Maximum Daily Rainfall Series Research Articles

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.

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.

Trends in flood events and their relationship to extreme rainfall in an urban area of Sahelian West Africa: The case study of Ouagadougou, Burkina Faso

Urban areas in Sahelian West Africa are highly vulnerable to extreme hydro‐meteorological events. In recent years, Burkina Faso has experienced several natural disasters with floods being the most frequent. This study investigates flood trends in Ouagadougou and their relationship to extreme rainfall events. Fourteen rainfall indices were analysed to characterise the frequency and intensity of extreme rainfall. A frequency analysis of annual maximum daily rainfall series was performed using three statistical distributions. The results showed that few of the rainfall indices have significant trends at 5% level over the period 1961–2015. The generalised extreme value distribution satisfactorily fit the time‐series of annual maximum daily rainfall. An analysis of media flood reports revealed that Burkina Faso experienced approximately three flood events per year throughout the period 1986–2016. In the 2000s, the number of flood events increased to five per year. Most flood events are caused by rainfall events with return periods of less than or equal to 5 years. The results indicated that the significant increase in flood risk in Ouagadougou is not only related to heavy rainfall but also due to human and environmental factors.

An assessment of temporal effect on extreme rainfall estimates

Abstract. This study assesses the temporal behaviour in terms of inter-decadal variability of extreme daily rainfall of stated return period relevant for hydrologic risk analysis using a novel regional parametric approach. The assessment is carried out based on annual maximum daily rainfall series of 180 meteorological stations of Yangtze River Basin over a 50-year period (1961–2010). The outcomes of the analysis reveal that while there were effects present indicating higher quantile values when estimated from data of the 1990s, it is found not to be noteworthy to exclude the data of any decade from the extreme rainfall estimation process for hydrologic risk analysis.

Heavy rainfall equations for the “Manuel Alves da Natividade” river watershed, Tocantins, Brazil

Knowledge of the equation that relates intensity, duration and frequency of heavy rainfall is of great technical interest in hydraulic projects. The aim of this study was to obtain the intensity-duration-frequency (IDF) relation­ship of heavy rainfall in the Manuel Alves da Natividade river watershed, located in the southeastern region of the state of Tocantins, Brazil. We used the rainfall disaggregation model, with adjustment of the heavy rainfall equation by nonlinear multiple regression, in addition to historical series of six pluviometric stations, obtained from the Hydrological Information System of the National Water Agency (Hidroweb-ANA). Gumbel probability distribution was adequate to model the frequency distribution of annual maximum daily rainfall series, according to the Kolmogorov-Smirnov test at 20% significance level. The heavy rainfall equations presented good statistical quality, with coefficients of determination above 0.92, which characterize them as useful tools in the context of water resource management in the Manuel Alves da Natividade river watershed.

Searching regional rainfall homogeneity using atmospheric fields

The correct identification of homogeneous areas in regional rainfall frequency analysis is fundamental to ensure the best selection of the probability distribution and the regional model which produce low bias and low root mean square error of quantiles estimation. In an attempt at rainfall spatial homogeneity, the paper explores a new approach that is based on meteo-climatic information. The results are verified ex-post using standard homogeneity tests applied to the annual maximum daily rainfall series. The first step of the proposed procedure selects two different types of homogeneous large regions: convective macro-regions, which contain high values of the Convective Available Potential Energy index, normally associated with convective rainfall events, and stratiform macro-regions, which are characterized by low values of the Q vector Divergence index, associated with dynamic instability and stratiform precipitation. These macro-regions are identified using Hot Spot Analysis to emphasize clusters of extreme values of the indexes. In the second step, inside each identified macro-region, homogeneous sub-regions are found using kriging interpolation on the mean direction of the Vertically Integrated Moisture Flux. To check the proposed procedure, two detailed examples of homogeneous sub-regions are examined.

Stochastic analyses of maximum daily rainfall series recorded at two stations across the Mediterranean Sea

Independence, stationarity, homogeneity, trend, and periodicity tests are applied on 48-year-long complete and 79-year-long incomplete maximum daily rainfall series recorded at Alexandria, Egypt, and on 61-year-long maximum daily rainfall series recorded at Antalya, Turkey, which are located at the southeastern and northeastern shores of the Mediterranean Sea. The results indicate no significant trend and no periodicity in mean, and both series are independent and homogeneous. Linear regression trend test applied to the 10 % highest part of the Alexandria series indicated a significant increasing trend. Next, frequency analysis is applied on each of these series by the probability distributions of Gumbel, general extreme-values, three-parameter log-normal, Pearson-3, log-Pearson-3, log-logistic, generalized Pareto, and Wakeby. The distributions, except for the generalized Pareto and Wakeby, pass the χ2 and Kolmogorov–Smirnov goodness-of-fit tests at 90 % probability. By visual inspection of the plots of histograms together with the probability density functions, and by the results of the χ2, Kolmogorov–Smirnov, and probability plot correlation coefficient tests, the general extreme-value distribution whose parameters are computed by the method of probability-weighted moments is deemed to be suitable for these two maximum daily rainfall series.

