Rainfall Thresholds Research Articles

A literature review: rainfall thresholds as flash flood monitoring for an early warning system

ABSTRACT Flash floods, increasingly common and impactful, can be better managed through effective early warning systems. This study reviews methods for determining rainfall thresholds using empirical, hydrological, and machine learning approaches. Empirical methods, based on historical data, identify patterns between rainfall and floods but struggle with spatial and temporal variations. Hydrological methods, which consider watershed characteristics, offer greater accuracy but require detailed data. Machine learning, with its capacity for real-time, adaptive analysis of big data, shows promise for improving predictions. Integrating these approaches can enhance early warning systems, making them more effective in the face of climate change and significantly reducing the impact of flash floods.

Landslide Hazard and Rainfall Threshold Assessment: Incorporating Shallow and Deep-Seated Failure Mechanisms with Physics-Based Models

Landslides pose a significant threat worldwide, leading to numerous fatalities and severe economic losses. The city of Manizales, located in the Colombian Andes, is particularly vulnerable due to its steep topography and permeable volcanic ash-derived soils. This study aims to assess landslide hazards in Manizales by integrating shallow planar and deep-seated circular failure mechanisms using physics-based models (TRIGRS and Scoops3D). By combining hazard zonation maps with rainfall thresholds calibrated through historical data, we provide a refined approach for early warning systems (EWS) in the region. Our results underscore the significance of the landslide hazard maps, which combine shallow planar and deep-seated circular failure scenarios. By categorizing urban areas into high, medium, and low-risk zones, we offer a practical framework for urban planning. Moreover, we developed physics-based rainfall thresholds for early landslide warning, simplifying their application while aiming to enhance regional predictive accuracy. This comprehensive approach equips local authorities with essential tools to mitigate landslide risks, refine hazard zoning, and strengthen early warning systems, promoting safer urban development in the Andean region and beyond, as the physics-based methods used are well-established and implemented globally.

Assessing rainfall threshold for shallow landslides triggering: a case study in the Alpes Maritimes region, France

AbstractIn this work, we use a statistical approach for modeling shallow landslide rainfall thresholds (Caine 1980) with a case study for the Alpes-Maritimes region (France). Cumulated rainfall / duration (ED) thresholds are obtained with the CTRL-T algorithm (Melillo and al. 2018) for different non-exceedance probabilities from a landslide and two climatic datasets. This tool allows to automatically define rainfall events that might trigger landslides, ensuring robustness and objectivity in this process. The first climate dataset stores high resolution gridded rainfall data (1km resolution, hourly), which provides rainfall data with high temporal and spatial accuracy. This dataset, coming from radar data, is calibrated with rainfall gauges, ensuring a higher accuracy of the rainfall measurements. It provides the rainfall records directly used in the threshold construction The second dataset contains lower resolution gridded rainfall, snow, temperature, and evapotranspiration data (8km resolution, daily); it enables to assess the region’s climate through parameters imported in CTRL-T. The thresholds are then validated using a method designed by Gariano and et al. (2015). Several improvements are made to the initial method. First, evapotranspiration values approximated in the process are replaced by values from the second climate dataset, the result accounting best for the regional climate. Then, computing duration values used for isolating events and sub-events for each mesh point allows to consider the heterogeneity of the Alpes-Maritimes climate. Rainfall thresholds are eventually obtained, successively from a set of probable conditions (MRC) and a set of highly probable conditions (MPRC). The validation process strengthens the analysis as well as enables to identify best performing thresholds. This work represents novel scientific progress towards landslide reliable warning systems by (a) making a case study of empirical rainfall thresholds for Alpes-Maritimes, (b) using high-resolution rainfall data and (c) adapting the method to climatically heterogeneous zones.

