scholarly journals Assessment of Rainfall Thresholds for Rain-Induced Landslide Activity in North Sikkim Road Corridor in Sikkim Himalaya, India

2019 ◽  
pp. 1-14 ◽  
Author(s):  
Bappaditya Koley ◽  
Anindita Nath ◽  
Subhajit Saraswati ◽  
Kaushik Bandyopadhyay ◽  
Bidhan Chandra Ray
Keyword(s):  
Rainfall Intensity ◽  
Lower Boundary ◽  
Daily Rainfall ◽  
Rainfall Threshold ◽  
Rainfall Data ◽  
Antecedent Rainfall ◽  
Rainfall Thresholds ◽  
Sikkim Himalaya ◽  
North East ◽  
The North

Land sliding is a perennial problem in the Eastern Himalayas. Out of 0.42 million km2 of Indian landmass prone to landslide, 42% fall in the North East Himalaya, specially Darjeeling and Sikkim Himalaya. Most of these landslides are triggered by excessive monsoon rainfall between June and October in almost every year. Various attempts in the global scenario have been made to establish rainfall thresholds in terms of intensity – duration of antecedent rainfall models on global, regional and local scale for triggering of the landslide. This paper describes local aspect of rainfall threshold for landslides based on daily rainfall data in and around north Sikkim road corridor region. Among 210 Landslides occurring from 2010 to 2016 were studied to analyze rainfall thresholds. Out of the 210 landslides, however, only 155 Landslides associated with rainfall data which were analyzed to yield a threshold relationship between rainfall intensity-duration and landslide initiation. The threshold relationship determined fits to lower boundary of the Landslide triggering rainfall events is I = 4.045 D - 0.25 (I=rainfall intensity (mm/h) and D=duration in (h)), revealed that for rainfall event of short time (24 h) duration with a rainfall intensity of 1.82 mm/h, the risk of landslides on this road corridor of the terrain is expected to be high. It is also observed that an intensity of 58 mm and 139 mm for 10-day and 20-day antecedent rainfall are required for the initiation of landslides in the study area. This threshold would help in improvement on traffic guidance and provide safety to the travelling tourists in this road corridor during the monsoon.

scholarly journals Can we get more out of rainfall thresholds? The temporal resolution tradeoff and the role of antecedent wetness and rainfall spatial variability

2020 ◽  
Author(s):  
Elena Leonarduzzi ◽  
Peter Molnar
Keyword(s):  
Temporal Resolution ◽  
Daily Rainfall ◽  
Rainfall Event ◽  
Rainfall Data ◽  
Antecedent Rainfall ◽  
Rainfall Thresholds ◽  
Total Rainfall ◽  
Hourly Rainfall ◽  
Landslide Triggering

<p>Rainfall event properties like maximum intensity, total rainfall depth, or their representation in the form of intensity-duration (ID) or total rainfall-duration (ED) curves, are commonly used to determine the triggering rainfall (event) conditions required for landslide initiation. This rainfall data-driven prediction of landsliding can be extended by the inclusion of antecedent wetness conditions. Although useful for first order assessment of landslide triggering conditions in warning systems, this approach relies heavily on data quality and temporal resolution, which may affect the overall predictive model performance as well as its reliability.</p><p>In this work, we address three key aspects of rainfall thresholds when applied at large spatial scales: (a) the tradeoffs between higher and lower temporal resolution (hourly or daily) (b) the spatial variability associated with long term rainfall, and (c) the added value of antecedent rainfall as predictor. We explore all of these by utilizing a long-term landslide inventory, containing more than 2500 records in Switzerland and 3 gridded rainfall records: a long daily rainfall dataset and two derived hourly products, disaggregated using stations or radar hourly measurements.</p><p>We observe that while predictive performances improve slightly when utilizing high quality hourly record (using radar information), the length of the record decreases, as well as the number of landslides in the inventory, which affects the reliability of the thresholds. A tradeoff has to be found between using long records of less accurate daily rainfall data and landslide timing, and shorter records with highly accurate hourly rainfall data and landslide timing. Even daily rainfall data may give reasonable predictive performance if thresholds are estimated with a long landslide inventory. Good quality hourly rainfall data significantly improve performance, but historical records tend to be shorter or less accurate (e.g. fewer stations available) and landslides with known timing are fewer. Considering antecedent rainfall, we observe that it is generally higher prior to landslide-triggering events and this can partially explain the false alarms and misses of an intensity-duration threshold. Nevertheless, in our study antecedent rainfall shows less predictive power by itself than the rainfall event characteristics. Finally, we show that we can improve the performances of the rainfall thresholds by accounting for local climatology in which we define new thresholds by normalizing the event characteristics with a chosen quantile of the local rainfall distribution or using the mean annual precipitation.</p>

scholarly journals A susceptibility-based rainfall threshold approach for landslide occurrence

