Annual Maximum Daily Rainfall Research Articles

Hydrological Modelling with HEC-HMS in Krueng Peudada Sub-Watershed Bireuen Regency

The flood that occurred in Krueng Peudada was influenced by several factors, such as watershed conditions. Heavy rains caused Krueng Peudada to overflow so that a number of villages adjacent to the river were submerged. Based on these problems, to minimize the occurrence of flooding in Krueng Peudada building, namely by carrying out occurrence of flooding in Krueng Peudada to be used to obtain peak discharge as a reference in building planning in Krueng Peudada using HEC-HMS (Hydrologic Engineering Center’s Hydrologic Modelling System). The benefit of the research is that the resulting model can be useful as a guide in calculating flood discharge considerations. The scope of this research includes the research location at Krueng Peudada using annual maximum daily rainfall data, calculating the return period of rainfall using of Gumbel probability distribution, Normal Log, Normal, and Pearson III Log as the basis for calculating the discharge and continuing the calculation of the hours with Manonobe and ABM. The results of calculations of the Nakayasu Synthetic Unit Hydrograph Method and running from HEC-HMS found that the flow that occurred in the Krueng Peudada two sub-watershed was 2903m3/second and 2723,0 m3/second, it can be concluded that in determining watershed management the flood discharge obtainded has error as much as 6,2%. The difference in the results obtained is due to the number of sub-watersheds in the watershed based on different flows and the value of the curve number for each area of the sub-watershed.

Rainfall characteristics during pre- and post-onset of local climate change in Aceh Besar District, Indonesia

Climate change caused by global warming, which is characterized by increasing temperatures on the earth’s surface, is the biggest problem in the world today. This study attempts to examine how local climate change has affected the characteristics of rainfall in Aceh Besar District, which is situated in the far north of Indonesia’s Sumatra Island. 2001 was assumed to be the onset of local climate change in the region. Using EasyFit, probability distribution that best fits the rainfall data during the pre- and post-onset of climate change is chosen. The results indicated that the annual rainfall amount has increased over the past decade. The probability of receiving an annual total rainfall exceeding the long-term average (1700 mm) has increased significantly after the local climate change. Meanwhile, although the lowest and highest amounts of annual maximum daily rainfall for pre- and post-onset periods remain the same, the frequency of the maximum daily rainfall in a year greater than 140 mm/day was doubled during the post-onset period. Consequently, local climate change has increased the expected amount of annual maximum daily rainfall for return periods of 2, 5, and 10 years.

Performance evaluation of a multi-site weather generator coupling maximum entropy resampling for estimating the probability distribution of annual maximum daily rainfall in the Loess Plateau

Performance evaluation of a multi-site weather generator coupling maximum entropy resampling for estimating the probability distribution of annual maximum daily rainfall in the Loess Plateau

Intensity-Duration-Frequency equations for Rio Grande do Sul - Brazil, based on stationary rainfall series

Heavy rainfall information is essential for environmental studies and water engineering. This study therefore aimed to adjust Intensity-Duration-Frequency (IDF) equations for 247 locations in the Rio Grande do Sul (RS) using stationary rainfall series. Mann-Kendall’s test was applied to identify the temporal trends in the Annual Maximum Daily Rainfall (AMDR) series of 271 rain gauges in RS. The Kappa, Generalized Extreme Value (GEV), Gumbel, two-parameters Log-Normal and three-parameters Log-Normal probabilistic distributions were adjusted to the AMDR series without significant temporal trend. The best distribution fit was given by Anderson-Darling’s test, so the AMDR was discretized up to 5 minutes. IDF equations coefficients were adjusted in RStudio, using Nash-Sutcliffe’s Coefficient and the Root-Mean-Square Error to evaluate them. In conclusion: the most suitable distributions for the AMDR were the multiparametric Kappa and GEV; the IDF equations coefficients adjustment was classified as “excellent”; coefficients a and b varied across the RS and are correlated with the AMDR and geographical positions; and the c and d coefficients were practically constant.
 Keywords: goodness of fit test, Kappa probabilistic distribution, trends test.

