Severe Rainfall Events Research Articles

Comparison of Different Quantitative Precipitation Estimation Methods Based on a Severe Rainfall Event in Tuscany, Italy, November 2023

Accurate quantitative precipitation estimation (QPE) is fundamental for a large number of hydrometeorological applications, especially when addressing extreme rainfall phenomena. This paper presents a comprehensive comparison of various rainfall estimation methods, specifically those relying on weather radar data, rain gauge data, and their fusion. The study evaluates the accuracy and reliability of each method in estimating rainfall for a severe event that occurred in Tuscany, Italy. The results obtained confirm that merging radar and rain gauge data outperforms both individual approaches by reducing errors and improving the overall reliability of precipitation estimates. This study highlights the importance of data fusion in enhancing the accuracy of QPE and also supports its application in operational contexts, providing further evidence for the greater reliability of merging methods.

The Necessity of Updating IDF Curves for the Sharjah Emirate, UAE: A Comparative Analysis of 2020 IDF Values in Light of Recent Urban Flooding (April 2024)

In the arid Arabian Peninsula, particularly within the United Arab Emirates (UAE), the perception of rainfall has shifted from a natural blessing to a significant challenge for infrastructure and community resilience. The unprecedented storm on 17 April 2024, exposed critical vulnerabilities in the UAE’s urban infrastructure and flood management practices, revealing substantial gaps in handling accumulated precipitation. This study addresses the necessity of updating the Intensity–Duration–Frequency (IDF) curves for the Sharjah Emirate by utilizing recent precipitation data from 2021 to April 2024, alongside previously published 2020 data. By recalibrating the IDF curves based on data from three meteorological stations, this study reveals a substantial increase in rainfall intensities across all durations and return periods. Rainfall intensities increased by an average of 36.76% in Sharjah, 26.52% in Al Dhaid, and 17.55% in Mleiha. These increases indicate a trend towards more severe and frequent rainfall events, emphasizing the urgent need to revise hydrological models and infrastructure designs to enhance flood resilience. This study contributes valuable insights for policymakers, urban planners, and disaster management authorities in the UAE and similar regions worldwide.

Temporal soil loss scenarios and erosional dynamics of a slopping landmass in the southwestern India before and after the 2018 severe rainfall and mega flood events

Temporal soil loss scenarios and erosional dynamics of a slopping landmass in the southwestern India before and after the 2018 severe rainfall and mega flood events

3D visualization of hurricane storm surge impact on urban infrastructure

As climate change intensifies, resulting in more severe rainfall events, coastal cities globally are witnessing significant life and property losses. A growingly crucial component for flood prevention and relief are urban storm flood simulations, which aid in informed decision-making for emergency management. The vastness of data and the intricacies of 3D computations can make visualizing the urban flood effects on infrastructure daunting. This study offers a 3D visualization of the repercussions of hurricane storm surge flooding on Galveston, TX residences, illustrating the impact on each structure and road across varied storm conditions. We employ target detection to pinpoint house door locations, using door inundation as a metric to gauge potential flood damage. Within a GIS-based framework, we model the damage scope for residences exposed to varying storm intensities. Our research achieves three core goals: 1) Estimating the storm inundation levels on homes across different storm conditions; 2) Assessing first-floor elevations to categorize housing damages into three distinct groups; and 3) Through visualization, showcasing the efficacy of a proposed dike designed to shield Galveston Island from future storm surge and flood events.

Development and Deployment of a Virtual Water Gauge System Utilizing the ResNet-50 Convolutional Neural Network for Real-Time River Water Level Monitoring: A Case Study of the Keelung River in Taiwan

Climate change has exacerbated severe rainfall events, leading to rapid and unpredictable fluctuations in river water levels. This environment necessitates the development of real-time, automated systems for water level detection. Due to degradation, traditional methods relying on physical river gauges are becoming progressively unreliable. This paper presents an innovative methodology that leverages ResNet-50, a Convolutional Neural Network (CNN) model, to identify distinct water level features in Closed-Circuit Television (CCTV) river imagery of the Chengmei Bridge on the Keelung River in Neihu District, Taiwan, under various weather conditions. This methodology creates a virtual water gauge system for the precise and timely detection of water levels, thereby eliminating the need for dependable physical gauges. Our study utilized image data from 1 March 2022 to 28 February 2023. This river, crucial to the ecosystems and economies of numerous cities, could instigate a range of consequences due to rapid increases in water levels. The proposed system integrates grid-based methods with infrastructure like CCTV cameras and Raspberry Pi devices for data processing. This integration facilitates real-time water level monitoring, even without physical gauges, thus reducing deployment costs. Preliminary results indicate an accuracy range of 83.6% to 96%, with clear days providing the highest accuracy and heavy rainfall the lowest. Future work will refine the model to boost accuracy during rainy conditions. This research introduces a promising real-time river water level monitoring solution, significantly contributing to flood control and disaster management strategies.