Construction of landslide warning by combining rainfall threshold and landslide susceptibility in the gully region of the Loess Plateau: A case of Lanzhou City, China

Regional forecasting of the landslide rainfall threshold is complex, and the empirical prediction of the rainfall threshold relies on historical statistical data. To obtain the intensity I ∼ duration (D) rainfall threshold, Lanzhou City was selected as the research object, and a field investigation, 3S technology, mathematical statistics and a Python language environment were employed to develop a set of automatic rainfall threshold calculation programs. Factor elimination, the information value method, weight of evidence, the frequency ratio, the density method and receiver operating characteristic (ROC) curve were applied to obtain landslide susceptibility, and the I ∼ D rainfall threshold (temporal probability) and landslide susceptibility (spatial probability) were combined to construct regional landslide rainfall threshold warning levels (red, orange, yellow, and blue). The results showed the rainfall duration of historical landslides ranged from 0.99 to 216 h, thereinto, it was accounted for 92.78 % with that of between 6 and 96 h, and the rainfall intensity was between 0.11 and 23.6 mm/h, and the α and β of the rainfall threshold curve was gradually increased with an increase in the rainfall threshold probability. Additionally, when the rainfall condition probability was the same, landslides occurrence was more frequent in Chenguan District, where there are large areas of bare land, and slope and annual rainfall are less than 10 °and 300 mm, respectively. Fifteen, nine, and six factors were used to obtain landslide susceptibility, and the accuracy of the ROC ranged from 0.78 to 0.80 under different methods and different factor combinations. The spatial distribution of landslide susceptibility was very similar. There were 35, 12, 25, and 108 landslides associated with red, orange, yellow, and blue levels, respectively, under the spatial–temporal probability, but 30, 26, 54, and 70, respectively, under temporal probability. Under the different conditions, there were 89, 69, and 22 landslide points with unchanged, increased, and decreased warning levels, respectively. The ability and accuracy of rainfall threshold warning and prediction were effectively improved by integrating the temporal and spatial probabilities of the rainfall threshold.

A coupled hydrological and hydrodynamic modeling approach for estimating rainfall thresholds of debris-flow occurrence

Abstract. Rainfall-induced hydrological processes and surface-water flow hydrodynamics may play a key role in initiating debris flows. In this study, a new framework based on an integrated hydrological and hydrodynamic model is proposed to estimate the intensity–duration (ID) rainfall thresholds that trigger debris flows. In the new framework, intensity–duration–frequency (IDF) analysis is carried out to generate design rainfall to drive the integrated models and calculate grid-based hydrodynamic indices (i.e., unit-width discharge). The hydrodynamic indices are subsequently compared with hydrodynamic thresholds to indicate the occurrence of debris flows and derive rainfall thresholds through the introduction of a zone threshold. The capability of the new framework in predicting the occurrence of debris flows is verified and confirmed by application to a small catchment in Zhejiang Province, China, where observed hydrological data are available. Compared with the traditional statistical approaches to derive intensity–duration (ID) thresholds, the current physically based framework can effectively take into account the hydrological processes controlled by meteorological conditions and spatial topographic properties, making it more suitable for application in ungauged catchments where historical debris-flow data are lacking.

A semi-automatic interpretation method for utilizing InSAR results to recognize active landslides considering causative factors

Synthetic Aperture Radar Interferometry (InSAR), which can map subtle ground displacement over large areas, has been widely utilized to recognize active landslides. Nevertheless, due to various origins of subtle ground displacement, their presence on slopes may not always reflect the occurrence of active landslides. Therefore, interpretation of exact landslide-correlated deformation from InSAR results can be very challenging, especially in mountainous areas, where natural phenomenon like soil creep, anthropogenic activities and erroneous deformational signals accumulated during InSAR processing can easily lead to misinterpretation. In this paper, a two-phase interpretation method applicable to regional-scale active landslide recognition utilizing InSAR results is presented. The first phase utilizes statistical threshold and clustering analysis to detect unstable regions mapped by InSAR. The second phase introduces landslide susceptibility combined with empirical rainfall threshold, which are considered as causative factors for active landslides triggered by rainfall, to screen unstable regions indicative of active landslides. A case study validated by field survey indicates that the proposed interpretation method, when compared to a baseline model reported in the literature, can achieve better interpretation accuracy and miss rate.