The Nonstationary Flood Hydrology of an Urbanizing Arid Watershed

Abstract The Las Vegas metropolitan area in Nevada has experienced extensive urban growth since 1950 coincident with regional and local climate change. This study explores the nonstationary flood history of the Las Vegas Wash (LVW) watershed by deconstructing it into its constituent physical drivers. Observations and reanalysis products are used to examine the hydroclimatology, hydrometeorology, and hydrology of flash flooding in the watershed. Annual peak flows have increased nonlinearly over the past seven decades, with an abrupt changepoint detected in the mid-1990s, which is attributed to the implementation of flood conveyance systems rather than changes in land use. The LVW watershed exhibits two pronounced flood seasons, associated with distinct synoptic atmospheric circulations: winter floods linked to inland-penetrating atmospheric rivers and summer floods linked to the North American monsoon. El Niño–Southern Oscillation also plays a role in modulating extreme rainfall and the resultant floods because annual maximum daily rainfall totals positively correlate with El Niño, with Spearman’s correlation coefficient of 0.36 (p value < 0.05). Winter maximum daily rainfall totals have increased since 1950, whereas summer daily rainfall maxima have decreased. The trends in hydrometeorological drivers interact with urbanization to shift flood seasonality toward more frequent winter floods in the LVW watershed. A process-based understanding of the flood hydrology of the watershed also provides insights into flood frequency analysis and flood forecasting.

Identification Of A Suitable Probability Distribution For Rainfall Analysis Of Bankura District In West Bengal

The daily rainfall data of 22 years (1992 to 1993 and 1997 to 2016) of Bankura district in West Bengal were collected from the Indian Meteorological Department (IMD), Kolkata. The data was then processed to identify the maximum rainfall received on any one day (24 hrs duration), in any week (7 days), in a month (4 weeks), in a monsoon season (4 months), pre-monsoon (4 months), post-monsoon(4 months) and in a year (365 days). The data were analysed to find out the standard deviation and coefficient of variation during all the six periods of study. The data showed that the annual daily maximum rainfall received at any time ranged between 58.7mm (minimum) to 258.8mm (maximum) indicating a large range of fluctuation during the period of study. These data were analysed using Anderson-Darling (AD) statistical goodness of fit teston the basis of probability plot to identify the best fit probability distribution for all the six period of study and the trend has been presented in this study. The lognormal distribution was found as the best fit probability distribution for the annual and monsoon period of study. The weibull (3P) was found as the best fit distribution for pre-monsoon and post-monsoon period of study. Weibull (3P), gamma (2P), lognormal, exponential and largest extreme value were observed in most of the weekly period of study as best fit probability distributions. The scientific results clearly established that the analytical procedure devised and tested in this study may be suitably applied for the identification of the best fit probability distribution of rainfall data.

Application of Large-scale Climate Simulation Data to Evaluate the Scale of Extreme Rainfall Events: A Case of the 2018 Hiroshima Extreme-scale Rainfall Event

This study aims to apply large-scale climate ensemble simulation data to evaluate the magnitude of extreme rainfall events. It was conducted on the basis of extreme rainfall that occurred in the Hiroshima region in 2018. This event recorded an extreme rainfall magnitude corresponding to a return period of 1,000 years at a 24-h rainfall duration; it is difficult to evaluate this magnitude with the concept of probability rainfall based on frequency analysis due to the short observation period. To overcome this limitation, the data for policy decision making for future (d4PDF) climate change database based on a large-scale climate ensemble simulation was used. These data provided 3,000 annual maximum daily rainfall values, which were used to empirically estimate the probability rainfall with a return period of 10-1,000 years based on a non-parametric approach without statistical methods. The estimated probability rainfall of the d4PDF was compared with the estimated probability obtained from the observed rainfall and frequency analysis. The difference between the two probability rainfall values was 3.53% for the return period of 50 years. However, as the return period increases, the error increases to more than 10%. This indicates that the estimation of the probability rainfall with a long-term return period using the observed data of a relatively short period may present uncertainties. Regarding the probability rainfall using d4PDF under present climate conditions, the Hiroshima event represented a return period of nearly 300 years. Meanwhile, for the probability rainfall using the d4PDF under future climate conditions, the Hiroshima event had a return period of nearly 100 years. Consequently, the magnitude of the probability rainfall increased in future climate conditions, and the probability of the occurrence of extreme rainfall corresponding to the Hiroshima event increased from 0.33% to 1%. Therefore, d4PDF can be used to quantitatively evaluate the magnitude of extreme rainfall events under present and future climate conditions.

