Heavy Rainfall Research Articles

Fire and smoke transport dynamics in a dead-end tunnel under heavy rainfall

Fire is one of the most common major accidents during tunnel construction and operation. This work conducted scale-model experiments to investigate the pool burning progress and flame characteristics in a dead-end tunnel with one end closed and the other open under heavy rainfall. The results show that, compared to opening both ends, the global burning time of fires is significantly decreased, and the quasi-stable burning phase is advanced under the combined effects of the closed end and heavy rainfall. The reason is that the increase in ceiling temperature, resulting from the accumulation of hot smoke, leads to an increase in radiative feedback of the fuel from the environment and an acceleration in the pool's burning rate. A coefficient (denoted by γ) is defined to evaluate the increase in the burning rate resulting from the dead-end. The enhancement ratio is found to be related to rainfall intensity, raindrop size, and pool size, and a correlation has been established based on the experimental results. The fire has an increased flame height and a reduced tilt angle under the impact of the dead-end. The changes in flame geometry parameters are mainly influenced by thermal buoyancy and the restriction of the dead-end. Prediction formulas for flame height and tilt angle under the combined effects of the dead-end and heavy rainfall have been proposed based on theoretical analysis. Under these combined effects, the tunnel space becomes eroded by dirty air.

Flood frequency analysis in the lower Burhi Dehing River in Assam, India using Gumbel Extreme Value and log Pearson Type III methods

Flood is a widespread climate-related hazard with the potential to occur in almost any geographical location where fluvial processes are active and can occur due to the involvement of multiple factors. Flood frequency analysis (FFA) is considered a valuable hydrologic tool to know the magnitude and frequency of the recurrence of floods. In this research, we have examined the Lower Burhi Dehing River (LBDR) in Assam, India, which frequently experiences severe flooding due to its meandering course induced by heavy monsoon rainfall. Therefore, the present research aims to analyze past flood events and current trends and predict future flood frequencies using Gumbel’s extreme value distribution and Log Pearson Type III Method. In this particular investigation, 25 years of annual maximum peak discharge data from 1972 to 1997 was employed to examine the discharge records of LBDR. This research estimated the return periods of different flood magnitudes, quantifying the likelihood of future floods with varying degrees of severity. In this regard, the return periods are estimated at 2, 5, 10, 20, 50, 100, and 200 years. The study reveals that the largest flood event occurred in 1972, with a discharge of 1134.4 m3/s, and the smallest flood event occurred in 1997, with a discharge of 214.65 m3/s. The 2-year flood is a relatively common event with a 50% probability of occurring in any given year, characterized by a moderate discharge of 1384.90 m3/s and minor impacts of 0.3665. The 5-year flood, occurring with a 20% chance annually, brings a significantly higher water level of 2008.81 m3/s and moderate-to-severe impacts of 1.4999, potentially causing widespread flooding. In the 10-year and 50-year return period, which has a probability of returning 10% and 2% chance annually, the discharge will reach 2421.89 m3/s and 3331.01 m3/s, respectively. The outcome of this research will sustainably guide policymakers and environmental planners to mitigate flood hazards.

Influence of mixed Rossby-Gravity waves on the North-East monsoon rainfall over south India

The northeast (NE) monsoon contributes considerable annual rainfall over southern peninsular India. Here, an attempt has been made to evaluate the role of convectively coupled mixed Rossby-Gravity (MRG) waves on the variabilities of NE monsoon rainfall. The gridded rainfall data from 1980 to 2021 from the India Meteorological Department shows large interannual variability in NE monsoon rainfall over southern peninsular India. Wavelet analysis of outgoing longwave radiation (OLR) data shows the dominance of short-period waves over the equatorial Indian Ocean during the years with above-normal NE monsoon rainfall compared to below-normal rainfall years. The space–time spectral analysis further confirms the dominance of short-period westward propagating convectively coupled equatorial waves (CCEW) during above-normal NE monsoon rainfall years. In contrast, the eastward component of CCEWs dominates during the below-normal rainfall years. The empirical orthogonal function (EOF) analysis of MRG waves filtered OLR data shows that the first two leading modes explain more than 14 % of variability over the Indian Ocean domain. Moreover, the vertically integrated moisture flux convergence indicates the role of MRG wave in triggering the extreme rainfall over southern peninsular India and also offers favorable conditions for the formation of cyclogenesis and, thereby, inducing heavy rainfall over the South Indian domain. Thus, the results suggest that the westward propagating MRG waves play a seminal role in modulating the NE monsoon rainfall over southern peninsular India.