Performance assessment of satellite rainfall estimates in rain detection capabilities at different thresholds over Nigeria

ABSTRACT In Nigeria, where rainfall plays a pivotal role in agriculture and disaster management, assessing the accuracy of satellite rainfall estimates at various thresholds is imperative. This study assesses the areal averages of nine satellite precipitation estimates (SPEs) against 48 ground-based raingauge observations in Nigeria. Employing categorical statistical metrics and compromise programming, the research assesses the performance of SPEs at seven rainfall thresholds. Results reveal that 82–94% of rainfall estimates align with ground truth, while 5–14% of rainy days are incorrectly detected by raingauges. Notably, SPEs tend to overestimate low rain between 1 mm and 5 mm d−1 and underestimate low heavy rain rates (10 mm ≤ rain < 20 mm), impacting flood monitoring and disaster preparedness. Global Precipitation Climatology Centre (CPCC), Tropical Applications of Meteorology using SATellite and ground-based observations (TAMSAT) African Rainfall Climatology And Time series (TARCAT), and Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) emerge as more reliable SPEs, particularly in highland regions. This research underscores the importance of choosing appropriate satellite precipitation estimates for enhanced disaster preparedness and resource management across different Nigerian regions.

Landslide predictions through combined rainfall threshold models

Landslide predictions through combined rainfall threshold models

Correction to: Comparative analysis of conventional and machine learning techniques for rainfall threshold evaluation under complex geological conditions

Correction to: Comparative analysis of conventional and machine learning techniques for rainfall threshold evaluation under complex geological conditions

Potential impact of climatic factors on malaria in Rwanda between 2012 and 2021: a time-series analysis

BackgroundMalaria remains an important public health problem, particularly in sub-Saharan Africa. In Rwanda, where malaria ranks among the leading causes of mortality and morbidity, disease transmission is influenced by climatic factors. However, there is a paucity of studies investigating the link between climate change and malaria dynamics, which hinders the development of effective national malaria response strategies. Addressing this critical gap, this study analyses how climatic factors influence malaria transmission across Rwanda, thereby informing tailored interventions and enhancing disease management frameworks.MethodsThe study analysed the potential impact of temperature and cumulative rainfall on malaria incidence in Rwanda from 2012 to 2021 using meteorological data from the Rwanda Meteorological Agency and malaria case records from the Rwanda Health Management and Information System. The analysis was performed in two stages. First, district-specific generalized linear models with a quasi-Poisson distribution were applied, which were enhanced by distributed lag non-linear models to explore non-linear and lagged effects. Second, random effects multivariate meta-analysis was employed to pool the estimates and to refine them through best linear unbiased predictions.ResultsA 1-month lag with specific temperature and rainfall thresholds influenced malaria incidence across Rwanda. Average temperature of 18.5 °C was associated with higher malaria risk, while temperature above 23.9 °C reduced the risk. Rainfall demonstrated a dual effect on malaria risk: conditions of low (below 73 mm per month) and high (above 223 mm per month) precipitation correlated with lower risk, while moderate rainfall (87 to 223 mm per month) correlated with higher risk. Seasonal patterns showed increased malaria risk during the major rainy season, while the short dry season presented lower risk.ConclusionThe study underscores the influence of temperature and rainfall on malaria transmission in Rwanda and calls for tailored interventions that are specific to location and season. The findings are crucial for informing policy that enhance preparedness and contribute to malaria elimination efforts. Future research should explore additional ecological and socioeconomic factors and their differential contribution to malaria transmission.

