NH3.8

There is a growing demand for constructing a complete and accurate landslide maps and inventories in a wide range, which leading explosive growth in extraction algorithm study based on remote sensing images. To the best of our knowledge, no study focused on deep learning-based methods for landslide detection on hyperspectral images.We proposes a deep learning frameworkwith constraints to detect landslides on hyperspectral image. The framework consists of two steps. First, a deep belief network is employed to extract the spectral–spatial features of a landslide. Second, we insert the high-level features and constraints into a logistic regression classifier for verifying the landslide. Experimental results demonstrated that the framework can achieve higher overall accuracy when compared to traditional hyperspectral image classification methods. The precision of the landslide detection on the whole image, obtained by the proposed method, can reach 97.91%, whereas the precision of the linear support vector machine, spectral information divergence, and spectral angle match are 94.36%, 84.50%, and 86.44%, respectively. Also, this article reveals that the high-level feature extraction system has a significant potential for landslide detection, especially in multi-source remote sensing.

How to cite: Li, Y.: Landslide Detection of Hyperspectral Remote Sensing Data Based on Deep Learning With Constrains, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-14070, https://doi.org/10.5194/egusphere-egu21-14070, 2021.

Landslides in the Pyrenees cause periodical damage to infrastructure and human lives. The European PyrMove project aims to develop cross-border methodologies to manage and reduce risk associated with these geological hazards. One of its approaches are the study of Multiple-Occurrence Regional Landslide Events (MORLE) generated by episodes of intense rainfalls that affect large areas. To prevent and manage MORLE crisis, an identification and categorization of the geological and meteorological factors determining the MORLEs that occurred in Catalonia during the 20th and 21st century were carried out, with special attention to the last 30 years. These events were contrasted to some relevant landslide events at worldwide scale. A new qualitative scale of magnitude multiple Regional Landslide event (mRL) has been conceived according two variables that provide the best reliability for the historical data: (1) the area of the affected region and (2) the magnitude of the largest inventoried landslide. To determine the magnitude of largest landslide we used the ICGC scale based on its size and the total mobilized energy (M). Finally, two MORLE that occurred in 1982 and 2003 in Catalonia have been studied in detail to collect basic information on geological phenomena. These preliminary works will make possible in the future to estimate the triggering precipitation thresholds that induce MORLE scenarios in Catalonia.

The magnitude scale of MORLE events allows contextualizing the Catalan MORLE in the World. In this approach, seventeen World’s MORLEs events have been described for this work. The main triggering factor of studied regional events has been earthquakes (56%) and intense rainfall or typhoons (44%). Their extension normally do not exceed 50,000 km2 and the number of landslides exceeds, in some cases, 50,000. MORLE’s magnitudes, are mostly 3 or higher, due to their large extension, and to the magnitude of the largest landslide, which normally reaches over the maximum degree within the established magnitude scale for landslides in Catalonia by ICGC (M). Damages and human losses have been difficult to quantify, however, at worldwide scale, most of the MORLEs recorded human losses (> 600 in some cases). The most catastrophic MORLE was in Wenchuan region, China, in October 2008, with more than 87,000 fatalities, 52,194 landslides and 410,000 km2 of affected regional area.

In Catalonia, 13 MORLEs have been registered from 1900 to present. Here, the main trigger factor has been intense precipitation and the affected areas usually do not exceed 10,000 km2. However, in some cases such as October 1982, which records the largest number of identified landslides (about 900), reached 20,000 km2. The magnitude of the largest event rarely exceeds category M4 in ICGC scale, being the majority category M3. Damages have been considerable in these events such as the most recent, triggered by Gloria storm in January 2020. For Catalonia, three general characteristics are notable: (1) East storm situations are the main generators of MORLE’s; (2) MORLEs usually reach magnitudes mRL3 o mRL4.

This work has been supported by the European Commission under the Interreg V-A-POCTEFA programme (grant no. PyrMove - EFA364/19).

