ICG2022-19

The disastrous earthquake that affected south-eastern Sicily in 1693 caused over 60,000 causalities and the total destruction of several villages and towns along the coastline between Siracusa, Ragusa, and Catania. During the aftermath of the earthquake, a tsunami struck the Ionian coasts of Sicily and the Messina Strait and was probably recorded even in the Aeolian Islands and Malta. Over the course of the past decades, the event has been highly debated regarding the location of the seismogenic source and the possible cause of the associated tsunami. The marine event has been correlated to both, a submarine landslide and a coseismic displacement at the seafloor. To better defining the most reliable sources and dynamics of the tsunami, we couple high-resolution marine seismic survey data with hydrodynamic modelling to simulate various scenarios of tsunami generation and propagation. Results from the simulations have been compared with geomorphological evidences of past tsunami impacts, described in previous work along the coast of south-eastern Sicily, and within historical chronicles and reports. The most reliable scenario considers the 1693 event composed by two different tsunami waves: a first wave generated by the coseismic fault displacement at the seafloor and a second wave generated by a submarine landslide, triggered by the earthquake shaking. Distinct tsunami modelling runs showed that a simultaneous movement between fault displacement and submarine mass movement could determine a destructive interference on the tsunami waves, with a reduction of wave height. For this reason, the second tsunami wave was probably generated with a maximum delay of few minutes after the one generated by the earthquake and induced a higher flooding. The double-source model could explain the observation why in the course of other destructive earthquakes which occurred in south-eastern Sicily, like that of 1169 AD, the associated tsunami caused less damages. This implies the need to better map, define and evaluate the coastal hazards offshore eastern Sicily accounting to this kind of tsunami events.

How to cite: Scicchitano, G., Gambino, S., Scardino, G., Barreca, G., Gross, F., Mastronuzzi, G., and Monaco, C.: The enigmatic 1693 AD tsunami in the eastern Mediterranean Sea: new insights on the triggering mechanisms and propagation dynamics, 10th International Conference on Geomorphology, Coimbra, Portugal, 12–16 Sep 2022, ICG2022-29, https://doi.org/10.5194/icg2022-29, 2022.

The 23 June 2020 La Crucecita earthquake occurred at 10:29 hr on the coast of Oaxaca in a Mw 7.4 megathrust event at 22.6 km depth and triggered a tsunami recorded at tide gauge stations and a DART off the coast of Mexico. We describe here details of the rapid response survey of vertical coseismic deformation, tsunami, geologic effects, ecological damage to coral reef ecosystems, and lessons from working in the field during the COVID-19 crisis. Moreover, ecological damage caused by earthquakes and tsunamis is an important problem barely addressed in recent studies. Coral reefs are some of the world’s most fragile ecosystems, they are highly sensitive to environmental changes. These communities had suffered a significant impact by both coseismic coastal uplift and the tsunami. We surveyed 44 km along the coast of Oaxaca. Because of the COVID-19 pandemic, some local communities enforced rules of confinement. They closed the access roads to their villages prohibiting the passage of outsiders to their community. We assessed coseismic coastal uplift by means of mortality caused by vertical displacement of intertidal organisms, bleaching of coral communities, resurveying of benchmarks, and measured tsunami runup. Our results show coastal uplift of 0.53 m near the epicenter, decreasing farther away from it, and up to 0.8 m, the latest related to exposure of the coast. Our values of coastal uplift, ca. 0.53 m fit well with 0.55 m of uplift reported by tide gauge data at Huatulco. These amounts of coastal uplift left coral communities exposed and not covered by seawater even at high tide, causing the death of these communities. Coastal uplift and low tide at the time of the event limited the tsunami inundation and runup on the Oaxaca coast. Nevertheless, we found tsunami inundation evidence at four confined coastal sites reaching a maximum runup of 1.5 m.  The enclosed morphology of these sites determined higher runup and tsunami inundation. Local coastal morphology effects are not detected in tsunami models lacking detailed bathymetry and topography. This issue needs to be addressed during tsunami hazard assessments. 

How to cite: Ramirez-Herrera, M. T., Corona, N., Nava, H., Romero, D., Torija, H., and Hernández Maguey, F.: Coseismic uplift and coral mortality caused by the 23 June 2020, Mw 7.4 La Crucecita, Oaxaca, Mexico earthquake and tsunami, 10th International Conference on Geomorphology, Coimbra, Portugal, 12–16 Sep 2022, ICG2022-444, https://doi.org/10.5194/icg2022-444, 2022.

Mountain environments are naturally exposed to debris flows, a mass movement which represents one of the major geomorphological hazard sources for urbanized alluvial fan. In the last decades, climate change has contributed to extreme precipitations increase, making debris flows both larger and more frequent than in the past. The assessment and management of the risk associated with these events, according to UE Flood Directive, is feasible and desirable by using appropriate practices and the best available technology that do not imply excessive costs.
In line with the above-mentioned European Directive, we present a multidisciplinary approach for the numerical modelling of the debris flow event that occurred on July 27-28, 2019 in Abbadia San Salvatore, a  village located in a catchment of the Mt. Amiata area (Southern Tuscany, Italy). Debris flow was triggered by an extreme rainfall of 110 mm/1 h causing a channelled erosive process, and the subsequent obstruction of a culvert at the entrance to the village, flooding and damaging it.
Mt. Amiata is an extinct Pleistocene volcano mainly consisting of trachidacitic lavas characterized by a pervasive saprolite weathering, resulting in a large amount of residual loose debris resting on the hillslopes and along the hydrographic network. Specific geological and engineering-geological field investigations were carried out to assess the availability of debris material and its hydrological behaviour, providing more constraints for numerical modelling. 
The Green-Ampt model, implemented in the FLO-2D software, was used for the evaluation of discharge values in the hydrographic network. Subsequently, the debris flow modelling was conducted applying the WEEZARD system, composed of a previously developed advanced two-phase debris-flow model (TRENT2D), re-coded as a web service. The mass movement was simulated to quantify erosive and depositional processes that occurred during the event. In addition, a specific approach was implemented to model the effect of the culvert that was clogged during the event.
Despite the challenging modelling aspects, the results in terms of debris volume, erosion rates, flooded area and timing of the culvert obstruction, are in agreement with observed data. 
The WEEZARD system has therefore proved to be an effective tool, in line with the indications of the European Directive. Moreover, the reconstruction was obtained using most of the a priori parameters setting. This shows that the used modelling approach has a good predictive capacity and can therefore be reasonably used to support further predictive hazard mapping analyses. Finally, another important element to be highlighted is that an accurate input model based on the integration of detailed geological-geomorphological investigations is necessary to obtain reliable modelling results.

How to cite: Amaddii, M., Rosatti, G., Zugliani, D., Marzini, L., and Disperati, L.: Back-analysis of the Abbadia San Salvatore (Mt. Amiata, Italy) debris flow of July 27-28, 2019 using the WEEZARD system, 10th International Conference on Geomorphology, Coimbra, Portugal, 12–16 Sep 2022, ICG2022-305, https://doi.org/10.5194/icg2022-305, 2022.

The effect of temperature on the stability of slopes in temperate climates is poorly constrained. Experiments demonstrate a clear thermo-hydro-mechanical (THM) response in expansive soils, and evidence of thermally-induced activity exists for some landslides. However, building a representative thermal variable suitable for catchment or regional-scale studies is challenging, owing to heterogeneities in materials and stress histories and the complexity of THM processes. We performed a landslide susceptibility modelling of the portion of Italian territory featuring clay deposits. We utilised the geo-lithological map of Italy and the Italian National Inventory (IFFI), that differentiates among landslide types, and we focused on slow flows, often associated with creep movements. We relied, as one of the inputs, on a ten-year series of Land Surface Temperature (LST) data from MODIS, freely available in Google Earth Engine, and implemented a slope unit-based Generalized Additive Model (GAM) approach to account for nonlinearities in the possible temperature-slope stability relationship. We produced a susceptibility map for clay deposits over the entire Italian territory, and observed a positive dependence of landslide abundance on LST on warmer and gentle slopes, where creep phenomena are common. Higher temperatures are in fact associated with decreased soil and water viscosity and hence enhanced shear creep rates in clays.

How to cite: Loche, M., Scaringi, G., and Lombardo, L.: Land Surface Temperature Controls Stability in Gentle Clay Slopes, 10th International Conference on Geomorphology, Coimbra, Portugal, 12–16 Sep 2022, ICG2022-411, https://doi.org/10.5194/icg2022-411, 2022.

