Extreme Rainfall Characteristics Research Articles

Analisis Karakteristik Hujan Ekstrim Menggunakan Model Iklim di Wilayah Gunung Merapi

Extreme rainfall is one of the trigger factors for debris floods in stratovolcanos. It caused by volcanic materials will be easily eroded in large quantity with surface water flow as the result of extreme rainfall. Extreme rainfall is avnatural phenomenon which is often related with climate change. In the future, changes in extreme rainfall characteristics may occur. Therefore, it’s necessary to conduct extreme rainfall analysis for historical and future periods. In this study, the characteristics of rainfall analyzed were the variability of extreme rain as shown by trend analysis of extreme rain indices namely RTOT. Hourly rainfall data at eight rain stations used as input. Future rainfall data was projected using the global climate model CanESM2 (RCP4.5 and RCP8.5 and downscaling process using Statistical Downscaling Model (SDSM). Comparison of the projection rainfall with historical rainfall shows a different trend at each station. Increasing trend occurred at four stations including Plosokerep, Pucanganom, Sopalan, and Talun stations, with the highest increasing trend occurring at Sopalan stations. In addition, there was also a decreasing trend that occurred at Ngandong station for both scenarios and at Sorasan station in the RCP8.5 scenario. The Jrakah and Randugunting stations show a steady trend.

Analysis of changes in extreme rainfall characteristics over Ambica river basin, India

ABSTRACT Significant variations are detected in extreme rainfall characteristics over Ambica river basin, Gujarat. The frequent flooding in Ambica basin has become a persistent phenomenon over last decade. Based on new knowledge and understanding in Extreme Value Theory (EVT), the discrepancies in extreme rainfall characteristics over entire basin are analyzed considering the daily rainfall series of 25 rain gauge stations for period of 1961–2017. At each station, the threshold value was selected as 90th percentile rainfall for computing the excesses of Peak over Threshold (POT) series. To examine the variations in rainfall pattern, basic rainfall indices viz. total annual rainfall (PRCPTOT), rainy days (RD) and simple daily intensity index (SDII) are calculated. To assess the non-uniformity of extreme rainfall, coefficient of variation (Cv) has been found. Non-parametric tests like Modified Mann-Kendall (MMK) test and Sen’s slope test were used for evaluating the trend. The western parts of the basin show a declining trend in PRCPTOT and SDII, wherein stationarity has been observed for RD. The stations in the central region show a significantly declining trend in total annual rainfall. The SDII and RD are found to be higher and lower respectively for the stations situated in eastern parts of the basin. It has been found that 20 %, 4 % and 17 % of stations exhibit significant decreasing trends in PRCPTOT, RD and SDII respectively. The western basin has lesser values of coefficient of variation (Cv) as compared to stations in eastern part. At the majority of stations, extreme rainfall duration and frequency were found to be stationary, whereas intensity of extreme rainfall is found to be non-stationary. Also, it has been found that 16 % of stations exhibit significant decreasing trends in extreme rainfall intensity and duration, which demonstrates the consequences to water availability in near future.

CHARACTERISTICS OF JOINT DISTRIBUTION MODELS AND RETURN PERIOD OF EXTREME RAINFALL VOLUME AND INTENSITY

This article highlights a new approach to finding a copula model that most closely matches extreme rainfall characteristics in South Sulawesi, Indonesia. In climatology research, data that does not meet normal assumptions are often found due to extreme observations; therefore, a method is needed to overcome the dependency of variables that do not meet the normality assumption. The copula approach is a method that can determine the relationship between such variables. Copula can describe the dependency structure between variables with different margins and model its tail dependencies. This study discusses the application of Archimedean copula in modeling the dependency structure of both variables, namely the intensity of the 75th percentile (I75) and volume of the 75th percentile (P75). For explaining the dependencies of both variables, the best copula model was selected using the empirical copula method. The results showed that 16 stations followed the Clayton copula model, 23 stations followed the Gumbel copula model, and 14 stations followed the Frank copula model. After knowing the distribution model characteristics and the intensity and volume of extreme rainfall, the return period value on the intensity and volume of extreme rainfall was calculated. The return period value can be useful as information and evaluation valuable material for preparing natural hazard mitigation programs.

