scholarly journals Rainfall-Induced Landslide Early Warning System based on corrected mesoscale numerical models: an application for the Southern Andes

2021 ◽  
Author(s):  
Ivo Fustos ◽  
Nataly Manque ◽  
Daniel Vásquez ◽  
Mauricio Hermosilla ◽  
Viviana Letelier
Keyword(s):  
Early Warning ◽  
Probabilistic Models ◽  
Daily Precipitation ◽  
Numerical Models ◽  
Wrf Model ◽  
Warning System ◽  
Early Warning Systems ◽  
Southern Andes ◽  
Daily Precipitation Data ◽  
Landslide Early Warning

Abstract. Rainfall-Induced Landslide Early Warning Systems (RILEWS) are critical tools for reducing and mitigating economic and social damages related to landslides. Despite this critical need, the Southern Andes does not yet possess an operational-scale system to support decision-makers. We propose RILEWS using a logistic regression system in the Southern Andes. The models were forced by corrected simulations of precipitation and geomorphological features. We evaluated the precipitation using the Weather and Research Forecast (WRF) model on an hourly scale. The precipitation was corrected using bias correction approaches with daily data from 12 meteorological stations. Four logistic and probabilistic models were then calibrated using Logit and Probit distributions. The predictor variables used were combinations of the slope, corrected daily precipitation and data preceding the events (7 and 30 days previous) for 57 Rainfall-Induced Landslides (RIL); validation was by ROC analysis. Our results showed that WRF does not represent the spatial variability of the precipitation. This situation was resolved by bias correcting. Specifically, the PP_M4a method with Bernoulli distribution for the occurrence and Gamma for the intensity produced lower MAE and RMSE values and higher correlation values. Finally, our RILEWS had a high predicting capacity with an AUC of 0.80 using daily precipitation data and slope. We conclude that our methodology is suitable at an operational level in the Southern Andes. Our contribution could become a useful tool in the mitigation of impacts related to climate change.

scholarly journals Brief communication "Landslide Early Warning System: toolbox and general concepts"

2013 ◽  
Vol 13 (1) ◽  
pp. 85-90 ◽  
Author(s):  
E. Intrieri ◽  
G. Gigli ◽  
N. Casagli ◽  
F. Nadim
Keyword(s):  
Early Warning ◽  
Early Warning System ◽  
Warning System ◽  
Early Warning Systems ◽  
End Users ◽  
Flow Chart ◽  
Warning Systems ◽  
Limited Experience ◽  
Practical Guidelines ◽  
Landslide Early Warning

Abstract. We define landslide Early Warning Systems and present practical guidelines to assist end-users with limited experience in the design of landslide Early Warning Systems (EWSs). In particular, two flow chart-based tools coming from the results of the SafeLand project (7th Framework Program) have been created to make them as simple and general as possible and in compliance with a variety of landslide types and settings at single slope scale. We point out that it is not possible to cover all the real landslide early warning situations that might occur, therefore it will be necessary for end-users to adapt the procedure to local peculiarities of the locations where the landslide EWS will be operated.

Development of early warning system for linear engineering - a case study on highway in Sichuan, China

2020 ◽  
Author(s):  
Ruihua Xiao
Keyword(s):  
Early Warning ◽  
Early Warning System ◽  
Warning System ◽  
Early Warning Systems ◽  
Highway Safety ◽  
Warning Systems ◽  
Safety Control ◽  
Early Warning Model ◽  
Landslide Early Warning ◽  
Warning Model

<p>For the recent years, highway safety control under extreme natural hazards in China has been facing critical challenges because of the latest extreme climates. Highway is a typical linear project, and neither the traditional single landslide monitoring and early warning model entirely dependent on displacement data, nor the regional meteorological early warning model entirely dependent on rainfall intensity and duration are suitable for it. In order to develop an efficient early warning system for highway safety, the authors have developed an early warning method based on both monitoring data obtained by GNSS and Crack meter, and meteorological data obtained by Radar. This early-warning system is not each of the local landslide early warning systems (Lo-LEWSs) or the territorial landslide early warning systems (Te-LEWSs), but a new system combining both of them. In this system, the minimum warning element is defined as the slope unit which can connect a single slope to the regional ones. By mapping the regional meteorological warning results to each of the slope units, and extending the warning results of the single landslides to the similar slope units, we can realize the organic combination of the two warning methods. It is hopeful to improve the hazard prevention and safety control for highway facilities during critical natural hazards with the progress of this study.</p>

scholarly journals Simulated or measured soil moisture: Which one is adding more value to regional landslide early warning?