How to cite: Buxó, P., Oller, P., Xifré, D., Fabregat, I., Marturià, J., and Janeras, M.: Identification, validation and assessment of Multiple Occurrence Regional Landslide Events (MORLE) in Catalonia (Spain) during the last one hundred years., EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-6171, https://doi.org/10.5194/egusphere-egu21-6171, 2021.

In Tai et al. (2020), the concept of idealized curved surface (ICS) is proposed to mimic the failure surface, and the application to a large-scale landslide yields good agreement with the satellite image for the post-failure flow paths. The ICS consists of two constant curvatures in the down-slope and cross-slope directions, respectively. Hence, it is convenient to evaluate the stability based on the moment of momentum with respect to the plausible ICS. In this study we are going introduce a new formula for the stability analysis, in which the balance of angular momentum is employed, so that the local failure thickness (above the ICS) and the local ground water level can be taken into account. That is, the depth distribution of the landslide body may also have significant impacts on the slope stability.

Motivated by the similarity between landslide and granular avalanches, the periodic sand avalanches on a heap are investigated by means of the snap shots of high-speed camera, where the sand is accumulated up to a specific volume before sliding down. It is found that the first failure takes place near the toe of the avalanching body and the rupture surface develops and moves upwards. The ICS and the associated stability analysis can well explain the initial failure near the toe. This concept can also be applied to the mystery of the Hsiaolin landslide, taking place in southern Taiwan in 2009, where the released volume is up to more than 22 Mm³ but the mean slope is around 21 degrees. In spite of a 2D analysis, it can be found that, with a reasonable groundwater level, the first failure could be suspected to develop around the toe part. Therefore, we speculate that the plausible state of the landslide is the rainfall induced rise of groundwater level, inducing the sequential landslides and resulting the resultant large-scale landslide event.

How to cite: Chi-Jyun, K., Chih-Ling, W., Hock-Kiet, W., and Yih-Chin, T.: Idealized curved surface for mimicking slope failure: toward the sequential failure, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-10520, https://doi.org/10.5194/egusphere-egu21-10520, 2021.

The canton Ticino, Switzerland, with an alpine setting and humid climate, is exposed to a great number of natural events, among which are gravitational movements. The StorMe inventory, which is compiled by the Forestry Section from the Republic and Canton of Ticino, contains all the relevant information regarding recorded natural events during the last 20 years, including rockfalls, debris flows, floods, landslides and avalanches (Galfetti et al., 2019).

Annually, millions of Swiss francs are invested for risk management for natural events (Galfetti et al., 2019): base studies, precautionary measures (such as mitigation works, and the protection and maintenance of woods) and monitoring. Considering landslide inventories are important sources of information for hazard and risk assessment, it is crucial to exploit the existing data in order to gain a better understanding of the specificities of the processes present at a regional scale.

The most significant statistical properties of landslides derive from geometrical parameters such as landslide area or volume; which can be used to calculate the size, frequency and potential distribution of future landslides as well as the contribution of sediment yield to erosion.

Here, a statistical analysis was carried out using the StorMe inventory and additional data, in order to better understand the spatial and temporal distribution of events in the study area, their geometric characteristics (distance and angle of propagation, volume) and their relation to the soil/rock type, land use, and climate. The inventory consists of both spatial attributes (points and polygons) and linked attributes. Additional input data included topographical, geological, land cover and previously created hazard maps; both in raster and vector formats.

Preprocessing of the available data included the calculation of derived attributes (slope, curvature, elevation, area, perimeter, among others…) and the joining of spatial and textual data. Bivariate and multivariate statistical analyses were carried out first on the whole inventory (including time series, spatial distribution, volume distribution, frequency–area distribution and inventory quality) and then analyses on controlling factors (mainly elevation, slope, lithology and land cover) for each different type of process was carried out.

Preliminary analysis results show a few general trends regarding chiefly the distribution of landslide types, volumes, propagation distance, and reach angle (Farböschung); as well as some local anomalies. More in-depth analysis using machine learning will be carried out in the future in order to determine main controlling factors for each movement type in the study area.

