Tree destruction induced by heavy rainfall, an overlooked type of forest degradation, has been exacerbated along with global climate change. On the Chinese Loess Plateau, especially in afforested gully catchments dominated by Robinia pseudoacacia, destructive rainfall events have increasingly led to widespread forest damage. Previous study has manifested the severity of heavy rainfall-induced tree destruction and its association with topographic change, yet the contributions of tree structure and forest structure remain poorly understood. In this study, we quantified the destroyed trees induced by heavy rainfall using light detection and ranging (LiDAR) techniques. We assessed the influence of tree structure (tree height, crown diameter, and crown area), forest structure (tree density, gap fraction, leaf area index, and canopy cover), and terrain parameters (elevation, slope, and terrain relief) using machine learning models (random forest and logistic regression). Based on these, we aimed to clarify the respective and combined contributions of structural and topographic factors to rainfall-induced tree destruction. Key findings revealed that when considered in isolation, greater tree height, crown diameter, crown area, leaf area index (LAI), and canopy cover suppressed tree destruction, whereas higher gap fractions increased the probability of tree destruction. However, the synergistic increases of tree structural factors (tree height, crown diameter, and crown area) and forest structural factors (LAI and canopy cover) significantly promoted tree destruction, which can counteract the inhibitory effect of terrain on destruction. In addition, increases in tree structure or canopy density (LAI and canopy cover) also increased the probability of tree destruction at the same elevation. Our findings challenge conventional assumptions in forest management by demonstrating the interaction of tree structure and canopy density can significantly promote tree destruction during heavy rainfall. This highlights the need to avoid overly dense afforestation in vulnerable landscapes and supports more adaptive, climate-resilient restoration strategies.

The practice of widening levees to mitigate frequent river flooding is globally prevalent. This paper addresses the pressing issue of sand-filled widened levee failures under the combined effect of heavy rainfall and high riverine water levels, as commonly observed in practice. The primary objective is to illuminate the triggering mechanism and characteristics of such levee failures using the well-designed physical model experiment and Material Point Method (MPM), thus guiding practical implementations. Experimentally, the macro-instability of the levee, manifested as slope failure within the sand-filled widened section, is primarily triggered by changes in the stress regime near the levee toe and continuous creep deformation. Upon failure initiation, the levee slope experiences a progressive failure mode, starting with local sliding, followed by global sliding, and ultimately transitioning into a flow-like behaviour, which characterises the slide-to-flow failure pattern. The slope failure along the interface between the original and new levees is the result of shear deformation rather than the cause. Parametric studies conducted using the calibrated MPM model reveal a critical threshold for the widening width, beyond which the volume of sliding mass and travel angle exhibit no further variation. Increasing the cohesion of the river sand used for levee widening demonstrates the most pronounced improvement in levee stability in the face of the combined effect of intense rainfall and elevated river levels. The MPM-based evaluation of common slope protection measures demonstrates the superior protective benefits of grouting reinforcement and impervious armour layer protection, providing valuable insights for reinforcement strategies in levee engineering applications.

Loess slopes are susceptible to rainfall due to the water sensitivity and collapsibility of loess. The aim of this study is to investigate the instability mode, failure mechanism and control effect of homogeneous loess landslide under rainfall by using physical model experiments and numerical simulation, combined with a new anchor cable with negative Poisson ratio (NPR) structural effect. The findings indicated that the loess slope's failure under heavy rainfall is characterized by progressive shallow flow-slip instability, encompassing three deformation modes and seven deformation characteristics. Water content, pore water pressure and earth pressure monitoring instruments capture the dynamic response of internal hydromechanical properties within the loess slope during intermittent heavy rainfall, clarifying its failure mechanism. Rainfall leads to soil softening and a reduction in strength. The effective stress of shallow soil and potential sliding surfaces diminishes due to decreased matrix suction and increased pore water pressure. The accumulation of internal and external deformation eventually leads to the disintegration of the shallow layer of the loess slope. Numerical simulation results indicated that rainfall significantly affects the shallow layer of the loess slope, with greater subsidence deformation observed at the slope's crest. Indoor and field monitoring findings revealed the pattern of Newton force on the loess slope in response to rainfall and demonstrated its seasonal dynamics, characterized by an increase during the thaw-collapse and flood periods, followed by a decrease in the frost-heave period.

