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Given the increasing frequency of short-duration heavy rainfall events in certain regions attributable to climate change, this study was aimed at establishing a stochastic rainfall generator based on the rainfall transition probability (RTP). The generator used the temporal distribution with short time intervals to simulate such events. Rainfall observations in Seoul and Gyeonggi province were considered. Using rainfall data derived in 1-min intervals from rainfall observations, 1-min RTPs were computed based on the Markov chain concept. This computation, accounting for both first- and second-order RTPs, differentiated by rainfall intensity, revealed various characteristics. The first- and second-order RTPs, corresponded to 0.0 mm, varied from 13 to 32% depending on the rainfall intensity. Subsequently, the 1-min RTP was used to generate 10-min rainfall scenarios tailored to independent rainstorm events (IREs). The parameters included rainfall amounts at the start and end points, rainfall duration, total rainfall amount, and features of the first and second peaks. These parameters were divided into three stages, and the resulting outcomes were examined. It was consistent that the introduction of conditions enhanced the similarity performance. Moreover, the performance of rainfall scenarios varied depending on the first- and second-order RTPs as well as the temporal distribution of IREs. The results indicate that by setting conditions appropriate for IREs and RTPs, rainfall scenarios with reasonable temporal distribution similarity can be obtained. The proposed method can consider the complexity of rainfall models and resulting uncertainties associated with rainfall occurrences. [ABSTRACT FROM AUTHOR]

Global warming has significantly increased the risk of heat waves (HWs) globally, with India being particularly vulnerable during the summer months (March-June; MAMJ). This study investigated the critical relationship between Indian summer monsoon rainfall (ISMR) and the occurrence of premonsoon HWs in subsequent years across the Indian subcontinent. It has been hypothesized that droughts during the ISMR could lead to more frequent HWs in the following MAMJ period. Using the Indian Meteorological Department's (IMD) gridded observed surface air daily maximum temperature (Tmax) dataset for the period 1951–2023, we analyzed the climatic patterns, interannual variability (IAV), and coefficient of variation (CV) of Tmax across India. The analysis compares two distinct periods: 1951–1999 (P1) and 2000–2023 (P2), with focus on Tmax trends and HW duration, distinguishing between short-duration HWs (SHWs, 2 days) and long-duration HWs (LHWs, 5 days or more). A key purpose of this study is to examine the relationship between the preceding all India summer monsoon rainfall (AISMR) and the occurance of various types of HW in the subsequent premonsoon season. In particular extreme AISMR events, such as droughts or excess rainfall, influence HW occurrence. The findings reveal a significant rise in Tmax across many regions of India during the MAMJ period, with the highest temperatures (> 37 °C) observed in northwestern, central, and eastern coastal areas. Northern India, particularly the Himalayan region, exhibits a greater interannual variability in Tmax, with June showing the most pronounced fluctuations. The study also highlights an increase in the frequency and intensity of HWs, especially in central and southern India, with the Chandigarh-Haryana-Delhi region recording the highest occurrences. A critical finding is the strong inverse relationship between the AISMR and conditions in the subsequent premonsoon season. Specifically, drought in the antecedent AISMR results in reduced soil moisture, which is strongly associated with higher premonsoon Tmax and an increased frequency of extreme heat events across India, particularly in regions prone to severe heat during this season. Drought conditions during AISMR are closely linked to higher HW frequencies in the following summer, especially in the central, northeast-central, and east-coastal regions. The frequencies of HW days, SHWs, and LHWs are significantly greater in years following AISMR droughts than in those following excess rainfall, indicating that drought years are more likely to lead to widespread HW activity. Despite the overall warming trends, some regions, such as the Indo-Gangetic Plain and parts of the Himalayan region, show cooling trends, although these trends are less widespread. The onset of the monsoon in June tends to reduce the intensity and spatial extent of warming, particularly in the central and eastern coastal regions, although significant HW trends persist in northwestern India and along the east coast. This study underscores the crucial role of AISMR in influencing HW events across India and highlights the need for adaptive strategies that account for the interactions between monsoon rainfall and HW risk, providing valuable insights for mitigating the impacts of HWs in the context of global warming. [ABSTRACT FROM AUTHOR]

Dry spells and rainfall variability significantly impact rain-fed agriculture in Ethiopia, necessitating targeted adaptation strategies. This study used rainfall data from Ethiopia's Meteorological Institute (1992–2021) to evaluate the likelihood of rainfall and dry spells lasting more than 7, 10, 15, and 20 days in northwestern Ethiopia. Markov chain modeling assessed the probabilities of these events, while Modified Mann–Kendall tests examined trends in dry days at a significance level of P < 0.05. The area has a Kiremt-dominated monomodal rain, with 80–100% rainfall probability following dry days in July–August. June (40–67%) and September (37–60%) experienced high dry days with moderate to high variability (CV: 26–45%), underscoring the need for monitoring dry day risks during planting and crop maturation. In contrast, July (10–19%) and August (10–29%) had lower frequencies of dry days. The Belg (77–89%) and Bega (79–95%) seasons showed high dry day frequencies with low variability, indicating that rain-fed agriculture is impractical during these times. Trend analysis revealed significant increases in dry days at Ebenat and decreases at Simada, with most stations showing rising dry day patterns. Dry spell analysis found minimal (0–10%) likelihood during Kiremt, but a 100% risk from late September to February. Spatial analysis revealed a high risk of extended dry spells: 20–40% in August, 40–60% in July, 100% in October, and 75–100% in September-June. May has a 60–80% chance of a 7-day dry spell, likely delaying the growing season to June and reducing yields for rain-fed crops, suggesting targeted interventions, like using drought-tolerant crops or mitigation strategies. Article Highlights: Rainfall Chances and Patterns in the study area Trends and Frequency of Dry Days Dry spell probability and Farming Risks [ABSTRACT FROM AUTHOR]

Nonstationary Rainfall frequency analysis (RFA) is used to assess how climate change is impacting the likelihood of extreme storms. A key limitation of covariate‐based approaches to nonstationary RFA is that without a physical basis, models selected based on the quality of fit to historical data cannot be reliably projected to estimate future quantiles. Here we propose to improve the physical representation of rainfall processes by using a peaks‐over‐threshold approach to separate the processes of storm intensity (impacted by thermodynamic drivers related to changes in atmospheric moisture) and storm arrival frequency (impacted by dynamic drivers that lead to changes in regional weather systems). Through stochastic experiments we demonstrate that quantiles can only be accurately projected beyond the observed climate when nonstationary models reflect the underlying nonstationary process. Through a case study we demonstrate how climate model projections of rainfall can be utilized to deduce nonstationary model structures, showing that changes in both the storm intensity and storm arrival frequency are needed to accurately estimate future quantiles. While here we propose a single simple physically informed approach for storm intensity, structuring the arrival frequency component requires a detailed understanding of atmospheric dynamics in the region of interest. Plain Language Summary: We explore how complexity can be added to probability models to estimate how the risks of extreme rainfalls may change in a warming climate. We propose a way to structure these models to separate changes due to increased moisture holding capacity in the atmosphere due to warming temperatures (thermodynamics) from changes in atmospheric circulation patterns that affect weather systems (dynamics). This separation allows us to introduce physically motivated models which represent the impact of climate change on these two processes. We demonstrate the value of using this approach through several stochastic experiments which demonstrate that shifts in extreme rainfall probabilities can only be estimated when the underlying physical processes are correctly represented. A case study is provided to demonstrate how rainfall projections from a climate model can be used to develop physically motivated models for both thermodynamic and dynamic processes. Key Points: Different nonstationary probability model structures are tested for their ability to estimate quantiles in future climatesA peaks‐over‐threshold approach is utilized to separate thermodynamic and dynamic drivers of rainfallPhysically motivated models are shown to be necessary to estimate extreme rainfall quantiles in a warming world [ABSTRACT FROM AUTHOR]

A method is proposed and demonstrated for extracting the spatial and temporal characteristics of individual rainfall events using hourly rainfall measurements at an array of rain gauges. The method is based on identifying the probabilities of overlapping rainfall events at the rain gauge locations. The method is demonstrated in a 47,000 km2 area covered by 150 rain gauges with 17 years of unbroken hourly data. The results show that the length scales of individual events in the wet season are in the range of 1.2–18.3 km depending on the event magnitude, and dry season events with corresponding magnitudes have length scales in the range of 1.5–35.9 km. The timescales of wet and dry season events generally increase with event magnitude in both seasons. The shapes and locations of cluster areas that tend to have overlapping rainfall events are substantially different between seasons, being smaller and more dispersed in the wet season and larger and lesser in number in the dry season. [ABSTRACT FROM AUTHOR]

