Spatial and temporal dynamics of monsoon-induced landslides in Nepal in 2020

2021 ◽  
Author(s):  
Kaushal Raj Gnyawali ◽  
Dwayne D. Tannant ◽  
Yogesh Bhattarai ◽  
Rijan Jayana ◽  
Rocky Talchabhadel
Keyword(s):  
Temporal Dynamics ◽  
Monsoon Season ◽  
Warning System ◽  
Early Morning ◽  
Online News ◽  
Temporal Database ◽  
Rainfall Thresholds ◽  
Landslide Occurrence ◽  
Late Night ◽  
Hourly Rainfall

<p>In the monsoon season, landslides are major disasters in Nepal, causing loss of life and economic impacts. The landslides triggered in the 2020 monsoon (June – September) in Nepal caused more than 300 fatalities and affected about 800 families. A spatial and temporal database of landslides in this region does not exist, which has hindered an understanding of landslide dynamics and the development of a regional early warning system (EWS). In this study, we prepare a time-stamped (hourly) geo-referenced database of the landslides triggered by the 2020 monsoon in Nepal and investigate their dynamic trends. We track landslides from online news for each day during the monsoon to map their location and time. The database contains 332 mapped landslides, out of which accurate time stamps are available for 126 landslides. The spatial pattern shows a large concentration of landslides in central Nepal (districts of Parbat, Kaski, Myagdi, Baglung, Gulmi, and Syangja). The temporal pattern reveals that landslides in this region occur mostly during late night or early morning. We estimate hourly rainfall thresholds for landslide occurrence from the Integrated Multi-satellitE Retrievals for GPM (IMERG) rainfall product. The database and analysis provide a basis for estimating regional rainfall thresholds for Nepal and the design of an EWS.</p>

scholarly journals Rainfall thresholds for shallow landslides occurrence in Calabria, southern Italy

2013 ◽  
Vol 1 (5) ◽  
pp. 5141-5179 ◽  
Author(s):  
C. Vennari ◽  
S. L. Gariano ◽  
L. Antronico ◽  
M. T. Brunetti ◽  
G. Iovine ◽  
...  
Keyword(s):  
Southern Italy ◽  
Central Italy ◽  
Warning System ◽  
Mediterranean Area ◽  
Google Earth ◽  
Shallow Landslides ◽  
Rainfall Thresholds ◽  
Rainfall Events ◽  
Landslide Occurrence ◽  
Hourly Rainfall

Abstract. In many areas, rainfall is the primary trigger of landslides. Determining the rainfall conditions responsible for landslide occurrence is important, and may contribute to save lives and properties. In a long-term national project for the definition of rainfall thresholds for possible landslide occurrence in Italy, and for the implementation of a national landslide warning system, we compiled a catalogue of 186 rainfall events that have resulted in 251 shallow landslides in Calabria, southern Italy, from January 1996 to September 2011. Landslides were located geographically using Google Earth®, and were given a mapping and a temporal accuracy. We used the landslide information, and sub-hourly rainfall measurements obtained from two complementary networks of rain gauges, to determine cumulated event vs. rainfall duration (ED) thresholds for Calabria. For the purpose, we adopted an existing method used to prepare rainfall thresholds and to estimate their associated uncertainties in central Italy. The regional thresholds for Calabria were found nearly identical to previous ED thresholds for Calabria obtained using a reduced set of landslide information, and slightly higher than the ED thresholds obtained for central Italy. We segmented the regional catalogue of rainfall events with landslides on lithology, soil regions, rainfall zones, and seasonal periods. The number of events in each subdivision was insufficient to determine reliable thresholds, but allowed for preliminary conclusions on the role of the environmental factors on the rainfall conditions responsible for shallow landslides in Calabria. We further segmented the regional catalogue based on administrative subdivisions used for hydro-meteorological monitoring and operational flood forecasting, and we determined separate ED thresholds for the Tyrrhenian and the Ionian coasts of Calabria. We expect the ED rainfall thresholds for Calabria to be used in regional and national landslide warning systems. The thresholds can also be used for landslide hazard and risk assessments, and for erosion and landscape evolution studies, in the study area and in similar physiographic regions in the Mediterranean area.