Triggered by continuous heavy rainfall, a catastrophic large-scale high-locality landslide occurred in Hengshanbei mountain slope of Shangxi Village, Longchuan County, Guangdong Province, China, on June 14, 2022, at 12:10 (UTC + 8). The landslide had an estimated volume of about 1.45 x 105 m3 and resulted in severe damage to the region. To investigate the causative mechanisms of this landslide, a comprehensive study was conducted, involving geological and hydrological surveys of the research area, combined with field investigations, satellite imagery, drone photography, data analysis of rainfall and landslide displacement monitoring, and laboratory experiments. The research focused on analyzing the process of landslide formation and development, trigger factors, destruction characteristics, and instability mechanisms. Additionally, the study employed the Mohr-Coulomb strength theory to explain stress variations during the landslide process. Findings indicated that: (1) the slope soil structure was loose with well-developed pores, mainly composed of kaolinite with strong water absorption properties, causing softening and disintegration of the soil when encountering water, resulting in reduced cohesion and internal friction angle, and overall poor soil properties; (2) continuous heavy rainfall infiltrated the slope through soil pores and eroded channels, increasing pore water pressure and reducing effective stress, subsequently reducing anti-sliding force and increasing sliding force; as well as (3) unfavorable terrain conditions, such as high landslide starting point and high-locality, significant height, and steep slope, lead to landslides running farther and being of larger scale. The study further highlighted that the intrinsic properties of the slope soil were the decisive internal cause of the landslide, while continuous heavy rainfall and adverse terrain were external triggering factors. These findings provide essential insights for understanding and preventing similar landslide disasters.

Heavy rainfall significantly impacts agriculture by damaging crops and causing substantial economic losses. The Paravanar River Basin, a coastal river basin in India, experiences heavy rainfall during the monsoon season. This study analyzed both ground-level rainfall measurements and farmers' experiences to understand the effects of heavy rainfall on agriculture. Rainfall data from nine rain gauge locations were analyzed across three cropping seasons: Kharif 1 (June to August), Kharif 2 (September to November), and Rabi (December to May). To determine the frequency of heavy rainfall events, a detailed analysis was conducted based on the standards set by the India Meteorological Department (IMD). Villages near stations showing increasing rainfall trends and a higher frequency of heavy rainfall events were classified as vulnerable. The primary crops cultivated in these vulnerable areas were identified through a questionnaire survey with local farmers. A detailed analysis of these crops was conducted to determine the cropping season most affected by heavy rainfall events. The impacts of heavy rainfall on the primary crops were assessed using the Delphi technique, a score-based crop risk assessment method. These impacts were categorized into eight distinct types. Among them, yield reduction, waterlogging, crop damage, soil erosion, and crop failure emerged as the most significant challenges in the study area. Additional impacts included nutrient loss, disrupted microbial activity, and disease outbreaks. Based on this evaluation, risks were classified into five categories: low risk, moderate risk, high risk, very high risk, and extreme risk. This categorization offers a framework for understanding potential consequences and making informed decisions. To address these challenges, the study recommended mitigation measures such as crop management, soil management, and drainage management. Farmers were also encouraged to conduct a cause-and-effect analysis. This bottom-up approach raised awareness among farmers and provided practical solutions to reduce crop losses and mitigate the effects of heavy rainfall.

Flash floods are one of the most dangerous hydrometeorological events in the world. The current study investigates flash floods on the northern Black Sea Coast. The data about stochastic and relatively stable factors of flash flood formation (such as hydrological, meteorological, lithological, geomorphological, and anthropogenic parameters) were collected for 22 events. The main trigger of flash floods is heavy rainfall of high intensity in the region but in some cases flash flood occurrence is connected with combinations of several non-critical factors. The small watershed area (<351 km(2)) of river basins experiencing flash floods promotes very rapid flow concentration. Analysis of extreme precipitation demonstrates significant increasing trends in river basins on the Crimean Peninsula and decreasing a maximum precipitation amount in 5 days (r5d) and 1 day (r1d) in river basins in the Caucasus Black Sea Coast in the 21st century as determined by processing of Integrated Multi-satellite Retrievals for Global precipitation measurement (IMEGR) satellite data. At the same time land network data indicates increasing r5d at the Anapa and r1d at the Tuapse meteorological stations in 1961-2020. More frequent occurrence of flash floods has been suggested in the area due to statistical analysis of the longest precipitation ranges. The main reason for significant social and economic damage is uncontrolled human activity in flooded areas on the northern Black Sea Coast. (c) 2024 International Research and Training Centre on Erosion and Sedimentation. Publishing services by Elsevier B.V. on behalf of KeAi Communications Co. Ltd. This is an open access article under the CC BY- NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Rainstorm events are becoming increasingly frequent due to the impacts of global warming, which results in widespread erosion disasters and related tree destruction. However, previous corresponding studies of forest damage have focused on typhoons or wildfires, ignoring the increasing risk of rainstorm erosion-induced tree destruction. It is unclear what scale of tree destruction can be caused by heavy rainfall. In this study, we used a tree segmentation method based on airborne light detection and ranging (LiDAR) technology to accurately quantify the tree destruction during heavy rainfall in a representative afforested catchment on the Chinese Loess Plateau. Additionally, topographic changes were calculated using pre- and post-heavy rainfall LiDAR datasets, and tree destruction was assessed by combining terrain information and tree structural parameters. The results showed that 3253 trees in the catchment (0.9 km2) were destroyed due to rainstorm erosion, among which 2845 trees were located on gully slope landform, accounting for 87.4 % of all destroyed trees. Tree destruction on steep gully slope (slope: 45.5 degrees-50.5 degrees) was mainly induced by rainstorm erosion, while that on both sides of the gully bed (altitude: 1137 m-1147 m) was mainly induced by sediment deposition. In the catchment, the deposition area that resulted in tree destruction (21265 m2) was greater than the erosion area (20020 m2). However, the damage caused by erosion was more destructive than that caused by deposition. There was a significant linear relationship between tree structural parameters and terrain in the forestland catchment. Our study provides a reference methodology for studies of forest damage due to extreme weather events worldwide, and has significant implications for ecosystem management and reforestation in the context of global change.