The occurrence of rainfall is significantly affected by climate change around the world. While in some places this is likely to result in increases in rainfall, both winter and summer rainfall in most parts of New South Wales (NSW), Australia are projected to decrease considerably due to climate change. This has the potential to impact on a range of hydraulic and hydrologic design considerations for water engineers, such as the design and construction of stormwater management systems. These systems are currently planned based on past extreme rain event data, and changes in extreme rainfall amounts due to climate change could lead to systems being seriously undersized (if extreme precipitation events become more common and/or higher in magnitude) or oversized (if extreme rainfall events become less frequent or decrease in magnitude). Both outcomes would have potentially serious consequences. Consequently, safe, efficient, and cost-effective urban drainage system design requires the consideration of impacts arising from climate change on the approximation of design rainfall. This study examines the impacts of climate change on the probability of occurrence of daily extreme rainfall in New South Wales (NSW), Australia. The analysis was performed for 29 selected meteorological stations located across NSW. Future design rainfall in this research was determined from the projected rainfall for different time periods (2020 to 2039, 2040 to 2059, 2060 to 2079, and 2080 to 2099). The results of this study show that design rainfall for the standard return periods was, in most cases, lower than that derived employing the design rainfall obtained from the Australian Bureau of Meteorology (BoM). While most of the analysed meteorological stations showed significantly different outcomes using the climate change scenario data, this varied considerably between stations and different time periods. This suggests that more work needs to be performed at the local scale to incorporate climate change predicted rainfall data into future stormwater system designs to ensure the best outcomes. [ABSTRACT FROM AUTHOR]

The seasonal prediction of the Indian summer monsoon (ISM) and Monsoon Intraseasonal Oscillations (MISO), as well as the Madden Julian Oscillations (MJO) that strongly modulate MISO, is important to the country for water and crop management. We have analyzed the precipitation, convection, and cloud in the selected models from the sixth generation Coupled Model Intercomparison Projects (CMIP6). This study highlights the significant differences in simulating MISO and MJO features between the selected CMIP6 models and their possible reasons. The mean and intraseasonal features of MISO and MJO varied significantly in CMIP6 models, which may impact better depiction of convection and total cloud fraction. The probability distributions of rainfall and OLR in CMIP6 models also indicate significant variations in simulating the MISO and mean ISM. The results demonstrate the importance of cloud and convection in CMIP6 models to depict realistic MISO and MJO and provide a road map for improving ISM climate prediction and projections. [ABSTRACT FROM AUTHOR]

Due to their severity and lack of predictability, understanding and forecasting extreme precipitation events (EPEs) is critical for disaster risk reduction. The present work documents the large‐scale environment of tropical EPEs based on a 42‐year data set combining dense rain‐gauge networks that cover several tropical small islands and coastal regions. Approximately 10%–30% of EPEs are associated with a tropical storm or cyclone (TC), except for Reunion, for which its high topography makes it reach 55%. TCs multiply the EPE probability by a factor of 4–15, especially during TCs of category 1 or higher. A composite analysis demonstrates that the remaining large part of EPEs occurs within large‐scale and strong moist, convective, and cyclonic wind anomalies resulting from the superimposition of intraseasonal, seasonal‐to‐annual, and interannual timescales. These intense anomalies come essentially from intraseasonal variability, and lower frequencies improve the effect of intraseasonal events in creating a favorable environment for EPEs. Plain Language Summary: Floods and landslides, mainly caused by extreme precipitation events (EPEs), are severe disasters that affect populations and economies. However, their prediction, especially in tropical areas, is challenging. Here, we study the main atmospheric configurations leading to EPEs using rain‐gauge data densely sampling several tropical small islands and coastal regions, covering the period 1979–2021. In areas prone to tropical storms and cyclones (TCs), these systems account for 10%–30% of EPEs, except for Reunion, whose high topography increases it up to 55%. TC‐related EPEs are also more intense than non‐TC‐related EPEs. TCs multiply the EPE probability of occurrence by 4–15 times, depending on the region and the TC category. Non‐TC‐related EPEs are shown to occur within specific large‐scale weather patterns in which moisture is highly increased, and convective storms are more active. These patterns involve phenomena occurring at intraseasonal (2–90‐day periods), seasonal‐to‐annual (91–365‐day periods), and interannual (periods above one year) timescales. The intraseasonal variability helps the most to develop this environment favorable to the occurrence of EPEs. Slower signals build a background on which intraseasonal variability can have more effects. The real‐time monitoring of these different features should improve EPE forecasting capabilities and deliver more early warnings. Key Points: Over most of the studied tropical small islands, only 10%–30% of extreme precipitation events occur near a tropical storm or cycloneTropical storms and cyclones favor heavier rainfall and raise the probability of occurrence of extreme precipitation by a factor of 4–15The other largest part of events occur in a large‐scale, intense, moist, and convective anomaly driven mainly by the intraseasonal timescale [ABSTRACT FROM AUTHOR]

This paper proposes a new post-processing method for model data in order to improve typhoon track and rainfall forecasts. The model data used in the article include low-resolution ensemble forecasts and high-resolution forecasts. The entire improvement method contains the following three steps. The first step is to correct the typhoon track forecast: three ensemble member optimization methods are applied to the low-resolution ensemble forecasts, and then the best optimization method is selected with the principle of the smallest average distance error. The results of rainfall forecasts show that the corrected rainfall forecast performs better than the original forecasts. The second step is to derive the high-resolution probability rainfall forecast: the neighborhood method is applied to the deterministic high-resolution rainfall forecast. The last step is to correct the typhoon rainfall forecast: the low- and high-resolution forecasts are blended using the probability-matching method with two different schemes. The results show that the forecasts of the two schemes perform better than the original forecast under all rainfall thresholds and all forecast lead times. In terms of bias score, a rain forecast from one scheme corrects the rainfall deviation from observation better for light and moderate rainfall, whereas a rain forecast from another scheme corrects the rainfall deviation better for heavy and torrential rainfall. The better performance of corrected rain forecasts in the case of Typhoon Lekima and Rumbia over eastern China is demonstrated. [ABSTRACT FROM AUTHOR]

Monthly precipitation data from 58 synoptic stations throughout Botswana, spanning 1981–2016, were used in this study. The data were examined using multivariate analysis to determine regions exhibiting distinct precipitation variability patterns and regimes. To accomplish this, the T-mode of principal component analysis was applied to the correlation matrix of the data. Based on the maximum loading values of the rotational principal component scores, the T-mode indicated three separate subregions with varying precipitation patterns over time. Four clusters with distinct rainfall patterns were identified when cluster analysis was performed on the principal component scores. An assessment of the homogeneity of the clusters was performed using L-moment's heterogeneity measure (H). Statistical analysis was employed to model annual rainfall data using five commonly used rainfall analysis probability distribution functions: normal, lognormal, gamma, Weibull, and Gumbel. The probability distributions with the greatest fit were determined based on the maximum overall score, which was calculated by adding the individual point scores of three chosen goodness-of-fit tests. Each cluster exhibited distinct probability distribution functions, with the gamma, Gumbel, lognormal, and Weibull distributions providing the most accurate descriptions. [ABSTRACT FROM AUTHOR]

Rainfall-induced slope failures and landslides pose significant hazards worldwide, impacting various regions, including the Kurumbadi area of Nilgiris, Tamil Nadu, India. Despite the region's vulnerability to such disasters, limited research has addressed the uncertainties surrounding soil parameters, particularly in the context of slope-stability analysis. The present study aims to fill this gap by conducting reliability analysis to assess the stability of hillslopes under different rainfall patterns. It specifically investigates how variations in soil compaction, including medium dense and loose soils, influence slope stability under both advanced and recorded rainfall patterns in the Kurumbadi area. Utilizing data from numerical analyses using Fast Lagrangian Analysis of Continua (FLAC), the study conducts comprehensive reliability analysis with Response Surface Methodology (RSM) and First-order Reliability Method (FORM), to evaluate slope stability. The findings reveal that advanced rainfall patterns significantly increase the likelihood of slope failure, highlighting the region's increased vulnerability to intense rainfall events. These insights contribute valuable knowledge to the assessment of slope instability across varying rainfall intensities and durations. Furthermore, the findings support the development of proactive measures, such as early-warning systems based on predefined threshold values, to mitigate the risks associated with rainfall-induced slope failures. [ABSTRACT FROM AUTHOR]