__________________________________________________________________________________

Galfetti, M.; Bottinelli, L.; Salvetti, A.; Re, L. and Coratelli, S. (2019). Pericoli naturali in Ticino: storia, cifre e strumenti di prevenzione. EXTRA DATI - Supplemento online della rivista Dati dell’Ufficio di statistica. Anno XIX – N.02

How to cite: Gutierrez, C. A., Jaboyedoff, M., Pedrazzini, A., and Derron, M.-H.: Preliminary statistical analysis of the Ticino landslide inventory, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-2790, https://doi.org/10.5194/egusphere-egu21-2790, 2021.

The most frequent and widespread landslides all over the world are induced by prolonged or heavy rainfall events. These phenomena often cause casualties and damages. Recent research on climate change has evidencing the link between the rainfall tendencies and the increase of damaging geohydrological events. This study has been carried out in the ambit of the EC Project INDECIS, whose aim is to develop an integrated approach to produce a series of climate indicators aimed at the high priority sectors of the Global Framework for Climate Services of the World Meteorological Organization (agriculture, risk reduction, energy, health, water), with the addition of tourism. The study area is Calabria, a region of Southern Italy frequently affected by mass movements and characterized by a highly variable climate. In this study, landslide occurrences in the period 1990-2018 have been collected for the whole territory of Calabria, and clustered according to the five provinces of the region. Moreover, 13 rainfall-based climatic indexes, among those proposed in the INDECIS project, have been calculated for each of the 79 rain gauges presenting complete and homogeneous databases. For each province and for the whole Calabria, the average and the maximum values of the climatic indices have been compared with the landslide occurrences in each year. The comparisons showed the best agreements with the following climatic indices: a) the total annual precipitation (RTA), the annual count of days when daily precipitation amount ≥ 10mm (R10mm), the annual count of days when daily precipitation amount ≥ 20mm (R20mm), the annual total precipitation when daily rainfall is greater than 95^th-percentile (R95TOT) and, secondarily, the annual count of days with daily rainfall >= 50 mm (D50mm). For the best matches, the curves interpolating the two databases have been also drawn. The obtained results can be useful to predict the impacts that tendencies of rainfall indices patterns can have on slope stabilities of the territory.

Acknowledgments:

The Project INDECIS is part of ERA4CS, an ERA-NET initiated by JPI Climate, and funded by FORMAS (SE), DLR (DE), BMWFW (AT), IFD (DK), MINECO (ES), ANR (FR) with co-funding by the European Union (Grant 690462)

How to cite: Petrucci, O., Aguilar, E., Pasqua, A. A., Vicente-Serrano, S., Zimbo, F., and Coscarelli, R.: Comparison of climatic indices with landslide occurrences in Calabria (Southern Italy), EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-405, https://doi.org/10.5194/egusphere-egu21-405, 2021.

In this study we present the results of a logistic regression model aimed at describing changes in probabilities for rockfall events in Germany in response to changes in meteorological and hydrological conditions.

The rockfall events for this study are taken from the landslide database for Germany (Damm and Klose, 2015). The meteorological variables we tested as predictors for the logistic regression model are daily precipitation from the REGNIE data set (Rauthe et al. 2013), hourly precipitation from the RADKLIM radar climatology (Winterrath et al., 2018) and temperature from the E-OBS data set (Cornes et al., 2018). As there is no observational soil moisture data set covering the entire country, we used soil moisture modelled with the state-of-the-art hydrological model mHM (Samaniego et al. 2010), which was calibrated using gauge measurements.

In order to select the best statistical model we tested a large number of physically plausible combinations of meteorological and hydrological predictors. Each model was checked using cross-validation. The decision on the final model was based on the value of the logarithmic skill score and on expert judgement.

