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

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.

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

10.5194/nhess-2017-361 ◽

2017 ◽

Cited By ~ 1

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.

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

Advances in Geosciences ◽

10.5194/adgeo-44-79-2017 ◽

2017 ◽

Vol 44 ◽

pp. 79-88 ◽

Cited By ~ 6

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

Landslides ◽

10.1007/s10346-020-01523-2 ◽

2020 ◽

Cited By ~ 2

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

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Rainfall thresholds for landslide early warning system in Nakhon Si Thammarat

Arabian Journal of Geosciences ◽

10.1007/s12517-016-2614-4 ◽

2016 ◽

Vol 9 (11) ◽

Cited By ~ 8

Author(s):

Chollada Kanjanakul ◽

Tanan Chub-uppakarn ◽

Tanit Chalermyanont

Keyword(s):

Early Warning ◽

Early Warning System ◽

Warning System ◽

Rainfall Thresholds ◽

Landslide Early Warning

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Estimating Rainfall Thresholds for Landslide Occurrence in the Bhutan Himalayas

Water ◽

10.3390/w11081616 ◽

2019 ◽

Vol 11 (8) ◽

pp. 1616 ◽

Cited By ~ 14

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.

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Improving the Performance of an Operational Flood Early Warning System with the Assimilation of Satellite-Soil-Moisture Data

10.1007/978-3-030-87007-2_3 ◽

2021 ◽

pp. 34-46

Author(s):

Santiago Narbondo ◽

Angela Gorgoglione ◽

Christian Chreties

Keyword(s):

Soil Moisture ◽

Early Warning ◽

Early Warning System ◽

Warning System ◽

Soil Moisture Data

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Technical Note: An operational landslide early warning system at regional scale based on space–time-variable rainfall thresholds

Natural Hazards and Earth System Science ◽

10.5194/nhess-15-853-2015 ◽

2015 ◽

Vol 15 (4) ◽

pp. 853-861 ◽

Cited By ~ 61

Author(s):

S. Segoni ◽

A. Battistini ◽

G. Rossi ◽

A. Rosi ◽

D. Lagomarsino ◽

...

Keyword(s):

Real Time ◽

Early Warning ◽

Early Warning System ◽

Warning System ◽

Rainfall Event ◽

Rainfall Data ◽

Limited Area ◽

Rainfall Thresholds ◽

Rain Gauges ◽

Starting Time

Abstract. We set up an early warning system for rainfall-induced landslides in Tuscany (23 000 km2). The system is based on a set of state-of-the-art intensity–duration rainfall thresholds (Segoni et al., 2014b) and makes use of LAMI (Limited Area Model Italy) rainfall forecasts and real-time rainfall data provided by an automated network of more than 300 rain gauges. The system was implemented in a WebGIS to ease the operational use in civil protection procedures: it is simple and intuitive to consult, and it provides different outputs. When switching among different views, the system is able to focus both on monitoring of real-time data and on forecasting at different lead times up to 48 h. Moreover, the system can switch between a basic data view where a synoptic scenario of the hazard can be shown all over the region and a more in-depth view were the rainfall path of rain gauges can be displayed and constantly compared with rainfall thresholds. To better account for the variability of the geomorphological and meteorological settings encountered in Tuscany, the region is subdivided into 25 alert zones, each provided with a specific threshold. The warning system reflects this subdivision: using a network of more than 300 rain gauges, it allows for the monitoring of each alert zone separately so that warnings can be issued independently. An important feature of the warning system is that the visualization of the thresholds in the WebGIS interface may vary in time depending on when the starting time of the rainfall event is set. The starting time of the rainfall event is considered as a variable by the early warning system: whenever new rainfall data are available, a recursive algorithm identifies the starting time for which the rainfall path is closest to or overcomes the threshold. This is considered the most hazardous condition, and it is displayed by the WebGIS interface. The early warning system is used to forecast and monitor the landslide hazard in the whole region, providing specific alert levels for 25 distinct alert zones. In addition, the system can be used to gather, analyze, display, explore, interpret and store rainfall data, thus representing a potential support to both decision makers and scientists.

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Landslide Early Warning in Indonesia: A Gap Analysis

10.5194/ems2021-339 ◽

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

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.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.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&#176;&#215;0.25&#176;. 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.&#160; 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.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.

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