2019 ◽  
Vol 19 (4) ◽  
pp. 775-789 ◽  
Author(s):  
Elise Monsieurs ◽  
Olivier Dewitte ◽  
Alain Demoulin
Keyword(s):  
Landslide Susceptibility ◽  
Rainfall Threshold ◽  
Exceedance Probability ◽  
Equal Weight ◽  
Antecedent Rainfall ◽  
Rainfall Thresholds ◽  
Landslide Occurrence ◽  
Continental Scale ◽  
Satellite Rainfall ◽  
Rainfall Estimates

Abstract. Rainfall threshold determination is a pressing issue in the landslide scientific community. While major improvements have been made towards more reproducible techniques for the identification of triggering conditions for landsliding, the now well-established rainfall intensity or event-duration thresholds for landsliding suffer from several limitations. Here, we propose a new approach of the frequentist method for threshold definition based on satellite-derived antecedent rainfall estimates directly coupled with landslide susceptibility data. Adopting a bootstrap statistical technique for the identification of threshold uncertainties at different exceedance probability levels, it results in thresholds expressed as AR = (α±Δα)⋅S(β±Δβ), where AR is antecedent rainfall (mm), S is landslide susceptibility, α and β are scaling parameters, and Δα and Δβ are their uncertainties. The main improvements of this approach consist in (1) using spatially continuous satellite rainfall data, (2) giving equal weight to rainfall characteristics and ground susceptibility factors in the definition of spatially varying rainfall thresholds, (3) proposing an exponential antecedent rainfall function that involves past daily rainfall in the exponent to account for the different lasting effect of large versus small rainfall, (4) quantitatively exploiting the lower parts of the cloud of data points, most meaningful for threshold estimation, and (5) merging the uncertainty on landslide date with the fit uncertainty in a single error estimation. We apply our approach in the western branch of the East African Rift based on landslides that occurred between 2001 and 2018, satellite rainfall estimates from the Tropical Rainfall Measurement Mission Multi-satellite Precipitation Analysis (TMPA 3B42 RT), and the continental-scale map of landslide susceptibility of Broeckx et al. (2018) and provide the first regional rainfall thresholds for landsliding in tropical Africa.

Towards the effective use of Artificial Neural Networks for accessing rainfall thresholds for rainfall-induced landslides, a study based on in-situ and satellite merged rainfall data

2021 ◽  
Author(s):  
Luísa Vieira Lucchese ◽  
Guilherme Garcia de Oliveira ◽  
Olavo Correa Pedrollo
Keyword(s):  
Neural Network ◽  
Rainfall Event ◽  
Rainfall Data ◽  
Extreme Rainfall Event ◽  
Ann Model ◽  
Antecedent Rainfall ◽  
Rainfall Thresholds ◽  
Satellite Rainfall ◽  
Artificial Neural