Mass movements are phenomena which, due to their random nature, generate very significant risks, affecting the human heritage. The Southern Escarpment of the Bamileke Plateau (ESPB) is a region with a rugged relief. This relief, associated with climatic bad weather, is the seat of numerous instabilities linked to mass movements. This study aims to establish a relationship between geomorphological factors and mass movements along the Southern Escarpment of the Bamileke Plateau. The methodological approach used in this work is based on the inventory of mass movements from field campaigns and the exploitation of satellite images as well as the mapping of geomorphological factors. The geomorphological map, the slope map and the slope direction map are the factor maps that were used to assess the occurrence and susceptibility to mass movements in the region. Based on the field observations and satellite images, a spatial distribution map of mass movement was obtained. This map reveals that landslides are the most significant mass movement in the study area with a representativeness rate of 90% compared to subsidence and rock falls which represent respectively 5% of recorded mass movements.  The steepest slopes are the areas with the highest concentration of landslides in the region. The susceptibility map obtained from the geomorphological factor maps reveals that 16.95% represent low probability areas, 43.39% represent moderate probability areas, 29.77% represent high probability areas and 9.89% represent very high probability areas. The superimposition of the susceptibility map on the spatial distribution map of mass movements allows the relevance of this susceptibility map to be validated and assessed. The information obtained on the regional geomorphology clearly explains the special distribution of recorded mass movements, especially landslides.

How to cite: Ndonbou, R. M., Nkouathio, D. G., Zangmo Tefogoum, G., Guedjeo, C. S., and Tematio, P.: Involvement of geomorphological factors on the occurrence of mass movements along the Southern Escarpment of the Bamileke Plateau (West-Cameroon), 10th International Conference on Geomorphology, Coimbra, Portugal, 12–16 Sep 2022, ICG2022-669, https://doi.org/10.5194/icg2022-669, 2022.

Abruzzo Region (Central Italy) is largely affected by landslide phenomena, widespread from the mountainous to the coastal areas. The area is located in the central-eastern part of the Italian peninsula. It is framed in a complex geological and geomorphological framework, closely connected to the combination of endogenous (morphotectonics) and exogenous processes (slope, fluvial, karst, and glacial processes). Landslide phenomena are linked to the interaction of geological, geomorphological, and climatic factors (instability factors) in response to trigger mechanisms, mostly represented by heavy rainfall events, seismicity, or human action. This work illustrates the results of multidisciplinary analyses carried out in recent years in different physiographic and geomorphological-structural contexts (chain, foothills, fluvial, and coastal areas). These analyses are based on the combination of classic and advanced methods, including morphometric analysis of the topography and hydrography, detailed geological-geomorphological field mapping, geostructural analysis, photogeological analysis, and numerical modeling. Selected case studies are chosen as representative of the main active geomorphological processes affecting different geomorphological/morphostructural environments, with reference to the predisposing and/or triggering factors. The main landslide cases analyzed and discussed in this work consist of: debris flow and rockfalls in a mountain area, widely altered by wildfire events (Montagna del Morrone case); complex landslides systems in the foothills, characterized by a very rough topography documenting the activity of long-term landslide phenomena (Ponzano and San Martino sulla Marrucina cases); sliding and complex landslides (topples and rockfalls) in hilly-piedmont areas, following a heavy snow precipitation event and a moderate seismic sequence (Castelnuovo di Campli case) and induced by episodic and localized cliff recession processes combined with wave-cut and gravity-induced slope processes (Abruzzo rock coast cases). The work outlines the importance of combining geological and geomorphological approaches with detailed field and laboratory data analysis to characterize morphologies, bedrock features, structural features and jointing, superficial continental deposits, and landforms distribution. This allows supporting large-scale analyses to evaluate hazards and risks posed by different landslides with different magnitudes in different environments. This work could represent a practical integrated approach in geomorphological studies for landslide hazard modeling at different spatial scales, readily available to interested stakeholders. Furthermore, it could provide a scientific basis for implementing sustainable territorial planning, emergency management, and loss-reduction measures.

How to cite: Paglia, G., Mancinelli, V., Esposito, G., Miccadei, E., Cinosi, J., Piattelli, V., and Fazzini, M.: Landslide hazard in the Abruzzo Region (Central Italy): landslides case studies in different geomorphological/morphostructural environments, 10th International Conference on Geomorphology, Coimbra, Portugal, 12–16 Sep 2022, ICG2022-148, https://doi.org/10.5194/icg2022-148, 2022.

Landslide susceptibility assessment implemented by statistical methods relies on a basic concept according to which past and future landslides depends on the same causes of past failures. As a consequence, statistical inference can explore the relationships between past phenomena and geo-environmental variables to spatially recognize landslide-prone areas. Coherently, the quality and the prediction skill of the model and the relative prediction image heavily depend on the completeness of the landslide scenario exploited. However, landslide archives are frequently biased due to more or less limits we face in reconstructing reliable and complete landslide maps. In this sense, exploiting public available archives compiled from local administration is a goal in landslides susceptibility evaluation. In fact, usually, these types of archives are multitemporal and collect potentially or real destructive phenomena based on direct observation of failures. The use of these types of archives allows obtaining immediately available landslides inventories, saving time and resources from mapping.

On the other hand, public landslide inventory may be wealthy close to urban areas while, usually, failures in the agricultural lands are sporadic or under-represented.

In this study, starting from the available slide (56 cases) and flow (115 cases) inventories of the Torto River basin (420 km², central-northern Sicily), which were prepared by the “Dipartimento Regionale dell’Autorità di Bacino del Distretto Idrografico Sicilia”, and a set of twelve geo-environmental predictors, two basin susceptibility models (for slides and flows, respectively) were prepared by applying Multivariate Adaptive Regression Splines (MARS). Good performance for the basin-scale models was then assessed according to cross-validation validation strategies. Then, in a randomly selected small sub-catchment (Sciara stream, 21.5 km²), the prediction images produced at a basin-scale were validated in recalling the landslides of two local systematic and multitemporal remotely recognized inventories. Despite the high performance of the basin-scale models, the results at the local scale showed a poor capacity of the models in detecting the two systematic archives, with a non-acceptable sensitivity (0.67 and 0.57 for slide and flow, respectively). The AUC (Area Under the Curve) values are non-acceptable (0.47 and 0.65 for slide and flow, respectively) also.

In order to “boost” the basin-scale susceptibility models, by exploiting a score weighted random selection of 30% of the mapping units of the Sciara stream sub-catchment, a stable/unstable status concerning slide and flow phenomena of the selected cases was assigned by intersecting with the remotely sensed inventories.

Two new basin-scale models were implemented by using these new “hybrid” archives. The two prediction images compared with the systematic inventories of the Sciara sub-catchment reveal an increment of the models’ performance, with high accuracy in predicting positive cases both for slide and local flow types. At the same time, persistent precision in detecting stable cases for local flow arises (~0.8), while a decrease in specificity due to an increment of False Positives suggests potentially new future activations for slide phenomena. However, the important increment of the AUC values (from 0.79 to 0.94 and from 0.8 to 0.94, for slide and local flow, respectively) testifies to a general improvement of the main models.

How to cite: Martinello, C., Mercurio, C., Cappadonia, C., Bellomo, V., Conte, A., Mineo, G., Azzara, G., and Rotigliano, E.: Incomplete landslide archive in landslide susceptibility assessment: a nested strategy for improving results., 10th International Conference on Geomorphology, Coimbra, Portugal, 12–16 Sep 2022, ICG2022-648, https://doi.org/10.5194/icg2022-648, 2022.

Vrancea seismic region, corresponding to the Curvature sector of the SE (Romanian) Carpathians, represents a very active geomorphic region, marked by a wide variety of fluvial, slope gravitational and seismic processes. The morpho-litho-structural traits, the active neotectonic movements, the climatic regime and the human influence are controlling a complex multi-hazard environment. Accordingly, numerous landslide hazard evaluation attempts have been developed during the last decade, and several landslide inventories were used to calibrate and validate statistic and probabilistic susceptibility models. However, comparing various results, it was seen that fine-tuned regional susceptibility models, performing properly from a statistical point of view, resulted in differently-distributed susceptibility classes, potentially inducing confusions among stakeholders and end-users. It is the purpose of this presentation to outline the geomorphic complexity of landslides typology in the study-area, and the induced epistemic uncertainties. Different landslide inventories (based on field mapping, optical remote sensing imagery, radar interpherometry) and bi/multivariate statistical approaches are being discussed, and slope sensitivity, geomorphic complexity of landslides, their evolution, frequency-magnitude relationship, triggering thresholds, morphodynamic sectors and connectivity are evaluated as potential sources of epistemic uncertainties. The presentation concludes with a series of recommendations meant to support the development of more robust and highly predictive susceptibility models and hazard evaluation. By enriching such methodology for evaluating the past, present and future behavior of such processes, the geomorphologists may foster numerous stakeholders to develop the optimal proactive measures for risk prevention and preparedness and the most suitable reactive actions for response and recovery.