Laboratory Model Tests on the Deformation and Failure of Terraced Loess Slopes Induced by Extreme Rainfall

Heavy rainfall is the main factor inducing the failure of loess slopes. However, the failure mechanism and mode of terraced loess slopes under heavy rainfall have not been well investigated and understood. This paper presents the experimental study on the deformation and failure of terraced loess slopes with different gradients under extreme rainfall conditions. The deformation and failure processes of the slope and the migration of the wetting front within the slope during rainfall were captured by the digital cameras installed on the top and side of the test box. In addition, the mechanical and hydrological responses of the slope, including earth pressure, water content, pore water pressure, and matric suction, were monitored and analyzed under rainfall infiltration and erosion. The experimental study shows that the deformation and failure of terraced loess slopes under heavy rainfall conditions exhibit the characteristic of progressive erosion damage. In general, the steeper the slope, the more severe the deformation and failure, and the shorter the time required for erosion failure. The data obtained from sensors embedded in the slope can reflect the mechanical and hydraulic characteristics of the slope in response to rainfall. The earth pressure and pore water pressure in the slope exhibit a fluctuating pattern with continued rainfall. The failure mode of terraced loess slopes under extreme rainfall can be summarized into five stages: erosion of slope surface and formation of small gullies and cracks, expansion of gullies and cracks along the slope surface, widening and deepening of gullies, local collapse and flow-slip of the slope, and large-scale collapse of the slope. The findings can provide preliminary data references for researchers to better understand the failure characteristics of terraced loess slopes under extreme rainfall and to further validate the results of numerical simulations and analytical solutions.

Comparative evaluation of multiscaler drought indices under different climatic conditions in Gujarat state of India.

The Standardized Precipitation Index (SPI) and the Standardized Precipitation Evapotranspiration Index (SPEI), two drought indices, have been compared for interchangeability and reliability under various climatic conditions in Gujarat, India. As the quality of the input is crucial for the accuracy of the index, weather records from surface observatories are preferable over grid and reanalysis data. This study found that on a short timescale (1-3months), SPEI diagnosed mild and moderate droughts more frequently than SPI, particularly in stations with relatively heavy rainfall. The indices in all timescales at all sites throughout the monsoon months displayed strong positive associations (r > 0.8). The correlation decreases but remains positive as the temporal scale is extended up to 8months. On a 9-months or longer scales that encompassed active monsoon rainfall months at all stations, correlation coefficients were between 0.8 and 0.9 for all months of the year. During monsoon months, high fractional matches were observed on a short scale. The months after the monsoon show a generalized diagonal pattern of high fractional match with the timescale for all stations. The kappa statistic followed a broad pattern comparable to the match fractions. The instances with poor agreements (R, Match and kappa < 0.3) had proportional bias between the indices. SPEI recognized more drought events at all stations in the short time periods, while the agreements increased with longer time scales. However, SPI detects high intensities in the subhumid.

Rainfall characteristics over the Congo Air Boundary Region in southern Africa: A comparison of station and gridded rainfall products