Mixture Distributions and the Hydroclimatology of Extreme Rainfall and Flooding in the Eastern United States

AbstractFlooding in the eastern United States reflects a mixture of flood-generating mechanisms, with landfalling tropical cyclones and extratropical systems playing central roles. The authors examine the climatology of heavy rainfall and flood magnitudes for the eastern United States through analyses of long-duration records of flood peaks and maximum daily rainfall series. Spatial heterogeneities in flood peak distributions due to orographic precipitation mechanisms in mountainous terrain, coastal circulations near land–ocean boundaries, and urbanization impacts on regional climate are central elements of flood peak distributions. Lagrangian analyses of rainfall distribution and storm evolution are presented for flood events in the eastern United States and used to motivate new directions for stochastic modeling of rainfall. Tropical cyclones are an important element of the upper tail of flood peak distributions throughout the eastern United States, but their relative importance varies widely, and abruptly, in space over the region. Nonstationarities and long-term persistence of flood peak and rainfall distributions are examined from the perspective of the impacts of human-induced climate change on flood-generating mechanisms. Analyses of flood frequency for the eastern United States, which are based on observations from a dense network of U.S. Geological Survey (USGS) stream gauging stations, provide insights into emerging problems in flood science.

The Estimation of Probable Maximum Precipitation

A brief overview of the different techniques used to estimate the probable maximum precipitation (PMP) is presented. As a particular case, the 1-day PMP over Catalonia has been calculated and mapped with a high spatial resolution. For this purpose, the annual maximum daily rainfall series from 145 pluviometric stations of the Instituto Nacional de Meteorología (Spanish Weather Service) in Catalonia have been analyzed. In order to obtain values of PMP, an enveloping frequency factor curve based on the actual rainfall data of stations in the region has been developed. This enveloping curve has been used to estimate 1-day PMP values of all the 145 stations. Applying the Cressman method, the spatial analysis of these values has been achieved. Monthly precipitation climatological data, obtained from the application of Geographic Information Systems techniques, have been used as the initial field for the analysis. The 1-day PMP at 1 km(2) spatial resolution over Catalonia has been objectively determined, varying from 200 to 550 mm. Structures with wavelength longer than approximately 35 km can be identified and, despite their general concordance, the obtained 1-day PMP spatial distribution shows remarkable differences compared to the annual mean precipitation arrangement over Catalonia.

The best fitting distribution of annual maximum rainfall in Peninsular Malaysia based on methods of L-moment and LQ-moment

The main objective of this study is to determine the best fitting distribution to describe the annual series of maximum daily rainfall from 1975 to 2004 for 50 rain gauge stations in Peninsular Malaysia based on L-moment and LQ-moment. Five three-parameter extreme-value distributions which are considered are generalized extreme value (GEV), generalized Pareto (GP), generalized logistic (GL), lognormal (LN3) and Pearson (P3) distributions. The estimation of parameters of these distributions is determined using the L-moment and LQ-moment. The adequacy of the distributions based on parameter estimates computed using the two methods are evaluated using goodness-of-fit tests. When the goodness-of-fit results for these distributions are compared, it is found that, on the average, the performance of L-moment is better than the performance of LQ-moment. Although the best fitting distribution may vary according to the method of estimation and stations considered, in most cases, data for the majority of the stations are found to follow the generalized logistic distribution.

Approche régionale pour l’estimation des distributions ponctuelles des pluies journalières dans le Languedoc-Roussillon (France) / Regional approach for the estimation of low-frequency distribution of daily rainfall in the Languedoc-Roussillon region, France

This paper aims to show the benefit of a regional approach for the estimation of rare daily rainfall. The studied region is Languedoc-Roussillon (south of France), where recent exceptional storms necessitate the revision of the statistical distributions, particularly their asymptotic tails over extreme values. The example of a large single-site time series of maximum daily rainfall at Marseille (1864–2002), very close to the studied region, shows a hyper-exponential behaviour for extreme events. At the regional scale, the homogenization process of daily maximum rainfall has been performed by considering that the coefficients of variation of the yearly maximum daily rainfall are stationary over the study zone. Two regional sample studies have been carried out on 15 and 23 gauges, randomly distributed in space, and a similar distribution could be fitted to both samples. As in the case of Marseille, the regional distribution shows a hyper-exponential asymptotic behaviour at the extreme values. The obtained regional distribution provides a systematic method for computation of rare daily rainfall that may be applied in every part of the studied region and, when compared with previous estimations, leads to a significant increase in the depth of rare rainfall.