Climate change impacts have the potential to alter the design rainfall estimates around the world. Decreasing trends in the summer and winter rainfall in New South Wales (NSW), Australia have already been observed due to climate variability and change. The derivation of design rainfall from historical rainfall, which is required for the design of stormwater management infrastructure, may be ineffective and costly. It is essential to consider climate change impacts in estimating design rainfall for the successful design of stormwater management infrastructure. In this study, the probability of the occurrence of daily extreme rainfall has been assessed under climate change conditions. The assessment was performed using data from 29 meteorological stations in NSW, Australia. For the evaluation of future design rainfall, the probability of the occurrence of extreme rainfall for different recurrence intervals was developed from daily extreme rainfall for the periods of 2020 to 2099 and compared with the current Australian Bureau of Meteorology (BoM) design rainfall estimates. The historical mean extreme rainfall across NSW varied from 37.71 mm to 147.3 mm, indicating the topographic and climatic influences on extreme rainfall. The outcomes of the study suggested that the future design rainfall will be significantly different from the current BoM estimates for most of the studied stations. The comparison of the results showed that future rainfall in NSW will change from −4.7% to +60% for a 100-year recurrence interval. However, for a 2-year recurrence interval, the potential design rainfall change varies from an approximately 8% increase to a 40% decrease. This study revealed that the currently designed stormwater management infrastructure will be idle in the changing climate. [ABSTRACT FROM AUTHOR]

This study investigates the impact of the Madden–Julian Oscillation (MJO) on the extreme rainfall events across 11 eastern coastal stations during the northeast monsoon season (November to March) over the Philippines from 1979 to 2019. The contribution of synoptic systems to these extreme rainfall events such as tropical cyclones (TCs), low‐pressure systems (LPS), cold surges (CS), and other disturbances as they coincide with a strong and active MJO were quantified. The results show that the probability of extreme rainfall occurrence increases first to as much as 20% in the southernmost stations in Phase 4. Then, it increases to more than 60% in central‐eastern stations in Phase 6. The extreme rainfall events were then classified into: MJO‐only, TC‐MJO, TC‐nonMJO, LPS‐MJO, LPS‐nonMJO, CS‐MJO, CS‐nonMJO, and others. The percentage contribution of MJO only, TC‐MJO, LPS‐MJO, and CS‐MJO to the total extreme rainfall events ranges from 9% to 16%, 0% to 3%, 2% to 4%, 1% to 9%, respectively. The relationship between MJO and flooding events in the Philippines was also examined. About 28 flood events or 266 flooding days were identified during the analysis period, wherein 50% of these events coincidentally occurred during strong and active phases of MJO. [ABSTRACT FROM AUTHOR]

Rainfall is one of the most important water sources for rice production in China. However, its temporal and spatial variability is leading to water shortages. The present study collected a long series of historical rainfall data from research sites during the rice growth period to analyze the characteristics of rainfall distribution and the correlation with rainfall utilization efficiency, aiming to investigate its impact on rice irrigation practices. It is found that the rainfall distribution varied greatly between the different locations and growth periods. The average rainfall of the whole growth period ranges from 135.5 mm to 694.5 mm. The rainfall curve exhibits a typical unimodal pattern with variations in the intensity, duration, and timing of peak precipitation across different growth periods. During the rice growth period, the cases in southern China are more prone to waterlogging for a high probability of continuous rainfall, and the cases in northern China are more prone to drought. The rainfall utilization efficiency of all cases exhibits a significant inter-year fluctuation range, negatively influenced by the rainfall amount and rainfall inhomogeneity. The efficiency in utilizing precipitation is diminished with greater and more uneven rainfall experienced during the growth period. These findings can provide a decision-making basis for optimizing rice irrigation strategies and enhancing rainfall utilization efficiency in diverse regions across China. [ABSTRACT FROM AUTHOR]

A larger antecedent effective precipitation (AEP) indicates a higher probability of a debris flow (Pdf) being triggered by subsequent rainfall. Scientific topics surrounding this qualitative conclusion that can be raised include what kinds of variation rules they follow and whether there is a boundary limit. To answer these questions, Jiangjia Gully in Dongchuan, Yunnan Province, China, is chosen as the study area, and numerical calculation, a rainfall scenario simulation, and the Monte Carlo integration method have been used to calculate the occurrence probability of debris flow under different AEP conditions and derive the functional relationship between Pdf and AEP. The relationship between Pdf and AEP can be quantified by a piecewise function. Pdf is equal to 15.88 %, even when AEP reaches 85 mm, indicating that debris flow by nature has an extremely small probability compared to the rainfall frequency. Data from 1094 rainfall events and 37 historical debris flow events are collected to verify the reasonability of the functional relationship. The results indicate that the piecewise functions are highly correlated with the observation results. Our study confirms the correctness of the qualitative description of the relationship between AEP and Pdf , clarifies that debris flow is a small-probability event compared to rainfall frequency, and quantitatively reveals the evolution law of debris flow occurrence probability with AEP. All the above discoveries can provide a clear reference for the early warning of debris flows. [ABSTRACT FROM AUTHOR]

The state of Iowa in the Central United States has experienced increasing flooding, with major events occurring most recently in 1993, 2008, 2011, and 2019. These floods caused over $23B in damage despite Iowa's three flood control reservoirs and expansive levee systems, suggesting the need for additional solutions. Iowa is home to over 4,000 small dams whose cumulative capacity more than double the state's current flood storage. These locations are operated passively, i.e., without the use of gated outlets to control basin storage utilization, thus limiting their flood mitigating potential. Here, the authors simulate gated outlets at 130 small dams within a 660 km2 watershed to (1) evaluate how effectively these storages can be activated across a watershed using gated outlets; and (2) quantify the utilization capacity of an activated distributed storage system for flow reduction. The authors used stochastic storm transposition to generate thousands of spatially variable rainfall events using Stage IV rainfall data within the Iowa domain at durations of 6, 12, 24, and 48 h and annual exceedance probabilities (AEPs) of 0.2, 0.1, 0.02, and 0.01. This expands the effective period of record, providing storms of various durations, intensities, and spatiotemporal distributions. An active management scheme was defined within the reservoir module of the hillslope link model designed to store water within the ponding locations. The study calculated the flow reductions that were achieved through this active scheme and found that flows were reduced for every rainfall duration and probability regardless of basin spatial scale. Reductions reached as high as 70% for a 6 h, 0.2 AEP event at a 93 km2 drainage area, while flows were reduced by roughly 12% for a 48 h, 0.01 AEP event at the basin outlet. This work establishes activated distributed storage as a meaningful flood reduction measure under realistic rainfall conditions at a variety of spatial scales. [ABSTRACT FROM AUTHOR]

Dashiling Gully, located in Miyun District, Beijing, exhibits a high susceptibility to debris flow due to its unique geological and topographical characteristics. The area is characterized by well-developed rock joints and fissures, intense weathering, a steep gradient, and a constricted gully morphology. These factors contribute to the accumulation of surface water and loose sediment, significantly increasing the risk of debris flow events. Following a comprehensive field geological investigation of Dashiling Gully, key parameters for simulation were obtained, including fluid weight, volume concentration, and rainfall. The formation and development conditions of potential mudslides were analyzed, and numerical simulations were conducted using FLO-2D software (version 2009) to assess scenarios with rainfall probabilities of 1 in 30, 50, and 100 years. The simulations accurately reconstructed the movement velocity, deposition depth, and other critical movement characteristics of mudslides under each rainfall scenario. Using ArcGIS, pre- and post-treatment hazard zoning maps were generated for Dashiling Gully. Furthermore, the efficacy of implementing a retaining wall as a mitigation measure was evaluated through additional numerical simulations. The results indicated that mudslide velocities ranged from 0 to 3 m/s, with deposition depths primarily between 0 and 3 m. The maximum recorded velocity reached 3.5 m/s, corresponding to a peak deposition depth of 4.31 m. Following the implementation of the retaining wall, the maximum deposition depth significantly decreased to 1.9 m, and high-risk zones were eliminated, demonstrating the intervention's effectiveness. This study provides a rigorous evaluation of mudslide movement characteristics and the impact of mitigation measures within Dashiling Gully. The findings offer valuable insights and serve as a reference for forecasting and mitigating similar mudslide events triggered by heavy rainfall in gully mudslides. [ABSTRACT FROM AUTHOR]

The choice of the most suitable probability distribution for rainfall varies across different geographical regions due to varying spatial and temporal variability. Therefore, there is an ongoing need to conduct research aimed at determining the optimal distribution models for various locations. This study was specifically designed to investigate the variability in rainfall patterns and identify the most appropriate probability distribution models for total monthly rainfall in selected agricultural areas in Nigeria. To achieve this, a comprehensive analysis was conducted on 41 years rainfall data spanning from 1982 to 2022. Ten different probability distributions were applied to fit these data for each calendar month as well as for the overall dataset. Applicable parameters (shape, scale, location, rate, logarithmic mean, and logarithmic standard deviation) for each distribution were estimated using maximum likelihood estimates. The selection of the best-fit distribution model for each month was based on three criteria, including the Akaike information criteria (AIC), Bayesian information criteria (BIC), and corrected Akaike information criteria (CAIC). The results revealed that the Pearson III and loglogistic distributions provided the best-fit model for rainfall data during most months at selected locations. However, when considering the overall rainfall across most locations, the Gamma distribution exhibited superior performance compared to the other distributions. This research contributes to the enhancement of integrated water resources management and facilitates the accurate evaluation and planning of water resources in selected agricultural hubs in Nigeria, with the ultimate aim of improving agricultural productivity. [ABSTRACT FROM AUTHOR]

