Search Results (40)

This study explores the impact of the 2022 Mw 6.8 Luding Earthquake on various geo-hazards and their corresponding rainfall thresholds. Focusing on the seismic intensity VI zone in Sichuan Province, China, we analyzed 1979 geo-hazard records and hourly precipitation data from 475 stations between 2010 and 2024. Empirical ID (intensity–duration) and AC (accumulated rainfall–continuous rainfall duration) rainfall threshold models are established based on these datasets. By comparing pre- and post-earthquake data, this study assesses changes in the spatial distribution and triggering rainfall thresholds of landslides, rockfalls, and debris flows. The results indicate a significant increase in geo-hazard risks post-earthquake, particularly near the Xianshuihe Fault, with rockfall risks exhibiting the most pronounced rise. Statistical analysis reveals that the rainfall thresholds required to trigger geo-hazards decreased notably after the earthquake: ID models indicate a decrease of approximately 20%, while AC models show a reduction of about 20% in the western zone and 10% in the eastern zone. A four-level early warning system is developed using empirical rainfall threshold models, offering tailored hazard alerts for different regions and geo-hazard types. The variation in threshold values between the east and west zones highlights the influence of differing topographic and climatic conditions. These findings provide critical insights for post-seismic hazard assessment and inform more effective, sustainable early warnings, thereby supporting more reliable and sustainable disaster risk management in earthquake-affected regions. Full article

Comprehensive, standardised, multi-temporal inventories of rainfall-induced landslides linked to soil moisture remain scarce, especially in tropical regions. Addressing this gap, we present a multi-source urban inventory for Brazil’s Baixada Santista region (1988–2024). A key advance is the introduction of geographical and temporal confidence classifications, which indicates precisely how each landslide’s location and occurrence date are known, thereby addressing a previously overlooked criterion in Brazil’s landslide data treatment. The inventory comprises 2534 records categorised by spatial (G1–G3) and temporal (T1–T3) confidence. Notable findings include the following: (i) confidence classifications enhance inventory reliability for research and early warning, though precise temporal data remains challenging; (ii) multi-source integration with UAV validation is key to robust inventories in urban tropical regions; (iii) soil moisture complements rainfall-based warnings, but requires local calibration for satellite-derived estimates; (iv) data gaps and biases underscore the need for standardised landslide documentation; and (v) the framework is transferable, providing a scalable model for Brazil and worldwide. Despite limitations, the inventory provides a foundation for (i) susceptibility and hazard modelling; (ii) empirical thresholds for early warning; and (iii) climate-related trend analyses. Overall, the framework offers a sustainable, practical, transferable method for worldwide and contributes to strengthening disaster information systems and early warning capacities. Full article

Reliable rainfall thresholds are critical for effective early warning and mitigating the risks of rainfall-induced landslides. Traditional statistical models have limitations in multi-variable modeling, while machine learning models face interpretability challenges. Explainable machine learning methods can address these challenges, but they are rarely applied to rainfall threshold modeling. In this study, we compared the performance of an empirical statistical model and machine learning models for predicting rainfall-induced landslides in Italy. Based on the optimal model, we visualized refined rainfall thresholds at three probability levels and employed SHAP (Shapley Additive Explanations) to enhance model explainability by quantifying the contribution of each input variable to the predictions. The results demonstrated that the XGBoost model achieved a good performance (AUC = 0.917 ± 0.026) with well-balanced sensitivity (0.792 ± 0.075) and specificity (0.812 ± 0.033) in landslide susceptibility modeling. Hydrological factors, particularly total rainfall, were identified as the dominant triggering mechanisms, with SHAP analysis confirming their substantially greater contribution compared to environmental factors in rainfall threshold modeling. The developed visualized threshold maps revealed distinct spatial variations in landslide-triggering rainfall thresholds across Italy, characterized by lower thresholds in gentle slope areas with moderate annual precipitation and higher thresholds in steep slope and mid-to-low-elevation regions, while these regional differences decreased under high-probability scenarios. This study offered a modeling approach for regional rainfall threshold assessment by integrating multi-variable modeling with explainable methods, contributing to the development of landslide early warning systems. Full article