Runoff Reduction Effects at Installation of LID Facilities under Different Climate Change Scenarios

In order to prepare for floods and droughts that occur as a result of climate change, various studies in water-related fields are being carried out in various countries around the world. Among them, special attention is being paid to the low-impact development (LID) technique. This study measured the annual maximum daily rainfall data from 2019 to 2100 by climate change scenario, which is the annual maximum daily rainfall series observed for rainfall stations, and tested the hydrological data using statistical analysis. After determining whether the data could be analyzed, the probability distribution was selected, and the parameters of the selected probability distribution were calculated using the L-moment method for each rainfall station. The probabilities of rainfall data were derived using GEV distribution, and the United States Environmental Protection Agency Storm Water Management Model (SWMM), a runoff simulation program, was used to compare and analyze the runoff reduction rate before and after the installation of a permeable pavement as an LID facility. The results of the analysis showed that representative concentration pathway (RCP) 4.5 and RCP 8.5 had the effect of reducing the runoff for more than 100 years at a 30% reduction rate compared with before installation.

Modelling annual maximum daily rainfall with the STORAGE (STOchastic RAinfall GEnerator) model

Abstract In this work, the capability of STORAGE (STOchastic RAinfall GEnerator) model for generating long and continuous rainfall series for the upper Vistula basin (southern Poland) is tested. Specifically, in the selected area, only parameters of depth–duration–frequency curves are known for sub-daily rainfall heights (which are usually estimated in an indirect way by using Lambor's equations from daily data), while continuous daily series with a sufficient sample size are available. Attention is focused on modelling the sample frequency distributions of daily annual maximum rainfall. The obtained results are promising for further elaborations, concerning the use of STORAGE synthetic continuous rainfall data as input for a continuous rainfall-runoff approach, to be preferred with respect to classical event-based modelling.

Influence of changes in extreme daily rainfall distribution on the stability of residual soil slopes

ABSTRACT Many landslides triggered by intense rainfall have occurred in mountainous areas in Thailand, causing major economic losses and infrastructure damage. Extreme daily rainfall is a significant trigger for hillslope instability. Increases in extreme daily rainfall intensity due to climate change may be one of the key factors responsible for the increased landslides. Thus, in this context, changes in the intensity of extreme daily rainfall in Chiang Mai Province in North Thailand and their effects on hillslope stability are analyzed. Extreme rainfall is modeled using a generalized extreme value distribution and estimated for various return periods. A numerical analysis of seepage and an infinite slope stability model are combined to understand the hillslope response under extreme rainfall conditions. The analysis period is divided into two periods of 34 years: 1952 to 1985 and 1986 to 2019. According to the analysis results, the distribution of extreme daily rainfall changes in terms of location. The average annual daily maximum rainfall increased by approximately 11.13%. The maximum decrease in the safety factor is approximately 4.5%; therefore, these changes in extreme daily rainfall should be considered in future landslide prevention policies.

STUDY ON ESTIMATING INITIAL ABSTRACTION (Ia) AND PONDING TIME (tp) BASED ON RAINFALL INTENSITY-INFILTRATION RATE

Initial abstraction (Ia) is one of the key parameters used in the well-known SCS-CN rainfall-runoff relationship method. The Ia parameter, which is recognized to have a relationship with runoff time concentration (tc) is frequently associated with ponding time (tp). The present study described a new approach to estimate Ia and tp through overlaying between rainfall intensity (i) and infiltration rate (f) curves. The infiltration rate measurement was established at thirteen (13) measurement points using double-ring infiltrometer while the hourly duration (t) and annual maximum daily rainfall data for 2009 – 2019 were used to determine rainfall hyetograph (i). The observed initial abstraction (Iaobs) and ponding time (tpobs) were obtained from the measured infiltration data at each point of infiltration measurement while the empirical initial abstraction (Iaemp) and ponding time (tpemp) were estimated by the method of overlaying between rainfall hyetograph and infiltration rate curves. The study found that the variabilities of Ia and tp were highly induced by the varying of infiltration rate (f) and rainfall intensity (i). The reliability analysis demonstrated that there is an intense association between Iaobs and Iaemp, as well as tpobs and tpemp which was indicated by the high value of the determination coefficient (R2). The results confirmed that the values of Iaemp and tpemp derived by overlaying curve of rainfall hyetograph and infiltration rate show a good agreement with Ia and tp from the observation (Iaobs, tpobs). Thus, it is reliable for estimating Ia and tp in the research location.