scholarly journals Application of FLaIR model for early warning system in Chibo Pashyor, Kalimpong, India for rainfall-induced landslides

2017 ◽  
Author(s):  
Abhirup Dikshit ◽  
Neelima Satyam
Keyword(s):  
Early Warning ◽  
Early Warning System ◽  
Risk Mitigation ◽  
Warning System ◽  
Landslide Risk ◽  
Rainfall Forecasting ◽  
Landslide Occurrence ◽  
Mobility Function ◽  
Hourly Rainfall ◽  
Rainfall Model

Abstract. The development of an early warning system for landslides due to rainfall has become an indispensable part for landslide risk mitigation. This paper explains the application of the hydrological FLaIR (Forecasting of Landslides Induced by Rainfall) model, correlating rainfall amount and landslide events. The FLaIR model comprises of two modules: RL (Rainfall-Landslide) which correlates rainfall and landslide occurrence and RF (Rainfall-Forecasting) which allows simulation of future rainfall events. The model can predetermine landslides based on identification of mobility function Y(.) which links actual rainfall and incidence of landslide occurrence. The critical value of mobility function was analyzed using 1st July 2015 event and applying it to 2016 monsoon to validate the results. These rainfall thresholds presented can be improved with intense hourly rainfall and landslide inventory data. This paper describes the details of the model and its performance for the study area.

scholarly journals Rainfall thresholds for shallow landslide occurrence in Calabria, southern Italy

2014 ◽  
Vol 14 (2) ◽  
pp. 317-330 ◽  
Author(s):  
C. Vennari ◽  
S. L. Gariano ◽  
L. Antronico ◽  
M. T. Brunetti ◽  
G. Iovine ◽  
...  
Keyword(s):  
Southern Italy ◽  
Central Italy ◽  
Mediterranean Area ◽  
Shallow Landslide ◽  
Google Earth ◽  
Shallow Landslides ◽  
Rainfall Thresholds ◽  
Rainfall Events ◽  
Landslide Occurrence ◽  
Hourly Rainfall

Abstract. In many areas, rainfall is the primary trigger of landslides. Determining the rainfall conditions responsible for landslide occurrence is important, and may contribute to saving lives and properties. In a long-term national project for the definition of rainfall thresholds for possible landslide occurrence in Italy, we compiled a catalogue of 186 rainfall events that resulted in 251 shallow landslides in Calabria, southern Italy, from January 1996 to September 2011. Landslides were located geographically using Google Earth®, and were given a mapping and a temporal accuracy. We used the landslide information, and sub-hourly rainfall measurements obtained from two complementary networks of rain gauges, to determine cumulated event vs. rainfall duration (ED) thresholds for Calabria. For this purpose, we adopted an existing method used to prepare rainfall thresholds and to estimate their associated uncertainties in central Italy. The regional thresholds for Calabria were found to be nearly identical to previous ED thresholds for Calabria obtained using a reduced set of landslide information, and slightly higher than the ED thresholds obtained for central Italy. We segmented the regional catalogue of rainfall events with landslides in Calabria into lithology, soil regions, rainfall zones, and seasonal periods. The number of events in each subdivision was insufficient to determine reliable thresholds, but allowed for preliminary conclusions about the role of the environmental factors in the rainfall conditions responsible for shallow landslides in Calabria. We further segmented the regional catalogue based on administrative subdivisions used for hydro-meteorological monitoring and operational flood forecasting, and we determined separate ED thresholds for the Tyrrhenian and the Ionian coasts of Calabria. We expect the ED rainfall thresholds for Calabria to be used in regional and national landslide warning systems. The thresholds can also be used for landslide hazard and risk assessments, and for erosion and landscape evolution studies, in the study area and in similar physiographic regions in the Mediterranean area.