Strong meridional rainfall gradients exist between the tropics and subtropics in southwestern Africa, bounded to the north by the moist Congo basin and to the south by the Kalahari Desert. This domain received relatively little scientific attention compared to the rest of southern Africa. In this study, the limited available station data are assessed against six gridded rainfall products (CHIRPS, PERSIANN-CDR, ERA5, GPCC and CPC) for various rainfall characteristics. The nearest neighbour approach was used to match the closest rainfall dataset pixel to each station location, with the assumption that each rain gauge represents observation of various pixels of products, irrespective of product resolution. Results reveal that ERA5, CHIRPS and PERSIANN-CDR tend to represent the monthly rainfall totals and number of rainy days well for most stations although magnitudes and monthly peaks differ. CPC and GPCC tend to perform poorly for magnitudes of rainfall, rainy days and monthly cycles especially for Angolan stations. These products also fail to adequately capture spatial distributions of rainfall, with poor representation of the strong gradients found in the region.Correlations between various gridded rainfall products mostly show good agreement in rainfall totals and rainy days. For early summer (October–November-December) moderate rainy days, ERA5 and CHIRPS products tend to have more days than the stations while CPC and GPCC products perform poorly over Angola and in the south. ERA5 generally overestimates rainfall in mountainous regions, while other products tend to underestimate it. Based on the Simple Daily Intensity Index, it was found that for most of the gridded rainfall products tend to overestimate rainfall during rainy days in the northern and wetter part of the domain. Furthermore, for heavy rainfall, CPC and GPCC tend to compare fairly well with stations in the southern and eastern parts of the domain but poorly with those in the western parts. PERSIANN-CDR tends to underestimate heavy rainy days for most stations in early and late summer (January–February-March-April). However, CHIRPS compares well at several stations, while ERA5 performs well for stations located in the south. This study helps provide useful guidance in choosing suitable rainfall gridded datasets for assessing long term rainfall cycles, daily rainfall characteristics as well as extremes over southwestern Africa.

Microphysical characteristics of torrential predecessor rain events over the Yangtze River Delta Area and the related tropical cyclones

The precipitation structures and microphysical characteristics of predecessor rain events (PREs) over the Yangtze River Delta area and related tropical cyclones (TCs) from 2014 to 2019 were investigated using Dual-frequency Precipitation radar data from Global Precipitation Measurement (GPM) for drop size distributions (DSDs). Results showed that the total mean rain rate of PREs was larger than that of TCs, primarily due to higher convective and stratiform rain rates, with enhanced fractional coverage of convective rain in PREs. Examination of microphysical characteristics revealed that a greater quantity of small-sized droplets and a smaller quantity of medium- as well as large-sized droplets contributed towards PREs in comparison with TCs. The conclusion still holds when partitioning DSDs based on different precipitation rate categories. Further investigation of DSDs using gamma functions illustrated that precipitation in PREs had lower average mass-weighted diameters (Dm) and enhanced normalized number concentration (Nw) compared with TCs; partitioning precipitation into convective and stratiform components illustrated a larger Dm and lower Nw in TCs than PREs. The analysis of microphysical and thermodynamical processes using the reanalysis data indicates that relatively intense convective activity with drier conditions may be favorable to enhancing raindrop growth through collision-coalescence processes, as a result of larger Dm in TCs than PREs. The empirical relations (Z–R algorithms) applied in different rain regimes (stratiform, convective, and total PREs) revealed significant diversities, relying on weather conditions and geographical locations. Plain language summaryThe Yangtze River Delta area is an important economic belt in China. Under climate change, observed frequent occurrences of weather extremes of heavy rainfall and tropical cyclones (TCs) exert adverse effects on economic development in this region. Thus, a deep understanding of the mechanism of TCs torrential rainfall in the Yangtze River Delta area is urgently necessary. In this study, we investigated the precipitation patterns and microphysical characteristics of predecessor rain events (PREs) in the Yangtze River Delta region, and their association with TCs in the South China Sea-Western North Pacific Ocean (SCS-WNPO) area from 2014 to 2019. We found that PREs had a higher total mean precipitation rate than TCs. Further examination using gamma functions demonstrated that PREs exhibited lower average mass-weighted diameters (Dm) and higher normalized intercept parameters (Nw) than TCs. This pattern persisted when distinguishing between convective and stratiform precipitation components. We believe that our study makes a significant contribution to the literature because these results provide valuable insights into the distinct precipitation characteristics of PREs and TCs in the study region and contribute to a better understanding of tropical cyclone-related rainfall patterns, and act as a scientific basis for disaster mitigation.