The spatiotemporal distribution of geomorphological incidents was examined in the present study, including the characteristics of obstruction distances and durations, by utilizing nationwide incident mitigation data and precipitation observation records from the period spanning 2019 to 2022. By comparing rainfall features over different temporal scales across various regions, the aim of the present study was to enhance the current comprehension of the patterns through which regional precipitation initiates incidents on highways by comparing rainfall characteristics over distinct temporal scales across diverse geographical areas. The findings indicate that: (1) The spatial distribution of highway incidents in China is significantly correlated with regional natural environments, predominantly concentrated in the southern parts of the country's second and third topographical terraces. The temporal distribution closely aligns with annual and monthly precipitation patterns, with the majority of occurrences taking place from June to September. Further, notable disparities in the distribution of highway-related incidents were observed among counties across most provinces; (2) National highways experience a notably higher frequency of incidents than expressways and provincial roads, with most obstruction lengths concentrated within 1 km and durations predominantly under 3 days; (3) The probability of daily rainfall inducing highway incidents is distinctly higher than that of short-duration rainfall, with eastern and southern China experiencing significantly greater inducing precipitation volumes than other regions. The majority of areas are susceptible to incidents within a 3-day window following heavy rainfall or within 24 h after intense short-duration rainfall. Moreover, it is observed that incidents are more closely associated with extreme precipitation occurring within a single day; (4) There is a certain lag between the timing of incidents and the occurrence of extreme short-duration heavy rainfall, with the highest frequency of incidents coinciding with continuous rainfall periods of 3 to 6 days. [ABSTRACT FROM AUTHOR]

Both the total amount of precipitation falling on Earth's surface and the fraction of the surface area on which it falls represent two key global climate indicators for Earth's global hydrological cycle. We show that the fraction of Earth's surface area receiving daily precipitation is closely connected to the global statistics of local wet-day frequency and mean precipitation intensity, based on the ERA5 reanalysis. Our analysis of the global statistical distribution of local temporal mean precipitation intensity μ revealed a close link between (1) its global spatial average ⟨ μ ⟩ and (2) the total daily precipitation falling on Earth's surface divided by the global surface area fraction on which it falls. This correlation highlights an important connection, since the wet-day frequency and the mean precipitation intensity represent two key parameters that may be used to approximately infer the probability of heavy rainfall on local scales. We also found a close match between the global mean surface temperature and both the total mass of 24-h precipitation falling on Earth's surface as well as surface area receiving 24-h precipitation in the ERA5 data, highlighting the dependency between the greenhouse effect and the global hydrological cycle. Moreover, the total planetary precipitation and the daily precipitation area represent links between the global warming and extreme precipitation amounts that traditionally have not been included in sets of essential climate indicators. A simple back-of-the-envelope calculation suggests that half of Δ ⟨ μ ⟩ / Δ T = 0.47 mm / day can be explained by increased 24-h precipitation and half by a reduced fractional area of 24-h precipitation. [ABSTRACT FROM AUTHOR]

The second-largest wildfire in the history of South Korea occurred in 2022 due to strong winds and dry climates. Quantitative evaluation of soil erosion is necessary to prevent subsequent sediment disasters in the wildfire areas. The erosion rates in two watersheds affected by the wildfires were assessed using the revised universal soil loss equation (RUSLE), a globally popular model, and the soil erosion model for mountain areas (SEMMA) developed in South Korea. The GIS-based models required the integration of maps of the erosivity factor, erodibility factor, length and slope factors, and cover and practice factors. The rainfall erosivity factor considering the 50-year and 80-year probability of rainfall increased from coastal to mountainous areas. For the LS factors, the traditional version (TV) was initially used, and the flow accumulation version (FAV) was additionally considered. The cover factor of the RUSLE and the vegetation index of the SEMMA were calculated using the normalized difference vegetation index (NDVI) extracted from Sentinel-2 images acquired before and after the wildfire. After one year following the wildfire, the NDVI increased compared to during the year of the wildfire. Although the RUSLE considered a low value of the P factor (0.28) for post-fire watersheds, it overestimated the erosion rate by from 3 to 15 times compared to the SEMMA. The erosion risk with the SEMMA simulation decreased with the elapsed time via the vegetation recovery and stabilization of topsoil. While the FAV of RUSLE oversimulated by 1.65~2.31 times compared to the TV, the FAV of SEMMA only increased by 1.03~1.19 times compared to the TV. The heavy rainfall of the 50-year probability due to Typhoon Khanun in 2023 generated rill and gully erosions, landslides, and sediment damage in the post-fire watershed on forest roads for transmission tower construction or logging. Both the RUSLE and SEMMA for the TV and FAV predicted high erosion risks for disturbed hillslopes; however, their accuracy varied in terms of the intensity and extent. According to a comparative analysis of the simulation results of the two models and the actual erosion situations caused by heavy rain, the FAV of SEMMA was found to simulate spatial heterogeneity and a reasonable erosion rate. [ABSTRACT FROM AUTHOR]

This study proposes a deep neural network (DNN)-based downscaling model incorporating kernel principal component analysis (KPCA) to investigate the precipitation uncertainty influenced by typhoons in Taiwan, which has a complex island topography. The best tracking data of tropical cyclones from the Joint Typhoon Warning Center (JTWC) are utilized to calculate typhoon and non-typhoon precipitation. KPCA is applied to extract nonlinear features of the BCC-CSM1-1 (Beijing Climate Center Climate System Model version 1.1) and CanESM2 (second-generation Canadian Earth System Model) GCM models. The length of the data used in the two GCM models span from January 1950 to December 2005 (historical data) and from January 2006 to December 2099 (scenario out data). The rainfall data are collected from the weather stations in Taichung and Hualien (cities of Taiwan) operated by the Central Weather Administration (CWA), Taiwan. The period of rainfall data in Taichung and in Hualien spans from January 1950 to December 2005. The proposed model is constructed with features extracted from the GCMs and historical monthly precipitation from Taichung and Hualien. The model we have built is used to estimate monthly precipitation and uncertainty in both Taichung and Hualien for future scenarios (rcp 4.5 and 8.5) of the GCMs. The results suggest that, in Taichung and Hualien, the summer precipitation is mostly within the normal range. The rainfall in the long term (January 2071 to December 2080) for both Taichung and Hualien typically fall between 100 mm and 200 mm. In the long term, the dry season (January to April, November, and December) precipitation for Taichung and that in the wet season (May to October) for Hualien are less and more affected by typhoons, respectively. The dry season precipitation is more affected by typhoons in Taichung than Hualien. In both Taichung and Hualien, the long-term probability of rainfall exceeding the historical average in the dry season is higher than that in the wet season. [ABSTRACT FROM AUTHOR]

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According to HadGEM3 (CMIP6) models, anthropogenic forcing reduced the probability of 2022-like June mean precipitation by about 32% (15%) and increased 5-day rainfall extreme probability by about 1.8 (1.3) times. [ABSTRACT FROM AUTHOR]

Sequential climate hazards, such as "warm and wet" compound extremes, have direct societal implications for highly urbanized regions and agricultural production. While typically extreme temperatures and rainfall are inversely correlated during the summer, extreme humid heatwaves often lead to atmospheric instability and moisture convection, increasing the likelihood of extreme precipitation (EP). Little is known about how heatwave characteristics, such as peak intensity and duration, influence EP at a regional scale. Using high‐resolution, sub‐daily station‐based observational records over five decades (1971–2021) across India, we find a robust increase in the frequency of compound humid heat‐peak precipitation events in all seasons. Our sensitivity analysis of the impact of humid heatwave characteristics on the subsequent sub‐daily rainfall extremes reveals that, with an increase in peak heatwave intensity for a given heatwave duration, >50% of sites show an increase in the magnitude of rainfall; conversely, with an increase in heatwave duration for a given peak heatwave intensity, around 67% sites show a decline in sub‐daily rainfall extremes. An asymmetrical shift toward above‐average precipitation extremes in response to humid heat stress is mainly clustered around low‐elevation, densely populated coastal areas and the irrigation‐intensive Indo‐Gangetic Plains. Plain Language Summary: Compound humid heatwave‐extreme rainfall events substantially impact society, as the sequential occurrence of such events has immense damage potential due to the limited recovery time compared to the situation where these events occur in isolation. Detecting spatiotemporal patterns in compound hot‐wet weather extremes is essential for disaster management, projecting future changes, and devising an early warning system for vulnerable populations. Using gauge‐based observations of more than five decades across India, we find a robust increase in the number of compound humid heatwave‐extreme precipitation events over climatologically homogeneous regions of India across all seasons. We analyze how the rainfall extremes depend on the characteristics of the preceding heatwave: we find that rainfall extremes increase with increasing heatwave intensity for more than 50% of sites; in contrast, rainfall extremes decrease with increasing heatwave duration for around 67% of sites. A shift toward higher rainfall in response to humid heat characteristics is apparent in densely populated coastal areas and irrigation‐dominated regions of the country. Key Points: We quantify the exceedance probability of sub‐daily rainfall peaks conditioned on preceding heatwavesA significant upward trend in the frequency of compound heatwave‐extreme precipitation events is detected across all seasonsSub‐daily rainfall peaks shows notable changes in their exceedance probability in response to changing heatwave properties [ABSTRACT FROM AUTHOR]