Changes in Extreme Daily Rainfall Characteristics in South Africa: 1921 – 2020

Changes in Extreme Daily Rainfall Characteristics in South Africa: 1921 – 2020

Characteristics of extreme rainfall in different gridded datasets over India during 1983–2015

Understanding the spatial and temporal occurrences of extreme rainfall events and their changes is crucial for reducing the risk associated with extreme events, especially under anthropogenic warming. This study presents a comparative analysis of twelve gridded rainfall datasets in representing the spatial and temporal variation of extreme rainfall events and trends in the recent past (1983–2015) in India. The selected datasets fall into four categories: gauge-based (APHRODITE, CPC, GPCC, REGEN), satellite-derived (CHIRPS, PERSIANN-CDR), reanalysis (ERA5, MERRA-2, PGF, and JRA-55), and product merged from these three types (WFDEI and MSWEP). For comparison, a gauge-based, high resolution (0.25° × 0.25°) daily gridded rainfall dataset, prepared by the India Meteorological Department (IMD) is used as a reference dataset. Eleven extreme climate indices, defined by the World Meteorological Organization's Expert Team on Climate Change Detection and Indices (ETCCDI) and the IMD are used to represent the magnitude, frequency, and duration of extreme rainfall events in India. The relative performance of these datasets in capturing extreme rainfall events is evaluated by computing model evaluation matrices i.e. correlation coefficient (CC) and Percentage Bias (PBIAS). Trends in extreme rainfall indices and their magnitudes are calculated using Mann-Kendell and Sen's slope methods respectively, to compare with the IMD dataset. Our study finds large uncertainties in representing extreme rainfall events where in most cases the datasets underestimate higher extreme events compared to the IMD data. GPCC and MSWEP better capture the magnitude, duration, and frequency of extreme rainfall events in India and are comparable to the pattern of the IMD dataset. CHIRPS and ERA5 perform better than other satellite and reanalysis rainfall datasets. All these selected datasets consistently underestimate extreme rainfall events over the northern Himalayan region. The gridded datasets are unable to capture the spatial pattern of observed trends in extreme rainfall indices in India when compared to the IMD dataset.

Retraction Note: Spatiotemporal evolution characteristics of extreme rainfall based on intelligent recognition and evaluation of music teaching effect in colleges and universities

Retraction Note: Spatiotemporal evolution characteristics of extreme rainfall based on intelligent recognition and evaluation of music teaching effect in colleges and universities

Climate change-induced variations in future extreme precipitation intensity-duration-frequency in flood-prone city of Adama, central Ethiopia.

The influences of climate change on the features of extreme rainfall events have become unprecedented that needs improved understanding at all levels for planning effective management strategies of the potential risks. This study aims to assess the potential influences of climate change on extreme rainfall characteristics in flood-vulnerable city of Adama. Daily precipitation records of 1967-2016 and projection of global circulation models (GCMs): CanESM2 and HadCM3 for 2021-2070 were disaggregated into shorter time resolutions using the Hyetos model. Gumbel type I probability distribution and power-regression model ([Formula: see text] were used for deducing intensity-duration-frequency (IDF) curves and for describing their functions, respectively. The extreme rainfall intensity of the historical and future periods for a range of storm durations and return periods were compared and contrasted. A close agreement is obtained between the observed and the modeled rainfall intensity with high values of coefficient of determination (> 0.996) and Nash-Sutcliffe efficiency (> 0.850). Besides, statistically significant (p < 0.05) direct linear relationship is found between the return periods and the coefficient parameter of the IDF models. Moreover, the intensity of extreme precipitation over 2021-2070 in Adama city would increase up to 49.5%, depending on storm duration and return period considered. This could have consequences of the way the city's drainage infrastructures are designed, operated, and sustained. Hence, flood-prone areas should be recognized in order to formulate effective strategies for mitigation and adaption of potential impacts. The standards for designing future drainage infrastructures should also be updated aiming to reflect the effects of climatic change.