Quantitatively detecting ground surface changes of slope failure caused by heavy rain using ALOS-2/PALSAR-2 data: a case study in Japan

Many SAR images have been utilized for geologic disasters investigations with the continuous launch of new Synthetic Aperture Radar (SAR) satellites such as ALOS-2/PALSAR-2. However, to proactively respond to transient slope failures caused by heavy rainfall, rapid extraction of areas of surface change accompanying slope failures is required. This study proposes two methods for quantitatively extracting slope failure areas using L-band SAR observations with slope units (SUs) as the evaluation units. The first method is based on the threshold method, which automates the selection of thresholds for various disaster-affected conditions, such as land use and topography. The second method is a machine-learning-based density ratio estimation method, which uses multi-temporal periodic observation data and pre- and post-disaster data to detect outliers through feature selection optimization. In the observation direction with the shortest satellite observation period, the F1 score (The F1 score is the harmonic mean of the precision and recall) of the threshold method for accuracy evaluation is 61.91%, and the F1 score of the density ratio method is 65.87%. Both methods can reduce the problem of low extraction accuracy caused by the effect of speckle noise. When slope failure occurs, both methods can extract the area of surface change within hours of a disaster. The method proposed in this study displays good applicability in supporting emergency rescue and the prevention of secondary disasters.

Characterizing Wet Season Precipitation in the Central Amazon Using a Mesoscale Convective System Tracking Algorithm

AbstractTo comprehensively characterize convective precipitation in the central Amazon region, we utilize the Python FLEXible object TRacKeR (PyFLEXTRKR) to track mesoscale convective systems (MCSs) observed through satellite measurements and simulated by the Weather Research and Forecasting model at a convection‐permitting resolution. This study spans a 2‐month period during the wet seasons of 2014 and 2015. We observe a strong correlation between the MCS track density and accumulated precipitation in the Amazon basin. Key factors contributing to precipitation, such as MCS properties (number, size, rainfall intensity, and movement), are thoroughly examined. Our analysis reveals that while the overall model produces fewer MCSs with smaller mean sizes compared to observations, it tends to overpredict total precipitation due to excessive rainfall intensity for heavy rainfall events (≥10 mm hr−1). These biases in simulated MCS properties could vary with the constraints on the convective background environment. Moreover, while the wet bias from heavy (convective) rainfall outweighs the dry bias in light (stratiform) rainfall, the latter can be crucial, particularly when MCS cloud cover is significantly underestimated. A case study for 1 April 2014 highlights the influence of environmental conditions on the MCS lifecycle and identifies an unrealistic model representation in both stratiform and convective precipitation features.

Efficient Modeling of Spatial Extremes over Large Geographical Domains

Various natural phenomena exhibit spatial extremal dependence at short spatial distances. However, existing models proposed in the spatial extremes literature often assume that extremal dependence persists across the entire domain. This is a strong limitation when modeling extremes over large geographical domains, and yet it has been mostly overlooked in the literature. We here develop a more realistic Bayesian framework based on a novel Gaussian scale mixture model, with the Gaussian process component defined by a stochastic partial differential equation yielding a sparse precision matrix, and the random scale component modeled as a low-rank Pareto-tailed or Weibull-tailed spatial process determined by compactly-supported basis functions. We show that our proposed model is approximately tail-stationary and that it can capture a wide range of extremal dependence structures. Its inherently sparse structure allows fast Bayesian computations in high spatial dimensions based on a customized Markov chain Monte Carlo algorithm prioritizing calibration in the tail. We fit our model to analyze heavy monsoon rainfall data in Bangladesh. Our study shows that our model outperforms natural competitors and that it fits precipitation extremes well. We finally use the fitted model to draw inference on long-term return levels for marginal precipitation and spatial aggregates.

Clay mineralogy in mylonite weathering products from Njimom (west Cameroon): origin and terracotta suitability

This study investigated the mineralogical assemblage of weathering products developed on mylonite rocks in west Cameroon. Smectite and vermiculite occurrence was studied, particularly to emphasize its origin and the potential of the samples in terracotta production. Samples characteristics were determined through X-Ray Diffraction (XRD) and X-ray Fluorescence (XRF) methods. Weathering products were observed along road trenches of 450 m long and 50 m height on a syenitic basement rock intruded by magmatic rock veins. Clay minerals are mainly composed of kaolinite and illite. 2:1 swelling clay minerals (vermiculite, montmorillonite) occur in some samples mainly originated from the contact between weathering materials and quartzo feldspatic bands. The main oxides are SiO2 (46–72%), Al2O3 (16–31%), Fe2O3 (2–16%) and K2O (0.1–6%). Vermiculite may be formed by fluid-mineral interactions during alteration of the chlorite in the plasmic microsystems of the soil. Structural changes in the upper part of the saprolite leading to the transformation of vermiculite into smectite under supergene alteration control, which predominates in humid tropical zones. Heavy rainfall leads to the formation of kaolinite at the expense of 2:1 clay minerals. Empirical classification revealed that the studied weathering products can be used to produce common bricks.

