Evaluation of rainfall threshold models for debris flow initiation in the Jiangjia Gully,Yunnan Province,China

Systematically determining the discriminatory power of various rainfall properties and their combinations in identifying debris flow occurrence is crucial for early warning systems.In this study,we evaluated the discriminatory power of different univariate and multivariate rainfall threshold models in identifying triggering conditions of debris flow in the Jiangjia Gully,Yunnan Province,China.The univariate models used single rainfall properties as indicators,including total rainfall(R_(tot)),rainfall duration(D),mean intensity(I_(mean)),absolute energy(Eabs),storm kinetic energy(E_(s)),antecedent rainfall(R_(a)),and maximum rainfall intensity over various durations(I_(max_dur)).The evaluation reveals that the I_(max_dur)and Eabs models have the best performance,followed by the E_(s),R_(tot),and I_(mean)models,while the D and R_(a)models have poor performances.Specifically,the I_(max_dur)model has the highest performance metrics at a 40-min duration.We used logistic regression to combine at least two rainfall properties to establish multivariate threshold models.The results show that adding D or R_(a)to the models dominated by Eabs,E_(s),R_(tot),or I_(mean)generally improve their performances,specifically when D is combined with I_(mean)or when R_(a)is combined with Eabs or E_(s).Including R_(a)in the I_(max_dur)model,it performs better than the univariate I_(max_dur)model.A power-law relationship between I_(max_dur)and R_(a)or between Eabs and R_(a)has better performance than the traditional I_(mean)–D model,while the performance of the E_(s)–R_(a)model is moderate.Our evaluation reemphasizes the important role of the maximum intensity over short durations in debris flow occurrence.It also highlights the importance of systematically investigating the role of R_(a)in establishing rainfall thresholds for triggering debris flow.Given the regional variations in rainfall patterns worldwide,it is necessary to evaluate the findings of this study across diverse watersheds.

Spatiotemporal Characteristics of Rainfall over Different Terrain Features in the Middle Reaches of the Yangtze River Basin during the Warm Seasons of 2016–20

Based on hourly rain gauge data during May–September of 2016–20,we analyze the spatiotemporal distributions of total rainfall(TR)and short-duration heavy rainfall(SDHR;hourly rainfall≥20 mm)and their diurnal variations over the middle reaches of the Yangtze River basin.For all three types of terrain(i.e.,mountain,foothill,and plain),the amount of TR and SDHR both maximize in June/July,and the contribution of SDHR to TR(CST)peaks in August(amount:23%;frequency:1.74%).Foothill rainfall is characterized by a high TR amount and a high CST(in amount);mountain rainfall is characterized by a high TR frequency but a small CST(in amount);and plain rainfall shows a low TR amount and frequency,but a high CST(in amount).Overall,stations with high TR(amount and frequency)are mainly located over the mountains and in the foothills,while those with high SDHR(amount and frequency)are mainly concentrated in the foothills and plains close to mountainous areas.For all three types of terrain,the diurnal variations of both TR and SDHR exhibit a double peak(weak early morning and strong late afternoon)and a phase shift from the early-morning peak to the late-afternoon peak from May to August.Around the late-afternoon peak,the amount of TR and SDHR in the foothills is larger than over the mountains and plains.The TR intensity in the foothills increases significantly from midnight to afternoon,suggesting that thermal instability may play an important role in this process.

Characterizing Rainfall of the Great Kei River Catchment, Eastern Cape Province, South Africa

This study seeks to understand long-term changes of rainfall for the Great Kei River catchment (GKRc) in South Africa for water resources management and planning. Monthly and annual rainfall time series data from 1950 to 2017 for 11 rainfall gauging stations are analyzed using various statistical methods. Data obtained from South African Weather Services (SAWS) was quality controlled to enable the use of Mann-Kendall (MK), Theil Sen’s method, Precipitation Concentration Index (PCI), among others to characterise rainfall. Rainfall in the catchment is seasonal (particularly wet in spring and summer) and highly variable with a PCI of 17.2. Years which received rain above and below the mean inter-annually were 46% and 54%, respectively. Seasonality trends also confirm that the GKRc has been progressively receiving less rainfall since 1950, especially in the autumn. The methods are novel in understanding historical and existing trends, variability and characteristics that control freshwater availability in this catchment.

