Heavy Rainfall Events Research Articles

Associated impact mechanism of heavy rain and floods in the middle and lower reaches of the Yangtze river basin based on ocean-atmosphere anomaly patterns

Heavy rainfall and flooding disasters are increasing due to global warming. A clear understanding of the mechanism of heavy rain and floods is the basic premise of disaster risk management. However, most previous studies emphasized more on the single anomalous signal from the average state in the whole season, which may neglect the combined influence of multiple signals in the ocean-atmosphere and differential characteristics of anomalous signals at different periods. Here, our study aimed to reveal the possible influence mechanism of heavy rain and floods in the middle and lower reaches of the Yangtze River Basin (MLRYRB) by systematically analyzing the monthly-scale and daily-scale ocean-atmosphere anomaly patterns in the preceding periods of heavy rainfall and flooding events. The results showed that heavy rainfall and flooding events were highly likely to occur in the region one month after El Niño decayed, with the flooding intensity in June having the negative correlation with the sea ice concentration anomaly in the Arctic with a lag of about 5 months (150 days). Besides, North Atlantic Oscillation, Western Pacific subtropical high, blocking, East Asian subtropical westerly jet, and the water vapor fluxes from the Arabian Sea and western Pacific Ocean could be used as the anomalous signals inducing heavy rain and floods. The daily-scale conceptual model inducing heavy rainfall and flooding events was built based on the patterns of all anomalous signals, which detailed the possible impact mechanism of heavy rain and floods in the region. By making targeted forecasts of anomalous signals and using this information in water resources planning and management based on climate mechanisms, it will have a significant impact on water management in the country.

A Sponge Village Flood Response Method Based on GIS and RS Analysis Formation—A Case Study of Jiangou Village

This study was conducted in response to the Beijing–Tianjin–Hebei mega heavy rainfall event at the end of July 2023, and the severely affected and representative Jiangou village in Beijing was selected as the study area. A variety of methods were used to synthesize and analyze the situation and propose an adaptive response to heavy rainfall and flooding in the village. Based on multi-source remote sensing (RS) data, a comprehensive topographic and hydrological characterization was carried out, and the precipitation before and after the disaster was analyzed; the flood inundation area was extracted using the improved normalized water body index (MNDWI) and OTSU thresholding methods, and the changes of water bodies during the flooding period were quantitatively analyzed; and an improved convolutional-neural-network-based building identification and extraction model was constructed to extract the research distribution of buildings in the area. The sponge city construction (SPCC) method was improved to obtain a method that can mitigate flood risk and adapt to villages by constructing small artificial lakes and local topographic buffers to improve the water storage and drainage capacity of villages. The study shows that these methods are innovative in flood hazard analysis and mitigation but still need further improvement in data accuracy, simulation depth, and system evaluation.

Cumulative frost heave and hydrothermal process of the high-speed railway subgrade under extreme climate conditions in northwest China

The mechanisms underlying the long-term deformation response and hydrothermal processes of high-speed railway subgrades in high altitude, seasonally frozen regions remain poorly understood, especially under extreme weather conditions. This study, based on an eight-year continuous field monitoring of the Lanzhou-Xinjiang High-Speed Railway (LXHR), presents several novel findings. From 2015 to 2022, air temperature, ground surface temperature, and subgrade surface temperature consistently increased, while the average annual rainfall showed a decreasing trend. During this period, sporadic heavy rainfall events occurred, and the climate demonstrated anomalous fluctuations. Such occasional heavy rainfall can cause a rise in the groundwater table, leading to an increase in frost heave within the subgrade. Given the same climatic conditions, embankments display a greater frozen depth compared to road cuts, but experience relatively less frost heave. Frost heave deformation within the subgrade comprises two components: residual deformation and inherent frost heave deformation. Residual deformation primarily contributes to the annual cumulative increase in frost heave, a process succinctly described as the transportation and deposition of fine particles (less than 2 mm in diameter) in coarse fillers by water, in close proximity to the frozen edge, ranging from 0.8 m to 1.2 m of the subgrade. In summary, the results of this study serve as a reminder for engineering designers and researchers to give more attention influence of residual deformation and the current climate change, in consideration of the stability and long-term service performance of subgrade under extreme weather conditions in the future.

