Annual Maximum Rainfall Series Research Articles

Development of intensity–duration–frequency relationships in Khon Kaen City, Thailand under changing climate using GCMs and a simple scaling method

Abstract This study analyses the annual maximum (AM) rainfall series (1991–2022) in Khon Kaen City, Thailand. The AM rainfall series ranging from 3 to 24 h was best fitted to the Log-Pearson Type-III distribution. Notably, our findings reveal linear relationships between the moments of rainfall intensities and durations establishing the practicality of the simple scaling method for disaggregating 24-h AM rainfall data. Additionally, the results of this method are influenced by factors such as sample size, rainfall durations and the chosen probability distribution. Comparisons between intensity–duration–frequency (IDF) curves obtained through the simple scaling method and those derived from traditional frequency analysis provide valuable insights. Furthermore, this method was applied to bias-corrected rainfall data of 15 global climate models facilitating the generation of future IDF curves under SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5 scenarios. Our results highlight that rainfall events in the SSP5-8.5 scenario are projected to exhibit higher intensities emphasizing the need to understand and prepare for increased rainfall extremes in the context of climate change. This research contributes valuable insights into rainfall analysis and prediction techniques, which are crucial for effective water resource management and climate adaptation strategies in the Khon Kaen region.

Rainfall frequency analysis using event-maximum rainfalls – An event-based mixture distribution modeling approach

Rainfall frequency analysis, an essential work for water resources management, is often conducted by using the annual maximum rainfall series. For rainfall stations with short record lengths and outliers presence, the use of annual maximum series for rainfall frequency analysis may yield design rainfall estimates of higher uncertainties. Moreover, for regions with cyclostationary climate patterns, the annual maximum rainfalls may be caused by different prevalent storm types, which differ in terms of their occurrence frequency and storm rainfall characteristics. In this study, we propose a novel event-maximum-rainfall-based mixture distribution modeling approach for rainfall frequency analysis. By considering the event-maximum rainfalls of individual storm events, the sample size for parameter estimation increases, and the uncertainty of design rainfall estimates reduces. Mixture distribution modeling enables a thorough investigation of the contributing probabilities of different storm types to the annual maximum rainfall. Through rigorous stochastic simulation, we demonstrated the superiority of the proposed approach over the conventional annual maximum rainfall approach. The proposed approach was applied to four representative rainfall stations in Taiwan, and the results revealed that the proposed approach is more robust than the conventional annual maximum rainfall approach. The results provide insights into the contributions of individual storm types to the annual maximum rainfall.

Stochastic Model of the Annual Maximum Rainfall Series Using Probability Distributions

Rainfall is a natural process that is often characterized by significant variability and uncertainty. Stochastic models of rainfall typically involve the use of probability distributions to describe the likelihood of different outcomes occurring. This study aimed to model the annual maximum of daily rainfall in Makassar City, Indonesia for the period 1980–2022, specifically focusing on the rainy season (November to April) using probability distributions to estimate return periods. The study used the Generalized Extreme Value (GEVD) and Gumbel distributions. The Kolmogorov-Smirnov test was used to determine the suitability of each distribution, and the likelihood ratio test was employed to determine the best-fit model. The Mann-Kendall test was used to detect any trends in the data. The results indicated that the Gumbel distribution was the best-fit model for data in November, December, January, March, and April, while GEV was appropriate for February. No trends were observed in any of the months. The study then estimated the maximum rainfall for various return periods. January produced the highest maximum rainfall estimates for the 2, 3, and 5-year return periods, while February produced the highest maximum rainfall estimates for the 10 and 20-year return periods. Information about maximum rainfall can be valuable for the government and other stakeholders in developing flood prevention strategies and mitigating the effects of heavy rainfall, particularly during the peak months of the rainy season in Makassar City, which are December, January, and February.

A Bayesian modelling approach for assessing non-stationarity in annual maximum rainfall under a changing climate

ABSTRACT Potential changes in hydro-meteorological events have been causing mass damage to the economy and lives. Among several other factors, the progression of climate change over a long time is expected to cause non-stationarity in annual maximum rainfall. Understanding the characteristics of annual maximum rainfall series is crucial for coastal cities as they are highly vulnerable due to the greatly varying weather patterns. In this paper, we propose stationary and non-stationary methods to model the effect of non-stationarity on the differing duration of annual maximum rainfall and demonstrate the impacts on nine coastal cities spread across the Arabian Sea and Bay of Bengal stretch of India. The Bayesian inference parameter estimation technique was used. It was found that while stationary models often fit well for longer-duration rainfall, non-stationary models often best fit the short duration.