Projecting Spring Consecutive Rainfall Events in the Three Gorges Reservoir Based on Triple-Nested Dynamical Downscaling

Spring consecutive rainfall events(CREs) are key triggers of geological hazards in the Three Gorges Reservoir area(TGR), China. However, previous projections of CREs based on the direct outputs of global climate models(GCMs) are subject to considerable uncertainties, largely caused by their coarse resolution. This study applies a triple-nested WRF(Weather Research and Forecasting) model dynamical downscaling, driven by a GCM, MIROC6(Model for Interdisciplinary Research on Climate, version 6), to improve the historical simulation and reduce the uncertainties in the future projection of CREs in the TGR. Results indicate that WRF has better performances in reproducing the observed rainfall in terms of the daily probability distribution, monthly evolution and duration of rainfall events, demonstrating the ability of WRF in simulating CREs. Thus, the triple-nested WRF is applied to project the future changes of CREs under the middle-of-the-road and fossil-fueled development scenarios. It is indicated that light and moderate rainfall and the duration of continuous rainfall spells will decrease in the TGR, leading to a decrease in the frequency of CREs. Meanwhile, the duration, rainfall amount, and intensity of CREs is projected to regional increase in the central-west TGR. These results are inconsistent with the raw projection of MIROC6. Observational diagnosis implies that CREs are mainly contributed by the vertical moisture advection. Such a synoptic contribution is captured well by WRF, which is not the case in MIROC6,indicating larger uncertainties in the CREs projected by MIROC6.

Assessment of Seasonal Rainfall Prediction in Ethiopia: Evaluating a Dynamic Recurrent Neural Network to Downscale ECMWF-SEAS5 Rainfall

Seasonal rainfall plays a vital role in both environmental dynamics and decision-making for rainfed agriculture in Ethiopia, a country often impacted by extreme climate events such as drought and flooding. Predicting the onset of the rainy season and providing localized rainfall forecasts for Ethiopia is challenging due to the changing spatiotemporal patterns and the country's rugged topography. The Climate Hazards Group Infra Red Precipitation with Station Data(CHIRPS), ERA5-Land total precipitation and temperature data are used from 1981–2022 to predict spatial rainfall by applying an artificial neural network(ANN). The recurrent neural network(RNN) is a nonlinear autoregressive network with exogenous input(NARX), which includes feed-forward connections and multiple network layers, employing the Levenberg Marquart algorithm. This method is applied to downscale data from the European Centre for Medium-range Weather Forecasts fifth-generation seasonal forecast system(ECMWF-SEAS5) and the Euro-Mediterranean Centre for Climate Change(CMCC) to the specific locations of rainfall stations in Ethiopia for the period 1980–2020. Across the stations, the results of NARX exhibit strong associations and reduced errors. The statistical results indicate that, except for the southwestern Ethiopian highlands, the downscaled monthly precipitation data exhibits high skill scores compared to the station records, demonstrating the effectiveness of the NARX approach for predicting local seasonal rainfall in Ethiopia's complex terrain. In addition to this spatial ANN of the summer season precipitation, temperature, as well as the combination of these two variables, show promising results.

Local Torrential Rainfall Event within a Mei-Yu Season Mesoscale Convective System:Importance of Back-Building Processes