Key words: Vrancea seismic region, Romania, Carpathians, landslides typology, epistemic uncertainty

How to cite: Micu, M.: Epistemic uncertainties induced by landslides typology in Vrancea seismic region (SE Carpathians, Romania) and their implication in hazard evaluation, 10th International Conference on Geomorphology, Coimbra, Portugal, 12–16 Sep 2022, ICG2022-731, https://doi.org/10.5194/icg2022-731, 2022.

Landslide susceptibility assessment is a key topic for land-use planning and for the overall safeguard of human activities. In this perspective, a wide range of methods and techniques have been proposed for the evaluation of landslide susceptibility, ranging from statistical methods to the latest deep learning technologies. Besides, Slope-Area plots are also exploited for the evaluation of surficial processes domains and the increasing availability of digital terrain models with higher resolution allows much detailed analyses.

On these premises, we compared slope-area plots produced with high resolution Lidar data with a landslide dataset produced following the LOOM data structure. The analysis has been carried out using only surficial phenomena like flows and falls. Moreover, such landslides have been decomposed into their principal components such as detachment, transit, and accumulation zones in order to perform an accurate evaluation of the geomorphic signature of such features. Each landslide has been also compared with the corresponding reference hillslope, defined as the set of enveloping Morse regions computed using Surface Network.

The plot of slope-area values of the training landslides and their reference hillslopes allows thresholding of the slope values at different contributing area bins, resulting in the mapping of those values exceeding the defined thresholds. Preliminary results show how such defined thresholds based on a proper training dataset could be a valid contribution to the overall topic of landslide susceptibility assessment based on geomorphological criteria at least for surficial landslide types like flow- and fall-like movements.

How to cite: Valiante, M. and Guida, D.: Geomorphic landslide susceptibility assessment using Slope-Area plots, 10th International Conference on Geomorphology, Coimbra, Portugal, 12–16 Sep 2022, ICG2022-665, https://doi.org/10.5194/icg2022-665, 2022.

In this study, the ability of stochastic models to predict landslide susceptibility in the southern sector of the “Via al Llano” highway (Colombia) was assessed. To this aim, an inventory of landslides occurred in the area was prepared by analyzing images available in Google Earth. Multivariate Adaptive Regression Splines (MARS) was employed to model the spatial distribution of the following five data sets of unstable cells selected within each landslide: i) the highest cell (data set MAX), ii) the highest 10% of cells (data set SUP), iii) the lowest cell (data set MIN), iv) the lowest 10% of cells (data set INF), and v) the entire landslide area (data set BODY). The goal of our experiment was to identify which of the calibration data set produces the best prediction of the landslide areas (BODY data set). The data sets were divided into calibration and validation groups of cells by randomly selecting 75% and 25% of the mapped landslides, respectively. In order to evaluate the robustness of the results, ten calibration and validation samples were extracted for each instability data set. The analysis revealed that the most important predictors were Normalized Difference Vegetation Index (NDVI), slope steepness, vertical distance to channel network, elevation and aspect. The receiver operating characteristic (ROC) curves and the area under the ROC curve (AUC), calculated for each of the five instability data sets, indicated that calibrating the models with the lowest landslide pixels (MIN or INF data sets) allows to obtain the most accurate prediction of the validation landslide bodies (BODY data set), achieving AUC values ranging between 0.80 and 0.85.

How to cite: Calderon-Cucunuba, L. P., Vargas-Cuervo, G., and Conoscenti, C.: Stochastic assessment of landslide susceptibility by using five different instability datasets: a case study from the southern sector of the “Via al Llano” highway (Colombia), 10th International Conference on Geomorphology, Coimbra, Portugal, 12–16 Sep 2022, ICG2022-316, https://doi.org/10.5194/icg2022-316, 2022.

Shallow landslides are frequently caused by an interplay of static predisposing factors (e.g., topography), mid-term preparatory factors (e.g., prolonged rainfall, seasonal changes of vegetation, snow melt), and short-term triggers (e.g., heavy rainfall). For large-area assessments, statistical analyses and data-driven approaches are often used to model landslide susceptibility based on spatial environmental variables or to derive critical landslide-triggering rainfall conditions through the definition of empirical rainfall thresholds. Attempts to integrate the spatial and temporal domains in the context of quantitative regional-scale landslide prediction are still rare.

This contribution focuses on the landslide-prone area of South Tyrol, northern Italy (7,400 km²) and presents a novel data-driven modelling procedure that integrates spatial predisposing factors and dynamic preparatory and triggering factors to predict the probability of landslide occurrence in space and time. The approach is based on time-stamped landslide inventory data from 2000 to 2021, high-resolution gridded daily precipitation observations for the same period, and a set of relevant static environmental variables (e.g., including topographic indices, lithology). Data preparation included an initial filtering of rainfall-induced landslide presence observations and a rule-based stratified sampling of landslide absence observations at landslide locations and at non-landslide locations. Cross-validation was implemented in the model developing stage to select optimal time windows to represent pre-landslide preparatory and triggering cumulative rainfall conditions. Modelling was based on a binomial Hierarchical Generalized Additive Model (HGAM) that considered the non-linear influence and interactions (i.e., via tensor products) of static and dynamic environmental variables on landslide occurrence (presence vs. absence) while simultaneously accounting for the nested data structure (i.e., multiple considerations of each location) and seasonal effects. The study also considered different biases inherent in the input data, such as the known underrepresentation of landslide data in locations far from infrastructure or potential reporting biases across years, by averaging-out associated random effect variables. The results were validated quantitatively (spatial and temporal cross-validation) and qualitatively (geomorphic plausibility) and visualized as maps and probability surface plots.

The assessment and validation confirmed the high generalizability and predictive performance of the model. A closer look at the derived relationships allowed to uncover the effects of predisposing, preparatory and triggering factors on landslide occurrence as well as the associated season-dependent variations. From our perspective, this new approach represents a good compromise between the high model flexibility for landslide prediction purposes and the high interpretability for understanding the underlying relationships.

The research leading to these results is related to the PROSLIDE project that received funding from the research program Research Südtirol/Alto Adige 2019 of the Autonomous Province of Bozen/Bolzano – Südtirol/Alto Adige.

How to cite: Steger, S., Moreno, M., Crespi, A., Zellner, P. J., Kohrs, R., Goetz, J., Gariano, S. L., Brunetti, M. T., Melillo, M., Peruccacci, S., de Vugt, L., Zieher, T., Rutzinger, M., Mair, V., and Pittore, M.: Applying a hierarchical Generalized Additive Model to integrate predisposing, preparatory and triggering factors for landslide prediction, 10th International Conference on Geomorphology, Coimbra, Portugal, 12–16 Sep 2022, ICG2022-388, https://doi.org/10.5194/icg2022-388, 2022.

Landslide susceptibility is generally defined as the likelihood of a landslide occurring in a certain area on the basis of local terrain conditions. The aim of susceptibility assessment is to estimate the areas where landslides are likely to occur, based on the assumption that landslides will likely occur under the same conditions under which they occurred in the past.

The purpose of this study is assessing shallow landslide for Negrone and Tanarello catchments which are the upper basins of Tanaro river, a mountainous area within the Palaeo-European Continental Margin of the Alpine External Belt (External Briançonnais sucessions) and the Ligurian units of Maritime Alps of the Alpine Axial Belt (Pennidic Domain). The lithology is composed of silicate and to a minor extent carbonate rocks.

In the last decades, the study area has repeatedly been affected by slope instability events, mainly related to debris flows, characterized by extremely rapid movements. By integrating a detailed (1:5.000) geological and geomorphological field survey with ortophoto interpretation and existing information provided by Arpa Piemonte, landslide inventories have been produced. Only focusing on source areas, susceptibility assessment to debris flow have been performed by using a logistic regression approach, considering as covariates lithology, land use and morphometric factors derived from a digital elevation model (DEM) with a 5 m resolution. Source area pixel have been split into training and validation subsets by adopting a random partition. A certain number of pixels equal to the training set has been randomly extracted among stable cells, to prepare the dataset for logistic regression. The best set of covariates in controlling the spatial distribution of debris flows have been identified by iteratively adding one variable at a time and comparing the results. Susceptibility model fitting and prediction skill have been assessed based on validation subsets. The role of the considered factors in predisposing debris flow failures has been evaluated, discussing differences in the outcomes.