Improvement of Extreme Value Modeling for Extreme Rainfall Using Large-Scale Climate Modes and Considering Model Uncertainty

Extreme value modeling for extreme rainfall is one of the most important processes in the field of hydrology. For the improvement of extreme value modeling and its physical meaning, large-scale climate modes have been widely used as covariates of distribution parameters, as they can physically account for climate variability. This study proposes a novel procedure for extreme value modeling of rainfall based on the significant relationship between the long-term trend of the annual maximum (AM) daily rainfall and large-scale climate indices. This procedure is characterized by two main steps: (a) identifying significant seasonal climate indices (SCIs), which impact the long-term trend of AM daily rainfall using statistical approaches, such as ensemble empirical mode decomposition, and (b) selecting an appropriate generalized extreme value (GEV) distribution among the stationary GEV and nonstationary GEV (NS-GEV) using time and SCIs as covariates by comparing their model fit and uncertainty. Our findings showed significant relationships between the long-term trend of AM daily rainfall over South Korea and SCIs (i.e., the Atlantic Meridional Mode, Atlantic Multidecadal Oscillation in the fall season, and North Atlantic Oscillation in the summer season). In addition, we proposed a model selection procedure considering both the Akaike information criterion and residual bootstrap method to select an appropriate GEV distribution among a total of 59 GEV candidates. As a result, the NS-GEV with SCI covariates generally showed the best performance over South Korea. We expect that this study can contribute to estimating more reliable extreme rainfall quantiles using climate covariates.

Spatial variation of catchment-oriented extreme rainfall in England and Wales

This paper presents the spatial variation of the annual maximum daily rainfall (AMDR) in more than 900 catchments of England and Wales over the last century with respect to different spatial features including geographic location, elevation, size, orientation and shape of catchments. A spatial pattern extraction and recognition (SPER) toolbox is employed to extract the spatial features of catchments where the AMDR is modelled by a well-tested Generalized Extreme Value (GEV) distribution. The results show that the GEV parameters μ and σ exhibit similar patterns and are usually larger with higher elevations. Increasing catchment size can decrease parameters due to areal averaging, however, in the middle-sized transition regions of the rainfall variation, e.g., east Wales, the trend reverses. For areas at higher elevation, parameters are greater in the west-northwest-oriented catchments while parameters in west-northwest or east-northeast-oriented catchments at lower elevation are similar or smaller than those with a north-south orientation. An elongated shape catchment usually has smaller parameters than a rounded-shape one. These findings reveal the heterogeneity of extreme rainfall distribution in space with respect to different spatial characteristics of catchments even under the same climate, which lays a basis for further catchment-based analysis concerning the relationship between hydrological response and geomorphic properties.

Trend and Seasonal Analysis of Annual One Day Maximum Rainfall

Saurashtra region is characterized by high temporal and spatial rainfall fluctuations. The daily maximum rainfall has a direct impact on the agricultural yield. It is thereby necessary to comprehend the trend changes in the annual daily maximum rainfall (ADMR). The daily rainfall data of 40 years (1981 to 2020), for 11 stations in Saurashtra was utilized for trend analysis of ADMRusing Mann Kendall's method and Sen's slope method. It was revealed from the Mann-Kendall test that significant positive trends were exhibited at Dwarka and Surendranagar. Further, Sen's slope and linear regression indicated that the ADMRat Rajkot and Surendranagar were having highest increasing trend. Trend analysis of ADMRcontribution to annual rainfall showed negative trend indicating better temporal distribution of rainfall. It was also revealed that the extreme events of rainfall usually occurred uniformly on certain days of the year from the results of directional statistics.