An extreme rainfall event occurred over Hangzhou,China,during the afternoon hours on 24 June 2013.This event occurred under suitable synoptic conditions and the maximum 4-h cumulative rainfall amount was over 150 mm.This rainfall event had two major rainbands.One was caused by a quasi-stationary convective line,and the other by a backbuilding convective line related to the interaction of the outflow boundary from the first rainband and an existing low-level mesoscale convergence line associated with a mei-yu frontal system.The rainfall event lasted 4 h,while the back-building process occurred in 2 h when the extreme rainfall center formed.So far,few studies have examined the back-building processes in the mei-yu season that are caused by the interaction of a mesoscale convergence line and a convective cold pool.The two rainbands are successfully reproduced by the Weather Research and Forecasting(WRF)model with fourlevel,two-way interactive nesting.In the model,new cells repeatedly occur at the west side of older cells,and the backbuilding process occurs in an environment with large CAPE,a low LFC,and plenty of water vapor.Outflows from older cells enhance the low-level convergence that forces new cells.High precipitation efficiency of the back-building training cells leads to accumulated precipitation of over 150 mm.Sensitivity experiments without evaporation of rainwater show that the convective cold pool plays an important role in the organization of the back-building process in the current extreme precipitation case.

Influences of tree characters on throughfall and stemflow from rainfall and fog in Popa Mountain Park, Myanmar

Available water for communities is insufficient in the central part of Myanmar due to limited rainfall and surface water resources.Over the last two decades,affores-tation and reforestation projects have been implemented in this region to provide sufficient water to local communities,expecting forested areas to store more rainwater than other land uses.However,there has been no research and very limited information on rainfall partitioning into throughfall(TF)and stemflow(SF),particularly concerning tree char-acters.Gross rainfall,TF under different canopy types,and SF of different tree types were measured in 2019.TF and SF were frequently observed even without rain but under foggy conditions.Therefore,both were partitioned into TF and SF from rainfall and fog individually.Sparser canopies resulted in larger TF from rainfall than denser canopies.However,a denser canopy delivered larger TF from fog than a sparser one.TF rates from rainfall in sparser and denser canopies were 54.5%and 51.5%,respectively,while those from fog were 15.2%and 27.2%,respectively.As a result,total TF rate in the denser canopy(70.7%)was significantly larger than that from the sparser one(64.3%).Short trees with small crown projection area and smooth bark(Type I)resulted in larger SF from rainfall than taller trees with large crown projection area and rough bark(Type II).How-ever,Type II trees resulted in larger SF from fog.SF rates by rainfall from Type I and II trees were 17.5%and 12.2%,respectively,while those by fog were 22.2%and 39.5%,respectively.No significant total SF rates were found for Type I(22.5%)and II trees(20.1%).A denser canopy results in larger TF,and Type I trees result in larger SF.In an area where foggy conditions occur frequently and for a lengthy period,however,Type II trees will result in larger SF.These three tree characters(dense canopies,short trees with small crown projection area and smooth bark,and tall trees with large crown projection area and rough bark)should be con-sidered for afforestation and reforestation projects in the Popa Mountain Park to enhance net water input by forests.

Comparison of Adaptive Simulation Observation Experiments of the Heavy Rainfall in South China and Sichuan Basin

This study examines the effectiveness of adaptive observation experiments using the ensemble transformation sensitivity(ETS) method to improve precipitation forecasts during heavy rainfall events in South China and the Sichuan Basin. High-resolution numerical models are employed to simulate adaptive observations. By identifying the sensitive areas of key weather system positions 42 hours before heavy rainfall events, the adaptive observations improve the prediction of jet streams, strong winds, and shear lines, which are essential for accurate heavy rainfall forecasting. This improvement is reflected in both the precipitation structure and location accuracy within the verification region. In South China, targeted observations enhance rainfall predictions by improving water vapor transport. In the Sichuan Basin, adaptive observations refine water vapor transport and adjust vortex dynamics. This research highlights the importance of accurately predicting shear lines and jet streams for forecasting heavy rainfall in these areas. Overall, this study found that adaptive observation enhances the precipitation forecast skills of the structure and location for heavy rainfall in South China and the Sichuan Basin, emphasizing their potential utility in operational numerical weather prediction.