How to cite: Buleo Tebar, V., Lucà, F., Bonasera, M., Licata, M., Fubelli, G., and Robustelli, G.: Landslide susceptibility assessment at a local scale: logistic regression analysis for upper Tanaro catchments, 10th International Conference on Geomorphology, Coimbra, Portugal, 12–16 Sep 2022, ICG2022-274, https://doi.org/10.5194/icg2022-274, 2022.

The present study endeavours to explore the efficacy of machine learning models in landslide predictions triggered by extreme rainfall events along Highway from Bandipora to Gurez, J&K, India. Random Forest (RF) and Logistic Regression (LR) Models were employed on account of their technical utility and logical procedure to transform the decision making into a series of algorithm steps that eventually culminate in low variance and minimum bias. The crux of these models was to find the optimal parameters for targeted feature i.e. landslide prediction. Hyper-parameter tuning was used to optimize the performance of the respective models. These models were evaluated for accuracy using the receiver operating characteristics, area under the curve (ROC-AUC) and false negative rate (FNR). When antecedent rainfall data is incorporated in the prediction models, both (LR and RF) performed better with an AUC of 0.825 and 0.843 respectively. The results reveal a positive correlation between antecedent precipitation and landslide occurrence rather than between single-day landslide and rainfall events. In case of FNR, LR and RF improved to14.32% and 15.92% respectively, when antecedent rainfall was included in the analysis. Comparing the two models, LR model’s performance is well within the acceptable limits of FNR and therefore could be preferred for landslide prediction and early warning over RF. LR model’s incorrect prediction rate is 8.48% without including antecedent precipitation data and 5.84% including antecedent precipitation data. Our study calls for wider use of Machinery Learning Models for developing early warning systems of landslides.

Keywords: -Rainfall Triggered; Machine Learning Models; Area Under Curve; False Negative Rate.

How to cite: Nanda, Dr. A. M., Ahmed, P. P., and Kanth, P. T. A.: Prediction of Rainfall Induced Landslides Using Machine Learning Models along Highway-Bandipora to Gurez Road, J&K, India, 10th International Conference on Geomorphology, Coimbra, Portugal, 12–16 Sep 2022, ICG2022-390, https://doi.org/10.5194/icg2022-390, 2022.

On January 13th, 2001, El Salvador was hit by an earthquake of magnitude 7.7 which triggered thousands of landslides, causing 1259 fatalities and extensive damage to infrastructures. The analysis of aerial images provided by the CNR (Centro Nacional de Registros de El Salvador), which were taken a few days after the event, allowed us to map 1005 seismically-induced landslides that occurred in a study area extended for 92 km². The objective of this experiment was to verify whether it is possible to predict the spatial distribution of these landslides through a stochastic approach that combines a rainfall-induced landslide susceptibility (SUSC) model, which is based on preparatory factors, and an earthquake-triggered landslide predictive (TRIGGER) model, which is based on seismic parameters such as peak ground acceleration (PGA) and distance to the epicenter (ED). The SUSC model was calibrated by using an inventory of 5609 landslides that occurred in November 2009 in the area of the San Vicente volcano, due to the simultaneous action of low-pressure system 96E and Hurricane Ida. The TRIGGER model was instead trained with the 20% of the earthquake-triggered landslides, whereas the remaining 80% was used to validate both the SUSC and TRIGGER models, as well as an ensemble model obtained by using as predictors PGA, ED and the landslide probability calculated by the SUSC model. In order to evaluate the robustness of the results, ten calibration and validation samples were randomly extracted from the 2001 landslide inventory. Multivariate adaptive regression splines (MARS) was used as modelling technique. The predictive performance of the models was evaluated by using receiver operating characteristics (ROC) curves and the area under the ROC curve (AUC).

The validation results revealed a slightly better performance of the SUSC model (average AUC = 0.719; AUC st.dev. = 0.008) with respect to the TRIGGER model (average AUC = 0.707; AUC st.dev. = 0.009). Moreover, the analysis highlighted that the best predictive ability is achieved by the ensemble model (average AUC = 0.743; AUC st.dev. = 0.006). These results suggest that, in the event that only some of the landslides triggered by an earthquake are known, as usually happens shortly after the event, it is possible to use the approach proposed in this study to identify those sites where the other landslides are more likely to have occurred.

This work is a part of the CASTES project, which is funded by the Italian Agency for Development Cooperation (AICS) and focuses on promoting research and training activities in the field of earth sciences in El Salvador (CA).

How to cite: Mercurio, C., Martinello, C., Argueta-Platero, A. A., Azzara, G., Rotigliano, E., and Conoscenti, C.: Predicting earthquake-induced landslides by using a stochastic modeling approach which combines preparatory and triggering factors: a case study of the co-seismic landslides occurred on January 2001 in El Salvador (CA), 10th International Conference on Geomorphology, Coimbra, Portugal, 12–16 Sep 2022, ICG2022-543, https://doi.org/10.5194/icg2022-543, 2022.

Several methodologies are described in the literature for the classification of terrain susceptibility to landslides, including probabilistic analysis based on landslide inventories and diagnoses based on the crossing of thematic maps. The latter approach prevails in Brazil, where thematic information has privileged geological maps and some geotechnical and geomorphological information. Other relevant categories, such as vegetation and land use are scant. This study poses a functionally based methodology of pertinent geological-geotechnical, geomorphological, vegetation cover and land use categories to assess terrain susceptibility to shallow planar landslides by following the geo-hydroecological approach. The D’Antas Creek basin (53 km2) located in Nova Friburgo, Rio de Janeiro state (SE-Brazil), was chosen as a pilot-area, as it was severely affected by 327 landslides in January 2011, 85% of which were triggered as shallow planar landslides with the slip surface around 1,5m + 0.5 m/deep. The geo-hydroecological approach follows empirical-analytical and integrative procedures of morphological, functional and historical knowledge, in a systemic view of the geographic space and articulating different hierarchical levels of the geo-ecosystem, as summarized in Coelho Netto et al. (2020). This approach is based on relevant functional parameters, indexes and categories affecting shallow planar landslides, all of which were synthetized in three cartographic bases: hydro-geomorphological; geological-geotechnical and vegetation-land use. Human-made activities were used to ponder their interferences to increase or to reduce the terrain susceptibility. The use of GIS (Geographic Information System) and multi-criteria analysis (i.e. Analytic Hierarchy Process - AHP) were incorporated into the geo-hydroecological approach. Soil geotechnical parameters increased the ability of the susceptibility model to identify potential areas for landslides. Integrated hydro-geomorphological conditions presented a more significant impact with the susceptibility classes. Vegetation and land use cover were reclassified on the basis of their ecological, hydrological and mechanical functionality, allowing a simplification of its classes. The final landslide susceptibility map included four categories (very high; high; medium; and low); areas classified as very high and high susceptibility overlaps with > 75% of the 2011 landslide scars. These results highlight the relevance of the geo-hydroecological approach to the definition of terrain susceptibility. Its systemic foundation and the organization of the database in a GIS environment remains open for updates, as new functional parameters are identified as relevant, favouring updated diagnoses to guide territorial ordering aimed at prevention, mitigation and/or adaptation of human occupation. This approach reaffirms the idea that the assessment of terrain susceptibility is a continuous process that must be constantly updated, according to the dynamics of landscape changes over space and time.

How to cite: Silva, R., Coelho Netto, A. L., Lacerda, W., and Nunes, F.: TERRAIN SUSCEPTIBILITY TO TRIGGER SHALLOW PLANAR LANDSLIDE ON STEEP SLOPES: a geo-hydroecological approach at D'Antas creek basin, Nova Friburgo (RJ - Brazil)., 10th International Conference on Geomorphology, Coimbra, Portugal, 12–16 Sep 2022, ICG2022-493, https://doi.org/10.5194/icg2022-493, 2022.