Enhancing interpretability of tree-based models for downstream salinity prediction: Decomposing feature importance using the Shapley additive explanation approach

To improve the interpretability of estimation processes in machine learning, we applied the Shapley additive explanation method to six tree-based models for predicting downstream salinity. Decision tree-based adaptive boosting (AdaBoost) demonstrated the highest performance, followed by extreme gradient boosting (XGBoost). Global post-decomposition in XGBoost identified water level, a major cause of salinity intrusion in actual environments, as the main contributor of salinity intrusion, while an interaction effect between water temperature and sea level pressure was identified as the main contributor in AdaBoost. Local post-decomposition in XGBoost enables visual interpretation of most of the salinity behaviors. However, the interaction effect between water level and sea level pressure identified as the main contributor by XGBoost was unlikely to drive the actual increase in salinity immediately after a heavy rainfall event. We conclude that highly accurate estimations are not necessarily interpretable and reliable and local post-decomposition can detect inconsistencies in estimation processes.

Limited sensitivity of permafrost soils to heavy rainfall across Svalbard ecosystems

Together with warming air temperatures, Arctic ecosystems are expected to experience increases in heavy rainfall events. Recent studies report accelerated degradation of permafrost under heavy rainfall, which could put significant amounts of soil carbon and infrastructure at risk. However, controlled experimental evidence of rainfall effects on permafrost thaw is scarce. We experimentally tested the impact and legacy effect of heavy rainfall events in early and late summer for five sites varying in topography and soil type on the High Arctic archipelago of Svalbard. We found that effects of heavy rainfall on soil thermal regimes are small and limited to one season. Thaw rates increased under heavy rainfall in a loess terrace site, but not in polygonal tundra soils with higher organic matter content and water tables. End-of-season active layer thickness was not affected. Rainfall application did not affect soil temperature trends, which appeared driven by timing of snowmelt and organic layer thickness, particularly during early summer. Late summer rainfall was associated with slower freeze-up and colder soil temperatures the following winter. This implies that rainfall impacts on Svalbard permafrost are limited, locally variable and of short duration. Our findings diverge from earlier reports of sustained increases in permafrost thaw following extreme rainfall, but are consistent with observations that maritime permafrost regions such as Svalbard show lower rainfall sensitivity than continental regions. Based on our experiment, no substantial in-situ effects of heavy rainfall are anticipated for thawing of permafrost on Svalbard under future warming. However, further work is needed to quantify permafrost response to local redistribution of active layer flow under natural rainfall extremes. In addition, replication of experiments across variable Arctic regions as well as long-term monitoring of active layers, soil moisture and local climate will be essential to develop a panarctic perspective on rainfall sensitivity of permafrost.

Heavy Rainfall Events in Selected Geographic Regions of Mexico, Associated with Hail Cannons

In this article, we document the use of hail cannons in Mexico to dispel or suppress heavy rain episodes, a common practice among farmers, without scientific evidence to support its effectiveness. This study uses two rain databases: one compiled from the Global Precipitation Measurement (GPM) mission and the other generated with the implementation of the Weather Research and Forecasting (WRF) model. The aim is to explore the association between heavy rain episodes and hail cannon locations. The analysis includes two geographic features: a pair of coordinates and a 3 km radius area of influence around each hail cannon. This dimension is based on the size and distribution of the heavy rainfall events. This study analyzes four years of half-hourly rain data using the Python ecosystem environment with machine learning libraries. The results show no relationship between the operation of hail cannons and the dissipation or attenuation of heavy rainfall events. However, this study highlights that the significant differences between the GPM and WRF databases in registering heavy rain events may be attributable to their own uncertainty. Despite the unavailability of ground-based observations, the inefficiency of hail cannons in affecting the occurrence of heavy rain events is evident. Overall, this study provides scientific evidence that hail cannons are inefficient in preventing the occurrence of heavy rain episodes.