Automated machine learning for rainfall-induced landslide hazard mapping in Luhe County of Guangdong Province,China

Landslide hazard mapping is essential for regional landslide hazard management.The main objective of this study is to construct a rainfall-induced landslide hazard map of Luhe County,China based on an automated machine learning framework(AutoGluon).A total of 2241 landslides were identified from satellite images before and after the rainfall event,and 10 impact factors including elevation,slope,aspect,normalized difference vegetation index(NDVI),topographic wetness index(TWI),lithology,land cover,distance to roads,distance to rivers,and rainfall were selected as indicators.The WeightedEnsemble model,which is an ensemble of 13 basic machine learning models weighted together,was used to output the landslide hazard assessment results.The results indicate that landslides mainly occurred in the central part of the study area,especially in Hetian and Shanghu.Totally 102.44 s were spent to train all the models,and the ensemble model WeightedEnsemble has an Area Under the Curve(AUC)value of92.36%in the test set.In addition,14.95%of the study area was determined to be at very high hazard,with a landslide density of 12.02 per square kilometer.This study serves as a significant reference for the prevention and mitigation of geological hazards and land use planning in Luhe County.

Effects of Initial and Boundary Conditions on Heavy Rainfall Simulation over the Yellow Sea and the Korean Peninsula:Comparison of ECMWF and NCEP Analysis Data Effects and Verification with Dropsonde Observation

This study evaluated the simulation performance of mesoscale convective system(MCS)-induced precipitation,focusing on three selected cases that originated from the Yellow Sea and propagated toward the Korean Peninsula.The evaluation was conducted for the European Centre for Medium-Range Weather Forecasts(ECMWF)and National Centers for Environmental Prediction(NCEP)analysis data,as well as the simulation result using them as initial and lateral boundary conditions for the Weather Research and Forecasting model.Particularly,temperature and humidity profiles from 3D dropsonde observations from the National Center for Meteorological Science of the Korea Meteorological Administration served as validation data.Results showed that the ECMWF analysis consistently had smaller errors compared to the NCEP analysis,which exhibited a cold and dry bias in the lower levels below 850 hPa.The model,in terms of the precipitation simulations,particularly for high-intensity precipitation over the Yellow Sea,demonstrated higher accuracy when applying ECMWF analysis data as the initial condition.This advantage also positively influenced the simulation of rainfall events on the Korean Peninsula by reasonably inducing convective-favorable thermodynamic features(i.e.,warm and humid lower-level atmosphere)over the Yellow Sea.In conclusion,this study provides specific information about two global analysis datasets and their impacts on MCS-induced heavy rainfall simulation by employing dropsonde observation data.Furthermore,it suggests the need to enhance the initial field for MCS-induced heavy rainfall simulation and the applicability of assimilating dropsonde data for this purpose in the future.

Three-dimensional numerical analysis of plant-soil hydraulic interactions on pore water pressure of vegetated slope under different rainfall patterns

Understanding the pore water pressure distribution in unsaturated soil is crucial in predicting shallow landslides triggered by rainfall,mainly when dealing with different temporal patterns of rainfall intensity.However,the hydrological response of vegetated slopes,especially three-dimensional(3D)slopes covered with shrubs,under different rainfall patterns remains unclear and requires further investigation.To address this issue,this study adopts a novel 3D numerical model for simulating hydraulic interactions between the root system of the shrub and the surrounding soil.Three series of numerical parametric studies are conducted to investigate the influences of slope inclination,rainfall pattern and rainfall duration.Four rainfall patterns(advanced,bimodal,delayed,and uniform)and two rainfall durations(4-h intense and 168-h mild rainfall)are considered to study the hydrological response of the slope.The computed results show that 17%higher transpiration-induced suction is found for a steeper slope,which remains even after a short,intense rainfall with a 100-year return period.The extreme rainfalls with advanced(PA),bimodal(PB)and uniform(PU)rainfall patterns need to be considered for the short rainfall duration(4 h),while the delayed(PD)and uniform(PU)rainfall patterns are highly recommended for long rainfall durations(168 h).The presence of plants can improve slope stability markedly under extreme rainfall with a short duration(4 h).For the long duration(168 h),the benefit of the plant in preserving pore-water pressure(PWP)and slope stability may not be sufficient.