Many examples of regional scale statistical landslide susceptibility assessments can be found in scientific literature. A real-life application of these maps for spatial planning decisions is less common. As result of the MoNOE research project (Method development for landslide susceptibility modelling in Lower Austria), a landslide susceptibility map has been created. Since 2014, this map is constantly used by provincial spatial planners and geologists to guide strategic settlement development in Lower Austria (approx. 19200 km²). Resulting from a multi-temporal inventory of 12,889 slides, a generalized additive model (GAM) was applied to predict the landslide susceptibility using a variety of meaningful morphological and geo-environmental predictors. These easily-applicable, local-scale (1:25,000) landslide susceptibility maps consist of three susceptibility classes. The three classes correspond to low landslide susceptibility (covering 78% of all pixels within the study area), moderate (16% of all pixels) and high (6% of all pixels). Although well accepted by the stakeholders, a few important questions recently arise: a) Is this map able to correctly predict new landslide events that occurred after the implementation of this map? b) With the inclusion of these new samples, is the terrain susceptibility still the same? c) If the terrain susceptibility has changed with the inclusion of the unused (partly recently mapped) samples, why and to what extent?

By aiming to answer these questions, a review project named MoNEW is currently in place, which has the overall objective to quantify the accuracy of the MoNOE spatial predictions. The new landslides were obtained from two main different sources: 1) recently occurred damage related landslides from a cadaster of landslide events (in German: “Baugrundkataster"), and 2) landslides mapped from hillshades of a high-resolution LiDAR DTM. Based on these new landslides, the final quality of MoNOE will be explored and the landslide susceptibility recalculated to identify potential differences. Therefore, the identical MoNOE methodological design will be applied to ensure comparability and quality control. Changes in the spatial prediction will be quantified and deeply explored.

First exploratory analysis has demonstrated that most of the new landslides occurred within the highest landslide susceptibility class, indicating an apparent good ability of the past MoNOE susceptibility model to predict these landslides. Depending on the inventory source, 34 to 64% of the landslides occurred within the higher susceptibility class (this percentage was 70% by design in the original MoNOE project). This variation might be explained by the positional accuracy and mapping methodologies of the new landslides. Additionally, it was observed that most of the new landslides occurring in other less susceptible classes (i.e., “low” and “moderate”) were actually located in close proximity to the highest susceptibility class. Given the applicability scale of the MoNOE landslide susceptibility map (1:25,000), these (mostly very low) quantified distances between the landslide locations and the high susceptibility pixels might be inside of the new landslide mapping accuracy. However, how much the landslide susceptibility of the terrain might change with the addition of these new samples is currently under analysis.

How to cite: Lima, P., Steger, S., Petschko, H., Goetz, J., Bertagnoli, M., Schweigl, J., and Glade, T.: How well do landslide susceptibility maps hold up over time? Reviewing the accuracy of maps implemented for spatial planning in Lower Austria, 10th International Conference on Geomorphology, Coimbra, Portugal, 12–16 Sep 2022, ICG2022-154, https://doi.org/10.5194/icg2022-154, 2022.

First approaches to analyze the propagation of hazardous, extremely rapid, landslides go back at least to the 1930s, when data-driven approaches such as angles of reach were used to estimate runout distances. Various much more sophisticated tools for the simulation of flow-type landsides including debris flows or rock avalanches, or cascading effects involving more than one type of phenomenon, have been developed since then, particularly throughout the last two decades. They build on increasingly complex physical-mathematical models, starting from comparatively simple and straightforward, depth-averaged Voellmy-type mixture models, moving to still depth-averaged two- and three-phase models able to simulate the interaction of landslides with water bodies, and currently proceeding to highly complex and highly flexible full 3D models. Phenomena such as erosion, entrainment, deposition, phase separation, or non-hydrostatic effects are increasingly well understood and incorporated into operational mass flow simulation tools.

However, this trend of increasing model complexity, supported by increasing physical process understanding and enhanced computational capacities, is not necessarily in line with the demand by natural hazard practitioners, who need capable but easy-to-handle simulation tools for their work. Besides the computational costs, it is mainly the multitude of often unknown, rather conceptual model parameters representing a barrier for practitioners towards using the more or even the most advanced approaches. Further, more complex models do not necessarily provide more accurate results than simpler ones, this always depends on the scope, purpose, and phenomenon. If only estimates of runout distances or impact areas are needed, very simple data-driven models may do the work. Even when it comes to flow thicknesses, velocities, or impact pressures, ordinary debris flows may still be better represented by comparatively simple mixture models than by parameter-hungry two- or three-phase models. Yet, more complex models are needed for more complex processes such as landslide-lake interactions or other types of process chains with dynamically changing material composition. Therefore, intelligent approaches have to be found to find an appropriate balance between simplicity and complexity.

Mainly based on seven years of experience with the depth-averaged multi-phase mass flow simulation framework r.avaflow, we will discuss the main challenges and ideas to find a useful balance between simplicity and complexity in the simulation of landslide runout.

How to cite: Mergili, M. and Fischer, J.-T.: Simplicity vs. complexity – a long-standing challenge in the simulation of landslide runout, 10th International Conference on Geomorphology, Coimbra, Portugal, 12–16 Sep 2022, ICG2022-474, https://doi.org/10.5194/icg2022-474, 2022.

It is widely accepted that climate changes will affect slope instability, and the presence and  prevalence of landslides are expected to be exacerbated.   Many areas of the world are experiencing increases in minimum, mean, and maximum temperatures and more frequent heavy precipitation.  Due to both climate change and projected population and urban growth, it is reasonable to assume the impact of debris flow  hazards will increase.  

With climate change and increasing temperatures, the likelihood of increased forest fires leads to an increased likelihood in post-fire debris flow frequency in those burn areas where other debris flow predisposing factors exist and rainfall amounts required to induce debris flows decrease. 

Future debris flow susceptibility models (RCP 2.6 and RCP 8.5) were developed and augmented with future wildfire probability, and areas of potential glacier retreat, both of which can subsequently act as amplifiers to global debris flow susceptibility.  The resulting debris flow susceptible areas are projected against future population and urbanization centers for a spatial view on human vulnerability.

How to cite: Kurilla, L. and Fubelli, G.: Future global debris flow susceptibility considering climate change, wildfire probability, and glacier retreat, 10th International Conference on Geomorphology, Coimbra, Portugal, 12–16 Sep 2022, ICG2022-69, https://doi.org/10.5194/icg2022-69, 2022.

The catchments draining the small plateau of Kimwenza in Kinshasa's south (DR Congo) are undergoing rapid and extensive peri-urbanization with limited planning. In this research, we study gully erosion processes that are associated with several infrastructure. The goal is to identify and characterize the primary erosion sites, to understand the factors of gully erosion, as well as to develop appropriate and effective methods for reducing gully erosion. To achieve this, we proceeded as follows: characterize the morphometry of the catchments, describe the surface water flow network, assess the runoff rates and propose an adequate drainage to collect runoff. Following the proliferation of street-gullies and the absence of water retention devices, it appears that these basins have a high drainage density ranging from approximately 1800 to more than 11000 m/km², with a general average of approximately 6570 m/km² following the field and laboratory investigations. Furthermore, all of the basins in this area have a somewhat rounded shape and a modest extension. This means they're more likely to have a quick rainwater concentration-time and, as a result, a quick response time. As a result, runoff flows can only be torrential, especially since the majority of this sector (80 percent) has slopes of more than 8%. The peak flow rate of surface flows can surpass 650 l/s/ha, according to an analysis of active showers and their intensities. Due to the soil's incoherent structure, which primarily consists of sands, 30 gullies were mapped, with an average length of 560 meters, a width of 27 meters, and a depth of 10 meters. Under these conditions, attenuating the gully phenomenon necessitates a significant reduction in runoff flows.

How to cite: Imwangana, F. M., Zola, B. K., Kangudia, J. M., and Sanghy, S. P.: Gully erosion associated with peri-urbanization: focus on the catchments in the south of Kinshasa, 10th International Conference on Geomorphology, Coimbra, Portugal, 12–16 Sep 2022, ICG2022-265, https://doi.org/10.5194/icg2022-265, 2022.

Landslides have caused significant losses in terms of lives and economic damage across the globe. Understanding their triggering factors, dynamics and potential impact is fundamental to implement more comprehensive disaster risk reduction measures to strengthen communities´ resilience. However, the lack of baseline data and impact information remains challenging. In order to improve our knowledge and to fill existing gaps between practitioners, disaster risk reduction institutions and other stakeholders, risk assessment and risk management projects can be appropriate starting points. Here, we present the results of a risk assessment analysis of three selected locations in Lower Austria.

