Analisis Sebaran Spasial Kerentanan Longsor Sebagai Upaya Mitigasi Bencana di Kecamatan Berastagi Kabupaten Karo Provinsi Sumatera Utara

Kabupaten Karo has several areas that have the potential to occurrence of a landslide.Therefore, the mitigation of landslide disaster is very important, it has long-term negative impacts on humanity and the environment. This study attempts to analyze the distribution of vulnerability landslides in Berastagi mitigation and management and provide the right environment. The causes of a movement occur landslides in Berastagi is the state of a steep slope around 35, 55 % - percent of lithological building materials / unstable material among other volcanic breccias and, riodasit tufa and the factor triggering rainfall andinfiltration water . The research method was descriptive , whereas scoring with Shapefile (SHP) data in 2019 was based on the 2004 Puslittanak. The results of the field are the weightings of the respective parameter with overlay uses arcgis 10.6 software. The result showed that the DouluVillage, SempaJaya Village, Raya Village, as well as Tambak Lau Mulgap II in the Berastagi District have a high vulnerability to landslides. Thus, recommendations for mitigation of calamity , by revegetation erosion including planting crops.

Comparing methods for determining landslide early warning thresholds: potential use of non-triggering rainfall for locations with scarce landslide data availability

Rainfall intensity-duration landslide-triggering thresholds have become widespread for the development of landslide early warning systems. Thresholds can be in principle determined using rainfall event datasets of three types: (a) rainfall events associated with landslides (triggering rainfall) only, (b) rainfall events not associated with landslides (non-triggering rainfall) only, (c) both triggering and non-triggering rainfall. In this paper, through Monte Carlo simulation, we compare these three possible approaches based on the following statistical properties: robustness, sampling variation, and performance. It is found that methods based only on triggering rainfall can be the worst with respect to those three investigated properties. Methods based on both triggering and non-triggering rainfall perform the best, as they could be built to provide the best trade-off between correct and wrong predictions; they are also robust, but still require a quite large sample to sufficiently limit the sampling variation of the threshold parameters. On the other side, methods based on non-triggering rainfall only, which are mostly overlooked in the literature, imply good robustness and low sampling variation, and performances that can often be acceptable and better than thresholds derived from only triggering events. To use solely triggering rainfall—which is the most common practice in the literature—yields to thresholds with the worse statistical properties, except when there is a clear separation between triggering and non-triggering events. Based on these results, it can be stated that methods based only on non-triggering rainfall deserve wider attention. Methods for threshold identification based on only non-triggering rainfall may have the practical advantage that can be in principle used where limited information on landslide occurrence is available (newly instrumented areas). The fact that relatively large samples (about 200 landslides events) are needed for a sufficiently precise estimation of threshold parameters when using triggering rainfall suggests that threshold determination in future applications may start from identifying thresholds from non-triggering events only, and then move to methods considering also the triggering events as landslide information starts to become more available.

Advancing rainfall-induced landslide detection using homogeneous slope units and distributed rainfall thresholds

<p>A better detection of landslide occurrence is critical for disaster prevention and mitigation, and a standing pursuit owing to increasing and widespread impact of slope failures on human activities and natural environment in a changing world. However, the detection of rainfall-induced landslide is limited in some areas by data scarcity and method applicability. In this study, we proposed distributed rainfall thresholds within homogeneous slope units, by considering the interaction of landslide-influencing geo-environmental conditions and landslide-triggering rainfall variables. Homogeneous slope units are extracted based on detailed terrain analysis. Various landforms are identified and used to obtain slope units with homogeneous slope traits. The concept behind the distributed rainfall threshold models is that rainfall threshold for landslide occurrence varies with geo-environmental conditions such as slope gradient. Thus, a link can be established between landslide-influencing geo-environmental conditions and landslide-triggering rainfall variables. We used elevation, slope, plan and profile curvature, mean annual precipitation and temperature, soil texture and land cover as independent variables. Rainfall duration and cumulated rainfall of landslide-triggering rainfall events are automatically calculated and used, the former as one of independent variables, and the latter as the dependent variable. A support vector regression (SVR) and a multiple linear regression (MLR) method are used. The error and correlation coefficient measurement indicate a better performance of SVR method. Compared with grid units, the model scores high accuracy for slope units. The models are implemented at a regional scale (Guangdong, China). The SVR model in slope units ran with error of 0.16 mm and correlation coefficient of 0.93.</p>

Identification of hydro-meteorological thresholds for rainfall-induced landslide prediction with clustering techniques