Intercomparison of Rainfall Estimates of Three Distributions for Computation of PFD using Rational Formula and SUH Approach

Peak flood discharge (PFD) for a given return period is considered as one of the most important parameters for planning and design of hydraulic structures, river protection works, and development of integrated water resources management projects. This can be achieved by adopting various methods such as flood frequency analysis, rational formula, envelope curves and Synthetic Unit Hydrograph (SUH) approach. Out of which, rational formula is applied for estimation of PFD for ungauged catchments with catchment area less than 25 km2 while SUH approach is adopted for ungauged catchments with catchment area more than 25 km2. This paper aims to investigate the study on intercomparison of probability distributions for estimation of extreme (i.e., 1-day maximum) rainfall for computation of PFD by adopting rational formula and SUH approach for the Vadhavan Port Project (VPP). The annual 1-day maximum rainfall series is extracted from the daily rainfall data observed at Dahanu site during the period 1969 to 2019 and also used for estimation of rainfall. The probability distributions viz., Extreme Value Type-1, 2-parameter Log Normal and Log Pearson Type-3 (LP3) adopted in extreme value analysis of rainfall is evaluated through diagnostic test using root mean square error and accordingly LP3 is adjudged as the best fit for estimation of rainfall. The 1-day maximum rainfall obtained from LP3 is considered to compute the rainfall intensity by applying one-third rule of IMD (India Meteorological Department) and also used for computation of PFD through rational formula. In SUH approach, the areal rainfall, physiographic and SUH parameters of the ungauged catchments are considered for computation of PFD. The study suggests the 25-year, 50-year and 100-year return period PFDs at six locations within the study area using rational formula and SUH approach could be considered for design purposes while designing any civil and hydraulic structures in the VPP.

True interval non-stationary Intensity-Duration-Frequency curves under changing climate and effect of temporal discretisation on rainfall extremes

The extreme rainfall intensity-duration-frequency (IDF) curves are used to design stormwater infrastructure in the urban areas and need to be developed keeping in view of the warming climate. The current study develops the non-stationary IDF relationships for 11 class-I urban cities of India under changing climate. The total 11,528 generalized extreme value distribution (GEV) models are developed to quantify non-stationarity in extreme rainfall by introducing a clock hour correction factor (CHCF) as a covariate. The observed hourly rainfall data of the selected cities located in five different regions of India, namely the western region (Mumbai, Ahmedabad, Jaipur), northern region (New Delhi, Dehradun), north-eastern region (Imphal), eastern region (Bhubaneswar, Kolkata), and southern region (Madurai, Chennai, Bengaluru) are collected and converted in the fixed and sliding interval annual maximum rainfall series of eight different durations. The said data set is used in the computation of CHCF, evaluation of discretisation effect on extreme rainfall events, trend analysis, development of stationary and non-stationary IDF curves. The CHCF values of seven (two) cities are above (below) 1.15 respectively; the other two cities show a value ∼ 1.15. The effect of temporal discretisation on the computation of extreme rainfall for 24-h (FW and SW) maximum rainfall depth was found to be highest in Bhubaneswar (50%) and Imphal (44%). The trend analysis revealed no statistically significant increasing (decreasing) trend in extreme rainfall for all durations for all cities under consideration except in the case of Bengaluru for 2-,3-, and 6-h durations (Chennai for a 48-h duration). The current study concluded that, under a non-stationarity climate, the shorter (1-h) and longer (24-h) duration extreme rainfall is influenced by local and global processes, respectively. Further, the short-duration extreme events are intensifying with the decrease in the return period. The 1-h duration non-stationary rainfall intensity for a 2-year return period and stationary rainfall intensity for a 5-year return period of 62.9 mm/h and 57 mm/h for Bengaluru, 77.7 mm/h and 77.2 mm/h for Mumbai, 60.8 mm/h and 60.7 mm/h for Ahmedabad, 47.8 mm/h and 49.4 mm/h for Jaipur, 59.8 mm/h and 59.3 mm/h for Madurai, 73.6 mm/h and 73 mm/h for Bhubaneswar, and 39.6 mm/h and 40.1 mm/h for Imphal, respectively. The ready-to-use IDF curves developed under stationary and non- stationary climate can be useful to design engineers in designing stormwater drainage systems in the selected class-I urban cities of India.