The Use of Laser Precipitation Monitor (LPM) of Disdrometer and Weather Radar to Determine the Microphysics Characteristics of Extreme Rainfall in Jakarta. (Jakarta Flood Case Study February 25, 2020)

Heavy rains on February 24, 2020, caused flooding in most parts of Jakarta and its surroundings. The one-day observation of accumulated rainfall from the Laser Precipitation Monitor (LPM) was recorded at 358.6 mm/day at the Kemayoran station on February 25, 2020, at 00.00 UTC (07.00 Jakarta Time). In this study, analysis of the microphysical characteristics of extreme rainfall using LPM installed at Kemayoran meteorology station and weather radar at Cengkareng meteorology station with a spatial radius of 250 km. LPM is used to measure the diameter of the raindrops, the velocity of falling raindrops, LPM reflectivity, and the amount of accumulated rainfall with time resolution per minute and stored in excel data format. While the weather radar is used to measure the reflectivity spatially and temporally in the data volume format (.vol). The method used is, first, to find the relationship between LPM reflectivity and the amount of LPM rainfall with regression analysis. Second, the radar reflectivity is converted into estimated rainfall intensity for the Jakarta area and its surroundings. The results of this study found a relationship between LPM reflectivity (X) and rainfall accumulation LPM (Y) to form a regression relationship with the formula Y = 0.013X with R2 = 0.3777. Based on the record of the LPM time series, the peak of rainfall occurred at 18.17 UTC with 1000 raindrops, the maximum fall speed was 10 m/s, and the maximum diameter is 8.5 millimeters. Based on the results of microphysical measurements of LPM, spatial plots, and vertical cross-section radar, it can be concluded that flooding in Jakarta is due to heavy rain from convective clouds.

RETRACTED ARTICLE: Spatiotemporal evolution characteristics of extreme rainfall based on intelligent recognition and evaluation of music teaching effect in colleges and universities

RETRACTED ARTICLE: Spatiotemporal evolution characteristics of extreme rainfall based on intelligent recognition and evaluation of music teaching effect in colleges and universities

The orographic influence on storm variability, extreme rainfall characteristics and rainfall-triggered landsliding in the central Nepalese Himalaya

The orographic influence on storm variability, extreme rainfall characteristics and rainfall-triggered landsliding in the central Nepalese Himalaya

A new framework for a multi-site stochastic daily rainfall model: Coupling a univariate Markov chain model with a multi-site rainfall event model

Multi-site rainfall models are useful tools to provide synthetic realizations of spatially-correlated rainfall at multiple stations, which are of great importance for flood and drought risk assessment and climate change impact analysis. Therefore, a good preservation of various observed rainfall characteristics including rainfall time-series statistics and rainfall event characteristics at individual stations and the inter-site correlations of these rainfall characteristics is very crucial. To achieve this purpose, this study aims to develop a multi-site stochastic daily rainfall model by coupling a univariate Markov chain with a multi-site rainfall event model (MSDRM-MCREM), based on our previously-developed single-site SDRM-MCREM. The univariate Markov chain model in MSDRM-MCREM is used to generate spatially-correlated multi-site rainfall occurrence time series and extract simulated rainfall events for individual stations based on continuous wet days. The multi-site rainfall event model is then constructed using Vine copulas to simulate spatially-correlated rainfall event characteristics of those simulated rainfall events that occur simultaneously at multiple stations, including rainfall durations, rainfall depths and temporal patterns. Subsequently, this model was applied to the Changshangang River basin in Zhejiang Province, East China and its performance in reproducing rainfall characteristics and spatial correlations was evaluated for three cases, i.e. simulations for two, three and four stations. Results show that except for overestimation of light rainfall, MSDRM-MCREM can simultaneously well preserve rainfall time-series statistics (i.e. different rainfall percentiles, mean monthly rainfall, standard deviations and probabilities and mean values of wet days), extreme rainfall (i.e. exceedance probabilities of annual maximum 1-day, 3-day and 5-day rainfall) and rainfall event characteristics (i.e. cumulative probabilities of wet spell, dry spell and rainfall depth, temporal patterns and occurrence probabilities of rainfall types for different depth-based event classes) at individual stations. In addition, the spatial correlations of rainfall characteristics have also been well maintained, including rainfall occurrence time series and rainfall event characteristics in different groups, with the inter-site correlations of rainfall time series being slightly underestimated.