The region of Lower Austria is particularly affected by landslides due to its complex geology and anthropogenic impact. The present research focuses on three earth mass movements with rotational characteristics in the regions of Erla, Behamberg, and Kreisbach. We developed different physical-based models to visualize different scenarios of potential runout areas and fluxes. Then, we performed an analysis of the cascading risk to estimate the potential economic damage in these regions to be able to propose adequate disaster risk reduction measures that could contribute to the region´s resilience.

How to cite: Jiménez Donato, Y. A., Lima, P., Arango, M., and Glade, T.: Risk assessment of earth mass movements in Lower Austria. Case study: NoeMotion Project, 10th International Conference on Geomorphology, Coimbra, Portugal, 12–16 Sep 2022, ICG2022-616, https://doi.org/10.5194/icg2022-616, 2022.

The Lisbon Metropolitan Area (LMA) is a Portuguese administrative region that encompasses 18 municipalities and is characterized by a high risk of landsliding as a consequence of the high susceptibility and exposure of people and assets. Therefore, this work aimed to assess the evolution of exposure levels to landsliding in each municipality of LMA, based on three timesteps (1995, 2011 and 2018), through the following summarized steps: 1) Landslide susceptibility modelling for LMA using an inventory that contains 1268 landslides, mostly rotational and translational slides; 2) Assessment of the relationship between the number of existing buildings and the urban area represented in the official land use map in 2011; 3) Assessment of the population exposed to landslides in 2011 (using Census data) within and outside the urban areas, based on the dasymetric distribution approach; 4) Backward and forward projection of population exposure to landsliding in the years of 1995 and 2018, for which official land use maps are available. Projections are based on relations found in 2) and 3). Overall, the results show that population exposure to landsliding has increased over time in LMA, but differently depending on the municipality considered. The relations found allow to estimate future population and assets exposure based on scenarios of urban expansion and population growth.

S.C. Oliveira was financed by the Portuguese Foundation for Science and Technology, I.P., under the framework of the project BeSafeSlide Landslide Early Warning soft technology prototype to improve community resilience and adaptation to environmental change (PTDC/GES-AMB/30052/2017). Ricardo A. C. Garcia was financed by the project Riskcoast - Development of tools to prevent and manage geological risks on the coast linked to climate change (SOE3/P4/EO868, Interreg Sudoe). CEG Research Unit UID/GEO/00295/2019.

How to cite: Melo, R., Zêzere, J. L., Oliveira, S. C., Garcia, R. A. C., and Oliveira, S.: Assessing exposure changes to landsliding in Lisbon Metropolitan Area, 10th International Conference on Geomorphology, Coimbra, Portugal, 12–16 Sep 2022, ICG2022-625, https://doi.org/10.5194/icg2022-625, 2022.

In the top of the Serra do Mar, at Rio de Janeiro state, the historic city of Petropolis has a long history of disasters related to extreme rainfall induced landslides and floods, which have become increasingly impactful in association with disorderly urban growth. The last critical event occurred on February 15, 2022, causing 236 deaths and major economic losses. A total number of 149 landslide scars were mapped with the support of a World View 3 satellite image from February 17th. These landslides reached a total area of 37 km², being more concentrated in the central part of the city, especially in the neighborhoods of Alto da Serra (Morro da Oficina), Chácara Flora (Morro do Turco) and Vila Felipe. The critical rains were spatially non-uniform, varying between 100 and 260 mm, in just 3 hours (between 16:00 and 19:00 hours); the highest number of landslides occurred when rainfall exceeded 180 mm (77.2%). Taking into account the geometry of the slopes, it was observed that 52% of these landslide scars occurred on slopes of converging water flows; 37% on divergent slopes and only 17% on planar slopes. Two types of landslide mechanisms prevailed: shallow planar slides and debris flows, which increased flooding in the rivers that drain the city. These rivers were artificially narrowed throughout the city's history, since the 19th century. The forest remnants, although highly degraded, functioned as natural dams for many landslides (45%), reducing the spread of sediment to the valley bottom.

How to cite: Coelho Netto, A. L., Fernandes, M., Nunes, F., Bertassoni, G., Bolsas, L., Facadio, A. C., Mefano e Silva, I., De Paula, A., Duek, T., Thaumaturgo, G., and Moreno, L.: The most recent disaster related to extreme rainfall induced landslides and floods: Petropolis, Rio de janeiro state, SE-Brazil., 10th International Conference on Geomorphology, Coimbra, Portugal, 12–16 Sep 2022, ICG2022-628, https://doi.org/10.5194/icg2022-628, 2022.

Greece, as the rest of the Mediterranean countries, faces wildland fires every year. Besides their short-term socioeconomic impacts, the ecological destruction and the loss of human lives, forest fires also increase the burnt areas’ susceptibility to floods, as the vegetation, which acted in a protective way against runoff and soil erosion, is massively removed. Among the most severe wildland fire events in Greece were those of summer 2021, which were synchronous to very severe heat waves that hit the broader area of the Balkan Peninsula. More than 3,600 Km2 of land were burnt and a significant amount of natural vegetation was removed. Four of the burnt areas are examined in this work, namely Attica, Northern Euboea, the Peloponnese, and Rhodes in order to assess their susceptibility to future flood events. The burnt areas were mapped, and their geological and geomorphological features were studied, while flood risk assessment was accomplished through logical rules applied in G.I.S. In this work we present the results of the flood risk assessment.

How to cite: Evelpidou, N., Karkani, A., Tzouxanioti, M., Spyrou, E., Gavalas, T., Saitis, G., and Petropoulos, A.: Assessment of fire effects to flood susceptibility: the case of the summer 2021 forest fires in Greece, 10th International Conference on Geomorphology, Coimbra, Portugal, 12–16 Sep 2022, ICG2022-278, https://doi.org/10.5194/icg2022-278, 2022.

The Ganga–Brahmaputra–Meghna Delta (GBMD) at the northern apex of the Bay of Bengal, is the world’s largest in respect of area (c. 120 × 10³ km²) as well as annual discharge of sediments (c. 10⁹ t). Contributed by the Ganga River and its numerous distributaries, the southwestern part of the GBMD is known as the Ganga Delta which spans over a number of districts in Bangladesh and West Bengal (India). The Indian state of West Bengal occupies the western portion of the GBMD, where the delta plains drained by the Ganga and its distributaries measure 42,371 km². The estimated population residing in this region in 2021 is about 76 million. The Ganga Delta is completely enveloped by the alluviums deposited by the floods and channel deposits in the last 10,000 years. Nearly every major river in the region is embanked on their both flanks to prevent overspilling during the high stages in the monsoon season (June–September). Floods occur nonetheless in years of exceptionally high rainfalls, often brought about by tropical cyclones, when these embankments are breached or overtopped by the river water.

This study aims to delineate the flood-prone zones of the Gangetic West Bengal (GWB) based on the highest floods that occurred between 1995 and 2020, and to extract data on extension, land use and resident population of the flood-susceptible area on different administrative levels.

Using five highest-magnitude flood events for five overlapping zones, it is found that 33% of the GWB is susceptible to inundation by floodwater. Overlying the inundation area over 226 administrative blocks of 14 districts of the region reveals that 51 highly populated blocks located close to the principal rivers are susceptible to flooding. The deepest flood localities of the east–central GWB noticeably coincides with the blocks with highest percentage of inundated area (>50%) and also with the blocks having fairly large population size. 77 out of 226 blocks are susceptible to inundation of 50% or more of their total area. On a higher level, if the distribution of flood inundation across the districts constituting the GWB is considered, the districts of Nadia and Murshidabad are found to have relatively more inundation area, with almost 16% of the total flood-susceptible area of the GWB lying within each of these districts, followed by Purba Medinipur (13%) and Malda (11%). The study connotes that floods and the region’s cultural landscape—consisting of farmlands, habitations, and lines of communications—are closely related. Floods occur despite all human endeavours to prevent them, affecting approximately 14,500 km² area and 18 million people.

How to cite: Das, S., Santra, P. K., Das, A., Mondal, S., Bandyopadhyay, S., and Rudra, K.: Demarcation of Flood-Prone Zones in the Indian Part of the Ganga Delta Based on the Highest Floods between 1995 and 2020, 10th International Conference on Geomorphology, Coimbra, Portugal, 12–16 Sep 2022, ICG2022-654, https://doi.org/10.5194/icg2022-654, 2022.