This study investigates the impact of urbanization on extreme winter rainfall in the South China Greater Bay Area (GBA) through the analysis of hourly station observations and simulations using the Weather Research and Forecasting Model with the Single Layer Urban Canopy Model (WRF-SLUCM). Data from 2008 to 2017 reveal that urban areas in the GBA experience lower 99th percentile hourly winter rainfall intensity compared to surrounding rural regions. However, urban locations exhibit higher annual maximum hourly rainfall (Rmax) and very extreme rainfall events (99.99th percentile) in winter, suggesting a positive influence of urbanization on extreme winter precipitation. A case study further underscores the role of the Urban Heat Island (UHI) effect in enhancing extreme rainfall intensity and probability in the GBA urban areas. Additionally, two extreme cases were dynamically downscaled using WRF-SLUCM, involving four parallel experiments: replacing urban land use with cropland (Nourban), using historical urban land use data from 1999 (99LS), projecting near-future urban land use for 2030 (30LS), and considering 2030 urban land use without anthropogenic heat (AH) (30LS-AH0). Synoptic analysis demonstrates that cold air intrusion suppresses the GBA UHI in Case 2013 but not in Case 2015. Reduced evaporation and humidity induced by urban surfaces significantly decrease urban precipitation in Case 2013. In contrast, the persistent UHI in Case 2015 enhances local convection and land–ocean circulation, leading to increased moisture flux convergence and amplified urban precipitation intensity and probability in 30LS compared to Nourban. This amplification is primarily attributed to AH, while the change in 99LS remains insignificant. These findings suggest that urban influences on extreme precipitation in the GBA persist during winter, particularly when the UHI effect is maintained. [ABSTRACT FROM AUTHOR]

Problems such as the expansion of impervious areas and changes in underlying surfaces have occurred in cities due to rapid urbanization, along with an increasing probability of extreme rainfall. Based on the normalized building index (NDBI) of underlying surfaces, calculated from remote sensing images in the Pearl River Delta from 1990 to 2020, this study determines the underlying surface changes in the Pearl River Delta. Based on the hourly rainfall data of meteorological stations in the Pearl River Delta region from 1990 to 2020, the extreme rainfall indexes are calculated to analyze the changes in extreme rainfall in the Pearl River Delta. Based on the NDBI of underlying surfaces and extreme rainfall calculated in the Pearl River Delta, the evolution of underlying surfaces and extreme rainfall is analyzed, as is the correlation between them, and the main conclusions are as follows: (1) From 1990 to 2020, the NDBI in highly urbanized areas in the Pearl River Delta was higher than that in non-highly urbanized areas. The NDBI in highly urbanized areas showed an increasing trend, and the growth rate tended to slow down; (2) From 1990 to 2020, extreme rainfall in highly urbanized areas of the Pearl River Delta was higher than in non-highly urbanized areas. Extreme rainfall in both highly urbanized areas and non-highly urbanized areas showed an increasing trend, with that in highly urbanized areas increasing faster; (3) The positive correlation between the NDBI and extreme rainfall indicators in highly urbanized areas is more significant than that in non-highly urbanized areas. [ABSTRACT FROM AUTHOR]

In recent decades, the increasing number and severity of wildfires have become a major concern in many regions of the world. Besides having direct consequences for forests, crops, and settlements, wildfires can have significant effects on the catchment's hydrology, altering their response to rainfall and promoting enhanced runoff and erosion. Increased runoff in burnt areas can result in catastrophic damage and loss of life from destructive debris flows and floods. Although observed in many regions of the world, post-wildfire debris flows/floods seem to prevail in the western USA and southern Europe. In Italy, post-wildfire debris flows/floods have been observed in a number of sectors of the Apennine range, and a total of 113 events occurred between 2001 and 2021 in the Campania region. A recent event occurred on August 24, 2020, in the Fosso delle Pescine catchment, on the southwestern slope of Mt. Omo in the L'Aquila municipality, hit by a severe wildfire at the beginning of the month. The consequences of the event on people and properties indicate the need for an evaluation of debris flow/flood hazard in both pre- and post-wildfire conditions. The analysis of these events may often be complex, requiring time and many input data. In this context, a simplified method of analysis which accounts for event initiation and propagation through a shallow water equation-based approach would represent a new and innovative solution for fast and preliminary debris flow/flood analyses. In this research, a reduced complexity analysis based on probability analysis of rainfall and 2D hydrodynamic modeling was carried out by estimating (i) wildfire extent and severity, (ii) sediment characteristics and availability, (iii) potential runoff induced by rainfall, and (iv) inundation by debris flow/flood. A debris flow/flood hazard model was developed on the basis of multiple return periods (10, 50, 100, and 300 years) for debris flow inundation scenarios derived by non-Newtonian 2D hydrodynamic modeling of debris flow hydrograph and qualitatively validated on the basis of the extent of alluvial fan deposits. Potential drawbacks of the model are related to the possible overestimation of surface runoff, related to the consideration of the whole catchment contributing area, and the limited volumetric sediment budget available at the headwater, which might not sustain a high return period event generation. A debris flow/flood event might originate from rainfall concentrated at the headwater of the catchment. [ABSTRACT FROM AUTHOR]

We develop the first climate risk Stress Test for the Peruvian financial system following a topdown approach. Focusing on the microeconomic channel, we evaluate how heavy rainfall and droughts, under a scenario of pure physical risk, will marginally affect the probability of default (PD) of borrowers by 2050. Using information from the Credit Registry, the National Oceanic and Atmospheric Administration (NOAA), and CMIP6 precipitation projections (37 modeling groups), we calibrate the marginal impacts differentiating by economic sector and geographical location. We find that, on average, by December 2050, the probability of default of the Peruvian financial system would increase by 4.9% with respect to December 2020. By geographic area, borrowers located on the northern coast (Piura, Lambayeque) and the southern highlands (Ayacucho, Cusco) would be negatively affected by heavy rainfall, while the rainforest (Madre de Dios, Ucayali) would be negatively affected by droughts. Moreover, the economic sectors affected by heavy rainfall or droughts would be agriculture, commerce, and transportation & communications. [ABSTRACT FROM AUTHOR]

Flash flood is the most hazardous type of flooding, mainly caused by extensive rainfall. It also can cause significant harm to a community's economy, ecology, and society without warning at an irrational pace. Therefore, this study was conducted to detect the time series element within the rainfall data, select the optimal model, and make predictions about the volume of rainfall in Selangor. A variety of univariate time series models were utilized, including the naïve model, decomposition model, Autoregressive Integrated Moving Average (ARIMA) model, exponential models, and combined models. Historical monthly rainfall data collected from Petaling station and Subang station from 2018 to 2022 were used to estimate the parameters of the models, and the model was evaluated for the smallest error of measurements. Previous research mostly focused on complex methodologies for forecasting rainfall. However, this research aimed to identify a simple tool for fast prediction of rainfall. The results showed that the combination of the ARIMA (2,0,3) model from Petaling Station and the ARIMA (4,0,4) model from Subang station were able to capture the trends and seasons in the time series with the lowest error of measurement on short-term predictions of rainfall volume. Furthermore, the study delves into the concept of combined time series models, which are blended using weighted performance measures to enhance prediction accuracy further. The research acknowledges certain limitations of univariate time series models, notably their inability to account for intricate interactions among environmental variables and potential long-term trends, such as those stemming from climate change. Overall, the study explores the potential of combining models to refine predictions for forecasting rainfall volume in Klang Valley. [ABSTRACT FROM AUTHOR]