scholarly journals Estimating Rainfall Thresholds for Landslide Occurrence in the Bhutan Himalayas

Water ◽  
2019 ◽  
Vol 11 (8) ◽  
pp. 1616 ◽  
Author(s):  
Abhirup Dikshit ◽  
Raju Sarkar ◽  
Biswajeet Pradhan ◽  
Saroj Acharya ◽  
Kelzang Dorji
Keyword(s):  
Early Warning ◽  
Early Warning System ◽  
Warning System ◽  
Antecedent Rainfall ◽  
Rainfall Thresholds ◽  
Landslide Occurrence ◽  
Rainfall Duration ◽  
The World ◽  
Landslide Data

Consistently over the years, particularly during monsoon seasons, landslides and related geohazards in Bhutan are causing enormous damage to human lives, property, and road networks. The determination of thresholds for rainfall triggered landslides is one of the most effective methods to develop an early warning system. Such thresholds are determined using a variety of rainfall parameters and have been successfully calculated for various regions of the world at different scales. Such thresholds can be used to forecast landslide events which could help in issuing an alert to civic authorities. A comprehensive study on the determination of rainfall thresholds characterizing landslide events for Bhutan is lacking. This paper focuses on defining event rainfall–duration thresholds for Chukha Dzongkhag, situated in south-west Bhutan. The study area is chosen due to the increase in frequency of landslides during monsoon along Phuentsholing-Thimphu highway, which passes through it and this highway is a major trade route of the country with the rest of the world. The present threshold method revolves around the use of a power law equation to determine event rainfall–duration thresholds. The thresholds have been established using available rainfall and landslide data for 2004–2014. The calculated threshold relationship is fitted to the lower boundary of the rainfall conditions leading to landslides and plotted in logarithmic coordinates. The results show that a rainfall event of 24 h with a cumulated rainfall of 53 mm can cause landslides. Later on, the outcome of antecedent rainfall varying from 3–30 days was also analysed to understand its effect on landslide incidences based on cumulative event rainfall. It is also observed that a minimum 10-day antecedent rainfall of 88 mm and a 20-day antecedent rainfall of 142 mm is required for landslide occurrence in the area. The thresholds presented can be improved with the availability of hourly rainfall data and the addition of more landslide data. These can also be used as an early warning system especially along the Phuentsholing–Thimphu Highway to prevent any disruptions of trade.

scholarly journals Rainfall Event–Duration Thresholds for Landslide Occurrences in China

Water ◽  
2020 ◽  
Vol 12 (2) ◽  
pp. 494 ◽  
Author(s):  
Shuangshuang He ◽  
Jun Wang ◽  
Songnan Liu
Keyword(s):  
Rainy Season ◽  
Regression Line ◽  
Warning System ◽  
Rainfall Threshold ◽  
Rainfall Event ◽  
Mean Annual Precipitation ◽  
National Scale ◽  
Rainfall Thresholds ◽  
Event Duration ◽  
Landslide Occurrence

A rainfall threshold for landslide occurrence at a national scale in China has rarely been developed in the early warning system for landslides. Based on 771 landslide events that occurred in China during 1998–2017, four groups of rainfall thresholds at different quantile levels of the quantile regression for landslide occurrences in China are defined, which include the original rainfall event–duration (E–D) thresholds and normalized (the accumulated rainfall is normalized by mean annual precipitation) (EMAP–D) rainfall thresholds based on the merged rainfall and the Climate Prediction Center Morphing technique (CMORPH) rainfall products, respectively. Each group consists of four sub-thresholds in rainy season and non-rainy season, and both are divided into short duration (<48 h) and long duration (≥48 h). The results show that the slope of the regression line for the thresholds in the events with long durations is larger than that with short durations. In addition, the rainfall thresholds in the non-rainy season are generally lower than those in the rainy season. The E–D thresholds defined in this paper are generally lower than other thresholds in previous studies on a global scale, and a regional or national scale in China. This might be due to there being more landslide events used in this paper, as well as the combined effects of special geological environment, climate condition and human activities in China. Compared with the previous landslide model, the positive rates of the rainfall thresholds for landslides have increased by 16%–20%, 10%–17% and 20%–38% in the whole year, rainy season and non-rainy season, respectively.