An asymmetric tropical cyclone rainfall model in the Northern Vietnam coast

AbstractRainfall associated with landfalling tropical cyclones (TCs) along the Northern Vietnam coast is examined to develop an asymmetric parametric TC‐induced rainfall model starting from the axisymmetric Rain‐Climatology and Persistence (R‐CLIPER) model. We recalibrated the R‐CLIPER model (original R‐CLIPER denoted as NHC) against observed rainfall patterns of 14 landfalling TCs from 2001 to 2021 in the Northern Vietnam coast, while relaxing the model's underlying linear relationships. The recalibrated R‐CLIPER (denoted as Fit‐Ax), still axisymmetric, suggests that some parameters are better correlated with the normalized maximum wind speed using logarithmic and exponential relationships. Fit‐Ax reduces the 12‐hr total rainfall overall root‐mean‐square errors (RMSEs) and Bias magnitudes in the before‐ and after‐landfall periods from NHC for the entire 500‐km TC domain. We further redistribute the Fit‐Ax rainfall intensity across the four quadrants with respect to the TC forward motion to account for the observed large asymmetry in quadrant rainfall (version denoted as Fit‐As). The vertical wind shear (VWS) and landfall (before or after) are considered in this redistribution. Fit‐As generally outperforms Fit‐Ax and NHC in reproducing the observed rainfall distribution for the 14 TCs. At the quadrant level, both Fit‐Ax and Fit‐As show significant improvement in Bias over NHC. Fit‐As is further better overall in RMSE and Skill when weighted by quadrant rainfall volume. In pattern matching, Fit‐As produces the best grid‐averaged Pearson correlation coefficients for 11 TCs. In addition, its equitable threat scores (ETSs) are best beyond the 20‐mm rainfall threshold, with the maximum of 0.299 at the 90‐mm rainfall threshold. Thus, our locally fitted asymmetric rainfall model demonstrates improved capability in reproducing the historical TC‐induced rainfall along the Northern Vietnam coast.

RegionGrow3D: A Deterministic Analysis for Characterizing Discrete Three‐Dimensional Landslide Source Areas on a Regional Scale

AbstractRegional‐scale characterization of shallow landslide hazards is important for reducing their destructive impact on society. These hazards are commonly characterized by (a) their location and likelihood using susceptibility maps, (b) landslide size and frequency using geomorphic scaling laws, and (c) the magnitude of disturbance required to cause landslides using initiation thresholds. Typically, this is accomplished through the use of inventories documenting the locations and triggering conditions of previous landslides. In the absence of comprehensive landslide inventories, physics‐based slope stability models can be used to estimate landslide initiation potential and provide plausible distributions of landslide characteristics for a range of environmental and forcing conditions. However, these models are sometimes limited in their ability to capture key mechanisms tied to discrete three‐dimensional (3D) landslide mechanics while possessing the computational efficiency required for broad‐scale application. In this study, the RegionGrow3D (RG3D) model is developed to broadly simulate the area, volume, and location of landslides on a regional scale (≥1,000 km2) using 3D, limit‐equilibrium (LE)‐based slope stability modeling. Furthermore, RG3D is incorporated into a susceptibility framework that quantifies landsliding uncertainty using a distribution of soil shear strengths and their associated probabilities, back‐calculated from inventoried landslides using 3D LE‐based landslide forensics. This framework is used to evaluate the influence of uncertainty tied to shear strength, rainfall scenarios, and antecedent soil moisture on potential landsliding and rainfall thresholds over a large region of the Oregon Coast Range, USA.