The final statistical model includes the local percentile of daily precipitation, total relative soil moisture and freeze-thawing cycles in the previous weeks as predictors. It was found that daily precipitation is the most important parameter in the model. An increase of daily precipitation from its median to its 80th percentile approximately doubles the probability for a rockfall event. Higher soil moisture and the occurrence of freeze-thaw cycles also increase the probability for rockfall events. 

Cornes, R. C. et al., 2018: An ensemble version of the E‐OBS temperature and precipitation data sets. Journal of Geophysical Research: Atmospheres, 123, 9391– 9409.

Damm, B., Klose, M., 2015. The landslide database for Germany: Closing the gap at national level. Geomorphology 249, 82–93

Rauthe, M. et al., 2013: A Central European precipitation climatology – Part I: Generation and validation of a high-reso-lution gridded daily data set (HYRAS), Vol. 22(3), p 235–256.

Samaniego, L. et al., 2010: Multiscale parameter regionalization of a grid-based hydrologic model at the mesoscale. Water Resour. Res., 46,W05523

Winterrath, T. et al., 2018: RADKLIM Version 2017.002: Reprocessed gauge-adjusted radar data, one-hour precipitation sums (RW), DOI: 10.5676/DWD/RADKLIM_RW_V2017.002.

How to cite: Nissen, K., Rupp, S., Guse, B., Ulbrich, U., Vorogushyn, S., and Damm, B.: Meteorological and hydrological conditions triggering rockfall events in Germany, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-5367, https://doi.org/10.5194/egusphere-egu21-5367, 2021.

Landslide activity in the Himalaya region is hypothesized to have increased over the last decades, as suggested by exiting landslide databases and disaster inventories. This trend has been linked to an enhancement of heavy rainfall events under warming climate, but also to anthropogenic factors that influences the slope stability as well as to an increase of exposed of people and infrastructures in prone areas. Yet, as recognized by the Intergovernmental Panel on Climate Change (IPCC), such positive trends are still unclear, mostly due to the lack of baseline data with enough spatio-temporal resolution. Focusing on Far-Western Nepal, we draw on remote sensing techniques to create a multi-temporal regional landslide inventory for the period 1992-2018 over an area covering 6,460 km2. To this end, we systematically interpret geomorphologically high-resolution satellite imagery from Google Earth. Besides, we analyze multispectral differences from Landsat images to interannual date the initiation or reactivation of the interpreted landslides. This massive effort includes the digitalization of 26,350 landslide events, of which 8,778 were dated at an annual scale. These events serve as a basis for the analyses of landslide frequency relationships and trends in relation to annual precipitation and temperature datasets, derived from ERA-5 climate reanalysis.
Our results show a strong correlation between the annual number of shallow landslides and the accumulated monsoon precipitation (r=0.74). Furthermore, warm and dry monsoons followed by especially rainy monsoons produce the highest incidence of shallow landslides (r=0.77). However, we find strong spatial variability in the strength of these relationships, which is linked to recent demographic development in the region. This highlights the role of anthropogenic drivers, and in particular, road cutting and land-use change, in amplifying the seasonal monsoon influence on slope stability. In parallel, the absence of any long-term trends in landslide activity, despite a widely reported increase in landslide disasters, points strongly to increasing exposure of people and infrastructure as the main driver of landslide disasters in this region of Nepal. Thus, our assessment could not determine evidence for any climate change signal related to landslide activity over this part of the Himalayas.

How to cite: Muñoz-Torrero Manchado, A., Allen, S., Ballesteros-Canovas, J. A., Dhakal, A., Dhital, M. R., and Stoffel, M.: Three decades of landslide activity in western Nepal: New insights into trends and climate drivers, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15848, https://doi.org/10.5194/egusphere-egu21-15848, 2021.