This study investigated the deformation characteristics and mechanisms of the Baiyansizu landslide under the coupled effects of crack development, rainfall infiltration, and road loading. Numerical simulations were performed using GeoStudio software (Version 2018; Seequent, 2018) to analyze geological factors and external disturbances affecting landslide deformation and seepage dynamics. Four additional landslides (Tanjiawan, Bazimen, Tudiling, and Chengnan) were selected as comparative cases to investigate differences in deformation characteristics and mechanisms across these cases. The results demonstrate that rear-edge deformation of the Baiyansizu landslide was predominantly governed by rainfall patterns, with effective rainfall exhibiting a dual regulatory mechanism: long-term rainfall reduced shear strength through sustained infiltration-induced progressive creep, whereas short-term rainstorms generated step-like deformation via transient pore water pressure amplification. GeoStudio simulations further revealed multi-physics coupling mechanisms and nonlinear stability evolution controls. These findings highlight that rear-edge fissures substantially amplify rainfall infiltration efficiency, thereby establishing these features as the predominant deformation determinant. Road loading was observed to accelerate shallow landslide deformation, with stability coefficient threshold values triggering accelerated creep phases when thresholds were exceeded. Through comparative analysis of five typical landslide cases, it was demonstrated that interactions between geological factors and external disturbances resulted in distinct deformation characteristics and mechanisms. Variations in landslide thickness, crack evolution, road loading magnitudes, and rainfall infiltration characteristics were identified as critical factors influencing deformation patterns. This research provides significant empirical insights and theoretical frameworks for landslide monitoring and early warning system development. Full article

Understanding rainfall partitioning by shrub canopies is essential for assessing water balance and improving hydrological models in cold regions. From 2010 to 2012, field experiments were conducted in the Hulu catchment of the Qilian Mountains, focusing on Potentilla fruticosa and Caragana jubata during the growing season. Throughfall, stemflow, and interception loss were measured using rain gauges, stemflow collars, and a water balance approach. A total of 197 natural rainfall events were recorded, and precipitation partitioning characteristics were analyzed in relation to rainfall intensity, amount, and vegetation traits. One-way ANOVA and regression analyses were used to test differences and correlations. The results showed that the critical rainfall threshold for generating throughfall and stemflow was 1.9 mm. For P. fruticosa, throughfall, stemflow, and interception loss accounted for 66.96%, 3.51%, and 29.53% of gross rainfall, respectively; the corresponding values for C. jubata were 67.31%, 7.27%, and 25.42%. Significant differences (p < 0.05) in stemflow were observed between species. Partitioning components were positively correlated with rainfall amount and stabilized at ~4 mm h⁻¹ intensity. Interception loss percentage decreased with intensity and plateaued at 2 mm h⁻¹ for P. fruticosa and 5 mm h⁻¹ for C. jubata. These findings provide empirical evidence for modeling shrub canopy rainfall redistribution in alpine environments. Full article

Arid and semi-arid environments are characterized by highly variable and unpredictable rainfall patterns, which significantly affect the structure and function of natural ecosystems. Understanding the interconnected relationship between climate variability, forage availability, and livestock dynamics in these regions is crucial to ensure sustainable management. This study provides novel insights into the effects of rainfall variability on natural forage resources and livestock populations in Botswana. In this arid region, traditional livestock farming remains a key economic and food security pillar. By employing a mathematical model based on plant–herbivore interactions, this article quantitatively evaluates the impact of changes in rainfall timing and intensity on forage biomass and, subsequently, livestock populations. A robust analysis of critical threshold values for ecosystem sustainability is possible when real-world climate data are incorporated. This study examines the effects of harvesting and rainfall variability on livestock dynamics across different locations in Botswana. Delayed rainfall leads to a sharp decline in livestock, while Sehitlwa sees biomass loss without a notable reduction in herd size. In Kgagodi, for example, livestock numbers decline by 37% without harvesting, but they remain stable with controlled harvesting. Conversely, Letlhakeng experiences a 6% increase in livestock numbers despite delayed rainfall, which results in a biomass decline. Both Mabutsane and Letlhakeng maintain stable livestock numbers. The findings confirm that early and intense rainfall enhances livestock productivity, while delayed or reduced rainfall leads to population decline, aligning with observed trends in historical data. Additionally, the study underscores the potential of adaptive livestock harvesting strategies as a viable approach to mitigating climate-related risks in grazing systems. As this work integrates theoretical modeling with empirical climate data, it contributes to understanding arid land dynamics, providing a predictive method for assessing ecosystem responses to climate variability. These insights are invaluable for policymakers, conservationists, and local farmers seeking sustainable livestock management practices in the face of changing climatic conditions. Full article