Rainfall-Runoff Response of A Suburban Area

Batu Pahat experiences flash floods whenever intense rainfall coincides with high tide. Among the factors which cause flash floods are due to Batu Pahat river catchment's flat and low-lying geographical features as well as high-intensity rainfall. The goals of the study are to analyse the frequency of rainfall events for SMK Munshi Sulaiman station and to investigate runoff due to severe rainfall events within the study area. The observed daily rainfall depth record between 2010 and 2021 was obtained from the Department of Irrigation and Drainage (DID) Malaysia. The frequency of rainfall events within the 11-year period is examined using empirical rainfall intensity formulation. Rainfall-runoff of the five most heavy rainfall events in 2021 within the study area are simulated using HEC-HMS. It has been found that most daily rainfall events have a frequency of a 1-year return period. Only three events were found to have a return period of up to 2-year ARI, with a maximum rainfall depth of 110.67 mm. Five heaviest rainfall events in 2021 were obtained from DID's real-time online platform for rainfall-runoff simulations. One of the events, which occurred in June 2021 has a frequency larger than a 100-year return period while other events have return periods ranging from 1-year to 5-year. Simulations produced by HEC-HMS for these events in January, April, May, June, and September 2021 have resulted in peak flow of 6.7 m3/s, 26.8 m3/s, 24.7 m3/s, 39.9 m3/s, and 39.9 m3/s, respectively. Based on frequency analysis, rainfall-runoff simulations, and field observations, it can be concluded that flash floods are highly possible due to high-intensity rainfall as well as lowland topographical features of the study area. Therefore, flood mitigation measures need to be carried out to improve the drainage system for the suburban area.

Assessing typhoon-induced compound flood drivers: a case study in Ho Chi Minh City, Vietnam

Abstract. We investigate the most severe rainfall event ever experienced in Ho Chi Minh City (HCMC), Vietnam. It occurred on 25 November 2018 when Typhoon (TY) Usagi directly hit HCMC. During this event, there was more than 300 mm of rainfall over 24 h which led to flooding and considerable material damage. We propose an in-depth study of TY-induced, compound flood drivers at a short timescale by focusing on the days before and after the event. We use a set of data analysis and signal processing tools to characterize and quantify both coastal and inland effects on the hydrosystem. We found that TY Usagi made landfall without forming a significant storm surge. The extreme rainfall does not translate into immediate river discharge but presents a 16 h time lag between peak precipitation and peak residual discharge. Nevertheless, increased river water levels can be seen at both urban and upstream stations with a similar time lag. At the upstream river station, residual discharge represents 1.5 % of available rainwater, and evidence of upstream widespread flooding was found. At the urban river station, we assess the potential surface runoff during the event to be 8.9 % of the upstream residual discharge. However, a time lag in peak river water level and peak rainfall was found and attributed to the combination of high tide and impervious streets which prevented the evacuation of rainwater and resulted in street flooding of up to 0.8 m. Overall, it was found that despite not having a significant storm surge, the coastal tidal forcing is the predominant compound flood driver even during severe, heavy rainfall with tidal fluctuations in river water level and respective discharge much larger than the residuals.

Assessment of cloud microphysics and cumulus convection schemes to model extreme rainfall events over the Paraiba do Sul River Basin

Highly urbanized areas with complex terrains and multiple land uses pose challenges to extreme precipitation forecasts. The present study investigates the performance of several cloud microphysics and cumulus convection parameterizations on a severe rainfall event over the Paraiba do Sul River Basin, an area periodically affected by heavy precipitation. The Weather Research and Forecasting (WRF) model was applied to perform high resolution simulations, and the results were compared against ground observations, weather radar data, and satellite-based precipitation estimates. The simulations tend to underestimate precipitation by an average bias of 55%; however, some experiments showed good fittings in terms of time correlations (over 90%). Overall, the radar vertical profile of reflectivity agrees with observations, where the height of the warm and mixed layers was accurately simulated, despite overpredictions of convective depth (by up to 3 km in regions of intense ascending currents). Typically, WRF was more sensitive to distinct microphysics schemes rather than cloud convection ones. Also, urban areas showed greater precipitation and hydrometeor content in comparison with non-urban ones, even under strong synoptic-scale forcing. The framework was able to reproduce convectively active environments, supporting its application on operational runs over urbanized regions for warning issuing and decision-making.