Rainfall is crucial for the development and management of water resources. Six hybrid soft computing models, including multilayer perceptron (MLP)–Henry gas solubility optimization (HGSO), MLP–bat algorithm (MLP–BA), MLP–particle swarm optimization (MLP–PSO), radial basis neural network function (RBFNN)–HGSO, RBFNN–PSO, and RBFGNN–BA, were used in this study to forecast monthly rainfall at two stations in Malaysia (Sara and Banding). Different statistical measures (mean absolute error (MAE) and Nash–Sutcliffe efficiency (NSE) and percentage of BIAS (PBIAS)) and a Taylor diagram were used to assess the models' performance. The results indicated that the MLP–HGSO performed better than the other models in forecasting rainfall at both stations. In addition, transition matrices were computed for each station and year based on the conditional probability of rainfall or absence of rainfall on a given month. The values of MAE for testing processes for the MLP–HGSO, MLP–PSO, MLP–BA, RBFNN–HGSO, RBFNN–BA, and RBFNN–PSO at the first station were 0.712, 0.755, 0.765, 0.717, 0.865, and 0.891, while the corresponding NSE and PBIAS values were 0.90–0.23, 0.83–0.29, 0.85–0.25, 0.87–0.27, 0.81–0.31, and 0.80–0.35, respectively. For the second station, the values of MAE were found 0.711, 0.743, 0.742, 0.719, 0.863 and 0.890 for the MLP–HGSO, MLP–PSO, MLP–BA, RBFNN–HGSO, RBFNN–BA, and RBFNN–PSO during testing processes and the corresponding NSE–PBIAS values were 0.92–0.22, 0.85–0.28, 0.89–0.26, 0.91–0.25, 0.83–0.31, 0.82–0.32, respectively. Based on the outputs of the MLP–HGSO, the highest rainfall was recorded in 2012 with a probability of 0.72, while the lowest rainfall was recorded in 2006 with a probability of 0.52 at the Sara Station. In addition, the results indicated that the MLP–HGSO performed better than the other models within the Banding Station. According to the findings, the hybrid MLP–HGSO was selected as an effective rainfall prediction model. [ABSTRACT FROM AUTHOR]

Background: Agriculture is the most sensitive sector to climate variability. In rain-fed ecosystems, rainfall is the single most essential component in crop planning. Crop planning using rainfall variability and probability analysis helps to improve the productivity. Methods: A study was taken for crop planning in Madurai district by analyzing the rainfall variability and probability analysis using 40 years (1982-2021) of weekly, monthly, seasonal and annual rainfall data. Result: The mean annual rainfall in Madurai district was 925.5 mm was spread over 74 rainy days with 24.7 per cent of coefficient of variation thereby the rainfall is dependable over years with decreasing trend. The probability analysis showed that receiving 20 mm rainfall per week was for 12 weeks (32 to 45th week) except 35 and 37th week which is helpful for sowing and planting. On the other hand, highest mean monthly rainfall was received in October (176 mm) followed by September (137.4 mm). Seasonal analysis resulted that Southwest monsoon had high rainfall quantity with mean value of 397.7 mm. Rice is the major crop cultivated in this region should be replaced by maize, green gram, black gram, cowpea, sorghum, ragi and minor millets which can withstand drought conditions with less crop duration. [ABSTRACT FROM AUTHOR]

Rainfall probability analysis improves predictions of the minimum assured rainfall to aid crop planning. An attempt has been made to look into the patterns of rainfall distribution, including weekly, seasonal, and annual rainfall, using data collected from the Fingeshwar tehsil of the Gariyaband district, Chhattisgarh, over a ten-year period (2011-2020). Using the Weibull plotting position function, expected weekly, monthly, seasonal, and yearly rainfall values were calculated for various probability levels. Based on a 10- year yearly average, the data revealed that 1074.4 mm of rain were actually seen, following an average of 52.2 rainy days. A rainfall amount with a 75% probability level predicts 862.9 mm annually. The largest amount of weekly rainfall, 49 mm, was predicted to fall in the 35th week, followed by 32.1 mm in the 25th standard week and the least amount, 0.0 mm, in the 20-22nd, 29, 37, 40- 42nd SMW. This prediction was made at a 75% chance level, same like the one before. According to a study of monthly rainfall at 70, 75, and 80% probability levels, the three crucial wet months are July, August, and September, with probabilities of getting a monthly rainfall between 0 and 50 mm. At a 70% probability level, the seasonal rainfall report projects 833 mm for the Kharif season. Thus, it can conclude that the kharif season's activities could start between the 22nd and the 23rd standard week and farmers can properly produce paddy crops in highland areas followed by any rabi crop in rabi season. [ABSTRACT FROM AUTHOR]

The current challenges of numerical weather prediction (NWP) of rainfall mainly stem from the complex and multiscale nature of rainfall. In recent years, as observation capability improved worldwide, there has been an increased feasibility to use data‐driven models to enhance forecasting performance with rainfall observation. Compared to traditional statistical and machine learning models, deep‐learning models show considerable promise in capturing the spatial‐temporal features of weather processes from multiple predictors, but the convolution‐based feature extractor is suboptimal due to the linear nature of convolution kernels. In this study, a multilevel forecasting model is proposed to forecast each rainfall level, in which each submodel adopts a graph neural network for feature extraction. Spatial and temporal propagation functions based on grid structure are designed to explicitly represent feature fusion and propagation of multiple predictors across multiple scales. On model training, a weight setting strategy that balances the impact of samples with different rainfall values on the total training loss is proposed, and a soft classification label is designed to convert observed rainfall into the probability of rainfall above each threshold. The proposed model was trained and validated on NWP data provided by European Center for Medium‐Range Weather Forecast, and results show significant improvement over the NWP in terms of threat score (TS) and Heidke Skill Score (HSS) scores. Analysis of the forecast results for two typical rainfall processes also illustrates that the proposed method can predict rainfall with more reasonable location and intensity. Plain Language Summary: Rainfall forecast plays an important role in human's daily life, and numerical weather prediction (NWP) has helped to greatly improve the performance of rainfall forecast. However, accurate forecasting is still challenging due to the complex characteristics of rainfall and uncertainty of NWP models. In recent years, there has been accumulated large amount of high‐precision rainfall observation data around the world. It becomes possible to enhance the forecasting performance with these data. However, the traditional statistical models either ignore the weather background information or struggle to capture the complex interactions between different variables produced by the NWP model. To handle these shortages, a neural network model based on grid structure is proposed, which can extract multiscale features from the output variables of the NWP model trough an information propagation mechanism. Meanwhile, a multilevel forecasting strategy is proposed for better handling different rainfall levels along with different uncertainties, from light rain to heavy downpours. The proposed model was applied to a real NWP data for the central part of East China, and the results illustrate significant improvement in terms of common forecast skills and intuitive feeling. Key Points: A graph neural network for extracting features from the output variables of the numerical weather prediction (NWP) model for rainfall forecastingA level‐specific training label and a weighted loss function based on the relative frequency of rainfall amount to better training the modelThe proposed method improves the forecast skill at all rainfall intensity levels and can yields more reasonable location and intensity of rainfall [ABSTRACT FROM AUTHOR]

The stability assessment of loess slopes is of great significance for slope reinforcement and safety assessment. This research studies the main factors affecting the stability of the loess slope through the summary and analyzes the failure cases of the loess slope in Shaanxi Province. The importance of influencing factors was studied through numerical simulation method, sensitivity analysis method, and gray correlation analysis method, and the weight value method was given. On this basis, we have developed the fuzzy comprehensive evaluation model method for assessing the stability of loess slopes based on the principle of maximum membership degree. Finally, the method was applied to the stability analysis of the actual loess slope, and the rationality and correctness of the loess slope stability evaluation method proposed in this paper were demonstrated. The results showed that, for the Shaanxi loess slope, the probability of instability of the positive slopes is far greater than that of negative slopes; the greater the slope gradient, the more unstable the loess slopes. Collapse mainly occurs in the range of 10–40 m slope height. There is a significant positive correlation between rainfall and the probability of loess landslides. The degree of correlation between the factors influencing slope stability and the safety factor can be categorized from strong to weak as follows: slope inclination > internal friction angle > height of the slope > gravitational forces > cohesion > Poisson's ratio > modulus of elasticity, and the influence of Poisson's ratio and elastic modulus can be ignored. The fuzzy comprehensive evaluation method based on the gray correlation degree method established in this paper was used to evaluate the stability of the loess slopes. The evaluation results attested to the actual data of slope monitoring. The evaluation method proves reasonable and feasible and can be well applied to the stability analysis of the loess slopes. [ABSTRACT FROM AUTHOR]