The phenomenon of stepwise landslides, characterized by displacement exhibiting a step-like pattern, is often influenced by reservoir operations and seasonal rainfall. Traditional early warning models face challenges in accurately predicting the sudden initiation and cessation of displacement, primarily because conventional indicators such as rate or acceleration are ineffective in these scenarios. This underscores the urgent need for innovative early warning models and indicators. Viewing step-like displacement through the lens of three phases—stop, start, and acceleration—aligns with the green-yellow-red warning paradigm of the Traffic Light System (TLS). This study introduces a novel early warning model based on the TLS, incorporating jerk, the derivative of displacement acceleration, as a critical indicator. Empirical data and theoretical analysis validate jerk’s significance, demonstrating its clear pattern before and after step-like deformations and its temporal alignment with the deformation’s conclusion. A comprehensive threshold network encompassing rate, acceleration, and jerk is established for the TLS. The model’s application to the Shuiwenzhan landslide case illustrates its capability to signal in a timely manner the onset and acceleration of step-like deformations with yellow and red lights, respectively. It also uniquely determines the deformation’s end through jerk differential analysis, which is a feat seldom achieved by previous models. Furthermore, leveraging the C5.0 machine learning algorithm, a comparison between the predictive capabilities of the TLS model and a pure rate threshold model reveals that the TLS model achieves a 93% accuracy rate, outperforming the latter by 7 percentage points. Additionally, in response to the shortcomings of existing warning and emergency response strategies for this landslide, a closed-loop management framework is proposed, grounded in the TLS. This framework encompasses four critical stages: hazard monitoring, warning issuance, emergency response, and post-event analysis. We also suggest support measures to ensure implementation of the framework. Full article

In recent years, the increasing frequency of extreme weather events has exacerbated the severity of geological disasters. Therefore, it is important to understand the mechanisms of geological disasters under extreme rainfall conditions. From June to October 2021, Baoji City, Shanxi Province, China, experienced some extreme and continuous heavy rainfalls, which triggered more than 30 geological disasters. Those geo-disasters threatened the lives of 831 people and the safety of 195 houses. The field investigations found that most of these geological disasters were devastating collapses that occurred in the loess layer, primarily due to the cave dwelling construction. The shear strength, montmorillonite content, disintegration degree, and plasticity index of two typical loesses, namely the Sanmen Formation stiff clay and the Hipparion red clay, were analyzed, and their water sensitivities were evaluated. The failure mechanisms of the landslides, ground fissures, and collapses were analyzed and most of them were controlled by the cave dwelling construction and the strong water sensitivity of the loess. This study provides data for understanding shallow geological disasters induced by extreme rainfall in the loess area, which are more threatening than large geological disasters. We proposed an intensity–duration (I–D) rainfall threshold as I = 90 D^−0.92, which relates the rainfall intensity (I) to the rainfall event duration (D). The empirical threshold provides some useful information for the early warning of collapses or landslides in similar geological settings in the loess area. Full article

Deriving rainfall thresholds is one of the most convenient and effective empirical methods for formulating landslide warnings. The previous rainfall threshold models only considered the threshold values for areas with landslide data. This study focuses on obtaining a threshold for each single landslide via the geostatistical interpolation of historical landslide–rainfall data. We collect the occurrence times and locations of landslides, along with the hourly rainfall data, for Dazhou. We integrate the short-term and long-term rainfall data preceding the landslide occurrences, categorizing them into four groups for analysis: 1 h–7 days (H1–7), 12 h–7 days (H12–D7), 24 h–7 days (H24–D7), and 72 h–7 days (H72–D7). Then, we construct a rainfall threshold distribution map based on the 2014–2020 data by means of Kriging interpolation. This process involves applying different splitting coefficients to distinguish the landslides triggered by short-term versus long-term rainfall. Subsequently, we validate these thresholds and splitting coefficients using the dataset for 2021. The results show that the best splitting coefficients for H1–D7, H12–D7, H24–D7, and H72–D7 are around 0.19, 0.52, 0.55, and 0.80, respectively. The accuracy of the predictions increases with the duration of the short-term rainfall, from 48% for H1–D7 to 67% for H72–D7. The performance of these threshold models indicates their potential for practical application in the sustainable development of geo-hazard prevention. Finally, we discuss the reliability and applicability of this method by considering various factors, including the influence of the interpolation techniques, data quality, weather forecast, and human activities. Full article