Prevention of well clogging during aquifer storage of turbid tile drainage water rich in dissolved organic carbon and nutrients

Well clogging was studied at an aquifer storage transfer and recovery (ASTR) site used to secure freshwater supply for a flower bulb farm. Tile drainage water (TDW) was collected from a 10-ha parcel, stored in a sandy brackish coastal aquifer via well injection in wet periods, and reused during dry periods. This ASTR application has been susceptible to clogging, as the TDW composition largely exceeded most clogging mitigation guidelines. TDW pretreatment by sand filtration did not cause substantial clogging at a smaller ASR site (2 ha) at the same farm. In the current (10 ha) system, sand filtration was substituted by 40-μm disc filters to lower costs (by 10,000–30,000 Euro) and reduce space (by 50–100 m2). This measure treated TDW insufficiently and injection wells rapidly clogged. Chemical, biological, and physical clogging occurred, as observed from elemental, organic carbon, 16S rRNA, and grain-size distribution analyses of the clogging material. Physical clogging by particles was the main cause, based on the strong relation between injected turbidity load and normalized well injectivity. Periodical backflushing of injection wells improved operation, although the disc filters clogged when the turbidity increased (up to 165 NTU) during a severe rainfall event (44 mm in 3 days). Automated periodical backflushing, together with regulating the maximum turbidity (<20 NTU) of the TDW, protected ASTR operation, but reduced the injected TDW volume by ~20–25%. The studied clogging-prevention measures collectively are only viable as an alternative for sand filtration when the injected volume remains sufficient to secure the farmer’s needs for irrigation.

Mitigation Strategy for Low-frequency Large-magnitude Debris Flows in Hong Kong

Potential hazards arising from low-frequency large-magnitude debris flows have become apparent under the adverse impact of climate change and extreme weather. Hong Kong has systematically managed landslide risks arising from man-made slopes and natural hillsides since 1977, owing to its unique geographic location, steep terrain, deep weathering profile and post-war rapid urban development. In recent years, evaluation of potential hazards arising from low-frequency large-magnitude debris flows and their possible dire consequences has developed into a key part of the landslide mitigation effort in Hong Kong in light of the occurrence of more frequent and more severe extreme rainfall events. Valuable experience is gained in identifying potential low-frequency large-magnitude debris flow hazards, assessing their potential impacts to the public located at the densely populated foothills and developing a holistic strategy to mitigate against potentially catastrophic consequences. This paper sets out the mitigation strategy for mitigating low-frequency large-magnitude debris flows in Hong Kong and gives a case study to demonstrate the approach.

Hydroclimatic intensity change in China during the past decades and its future trend based on CMIP5/6

Changes in the hydrological cycle have far reaching impacts on agricultural productivity, transport availability, energy supply. For example, increasing intensity and declining frequency of rainfall in China have led to a prolonged period of drought as well as more floods, with serious impacts on economics and environment. Different hydrological indices, for instance the accumulated precipitation, the Palmer drought severity index etc., which can reflect the intensity of precipitation or drought, have been proposed to understand the change of hydrological cycle. However, they lead to obvious disagreements in case of China's hydrological cycle because various indices emphasis mainly on either wet or dry feature of the hydrological cycle. The disagreements interfere our ability to manage environmental assets, drought risks, and flood hazards. To reconcile these disagreements, this study has utilized a hydroclimatic intensity index (HY-INT) which is proposed innovatively by previous study with advantages in quantifying the intensity of hydrological cycle. HY-INT integrates and amplifies the signals of precipitation intensity and dry spell length, viewing the response of these two metrics to warming as deeply interconnected. An increase of HY-INT represents the accelerated hydrological cycle and the increased risks of wet and/or dry extremes. Based on the daily gauged precipitation at 540 stations within mainland China, this study has assessed the change of HY-INT in China during 1961–2017. The results show an increasing trend of HY-INT in central-east China, indicating the increasing intensity of hydrological cycle (i.e., more severe droughts and/or rainfall events). While a decreasing trend of HY-INT is observed in northwestern China which implies less wet-dry extremes. Future change of HY-INT is further assessed based on the outputs from climate models in the Coupled Model Inter-comparison Project phase 5 and phase 6 (CMIP5 and CMIP6) under the more realistic medium stabilization emission scenarios. The models are weighted by the performances in reproducing both spatial distribution and inter-annual variability of observed HY-INT. Results of weighted multi-model ensemble suggest an increased hydrological cycle over most part of China, especially in the Yangtze River Basin, indicating more wet-dry extremes. While an exception is seen in northwest China where the HY-INT will decline due to the dominance of the decreased dry spell length over the increased precipitation intensity. This reveals an alleviated intensity of hydrological cycle and fewer wet-dry extreme events therein, similar to the change in recent decades. The changes of HY-INT are attributed to the different variation patterns in the precipitation probability, which are associated with the change in atmospheric instability and moisture content.