Climate Change Trends in a European Coastal Metropolitan Area: Rainfall, Temperature, and Extreme Events (1864–2021)

This paper summarises an updated climate change trends analysis—developed for the period from 1 October 1864 to 30 September 2021 within the scope of a Horizon 2020-funded project to increase climate resilience in European coastal cities—for a representative site of the Lisbon Metropolitan Area (Portugal). By using long ground-based daily records of rainfall and surface temperature at the Lisboa-Geofísico climatological station, the analysis aimed to identify (i) long-term and recent climate trends in rainfall and temperature, (ii) changes in extreme rainfalls, heatwaves, and droughts, and (iii) possible effects of the coupled changes of minimum and maximum daily temperatures (Tmin and Tmax, respectively) on drought development based on the diurnal temperature range (DTR) indicator. To detect these trends and quantify their magnitude, the Mann−Kendall and Sen’s slope estimator tests were implemented. The analysis of the mean annual temperatures indicated that the study area has warmed ∼1.91 °C through the 157 analysed years. Results evidenced statistically significant upward trends in both Tmin and Tmax, and in the number of Tmax heatwave days. In what concerns the extreme hydrological events, the analysis of annual maximum rainfall series and peaks-over-threshold (POT) techniques showed more frequent and intense events in recent years, reaching up to ∼120.0 mm in a single day. With regard to drought, the study proved that the characterisation based on the commonly used standardised precipitation index (SPI) might differ from that based on the standardised precipitation evapotranspiration index (SPEI), as the latter can take into account not only rainfall but also temperature, an important trigger for the development of drought. According to the SPEI index, severe and extreme drought conditions have been more frequent in the last 60 years than in any other recorded period. Finally, a decreasing DTR trend towards the present was found to influence evapotranspiration rates and thus drought characteristics.

Ranking of general circulation models for Surat City by using a hybrid approach

Abstract Climate change is expected to worsen flood risks by increasing precipitation in and around Surat City. Thus, to study the effect of climate change on Surat City's stormwater drainage network, ranking of general circulation models (GCMs) and generation of future annual maximum rainfall series is needed, which has not been performed by any reviewed study and is performed in the present study by using a hybrid approach. The ‘hybrid approach’ refers to the combination of past performance approach used for ranking of GCMs and envelope approach based on future climate projections. To rank 21 GCMs belonging to Coupled Model Intercomparison Project Phase 5, a past performance approach is employed by using four performance indicators, which are evaluated on the basis of Surat's simulated and observed monthly rainfall data corresponding to the period 1969–2005. By using an entropy method, weights are assigned to different performance indicators and then ranking of GCMs is performed by employing the TOPSIS method. The top five ranked GCMs are used to generate future annual maximum rainfall series by employing the Reliability Ensemble Averaging method corresponding to Representative Concentration Pathways scenarios 4.5 and 8.5. This study will be helpful for future climate and hydrologic studies to be performed in the study area.

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.

Estimation of Intensity-Duration-Frequency (IDF) Curves from Large Scale Atmospheric Dataset by Statistical Downscaling

The study proposes a new approach that combined statistical downscaling, bias correction, and disaggregation of rainfall techniques in order to derive the IDF curve from large scale atmospheric reanalysis data. The applied methodology details the NCEP/NCAR reanalysis dataset being downscaled by an ANN-based approach to estimate the daily rainfall of Izmir. The annual maximum rainfall series of the study area were sampled from the daily downscaled rainfall series. The sampled daily maximum rainfalls were then bias-corrected by the quantile mapping method and disaggregated into the annual maximum standard-duration rainfall heights regarding the rainfalls' scale-invariant properties. Finally, the IDF curves of the study area were determined by using the disaggregated rainfall heights. The results confirmed that the IDF curves dependent on short-duration extreme rainfall heights could be reasonably estimated from the large-scale atmospheric variables using the statistical downscaling approach.