Geostationary Satellite-Based Overshooting Top Detections and Their Relationship to Severe Weather over Eastern China

Overshooting tops (OTs), prominent signatures within deep convective storms, are produced by intense updrafts and are closely linked to heavy rainfall, strong winds, and other severe weather conditions. Using an OT dataset derived from multiyear observations of precipitation radar on board the Global Precipitation Measurement core observatory as a reference, the performances of two commonly used OT detection algorithms are evaluated for the Himawari-8 and Fengyun-4A satellites. The results indicate that the infrared contour-based algorithm based on Himawari-8 is the most effective for objective OT detection in eastern China. It exhibits a probability of detection (POD) of 62.1% and a false-alarm ratio (FAR) of 36.6%, outperforming others by achieving a greater POD and a lower FAR. Furthermore, based on the severe weather records from surface meteorological stations and nearby OT detections, a strong relationship is revealed between GEO-detected OTs and the occurrence of short-term heavy rainfall (e.g., ≥20 mm h−1) and extreme wind speed (e.g., ≥17.2 m s−1) events. The OT matched percentages for these events are 61.8% and 54.0%, respectively. This suggests that GEO satellite-based OT data can serve as an important objective product for forecasters to increase their understanding of severe convective storms.

Impact of High-Resolution Land Cover on Simulation of a Warm-Sector Torrential Rainfall Event in Guangzhou

This study on the warm-sector heavy rainfall event in Guangzhou on 7 May 2017, examined the effects and mechanisms of incorporating 30 m high-resolution land surface data into its numerical simulation. The updated 1km numerical model, integrating 30 m high-resolution land surface data, successfully captured the initiation, back-building, and organized development of warm-sector convections in Huadu and Zengcheng District. The analysis revealed that the high spatial resolution of the surface data led to a reduced urban area footprint (urban −6.31%), increased vegetation cover (forest 11.63%, croplands 1%), and enhanced surface runoff (water 2.77%) compared with a model’s default land cover (900 m). These changes mitigated the urban heat island (UHI) effect within the metropolitan area and decreased the surface sensible heat flux. This reduction contributed to a pronounced temperature gradient between Huadu Mountain and the urban area. Additionally, a stronger high-pressure recirculation and sea–land breezes facilitated the transport of warm and moist air from the sea inland, creating a humid corridor along the sea–land interface. The consistent influx of warm and moist air near the mountain front, where strong temperature gradients were present, forcibly triggered warm-sector convection, intensifying its organization. This study highlighted the critical role of high-resolution land surface data in the accurate numerical simulation of warm-sector heavy rainfall.

The added value of Himawari-8 satellite radiance data assimilation for very heavy rainfall prediction in the Indonesian Maritime Continent (Case study: East Kalimantan)

Abstract This study evaluates the impact of assimilating Advanced Himawari Imager (AHI) radiance data on the 8-10 water vapor channel using the three-dimensional variational (3DVAR) technique for the very heavy rainfall events in East Kalimantan on June 2-4, 2019. We utilized the Weather Research and Forecasting (WRF) and WRF Data Assimilation (WRFDA) system to run assimilation and prediction model simulations. To compare the assimilated model with AHI data and without assimilation, we devised two experimental schemes: 3DVAR and NODA. We assessed the enhancement in model prediction by comparing simulation results based on convection evolution, vertically integrated moisture flux convergence (VIMFC) increment, and rain prediction skill metrics, including Threat Score (TS), Equitable Threat Score (ETS), and Fractions Skill Score (FSS). Our findings suggest that the incorporation of AHI data results in a substantial improvement of the WRF model’s prediction of the very heavy rainfall events in East Kalimantan.

Comparison of 1D, coupled 1D–2D, and 2D shallow water numerical modeling for dam-break flow analysis of Way-Ela dam, Indonesia

This study analyzes the flood inundation area using shallow water numerical modeling with HEC-RAS 6.3 software by comparing 1D, coupled 1D–2D, and 2D approaches. As a case study, the 2013 Way-Ela dam-break event in Indonesia is selected. Way-Ela Dam naturally formed by landslides in 2012, collapsed due to a piping mechanism after a heavy rainfall event. To estimate the breach outflow hydrograph, an empirical parametric model based on regression formula is used. Compared with the observed data, the numerical results show the 2D model produces the most accurate results among others, with reasonable computational time, while the 1D model, despite being computationally very efficient, misinterprets the flood extent map. The coupled 1D–2D model produces results similar to that of the 2D model; however, this coupled approach, which is expected to be more computationally efficient than the 2D one, interestingly yields a significantly longer calculation time. Some possible reasons are thus discussed. Additionally, comparisons for the water level and velocity are also presented in several locations to point out the difference between each model. Our finding informs the selection of an appropriate hydrodynamic model for dam-break simulations, balancing the result accuracy and computational cost.

