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Abstract. Deforestation is associated with a decrease in slope stability through the alteration of hydrological and geotechnical conditions. As such, deforestation increases landslide activity over short, decadal timescales. However, over longer timescales (0.1–10 Myr) the location and timing of landsliding is controlled by the interaction between uplift and fluvial incision. Yet, the interaction between (human-induced) deforestation and landscape evolution has hitherto not been explicitly considered. We address this issue in the North Tanganyika–Kivu rift region (East African Rift). In recent decades, the regional population has grown exponentially, and the associated expansion of cultivated and urban land has resulted in widespread deforestation. In the past 11 Myr, active continental rifting and tectonic processes have forged two parallel mountainous rift shoulders that are continuously rejuvenated (i.e., actively incised) through knickpoint retreat, enforcing topographic steepening. In order to link deforestation and rejuvenation to landslide erosion, we compiled an inventory of nearly 8000 recent shallow landslides in © Google Earth imagery from 2000–2019. To accurately calculate landslide erosion rates, we developed a new methodology to remediate inventory biases linked to the spatial and temporal inconsistency of this satellite imagery. Moreover, to account for the impact of rock strength on both landslide occurrence and knickpoint retreat, we limit our analysis to rock types with threshold angles of 24–28∘. Rejuvenated landscapes were defined as the areas draining towards Lake Kivu or Lake Tanganyika and downstream of retreating knickpoints. We find that shallow landslide erosion rates in these rejuvenated landscapes are roughly 40 % higher than in the surrounding relict landscapes. In contrast, we find that slope exerts a stronger control on landslide erosion in relict landscapes. These two results are reconciled by the observation that landslide erosion generally increases with slope gradient and that the relief is on average steeper in rejuvenated landscapes. The weaker effect of slope steepness on landslide erosion rates in the rejuvenated landscapes could be the result of three factors: the absence of earthquake-induced landslide events in our landslide inventory, a thinner regolith mantle, and a drier climate. More frequent extreme rainfall events in the relict landscapes, and the presence of a thicker regolith, may explain a stronger landslide response to deforestation compared to rejuvenated landscapes. Overall, deforestation initiates a landslide peak that lasts approximately 15 years and increases landslide erosion by a factor 2 to 8. Eventually, landslide erosion in deforested land falls back to a level similar to that observed under forest conditions, most likely due to the depletion of the most unstable regolith. Landslides are not only more abundant in rejuvenated landscapes but are also smaller in size, which may again be a consequence of a thinner regolith mantle and/or seismic activity that fractures the bedrock and reduces the minimal critical area for slope failure. With this paper, we highlight the importance of considering the geomorphological context when studying the impact of recent land use changes on landslide activity.

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Determining rainfall thresholds for landsliding is crucial in landslide hazard evaluation and early warning system development, yet challenging in data-scarce regions. Using freely available satellite rainfall data in a reproducible automated procedure, the bootstrap-based frequentist threshold approach, coupling antecedent rainfall (AR) and landslide susceptibility data as proposed by Monsieurs et al., has proved to provide a physically meaningful regional AR threshold equation in the western branch of the East African Rift. However, previous studies could only rely on globaland continental-scale rainfall and susceptibility data. Here, we use newly available regional-scale susceptibility data to test the robustness of the method to different data configurations. This leads us to improve the threshold method through using stratified data selection to better exploit the data distribution over the whole range of susceptibility. In addition, we discuss the effect of outliers in small data sets on the estimation of parameter uncertainties and the interest of not using the bootstrap technique in such cases. Thus improved, the method effectiveness shows strongly reduced sensitivity to the used susceptibility data and is satisfyingly validated by new landslide occurrences in the East African Rift, therefore successfully passing first transferability tests. parameters through a spatially extended infinite-slope stability model [25]. However, the large required data input for well-calibrated process-based thresholds explains their current limitation to mostly applications at the hillslope scale or through numerical simulations [4,21,[25][26][27].The estimation of empirical rainfall thresholds is also associated with additional sources of uncertainty. Firstly, landslide inventories are inherently biased towards high-impact landslide events and regions that are most accessible, while their accuracy is constrained by the scientific validity of the reporting sources, especially in data-scarce low-capacity environments [1,[28][29][30][31]. Secondly, rainfall data comprise uncertainties related to the spatial representativeness of rain gauges or biases in satellite-derived estimates [32,33]. Thirdly, the definition of rainfall parameters, with intensity and duration forming the most frequently used parameter couple [3,5], varies strongly across studies [3]. Finally, the latter parameters' interdependence is problematic, obscuring the physical processes associated with the calculated thresholds [34].In order to account for and characterize threshold uncertainties, a growing number of reproducible statistical techniques have been developed [3]. A weakness of such methods is, however, that they are generally tailored to a specific area and available data sets, which often prevents straightforward transferability to other regions and data sets [35]. Nevertheless, transferability is not only essential for evaluating and comparing landslide hazard over different regions of the world [10,36], but also valuable in the context of the in...

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Accurate and detailed multitemporal inventories of landslides and their process characterization are crucial for the evaluation of landslide hazards and the implementation of disaster risk reduction strategies in densely-populated mountainous regions. Such investigations are, however, rare in many regions of the tropical African highlands, where landslide research is often in its infancy and not adapted to the local needs. Here, we have produced a comprehensive multitemporal investigation of the landslide processes in the hillslopes of Bujumbura, situated in the landslide-prone East African Rift. We inventoried more than 1200 landslides by combining careful field investigation and visual analysis of satellite images, very-high-resolution topographic data, and historical aerial photographs. More than 20% of the hillslopes of the city are affected by landslides. Recent landslides (post-1950s) are mostly shallow, triggered by rainfall, and located on the steepest slopes. The presence of roads and river quarrying can also control their occurrence. Deep-seated landslides typically concentrate in landscapes that have been rejuvenated through knickpoint retreat. The difference in size distributions between old and recent deep-seated landslides suggests the long-term influence of potentially changing slope-failure drivers. Of the deep-seated landslides, 66% are currently active, those being mostly earthflows connected to the river system. Gully systems causing landslides are commonly associated with the urbanization of the hillslopes. Our results provide a much more accurate record of landslide processes and their impacts in the region than was previously available. These insights will be useful for land management and disaster risk reduction strategies.

Abstract. In recent decades, large wildfires have inflicted considerable damage on valuable Natura 2000 regions in Belgium. Despite these events and the general perception that global change will exacerbate wildfire prevalence, this has not been studied yet in the Belgian context. Therefore, the national government initiated the national action plan on wildfires in order to evaluate the wildfire risk, on the one hand, and the materials, procedures, and training of fire services, on the other hand. This study focuses on the spatial distribution of the ignition probability, a component of the wildfire risk framework. In a first stage, we compile a historical wildfire database using (i) newspaper articles between 1994 and 2016 and (ii) a list of wildfire interventions between 2010 and 2013, provided by the government. In a second stage, we use a straightforward method relying on Bayes' rule and a limited number of covariates to calculate the ignition probability. It appears that most wildfire-prone areas in Belgium are located in heathland where military exercises are held. The provinces that have the largest relative areas with a high or very high wildfire risk are Limburg and Antwerp. Our study also revealed that most wildfire ignitions in Belgium are caused by humans (both arson and negligence) and that natural causes such as lightning are rather scarce. Wildfire prevention can be improved by (i) excluding military activity in fire-prone areas during the fire season, (ii) improving collaboration with foreign emergency services, (iii) concentrating the dedicated resources in the areas that display the highest ignition probabilities, (iv) improving fire detection methods, and (v) raising more awareness among the public.