scholarly journals Extent of diurnal cycle of rainfall and its intra seasonal variation over coastal Tamil Nadu during north east monsoon season

MAUSAM ◽  
2022 ◽  
Vol 73 (1) ◽  
pp. 1-18
Author(s):  
Y.E.A. RAJ ◽  
B. AMUDHA
Keyword(s):  
Diurnal Variation ◽  
Diurnal Cycle ◽  
Tamil Nadu ◽  
Monsoon Season ◽  
Statistical Significance ◽  
Early Morning ◽  
Rainfall Anomaly ◽  
Land Breeze ◽  
North East ◽  
Hourly Rainfall

The diurnal variation of north east monsoon rainfall of coastal Tamil Nadu represented by four coastal stations Chennai Nungambakkam (Nbk), Chennai Meenambakkam (Mbk), Nagapattinam (Npt) and Pamban (Pbn)  was  studied in detail based on hourly rainfall data of rainy days only, for the period 1 Oct-31 Dec for the 47/48  year period 1969-2016/2017.  Mean Octet rainfall and its anomaly were computed for the 8 octets  00-03,…., 21-24 hrs of the day and the anomaly was tested for statistical significance. Various analysis for the individual months of Oct, Nov, Dec and the entire period Oct-Dec were separately conducted.  The basic technique of evolutionary histogram analysis supplemented by harmonic analysis of octet mean rainfall anomaly was used to detect the diurnal cycle signal. Two indices  named as  diurnal variation of  rainfall index and coefficient of mean absolute octet rainfall anomaly representing the intensity of diurnal variation  in dimensionless numbers were defined,  computed  and interpreted. The analysis based on the above techniques revealed that the diurnal signal which shows an early morning maximum and late afternoon minimum of octet rainfall is well defined in Oct, decreases in Nov and further decreases in Dec for all the 4 stations. Though the diurnal variation manifests a well defined pattern in Dec the signal is not statistically significant in most cases. For Nbk and Mbk there is a weak secondary peak of octet rainfall anomaly occurring in the forenoon and afternoon respectively in Oct and Dec suggesting the presence of semi-diurnal variation of rainfall. Stationwise, the diurnal signal is most well defined for each month/season in Pbn followed by Npt, Nbk and then Mbk.   The physical causes behind the diurnal signal and its decrease as the north east monsoon season advances from Oct to Dec have been deliberated. The well known feature of nocturnal maximum of oceanic convection influencing a coastal station with maritime climate and the higher saturation at the lower levels of the upper atmosphere in the early morning hours have been advanced as some of the causes. For the much more complex feature of decrease of diurnal signal with the  advancement of the season, the decrease of minimum surface temperature over coastal Tamil Nadu from Oct to Dec causing an early morning conceptual land breeze has been shown as one of the plausible causes  based on analysis of temperature and wind.  Scope for further work based on data from automatic weather stations, weather satellites and Doppler Weather Radars has been discussed.

scholarly journals Empirical Rainfall Thresholds for Landslide Occurrence in Serra do Mar, Angra dos Reis, Brazil

2021 ◽  
Author(s):  
Daniel Germain ◽  
Sébastien Roy ◽  
Antonio Jose Teixera Guerra
Keyword(s):  
Temporal Dynamics ◽  
Regional Scale ◽  
Daily Rainfall ◽  
Wet Season ◽  
Rainfall Thresholds ◽  
Landslide Occurrence ◽  
Serra Do Mar ◽  
Temporal Prediction ◽  
Northern Regions ◽  
Highly Correlated