Determination of the critical rainfall of runoff-initiated debris flows by the perspective of physical mechanics and Shields stress

The critical rainfall of runoff-initiated debris flows is utmost importance for local early hazard forecasting.This paper presents research on the critical rainfall of runoff-initiated debris flows through comparisons between slope gradients and three key factors,including topographic contributing area,dimensionless discharge,and Shields stress.The rainfall amount was estimated by utilizing in-situ rainfall records and a slope-dependent Shields stress model was created.The created model can predict critical Shields stress more accurately than the other two models.Furthermore,a new dimensionless discharge equation was proposed based on the corresponding discharge-gradient datasets.The new equation,along with factors such as contributing area above bed failure sites,channel width,and mean diameter of debris flow deposits,predicts a smaller rainfall amount than the in-situ measured records.Although the slope-dependent Shields stress model performs well and the estimated rainfall amount is lower than the in-situ records,the sediment initiation in the experiments falls within sheet flow regime due to a large Shields stress.Therefore,further sediment initiation experiments at a steeper slope range are expected in the future to ensure that the sediment transport belongs to mass failure regime characterized by a low level of Shields stress.Finally,a more accurate hazard forecast on the runoff-initiated debris flow holds promise when the corresponding critical slope-dependent dimensionless discharge of no motion,fluvial sediment transport,mass flow regime,and sheet flow regime are considered.

Estimating rainfall intensity based on surveillance audio and deep-learning

Rainfall data with high spatial and temporal resolutions are essential for urban hydrological modeling.Ubiquitous surveillance cameras can continuously record rainfall events through video and audio,so they have been recognized as potential rain gauges to supplement professional rainfall observation networks.Since video-based rainfall estimation methods can be affected by variable backgrounds and lighting conditions,audio-based approaches could be a supplement without suffering from these conditions.However,most audio-based approaches focus on rainfall-level classification rather than rainfall intensity estimation.Here,we introduce a dataset named Surveillance Audio Rainfall Intensity Dataset(SARID)and a deep learning model for estimating rainfall intensity.First,we created the dataset through audio of six real-world rainfall events.This dataset's audio recordings are segmented into 12,066 pieces and annotated with rainfall intensity and environmental information,such as underlying surfaces,temperature,humidity,and wind.Then,we developed a deep learning-based baseline using Mel-Frequency Cepstral Coefficients(MFCC)and Transformer architecture to estimate rainfall intensity from surveillance audio.Validated from ground truth data,our baseline achieves a root mean absolute error of 0.88 mm h^(-1) and a coefficient of correlation of 0.765.Our findings demonstrate the potential of surveillance audio-based models as practical and effective tools for rainfall observation systems,initiating a new chapter in rainfall intensity estimation.It offers a novel data source for high-resolution hydrological sensing and contributes to the broader landscape of urban sensing,emergency response,and resilience.

Stability analysis of loose accumulation slopes under rainfall:case study of a high‑speed railway in Southwest China

The high and steep slopes along a high-speed railway in the mountainous area of Southwest China are mostly composed of loose accumulations of debris with large internal pores and poor stability,which can easily induce adverse geological disasters under rainfall conditions.To ensure the smooth construction of the high-speed railway and the subsequent safe operation,it is necessary to master the stability evolution process of the loose accumulation slope under rainfall.This article simulates rainfall using the finite element analysis software’s hydromechanical coupling module.The slope stability under various rainfall situations is calculated and analysed based on the strength reduction method.To validate the simulation results,a field monitoring system is established to study the deformation characteristics of the slope under rainfall.The results show that rainfall duration is the key factor affecting slope stability.Given a constant amount of rainfall,the stability of the slope decreases with increasing duration of rainfall.Moreover,when the amount and duration of rainfall are constant,continuous rainfall has a greater impact on slope stability than intermittent rainfall.The setting of the field retaining structures has a significant role in improving slope stability.The field monitoring data show that the slope is in the initial deformation stage and has good stability,which verifies the rationality of the numerical simulation method.The research results can provide some references for understanding the influence of rainfall on the stability of loose accumulation slopes along high-speed railways and establishing a monitoring system.