This research examines the intricate changes in the number of occurrences and cumulative rainfall of exceptional events in Portugal spanning 42 hydrological years (from 1981/1982 to 2022/2023). The study has two primary objectives: assessing the hydrological spatial dynamics of a region susceptible to climate-induced variations in exceptional rainfall and evaluating the proficiency of a ERA5-Land reanalysis rainfall dataset in capturing exceptional rainfall. Confronting methodological and data-related challenges (e.g., incomplete record series), the investigation uses continuous daily ERA5-Land rainfall series. Validation against the Sistema Nacional de Informação de Recursos Hídricos (SNIRH) and the Portuguese Institute for Sea and Atmosphere (IPMA) ensures the reliability of ERA5-Land data. Empirical non-exceedance probability curves reveal a broad consensus between reanalysis data and observational records, establishing the dataset’s suitability for subsequent analysis. Spatial representations of occurrences, cumulative rainfall, and rainfall intensity of events above thresholds throughout the overall 42-year period and two subperiods (late: 1981/1982–2001/2002; and recent: 2002/2003–2022/2023) are presented, illustrating spatial and temporal variations. A noteworthy shift in the spatial distribution of intense events from south to north is observed, emphasising the dynamism of such hydrological processes. The study introduces a novel dimension with a severity heat map, combining some key findings from the occurrences and cumulative rainfall through subperiods. This study significantly contributes to the understanding of hydrological dynamics in Portugal, providing valuable insights for risk management and the development of sustainable strategies tailored to the evolving patterns of exceptional rainfall. Full article

This study builds upon deterministic evaluations of the extensive cumulative rainfall thresholds associated with shallow landslides in the Gaoping River Basin, with a specific focus on the necessary response times during typhoon and intense rainfall events. Following the significant impact of Typhoon Morakot on the Liugui area, our investigation enhances previous research by employing a downscaled approach. We aim to establish early warning models for village-level, intermediate-scale landslide cumulative rainfall thresholds and to create action thresholds for small-scale, key landslide-prone slopes. Our inquiry not only combines various analytical models but also validates their reliability through comprehensive case studies. Comparative analysis with the empirical values set by the Soil and Water Conservation Bureau (SWCB) and the National Center for Disaster Reduction (NCDR) provides a median response time of 6 h, confirming that our findings are consistent with the response time frameworks established by these institutions, thus validating their effectiveness for typhoon-related landslide alerts. The results not only highlight the reference value of applying downscaled cumulative rainfall thresholds at the village level but also emphasize the significance of the evaluated warning thresholds as viable benchmarks for early warnings in landslide disaster management during Taiwan’s flood and typhoon seasons. This research offers a refined methodological approach to landslide early warning systems and provides scientific support for decision making by local governments and disaster response teams. Full article

Flash floods are ferocious and destructive, making their forecasting and early warning difficult and easily causing casualties. In order to improve the accuracy of early warning, a dynamic early warning index system was established based on the distributed spatio-temporally mixed model through a case study of riverside villages in Hubei Province. Fully taking into account previous rainfall and assuming different rainfall conditions, this work developed a dynamic early warning threshold chart by determining critical rainfall thresholds at different soil moisture levels (dry, normal, wet, and saturated) through pilot calculations, to support a quick query of the critical rainfall at any soil moisture level. The research results show that of the 74 counties and districts in Hubei Province, more than 50% witnessed higher mean critical rainfall than empirical thresholds when the soil was saturated, and about 90% did so when the soil was dry. In 881 towns, a total of 456 early warnings were generated based on dynamic thresholds from 2020 to 2022, 15.2% more than those based on empirical thresholds. From the perspective of total rainfall, dynamic early warnings were generated more frequently in wet years, while empirical early warnings were more frequent in dry years, and the frequency of two warnings were roughly the same in normal years. There were more early warnings based on empirical thresholds in May each year, but more based on dynamic thresholds in June and July, and early warnings generated based on the two methods were almost equal in August and September. Spatially, after dynamic early warning thresholds were adopted, Shiyan and Xiangyang, both northwestern cities in Hubei Province, witnessed significant increases in early warnings. In terms of the early warning mechanism, dynamic early warning took into account the impact of soil moisture and analyzed the flood discharge capacity of river channels according to the flood stage of the riverside villages. On this basis, the rainfall early warning thresholds under different conditions were determined. This is a refined early warning method that could improve the accuracy of flash flood warnings in Hubei Province. Full article