In the tropical environment such as Brazil, the frequency of rainfall-induced landslides is particularly high because of the rugged terrain, heavy rainfall, increasing urbanization, and the orographic effect of mountain ranges. Since such landslides repeatedly interfere with human activities and infrastructures, improved knowledge related to spatial and temporal prediction of the phenomenon is of interest for risk management. This study is an analysis of empirical rainfall thresholds, which aims to establish local and regional scale correlations between rainfall and the triggering of landslides in Angra dos Reis in the State of Rio de Janeiro. A statistical analysis combining quantile regression and binary logistic regression was performed on 1640 and 526 landslides triggered by daily rainfall over a 6-year period in the municipality and the urban center of Angra dos Reis, in order to establish probabilistic rainfall duration thresholds and assess the role of antecedent rainfall. The results show that the frequency of landslides is highly correlated with rainfall events, and surprisingly the thresholds in dry season are lower than those in wet season. The aspect of the slopes also seems to play an important role as demonstrated by the different thresholds between the southern and northern regions. Finally, the results presented in this study provide new insight into the spatial and temporal dynamics of landslides and rainfall conditions leading to their activation in this tropical and mountainous environment.

scholarly journals The optimal rainfall thresholds and probabilistic rainfall conditions for a landslide early warning system for Chuncheon, Republic of Korea

Landslides ◽  
2021 ◽  
Author(s):  
Won Young Lee ◽  
Seon Ki Park ◽  
Hyo Hyun Sung
Keyword(s):  
Early Warning ◽  
Early Warning System ◽  
Bayesian Model ◽  
Warning System ◽  
Probability Of Detection ◽  
Republic Of Korea ◽  
Maximum Rainfall ◽  
Landslide Occurrence ◽  
Hourly Rainfall ◽  
Landslide Early Warning

AbstractThe purpose of this study is to establish the criteria for a landslide early warning system (LEWS). We accomplished this by deriving optimal thresholds for the cumulative event rainfall–duration (ED) and identifying the characteristics of the rainfall variables associated with a high probability of landslide occurrence via a Bayesian model. We have established these system criteria using rainfall and landslide data for Chuncheon, Republic of Korea. Heavy rainfall is the leading cause of landslides in Chuncheon; thus, it is crucial to determine the rainfall conditions that trigger landslides. Hourly rainfall data spanning 1999 to 2017 from seven gauging stations were utilized to establish the ED thresholds and the Bayesian model. We used three different calibration periods of rainfall events split by 12, 24, 48, and 96 non-rainfall hours to calibrate the ED thresholds. Finally, the optimal threshold was determined by comparing the results of the contingency table and the skill scores that maximize the probability of detection (POD) score and minimize the probability of false detection (POFD) score. In the LEWS, by considering the first level as “normal,” we developed subsequent step-by-step warning levels based on the Bayesian model as well as the ED thresholds. We propose the second level, “watch,” when the rainfall condition is above the ED thresholds. We then adopt the third level, “warning,” and the fourth level, “severe warning,” based on the probability of landslide occurrence determined via a Bayesian model that considers several factors including the rainfall conditions of landslide vs. non-landslide and various rainfall variables such as hourly maximum rainfall and 3-day antecedent rainfall conditions. The proposed alert level predicted a total of 98.2% of the landslide occurrences at the levels of “severe warning” and “warning” as a result of the model fitness verification. The false alarm rate is 0% for the severe warning level and 47.4% for the warning level. We propose using the optimal ED thresholds to forecast when landslides are likely to occur in the local region. Additionally, we propose the ranges of rainfall variables that represent a high landslide probability based on the Bayesian model to set the landslide warning standard that fits the local area’s characteristics.

scholarly journals Determination of Empirical Rainfall Thresholds for Shallow Landslides in Slovenia Using an Automatic Tool

Water ◽  
2020 ◽  
Vol 12 (5) ◽  
pp. 1449 ◽  
Author(s):  
Galena Jordanova ◽  
Stefano Luigi Gariano ◽  
Massimo Melillo ◽  
Silvia Peruccacci ◽  
Maria Teresa Brunetti ◽  
...  
Keyword(s):  
Sedimentary Rocks ◽  
Warning System ◽  
Shallow Landslides ◽  
Annual Rainfall ◽  
Rainfall Thresholds ◽  
Landslide Occurrence ◽  
Automatic Tool ◽  
Clastic Sedimentary ◽  
Trigger Mechanisms