Frequency analysis of annual maximum rainfall series of fifteen meteorological observatories of Sindh, Pakistan

The study is an application of L-moments based regional frequency analysis using annual maximum rainfall series of fifteen sites of Sindh, Pakistan. Initial screening reveals that the observed data series at each site has no serial correlation and is randomly and identically distributed. The discordancy measure (Di) show that none of the site in a group of fifteen sites is discordant. The regional heterogeneity measure (H) shows that the region of fifteen sites is homogeneous. L-moment ratio diagram and |ZDIST| statistic are in favor of generalized logistic distribution (GLO) as best-fitted regional distribution. Parameters of GLO distribution and regional as well as at-site quantiles for various return periods have been estimated. Results show that the estimated quantiles of each site are greater than the average values of observed data series at corresponding sites. Random forest regression has been used for the estimation of quantiles at ungauged sites.

Comprehensive Extreme Rainfall Analysis: A study on Ahmedabad region, India

ABSTRACT Recently, the Central Public Health and Environmental Engineering Organization (CPHEEO), India, emphasized extreme rainfall analysis based on the probabilistic approach. The extreme rainfall analysis was performed using hourly rainfall data for Ahmedabad city, India. The analysis was carried out to obtain (a) clock hour correction factor (CHCF) for annual maximum rainfall series (AMS) and partial duration maximum rainfall series (PDS); (b) empirical factors for converting PDS to AMS for different return period extreme rainfall; and (c) conversion factors for various return period 24-h maximum rainfall. The AMS and PDS were fitted with generalized extreme value and generalized Pareto distribution, respectively, for fixed window (FW) and sliding window (SW). The CHCF for daily maximum rainfall for AMS and PDS were 1.134 and 1.139, respectively, against 1.15 suggested by IS 5542: 2003. The empirical factor for converting PDS to AMS for a 2-year return period of 24-h extreme rainfall (FW) was found to be 0.86. The conversion factor for 24-hextreme rainfall PDS (FW) from a 50-year return period to a 5-year return period was found to be 0.54. The empirical and conversion factors derived in the present study for Ahmedabad city are site-specific.

ASSESSMENT OF THE BEST PROBABILITY DISTRIBUTION METHOD IN RAINFALL FREQUENCY ANALYSIS FOR A TROPICAL REGION

The Intensity-Duration-Frequency (IDF) curve defines the relationship between rainfall intensities at certain durations and with the frequencies. The IDF Curve is extensively used in many applications such as flood modelling and peak discharge estimation. Over the years, the frequent occurrence of flood has become a great challenge in Kelantan river basin. Herein, IDF curves using frequency analyses based on different distributions were developed and compared. The historical rainfall data at eight rainfall stations for the period of 1985-2019 were selected for the assessment purpose. The Gumbel, Normal and Log Pearson Type III distributions were fitted into the annual maximum rainfall series for durations varying from 30 minutes to 24 hours. The goodness of fit tests were then used to evaluate the performances of each frequency distribution. It was found that the Gumbel distribution gave the highest passing rate followed by the Log Pearson Type III and then the Normal distributions. The Gumbel distribution resulted in respective 86% and 75% passing rate since most of the p-values generated by both the K-S and the Mann-Whitney test were greater than 5% of significance level leading to the acceptance of the null hypothesis. Thus, the Gumbel distribution is suggested for the frequency analyses in this study.

Trend and nonstationary relation of extreme rainfall: Central Anatolia, Turkey

The frequency of extreme rainfall occurrence is expected to increase in the future and neglecting these changes will result in the underestimation of extreme events. Nonstationary extreme value modelling is one of the ways to incorporate changing conditions into analyses. Although the definition of nonstationary is still debated, the existence of nonstationarity is determined by the presence of significant monotonic upward or downward trends and/or shifts in the mean or variance. On the other hand, trend tests may not be a sign of nonstationarity and a lack of significant trend cannot be accepted as time series being stationary. Thus, this study investigated the relation between trend and nonstationarity for 5, 10, 15, and 30 min and 1, 3, 6, and 24 h annual maximum rainfall series at 13 stations in Central Anatolia, Turkey. Trend tests such as Mann–Kendall (MK), Cox–Stuart (CS), and Pettitt’s (P) tests were applied and nonstationary generalized extreme value models were generated. MK test and CS test results showed that 33% and 27% of 104 time series indicate a significant trend (with p < 0.01–p < 0.05–p < 0.1 significance level), respectively. Moreover, 43% of time series have outperformed nonstationary (NST) models that used time as covariate. Among five different time-variant nonstationary models, the model with a location parameter as a linear function of time and the model with a location and scale parameter as a linear function of time performed better. Considering the rainfall series with a significant trend, increasing trend power may increase how well fitted nonstationary models are. However, it is not necessary to have a significant trend to obtain outperforming nonstationary models. This study supported that it is not necessarily time series to have a trend to perform better nonstationary models and acceptance of nonstationarity solely depending on the presence of trend may be misleading.