Dry spells occurring during the rainy season have significant implications for agricultural productivity and socioeconomic development, particularly in rainfed agricultural countries such as Senegal. This study employs various chaos-theory-based tools, including the lacunarity method, rescaled analysis, and the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) method, to investigate the distribution, predictability, and multiscale properties of the annual series of maximum dry spell length (AMDSL) in Senegal during the rainy season. The analysis focuses on 29 stations across Senegal, spanning the period from 1951 to 2010. The findings reveal persistent behavior in the AMDSL across nearly all stations, indicating that predictive models based on extrapolating past time trends could enhance AMDSL forecasting. Furthermore, a well-defined spatial distribution of the lacunarity exponent β is observed, which exhibits a discernible relationship with rainfall patterns in Senegal. Notably, the lacunarity exponent displays a south-to-north gradient for all thresholds, suggesting its potential for distinguishing between different drought regimes and zones while aiding in the understanding of spatiotemporal rainfall variability patterns. Moreover, the analysis identifies five significant intrinsic mode functions (IMFs) characterized by different periods, including interannual, interdecadal, and multidecadal oscillations. These IMFs, along with a nonlinear trend, are identified as the driving forces behind AMDSL variations in Senegal. Among the inter-annual oscillations, a 3-year quasi-period emerges as the primary contributor and main component influencing AMDSL variability. Additionally, four distinct morphological types of nonlinear trends in AMDSL variations are identified, with increasing–decreasing and increasing trends being the most prevalent. These findings contribute to a better understanding of the variability in annual maximum dry spell lengths, particularly in the context of climate change, and provide valuable insights for improving AMDSL forecasting. Overall, this study enhances our comprehension of the complex dynamics underlying dry spell occurrences during the rainy season and presents potential avenues for predicting and managing the AMDSL in Senegal. Full article

Assimilating the proper amount of water vapor into a numerical weather prediction (NWP) model is essential in accurately forecasting a heavy rainfall. Radar data assimilation can effectively initialize the three-dimensional structure, intensity, and movement of precipitation fields to an NWP at a high resolution (±250 m). However, the in-cloud water vapor amount estimated from radar reflectivity is empirical and assumes that the air is saturated when the reflectivity exceeds a certain threshold. Previous studies show that this assumption tends to overpredict the rainfall intensity in the early hours of the prediction. The purpose of this study is to reduce the initial value error associated with the amount of water vapor in radar reflectivity by introducing advanced remote sensing data. The ongoing research shows that errors can be largely solved by assimilating satellite all-sky radiances and global positioning system radio occultation (GPSRO) refractivity to enhance the moisture analysis during the cycling period. The impacts of assimilating moisture variables from satellite all-sky radiances and GPSRO refractivity in addition to hydrometeor variables from radar reflectivity generate proper amounts of moisture and hydrometeors at all levels of the initial state. Additionally, the assimilation of satellite atmospheric motion vectors (AMVs) improves wind information and the atmospheric dynamics driving the moisture field which, in turn, increase the accuracy of the moisture convergence and fluxes at the core of the convection. As a result, the accuracy of the timing and intensity of a heavy rainfall prediction is improved, and the hourly and accumulated forecast errors are reduced. Full article

Empirical rainfall thresholds for predicting rainfall-triggered shallow landslides are proposed for Guangzhou city, which is prone to widespread geological hazards during the annual flood season due to the subtropical monsoon climate and frequent tropical storms and typhoons. In this study, the cumulated event rainfall (E, in mm), the duration of rainfall event (D, in day) (E–D) thresholds, normalized cumulated event rainfall, and the duration of rainfall event (EMAP–D) thresholds were defined. Thresholds based on five lithological units were obtained at 5%, 20%, and 50% probability levels using quantile regression methods. More than two-thirds of the landslides occurred within units of intrusive rock. The 20-day cumulative rainfall of 97 mm integrating cumulative event rainfall and the duration of rainfall events (CED) is introduced into the three-dimensional spatial threshold. The areas under the receiver operating characteristic curves for the CED threshold and E–D threshold were 0.944 and 0.914, respectively, and the true-positive rate of the CED threshold with the same probability level was slightly lower than that of the E–D threshold, but the CED threshold false-positive rate was much better than the E–D threshold, which can significantly reduce false alarm rate since many non-triggering rainfalls were filtered out. Full article