Unstable evolution of railway slope under the rainfall-vibration joint action

Understanding the unstable evolution of railway slopes is the premise for preventing slope failure and ensuring the safe operation of trains.However,as two major factors affecting the stability of railway slopes,few scholars have explored the unstable evolution of railway slopes under the joint action of rainfall-vibration.Based on the model test of sandy soil slope,the unstable evolution process of slope under locomotive vibration,rainfall,and rainfall-vibration joint action conditions was simulated in this paper.By comparing and analyzing the variation trends of soil pressure and water content of slope under these conditions,the change laws of pore pressure under the influence of vibration and rainfall were explored.The main control factors affecting the stability of slope structure under the joint action conditions were further defined.Combined with the slope failure phenomena under these three conditions,the causes of slope instability resulting from each leading factor were clarified.Finally,according to the above conclusions,the unstable evolution of the slope under the rainfall-vibration joint action was determined.The test results show that the unstable evolution process of sandy soil slope,under the rainfall-vibration joint action,can be divided into:rainfall erosion cracking,vibration promotion penetrating,and slope instability sliding three stages.In the process of slope unstable evolution,rainfall and vibration play the roles of inducing and promoting slide respectively.In addition,the deep cracks,which are the premise for the formation of the sliding surface,and the violent irregular fluctuation of soil pressure,which reflects the near penetration of the sliding surface,constitute the instability characteristics of the railway slope together.This paper reveals the unstable evolution of sandy soil slopes under the joint action of rainfall-vibration,hoping to provide the theoretical basis for the early warning and prevention technology of railway slopes.

Ingredients-based Methodology and Fuzzy Logic Combined Short-Duration Heavy Rainfall Short-Range Forecasting:An Improved Scheme

Short-duration heavy rainfall(SHR),as delineated by the National Meteorological Center of the China Me-teorological Administration,is characterized by hourly rainfall amounts no less than 20.0 mm.SHR is one of the most common convective weather phenomena that can cause severe damage.Short-range forecasting of SHR is an important part of operational severe weather prediction.In the present study,an improved objective SHR forecasting scheme was developed by adopting the ingredients-based methodology and using the fuzzy logic approach.The 1.0°×1.0°National Centers for Environmental Prediction(NCEP)final analysis data and the ordinary rainfall(0.1-19.9 mm h-1)and SHR observational data from 411 stations were used in the improved scheme.The best lifted index,the total precipitable water,the 925 hPa specific humidity(Q 925),and the 925 hPa divergence(DIV 925)were selected as predictors based on objective analysis.Continuously distributed membership functions of predictors were obtained based on relative frequency analysis.The weights of predictors were also objectively determined.Experiments with a typhoon SHR case and a spring SHR case show that the main possible areas could be captured by the improved scheme.Verification of SHR forecasts within 96 hours with NCEP global forecasts 1.0°×1.0°data initiated at 08:00 Beijing Time during the warm seasons in 2015 show the results were improved from both deterministic and probabilistic perspectives.This study provides an objectively feasible choice for short-range guidance forecasts of SHR.The scheme can be applied to other convective phenomena.

Exploring the groundwater response to rainfall in a translational landslide using the master recession curve method and cross-correlation function

Rainfall is a common trigger for landslide reactivation,as it raises groundwater levels and reduces bedrock or soil shear resistance.This study focuses on the Kualiangzi landslide in the southern region of Sichuan Province,China.Real-time monitoring of groundwater levels and rainfall from July 2013 to September 2016 is analyzed.Groundwater table increments,considering groundwater drainage rate,were calculated using the water-table fluctuation and master recession curve method and the response time of the groundwater table to rainfall events was estimated using the cross-correlation function.Results reveal that groundwater level declines from tension troughs to landslide fronts in the rainy season,with a significant positive correlation between the groundwater level in the tension trough and landslide surface displacement.Evaluated spring elevations for groundwater discharge range from 410 m to 440 m,which is in agreement with the actual spring elevations(390-423 m).Lag times of groundwater response to rainfall decreases with cumulative rainfall of the rainy periods.In the middle part of the landslide,two responses between rainfall and groundwater levels indicate two water movement pathways:Vertical cracks or fractures resulting from the slow landslide movement,and matrix pore space in unconsolidated sediment.Variations in peak values of the cross-correlation function suggest early dominance of the uniform matrix flow and later dominance of preferential flow during the rainy period.