Pathways to adoption and mitigation: A dynamic perspective on good agricultural practices in Rural Malawi

Many researchers have noted the limited adoption of farming management practices that should increase the resilience of smallholder farmers to weather shocks and mitigate their impact on the changing climate in sub-Saharan Africa. In this paper, we evaluate the dynamics of adopting “good agricultural practices” in Malawi, using data from a three-wave panel collected as part of an impact assessment of the Sustainable Agricultural Production Programme, funded by the International Fund for Agricultural Development. In addition to project impacts, we also evaluate additional mechanisms though which farmers may learn about the costs and benefits of different practices. We also evaluate the extent to which climatic conditions – such as being located in drought-prone or heavy rainfall areas – drive adoption decisions. Given the three waves of data, we first look at the range of adoption pathways observed, through the use of an adoption pathway trees. We identify six pathways, noting that adoption is not continuous for a large percentage of households. We then run a multinomial logit to assess the factors that increase the likelihood of falling into different adoption categories vis-a-vis remaining a never adopter. Results suggest that learning through information dissemination, such as through the SAPP project, and wider learning opportunities significantly increased the likelihood of pursuing different adoption pathways, while climatic conditions and learning through observing have limited impacts. On the other hand, for land-intensive management practices, being located in drought-prone areas or being located in areas prone to heavy rainfall increased the likelihood of pursuing different adoption pathways, as did greater ability to learn by observing. Learning by information sharing had limited impacts for land-intensive adoption pathway decisions. Overall, results suggest that information dissemination is important, though the mechanism differs by type of practice promoted. Flexibility in adoption status is an attribute of this system and there is a need to identify and promote practices that are both flexible and increase resilience to climate change.

A novel multi-hazard risk assessment framework for coastal cities under climate change

Coastal cities, as centres of human habitation, economic activity and biodiversity, are confronting the ever-escalating challenges posed by climate change. In this work, a novel Multi-Hazard Risk Assessment framework is presented with the focus on Coastal City Living Labs. The methodology provides a comprehensive assessment of climate-related hazards, including sea-level rise, coastal flooding, coastal erosion, land flooding, heavy precipitation, extreme temperatures, heatwaves, cold spells, landslides and strong winds. Its application is illustrated through a case study: the Coastal City Living Lab of Benidorm, Spain. The methodology incorporates remote sensing data from various satellite sources, such as ERA5, Urban Atlas and MERIT DEM, to evaluate multiple hazards through a systematic and standardized indicator-based approach, offering a holistic risk profile that allows for comparison with other European coastal cities. The integration of remote sensing data enhances the accuracy and resolution of hazard indicators, providing detailed insights into the spatiotemporal dynamics of climate risks. The incorporation of local expertise through the Coastal City Living Lab concept enriches data collection and ensures context-specific adequacy. The integration of local studies and historical extreme climate events enhances the validity and context of the risk indicators. The findings align with regional trends and reveal specific vulnerabilities, particularly related to heatwaves, heavy rainfall, and coastal flooding. Despite its strengths, the MHRA methodology faces limitations, including reliance on outdated datasets and the complexity of integrating multiple hazards. Continuous updates and adaptive management strategies are essential to maintain the accuracy and relevance of risk assessments. The broader implications of the methodology for global coastal cities highlight its potential as a model for developing targeted adaptation strategies.

Microbial source tracking and identification of fecal contamination patterns in saltwater estuaries between base- and storm-flow events using dual genome-based approaches