Following the projected increase in extreme precipitation, an increase in extreme snowfall may be expected in cold regions, e.g., for high latitudes or at high elevations. By contrast, in low- to medium-elevation areas, the probability of experiencing rainfall instead of snowfall is generally projected to increase due to warming conditions. Yet, in mountainous areas, despite the likely existence of these contrasted trends according to elevation, changes in extreme snowfall with warming remain poorly quantified. This paper assesses projected changes in heavy and extreme snowfall, i.e., in mean annual maxima and 100-year return levels, in the French Alps as a function of elevation and global warming level. We apply a recent methodology, based on the analysis of annual maxima with non-stationary extreme value models, to an ensemble of 20 adjusted general circulation model–regional climate model (GCM–RCM) pairs from the EURO-CORDEX experiment under the Representative Concentration Pathway 8.5 (RCP8.5) scenario. For each of the 23 massifs of the French Alps, maxima in the hydrological sense (1 August to 31 July) are provided from 1951 to 2100 and every 300 m of elevations between 900 and 3600 m. Results rely on relative or absolute changes computed with respect to current climate conditions (corresponding here to +1 ∘ C global warming level) at the massif scale and averaged over all massifs. Overall, daily mean annual maxima of snowfall are projected to decrease below 3000 m and increase above 3600 m, while 100-year return levels are projected to decrease below 2400 m and increase above 3300 m. At elevations in between, values are on average projected to increase until +3 ∘ C of global warming and then decrease. At +4 ∘ C, average relative changes in mean annual maxima and 100-year return levels, respectively, vary from -26% and -15% at 900 m to +3% and +8% at 3600 m. Finally, for each global warming level between +1.5 and +4 ∘ C, we compute the elevation threshold that separates contrasted trends, i.e., where the average relative change equals zero. This elevation threshold is shown to be lower for higher return periods, and it is projected to rise from 3000 m at +1.5 ∘ C to 3350 m at +4 ∘ C for mean annual maxima and from 2600 to 3000 m for 100-year return levels. These results have implications for the management of risks related to extreme snowfall. [ABSTRACT FROM AUTHOR]

The main objective of this work is to evaluate the landslide hazard using temporal and spatial probability analysis at a large scale for the urban area of Azazga in northern Algeria. The historical landslide events for a period from 1952 to 2019 were collected and mapped from various sources, and a database was built. The daily and cumulative antecedent rainfall data related to landslide events were prepared from rain stations located in and around the urban area of Azazga. The rainfall threshold model for landslide occurrence was developed by analyzing the relationship between the daily and cumulative values of antecedent rainfall related to landslide events. The results show that 30-day antecedent rainfall is the most reliable predictor of current landslide events. The resultant rainfall threshold model was validated further using the 2012 rainfall and landslide events, which are not included in the threshold model. The results were used to estimate the temporal probability of a landslide occurrence using a Poisson probability model. For the spatial prediction probability of landslide initiation, four landslide susceptibility maps were previously prepared using statistical models. Finally, landslide hazard maps were produced by combining the temporal and spatial probabilities of landslides for three return periods: 1, 3, and 5 years. The obtained results could be considered as a first step toward reducing landslide risk and developing an early warning system. [ABSTRACT FROM AUTHOR]

Extreme precipitation events (EPE) are often associated with severe floods and significant damages in Central Sahel. To better understand their formation and improve their forecasts, we investigate the sub‐seasonal drivers of EPEs. A composite analysis reveals that moist, cyclonic and upper‐level divergence anomalies are found on average as a result of several tropical waves. The equatorial Rossby wave (ER) dominates at large scale providing a moist and convectively‐active anomaly over the northern Sahel together with a smaller‐scale African Easterly Wave (AEW). The Madden‐Julian Oscillation provides upper‐level divergence anomalies and a Kelvin wave increases convection during the EPE. Statistics show the prevalence of AEW and emphasize ER as a key driver of EPE. The co‐occurrences of several tropical waves, especially those involving AEW, ER, and Kelvin waves, increase the probability of EPE. Monitoring these tropical waves combinations could improve EPEs forecasts. Plain Language Summary: The drivers of extreme precipitation events (EPE) over the Central Sahel are studied at subseasonal scales. A statistical approach is adopted to build an average extreme rainfall event. EPEs occur within a large‐scale moist anomaly, an upper‐level divergence, and at shorter scales an intense vortex. These features are provided by multiple tropical meteorological systems, called tropical waves, that have typical spatio‐temporal scales greater than that of a convective storm. The four tropical waves studied here contribute to this favorable environment. However two of them—an Equatorial Rossby wave, rather slow, and an African Easterly Wave (AEW) (more rapid)—explain the largest part of the favorable conditions leading to an EPE. Statistics show that a single intense AEW or the combination of multiple tropical waves increases the probability of EPE. Monitoring these tropical waves combinations could improve EPEs forecasts. Key Points: Tropical waves are key to build the atmospheric environment conducive to an extreme precipitation eventEquatorial Rossby waves appear as a new driver of extreme rainfall in the Central Sahel when combined with African Easterly WavesThe combination of African Easterly Wave with an equatorial Rossby wave and/or Kelvin wave, increase the probability of extreme rainfall [ABSTRACT FROM AUTHOR]

The definition of landslide hazard is a step-like procedure that encompasses the quantification of its spatial and temporal attributes, i.e., a reliable definition of landslide susceptibility and a detailed analysis of landslide recurrence. However, available information is often incomplete, fragmented and unsuitable for reliable quantitative analysis. Nevertheless, landslide hazard evaluation has a key role in the implementation of risk mitigation policies and an effort should be done to retrieve information and make it useful for this purpose. In this research, we go through this topic of optimising the information available in catalogues, starting from landslide inventory review and constitution of a boosted training dataset, propaedeutic for susceptibility analysis based on machine learning methods. The temporal recurrence of landslide events has been approached here either through the definitions of large-scale quantitative hazard descriptors or by analysis of historical rainfall (i.e., the main triggering factor for the considered shallow earth slope failures) databases through the definition of rainfall probability curves. Spatial and temporal attributes were integrated, selecting potential landslide source areas ranked in terms of hazard. Data integration was also pursued through persistent scatterer interferometry analysis which pointed out areas of interest within potential landslide source areas featured by ongoing ground movement. The consequential approach led to the definition of the first hazard product of the city of Rome at a local scale functional for advisory purposes or the statutory level, representing a thematic layer able to orient the risk managers and infrastructure stakeholders. [ABSTRACT FROM AUTHOR]

Meta-Gaussian models are ubiquitous in the statistical literature. They provide a flexible building block to represent non-Gaussian distributions which inherit modeling and inference methods available in the Gaussian framework. In particular they have been widely used for modeling rainfall distributions. The first step when working with meta-Gaussian models consists in choosing an appropriate transformation which allows to map the Gaussian distribution to the target rainfall distribution. Many transfer functions have been proposed in the literature but most of them are not appropriate to describe heavy-tailed distributions, which is known to be a usual feature for rainfall at sub-daily scales. In this context, we propose and study a new meta-Gaussian model that can handle heavy-tailed observations. It leads to a four parameter model for which each parameter is linked to a different part of the distribution: a first one describes the probability of rainfall occurrence, two of them are related to the lower and upper tailed features of the distribution, and the last one is just a scaling parameter. Theoretical arguments are given to justify the proposed model. A statistical analysis of seven French rain gauges indicates the flexibility of our approach under different climatological regions and different aggregation times, here from 6 min to 24 h. Our distribution outperforms other meta-Gaussian models that have been proposed in the literature and, in particular, it captures well heavier tail behaviours below the hourly scale. [ABSTRACT FROM AUTHOR]

Copyright of Plateau Meteorology is the property of Plateau Meteorology Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Rainfall and earthquakes are the primary causes of landslides. In order to reveal the occurrence mechanisms and spatial differentiations of the rainfall-seismic coupling effect induced landslide, the landslide hazard assessment was carried out in Boshan District as an example. First, the different PGA exceedance probabilities in the next 50 years for 48 sites in Boshan District were calculated using the CPSHA method of the fifth-generation seismic ground motion parameter zonation map in China. The seismic hazard maps of Boshan District were drawn with the Kriging interpolation method. Second, the Green-Ampt infiltration model was improved by the characteristic parameters of the Philip infiltration model, and the formula for the variation of rainfall infiltration depth with rainfall duration was derived. Lastly, on the basis of the above study, the formula of landslide safety factor considering pore water pressure was derived based on the Newmark cumulative displacement model, and the slope cumulative displacement and the landslide probability under the sheer seismic effect and the rainfall-seismic coupling effect were calculated, and the landslide hazard assessment results with different PGA exceedance probabilities and different rainfall recurrence intervals were compared. The research results show that under the effect of PGA with an exceedance probability of 10% in the next 50 years, the maximum slope cumulative displacement is 2.52 cm, and the maximum probability of seismic landslide is 29.6%. The areas with maximum cumulative displacement are the east of Yuanquan Town and the south of Chishang Town. Under the coupling effect of PGA with an exceedance probability of 10% in the next 50 years and the once-in-50-year rainfall, the maximum slope cumulative displacement is 3.76 cm, and the maximum probability of the rainfall-seismic coupling effect induced landslide is 33.2%. The areas with maximum cumulative displacement are the central part of Yucheng Town, the south of Chishang Town and Boshan Town, and the east of Yuanquan Town. As a result of either the sheer seismic effect and the rainfall-seismic coupling effect, the slope cumulative displacement and landslide probabilities with lower PGA exceedance probabilities and higher rainfall recurrence intervals will increase significantly. [ABSTRACT FROM AUTHOR]