The Influence of Variable (Monsoon) Rainfall on Sedimentation in the Roaches Grit and Other Upper Carboniferous Delta Sequences in the UK Pennine Basin

The Roaches Grit in the UK Pennine Basin was a complex deep water deltaic sequence deposited during the Late Carboniferous glacial period. The channels of the upper part of the Roaches Grit, deposited towards the end of the cyclothem after the eustatic minimum, contain evidence for very high seasonal discharges related to strong monsoon rainfall in the catchment areas. In some channels, intense turbulence near the delta front, led to knick point recession and deep incision. These channels were filled with sediments during reduced discharge, including very large sets of cross-bedding up to 16 m thick. Channels were short-lived with frequent avulsions. Over time slightly lower discharges formed laterally migrating channels dominated by bar forms. Different discharge-controlled processes operated on the reactivated delta slope. Incised channels generated turbidity currents during floods which transported sediments directly into the basin far from the delta. Migrating channels built mouth bars;resedimentation during floods formed density currents which then deposited sediment on the lower parts of the slope.

Occurrence of Extreme Rainfall and Flood Risks in Yopougon, Abidjan, Southeast Côte d’Ivoire from 1971 to 2022

Yopougon, located in the western part of the Autonomous District of Abidjan, is the most heavily populated municipality in Côte d’Ivoire. However, this area is prone to floods and landslides during the rainy season. The study aims to assess recent flood risks in the municipality of Yopougon of the Autonomous District of Abidjan. To achieve this objective, the study analyzed two types of data: daily rainfall from 1971 to 2022 and parameters derived from a Numerical Field and Altitude Model (NFAM). The study examined six rainfall parameters using statistical analysis and combined land use maps obtained from the NFAM of Yopougon. The results indicated that, in 67% of cases, extreme rainfall occurred mainly between week 3 of May and week 1 of July. The peak of extreme rainfall was observed in week 2 of June with 15% of cases. These are critical periods of flood risks in the Autonomous District of Abidjan, especially in Yopougon. In addition, there was variability of rainfall parameters in the Autonomous District of Abidjan. This was characterized by a drop of annual and seasonal rainfall, and an increase of numbers of rainy days. Flood risks in Yopougon are, therefore, due to the regular occurrence of rainy events. Recent floods in Yopougon were caused by normal rains ranging from 55 millimeters (mm) to 153 mm with a return period of less than five years. Abnormal heavy rains of a case study on June 20-21, 2022 in Yopougon were detected by outputs global climate models. Areas of very high risk of flood covered 18% of Yopougon, while 31% were at high risk. Climate information from this study can assist authorities to take in advance adaptation and management measures.

Analysis of the Heavy Rainfall Process in Mangshi City on August 8, 2023

On August 7,2023,Mangshi City,Dehong Prefecture experienced a local heavy rainstorm,and the geological disaster caused by the heavy rainfall caused casualties and property losses.Based on the real-time observation data of automatic stations,Doppler weather radar detection and meteorological risk warning products,the disaster situation,social impact,forecast and early warning service,causes of heavy precipitation and forecast and early warning inspection were summarized and analyzed.The results show that the heavy rainfall was prominent locally,lasted for a long time and accumulated a large amount of rainfall.There were biases in model products,and it was difficult for forecasters to make subjective corrections in complex terrain.The analysis ideas and focus points of heavy rainfall forecast,the improvement ideas and technical schemes of forecast deviation,and the improvement ideas and suggestions of services were summarized.It provides a reference for the forecast and early warning of severe weather in the future.