<p>Rainfall-induced landslides have caused destruction and deaths in South America. Accessing its triggers can help researchers and policymakers to understand the nature of the events and to develop more effective warning systems. In this research, triggering rainfall for rainfall-induced landslides is evaluated. The soil moisture effect is indirectly represented by the antecedent rainfall, which is an input of the ANN model. The area of the Rolante river basin, in Rio Grande do Sul state, Brazil, is chosen for our analysis. On January 5<sup>th</sup>, 2017, an extreme rainfall event caused a series of landslides and debris flows in this basin. The landslide scars were mapped using satellite imagery. To calculate the rainfall that triggered the landslides, it was necessary to compute the antecedent rainfall that occurred within the given area. The use of satellite rainfall data is a useful tool, even more so if no gauges are available for the location and time of the rainfall event, which is the case. Remote sensing products that merge the data from in situ stations with satellite rainfall data are increasingly popular. For this research, we employ the data from MERGE (Rozante et al., 2010), that is one of these products, and is focused specifically on Brazilian gauges and territory. For each 12.5x12.5m raster pixel, the rainfall is interpolated to the points and the rainfall volume from the last 24h before the event is accumulated. This is added as training data in our Artificial Neural Network (ANN), along with 11 terrain attributes based on ALOS PALSAR (ASF DAAC, 2015) elevation data and generated by using SAGA GIS. These attributes were presented and analyzed in Lucchese et al. (2020). Sampling follows the procedure suggested in Lucchese et al. (2021, in press). The ANN model is a feedforward neural network with one hidden layer consisting of 20 neurons. The ANN is trained by backpropagation method and cross-validation is used to ensure the correct adjustment of the weights. Metrics are calculated on a separate sample, called verification sample, to avoid bias. After training, and provided with relevant information, the ANN model can estimate the 24h-rainfall thresholds in the region, based on the 2017 event only. The result is a discretized map of rainfall thresholds defined by the execution of the trained ANN. Each pixel of the resulting map should represent the volume of rainfall in 24h necessary to trigger a landslide in that point. As expected, lower thresholds (30 - 60 mm) are located in scarped slopes and the regions where the landslides occurred. However, lowlands and the plateau, which are areas known not to be prone to landslides, show higher rainfall thresholds, although not as high as expected (75 - 95 mm). Mean absolute error for this model is 16.18 mm. The inclusion of more variables and events to the ANN training should favor achieving more reliable outcomes, although, our results are able to show that this methodology has potential to be used for landslide monitoring and prediction.</p>

How to account for misclassification costs in shallow-landslide rainfall thresholds?

2021 ◽  
Author(s):  
Paolo Frattini ◽  
Gianluca Sala ◽  
Camilla Lanfranconi ◽  
Giulia Rusconi ◽  
Giovanni Crosta
Keyword(s):  
False Alarm ◽  
Rainfall Intensity ◽  
Rainfall Threshold ◽  
Shallow Landslides ◽  
False Alarms ◽  
Rainfall Thresholds ◽  
Rainfall Events ◽  
Misclassification Costs ◽  
Triggering Events ◽  
The Cost

<p>Rainfall is one of the most significant triggering factors for shallow landslides. The early warning for such phenomena requires the definition of a threshold based on a critical rainfall condition that may lead to diffuse landsliding. The developing of these thresholds is frequently done through empirical or statistical approaches that aim at identifying thresholds between rainfall events that triggered or non-triggered landslides. Such approaches present several problems related to the identification of the exact amount of rainfall that triggered landslides, the local geo-environmental conditions at the landslide site, and the minimum rainfall amount used to define the non-triggering events. Furthermore, these thresholds lead to misclassifications (false negative or false positive) that always induce costs for the society. The aim of this research is to address these limitations, accounting for classification costs in order to select the optimal thresholds for landslide risk management.</p><p>Starting from a database of shallow landslides occurred during five regional-scale rainfall events in the Italian Central Alps, we extracted the triggering rainfall intensities by adjusting rain gouge data with weather radar data. This adjustment significantly improved the information regarding the rainfall intensity at the landslide site and, although an uncertainty related to the exact timing of occurrence has still remained. Therefore, we identified the rainfall thresholds through the Receiver Operating Characteristic (ROC) approach, by identifying the optimal rainfall intensity that separates triggering and non-triggering events. To evaluate the effect related to the application of different minimum rainfall for non-triggering events, we have adopted three different values obtaining similar results, thus demonstrating that the ROC approach is not sensitive to the choice of the minimum rainfall threshold. In order to include the effect of misclassification costs we have developed cost-sensitive rainfall threshold curves by using cost-curve approach (Drummond and Holte 2000). As far as we know, this is the first attempt to build a cost-sensitive rainfall threshold for landslides that allows to explicitly account for misclassification costs. For the development of the cost-sensitive threshold curve, we had to define a reference cost scenario in which we have quantified several cost items for both missed alarms and false alarms. By using this scenario, the cost-sensitive rainfall threshold results to be lower than the ROC threshold to minimize the missed alarms, the costs of which are seven times greater than the false alarm costs. Since the misclassification costs could vary according to different socio-economic contexts and emergency organization, we developed different extreme scenarios to evaluate the sensitivity of misclassification costs on the rainfall thresholds. In the scenario with maximum false-alarm cost and minimum missed-alarm cost, the rainfall threshold increases in order to minimize the false alarms. Conversely, the rainfall thresholds decreases in the scenario with minimum false-alarm cost and maximum missed-alarm costs. We found that the range of variation between the curves of these extreme scenarios is as much as half an order of magnitude.</p>

scholarly journals Rainfall Threshold Estimation and Landslide Forecasting for Kalimpong, India Using SIGMA Model