Rainfall-triggered shallow landslides represent a major threat to people and infrastructure worldwide. Predicting the possibility of a landslide occurrence accurately means understanding the trigger mechanisms adequately. Rainfall is the main cause of slope failures in Slovenia, and rainfall thresholds are among the most-used tools to predict the possible occurrence of rainfall-triggered landslides. The recent validation of the prototype landslide early system in Slovenia highlighted the need to define new reliable rainfall thresholds. In this study, several empirical thresholds are determined using an automatic tool. The thresholds are represented by a power law curve that links the cumulated event rainfall (E, in mm) with the duration of the rainfall event (D, in h). By eliminating all subjective criteria thanks to the automated calculation, thresholds at diverse non-exceedance probabilities are defined and validated, and the uncertainties associated with their parameters are estimated. Additional thresholds are also calculated for two different environmental classifications. The first classification is based on mean annual rainfall (MAR) with the national territory divided into three classes. The area with the highest MAR has the highest thresholds, which indicates a likely adaptation of the landscape to higher amounts of rainfall. The second classification is based on four lithological units. Two-thirds of the considered landslides occur in the unit of any type of clastic sedimentary rocks, which proves an influence of the lithology on the occurrence of shallow landslides. Sedimentary rocks that are prone to weathering have the lowest thresholds, while magmatic and metamorphic rocks have the highest thresholds. Thresholds obtained for both classifications are far less reliable due to the low number of empirical points and can only be used as indicators of rainfall conditions for each of the classes. Finally, the new national thresholds for Slovenia are also compared with other regional, national, and global thresholds. The thresholds can be used to define probabilistic schemes aiming at the operative prediction of rainfall-induced shallow landslides in Slovenia, in the framework of the Slovenian prototype early warning system.

scholarly journals Data limitations and potential of hourly and daily rainfall thresholds for shallow landslides

2020 ◽  
Author(s):  
Elena Leonarduzzi ◽  
Peter Molnar
Keyword(s):  
Daily Rainfall ◽  
Predictive Performance ◽  
Rainfall Threshold ◽  
Mean Annual Precipitation ◽  
Threshold Estimation ◽  
Rainfall Thresholds ◽  
Landslide Occurrence ◽  
Data Limitations ◽  
Hourly Rainfall ◽  
The Impact

Abstract. Rainfall thresholds are a simple and widely used method to predict landslide occurrence. In this paper we provide a comprehensive data-driven assessment of the effects of rainfall temporal resolution (hourly versus daily) on landslide prediction performance in Switzerland, with sensitivity to two other important aspects which appear in many landslide studies – the normalisation of rainfall, which accounts for local climatology, and the inclusion of antecedent rainfall as a proxy of soil water state prior to landsliding. We use an extensive landslide inventory with over 3800 events and several daily and hourly, station and gridded rainfall datasets, to explore different scenarios of rainfall threshold estimation. Our results show that although hourly rainfall did show best predictive performance for landslides, daily data were not far behind, and the benefits of hourly resolutions can be masked by the higher uncertainties in threshold estimation connected to using short records. We tested the impact of several typical actions of users, like assigning the nearest raingauge to a landslide location and filling in unknown timing, and report their effects on predictive performance. We find that localisation of rainfall thresholds through normalisation compensates for the spatial heterogeneity in rainfall regimes and landslide erosion process rates and is a good alternative to regionalisation. On top of normalisation by mean annual precipitation or a high rainfall quantile, we recommend that non-triggering rainfall be included in rainfall threshold estimation if possible. Finally, we demonstrate that there is predictive skill in antecedent rain as a proxy of soil wetness state, despite the large heterogeneity of the study domain, although it may not be straightforward to build this into rainfall threshold curves.

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