<p>Operational early warning systems for rainfall-induced landslides (LEWS) usually rely on simple empirical thresholds based on the statistical analysis of either triggering rainfall characteristics, e.g. intensity and duration (Guzzetti et al., 2007). The main pro of this simplified approach is that it requires only rainfall records, at the desired spatial and temporal resolution, and a database of landslides with known time and location. The effect of the hydrologic conditions of the slopes prior the onset of the triggering rainfall is usually neglected, limiting the performance of the LEWS, which often give rise to false and missing alarms. To address this issue, antecedent precipitation is sometimes included in the definition of the threshold, but the identification of the antecedent precipitation duration is doubtful, as this approach neglects non-linear hydrological processes affecting slope response. Hydro-meteorological thresholds, linking a variable accounting for the antecedent hydrologic conditions with a characteristic of the triggering rainfall, have been recently proposed (Bogaard and Greco, 2018).</p><p>In this study, hydro-meteorological thresholds for landslide prediction are identified for a slope in southern Italy, characterized by an unsaturated pyroclastic soil cover laying upon fractured limestone bedrock and subject to rainfall-induced shallow landslides. To this aim, a synthetic 1000 years long hourly point rainfall record is generated with the Neyman-Scott rectangular pulse stochastic model, calibrated thanks to available measured rainfall. The response of the slope to the synthetic rainfall record is simulated by means of a physically-based model, which couples unsaturated flow in the soil cover with a temporary perched aquifer in the limestone bedrock, and allows estimating all the terms of slope water balance (Greco et al., 2018). The stability of the slope is eventually assessed under the infinite slope hypothesis, allowing the identification of the occurrence of landslides.</p><p>The obtained synthetic dataset of rainfall and hydrologic variables has been exploited for the definition of hydro-meteorological thresholds. All the combinations of hydrologic variables with triggering rainfall height have been analyzed with several clustering techniques, so to identify the most effective combinations for landslide predictions.</p><p> </p><p>References:</p><p>Bogaard TA, Greco R (2018). Invited perspectives: Hydrological perspectives on precipitation intensity-duration thresholds for landslide initiation: proposing hydro-meteorological thresholds, Nat Hazards Earth Syst Sci, 18: 31–39.</p><p>Greco R, Marino P, Santonastaso GF, Damiano E (2018). Interaction between Perched Epikarst Aquifer and Unsaturated Soil Cover in the Initiation of Shallow Landslides in Pyroclastic Soils, Water, 10: 948.</p><p>Guzzetti F, Peruccacci S, Rossi M, Stark CP (2007). Rainfall thresholds for the initiation of landslides in central and southern Europe, Meteorol Atmos Phys, 98: 239–267.</p>

Monitoring Debris-Flow Surges and Triggering Rainfall at the Lattenbach Creek, Austria

ABSTRACT Debris-flow events often comprise a sequence of surges, sometimes termed “roll waves.” The reason for this surging behavior is still a matter of debate. Explanations include the growth of hydraulic instabilities, periodic sediment deposition and release, or grain size sorting. High-resolution field measurements together with triggering rainfall characteristics are rare. We present results for 3 years of monitoring debris-flow events at Lattenbach Creek in the western part of Austria. The monitoring system includes a weather station in the headwaters of the creek, radar sensors for measuring flow depth at different locations along the channel, as well as a two-dimensional rotational laser sensor installed over a fixed cross section that yields a three-dimensional surface model of the passing debris-flow event. We find that the debris flows at Lattenbach Creek were all triggered by rainstorms of less than 2 hours and exhibited surges for each observed event. The velocities of the surges were up to twice as high as the front velocity. Often, the first surges that included boulders and woody debris had the highest flow depth and discharge and showed an irregular geometry. The shape of the surges in the second half of the flow, which carried smaller grain sizes and less woody debris, were rather regular and showed a striking geometric similarity, but still high velocities. The results of our monitoring efforts aim to improve our understanding of the surging behavior of debris flows and provide data for model testing for the scientific community.

Debris flows recorded in the Moscardo catchment (Italian Alps) between 1990 and 2019

This paper presents debris-flow data recorded in the Moscardo Torrent (eastern Italian Alps) between 1990 and 2019. In this time interval, 30 debris flows were observed: 26 of them were monitored by sensors installed on the channel, while four were only documented through post-event observations. Monitored data consist of debris-flow hydrographs, measured utilizing ultrasonic sensors, and rainfall. Debris flows in the Moscardo Torrent occur from early June to the end of September, with higher frequency in the first part of summer. The paper presents data on triggering rainfall, flow velocity, peak discharge, and volume for the monitored hydrographs. Simplified triangular hydrographs and dimensionless hydrographs were derived to show the basic features of the debris flows in the Moscardo Torrent (time to peak, surge duration, flow depth) and permitting comparison with other instrumented catchments. The dataset is made available to the public with the following DOI: https://doi.org/10.1594/PANGAEA.919707.