Water ◽  
2020 ◽  
Vol 12 (4) ◽  
pp. 1195 ◽  
Author(s):  
Minu Treesa Abraham ◽  
Neelima Satyam ◽  
Sai Kushal ◽  
Ascanio Rosi ◽  
Biswajeet Pradhan ◽  
...  
Keyword(s):  
Early Warning ◽  
Sigma Model ◽  
Warning System ◽  
Daily Rainfall ◽  
Early Warning Systems ◽  
Rainfall Threshold ◽  
Integrated System ◽  
Warning Systems ◽  
Rainfall Forecasting ◽  
Rainfall Thresholds

Rainfall-induced landslides are among the most devastating natural disasters in hilly terrains and the reduction of the related risk has become paramount for public authorities. Between the several possible approaches, one of the most used is the development of early warning systems, so as the population can be rapidly warned, and the loss related to landslide can be reduced. Early warning systems which can forecast such disasters must hence be developed for zones which are susceptible to landslides, and have to be based on reliable scientific bases such as the SIGMA (sistema integrato gestione monitoraggio allerta—integrated system for management, monitoring and alerting) model, which is used in the regional landslide warning system developed for Emilia Romagna in Italy. The model uses statistical distribution of cumulative rainfall values as input and rainfall thresholds are defined as multiples of standard deviation. In this paper, the SIGMA model has been applied to the Kalimpong town in the Darjeeling Himalayas, which is among the regions most affected by landslides. The objectives of the study is twofold: (i) the definition of local rainfall thresholds for landslide occurrences in the Kalimpong region; (ii) testing the applicability of the SIGMA model in a physical setting completely different from one of the areas where it was first conceived and developed. To achieve these purposes, a calibration dataset of daily rainfall and landslides from 2010 to 2015 has been used; the results have then been validated using 2016 and 2017 data, which represent an independent dataset from the calibration one. The validation showed that the model correctly predicted all the reported landslide events in the region. Statistically, the SIGMA model for Kalimpong town is found to have 92% efficiency with a likelihood ratio of 11.28. This performance was deemed satisfactory, thus SIGMA can be integrated with rainfall forecasting and can be used to develop a landslide early warning system.

scholarly journals Rainfall-triggered landslides in the Lisbon region over 2006 and relationships with the North Atlantic Oscillation

2008 ◽  
Vol 8 (3) ◽  
pp. 483-499 ◽  
Author(s):  
J. L. Zêzere ◽  
R. M. Trigo ◽  
M. Fragoso ◽  
S. C. Oliveira ◽  
R. A. C. Garcia
Keyword(s):  
North Atlantic Oscillation ◽  
North Atlantic ◽  
Rainfall Threshold ◽  
Antecedent Rainfall ◽  
Landslide Activity ◽  
The North ◽  
Nao Index ◽  
Landslide Event ◽  
The North Atlantic ◽  
The North Atlantic Oscillation

Abstract. Landslides occurred in the Lisbon area during the last 50 years were almost always induced by rainfall and have been used to establish rainfall thresholds for regional landslide activity. In 2006, three new rainfall-triggered landslide events occurred in the study area, namely on the 20 March, the 25–27 October, and the 28 November. Landslide events occurred in March and October 2006 include shallow translational slides and few debris flows, and the corresponding absolute antecedent rainfall was found to be above the threshold for durations ranging from 4 to 10 days. These events also fit the combined threshold of daily precipitation and 5 days calibrated antecedent rainfall values. Likewise the landslide event that took place in late November 2006 includes some slope movements with deeper slip surfaces, when compared with landslides dating from March and October. Moreover, the corresponding absolute antecedent rainfall was also found to be above the 40-day period rainfall threshold. Here we characterize in detail the short and long-term atmospheric circulation conditions that were responsible for the intense rainfall episodes that have triggered the corresponding landslide events. It is shown that the three rainfall episodes correspond to considerably different synoptic atmospheric patterns, with the March episode being associated to an intense cut-off low system while the October and November episodes appear to be related to more typical Atlantic low pressure systems (and associated fronts) travelling eastwards. Finally, we analyse the role played by the North Atlantic Oscillation (NAO) during those months marked by landslide activity. It is shown that the NAO index was consistently negative (usually associated with above average precipitation) for the months prior to the landslide events, i.e. between October 2005 and March 2006, and again between August and October 2006.

scholarly journals Spatial and temporal variations of rainy days and mean daily rainfall intensity in northern Nigeria.