Characteristics of extreme rainfall and rainbands evolution of Super Typhoon Lekima (2019) during its landfall

As one of the most devastating tropical cyclones over the western North Pacific Ocean, Super Typhoon Lekima (2019) has caused a wide range of heavy rainfall in China. Based on the CMA Multi-source merged Precipitation Analysis System (CMPAS)-hourly data set, both the temporal and spatial distribution of extreme rainfall is analyzed. It is found that the heavy rainfall associated with Lekima includes three main episodes with peaks at 3, 14 and 24 h after landfall, respectively. The first two rainfall episodes are related to the symmetric outburst of the inner rainband and the persistence of outer rainband. The third rainfall episode is caused by the influence of cold, dry air from higher latitudes and the peripheral circulation of the warm moist tropical storm. The averaged rainrate of inner rainbands underwent an obvious outburst within 6 h after landfall. The asymmetric component of the inner rainbands experienced a transport from North (West) quadrant to East (South) quadrant after landfall which was related to the storm motion other than the Vertical Wind Shear (VWS). Meanwhile the outer rainband in the vicinity of three times of the Radius of Maximum Wind (RMW) was active over a 12-h period since the decay of the inner rainband. The asymmetric component of the outer rainband experienced two significant cyclonical migrations in the northern semicircle.

Spatial and temporal evolution characteristics of extreme rainfall in the Pearl River Delta under high urbanization

To analyze the relationship between the urbanization development degree and extreme rainfall change, hourly rainfall data from 22 rainfall stations in the Pearl River Delta region from 1973 to 2012 were selected. Methods such as spatial analysis, linear regression, sliding average, and the Mann-Kendall trend test were used to analyze the spatiotemporal distribution and variation characteristics of extreme rainfall and rainstorm patterns in the Pearl River Delta region under the sense of high urbanization. The results showed that extreme rainfall in highly urbanized areas of the Pearl River Delta increased by 44.3 mm/(10 a), showing a significant increase in the trend, while the other adjacent areas showed no significant change. The significant increase in extreme rainfall during the first rainy season was the main reason for the increase in the annual extreme rainfall in highly urbanized areas. In addition, the rainstorm pattern in the Pearl River Delta region was dominated by unimodal patterns, among which the proportion of type Ⅰ rainstorms was the highest (approximately 33.7%). The occurrence frequency of type Ⅰ rainstorms in highly urbanized areas has increased significantly, which easily leads to an increase in rainstorm flooding events. Therefore, flood control and drainage work in highly urbanized areas should be strengthened.

Spatial analysis of return period based copula on extreme rainfall data in South Sulawesi

The extreme rainfall in an area makes the area vulnerable to various disasters. To reduce the risk of damage caused by floods, it is important to know the characteristics of extreme rainfall. Generally, the characteristics of extreme rainfall are described by one variable. However, most of the extreme rainfall events also need to be explained based on the return period they occur using several variables with copula approach. This study model to the characteristics of extreme rainfall with two variables, they are namely extreme rainfall intensity and extreme rainfall volume. The purpose of this study was to analyze the spatial distribution pattern of the return period from extreme rainfall in South Sulawesi. To determine the characteristics of the return period distribution in South Sulawesi, spatial analysis is carried out using the Moran’s index and the LISA index. The results of the spatial autocorrelation analysis with the Moran’s index show that there is a relationship between several return period values in South Sulawesi, with the Moran’s index value of 0.209. This means that the value of the return period in South Sulawesi has a clustered relationship pattern. Furthermore, the results of the spatial autocorrelation analysis with LISA show that there are seven sub-districts identified as having local spatial autocorrelation. The conclusion obtained from Moran’s scatterplot is that 15 sub-districts are the main concern in preventing natural disasters because extreme rainfall in these 15 sub-districts tends to occur more frequently, so that it can lead to various natural disasters.