Wildfire changes the hydrologic and geomorphic response of watersheds, resulting in a cascade of increased hazards for sediment-laden floods, shallow landslides, and debris flows. This phenomenon has long been studied in regions where landscape evolution is driven by repeated fire-flood sequences such as Southern California. However, comparing mass movement hazards across multiple regions presents a challenge because most landslide inventories are limited to local or regional spatial scales. This study seeks to identify unique triggering characteristics of post-fire landslides through a comparison of the precipitation characteristics preceding landslides at both burned and unburned locations spanning six global regions. Regional inter-comparison was facilitated by selecting landslide events from the NASA Global Landslide Catalog (GLC), and then establishing fire and precipitation histories for each site using MODIS global burned area and CHIRPS precipitation data. In addition, since the GLC did not contain a sufficient percentage of burned locations in any part of Europe, a parallel analysis incorporates nationally maintained landslide inventories from several European countries. Analysis of normalized seven-day accumulated precipitation for sites across all regions shows that post-wildfire landslides are preceded by less precipitation than landslides without antecedent wildfire events. This supports the hypothesis that fire increases the rainfall-driven landslide hazards. A regional examination of landslide susceptibility using normalized triggering storm volumes as a proxy indicator reveals a distinct sensitivity to fire across several regions of the western US. However, in other regions wildfire appears to have a limited or even opposite impact on landslide susceptibility. Unburned locations also tend to see a sharper ramp-up of precipitation leading up to the date of the landslide relative to burned sites in regions of the Western US. In other regions the storm timing is similar or, a in Central America, even longer in burned locations. The landslide-triggering storms of post-fire landslides also exhibit different seasonality from other rainfall-triggered landslides, with a variety of seasonal shifts ranging from approximately six months in the Pacific Northwest of the US to one week in the Himalayas. These results suggest that the apparent inconsistency in the impact of wildfire on landslide hazards may be due to a combination of two factors: interactions between fire and precipitation seasonality and variability in soils and vegetation. For example, in the Intermountain West of the US and Southeast Asia landslides are preceded by a thirty-day or longer dry period while in all other regions there is no difference in the precipitation prior to the triggering storm event, which suggests different landslide mechanisms such as dry ravel, which rely on low rather than high soil moisture levels, may be a factor. Overall, this work offers an exploration of regional differences in the characteristics of rainfall-triggered landslides over a broad spatial scale encompassing a variety of climates, terrains, and ecoregions.

How to cite: Culler, E., Livneh, B., Tiampo, K., and Rajagopalan, B.: A data-driven evaluation of post-fire landslide susceptibility, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-14025, https://doi.org/10.5194/egusphere-egu21-14025, 2021.

How to cite: Thiery, Y., Kaonga, H., Mtumbuka, H., and Rohmer, J.: Landslide hazard assessment and mapping for Malawi (Southeastern Africa): from susceptibility to hazard by integration of temporal exceedance probabilities related to tropical meteorological events, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-6636, https://doi.org/10.5194/egusphere-egu21-6636, 2021.

Landslides problems are one of the major natural hazards in the mountainous region. Every year due to the increase in anthropogenic factors and changing climate, the problem of landslides is increasing, which leads to huge loss of property and life. Landslide is a common and regular phenomenon in most of the northeastern states of India.  However, in recent past years, Manipur has experienced several landslides including mudslides during the rainy season. Manipur is a geologically young and geodynamically active area with many streams flowing parallel to fault lines. As a first step toward hazard management, a landslide susceptibility map is the prime necessity of the region. In this study, we have prepared a landslide hazard map of the state using freely available earth observations datasets and multi-criteria decision making technique, i.e., Analytic Hierarchy Process (AHP). For this purpose, lithology, rainfall, slope, aspect, relative relief, Topographic Wetness Index, and distance from road, river and fault were used as the parameters in AHP based on the understanding of their influence towards landslide in that region. The hazard map is classified into four hazard zones: Very High, High, Moderate, and Low. About 40% of the state falls under very high and high hazard zone, and the hilly regions such as Senapati and Chandel district are more susceptible to the landslide. Among the factors, slope and rainfall have a more significant contribution towards landslide hazard. It is also observed that areas nearer to NH-39 that lies in the fault zones i.e., Mao is also susceptible to high hazard. The landslide susceptibility map gives an first-hand impression for future land use planning and hazard mitigation purpose.