MAUSAM ◽  
2021 ◽  
Vol 44 (1) ◽  
pp. 85-92
Author(s):  
E. O. OLADIPO ◽  
S. SALAHU
Keyword(s):  
Rainfall Intensity ◽  
Daily Rainfall ◽  
Temporal Variations ◽  
Spatial And Temporal Variations ◽  
Northern Nigeria ◽  
Rainfall Trend ◽  
North Central ◽  
The North ◽  
The Mean ◽  
Rainy Days

The spatial and temporal variations of rainy Gays arid daily rainfall intensity for northern Nigeria for using 54 years data are analysed, The extent and nature of non-random changes, such as trend and fluctuations are Investigated. In general, both, the rainy day frequency and mean daily rainfall intensity decreases northwards except for localized orographic effect in the north central Part of the region. There is statistical evidence or decreasing trend in the, number of rainy days over the period of study, but the trend analysis showed no significance or the mean daily rainfall intensity. This suggests that the recent decreasing rainfall trend In the region particularly In the Sahellan zone, In the result of decrease In the frequency of rainy days and not due to any significant change In the rainfall intensity.  

scholarly journals The Determination of Rainfall Threshold Triggering Landslides Using Remote Sensing

2021 ◽  
Vol 893 (1) ◽  
pp. 012011
Author(s):  
L Agustina ◽  
A Safril
Keyword(s):  
Remote Sensing ◽  
Rainfall Intensity ◽  
Nearest Neighbor ◽  
Remote Sensing Data ◽  
Rainfall Threshold ◽  
Rainfall Data ◽  
Bilinear Method ◽  
Sensing Data ◽  
Central Java ◽  
Hourly Rainfall

Abstract Landslide is one of the natural disasters that can cause a lot of loss, both material and fatalities. Banjarnegara Regency is one of Central Java Province regencies where landslides often occur due to the region's topography and high intensity rainfall.. Therefore, it is necessary to determine the threshold of rainfall that can trigger landslides to be used as an early warning for landslides. The rainfall data used for the threshold is daily and hourly rainfall intensity from remote sensing data that provides complete data but relatively rough resolution. So that remote sensing data need to be re-sampled. The remote sensing data used is CMORPH satellite data that has been re-sampled for detailing existing information of rainfall data. The resampling method used is the bilinear method and nearest neighbor by choosing between the two based on the highest correlation. Threshold calculation using Cumulative Threshold (CT) method resulted equation P3 = 7.0354 - 1.0195P15 and Intensity Duration (ID) method resulted equation I = 1.785D-0305. The peak rainfall intensity occurs at the threshold of 97-120 hours before a landslide occur.

Development of Rainfall threshold model for the watershed/sub-watershed landscape at data scarce situation – a case study of mid-hill region, Nepal.

2021 ◽  
Author(s):  
Sanjaya Devkota ◽  
Deepak Kc ◽  
Michel Jaboyedoff ◽  
Govind Acharya
Keyword(s):  
Threshold Model ◽  
Monsoon Season ◽  
Warning System ◽  
Daily Rainfall ◽  
Rainfall Threshold ◽  
Rainfall Data ◽  
Coefficient Of Determination ◽  
Susceptibility Map ◽  
Hill Region ◽  
Threshold Exceedance

