scholarly journals Brief communication: Using punctual soil moisture estimates to improve the performances of a regional scale landslide early warning system

2017 ◽  
Author(s):  
Samuele Segoni ◽  
Ascanio Rosi ◽  
Daniela Lagomarsino ◽  
Riccardo Fanti ◽  
Nicola Casagli
Keyword(s):  
Soil Moisture ◽  
Early Warning ◽  
Early Warning System ◽  
Regional Scale ◽  
Back Analysis ◽  
Warning System ◽  
Threshold Value ◽  
False Alarms ◽  
Rainfall Thresholds ◽  
Landslide Early Warning

Abstract. We improved a state-of-art RSLEWS (regional scale landslide early warning system) based on rainfall thresholds by integrating punctual soil moisture estimates. We tested two approaches. The simplest can be easily applied to improve other RSLEWS: it is based on a soil moisture threshold value under which rainfall thresholds are not used because landslides are never expected to occur. Another approach deeply modifies the original RSLEWS: thresholds based on antecedent rainfall accumulated over long periods were substituted by soil moisture thresholds. A back analysis demonstrated that both approaches reduced consistently false alarms, while the second approach reduced missed alarms as well.

scholarly journals Brief communication: Using averaged soil moisture estimates to improve the performances of a regional-scale landslide early warning system

2018 ◽  
Vol 18 (3) ◽  
pp. 807-812 ◽  
Author(s):  
Samuele Segoni ◽  
Ascanio Rosi ◽  
Daniela Lagomarsino ◽  
Riccardo Fanti ◽  
Nicola Casagli
Keyword(s):  
Soil Moisture ◽  
Early Warning ◽  
Early Warning System ◽  
Preliminary Investigation ◽  
Regional Scale ◽  
Back Analysis ◽  
Warning System ◽  
False Alarms ◽  
Rainfall Thresholds ◽  
Landslide Early Warning

Abstract. We communicate the results of a preliminary investigation aimed at improving a state-of-the-art RSLEWS (regional-scale landslide early warning system) based on rainfall thresholds by integrating mean soil moisture values averaged over the territorial units of the system. We tested two approaches. The simplest can be easily applied to improve other RSLEWS: it is based on a soil moisture threshold value under which rainfall thresholds are not used because landslides are not expected to occur. Another approach deeply modifies the original RSLEWS: thresholds based on antecedent rainfall accumulated over long periods are substituted with soil moisture thresholds. A back analysis demonstrated that both approaches consistently reduced false alarms, while the second approach reduced missed alarms as well.

Landwarn: An Operative Early Warning System for Landslides Forecasting Based on Rainfall Thresholds and Soil Moisture

2013 ◽  
pp. 627-634 ◽  
Author(s):  
Francesco Ponziani ◽  
Nicola Berni ◽  
Marco Stelluti ◽  
Renato Zauri ◽  
Claudia Pandolfo ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Early Warning ◽  
Early Warning System ◽  
Warning System ◽  
Rainfall Thresholds

scholarly journals Definition of 3D rainfall thresholds to increase operative landslide early warning system performances

Landslides ◽  
2020 ◽  
Author(s):  
Ascanio Rosi ◽  
Samuele Segoni ◽  
Vanessa Canavesi ◽  
Antonio Monni ◽  
Angela Gallucci ◽  
...  
Keyword(s):  
Early Warning ◽  
Early Warning System ◽  
Warning System ◽  
Rainfall Thresholds ◽  
Landslide Early Warning ◽  
Definition Of

Updating and tuning a regional-scale landslide early warning system

Landslides ◽  
2012 ◽  
Vol 10 (1) ◽  
pp. 91-97 ◽  
Author(s):  
D. Lagomarsino ◽  
S. Segoni ◽  
R. Fanti ◽  
F. Catani
Keyword(s):  
Early Warning ◽  
Early Warning System ◽  
Regional Scale ◽  
Warning System ◽  
Landslide Early Warning

scholarly journals Rainfall Thresholds for Prediction of Landslides in Idukki, India: An Empirical Approach

Water ◽  
2019 ◽  
Vol 11 (10) ◽  
pp. 2113 ◽  
Author(s):  
Minu Treesa Abraham ◽  
Deekshith Pothuraju ◽  
Neelima Satyam
Keyword(s):  
Early Warning ◽  
Early Warning System ◽  
Regional Scale ◽  
Warning System ◽  
Antecedent Rainfall ◽  
Rainfall Forecasting ◽  
Study Region ◽  
Landslide Occurrence ◽  
South Indian ◽  
Landslide Early Warning