Study on the Characteristics of Low-level Jets in Short-term Heavy Rainfall under the Influence of Northeast Cold Vortex in Shenyang Based on Wind Profile Radar

To enhance the understanding of the mechanisms behind heavy rainstorms influenced by the northeast cold vortex in Shenyang, we analyzed the intensity, height, and index of low-level jets during short-term heavy rainfall events from May to September between 2017 and 2021. The findings are as follows: (1) The maximum wind speed below 2 km generally follows a normal distribution, with low-level jets already present prior to approximately 30% of the heavy rainfall events. The maximum wind speed at corresponding percentiles shows a slight increase as the rainstorm approaches. However, the frequency of low-level jets decreases, and wind speeds significantly drop during the rainstorm. (2) The height distribution of low-level jets increases with altitude, reaching its lowest point 2 to 3 hours before the onset of heavy rainfall. At this time, low-level jets exhibit their strongest and most vigorous development. Combined with the warm, moist air from the southwest providing moisture and thermal energy, these conditions can trigger the onset of heavy rainstorms. (3) The hourly distribution of the low-level jet index (I) remains relatively consistent leading up to a rainstorm. As the rainstorm nears, wind speeds decrease, and the minimum height of low-level jets rises, resulting in a decrease in the low-level jet index (I).

Correction to: Patterns and drivers of heavy and extreme hourly rainfall events over Metro Manila, Philippines

Correction to: Patterns and drivers of heavy and extreme hourly rainfall events over Metro Manila, Philippines

Two stages of subsidence and its formation mechanisms in Mid-Late Triassic Ordos Basin, NW China

Based on a large number of newly added deep well data in recent years, the subsidence of the Ordos Basin in the Mid-Late Triassic is systematically studied, and it is proposed that the Ordos Basin experienced two important subsidence events during this depositional period. Through contrastive analysis of the two stages of tectonic subsidence, including stratigraphic characteristics, lithology combination, location of catchment area and sedimentary evolution, it is proposed that both of them are responses to the Indosinian Qinling tectonic activity on the edge of the craton basin. The early subsidence occurred in the Chang 10 Member was featured by high amplitude, large debris supply and fast deposition rate, with coarse debris filling and rapid subsidence accompanied by rapid accumulation, resulting in strata thickness increasing from northeast to southwest in wedge-shape. The subsidence center was located in Huanxian–Zhenyuan–Qingyang–Zhengning areas of southwestern basin with the strata thickness of 800–1 300 m. The subsidence center deviating from the depocenter developed multiple catchment areas, until then, unified lake basin has not been formed yet. Under the combined action of subsidence and Carnian heavy rainfall event during the deposition period of Chang 7 Member, a large deep-water depression was formed with slow deposition rate, and the subsidence center coincided with the depocenter basically in the Mahuangshan–Huachi–Huangling areas. The deep-water sediments were 120–320 m thick in the subsidence center, characterized by fine grain. There are differences in the mechanism between the two stages of subsidence. The early one was the response to the northward subduction of the MianLüe Ocean and intense depression under compression in Qinling during Mid-Triassic. The later subsidence is controlled by the weak extensional tectonic environment of the post-collision stage during Late Triassic.