Spatial patterns and trends of extreme rainfall over the southern coastal belt of West Africa

The southern coastal belt of West Africa (SCWA) with its high population density and many major cities, combined to the low elevation and poor urban planning, is very vulnerable to floods resulting from extreme rainfall events. The aim of this paper is to analyze the characteristics of extreme rainfall in the SCWA during the 1981–2015 period, in terms of frequency, intensity, seasonality, and trends. Therefore, daily rainfall of 31 stations located in the southern part of Cote d’Ivoire, Ghana, Togo, and Benin and rainfall estimation products combining in situ observations and satellites rainfall estimation data have been used. For each station and pixel, the local 95th percentile (P95) computed on all rain days of at least 1 mm was used to define extreme rainfall events. Rainfall on the coastal belt is heavier than further inland, with P95 values reaching 82 and 52 mm/day for coastal and continental stations, respectively. Extreme rainfall along the coast occurs predominantly between May and July. Interannual variations of different indicators of extreme rainfall show a broad agreement between rain gauge data and rainfall estimates from CHIRPS (Climate Hazards Group InfraRed Precipitation with Station) data. In the southern part of Cote d’Ivoire and Togo/Benin, increase of number of extreme rainfall event (NP95) and stability number of days with rainfall less than P95 (NL95) are recorded, which induces an increase of total rainfall. But, in the southern part of Ghana, there is a stable total rainfall due to an increase in NP95 compensated by a decrease in NL95.

Influence of threshold selection in modeling peaks over threshold based nonstationary extreme rainfall series

Recent studies report that the extreme rainfall characteristics in most parts of the globe exhibit temporal non-stationarity. Therefore, modeling the nonstationary behavior of extreme rainfall for different water resources applications is vital. When modeling non-stationarity in extreme rainfall series, previous studies consider a single threshold value in the peaks over threshold (POT) approach to extract extreme rainfall series. However, extreme rainfall series extracted with different threshold values may have a different degree of non-stationarity. Consequently, it is essential to understand the effect of threshold selection in modeling peaks over threshold based nonstationary extreme rainfall series. This study aims at quantifying the threshold uncertainty (i.e., uncertainty in extreme rainfall return levels due to the choice of the threshold) in modeling peaks over threshold based nonstationary extreme rainfall series using the Generalized Pareto Distribution (GPD). To study the threshold uncertainty, extreme rainfall series over India from the India Meteorological Department’s high-resolution gridded (0.25° Longitude × 0.25° Latitude) daily rainfall dataset is used. For modeling non-stationarity in extreme rainfall series, different indices representing four physical processes, namely, global warming, El Niño–Southern Oscillation (ENSO), Indian Ocean Dipole (IOD) and local temperature anomaly are linked with the scale parameter of the GPD. Uncertainties in extreme rainfall return levels calculated over India indicate that the uncertainty created due to the choice of threshold is 54% higher under the nonstationary condition when compared to the stationary condition.

Characteristics of extreme rainfall in South China during the late rice growth period

AbstractExtreme rainfall has significant impacts on human society and agricultural development, as well as severe effects on rice development. However, few studies have investigated the characteristics of extreme rainfall during different stages of rice growth period. Taking the South China region as a case study, the characteristics of extreme daily rainfall in the late rice growth period (July to November) from 1960 to 2009 were investigated using the percentile method. The results indicated that the extreme rainfall threshold varies between stages of rice growth period. The regional average extreme rainfall threshold exceeded the traditional heavy rainfall threshold (50 mm/day) during the seeding (60.6 mm/day), tillering (56.6 mm/day) and booting stage (51 mm/day) of late rice growth period. Regions with high extreme rainfall threshold are observed in coastal cities during each stage. Typhoons are the most important weather system that causes extreme rainfall, especially in the first three stages of the late rice growth period. Pronounced annual variability is found in a number of cases of extreme rainfall and typhoon‐caused (TC) extreme rainfall. Extreme rainfall, ranging from 100–200 mm/day, is most common for cases with TC and none‐typhoon‐caused (NTC) extreme rainfall. Finally, flood risk regions more easily form in the coastal cities in cases of TC extreme rainfall, but for NTC extreme rainfall, the north‐western region of South China is the main flood risk region.