Extreme rainfall estimation at ungauged locations: Information that needs to be included in low-lying monsoon climate regions like Bangladesh

Bangladesh, a tropical low-lying region, is stranded between the Bay of Bengal in the south and the Himalayas in the north. The unique characteristics allow receiving huge rainfall, in particular, in the monsoon season. The physical phenomena such as the Bay of Bengal, the Himalayas and the monsoon season dominate the extreme rainfall pattern in Bangladesh. However, how to include that information to good effect in the estimation of extreme rainfall at ungauged locations is yet to be investigated. The objective of this work is to examine the information that needs to be included in the estimation of extreme rainfall in such regions. The approach that permits parameters of an extreme value distribution to vary spatially is used to determine the probability model. The methodology, subsequently, authorizes the frequency analysis to be performed at ungauged conditions. The geostatistical technique in the form of kriging with external drift (KED) which has the ability to take secondary information is used to produce spatial maps of the parameters. This study primarily assessed KED interpolation scheme and its ability to accurately predict the model parameters. The scheme was compared against the more widely used ordinary kriging (OK) model. The cross- validation was used to find the robust scheme for each parameter. Annual maximum daily rainfall data from 34 measuring stations were used for the assessment. The KED with the covariate annual mean monsoon rainfall (AMMR) appears to be the best model for location parameter. However, for the scale and shape parameter the KED models were unable to score past the OK model. The availability of parameter maps allows the estimation of extreme rainfall at locations where rainfall records were absent and offers a much better elucidation than the traditional at-site quantile based interpolation. Overall, the inclusion of monsoon rainfall and spatial information are deemed necessary for the estimation of extreme rainfall at ungauged locations in a low-lying monsoon region.

A comprehensive study on the Bayesian modelling of extreme rainfall: A case study from Pakistan

AbstractIn this paper, the modelling of extreme rainfall is carried out in Pakistan by analysing annual daily maximum rainfall data via frequentist and Bayesian approaches. In frequentist settings, the parameters and return levels of the best fitted probabilistic model (i.e., generalized extreme value) are estimated using maximum likelihood and linear moments method. On the other side, under the Bayesian framework, the parameters and return levels are calculated both for noninformative and informative priors. This task is completed with the help of the Markov Chain Monte Carlo method using the Metropolis‐Hasting algorithm. This study also highlights a procedure to build an informative prior through historical records of the underlying processes from other nearby weather stations. The findings attained from the Bayesian paradigm demonstrate that the posterior inference could be affected by the choice of past knowledge used for the construction of informative priors. Additionally, the best method for the modelling of extreme rainfall over the country is decided with the support of assessment measures. In general, the Bayesian paradigm linked with the informative priors offers an adequate estimations scheme in terms of accuracy as compared to frequentist methods, accounting for ambiguity in parameters and return levels. Hence, these findings are very helpful in adopting accurate flood protection measures and designing infrastructures over the country.

Selection of the best probability models for daily annual maximum rainfalls in Egypt

Twelve commonly used probability distributions are evaluated to identify the most suitable model that could provide accurate extreme rainfall estimates in Egypt. Three popular parameter estimation methods are applied: the method of moments, L-moments and maximum likelihood. The performance of the models is evaluated based on several numerical and graphical goodness-of-fit criteria. The proposed procedure is applied to annual maximum daily rainfall data from a network of 31 stations located in Egypt. The results indicate that no single distribution performed the best at all stations. Log-Normal, Log-Pearson Type III and Exponential are the top three distributions for the frequency analysis of daily annual extreme rainfalls in Egypt, i.e. they are selected as the “optimum” models for 23%, 19% and 19% of the total stations, respectively. In contrast, the distributions: Normal, Gumbel, Logistic and Generalized Logistic are not suitable for describing the extreme rainfalls in the country. The performances of both L-moments and maximum likelihood methods are almost equal and much better than that of the method of moments. Additionally, Depth-Duration-Frequency curves were established for 18 stations by using the “optimum” model, which can support the design of hydraulic structures. The findings from this study would be helpful for rainfall frequency analysis in similar arid countries.