2021 ◽  
Author(s):  
Adrian Wicki ◽  
Per-Erik Jansson ◽  
Peter Lehmann ◽  
Christian Hauck ◽  
Manfred Stähli
Keyword(s):  
Soil Moisture ◽  
Early Warning ◽  
Prediction Models ◽  
Numerical Models ◽  
Forecast Model ◽  
Early Warning Systems ◽  
Transfer Model ◽  
Statistical Framework ◽  
Landslide Early Warning

Abstract. The inclusion of soil wetness information in empirical landslide prediction models was shown to improve the forecast goodness of regional landslide early warning systems (LEWS). However, it is still unclear which source of information – numerical models or in-situ measurements – are of higher value for this purpose. In this study, soil moisture dynamics at 133 grassland sites in Switzerland were simulated for the period of 1981 to 2019 using a physically-based 1D soil moisture transfer model (CoupModel). A common parametrization set was defined for all sites except for site-specific soil hydrological properties, and the model performance was assessed at a subset of 14 sites where in-situ soil moisture measurements were available on the same plot. A previously developed statistical framework was applied to fit an empirical landslide forecast model, and ROC analysis was used to assess the forecast goodness. To assess the sensitivity of the landslide forecasts, the statistical framework was applied to different CoupModel parametrizations, to various distances between simulation sites and landslides, and to measured soil moisture from a subset of 35 sites for comparison with a measurement-based forecast model. We found that (i) simulated soil moisture is a skilful predictor for regional landslide activity, (ii) that it is sensitive to the formulation of the upper and lower boundary conditions, and (iii) that the information content is strongly distance-dependent. Compared to a measurement-based landslide forecast model, the model-based forecast performs better as the homogenization of hydrological processes and the site representation can lead to a better representation of triggering event conditions. However, it is limited in reproducing critical antecedent saturation conditions due to an inadequate representation of the long-term water storage.

scholarly journals Snow accumulation/melting model (SAMM) for integrated use in regional scale landslide early warning systems

2013 ◽  
Vol 17 (3) ◽  
pp. 1229-1240 ◽  
Author(s):  
G. Martelloni ◽  
S. Segoni ◽  
D. Lagomarsino ◽  
R. Fanti ◽  
F. Catani
Keyword(s):  
Early Warning ◽  
Regional Scale ◽  
Warning System ◽  
Early Warning Systems ◽  
Snow Accumulation ◽  
Point Of View ◽  
Warning Systems ◽  
Temperature Index ◽  
Landslide Early Warning ◽  
Optimal Values

Abstract. We propose a simple snow accumulation/melting model (SAMM) to be applied at regional scale in conjunction with landslide warning systems based on empirical rainfall thresholds. SAMM is based on two modules modelling the snow accumulation and the snowmelt processes. Each module is composed by two equations: a conservation of mass equation is solved to model snowpack thickness and an empirical equation for the snow density. The model depends on 13 empirical parameters, whose optimal values were defined with an optimisation algorithm (simplex flexible) using calibration measures of snowpack thickness. From an operational point of view, SAMM uses as input data only temperature and rainfall measurements, bringing about the additional benefit of a relatively easy implementation. After performing a cross validation and a comparison with two simpler temperature index models, we simulated an operational employment in a regional scale landslide early warning system (EWS) and we found that the EWS forecasting effectiveness was substantially improved when used in conjunction with SAMM.

Trigger atmospheric conditions for RIL in the Southern Andes.

2020 ◽  
Author(s):  
Nataly Manque Roa ◽  
Ivo Fustos-Toribio ◽  
Marcelo Somos-Valenzuela
Keyword(s):  
Early Warning ◽  
Early Warning System ◽  
Critical Infrastructure ◽  
Wrf Model ◽  
Deep Understanding ◽  
Warning System ◽  
Climate Change Scenarios ◽  
Atmospheric Conditions ◽  
Southern Andes ◽  
The Future

<p>Rainfall-Induced Landslides (RIL) are one of the most important natural hazards due to their damage to populated areas, critical infrastructure, and roads. Therefore, their deep understanding is critical for decision-makers. The Southern Andes (~ 41.1ºS, 72.5ºW) has undergone recurring RIL processes in recent years, which have affected interurban connectivity with strong social impacts. The objective of this study is to understand the atmospheric conditions that could trigger RIL at the Southern Andes. We propose a correction of high-resolution atmospheric simulations based on the Weather and Research Forecast (WRF) model. Our results were corrected by meteorological in-situ stations using geostatistical techniques. We identify precursor signals at different pressure heights that could be used to the future in an early warning system. Our proposed methodology will support the generation of public policies in the context of climate change scenarios in catchments with low-dense instrumentation and low uncertainty. Hence, our database will provide new hydrometeorological perspectives in RIL studies. To the future, these results will allow the development of an early warning system applicable in the central-southern zone of Chile.</p>

scholarly journals Investigation of Critical Geotechnical, Petrological and Mineralogical Parameters for Landslides in Deeply Weathered Dunite Rock (Medellín, Colombia)