Changes of vertical distribution of Microcystis colonies driven by short-term rainfall: Disappearance and reformation of surface bloom

Global climate warming and human activities have increased the magnitude and frequency of Microcystis surface blooms, posing significant threats to freshwater ecosystems and human health over recent decades. Heavy rainfall events have been reported to cause the disappearance of these blooms. Although some studies have employed turbulence models to analyze the movement characteristics of Microcystis colonies, the impact of rainfall is complex, comprehensive investigations on their vertical migration induced by short-term rainfall are still necessary. Utilizing monitoring data from eutrophic ponds and controlled simulation experiments, this study examines the short-term impacts of rainfall on the vertical distribution of Microcystis in the water column. Our findings indicate that rainfall contributes to the disappearance of Microcystis blooms by reducing the quantity of small to medium-sized colonies (0–100 μm) at the surface, subsequently decreasing the overall Microcystis biomass. As rainfall intensity increases, larger colonies migrate deeper into the water column. At a rainfall threshold of 666 mm, the difference in the median volume diameter (DV50) of Microcystis colonies between the surface and bottom reaches a minimal value of 3.09%. Post-rainfall, these colonies rapidly ascend, aggregate into larger formations, and re-establish surface blooms. The greater the rainfall, the smaller the resultant Microcystis biomass, albeit with larger aggregated colony sizes. When rainfall exceeds 222 mm, the recovery rate of surface Microcystis biomass remains below 100%, decreasing to 19.48% at 666 mm of rainfall, while the median volume diameter (DV50) of the colonies increases to 139.07% of its pre-rainfall level. Furthermore, compared to pre-rainfall conditions, the photosynthetic activity of the surface Microcystis colonies was enhanced and the secretion of EPS was increased under heavy rainfall conditions. Our results identify a critical response time of 30 min for Microcystis colonies to rainfall, after which the response ceases to intensify. These insights are crucial for predicting post-rain Microcystis bloom dynamics and aiding management authorities in timely interventions.

Erosive Rainfall Thresholds Identification Using Statistical Approaches in a Karst Yellow Soil Mountain Erosion-Prone Region in Southwest China

Karst yellow soil is one of the most important cultivated soils in southwest China. At present, only a few studies have dealt with rainfall erosivity and erosive rainfall thresholds in the karst yellow soil region. This paper utilizes statistical methods to identify erosive rainfall thresholds and slope erosion-prone areas in the Qianzhong region. This analysis is based on long-term experimental data from 10 experimental stations and 69 experimental plots within the region in 2006 to 2022. The findings show the following: The rainfall amount threshold was 12.66 mm for woodland plots, 10.57 mm for grassland plots, 9.94 mm for farmland plots, and 8.93 mm for fallow plots. Soil and water conservation measures in forestry and grassland effectively increase the rainfall amount thresholds. Compared to farmland, the rainfall threshold increased by 27.32% for woodland and 6.32% for grassland. Bare land and farmland are erosion-prone areas in the karst yellow soil region. The erosive rainfall thresholds for farmland plots with slopes of 13°, 15°, 20°, 23°, and 25° were 10.41 mm, 10.28 mm, 9.66 mm, 9.52 mm, and 9.15 mm, respectively. With the increase in the 13–25° slope gradient of farmland, the initial rainfall required for runoff generation leads to a reduction. The wrong selection indices (WSI) of all landcover plots were less than 10%, and the efficiency indices (EFF) were between 80.43% and 90.25%. The relative error index (REI) of the erosive rainfall thresholds for all landcover runoff plots was less than 0.50%, very close to 0, indicating that these thresholds have small errors and high accuracy. This study gained a better understanding of natural rainfall-induced erosion characteristics in the study area, determined rainfall thresholds for distinguishing erosive rainfall events from non-erosive across different landcover types, and reduced the workload of calculating rainfall erosivity while enhancing the accuracy of soil erosion forecasting and simulation in the karst mountain yellow soil area.