Due to its impact on the derivation of hydrological models and the safe design of sewer and storm networks, the anticipation of rainfall events should be intensively studied. In this regard, studying the rainfall events in Karbala, Iraq, has specific importance after the rapid increase in population in this city and the impact of climate change beyond 2003. Herein, the maximum daily rainfall depth of Karbala city has been analyzed to investigate and extract its probability of future occurrence using frequency analysis. Eight theoretical probability distributions, which are Weibull, Gamma, Gumbel, Log normal, Generalized Extreme Value, Normal, Exponential, and Log-Pearson Type III, were fitted using Hyfran Plus software to simulate the characteristics of the observed probability of rainfall depth. Based on the results of four statistical indicators, the exponential distribution revealed the best performance as compared with other distributions. Accordingly, ‎the synthetic storms of 24 hr were derived‎ for cumulative and incremental distribution of rainfall depth using the Soil Conservation Service method type ΙΙ for the arid and semiarid region to recurrence periods ‎‎(T = 2, 5, 10, 15, 25, 50, 100, 200, and 500), also the rainfall intensity duration frequency curves were developed. As an essential finding, new validated empirical formula has been proposed to optimize the coefficients of the location of Karbala city which may fill the gap for predicting the rainfall intensity and improving storm management strategies in Iraq. [ABSTRACT FROM AUTHOR]

Equatorial waves are a major driver of widespread convection in Southeast Asia and the tropics more widely, a region in which accurate heavy rainfall forecasts are still a challenge. Conditioning rainfall over land on local equatorial wave phases finds that heavy rainfall can be between 2 and 4 times more likely to occur in Indonesia, Malaysia, Vietnam, and the Philippines. Equatorial waves are identified in a global numerical weather prediction ensemble forecast [Met Office Global and Regional Ensemble Prediction System (MOGREPS-G)]. Skill in the ensemble forecast of wave activity is highly dependent on region and time of year, although generally forecasts of equatorial Rossby waves and westward-moving mixed Rossby–gravity waves are substantially more skillful than for the eastward-moving Kelvin wave. The observed statistical relationship between wave phases and rainfall is combined with ensemble forecasts of dynamical wave fields to construct hybrid dynamical–statistical forecasts of rainfall probability using a Bayesian approach. The Brier skill score is used to assess the skill of forecasts of rainfall probability. Skill in the hybrid forecasts can exceed that of probabilistic rainfall forecasts taken directly from MOGREPS-G and can be linked to both the skill in forecasts of wave activity and the relationship between equatorial waves and heavy rainfall in the relevant region. The results show that there is potential for improvements of forecasts of high-impact weather using this method as forecasts of large-scale waves improve. [ABSTRACT FROM AUTHOR]

This study aims to understand the process that determines the regional rainfall distribution in the equatorial South China Sea (SCS) and its surrounding during the cold surge (CS) days by looking at the different vorticity phases of active equatorial Rossby (R1) waves and the dynamics of the CSs related to the R1 wave. The CS can be thought of as an anticyclonic outflow from midlatitude and behaves like a dispersive group of R1 meridional modes. From the study, it shows the vorticity of the R1 wave arranges itself in such a way that the anticyclonic phase is located to the west and the cyclonic phase is located to the east of the cold CS axis. The CS winds in the equatorial SCS occur early, remain longer, and possess a larger zonal component in the active cyclonic phase compared with the active anticyclonic phase. During the active cyclonic vorticity phase, the rainfall anomalies pattern in the equatorial SCS changes from a "tick‐mark‐like" pattern to a "V‐shape" pattern as a consequence of the westward propagation of the wave. The probability of rainfall increases near the centre of cyclonic vorticity, but for anticyclonic vorticity the probability increases in the southern part of the vorticity centre. There is both increased (decreased) intensity and probability of extreme rainfall events during the active cyclonic (anticyclonic) vorticity phase on CS days. Overall, the rainfall anomalies are largely dominated by changes in the intensity on wet days rather than the number of wet days. These changes indicate the roles of the R1 wave vorticity phase in the rainfall distribution during CS days. The timing of enhancement and suppression of rain will provide additional valuable information to help improve forecast extreme weather events. [ABSTRACT FROM AUTHOR]

In order to improve the calculation accuracy of the rainfall probability distribution in related applications, this study aimed to select a theoretical function from applicable functions for three classes of the class-conditional probability density function (CCPD) of hourly rainfall series. The three applicable functions are generalized gamma distribution (GΓD), generalized normal distribution (GND), and Weibull distribution. For the reason that it is hard to distinguish the advantages and disadvantages of the three functions by the probability plot and error analysis of fitted values, optimization criteria are proposed, which are the Bayesian information criterion (BIC) and the estimated accuracy of both the annual average rainfall (AAR) and the annual average continuous rainfall (AACR). The results show that by using three applicable functions in 15 regions, the relative fitting deviations for CCPD1 were less than 2.3% and less than 3.3% for ln(CCPD1). The goodness-of-fit values were all above 0.98 for CCPD1 and greater than 0.94 for ln(CCPD1). The fitting effect of the Weibull distribution was relatively poor from the perspective of the probability plot and error analysis of the fitted values, while the three applicable functions could be used to fit CCPD. GΓD had the highest fitting accuracy for the three classes of CCPDs, but there is concern about overfitting due to its broad spectrum. GND, with fewer parameters, had comparable performance to GΓD, and when fitting CCPD1 using GND, the mean relative fitting deviation was 0.6%, the coefficient of determination was 0.999, and for ln(CCPD1), they were 1.45% and 0.989. At the same time, GND performed well in estimating the AARs, with an 8.6% relative error and a 0.92 correlation coefficient in the fifteen regions, indicating that GND can well reflect the spatial variation characteristics of the AAR. Moreover, the function form of GND is simple. GND follows the parsimonious principle, and it is suitable for the whole domain. Therefore, GND is recommended as the theoretical density function based on the optimization criteria. The genetic algorithm was adopted to obtain the approximate solution of the parameters through optimization, which can simplify the derivation and calculation steps. The multiplicative and additive fitting errors were both used in the objective functions, which gave comprehensive consideration to both ends of the fitting curve. [ABSTRACT FROM AUTHOR]

Summary: To address the problem that Ka‐band satellite communication signal transmission is easily affected by rainfall and terminal environment, combining the characteristics of high‐speed movement of LEO satellites and the wave propagation characteristics of satellite‐ground links, this paper establishes a Markov synthesis model of four‐state satellite channels based on Ka‐band that integrates rainfall attenuation and terminal shadow attenuation, and a scheme for adaptive coding and modulation selection based on the DVB‐S2 standard is proposed. Based on this, a rainfall fading probability density function (PDF) based on the satellite elevation angle variation is derived, and a more efficient and streamlined set of modulation and coding(MODCOD) is obtained through simulations and calculations. The simulation results show that the proposed scheme not only effectively solves the problem of severe fading of the transmission signal due to rainfall, ground movement environment and satellite mobility but also significantly reduces the system complexity of the original DVB‐S2 standard scheme with little loss of efficiency. [ABSTRACT FROM AUTHOR]

This study chiefly aimed to perform flash flood susceptibility mapping by means of machine learning methods based on the records attained in the Kentucky River basin over the last two decades. To carry out analysis, one of the widely adopted practical tree-based machine learning tools, i.e., the random forest (RF) method, was utilized, while the hyperparameters (i.e., number of trees and maximum tree depth) of the RF algorithm were tuned via the particle swarm optimization (PSO) strategy. In this vein, a total of 343 flash-flooded and the same number of random (non-flash flooded) points were assigned within the Kentucky River basin boundaries. In addition, a total of 12 factors triggering flash floods have been introduced to the corresponding points and the predictions were conducted in this regard. Many performance evaluation indicators considered within the scope of this study illustrated that the hybrid PSO-RF model revealed quite accurate predictive results based on the blinded testing set; such that both flash-flooded and non-flash flooded points exist in the test set were estimated with an accuracy of 70%. In addition, one of the promising performance indicators in assessing binary classification implementations, called AUROC, was calculated as 0.79. Further analysis regarding the individual impacts of the triggering factors also highlighted that the heavy rainfall probability factor was found to be the most effective variable, followed by topography, NDVI, and curve number, respectively. On the other hand, it was concluded that the effect of the lithology on the flash flood modeling is considerably lower compared to its counterparts. Overall, the results acquired in the light of all these findings have important potential in terms of both contributing to the flood susceptibility mapping literature and guiding with respect to the measures that should be taken prior to the flash flood incidents in the corresponding region. [ABSTRACT FROM AUTHOR]