Fecal contamination from natural and anthropogenic sources poses significant threats to saltwater estuaries, particularly after storms or heavy rainfall. Monitoring fecal contamination is essential for protecting these vulnerable ecosystems having important ecological and economic values. In this study, we investigated the abundance, sources, and potential causes of fecal contamination at three marine and seven freshwater stations across Vaughn Bay (WA, USA), a shellfish growing district, during base- and storm-flow events. Additionally, we evaluated the performance of fecal indicator bacteria (FIB) quantification, optical brightener assessment, and qPCR analysis for fecal contamination quantification. We compared the effectiveness of qPCR-based microbial source tracking (MST), which targeted a broad range of hosts including, such as humans, birds, cows, horses, ruminants, dogs, and pigs, with sequencing-based MST in identifying fecal contamination sources. Both MST analysis approaches identified birds and humans as the primary sources of fecal contamination. For marine water stations, freshwater creeks VBU001, VBU002, and VB047, along with drain VB007, were identified as the main sources of human-derived fecal contamination in Vaughn Bay, based on Kendall's tau analysis (τ: 0.58–0.97). This information indicates that the septic systems in the catchment areas of these creeks and drains require further investigation to achieve effective fecal contamination control. Optical brightener, FIB enumeration and qPCR quantification results were generally higher during storm-flow events, although they showed poor correlation with each other (Pearson r < 0.40), likely due to physiological and phylogenetic differences among the target organisms of these methods. However, the sequencing-based method faces challenges in precise quantitative identification of differences in fecal contamination between base- and storm-flow events. Due to its high-throughput and cost-effectiveness, we recommend using sequencing-based analysis for large-scale identification of the primary sources of fecal contamination in water environments, followed by targeted qPCR quantification of MST markers for more precise assessments.

Capturing Urban Heterogeneity Enhances Tropical Cyclones Simulation in Houston

AbstractUrbanization in the coastal region has increased socioeconomic losses from landfalling tropical cyclones (TCs). Although previous studies have explored the broad impacts of urbanization on TCs, the effect of heterogeneity caused by local intra‐urban variability has not been examined. To address this gap, this study utilized the urban Local Climate Zone (LCZ) to capture the urban landscape heterogeneity and the impacts of this representation on the simulation of the post‐landfall TCs characteristics in Houston. Taking the case of two recent TCs: Hurricane Harvey (occurred in 2017) and Tropical Storm Imelda (occurred in 2019), the study evaluated the impact of urban heterogeneity on 10‐m winds, 2‐m temperature, land surface temperature, and precipitation across Houston. The consideration of intra‐urban heterogeneity using LCZ can improve the 10‐m winds, 2‐m temperature, and spatial pattern of land surface temperature. Although the cumulative rainfall remained largely similar within the experiments (with and without LCZ), incorporating intra‐urban heterogeneity notably modified the spatial structure of urban rainfall, particularly in simulating heavy rainfall hot spots. These findings are consistent for both TCs and demonstrate the positive impact of incorporating intra‐urban heterogeneity on the landfalling TC simulations over the Houston area.

Shear lines trigger heavy rainfalls in the Philippines during the winter monsoon

Heavy rainfall events (HREs) occur almost throughout the year in the Philippines, with relatively limited research during the winter monsoon. This study analyzes the 20-year (2003–2022) daily precipitation from 55 rain gauges and Integrated Multi-satellitE Retrievals for GPM (IMERG) from November to February. HREs are classified into three clusters by employing a cluster analysis on the most pertinent principal modes extracted from the principal component analysis. Each cluster exhibits a distinct heavy rainfall spatial pattern, mostly showing more than 50 mm/day of rainfall in the eastern part of the country. We noted that heavy rainfall in the Philippines during the winter monsoon occurs during a strong East Asian Winter Monsoon and caused by the interaction of shear line and low-level cyclonic vortex. The different location of rainfall maxima in each HRE cluster is a result of the variation of locations of the shear line and cyclonic vortex.

Study of Natural Disaster in Manali Valley (Himachal Pradesh), India on 09 July 2023

Continuous heavy rainfall in July 2023, particularly from the 7th to the 10th, led to devastating flash floods, cloudbursts, and landslides in Himachal Pradesh, causing extensive damage to infrastructure, properties, and loss of lives. The most severely affected districts were Kullu, Mandi, Sirmaur, Shimla, Solan, and nearby areas. Manali sub-division, located in Himachal Pradesh, spans an altitude range of 1074 to 4017 meters above sea level. The region's climate is shaped by its mountainous terrain, with the Beas Valley's weather influenced by factors like relief, aspect, and altitude. The Pir Panjal Range's windward slopes create a barrier to monsoon winds, resulting in heavy rainfall and cloudbursts in the area. The report from the India Meteorological Department (IMD) in Shimla, Himachal Pradesh, on July 12, 2023, highlighted the active monsoon conditions in the state from July 7th to 10th, exacerbated by a Western Disturbance (WD). This combination led to intense and unprecedented rainfall, causing extensive damage to public and private properties, particularly in hilly regions. The report mentioned that similar disasters have occurred in the past due to heavy rainfall, cloudbursts, and landslides, possibly exacerbated by factors like unscientific construction, climate change, and increased human activities, including tourism. The report emphasized the need for accurate predictions and proactive planning to mitigate such disasters in the future.