Heavy rainfall event in Nova Friburgo (Brazil): numerical sensitivity analysis using different parameterization combinations in the WRF model

Heavy rainfall event in Nova Friburgo (Brazil): numerical sensitivity analysis using different parameterization combinations in the WRF model

Distribution Characteristics of the Short-term Heavy Rainfall of an MCC in Shandong Province

This study employs a comprehensive approach, utilizing conventional meteorological observation data, Fengyun satellite cloud imagery, and ERA5 reanalysis data to examine a short-term heavy rainfall event and its underlying causes in Shandong Province from August 14 to 16, 2020. The findings reveal that: (1) This event represents a typical Mesoscale Convective Complex (MCC)-induced rainfall process, with the primary precipitation zone in central and southern Shandong. The most intense hourly rainfall is observed along the side of the MCC with the steepest Tropical Brightness Temperature Blackbody (TBB) gradient. The heaviest hourly rainfall happens right around the time of the lowest TBB interval. (2) The rainfall event is marked by a vertical atmospheric structure with a high-altitude westerly jet and a low-level southwest jet, which makes it easy for water vapour to move and shear lines to form. Favourable conditions for water vapour convergence and high specific humidity are noted. During episodes of heavy precipitation, there is a marked upward motion in the lower troposphere, aligning with the vertical pattern of low-level convergence and high-level divergence.

Urban stormwater green infrastructure: Evaluating the public health service role of bioretention using microbial source tracking and bacterial community analyses

Bioretention cells (BRCs) control stormwater flow on-site during precipitation, reducing runoff and improving water quality through chemical, physical, and biological processes. While BRCs are effective in these aspects, they provide habitats for wildlife and may face microbial hazards from fecal shedding, posing a potential threat to human health and the nearby environment. However, limited knowledge exists regarding the ability to control microbial hazards (e.g., beyond using typical indicator bacteria) through stormwater biofiltration. Therefore, the purpose of this study is to characterize changes in the bacterial community of urban stormwater undergoing bioretention treatment, with the goal of assessing the public health implications of these green infrastructure solutions. Samples from BRC inflow and outflow in Columbus, Ohio, were collected post-heavy storms from October 2021 to March 2022. Conventional culture-based E. coli monitoring and microbial source tracking (MST) were conducted to identify major fecal contamination extent and its sources (i.e., human, canine, avian, and ruminant). Droplet digital polymerase chain reaction (ddPCR) was utilized to quantify the level of host-associated fecal contamination in addition to three antibiotic resistant genes (ARGs): tetracycline resistance gene (tetQ), sulfonamide resistance gene (sul1), and Klebsiella pneumoniae carbapenemase resistance gene (blaKPC). Subsequently, 16S rRNA gene sequencing was conducted to characterize bacterial community differences between stormwater BRC inflow and outflow. Untreated urban stormwater reflects anthropogenic contamination, suggesting it as a potential source of contamination to waterbodies and urban environments. When comparing inlet and outlet BRC samples, urban stormwater treated via biofiltration did not increase microbial hazards, and changes in bacterial taxa and alpha diversity were negligible. Beta diversity results reveal a significant shift in bacterial community structure, while simultaneously enhancing the water quality (i.e., reduction of metals, total suspended solids, total nitrogen) of urban stormwater. Significant correlations were found between the bacterial community diversity of urban stormwater with fecal contamination (e.g. dog) and ARG (sul1), rainfall intensity, and water quality (hardness, total phosphorous). The study concludes that bioretention technology can sustainably maintain urban microbial water quality without posing additional public health risks, making it a viable green infrastructure solution for heavy rainfall events exacerbated by climate change.

Quantifying annual microplastic emissions of an urban catchment: Surface runoff vs wastewater sources

Urban clusters are recognized as hotspots of microplastic pollution, and the associated urban rivers convey microplastics into the global oceans. Despite this knowledge, the relative contributions of various sources to the annual microplastic emissions from urban catchments remain scarcely quantified. Here, we quantified microplastic emissions from a riverine urban catchment in Japan. The total microplastics (size range: 10–5000 μm) released from the catchment amounted to 269.1 tons/annum, of which 78.1% is contributed by surface runoff and other uncontrolled emissions (UCE), and 21.1% emerges from the regulated wastewater (controlled emissions; CE), implying that approximately one-fifth is intercepted and removed by the wastewater treatment plants (WWTPs). This further indicated higher microplastic pollution by unmanaged surface runoff compared to untreated wastewater. In the dry season, WWTPs contributed significantly to the reduction of total microplastic emissions (95%) compared to wet periods (8%). On an annual scale, the treated effluent occupies only 0.1% of the total microplastics released to the river network (212.4 tons/annum), while the remaining portion is dominated by UCE, i.e., primarily surface runoff emissions (98.9%), and trivially by the background microplastic inputs that are potentially derived through atmospheric depositions in dry days (1.0%). It was shown that moderate and heavy rainfall events which occur during 18% of the year (within the context of Japan), leading to 95% of the annual microplastic emissions, are crucial for pollution control of urban rivers. Furthermore, our study demonstrated that surface area-normalized microplastic emissions from an urban catchment (∼0.8 tons/km2/annum) is globally relevant, especially for planning microplastic interventions for developed cities.