<p>Landslides are common in the mid-hill region of Nepal where the terrain slopes are steep and consist of fragile geo-morphology. In Nepal, the casual and triggering factors of the landslides are respectively the underlying geology, intense rainfall and unplanned construction of rural roads is highly recognized, which is however less known and limited in study. Establishment of rainfall threshold for landslides at the watershed landscape is data driven, which is scared in the context of Nepal. The only available long term daily rainfall and sparsely available historical landslides date has been used to develop the rainfall threshold model for the two watersheds in central and western mid-hill regions respectively the Sindhukhola and Sotkhola in Bagmati and Karnali Provinces of Nepal. The watersheds are located in two distinct hydro-climatic regions in terms of rainfall amount and intensity. Historical daily (monsoonal) rainfall data of over four decades (1970-2016) were analyzed available from the Department of Hydrology and Meteorology (DHM)/Government of Nepal and five days’ antecedent rain was calculated. With the limitedly available temporal landslides data, correlation was examined among the 5-days antecedent rain (mm/5days) and daily rainfall (mm/day) portrayed the rainfall threshold (RT) model (Sindhukhola=180-1.07R<sub>T5adt</sub> and Sotkhola = 110-0.83*R<sub>T5adt</sub>). Utilizing the five days’ antecedent rain fitted into the model, results the threshold rainfall. Deducting the five days’ antecedent rains to the RT described the threshold exceedance (R) for the landslides. The model can be plotted in simple spreadsheet (landslides date in Y-axis and threshold exceedance R in X-axis) to visualize the changes in the threshold exceedance over time, whenever the threshold exceedance progressively and rapidly increased and crossed the threshold line and reached to the positive (> 0) zone, the plots allows for the landslides warning notice. In case of the threshold exceedance is further increased there is likely to have landslides in the watersheds. The model was validated with the 35 dated landslides recorded in monsoon 2015 in Sotkhola watershed. The result indicated that the model preserves 72% coefficient of determination (R<sup>2</sup>) where there were landslides in the watershed during 2015 monsoon. Due to the simplicity and at the data scarce situation, the model was found to be useful to forecast the landslides during the monsoon season in the region. The model; however, can be improved for better performance whenever the higher resolution time-series landslides data and automated weather stations are available in the watersheds. Linking this model to the proper landslide susceptibility map, and the real time rainfall data through mobile communication techniques, landslide early warning system can be established.</p><p><strong>KEYWORDS: </strong>landslide, rainfall threshold, data-scare, antecedent rainfall</p><p><strong>References:</strong></p><p>Aleotti, P. (2004). A warning system for rainfall-induced shallow failures. Engineering Geology, 73(3-4), 247-265.</p><p>Jaiswal, P. and van Westen, C.J., 2009. Estimating temporal probability for landslide initiation along transportation routes based on rainfall thresholds. Geomorphology, 112(1-2): 96-105.</p><p><strong>Acknowledgement:</strong> Comprehensive Disaster Risk Management Programme – UNDP in Nepal for the opportunity to conduct this research.</p>

scholarly journals A susceptibility-based rainfall threshold approach for landslide occurrence

2018 ◽  
Author(s):  
Elise Monsieurs ◽  
Olivier Dewitte ◽  
Alain Demoulin
Keyword(s):  
Landslide Susceptibility ◽  
Rainfall Threshold ◽  
Exceedance Probability ◽  
Equal Weight ◽  
Antecedent Rainfall ◽  
Rainfall Thresholds ◽  
Landslide Occurrence ◽  
Continental Scale ◽  
Satellite Rainfall ◽  
Rainfall Estimates

Abstract. Rainfall threshold determination is a pressing issue in the landslide scientific community. While main improvements have been made towards more reproducible techniques for the identification of triggering conditions for landsliding, the now well-established rainfall intensity or event – duration thresholds for landsliding suffer from several limitations. Here, we propose a new approach of the frequentist method for threshold definition based on satellite-derived antecedent rainfall estimates directly coupled with landslide susceptibility data. Adopting a bootstrap statistical technique for the identification of threshold uncertainties at different exceedance probability levels, it results in thresholds expressed as AR = (α ± ∆α) * S(β ± ∆β), where AR is antecedent rainfall (mm), S is landslide susceptibility, α and β are scaling parameters, and ∆α and ∆β are their uncertainties. The main improvements of this approach consist in: (1) using spatially continuous satellite rainfall data, (2) giving equal weight to rainfall characteristics and ground susceptibility factors in the definition of spatially varying rainfall thresholds, (3) proposing an exponential antecedent rainfall function that involves past daily rainfall in the exponent to account for the different lasting effect of large versus small rainfall, (4) quantitatively exploiting the lower parts of the cloud of data points, most meaningful for threshold estimation, and (5) merging the uncertainty on landslide date with the fit uncertainty in a single error estimation. We apply our approach in the western branch of the East African Rift based on landslides that occurred between 2001 and 2018, satellite rainfall estimates from the Tropical Rainfall Measurement Mission Multi-satellite Precipitation Analysis (TMPA 3B42 RT), and the continental-scale map of landslide susceptibility of Broeckx et al. (2018) and provide first regional rainfall thresholds for landsliding in tropical Africa.

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