Analysis of annual maximum and partial duration rainfall series

Kelantan River Basin is affected by two significant monsoon seasons, namely the Northeast and Southwest monsoons that lead to flood and heavy downpour events. Consequently, analysis of rainfall series is gaining more attention from researchers. The aim of this study is to analyse the annual maximum series (AMS) and partial duration series (PDS) by fitting different probability distributions. Generalized Extreme Value (GEV), Generalized Pareto (GP), Log Pearson Type 3 (LP3), Log Normal (LN) and Log Normal 3 (LN3) were used in this study. The performances of these probability distributions were evaluated using different goodness-of-fit tests, namely the chi-square (χ2), Kolmogorov-Smirnov (KS) and Anderson-Darling (AD) tests. Subsequently, the performances of probability distributions were compared and the best fit probability distribution was selected. The GEV and GP distributions were selected as the best fit probability distributions for AMS and PDS, respectively. The findings can provide useful information for flood mitigation and water resources management.

Analysis of Annual Maximum Rainfall Series using Method of L-Moments in Some Selected Sites in South-South Geographical Zone, Nigeria

Determination of the extent of peak rainfall for different return periods is an essential ingredient for the accurate design of hydraulic structures such as drains, dams and culverts as well as detection of flood risk areas. The focus of this study is to analyze annual maximum daily rainfall series in some selected sites within the coastal region of Nigeria using three parameter probability distribution models, namely, Generalized Logistics (GLO), Generalized Extreme Value (GEV) and Generalized Pareto (GPA) with the view of identifying the best fit probability distribution model per station that can be employed to estimate the rainfall magnitude for selected return periods. Specific time series analysis test, namely, detection of outlier and homogeneity test were performed to certify that the data utilized are adequate and suitable. Descriptive statistics such as sample mean, variance, standard deviation, kurtosis, skewness, and coefficient of variation were computed using basic statistical equations. The probability weighted moment parameters (b0, b1, b2 and b3), L-moment values (λ1, λ2, λ3 and λ4) and ratios (τ2, τ 3 and τ4) including the distribution parameters, namely, shape (k), scale (α) and location (ξ) parameters were computed based on L-moments procedures. To select the best-fit probability distribution model per station, carefully chosen goodness-of-fit statistics, namely, root mean square error, relative root mean square error, maximum absolute deviation index, maximum absolute error and probability plot correlation coefficient were employed since they can adequately assess the fitted distribution at a site. Results obtained indicate that the GLO is the best fit distribution for analyzing annual maximum daily rainfall series from Warri and Calabar while GPA for Port Harcourt and Uyo.

Visual and statistical inference of hourly and sub-hourly extreme rainfall trends Central Anatolia, Turkey case

The analysis of long-term rainfall data in a changing climate is important because it has many sectoral applications such as agriculture, infrastructure, and water resources management. Statistical analyses of the annual maximum rainfall data were performed using the Mann–Kendall (MK) trend test to evaluate the annual maximum trend characteristics of the rainfall time series, the innovative trend analysis (ITA) method to detect categorial trends, and the ITA indicator to digitize the ITA results. Storm durations of 5, 10, 15, 30 min and 1, 3, 6, 24 h annual maximum rainfall series at 13 central stations in Central Anatolia, Turkey were used. According to the MK test results, there were no significant upward or downward monotonic trend at four stations, whereas the remaining nine stations showed a significant upward or downward monotonic trend. Significant negative and positive trends were identified for the sub-hourly and hourly rainfall, whereas significant positive trends were detected for hourly storm durations. Significant trend results were mostly consistent with the general ITA results. The sub-hourly storm duration data were more consistent in terms of significant trends. Conversely, when evaluated according to low, medium, and high data values in the rainfall series (categories), the high data values showed different trends. Although no trend was detected with the MK test, the ITA results showed an upward or downward trend for 25 rainfall series. 29 of 30 significant MK test results were consistent with the ITA indicator results, compared with 24 of 30 results of the visually inspected ITA results.