A flash flood is a very quick increase in the discharge of a stream, generally caused by short duration and high intensity rainfalls. This phenomenon is very frequent in Europe and in Italy too, where generally occurs in small catchments with short response time, causing many damages in the outlet zones. In historical times, numerous flash flood events occurred in Campania Region (Southern Italy), with an increase in frequency in the last decades, associated with the present trend of climate change. 
This note summarizes the results of the last ten years studies on the flood events occurred in the torrential basin-fan systems of the Campania region, aiming to contribute to the assessment of the hazard and risk condition. In these watersheds flash flood events resulted in a series of phenomena, passing from debris flow to debris flood and water flood, which produced serious damage to urban centers and cultivated areas. Unfortunately, these catchments are often not monitored and the lack of information on rainfall and flow data makes more difficult to apply hydrological and hydraulic models to evaluate hazard and risk. Hence, in these settings, geomorphological study and post-event field campaign have been useful to identify the flooding prone areas and the magnitude of the occurred events. The latter has been achieved by the estimate of the maximum water heights of the flow, of the thickness of eroded and/or deposited material and of the relative particle size of the deposits and finally of the volumes. The acquisition of remotely sensed images by means of UAV (drones) enhanced the field surveys to obtain these data. More in detail, the integration of morphometric analysis in GIS, with field and remotely sensed data permitted to draw several thematic maps, that together with the identification of the damage states have been exploited to produce empirical vulnerability curves for these events. 
Collecting such data represents a fundamental issue for calibrating hydraulic flow models for the hazard evaluation, while the development of vulnerability tools can be adopted to assess the risk and define the best mitigation strategies.

How to cite: Forte, G., De Falco, M., Santangelo, N., and Santo, A.: Geomorphological approach to flash flood hazard and vulnerability evaluation in torrential basin-fan systems in Campania (South Italy), 10th International Conference on Geomorphology, Coimbra, Portugal, 12–16 Sep 2022, ICG2022-146, https://doi.org/10.5194/icg2022-146, 2022.

The passage of the storm Alex favored on October 2, 2020 the triggering of very intense rainfall in the coastal valleys of the Alpes-Maritimes, including that of the Roya, which caused many devastating hydro-geomorphological processes, mainly floods and landslides. The destruction, which has affected modern and ancient infrastructures, has been considerable, with dramatic social and economic consequences. The processes have also unearthed archaeological remains dating from the Bronze Age to the modern era.

Meteorological analyses confirm the exceptional character of this Mediterranean episode, while marine sedimentary records made in the deltas of the Var crossed with C14 dating of ancient torrential deposits identified in the Roya watershed indicate a return time of about half a millennium.

The major difficulty in the case of the Roya is that the reactivation of the entire late glacial active band has upset the geometry of the river's major bed compared to what it was before Storm Alex. In the areas of torrential deposits, there is a widening of the bed of the order of 1 to 10 and a rise of several meters in the height of the alluvial table. This major modification implies that before defining the new risk zones and deciding on the reconstruction of the infrastructures, the impact of the floods recorded in the past should be modeled according to this new geometry of the bed. Moreover, in the context of global warming, it is not excluded that we observe a higher recurrence of events such as storm Alex. We present the preliminary results of our research program (STORY: "Risks and societies in the Roya basin: multidisciplinary and multi-temporal analysis, from the slopes to the sea") and discuss the difficulties in reconciling the perceptions of the different actors, the necessary time of research not always easy to articulate with the urgency of reconstruction and the vital need to open up sectors that otherwise risk being permanently abandoned by the populations.

Keywords: Holocene, flash floods, climate change, risk management

How to cite: Fouache, E., Desruelles, S., Gorini, C., Bianchi, N., and Marchiel, A.: Evaluation of the return time of flash floods generated in the Roya Valley (Alpes Maritime, France) on October 2, 2020: What challenges does this type of event pose for risk management?, 10th International Conference on Geomorphology, Coimbra, Portugal, 12–16 Sep 2022, ICG2022-7, https://doi.org/10.5194/icg2022-7, 2022.

The flood hazard is affected by several natural factors, such as impermeable soil, depressions and unsuitable land cover. Human impact is also not negligible in the river basin. We can observe inappropriate tillage accelerating erosion or storage of various materials and waste in the immediate proximity and directly in watercourses. The size of the inundation area is affected by the capacity of the watercourse and its ability to transmit a flood wave.

Our medium-term goal is to enrich the preliminary flood hazard assessment with watercourse channel properties such as flow, depth and capacity. We want to determine these parameters quickly for large areas using detailed data. The sub-goal on which this paper is focused is the detection of the watercourse channel as an area. Hence we are focused on detecting the right and left bank of the channel. We test our methodology in a selected area of Slovakia with an area of 320 km². The input is LiDAR data with an average density of 32 points per m² and average height accuracy of 0.05 m. From the above data, we created a digital elevation model (DEM) with a cell size of 1 m and derived layers such as slope, topographic position index, topographic wetness index, and topographic openness. Subsequently, we used these layers in the machine learning random forest (RF) tool for the supervised classification of the spatial limits of the channels represented by the banks. We adjusted the classification output from other depressions or potential misclassification based on proximity analysis and a morphometric approach. For validation, we use a manually edited layer of channel lines based on detailed orthophoto from 2017 and DEM.

Keywords: LiDAR, channel detection, channel morphology, banks, random forest

How to cite: Michaleje, L. and Rusnák, M.: Semi-automatic channel detection based on detailed LiDAR data for preliminary flood hazard assessment, 10th International Conference on Geomorphology, Coimbra, Portugal, 12–16 Sep 2022, ICG2022-593, https://doi.org/10.5194/icg2022-593, 2022.

Nagavali river has come into the limelight since 2005 because the Govt of India had announced for constructing ten hydro-powers projects in the river basin. Nagavali river is flood-prone. The river flows through a complex structural longitudinal valley between eastern and western ridges in Orissa and Andhra Pradesh before meeting with the Bay of Bengal. Out of those ten project sites, two sites, i.e., at Hathipahar region in Rayagada, Orissa and the Thotapalli village in Vizianagaram District, Andhra Pradesh, have been selected for the risk assessment.  The Hathipahar region is situated over a piedmont slope of the eastern ridge (formed of Khandelite-granite), and the Thotapalli site is located over a rolling plain. In July 2006, due to torrential rainfall and flash flood, the under constructed dam at Hathipahar collapsed. The valley experienced vast changes in channel shifting (about 550 meters), headword erosion, valley incision and soil loss. The Thotapalli site was not affected, and thus, the construction was completed in 2010. Consequent flood events of   2011 and 2015 caused massive erosion and enlargement of the reservoir from 2.1 sq. km. to 9.16 sq. km, and the upstream course of the river merged with the reservoir.

The present research aims at estimating damage and assessing socio-environmental risk along with these two sites. For assessing flood risk, the SCS-CN model has been used and validated with real-time discharge data, land use-land cover change, spatio-temporal variations in channel width-depth, and sedimentology. Google Earth and SRTM DEM have been used for detecting such changes. Field surveys have been carried out to prepare a micro-level elevation model, damage estimation and lithological formation of the area. The research also focuses on assessing the possibility of continuing the proposed hydro-power sites since the existing sample sites are already experiencing massive erosions and destructions.

Keywords: piedmont slope, flash flood; hydro-power project; SCS-CN model; risk assessment

How to cite: Bandyopadhyay, S. and De, S. K.: Risk Assessments of Hydro-Power Projects along the Nagavali River, Odisha-Andhra Pradesh, India, 10th International Conference on Geomorphology, Coimbra, Portugal, 12–16 Sep 2022, ICG2022-222, https://doi.org/10.5194/icg2022-222, 2022.

Coastal erosion worldwide affects seaside resorts, linear infrastructures (promenades, railways, and coastal roads), small entrepreneurs’ activities, and local economies. On the other hand, the development of these human activities often leads to coastal dynamics and coastal erosion in general. With ever-changing environmental conditions due to both climate change and human activities, coastal area and particularly sandy beaches are being subjected to phenomenon of different kinds.  Local authorities and scientific communities have been working and searching for solutions to contrast these processes. Different solutions have been adopted regarding beach protection including hard engineering structures, seawalls, and beach nourishment. However, these solutions have been proven not to be cost effectives and sometimes they have been inefficient in terms of sustainability and resiliency. Within the last 50 years, the pocket beach of San Vito lo Capo located on the Northwestern coast of Sicily has undergone repetitive modifications in terms of beach morphology and grain sediment sizes. Moreover, this beach has been identified as an important tourist destination and contributes efficiently on local economic development.