How to cite: Singh, D. and Laha, A.: Application of earth observation datasets and Analytic Hierarchy Process in the mapping of Landslide hazard zones of Manipur, India, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15789, https://doi.org/10.5194/egusphere-egu21-15789, 2021.

The Dorog Basin was a mining area in northern central Hungary for more than two centuries. Tunnel mining and quarrying of Eocene coal was the main industrial activity in the basin from the mid-19^th century until the late 1990s. Extensive quarrying of the Cretaceous marl and Triassic limestone for the cement industry is also present in the area, along with pits of sand and fire clay and travertine quarries. Though the waste treatment is controlled by law and strict directives, the morphology and the material characteristics of the waste heaps are often enough to increase the chance of slope failures. As the mining waste heaps and tailings are often adjacent to residential and agricultural areas, they are considered as hazard sources. The combined use of remote sensing and machine learning methods can help to evaluate the stability of the waste heaps and select the sites where further hazard assessment is needed on the field.

The slopes of the area were sorted into six stability categories (scarps, transitional slopes, debris, low-lying accumulation areas, hilltops, stabile slopes) with random forest machine learning classification. The sample areas for the analysis were selected based on geomorphological mapping in the area and the re-evaluation of the recorded landslides from the landslide inventory. The classifier (Rstudio) analysed one lithological and two to six morphometric predictor variables. We tested several sets of different variables and selected the best performing set, which included the slope angle, profile curvature, TWI, mean upslope area, and the normalized height morphometric indices.

After the classification, the distribution of the stability categories was computed for three different areas: the mining waste heaps, the remediated quarries, and the natural slopes. The mining waste sites and the quarries were delineated using the national mining waste inventory, satellite images and topographic maps. Then a likelihood ratio analysis was done to calculate the relative frequencies of the stability categories in the different area types. It was expected that the stability category representing the slope debris at rest will be the most frequent in the waste heap areas. The statistical analysis reinforced this hypothesis by resulting a 54% larger likelihood compared to the natural slopes. It was also revealed that the most dangerous category, the scarps, are less likely on the waste heaps than on the natural slopes, which is a reassuring result. However, the transitional types (slopes that are still in movement) are more likely by 25% on the waste heaps. Even this slightly increased likelihood makes the local villages more prone to hazardous events, so an increased concern is also justified.

From the part of G.A. financial support was provided from the NRDI Fund of Hungary, Thematic Excellence Programme no. TKP2020-NKA-06 (National Challenges Subprogramme) funding scheme. D. G.: The study was supported by the ÚNKP-19-3 New National Excellence Program of the Ministry for Innovation and Technology, Hungary.

How to cite: Albert, G., Gerzsenyi, D., and Pogácsás, R.: Slope movement hazards of the mining-dumps in the Dorog Basin, Hungary, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-7673, https://doi.org/10.5194/egusphere-egu21-7673, 2021.

Landslide hazard assessment in India using historical data faces three challenges: (i) difficulty of obtaining systematic landslide occurrence data; (ii) under-representation of small-scale landslides; (iii) lack of recording of the physical/anthropogenic influences on landsliding. Here we show development of a Bayesian Belief Network (BBN) for a multi-hazard landslide assessment using experts’ judgements. Experts were chosen based on their experience on landslides and/or in Darjeeling Himalayas. A BBN produces a probability estimation of possible events and is a graph containing a set of variables (nodes) and conditional (in)dependencies between the nodes (arcs).