2021 ◽  
Vol 18 (21) ◽  
pp. 11141
Author(s):  
Tamara Breuninger ◽  
Bettina Menschik ◽  
Agnes Demharter ◽  
Moritz Gamperl ◽  
Kurosch Thuro
Keyword(s):  
Rock Mass ◽  
Early Warning ◽  
Hazard Analysis ◽  
Low Cost ◽  
Warning System ◽  
Field Studies ◽  
Early Warning Systems ◽  
Small Scale ◽  
Prone Area ◽  
Landslide Early Warning

The current study site of the project Inform@Risk is located at a landslide prone area at the eastern slopes of the city of Medellín, Colombia, which are composed of the deeply weathered Medellín Dunite, an ultramafic Triassic rock. The dunite rock mass can be characterized by small-scale changes, which influence the landslide exposition to a major extent. Due to the main aim of the project, to establish a low-cost landslide early warning system (EWS) in this area, detailed field studies, drillings, laboratory and mineralogical tests were conducted. The results suggest that the dunite rock mass shows a high degree of serpentinization and is heavily weathered up to 50 m depth. The rock is permeated by pseudokarst, which was already found in other regions of this unit. Within the actual project, a hypothesis has for the first time been established, explaining the generation of the pseudokarst features caused by weathering and dissolution processes. These parameters result in a highly inhomogeneous rock mass and nearly no direct correlation of weathering with depth. In addition, the theory of a secondary, weathering serpentinization was established, explaining the solution weathering creating the pseudokarst structures. This contribution aims to emphasize the role of detailed geological data evaluation in the context of hazard analysis as an indispensable data basis for landslide early warning systems.

scholarly journals Development of a Landslide Early Warning System in Indonesia

Geosciences ◽  
2019 ◽  
Vol 9 (10) ◽  
pp. 451 ◽  
Author(s):  
Rokhmat Hidayat ◽  
Samuel Jonson Sutanto ◽  
Alidina Hidayah ◽  
Banata Ridwan ◽  
Arif Mulyana
Keyword(s):  
Slope Stability ◽  
Early Warning ◽  
Early Warning System ◽  
Warning System ◽  
Economic Losses ◽  
Precipitation Data ◽  
Threshold Method ◽  
Landslide Occurrence ◽  
Daily Precipitation Data ◽  
Landslide Early Warning

Landslides are one of the most disastrous natural hazards in Indonesia, in terms of number of fatalities and economic losses. Therefore, Balai Litbang Sabo (BLS) has developed a Landslide Early Warning System (LEWS) for Indonesia, based on a Delft–FEWS (Flood Early Warning System) platform. This system utilizes daily precipitation data, a rainfall threshold method, and a Transient Rainfall Infiltration and Grid-based Regional Slope-stability model (TRIGRS) to predict landslide occurrences. For precipitation data, we use a combination of 1-day and 3-day cumulative observed and forecasted precipitation data, obtained from the Tropical Rainfall Measuring Mission (TRMM) and the Indonesian Meteorological Climatological and Geophysical Agency (BMKG). The TRIGRS model is used to simulate the slope stability in regions that are predicted to have a high probability of landslide occurrence. Our results show that the landslides, which occurred in Pacitan (28 November 2017) and Brebes regions (22 February 2018), could be detected by the LEWS from one to three days in advance. The TRIGRS model supports the warning signals issued by the LEWS, with a simulated factor of safety values lower than 1 in these locations. The ability of the Indonesian LEWS to detect landslide occurrences in Pacitan and Brebes indicates that the LEWS shows good potential to detect landslide occurrences a few days in advance. However, this system is still undergoing further developments for better landslide prediction.

scholarly journals Assessing the performance of regional landslide early warning models: the EDuMaP method

2015 ◽  
Vol 3 (10) ◽  
pp. 6021-6074 ◽  
Author(s):  
M. Calvello ◽  
L. Piciullo
Keyword(s):  
Early Warning ◽  
Early Warning System ◽  
Model Performance ◽  
Warning System ◽  
Early Warning Systems ◽  
Performance Criteria ◽  
Warning Systems ◽  
Technical Performance ◽  
Landslide Early Warning ◽  
Warning Model