The aim of this study is to conduct a comprehensive geomorphological and sedimentological analyzes in order to determine its natural resilience and eventually propose solutions to increase its protection and its sustainable management.

To achieve this objective, emerged and submerged sediments have been sampled and analyzed, single beam echo sounding and AUV photogrammetry surveys have been performed for morpho-topographic analyses, and a morpho-dynamic shoreline analysis has been conducted based on the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) satellite images acquired from 2001 to 2017.

The results indicate that the sediments tend to move along the coastline generally from center to the western part of the beach with no loss of the sediments for the beach. Furthermore, wave penetration and wind analyses revealed the presence of non-destructive wave with maximum penetration reaching the urban area.  Such ability of the beach to dissipate the wave energy can be enhanced by using sever beaches to dissipate the winter waves. While morpho-dynamic evolution of the beach can be managed by establishing a seasonal re-equilibrium of internal sediment distribution.

How to cite: Muzirafuti, A., Randazzo, G., and Lanza, S.: A Resilient Solution for Management of Beach Tourist Destination (NW Sicily, Italy), 10th International Conference on Geomorphology, Coimbra, Portugal, 12–16 Sep 2022, ICG2022-288, https://doi.org/10.5194/icg2022-288, 2022.

Catastrophic outburst floods are formed due to the destruction of moraine dams of the periglacial lakes. Outburst floods are dangerous hydrological phenomena that cause the large material damage and often lead to human losses. These dangerous hydrological phenomena are sudden and fleeting due to this the organization of observations is extremely difficult and unsafe. To study these hydrological phenomena the methods of mathematical modelling and physical modelling are most often used. This paper presents a methodology developed by the authors for calculating the outburst flood hydrograph, which is formed during the destruction of an earth dam due to water overflow through the crest. The methodology was verified using the results of physical experiments carried out on a special experimental setup and in natural conditions. The quantitative characteristics of outburst floods obtained as a result of physical experiments were used for comparison with the data obtained in mathematical modelling. Comparison of the simulated outburst flood hydrographs with obtained hydrographs as a result of physical experiments showed their convergence. The paper also presents the results of numerical experiments, which allowed to obtain the dependence of the discharge on the initial size of the breach, the specific gravity of the material from which the dam is built, the percentage of clay in this material and the roughness coefficient value. The results of the work were considered satisfactory and demonstrated the efficiency of the calculation method.

The work was supported by the RFBR grant No. 20-05-00343 A "Identification of the features of the outburst process of lakes of oases of Antarctica based on field research data and mathematical modeling."

How to cite: Pryakhina, G., Rasputina, V., and Popov, S.: Modelling of the outburst flood hydrograph due to the moraine lakes outbursts, 10th International Conference on Geomorphology, Coimbra, Portugal, 12–16 Sep 2022, ICG2022-73, https://doi.org/10.5194/icg2022-73, 2022.

Abstract: Vulnerability not only establishes the relationships people have with their environment but also links the social forces and institutions and the cultural values that sustain them. Physiographically, a deltaic plain, the Sundarban region is a dynamic ecosystem which frequently witnesses physical and social vulnerability brought about by natural hazards like tropical cyclones resulting in saltwater flooding. The Bay of Bengal basin records the highest number of tropical cyclones globally. About 8 storms with sustained wind speeds more than 63 km/hr form in the Bay of Bengal, of which, 2 become tropical cyclones. Cyclone Yaas was identified as a very severe cyclonic storm, originally formed as a tropical disturbance identified by the India Meteorological Department (IMD) on 23^rd May 2021. Yaas made it’s landfall at Dhamra of Orissa state of India, on 26^th May 2021, coinciding with spring tide. Coastal flooding was reported from Maipith, Bhubaneswari village panchayats along the rivers Matla, Thakuran and Nimania of Kultali community development block, Dhaspara, Sumatinagar, Kastala, Kochuberia, Mahishamari, Muriganga, Shilpara village panchayats of Sagar community development block, Amrabati, Lakshmipur, Fraserganj of Namkhana community development block, Gobardhanpur, Mridangabhanga, Krishnadaspur, East Chintamanipur, Kumarpur of Patharpratima community development block. Within the last 7 years, capacity building in the form of concrete houses under Indira Awas Yojana, cyclone shelters, metalled roads has increased. However, in the time of disasters even the settled portions get submerged in water as the settled portions are located at a lower height than the high water level. The local demand of concrete embankments is debatable and in some places the use of brick pitching, brick block pitching, porcupine mesh and Aila bandh have not been successful in preventing breaching and consequent saltwater inundation. Vulnerability is severe in case of Basanti, Gosaba, Kultali, Namkhana, Patharpratima community development blocks and high in case of Kakdwip, Sagar and Hingalganj community development blocks (Sahana et al, 2019). This paper takes all these phenomena into account and provides certain probable mitigation strategies which include the mapping of erosion accretion sites by comparative analyses of toposheets and satellite images, site specific mangrove aforestation, improved construction of embankments following expert and local opinions, revival of the decayed creeks to prevent waterlogging at times of disaster and rehabilitation of people from sites of continuous erosion to community shelters located at elevated areas alongwith tidal river management as practiced in Bangladesh where the rivers are temporarily allowed their spill areas.

How to cite: Das, K. and Das, K.: Vulnerabilities in Post Yaas Environment and Probable Mitigation Strategies: Case Studies from Selected Sites, Sundarban, India, 10th International Conference on Geomorphology, Coimbra, Portugal, 12–16 Sep 2022, ICG2022-341, https://doi.org/10.5194/icg2022-341, 2022.

All populations are, at some level, directly or indirectly affected by climate change. Sea level rise (SLR) changes can have strong effects in population exposure conditions in coastal areas, being one of the most critical environmental threats in 21st century.

Portugal, like most coastal countries, have high concentrations of settlements, population and activities near the coast, situation that has been intensified from the second half of the 20th century and during the 21st century. Despite of mitigation and adaptation measures based on SLR scenarios, Portugal already faces the expansion of SLR hazard zones, with a rising rate of 2.1±0.1 mm/year between 1977 and 2000 [1], and an increasing exposure and vulnerability to coastal flooding. Moreover, the IPCC/NASA SSP5 scenario for 2100 estimates values between 0.74m e 0.81m for the Portuguese coastline [1]. In addition, an increasing frequency of extreme sea level events is expected, namely associated to the SSP5-8.5 [2]. Despite 30 years of regulatory land use planning that incorporates hazard preventive measures and restrictions, Portuguese coastal communities have long before occupied SLR hazard zones in an ongoing process. In this context, knowledge of processes and impacts in the past is fundamental to assess future conditions and consequences, based on SLR scenarios.

The main goal of this work is the diagnosis of land use land cover (LULC) changes focused on built-environments spatiotemporal dynamics (1995 to 2018) in a SLR context. The results will support exposure assessment for 2040, 2070 and 2100 scenarios for the Portuguese mainland coastal zone at a sub-municipality level.

This work uses a validated SLR hazard baseline [1] as reference to measure the recent and actual exposure of the potentially elements at risk. For LULC one used the official cartography (1995 to 2018), produced by the General Directorate for Territorial Development in Portugal.

To assess the amount and patterns of LULC changes, memoryless stochastic methods are used, extracting two categories of data:  i) the persistence, gain, loss, total change, absolute value of net change, and swapping tendency, as well several ratios; ii)   measurement of systematic and random LULC changes by using the off-diagonal entries of the transition matrix for the significant inter-class transitions detection.

References

[1] https://sealevel.nasa.gov/ipcc-ar6-sea-level-projection-tool.

[2] IPCC (2021). Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. V. Masson-Delmotte, P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, B. Zhou (eds.), Cambridge University Press.

Acknowledgements

To project HighWaters (EXPL/GES-AMB/1246/2021). Pedro P. Santos is financed by FCT I.P. (CEECIND/00268/2017). This work was financed by the Research Unit UIDB/00295/2020 and UIDP/00295/2020.

How to cite: Reis, E., Rocha, J., Silva, A., Trindade, J., Zêzere, J. L., Pereira, S., Oliveira, S., Garcia, R., and Santos, P.: Spatial and temporal changes in urban fabric exposure to Sea Level Rise, from 1995 to 2018, in mainland Portugal, 10th International Conference on Geomorphology, Coimbra, Portugal, 12–16 Sep 2022, ICG2022-495, https://doi.org/10.5194/icg2022-495, 2022.