A case study of the “21.7” Henan extremely rainfall event: From the perspective of water vapor monitored with GNSS tomography

Water vapor plays a vital role in the development of heavy rainfall system. The “21.7” Henan rainfall event, a record-breaking event, was investigated from the perspective of water vapor based on in-situ measurements, namely the 220 continuously operating GNSS ground stations in Henan Province, with GNSS tomography technique. Results showed that during and after the heavy rainfall, the error RMS of water vapor density from tomography was 21 % lower than that of GFS prediction data at the height of about 1450 m by comparing with radiosonde data. Taking ERA5 as the reference, the average relative error of tomographic water vapor density at the height below 4 km was within 10 % before and during the rainfall. The analyses of spatiotemporal variations of water vapor illustrated that tomographic products can, compared to ERA5 reanalysis data, more distinctly reflect the formation and convergence process of low-level water vapor bands before the occurrence of the extreme rainfall, as well as the fracture of the water vapor bands before the end of the rainfall. By combining the tomographic water vapor and GFS wind field data, the water vapor flux divergence can be calculated to further elucidate the movement of water vapor where significant variations of water vapor at the height of 10 to12 km (near the tropopause) and 1 to 4 km in both the vertical and horizontal directions were found before the extreme precipitation.

Projected changes of thermal, rainfall and drought indices in Morocco from high resolution climate models

Abstract Over the past decade, global changes in temperature, average and extreme precipitation have been observed many times. The increase in temperature or even the change in precipitation systems is directly affect extreme weather events, for example, heat waves, heavy rainfall, storms, and droughts are becoming more frequent and more powerful worldwide due to climate change caused by several factors including human activity. This is why there is a necessity to monitor future changes in climate extremes. The objective of this paper is to evaluate the future changes in thermal and rainfall extremes projected by the four climate models over time horizon 2041-2060 relative to the 1981-2010 reference period, based on the 5 climate indices, including Tmm, Tx90p, WSDI, PRCPTOT and SPI. The projections are given under the RCP 4.5 medium scenario. The analysis in thermal aspect shows that Morocco could experience by 2041-2060, a significant warming, has manifested by an increase in average temperature. Future changes in rainfall indices show that the spatial distribution of rainfall in Morocco could change significantly by 2041-2060 compared to the reference period 1971-2010. The four climate models agree on a decrease in annual precipitation that could affect most of the Kingdom. In the same way to dryness, the future changes of the SPI index that characterizes the drought, indicates a move to a dry climate.

Special Issue on NIED Frontier Research on Science and Technology for Disaster Risk Reduction and Resilience 2024

We are pleased to publish this special issue, dedicated to the NIED Frontier Research on Science and Technology for Disaster Risk Reduction and Resilience 2024. The fifth NIED mid-/long-term plan period began in 2023, and this issue includes the first results of six papers, two reviews, one survey report, one note, and one material. Recently, violent natural disasters have triggered the development of effective countermeasures. Akita, Yamaguchi et al., and Hirashima et al. dealt with climate hazards such as heavy rainfall, snow, and ice disasters. Yamazaki-Honda reported the progress and challenges of disaster reduction and climate change adaptation using DRR strategies. Yadab et al., Akita, and Naito et al. reported data analysis and suggestions to evaluate seismic and volcanic hazards. Nagata et al. proposed the ICT tool “YOU@RISK” to educate regarding volcanic disaster risks. Sato et al. emphasized the importance and effectiveness of information technology. Kimura et al. emphasized preparedness and elaborated on measures to improve preparedness for all natural hazards. These new outcomes will promote the development of a sustainable society against these hazards and will be flagships to proceed with NIED research projects.