Abstract. The paper proposes the evaluation of the technical performance of a regional landslide early warning system by means of an original approach, called EDuMaP method, comprising three successive steps: identification and analysis of the Events (E), i.e. landslide events and warning events derived from available landslides and warnings databases; definition and computation of a Duration Matrix (DuMa), whose elements report the time associated with the occurrence of landslide events in relation to the occurrence of warning events, in their respective classes; evaluation of the early warning model Performance (P) by means of performance criteria and indicators applied to the duration matrix. During the first step, the analyst takes into account the features of the warning model by means of ten input parameters, which are used to identify and classify landslide and warning events according to their spatial and temporal characteristics. In the second step, the analyst computes a time-based duration matrix having a number of rows and columns equal to the number of classes defined for the warning and landslide events, respectively. In the third step, the analyst computes a series of model performance indicators derived from a set of performance criteria, which need to be defined by considering, once again, the features of the warning model. The proposed method is based on a framework clearly distinguishing between local and regional landslide early warning systems as well as among correlation laws, warning models and warning systems. The applicability, potentialities and limitations of the EDuMaP method are tested and discussed using real landslides and warnings data from the municipal early warning system operating in Rio de Janeiro (Brazil).

scholarly journals Advanced numerical models for the propagation of floods with high-sediment concentrations in mountain rivers

2018 ◽  
Vol 40 ◽  
pp. 06039
Author(s):  
María Teresa Contreras ◽  
Jorge Gironás ◽  
Joannes Westerink ◽  
Cristián Escauriaza
Keyword(s):  
Early Warning ◽  
Flood Hazard ◽  
Numerical Models ◽  
Warning System ◽  
Mass Conservation ◽  
Sediment Concentration ◽  
Early Warning Systems ◽  
Mountain Range ◽  
The Andes ◽  
Wide Range

Rapid floods induced by extreme precipitation are common events in regions near the Andes mountain range. Growing urban development, combined with the changing climate and the influence of El Niño, have increased the exposure of the population in many regions of South America. Simulations of flash floods in these watersheds are very challenging, due to the complex morphology, the insufficient hydrometeorological data, and the uncertainty posed by the variability of sediment concentration. To address these issues, we develop a high-resolution numerical model of the non-linear shallow water equations, coupled with the mass conservation of sediment, and considering the density effects and changes of rheology in the momentum equation. Based on these simulations we develop a real-time early-warning system, by creating a surrogate model or meta-model from the simulations. Using a small set of parameters, we define storms for a wide range of meteorological conditions, and utilize the high-fidelity model results to create a database of flood propagation under different conditions. Through this second model we perform a sophisticated interpolation/regression, and approximate efficiently the flow depths and velocities. This is the first application of its kind in the Andes region, which can be used to improve the prediction of flood hazard in real conditions, employing low computational resources. We also create a framework to develop early warning systems, and to help decision makers and city planners in these mountain regions.

scholarly journals Snow Accumulation-Melting Model (SAMM) for integrated use in regional scale landslide early warning systems

2012 ◽  
Vol 9 (8) ◽  
pp. 9391-9423 ◽  
Author(s):  
G. Martelloni ◽  
S. Segoni ◽  
D. Lagomarsino ◽  
R. Fanti ◽  
F. Catani
Keyword(s):  
Early Warning ◽  
Regional Scale ◽  
Warning System ◽  
Early Warning Systems ◽  
Snow Accumulation ◽  
Point Of View ◽  
Warning Systems ◽  
Additional Advantage ◽  
Landslide Early Warning ◽  
Snow Model

Abstract. We propose a simple snow accumulation-melting model (SAMM) to be applied at the regional scale in conjunction with landslide warning systems based on empirical rainfall thresholds. SAMM follows an intermediate approach between physically based models and empirical temperature index models. It is based on two modules modelling the snow accumulation and the snowmelt processes. Each module is composed by two equations: a conservation of mass equation is solved to model snowpack thickness and an empirical equation for the snow density. The model depends on 13 empirical parameters, whose optimal values were defined with an optimization algorithm (simplex flexible) using calibration measures of snowpack thickness. From an operational point of view, SAMM uses as input data only temperature and rainfall measurements, bringing the additional advantage of a relatively easy implementation. The snow model validation gave satisfactory results; moreover we simulated an operational employment in a regional scale landslide early warning system (EWS) and found that the EWS forecasting effectiveness was substantially improved when used in conjunction with SAMM.

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