Idukki is a South Indian district in the state of Kerala, which is highly susceptible to landslides. This hilly area which is a hub of a wide variety of flora and fauna, has been suffering from slope stability issues due to heavy rainfall. A well-established landslide early warning system for the region is the need of the hour, considering the recent landslide disasters in 2018 and 2019. This study is an attempt to define a regional scale rainfall threshold for landslide occurrence in Idukki district, as the first step of establishing a landslide early warning system. Using the rainfall and landslide database from 2010 to 2018, an intensity-duration threshold was derived as I = 0.9D-0.16 for the Idukki district. The effect of antecedent rainfall conditions in triggering landslide events was explored in detail using cumulative rainfalls of 3 days, 10 days, 20 days, 30 days, and 40 days prior to failure. As the number of days prior to landslide increases, the distribution of landslide events shifts towards antecedent rainfall conditions. The biasness increased from 72.12% to 99.56% when the number of days was increased from 3 to 40. The derived equations can be used along with a rainfall forecasting system for landslide early warning in the study region.

scholarly journals Landslide Early Warning in Indonesia: A Gap Analysis

2021 ◽  
Author(s):  
Ratna Satyaningsih ◽  
Victor Jetten ◽  
Janneke Ettema ◽  
Ardhasena Sopaheluwakan ◽  
Danang Eko Nuryanto ◽  
...  
Keyword(s):  
Spatial Resolution ◽  
Early Warning ◽  
Early Warning System ◽  
Warning System ◽  
Community Response ◽  
Catchment Scale ◽  
Rainfall Thresholds ◽  
Landslide Prediction ◽  
Rainfall Forecast ◽  
Landslide Early Warning

<p>For the last decade, rainfall-triggered landslides have been one of the major hazards in Indonesia. According to the National Agency for Disaster Management (BNPB) reports, from 2010 to 2020, a total of 5822 landslides occurred in Indonesia and caused 1812 casualties, 1627 injured, and 234 missing. More than 75% of those landslides occurred in Java, the most populous island in the region. Settlements alongside agricultural fields often are located in areas that are susceptible to landslides. As relocation would be costly, a landslide early warning system (LEWS) could provide the necessary information for communities susceptible to landslides to prepare for the upcoming hazard. The objective of this study is to map the issues with the existing landslide early warning system in Indonesia and our plan to improve landslide forecasting by tailoring available rainfall forecasts and monitoring.</p><p>The United Nations International Strategy for Disaster Reduction (UNISDR) has defined an end-to-end early warning system that essentially comprises knowledge risk, hazard forecasting, alerts dissemination, and community response. In the definition, the UNISDR also highlighted timely and meaningful warning information for appropriate preparedness and action in a sufficient time. Landslide prediction itself is challenging in terms of when and where precisely the landslides occur as different landslide types have different characteristics and trigger mechanisms. Moreover, when rainfall forecast data is used as input for a physically-based hydrological and landslide model, the uncertainty and accuracy of the rainfall will affect the forecast skill.</p><p>National LEWS with a longer lead-time is operational, utilizing generic rainfall thresholds derived from 1-day and 3-day cumulative rainfall triggering landslides occurred in Indonesia (mostly in the Java Island) as warning signals. The rainfall thresholds were derived from NASA Tropical Rainfall Measuring Mission (TRMM) rainfall estimates with a spatial resolution of 0.25°×0.25°. Different studies showed that the thresholds derived from that product are lower than those derived from raingauge measurements, potentially leading to more false alerts. These thresholds are applied for all catchments in Indonesia even though the region has different climate regimes and geomorphological characteristics, leading to insufficient accuracy for the local landslide prediction.  As for the forecast, the current LEWS applies rainfall forecast with the same spatial resolution as TRMM, which is not suitable for (sub-)catchment-scale prediction.</p><p>This study proposes an approach to tailor rainfall data from various high-resolution sources, like radar, NWP models, and satellite, where historical landslide data are to be used to derive dynamical rainfall thresholds at local scale.</p>

Towards establishing rainfall thresholds for a real-time landslide early warning system in Sikkim, India

