high sediment loads in other fluvial environments.

The focus of this study is to describe the hydraulic effects of stormwater discharge, thus sediment transport occurring as a result of increased discharge from a stormwater detention pond, based on measurements made in a small high-slope Danish stream. In order to extrapolate the findings and predict the result of larger discharge flow rates from the detention pond in this study, 11 traditional threshold equations were tested, and results were compared to the sediment transport experiment with five formulas predicting the threshold based on shear stress and six based on stream power. The sediment transport experiment was constructed as a staircase pattern, stepwise increasing the discharge. During the experiment, measurements of sediment transport in the stream were made in two stations downstream from the point of discharge. Results from those measurements showed that there was no notable correlation between suspended sediment transport and bed sediment transport, and that suspended transport peaked during the periods of low flow conditions. Bed sediment transport peaked before the maximum flow, indicating that the available sediment for transport is a limiting factor. When comparing the calculated threshold of the collected sediment particle sizes to the shear stress and stream power calculated during the experiment, all 11 tested formulas overestimated the sediment transport and particle size moved by a specific flow. This result is in correspondence with results found in other experiments, and here the expected explanation is that the form roughness of the stream bed makes less energy available for sediment transport. This implies that the hydraulic impact from discharge of stormwater into small streams has to be evaluated on a caseby-case basis, rather than relying on general threshold sediment transport models.

Four coarse-grained braided channels undergoing rapid deposition in the hyper-arid Arava Rift Valley have been studied to determine the dynamics of strata deposition by monitored flood events and to ascertain the sedimentologic character of these event deposits. The systems span granule-sand to sandy-gravel bed material having longitudinal bars with simultaneously increasing grains size, slope and bed relief. Scour and fill were monitored along cross-sections using scour chains and repeat surveys, while event strata (=fill) were described with reference to sediment calibre, type of support, grading, thickness and location. Unlike many previous investigations of gravel sedimentology, the source of information for this study involves not only stratigraphic description but also observation of the concomitant dynamics of the channel bed as expressed by the extent of event deposition. The texture of the deposited sediment generally coarsens in sympathy with the surface bed texture of the studied channels. The thickness of event-deposited sediments increases as the magnitude of flow events increases. The stratigraphic record is characterized by: (1) sub-parallel stratification, (2) no grading, (3) clast-supported gravel infilled with sand-granule matrices and (4) rapid sedimentation rates. This character is hypothesized to mirror the elevated bedload fluxes in this hyper-arid fluvial setting. Lacking densely-vegetated banks, the proximal braidplain of this arid environment often contains no extensive fine-textured floodplain successions. The depth of fill as well as the extent of strata obliteration increase with flood magnitude, explaining the increased preservation potential of event strata with increase in flood magnitude. It also explains why most event strata, including large ones, are only partially preserved.

Mapping flood susceptibility is essential for identifying flood-prone areas and informing flood risk management strategies. This study applies a multi-criteria decision-making approach to assess flood vulnerability in Ain Smara and its surrounding areas in Constantine, Algeria, which are highly susceptible to flooding. The analysis combines the Analytical Hierarchy Process (AHP), Geographic Information System (GIS), Remote Sensing (RS), and the Google Earth Engine (GEE) platform. Ten key flood-related criteria were selected, including the Topographic Wetness Index, Elevation, Slope, Precipitation, Land Cover/Land Use, Normalized Difference Vegetation Index (NDVI), Distance from Rivers, Distance from Roads, Drainage Density, and Lithology, along with more than 25 sub-criteria. A pairwise comparison matrix (PCM) was used to assign relative importance to each criterion based on its impact on flood susceptibility. The Topographic Wetness Index was identified as the most significant criterion, while Distance from Roads was deemed the least significant. The results reveal that approximately 4.00% of the area is classified as having high to very high flood susceptibility, particularly near the Rhumel River. Roughly 30% of the area has low to very low susceptibility, and the remaining 66% is categorised as moderately susceptible to flooding. Notably, most of the identified flood-prone zones correspond with areas that experienced flooding events in 2019, 2020, and 2021, predominantly within 1,000 meters of river courses. These findings demonstrate the GIS-AHP technique’s effectiveness in generating reliable flood susceptibility maps, especially when numerous criteria are considered.

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Accumulation of instream large wood (i.e., fallen trees, trunks, branches, and roots) at bridges during floods may exacerbate scour and cause structural failure. Yet, explaining and predicting the likelihood of a bridge trapping wood remains challenging. Quantitative data regarding wood accumulation at bridges are scarce, and most studies have been based on flume experiments. Equations proposed to estimate the accumulation probability use variables such as flow velocity, Froude number, and approaching wood volume or size which are difficult to obtain. Other evaluations based on technical reports and information regarding wood removal prevail qualitative. But a data-driven approach combining multiple quantitative accessible variables at the local and catchment scales remains lacking. As a result, the controlling parameters explaining whether a bridge is prone to trap wood are still unclear. This work aims to fill this gap by analysing a database of 49 bridges across the United Kingdo...

The purpose of this study is to prepare landslide susceptibility map using a spatial multi criteria evaluation approach (SMCE) in a landslide-prone area (Haraz) in Iran. In the first stage, landslide locations were identified in the study area from interpretation of aerial photographs, and field surveys. In the second stage, twelve data layers were used as landslide conditioning factors for susceptibility mapping. These factors are slope, aspect, altitude, lithology, land use, distance from rivers, distance from roads, distance from faults, topographic wetness index, stream power index, stream transport index, and plan curvature. Next, landslide-susceptible areas were analyzed using the SMCE approach and mapped using landslide conditioning factors. For verification, the results of the analyses was compared with the field-verified landslide locations. Additionally, the receiver operating characteristics (ROC) curves for all landslide susceptibility models were drawn and the area under curve values was calculated. Landslide locations were used to validate results of the landslide susceptibility map generated using the SMCE approach and the verification results showed a 76.84% accuracy. According to the results of the AUC evaluation, the produced map has exhibited good performance.

A physically-based mathematical model is developed to simulate flow on a graded terrace idealized as an erodible and pervious overland flow plane and a terrace channel. A nonlinear kinematic wave scheme is adopted for routing the water and the sediment discharges through the overland and the channel flows. The erosion and the sediment transport capacities, the erosion rate and the sediment concentration in the overland flow are interrelated through a closed form equation. The transport capacity of the channel is determined by using the unit stream power concept. A finite difference method is employed to solve the partial differential equations of the mathematical model. The model is verified by a set of observed data.