Determination of Heavy Rain Damage-Triggering Rainfall Criteria Based on Data Mining

Heavy rainfall occurs over the Korean peninsula mainly because of typhoons and a localized heavy rainfall, leading to severe flooding and landslide risk. KMA (Korean Meteorological Administration) has the criteria for issuing a Heavy Rain Advisory (HRA) over the peninsula even though each region or local government has different conditions in capability of disaster prevention (CDP) and different characteristics in rainfall and heavy rain damage. Therefore, the aim of this study is to suggest the methodology for the determination of Heavy rain Damage-Triggering Rainfall Criteria (HD-TRC) that HRA can be issued in each region. The study regions are local governments in Gyeonggi-province, Seoul-city, and Incheon-city in Korea. HD-TRC can be determined based on rainfall and heavy rain damage data. The data from 2005 to 2018 are collected and then the data for flood or rainy season from June to September are extracted. The rainfall data is provided in KMA and heavy rain damage data during disaster periods (DPs) can be obtained from the statistical yearbook of natural disaster (SYND) published by MOIS (Minstry of Interior and Safety) every year. Training set of 2005 to 2014 is used for obtaining HD-TRC and test set of 2015 to 2018 is used for evaluating three criteria of HD-TRC, Advanced HD-TRC, and HRA. Analysis for determining the best criteria is performed through data mining processes as follows: (1) Maximum rainfalls in durations of 1 to 24-hr (X1) and antecedent rainfalls of 1 to 7-day (X2) are obtained and used as independent variables. Heavy rain damage data are divided into damage day (1) and no damage day (0) used as dependent variables (Y). Principal component analysis (PCA) is performed and PCs (principal components) are obtained as PC.X1 and PC.X2 for independent variables. Then Risk Index (RI) is defined as PC.X1 + PC.X2 and RIs become the candidates for HD-TRC. The predicted damage (Ŷ) is obtained based on RIs and confusion matrix is constructed then the best HD-TRC is determined through the evaluation of classification performance. (2) However, ‘abnormal days’ (ADs) in a DP that the damage is occurred exists. The ADs mean the days which we do not have rainfall or have small rainfall amount during DP. Say, ADs have too small rainfall to damage even during DP. The ADs are defined as days below rainfall of 20 mm and 5 cases of ADs are also defined as 0, 0–5, 0–10, 0–15, and 0–20 mm in this study. We count total days in all the DPs and in ADs for a case. The ratio of ADs to total days during DPs could be the occurrence probability or prior probability (PP) of ADs for a case and 5 PPs are obtained. Also, the average AD for each case can be obtained and defined as risk range (RR). Then we define Advanced HD-TRC using MCS (Monte Carlo Simulation) linked with PP, RR, and from HD-TRC for each case. Therefore, HD-TRC is determined based on RI and Advanced HD-TRC for each case based on PP and RR. Finally, three criteria of HD-TRC, Advanced HD-TRC, and HRA are compared based on performance evaluation by test set. As a result, Advanced HD-TRC shows the best performance and so the suggested methodology can be used for regional heavy rain damage warning information.

Debris flows recorded in the Moscardo catchment (Italian Alps) between 1990 and 2019

This paper presents debris-flows data recorded in the Moscardo Torrent (eastern Italian Alps) between 1990 and 2019. In this time interval, 30 debris flows were observed, 26 of them were monitored by sensors installed on the channel, while four were only documented through post-event observations. Monitored data consist of debris-flow hydrographs, measured utilizing ultrasonic sensors, and rainfall. Debris flows in the Moscardo Torrent occur from early June to the end of September, with higher frequency in the first part of summer. The paper presents data on triggering rainfall, flow velocity, peak discharge, and volume for the monitored hydrographs. Simplified triangular hydrographs and dimensionless hydrographs were derived to show the basic features of the debris flows in the Moscardo Torrent (time to peak, surge duration, flow depth) and permitting comparison with other instrumented catchments. The dataset is made available to the public with the following DOI: https://doi.pangaea.de/10.1594/PANGAEA.919707 .

The role of geomorphology, rainfall and soil moisture in the occurrence of landslides triggered by 2018 Typhoon Mangkhut in the Philippines

In 2018, Typhoon Mangkhut (locally known as Typhoon Ompong) triggered thousands of landslides in the area of Itogon (Philippines). A landslide inventory of 1101 landslides over a 570 km2 area is used to study the geomorphological characteristics and land cover more prone to landsliding as well as the rainfall and soil moisture conditions that led to widespread failure. Landslides mostly occurred in slopes covered by wooded grassland in clayey materials, predominantly facing East–Southeast. The analysis of both satellite rainfall (GPM IMERG) and soil moisture (SMAP-L4) finds that, in addition to rainfall from the typhoon, soil water content plays an important role in the triggering mechanism. Rainfall associated with Typhoon Mangkhut is compared with 33 high intensity rainfall events that did not trigger regional landslide events in 2018 and with previously published rainfall thresholds. Results show that: (a) it was one of the most intense rainfall events in the year but not the highest, and (b) despite satellite data tending to underestimate intense rainfall, previous published regional and global thresholds are to be too low to discriminate between landslide triggering and non-triggering rainfall events. This work highlights the potential of satellite products for hazard assessment and early warning in areas of high landslide activity where ground-based data is scarce.