In the context of climate change, rainstorm events are becoming increasingly frequent. In particular, on the Loess Plateau, heavy rainstorms are the primary cause of soil erosion. This study investigated and analysed different types of soil erosion hotspots and influencing factors in small watersheds under different rainstorm events in different areas of the Loess Plateau. The results indicate that the erosion intensities of rills, gullies, landslides and collapses ranged from 13600-46244, 1982-772201, 1163-172153 t km-2 and 1867-94985 t km-2, respectively. Newly constructed terraces exhibited an erosion intensity 1.6 times greater than that of old terraces, while terraces constructed before the rainy season in the current year exhibited an erosion damage intensity 2.6 times greater than that of terraces constructed after the rainy season in the previous year. In addition, under rainstorm conditions, landslides represented the most severe type of erosion in the watersheds, with the maximum amount of erosion accounting for more than 90 % of the total erosion amount, followed by gully or collapse erosion, with the collapse of terrace risers as the main contributor. Slope cultivation land, unpaved roads, terrace risers, and valley slopes below the gully shoulder line were identified as erosion hotspot areas. Rainstorm erosion was significantly influenced by the land use type and slope, which explained 14.2 %-41.5 % and 9.7 %-15.1 %, respectively, of the total variance in erosion intensity. We suggest that soil erosion prevention and control efforts on the Loess Plateau should focus on landslides on valley slopes below gully shoulder lines, followed by gullies on unpaved roads and the collapse of terraced fields. Drainage ditches and water cellars should be constructed above the gully shoulder line and on the inside of roads and terraces, thereby reducing erosion. Our research is crucial for optimizing and adjusting watershed management measures and preventing rainstorm erosion disasters.

Understanding the changes of sediment concentration in rivers, especially under heavy rains, is of great significance to accurately evaluate the effectiveness of soil and water conservation measures in the Loess Hilly Area. On July 26, 2017, an extremely heavy rain (maximum rain intensity of 66.6 mm/h, the highest rainfall of 256.8 mm in 10 h, and a return period of 100 years) occurred over the 821 km2 Xiaoli River watershed in the Wuding River watershed. In order to understand the sediment concentration during the heavy rain event, 10 dam-controlled catchments in the center of the rainstorm (3 in the Xiaoli River watershed and 7 in its adjacent watersheds) without drainage and damage were selected. All the retained runoff and sediment in the check dams were measured by the cross-sectional survey according to the flood marks left by the heavy rain. Combined with the observed runoff and sediment transport data by the hydrological station at the outlet of the Xiaoli River watershed as well as the deposited sediment investigated in the watershed during the heavy rain event, the change of sediment concentration and the role of check dam were analyzed. Results showed that under heavy rain, all the sediment concentrations of runoff in the dam-controlled catchments ranged from 344 to 552 kg/m3, suggesting that the sediment retaining effect of slope measures (woodland, grassland, and terrace) is limited and cannot prevent the occurrence of hyperconcentrated flows. The gully measures (check dams) in the Xiaoli River watershed can hold a large amount of sediment under heavy rain and make the runoff no longer a hyperconcentrated flow at the watershed outlet. In order to control the transport of hyperconcentrated flows to the downstream, more attention should be paid to the construction of check dams while carrying out vegetation ecological construction in the Loess Hilly Area.

In 2022, the Pakistan witnessed the hottest spring and wettest summer in history. And devastating floods inundated a large portion of Pakistan and caused enormous damages. However, the primary water source and its contributions to these unprecedented floods remain unclear. Based on the reservoir inflow measurements, Multi-Source Weighted-Ensemble Precipitation (MSWEP), the fifth generation ECMWF atmospheric reanalysis (ERA5) products, this study quantified the contributions of monsoon precipitation, antecedent snowmelts, and orographic precipitation enhancement to floods in Pakistan. We found that the Indus experienced at least four inflow uprushes, which was mainly supplied by precipitation and snowmelt; In upper Indus, abnormally high temperature continued to influence the whole summer and lead to large amounts of snowmelts which not only was a key water supply to the flood but also provided favorable soil moisture conditions for the latter precipitation. Before July, the snowmelt has higher contributions than the precipitation to the streamflow of Indus River, with contribution value of more than 60%. Moreover, the snowmelt could still supply 20%-40% water to the lower Indus in July and August; The leading driver of 2022 mega-floods over the southern Pakistan in July and August was dominated by the precipitation, where terrain disturbance induced precipitation account to approximately 33% over the southern Pakistan. The results help to understand the mechanisms of flood formation, and to better predict future flood risks over complex terrain regions.