The characteristics and formation mechanism of double-band radar echoes formed by a severe rainfall occurred in the Sichuan Basin under the background of two vortices coupling

During 29–30 UTC June 2013, a severe rainfall event with a long and narrow region of strong precipitation occurred in the central of the Sichuan Basin (SCB). Under the combined influence of a Tibetan Plateau vortex (TPV) and a southwest vortex (SWV), two banded strong radar echoes existed and developed simultaneously over the SCB. The analysis reveals that the vertical wind shear (VWS) caused by the circulations of the TPV and the SWV was the dominant factor of the formation and development of the radar echoes over the SCB. During the coupling period of the two vortices, the SWV provided abundant water vapor at the middle and lower levels over the SCB and the updrafts of the two vortices break through that formed deep convection, which made the precipitation in the SCB reach the maximum intensity. The enhancement of horizontal vorticity caused by the baroclinicity and the secondary circulation related to the two vortices created conditions for the formation of the double-band radar echoes. The matching degree of water vapor and heating conditions accompanying the circulation of the two vortices could affect the developments of convective storms and precipitation.

On the Evolution of a Long‐Lived Mesoscale Convective Vortex that Acted as a Crucial Condition for the Extremely Strong Hourly Precipitation in Zhengzhou

AbstractFrom 17–22 July 2021, Henan Province experienced the most severe torrential rainfall event since 1975 with a maximum hourly precipitation of 201.9 mm appeared in Zhengzhou, which was the largest hourly rainfall thus far observed by meteorological observation stations over the Chinese Mainland. The appearance of a long‐lived (21‐hr) northwestward‐moving mesoscale convective vortex (MCV) and its interaction with its parent mesoscale convective system (MCS) was crucial to produce the extremely strong heavy rainfall in Zhengzhou. The backward trajectory analysis indicates that air particles in the lower troposphere beneath the MCS over Henan contributed mostly to the MCV's formation. These air particles experienced notable ascending motions and condensation with their strong cyclonic vorticity mostly produced 1‐hr before the MCV's formation. Vorticity budget denotes that strong upward transport of cyclonic vorticity and convergence‐related vertical stretching, both of which were mainly due to convection associated with the parent MCS, acted as dominant factors for the MCV's formation. After formation, the MCV first coupled with its parent MCS, during which its intensity, thickness, and precipitation were all maximized; then, it moved northwestward and decoupled from the MCS, during which it weakened rapidly and finally dissipated. Convection‐related upward cyclonic vorticity transport and inward horizontal advection of cyclonic vorticity associated with an inverted trough over the Henan Province dominated the vortex's development/maintenance in the coupling stage; whereas outward horizontal advection of cyclonic vorticity dominated the MCV's dissipation after it completely decoupled from its parent MCS. These differ notably from the findings documented in previous MCV‐related literature.

Development of Near Real-Time PWV Estimation System for Monitoring the Meteorological Events in Thailand

Nowadays, Global Navigation Satellite System (GNSS) observation is widely used as an alternative approach to estimate the Precipitable Water Vapor (PWV). The process of Precise Point Position (PPP) is now well developed and commonly applied to process GNSS ground-based observations. This study developed the system to acquire data from 121 permanent reference stations of GNSS CORS spatially distributed across Thailand to analyze and monitor GNSS-PWV. Several other meteorological data like surface pressure, temperature, and humidity were collected from 478 telemetry stations. The developed system reduces the time for data processing, making the approach more promising for meteorological applications. The comparison of GNSS-PWV from CODE and IGS data correction shows an average correlation of 0.29. However, there is a potential for monitoring the meteorological phenomenon based on the analysis between near real-time GNSS-PWV and average rainfall rate for specific rainfall duration and a selected frequency called rainfall intensity in the central of Thailand during the severe rainfall events.