Landslides ◽  
2019 ◽  
Vol 16 (12) ◽  
pp. 2395-2408 ◽  
Author(s):  
Geethu Thottungal Harilal ◽  
Dhanya Madhu ◽  
Maneesha Vinodini Ramesh ◽  
Divya Pullarkatt
Keyword(s):  
Real Time ◽  
Early Warning ◽  
Early Warning System ◽  
Warning System ◽  
Rainfall Thresholds ◽  
Landslide Early Warning

scholarly journals A Regional-Scale Landslide Early Warning System Based on the Sequential Evaluation Method: Development and Performance Analysis

2020 ◽  
Vol 10 (17) ◽  
pp. 5788
Author(s):  
Joon-Young Park ◽  
Seung-Rae Lee ◽  
Yun-Tae Kim ◽  
Sinhang Kang ◽  
Deuk-Hwan Lee
Keyword(s):  
Performance Analysis ◽  
Early Warning ◽  
Debris Flows ◽  
Early Warning System ◽  
Evaluation Method ◽  
Regional Scale ◽  
Warning System ◽  
High Class ◽  
Landslide Early Warning ◽  
Sequential Evaluation

A regional-scale landslide early warning system was developed in collaboration with a city government. The structure and distinctive features of the system are described in detail. This system employs the principles of the sequential evaluation method that consecutively applies three different evaluation stages: statistical, physically based, and geomorphological evaluations. Based on this method, the system determines five phases of warning levels with improved levels of certainty and credibility. In particular, the warning levels are systematically derived to enable the discrimination of slope failures and debris flows. To provide intuitive and pragmatic information regarding the warning capabilities of the system, a comprehensive performance analysis was conducted. Early warning level maps were generated and a historical landslide database was established for the study period from 2009 to 2016. As a result, 81% of historical slope failures and 86% of historical debris flows were correctly predicted by high-class warning levels. Miscellaneous details associated to the timing efficiency of warnings were also investigated. Most notably, five high-class warning level events and four landslide events were recorded for a study region during the eight-year period. The four landslide events were all successfully captured by four out of the five warning events.

scholarly journals Flood and landslide warning based on rainfall thresholds and soil moisture indexes: the HEWS (Hydrohazards Early Warning System) for Sicily

2017 ◽  
Vol 44 ◽  
pp. 79-88 ◽  
Author(s):  
Giuseppina Brigandì ◽  
Giuseppe Tito Aronica ◽  
Brunella Bonaccorso ◽  
Roberto Gueli ◽  
Giuseppe Basile
Keyword(s):  
Soil Moisture ◽  
Early Warning ◽  
Early Warning System ◽  
Warning System ◽  
Rainfall Runoff ◽  
Rainfall Thresholds ◽  
Rainfall Runoff Model ◽  
Moisture Conditions ◽  
Runoff Model ◽  
Scs Cn

Abstract. The main focus of the paper is to present a flood and landslide early warning system, named HEWS (Hydrohazards Early Warning System), specifically developed for the Civil Protection Department of Sicily, based on the combined use of rainfall thresholds, soil moisture modelling and quantitative precipitation forecast (QPF). The warning system is referred to 9 different Alert Zones in which Sicily has been divided into and based on a threshold system of three different increasing critical levels: ordinary, moderate and high. In this system, for early flood warning, a Soil Moisture Accounting (SMA) model provides daily soil moisture conditions, which allow to select a specific set of three rainfall thresholds, one for each critical level considered, to be used for issue the alert bulletin. Wetness indexes, representative of the soil moisture conditions of a catchment, are calculated using a simple, spatially-lumped rainfall–streamflow model, based on the SCS-CN method, and on the unit hydrograph approach, that require daily observed and/or predicted rainfall, and temperature data as input. For the calibration of this model daily continuous time series of rainfall, streamflow and air temperature data are used. An event based lumped rainfall–runoff model has been, instead, used for the derivation of the rainfall thresholds for each catchment in Sicily characterised by an area larger than 50 km2. In particular, a Kinematic Instantaneous Unit Hydrograph based lumped rainfall–runoff model with the SCS-CN routine for net rainfall was developed for this purpose. For rainfall-induced shallow landslide warning, empirical rainfall thresholds provided by Gariano et al. (2015) have been included in the system. They were derived on an empirical basis starting from a catalogue of 265 shallow landslides in Sicily in the period 2002–2012. Finally, Delft-FEWS operational forecasting platform has been applied to link input data, SMA model and rainfall threshold models to produce warning on a daily basis for the entire region.

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