Comparative analysis of conventional and machine learning techniques for rainfall threshold evaluation under complex geological conditions

AbstractThis research focuses on the essential task of defining rainfall thresholds in regions with complex geological features, specifically at a regional scale. It examines a variety of methodologies, from traditional empirical-statistical methods to cutting-edge machine learning (ML) techniques, for establishing these thresholds. The Emilia-Romagna region in Italy, known for its intricate geological structure and prevalence of weak rocks that often lead to large and deep-seated landslides, serves as the study area. The region’s complex interplay between rainfall and landslide incidences poses a significant challenge in accurately determining rainfall thresholds. The effectiveness of ML methods is compared against conventional empirical-statistical approaches, evaluating factors such as prediction accuracy, model complexity, and the interpretability of results for use by regional landslide warning system operators. The findings indicate that machine learning techniques have an edge over traditional methods, yielding higher performance scores and fewer false positives. Nevertheless, these advancements are modest when considering the increased complexity of ML methods and the incorporation of additional rainfall parameters. This underlines the continued need for improvements in data quality and volume. The study stresses the importance of enhancing data collection and analysis techniques, especially in an era where advanced AI tools are increasingly available, to improve the accuracy of predicting rainfall thresholds for effective landslide warning systems.

Precipitable water vapor retrieval for rainfall forecasting using BDS-3 PPP-B2b signal in the coastal region of China

Abstract Currently, rainfall cannot be accurately forecast because of poor network communication at the ocean. The advantage of the BeiDou Global Navigation Satellite System (BDS-3) precise point positioning (PPP-B2b) signal, which does not rely on network communication to receive data, is that it can provide precipitable water vapor (PWV) retrieval application services for the open seas in eastern China, where the communication system presents difficulties. In this study, data from stations in the coastal region of China were used to establish a rainfall forecasting method for monitoring extreme weather on the sea. First, the service performance of PPP-B2b was explored. Then, based on 17 Chinese coastal stations, the PWV accuracy was evaluated. Finally, based on an analysis of the relationship between PWV and actual rainfall, a threshold rainfall forecasting method based on a sliding window was constructed. The experimental results show that the PWV accuracy varies slightly depending on the geographic location, in which the mean absolute error in the North Sea region is the smallest (2.1 mm), that of the South China Sea region is the largest (2.60 mm), and that of the East China Sea region is in the middle (2.48 mm). The optimal predictors of the constructed 12 h sliding-window threshold rainfall prediction method are a PWV maximum of 49 mm, PWV increase of 5 mm, and PWV increase rate of 1.2 mm h−1. The prediction results can reach a critical success index value of more than 45%, indicating high prediction accuracy and applicability to the coastal region of China during the same period.

A typhoon-induced debris flow warning model integrating rainfall thresholds with geological factors

Typhoon-induced debris flows pose a significant threat to the southeastern coastal regions of China. The existing typhoon-induced debris flow warning models, however, are largely limited in the refinement of their rainfall thresholds and in their lack of integration with geological factors. This study proposes a typhoon-induced debris flow warning model based on refined rainfall thresholds by integrating them with geological factors. Firstly, based on the characteristic rainfall of 159 debris flows in seven typhoon rainstorms between 1999 and 2019, the rainfall thresholds of debris flow under different geological conditions were obtained. Close relationships were noticed between rainfall thresholds and the number and density of debris flow disasters. The deterministic coefficient method (DCM) and sensitivity index were used to derive the weights of the geological factors, and the geological groups of the rock masses and vegetation type were the most important geological factors for debris flows. Finally, a typhoon-induced debris flow warning model was constructed, and refined rainfall thresholds were obtained. Due to different geological backgrounds, there were significant differences in the warning rainfall thresholds for debris flow disasters in different regions of the study area. The ROC indicator showed the high accuracy of this debris flow warning model. This research provides a scientific basis for the early warning and prediction of debris flows in typhoon-prone area of China.