Regional frequency analysis of annual daily rainfall maxima in Skåne, Sweden

AbstractExtreme daily rainfall events are critical for the urban drainage system, human life, agriculture and small catchments. The information about extreme rainfall magnitudes and frequencies is immensely important for civil engineers, city planners, scientists related to water management, rescue operations and flood control works. This study illustrates the results of regional frequency analysis (RFA) of annual maximum daily rainfall (AMDR) of Skåne County, Sweden. L‐moments based heterogeneity measure (H) reveals that the Skåne County is a homogeneous region. Based on the L‐moment ratio diagram and ZDist statistic results, the generalized normal (GNO) distribution is selected as the most suitable regional distribution. The accuracy measures used in K‐fold cross validation indicate that support vector machine (SVM) model is an appropriate model to find the index rainfall at ungauged sites in the region. The sites characteristics, elevation and latitude are identified as the most important variables to explain the variation in mean annual maximum daily rainfall (MAMDR). Finally, spatial maps of predicted MAMDR for different return periods are constructed by using index rainfall combined with regional quantiles. Spatial maps offer an overall view of the expected MAMDR in the region that is helpful for multiple decision makers including infrastructure planners, city planners, emergency managers, engineers and many others.

Statistical analysis of extreme value of meteorological elements observed for the last 31 years (1989–2019) at Narora site

Understanding of extreme weather conditions at the site of interest is essentially required in the design of engineering structures so that the structures can withstand weather stresses. This paper presents an analysis of extreme values of meteorological elements observed at Narora site for the last three decades (1989–2019). The elements considered are extreme air temperature (°C), minimum relative humidity (%), extreme wind gust (km/h), maximum rainfall (mm) in a day and a month, and annual rainfall. The extreme value analysis reveals that the maximum air temperature, maximum wind gust at 30 m, maximum monthly rainfall, and maximum annual rainfall obey Fisher-Tippett Type-1/Gumbel extreme value distribution, whereas minimum air temperature, minimum relative humidity (%), annual daily maximum rainfall (mm), and annual minimum rainfall (mm) obey Fisher-Tippett Type-2/Frechet extreme value distribution function. Distribution function parameters, i.e., location, scale, and shape parameter for each variable, have been determined. Extreme values corresponding to return periods of 50, 100, and 1000 years are worked out using best fit linear regression curve as a compliance of the Atomic Energy Regulatory Board Safety Guide Recommendations. The derived extreme values are particularly useful to designer for arriving at suitable design basis values of different elements to ensure the safety of the reactors and other civil structures in Narora region, with respect to stresses due to weather conditions. Extreme values corresponding to return periods of 50 and 100 years at Narora are compared with corresponding values at other three nuclear reactor sites in India, namely, Tarapur, Kalpakkam, and Trombay. In addition, the time series pattern analysis of rainfall for 31 years at the Narora site closely following the 2-year moving average rainfall data pattern. These results can be used for water harvesting, irrigation, and floods management plans in the future.

Sensitivity of Statistical Models for Extremes Rainfall Adjustment Regarding Data Size: Case of Ivory Coast

The objective of this study is to analyze the sensitivity of the statistical models regarding the size of samples. The study carried out in Ivory Coast is based on annual maximum daily rainfall data collected from 26 stations. The methodological approach is based on the statistical modeling of maximum daily rainfall. Adjustments were made on several sample sizes and several return periods (2, 5, 10, 20, 50 and 100 years). The main results have shown that the 30 years series (1931-1960; 1961-1990; 1991-2020) are better adjusted by the Gumbel (26.92% - 53.85%) and Inverse Gamma (26.92% - 46.15%). Concerning the 60-years series (1931-1990; 1961-2020), they are better adjusted by the Inverse Gamma (30.77%), Gamma (15.38% - 46.15%) and Gumbel (15.38% - 42.31%). The full chronicle 1931-2020 (90 years) presents a notable supremacy of 50% of Gumbel model over the Gamma (34.62%) and Gamma Inverse (15.38%) model. It is noted that the Gumbel is the most dominant model overall and more particularly in wet periods. The data for periods with normal and dry trends were better fitted by Gamma and Inverse Gamma.