Assessment of the WRF model in reproducing a flash-flood heavy rainfall event over Kosovo

Assessment of the WRF model in reproducing a flash-flood heavy rainfall event over Kosovo

Integrating portable X-ray fluorescence site survey and ArcGIS models for rapid risk assessment and mitigation strategies at an abandoned arsenic mine site: a case study

ABSTRACT Australia’s metalliferous abandoned mine sites (MAMSs), pose tangible threats to the environment and human health. To address these concerns, our study utilised state-of-the-art handheld XRF technology to conduct a real-time assessment of the Mole River arsenic mine site. The data revealed notably elevated levels of arsenic and manganese, with the southeast corner of the site identified as a contaminant hotspot. We used a tiered risk assessment approach to compare the detected contaminant concentrations to the Australian health investigation levels (tier 1). This led us to a broader examination of erosion vulnerabilities and the potential migration of contaminants (tier 2). Further, a hydrological assessment (tier 3) identified significant erosion in southern regions, indicating the potential for contaminants to be transported off-site through surface water runoff to Sam’s Creek and Mole River. The proximity of a reservoir to these runoff pathways brought forth additional challenges, especially during heavy rainfall events. Subsequent laboratory analysis of water samples reinforced our findings, as they confirmed heightened arsenic concentrations in Mole River downstream, accentuating the potential risks to ecosystems and human health. By integrating the XRF contour map and erosion assessment with the RUSLE model, valuable insights are gained into critical hotspots with high contamination and erosion potential. By directing rehabilitation efforts towards critical hotspots, resources can be allocated more efficiently and cost-effectively.

Impacts of Fengyun-4A and Ground-Based Observation Data Assimilation on the Forecast of Kaifeng’s Heavy Rainfall (2022) and Mechanism Analysis of the Event

The advancement of Numerical Weather Prediction (NWP) is pivotal for enhancing high-impact weather forecasting and warning systems. However, due to the high spatial and temporal inhomogeneity, the moisture field is difficult to describe by initial conditions in NWP models, which is the essential thermodynamic variable in the simulation of various physical processes. Data Assimilation techniques are central to addressing these challenges, integrating observational data with background fields to refine initial conditions and improve forecasting accuracy. This study evaluates the effectiveness of integrating observations from the Fengyun-4A (FY-4A) Advanced Geosynchronous Radiation Imager (AGRI) and ground-based microwave radiometer (MWR) in forecasts and mechanism analysis of a heavy rainfall event in the Kaifeng region of central China. Our findings reveal that jointly assimilating AGRI radiance and MWR data significantly enhances the model’s humidity profile accuracy across all atmospheric layers, resulting in improved heavy rainfall predictions. Analysis of the moisture sources indicates that the storm’s water vapor predominantly originates from westward air movement ahead of a high-altitude trough, with sustained channeling towards the rainfall zone, ensuring a continuous supply of moisture. The storm’s development is further facilitated by a series of atmospheric processes, including the interplay of high and low-level vorticity and divergence, vertical updrafts, the formation of a low-level jet, and the generation of unstable atmospheric energy. Additionally, this study examines the influence of Tai-hang Mountain’s terrain on precipitation patterns in the Kaifeng area. Our experiments, comparing a control setup (CTL) with varied terrain heights, demonstrate that reducing terrain height by 50–60% significantly decreases precipitation coverage and intensity. In contrast, increasing terrain height enhances precipitation, although this effect plateaus when the elevation increase exceeds 100%, closely mirroring the precipitation changes observed with a 75% terrain height increment.