An AI-Based Method for Estimating the Potential Runout Distance of Post-Seismic Debris Flows

The widely distributed sediments following an earthquake presents a continuous threat to local residential areas and infrastructure. These materials become more easily mobilized due to reduced rainfall thresholds. Before establishing an effective management plan for debris flow hazards, it is crucial to determine the potential reach of these sediments. In this study, a deep learning-based method—Dual Attention Network (DAN)—was developed to predict the runout distance of potential debris flows after the 2022 Luding Earthquake, taking into account the topography and precipitation conditions. Given that the availability of reliable precipitation data remains a challenge, attributable to the scarcity of rain gauge stations and the relatively coarse resolution of satellite-based observations, our approach involved three key steps. First, we employed the DAN model to refine the Global Precipitation Measurement (GPM) data, enhancing its spatial and temporal resolution. This refinement was achieved by leveraging the correlation between precipitation and regional environment factors (REVs) at a seasonal scale. Second, the downscaled GPM underwent calibration using observations from rain gauge stations. Third, mean absolute error (MAE), mean square error (MSE), and root mean square error (RMSE) were employed to evaluate the performance of both the downscaling and calibration processes. Then the calibrated precipitation, catchment area, channel length, average channel gradient, and sediment volume were selected to develop a prediction model based on debris flows following the Wenchuan Earthquake. This model was applied to estimate the runout distance of potential debris flows after the Luding Earthquake. The results show that: (1) The calibrated GPM achieves an average MAE of 1.56 mm, surpassing the MAEs of original GPM (4.25 mm) and downscaled GPM (3.83 mm); (2) The developed prediction model reduces the prediction error by 40 m in comparison to an empirical equation; (3) The potential runout distance of debris flows after the Luding Earthquake reaches 0.77 km when intraday rainfall is 100 mm, while the minimum distance value is only 0.06 km. Overall, the developed model offers a scientific support for decision makers in taking reasonable measurements for loss reduction caused by post-seismic debris flows.

A New Post-Processing Method for Improving Track and Rainfall Ensemble Forecasts for Typhoons over Eastern China

This paper proposes a new post-processing method for model data in order to improve typhoon track and rainfall forecasts. The model data used in the article include low-resolution ensemble forecasts and high-resolution forecasts. The entire improvement method contains the following three steps. The first step is to correct the typhoon track forecast: three ensemble member optimization methods are applied to the low-resolution ensemble forecasts, and then the best optimization method is selected with the principle of the smallest average distance error. The results of rainfall forecasts show that the corrected rainfall forecast performs better than the original forecasts. The second step is to derive the high-resolution probability rainfall forecast: the neighborhood method is applied to the deterministic high-resolution rainfall forecast. The last step is to correct the typhoon rainfall forecast: the low- and high-resolution forecasts are blended using the probability-matching method with two different schemes. The results show that the forecasts of the two schemes perform better than the original forecast under all rainfall thresholds and all forecast lead times. In terms of bias score, a rain forecast from one scheme corrects the rainfall deviation from observation better for light and moderate rainfall, whereas a rain forecast from another scheme corrects the rainfall deviation better for heavy and torrential rainfall. The better performance of corrected rain forecasts in the case of Typhoon Lekima and Rumbia over eastern China is demonstrated.

Influence of Cumulative Geotechnical Deterioration on Mass Movement at a Medium-Scale Regional Analysis (Cortinas Sector, Toledo, Colombia)

Landslides in Colombia represent a serious threat to the safety of local communities and the surrounding infrastructure, especially in the mountain range zone. These events occur due to the variation and correlation of endogenous conditions existing in each area, such as geology, geomorphology and coverage, which are triggered by rainfall, seismic events or anthropic activities. This article aims to analyze the geoenvironmental conditions between 2016 and 2021 in the sector known as Cortinas (Toledo, Colombia), applying, for this purpose, the innovative concept of “accumulated geotechnical deterioration” in order to explain the evolution of susceptibility over time from the perspective of prediction, which under traditional methodologies is not properly considered, since unlike what has been thought, the conditioning factors do change in the short and medium term, especially in tropical areas. As a result of this part of the research, the hypothesis was validated that it is necessary for the terrain to be under certain specific conditions for an instability event to occur, which does not depend only on certain critical thresholds of rainfall and earthquakes.