A New Look at Storm Separation Technique in Estimation of Probable Maximum Precipitation in Mountainous Areas

Storm separation is a key step when carrying out storm transposition analysis for Probable Maximum Precipitation (PMP) estimation in mountainous areas. The World Meteorological Organization (WMO) has recommended the step-duration-orographic-intensification-factor (SDOIF) method since 2009 as an effective storm separation technique to identify the amounts of precipitation caused by topography from those caused by atmospheric dynamics. The orographic intensification factors (OIFs) are usually developed based on annual maximum rainfall series under such assumption that the mechanism of annual maximum rainfalls is close to that of the PMP-level rainfall. In this paper, an alternative storm separation technique using rainfall quantiles, instead of annual maximum rainfalls, with rare return periods estimated via Regional L-moments Analysis (RLMA) to calculate the OIFs is proposed. Based on Taiwan’s historical 4- and 24-h precipitation data, comparisons of the OIFs obtained from annual maximum rainfalls with that from extreme rainfall quantiles at different return periods, as well as the PMP estimates of Hong Kong from transposing the different corresponding separated nonorographic rainfalls, were conducted. The results show that the OIFs obtained from rainfall quantiles with certain rare probabilities are more stable and reasonable in terms of stability and spatial distribution pattern.

Calibration of NSRP Models from Extreme Value Distributions

In this work, the authors investigated the feasibility of calibrating a model which is suitable for the generation of continuous high-resolution rainfall series, by using only data from annual maximum rainfall (AMR) series, which are usually longer than continuous high-resolution data, or they are the unique available data set for many locations. In detail, the basic version of the Neyman–Scott Rectangular Pulses (NSRP) model was considered, and numerical experiments were carried out, in order to analyze which parameters can mostly influence the extreme value frequency distributions, and whether heavy rainfall reproduction can be improved with respect to the usual calibration with continuous data. The obtained results were highly promising, as the authors found acceptable relationships among extreme value distributions and statistical properties of intensity and duration for the pulses. Moreover, the proposed procedure is flexible, and it is clearly applicable for a generic rainfall generator, in which probability distributions and shape of the pulses, and extreme value distributions can assume any mathematical expression.

Simple to Use Microsoft Excel Template for Estimating the Parameters of Some Selected Probability Distribution Model by Method of L-Moment

The focus of this research was to design a simple to use Microsoft excel algorithm that will aid in the estimation of the parameters of generalized extreme value probability distribution (GEV), generalized logistics probability distribution (GLO) and generalized pareto probability distribution (GPA), calculate the predicted rainfall/discharge based on L-moment procedures and compute the quantile estimates at various return periods.The algorithm was design based on the underlying mathematics of L-moment and has the capacity to handle forty (40) year’s annual maximum series of either rainfall or discharge data which must first be ranked in ascending order of magnitude. Basic descriptive statistics such as the sample mean, variance, standard deviation, skewness, kurtosis, coefficient of variation have been built into the algorithm. Other exciting features include; the computation of Probability weighted moment parameters (b0, b1, b2 and b3), L-Moment values (ƛ1, ƛ2, ƛ3 and ƛ4) , L-Moment ratio values (Ʈ2, Ʈ3 and Ʈ4), and goodness of fit statistics (RRMSE, RMSE, MAE, MADI and PPCC). Others include; the shape (k), scale (α) and location (ξ) parameters of GEV, GPA and GLO probability distributions. To test the performance of the algorithm, forty (40) year’s annual maximum rainfall data from Benin City was used. Basic time series analysis such as test of normality, test of homogeneity and outlier detection was conducted to ensure that the data used are adequate and suitable.Results obtained revealed that generalized logistics probability distribution GLO was the best fit distribution model for analyzing the annual maximum rainfall series at the study site. The predicted rainfall quantile magnitude (Qt) based on the GLO model ranges from 425.877mm at 2years return period to 762.759mm at 200years return period. The coefficient of determination (r 2 ) for the observed versus predicted rainfall based on the best fit model was observed to be 0.9793. It was thereafter concluded that L-moments and L –moment ratios are useful summary statistics for analyzing rainfall data. Keywords: L-moments, probability distribution, normality test, goodness of fit statistics, coefficient of variation. DOI : 10.7176/CER/11-9-05 Publication date :October 31 st 2019