To better understand the relative weighting of potential causes of landslides in our case study area -Darjeeling Himalayas- we carried out four steps. (Step 1) We reviewed 29 peer- and grey-literature sources to list 13 physical/anthropogenic variables that might influence landsliding. (Step 2) We interviewed 11 experts about the importance of these 13 variables and asked for additional potential variables (resulting in 35 variables). (Step 3) We used interviews plus questionnaire to ask 16 experts to rate each of the 35 variables (scale 1-10) as to their potential to influence landsliding. The experts also added 7 more variables (resulting in 46 variables). (Step 4) Based on the ratings and interviews, we chose 35 out of 46 variables as our BBN nodes and from these the BBN arcs. Examples of these variables include rainfall, wildfires, geological weathering, planned infrastructure loading, cultivation (planned/unplanned), railway/road construction changing slope angle (planned), relief, slope, soil cohesion. Based on this study, we found that judgement of local people/academicians/technical experts can be of help whilst developing a BBN structure, allowing us to calculate probabilistic relationships between the nodes in a BBN. This process, therefore, can be utilised for landslide-based multi-hazard assessment in low data regions.

How to cite: Choudhury, S., Malamud, B. D., and Donovan, A.: Structuring a Bayesian belief network using expert knowledge for landslide hazard assessment, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-16047, https://doi.org/10.5194/egusphere-egu21-16047, 2021.

On the nexus of humans and their environment, landslide risk is in essence dynamic. In mountainous areas over the world, the need for agricultural land incites people to settle on steeper (more landslide-prone) terrain at the expense of ecosystems. At the same time, the degradation of ecosystems, for example through deforestation, leads to a considerable increase in landslide hazard. Although the link between deforestation and landslide hazard/risk has been widely recognized, it remains poorly quantified. This is especially the case in the Global South where historical land cover and landslide records are scarce.  

In this study, we investigate 58 years of forest cover changes, population dynamics, and landslide risk in the Kivu Rift. This mountainous region presents similar geomorphic and climatic conditions across three countries: Burundi, the eastern part of the Democratic Republic of the Congo (DRC), and Rwanda. First, we use contemporary landslide and deforestation data (2000-2016) to explicitly quantify the interactions between these two processes. Second, we reconstruct the annual forest cover changes between 1958 and 2016 by means of a cellular automaton of which the output converges to four forest cover products (1958, 1988, 2001, 2016). We derive the 1958 forest data from an inventory of nearly 2,400 panchromatic aerial photographs, available at the Royal Museum for Central Africa. The forest data for 1988, 2001, and 2016 are readily available and derived from satellite imagery. Next, we estimate the yearly historical landslide hazard dynamics by applying the contemporary deforestation-landslide relationship to the historical forest cover changes. Finally, an approximation of the landslide risk (expected fatalities per 100,000 inhabitants), is calculated for four epochs (1975, 1990, 2000, 2015) and derived from the product of the corresponding hazard map and population density grids.

During our entire period of observation, the landslide risk is higher in the DRC than in Rwanda and Burundi. While the risk in Rwanda and Burundi displays a slightly decreasing trend, the risk seems more volatile in the DRC. Here, the initial risk in 1975 is high due to the concentration of a small population along the steep northwestern coast of Lake Kivu. In the following 15 years, the risk in the DRC decreases sharply, only to soar again in the nineties. This sudden increase in risk can be linked to two factors: demographic changes and environmental degradation. During the nineties, the location of the Congolese people shifted towards steeper terrain. This shift is explained by the relocation of hundreds of thousands of Rwandan refugees and internally displaced people following the First and Second Congo War, but also by the economic opportunities provided by the booming, often informal, mining industry. Deforestation has also contributed to the higher landslide risk in the DRC, as large parts of the primary forest have been cut to satisfy the land and fuelwood demand of the fast-growing population.

With our analysis, we demonstrate that a landslide risk assessment is more than the reflection of the current environmental conditions. The legacy of environmental and societal dynamics resonates in contemporary landslide risk.

How to cite: Depicker, A., Jacobs, L., Mboga, N., Smets, B., Van Rompaey, A., Lennert, M., Kervyn, F., Michellier, C., Dewitte, O., and Govers, G.: Landslide risk trends in the Kivu Rift and the impact of environmental and societal dynamics, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-8927, https://doi.org/10.5194/egusphere-egu21-8927, 2021.