Using InSAR Time Series to Monitor Surface Fractures and Fissures in the Al-Yutamah Valley, Western Arabia

Western Arabia routinely experiences geophysical phenomena that deform the surface of the earth in a variety of ways. These phenomena include earthquakes, volcanic eruptions, sinkholes, and earth fissuring and fracturing. We perform a time-series analysis of interferometric synthetic aperture radar (InSAR) observations derived from the ESA Sentinel-1 radar satellite constellation to map regional surface displacements in western Arabia as a function of time. We rely on InSAR products generated by the JPL-Caltech ARIA project to detect regions with short wavelength anomalies, and then manually reprocess InSAR products at a higher resolution for these regions to maximize spatial and temporal coverage. We post-process InSAR products using MintPy workflows to develop the InSAR time series. We report short wavelength anomalies localized within alluvial valleys across western Arabia and find a 5 cm/year line-of-sight surface displacement within the Al-Yutamah Valley. Part of the observed subsidence is correlated with surface fractures that developed in conjunction with severe rainfall events in regions characterized mainly by alluvial sediments at the surface. Regions of observed subsidence that are not associated with any surface fractures or fissures are correlated with the presence of basalt layers at the surface. Both regions are subject to groundwater exploitation. The observed subsidence is inferred to be driven by groundwater withdrawal perhaps modulated by the presence of a preexisting depositional environment (e.g., paleo-lake deposits) that promotes unconsolidated soil compaction.

ASSESSING LAND SUSCEPTIBILITY FOR POSSIBLE GROUNDWATER POLLUTION DUE TO LEACHING – A CASE STUDY ON ROMANIA

Pollution can occur in any environment, but the way the pollutants spread over depends on the environmental features. Some pollutants remain more or less confined to the contamination site, others do not. This paper proposes a territory zoning showing lands susceptibility for possible groundwater pollution through leaching in Romania, if pollution occurs. The method uses a GIS technique and takes into account soil permeability and texture, relief type, depth to groundwater and climatic water deficit. Six land susceptibility classes were obtained for the whole territory. The highest vulnerability to groundwater pollution was assessed for the most permeable sandy soils, or near - river soils, whereas the most resilient environment was assessed for the low permeable clayey soils. This land classification is aimed at drawing attention to stakeholders in order to rank and take the most appropriate measures to prevent and control pollution, if occurring. The regions that are most vulnerable to pollution should be managed with more care than the most resilient regions. If global warming continues, more severe rainfall events are expected to happen, thus enhancing the leaching of pollutants toward groundwater, specifically for the most vulnerable classes.

A paradigm of extreme rainfall pluvial floods in complex urban areas: the flood event of 15 July 2020 in Palermo (Italy)

Abstract. In the last few years, some regions of the Mediterranean area have witnessed a progressive increase in extreme events, such as urban and flash floods, as a response to the increasingly frequent and severe extreme rainfall events, which are often exacerbated by the ever-growing urbanization. In such a context, the urban drainage systems may not be sufficient to convey the rainwater, thus increasing the risk deriving from the occurrence of such events. This study focuses on a particularly intense urban flood that occurred in Palermo (Italy) on 15 July 2020; it represents a typical pluvial flood due to extreme rainfall on a complex urban area that many cities have experienced in recent years, especially in the Mediterranean region. A conceptual hydrological model and a 2D hydraulic model, particularly suitable for simulations in a very complex urban context, have been used to simulate the event. Results have been qualitatively validated by means of crowdsourced information and satellite images. The experience of Palermo, which has highlighted the urgent need for a shift in the way stormwater in urban settlements is managed, can be assumed to be a paradigm for modeling pluvial floods in complex urban areas under extreme rainfall conditions. Although the approaches and the related policies cannot be identical for all cities, the modeling framework used here to assess the impacts of the event under study and some conclusive remarks could be easily transferred to other, different urban contexts.