Analisis Karakteristik Hujan Ekstrim Untuk Mendukung Pengembangan Peringatan Dini Lahar Dingin di Lereng Gunung Merapi

Mt. Merapi cold lava disasters in 2010 had caused a lot of public infrastructure and facilities in the area around Mt. Merapi were damaged, due to the occurrence of debris flows triggered by extreme rainfall. Analysis of extreme rainfall characteristics are conducted to determine the pattern of distribution of the amount of hourly rainfall in the slopes of Mt. Merapi. To reduce the negative impact caused by cold lava flood, it is necessary to plan an Early Warning System (EWS) and the proper evacuation measures. EWS based Rainfall intensity, can refer to the Critical Line Curve. The research results showed the greatest rainfall intensity occurred in the Mt. Maron station Pwith the cumulative relative frequency of rainfall intensity &gt;20 mm/hr in between the years 1988 to 2010 with 6.74%. Analysis of the incidence of the most extreme hourly rainfall of 14 rainfall stations in the slopes of Mt. Merapi in 1988 to 2010 occurred in kemasan station in 2010 with 621.5 mm in January at 6:00 p.m. to 7:00 p.m. Critical Line Curve Kali Gendol rain station Batur, Deles and Sorosan, non-causing rainfall in the dangerous area is 24 events (13.79%), causing rainfall in the dangerous area is 4 events (2.30%) and non-causing rainfall in the safety area is 146 events (83.91%). Cumulative Relative frequency rainfall intensity &gt;20 mm/hr at rainfall stations close to the Kali Gendol i.e. Batur, Deles and Sorosan rainfall station from the highest to the lowest are 2.74%, 2.33% and 1.70%, respectively.

Evidence of a continent-wide shift of episodic rainfall in Australia

Extreme daily rainfall has intensified and become more frequent globally. However, in Australia, long-term changes in the characteristics of extreme rainfall are not well understood. Commonly used indices that examine the characteristics of rainfall rarely show statistically significant long-term historical changes in mean and extreme rainfall events. Here we use a rainfall event-based approach to study observed changes in intensity and frequency of rainfall events in Australia. This approach defines rain events as n consecutive days of rain to account for varying event duration. The intensity of a rainfall event is defined as the average of the daily accumulation of rainfall over the event duration. We find that short-duration rainfall events (1–2 day) have become more frequent and have intensified in large parts of Australia since the beginning of the 20th century. The frequency of longer duration rainfall events (3–4 day and 5–6 day) show substantial and statistically significant reductions in the south of Australia and increases in some parts of northern Australia. The frequency of rainfall events lasting >6 days has increased in the north and decreased in the south. The robust increase in short-duration episodic rainfall events across Australia and decrease in the frequency of extended episodic events imply rainfall has become more sporadic in Australia. Changes in rainfall intensities are less consistent compared to changes in frequency across the continent. We find an increase in intensity in the 1–2 and 3–4 day events, but the trends in the intensity of those events lasting >4 days are mostly not significant. Overall, changes in the characteristics of rainfall events of duration n days provide more spatially coherent results than those presented previously.

Characteristics and Causes of Extreme Rainfall Induced by Binary Tropical Cyclones over China

Binary tropical cyclones (BTC) often bring disastrous rainfall to China. From the viewpoint of the extreme of the BTC maximum daily rainfall, the characteristics of BTC extreme rainfall (BTCER) during 1960–2018 are analyzed, using daily rainfall data; and some representative large-scale mean flows, in which the associated BTCs are embedded, are analyzed. Results show that the frequency of BTCER shows a decreasing trend [−0.49 (10 yr)−1] and is mainly distributed within the BTC heavy rainstorm interval (100 mm ≤ BTCER <250 mm). BTCER occurs mostly from July to September with a peak in August. Three BTCER typical regions— Minbei, the Pearl River Delta (PRD), and Taiwan—are identified according to the clustering of stations with high BTCER frequency and large BTCER. A further analysis of the 850-hPa BTC composite horizontal wind and water vapor flux over the PRD region shows the existence of two water vapor transport channels, which transport water vapor to the western tropical cyclone. In the first of these channels, the transport takes place via the southwest monsoon, which accounts for 58% of the total moisture, and an easterly flow associated with eastern tropical cyclone accounts for the remaining 42%.