Mass movement such as landslides and rock fall is a prominent source of sediment in active mountain belt. Earthquake triggered landslides can generate substantial loose sediment and have significant geomorphic effects on long term landscape evolution. More importantly, these landslide impacts to land surface vary a lot due to the divergence of landslide characteristics and surrounding environment settings. Downslope and downstream transport of sediment into the channel network is fairly sensitive to climatic perturbations especially for extreme rainfall events. A wide variety of studies attempt to quantify or determine the contribution of landslide generated material to gross sediment budget and the corresponding retention time scale of landslide generated deposit in the mountain basin, whereas no established techniques can explicitly fingerprint/track landslide derived sediment. In this study, we first generated the hourly future extreme rainfall under two emission scenario (RCP4.5, RCP8.5) using ‘NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP)’ dataset. A new tracing function is incorporated into CAESAR-lisflood to track the landslide derived sediment footprint and dynamics in response to climate change. The landscape evolution at the Hongxi catchment, which is suffered tremendous damage from Wenchuan earthquake (Ms 8.0), are then simulated using CAESAR-lisflood under two climate scenarios. The results show that more than 80 percent of material generated by seismic landslides are still retained at the hillslope even after a sufficient time (e.g. 100 year). This study is to compare the spatial-temporal evolution pattern of landslides-derived sediment under two climatic scenarios (RCP4.5, RCP8.5), thus probing into the landslide generated sediment transport and budget respond to the climate change especially the impact of extreme rainfall events. Numerical modelling can provide a quick and effective tool for broad scale predictions of sediment produced by landslide events under different climatic predictions, which is of great importance for seismic induced disaster protection and reduction under climate change.

How to cite: Xie, J. and Coulthard, T.: Tracing seismic landslide-derived sediment dynamics in response to climate change, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-9004, https://doi.org/10.5194/egusphere-egu21-9004, 2021.

Transport of continental shelf sediments to the deep ocean can be studied from displaced symbiont-bearing larger benthic foraminifera (LBF) found in turbidite deposits. The LBF habitat depth, physical characteristics and preservation serve as indicators for understanding sediment transport dynamics near the seabed and in the water column. Here, an experiment was designed to explore turbulent sediment transport in a closed flume system using simulated high current velocities. Shelf sediments from the Gulf of Eilat/Aqaba (GEA), dominated by Amphistegina papillosa and Operculina ammonoides, were subjected to 60 and 80 cm/sec current velocities while collected in a 10-cm vertical sediment trap. LBF abundance, shell physical properties and preservation state were analyzed and compared with the original bulk shelf sediments. The experiment results showed that at 80 cm/sec velocity LBF shells of all sizes and preservation states are efficiently resuspended and transported in large quantities throughout the water column, as opposed to their transport as bedload by the lower velocity current. LBF shape also has a role in the transport distances and accumulation depths. O. ammonoides shells were found more portable, compared to A. papillosa, due to their flatter discoid shape. The results suggest that a threshold velocity of ~80 cm/sec was needed to generate the thick coarse deposits found in the GEA slope sedimentary record, which were previously suggested to be triggered by large magnitude seismic events. Lower velocities probably winnowed minor amounts of LBF shells (with little or no coarser sediments) that were deposited as thin layers and may point to lower magnitude seismic triggers. In conclusion, LBF shells are transported and deposited in turbidites according to their hydrodynamic properties, resulting in assemblage differentiation along the transport pathway. This study shows the fossil biogenic composition in turbidites  includes valuable information on current velocities, transport dynamics and possible triggers in the geological record.  

How to cite: Ash-Mor, A., Almogi-Labin, A., Bouchet, V. M. P., Seuront, L., Guy-Haim, T., Ben-Avraham, Z., and Bookman, R.: Going with the flow: Experimental simulation of sediment turbid transport from a foraminifera perspective, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-7111, https://doi.org/10.5194/egusphere-egu21-7111, 2021.