Potential impact of aerosols on convective clouds revealed by Himawari-8 observations over different terrain types in eastern China

Abstract. Convective clouds are common and play a major role in Earth's water cycle and energy balance; they may even develop into storms and cause severe rainfall events. To understand the convective cloud development process, this study investigates the impact of aerosols on convective clouds by considering the influence of both topography and diurnal variation in radiation. By combining texture analysis, clustering, and thresholding methods, we identify all convective clouds in two warm seasons (May–September, 2016/17) in eastern China based on Himawari-8 Level 1 data. Having large diurnally resolved cloud data together with surface meteorological and environmental measurements, we investigate convective cloud properties and their variation, stratified by elevation and diurnal change. We then analyze the potential impact of aerosol on convective clouds under different meteorological conditions and topographies. In general, convective clouds tend to occur preferentially under polluted conditions in the morning, which reverses in the afternoon. Convective cloud fraction first increases then decreases with aerosol loading, which may contribute to this phenomenon. Topography and diurnal meteorological variations may affect the strength of aerosol microphysical and radiative effects. Updraft is always stronger along the windward slopes of mountains and plateaus, especially in northern China. The prevailing southerly wind near the foothills of mountains and plateaus is likely to contribute to this windward strengthening of updraft and to bring more pollutant into the mountains, thereby strengthening the microphysical effect, invigorating convective clouds. By comparison, over plain, aerosols decrease surface heating and suppress convection by blocking solar radiation reaching the surface.

Probabilistic Attenuation Nowcasting for the 5G Telecommunication Networks

In this letter, we propose a novel approach to produce attenuation forecasts for microwave links using a probabilistic approach. It uses ensembles of forecast rainfall fields to easily derive attenuation forecasts for specific frequencies. The proposed approach uses the short-term ensemble prediction system (STEPS) to generate ensembles of high, spatial and temporal, resolution forecast rainfall fields based on observed weather radar fields with lead times of 15–90 min. Attenuation forecasts could eventually be used by telecommunication operators to drive the operation of wireless networks and ensure their maintenance during severe and extreme rainfall events. This study used 109 microwave links ranging from 15 to 40 GHz to verify the results of this probabilistic attenuation forecast. Results suggest that the STEPS-based attenuation forecasts were within the narrow span of the 90% confidence region for all microwave links tested up to 30 min lead time, decreasing for longer lead times. Examples of how the proposed approach can be used to derive a detailed probabilistic attenuation forecast for multiple lead times within a domain of few kilometers, as well as probability of attenuation maps for large areas are shown.

Usage of antecedent soil moisture for improving the performance of rainfall thresholds for landslide early warning

Landslides triggered by heavy rains are increasing in number and creating severe losses in hilly regions across the world. Rainfall thresholds on regional and local-scales are being used for forecasting such events, for efficient early warning. Empirical and probabilistic approaches for defining rainfall thresholds are traditional tools which are being used as part of the forecasting system for rainfall induced landslides. Such methods are easy-to-use and are based on statistical analyses. They can be derived without looking into the complex hydro-geological processes involved in slope failures, but are often associated with the disadvantage of higher false alarms, limiting their applications in a regional landslide early warning system (LEWS). This study is an attempt to improve the performance of conventional meteorological thresholds by considering the effect of soil moisture, using a probabilistic approach. Idukki district in southern part of India is highly susceptible to landslides and has witnessed major socio-economical setbacks in the recent disasters happened in 2018 and 2019. This tourist hub is now in need of a landslide forecasting system, which can help in landslide risk reduction. This study attempts to understand the effect of averaged soil moisture estimates derived from passive microwave remote sensing data, for improving the performance of conventional empirical and probabilistic thresholds. For defining empirical thresholds, an algorithm-based approach such as Calculation of Thresholds for Rainfall-induced Landslides Tool (CTRL-T) has been used. Probabilistic thresholds were defined using a Bayesian approach, finding the posterior probability of occurrence using the marginal and conditional probabilities of the control parameters along with the prior probability of occurrence of landslide. The derived rainfall thresholds were quantitatively compared with the Bayesian probabilistic threshold derived using rainfall severity and soil wetness using an area under the curve (AUC) based on receiver operating characteristics (ROC) curve method. The results show that when the antecedent moisture content in soil is less, only severe rainfall events can trigger landslides in the study area; while less severe rainfall events can also trigger landslides when the soil is wet. The role of soil wetness in the initiation is used to improve the performance of the conventional methods, and a ROC approach was used for the statistical comparison of different models. Further, the results indicated that the probabilistic threshold using rainfall severity and soil wetness outperformed the conventional approaches with AUC of 0.96, being the most sensitive and specific among the models considered. This result opens new promising perspectives for the development of an operational LEWS in the Idukki district based on a combination of rainfall and soil moisture data. Moreover, this work contributes to strengthen the advancing trend of hydro-meteorological thresholds based on soil moisture, which is gaining a growing attention in landslide studies and that, to date, was lacking evidences in monsoon regions.