scholarly journals Extreme Rainfall Return Periods using Gumbel and Gamma Distribution

Extreme rainfall amount at various return periods is one of the key inputs in the design of various hydraulic structures. In order to reduce damages that may arise due to extreme rainfall, it is very important to estimate accurately by a suitable probability distribution. Gumbel and Gamma distributions are widely applied to fit the extreme rainfall events. In the present work, an attempt is made to find maximum rainfall that could occur at various return periods, (10, 20, 50, 75, 100 and 200 years) for Tiruchirappalli city located in India. The rainfall data starting from the year 1904 to 2010 is used to predict extreme rainfall. Akaike Information Criteria (AIC) and Bayesian information criteria (BIC) were employed to determine the best probability distribution for rainfall data belongs to Tiruchirappalli station.

Water ◽  
2020 ◽  
Vol 12 (5) ◽  
pp. 1397 ◽  
Author(s):  
Óscar E. Coronado-Hernández ◽  
Ernesto Merlano-Sabalza ◽  
Zaid Díaz-Vergara ◽  
Jairo R. Coronado-Hernández

Frequency analysis of extreme events is used to estimate the maximum rainfall associated with different return periods and is used in planning hydraulic structures. When carrying out this type of analysis in engineering projects, the hydrological distributions that best fit the trend of maximum 24 h rainfall data are unknown. This study collected maximum 24 h rainfall records from 362 stations distributed throughout Colombia, with the goal of guiding hydraulic planners by suggesting the probability distributions they should use before beginning their analysis. The generalized extreme value (GEV) probability distribution, using the weighted moments method, presented the best fits of frequency analysis of maximum daily precipitation for various return periods for selected rainfall stations in Colombia.


2021 ◽  
Author(s):  
Jinfeng Wu ◽  
João Pedro Nunes ◽  
Jantiene E. M. Baartman

<p>Wildfires have become a major concern to society in recent decades because increases in the number and severity of wildfires have negative effects on soil and water resources, especially in headwater areas. Models are typically applied to estimate the potential adverse effects of fire. However, few modeling studies have been conducted for meso-scale catchments, and only a fraction of these studies include transport and deposition of eroded material within the catchment or represent spatial erosion patterns. In this study, we firstly designed the procedure of event-based automatic calibration using PEST, parameters ensemble, and jack-knife cross-validation that is suitable for event-based OpenLISEM calibration and validation, especially in data-scarce burned areas. The calibrated and validated OpenLISEM proved capable of providing reasonable accurate predictions of hydrological responses and sediment yields in this burned catchment. Then the model was applied with design storms of six different return periods (0.2, 0.5, 1, 2, 5, and 10 years) to simulate and evaluate pre- and post-wildfire hydrological and erosion responses at the catchment scale. Our results show rainfall amount and intensity play a more important role than fire occurrence in the catchment water discharge and sediment yields, while fire occurrence is regarded as an important factor for peak water discharge, indicating that high post-fire hydro-sedimentary responses are frequently related to extreme rainfall events. The results also suggest a partial shift from flow to splash erosion after fire, especially for higher return periods, explained by a combination of higher splash erosion in burnt upstream areas with a limited sediment transport capacity of surface runoff, preventing flow erosion in downstream areas. In consequence, the pre-fire erosion risk in the croplands of this catchment is partly shifted to a post-fire erosion risk in upper slope forest and natural areas, especially for storms with lower return periods, although erosion risks in croplands are important both before and after fires. This is relevant, as a shift of sediment sources to burnt areas might lead to downstream contamination even if sediment yields remain small. These findings have significant implications to identify areas for post-wildfire stabilization and rehabilitation, which is particularly important given the predicted increase in the occurrence of fires and extreme rainfall events with climate change.</p>


2020 ◽  
Vol 3 (1) ◽  
pp. 288-305
Author(s):  
Philip Mzava ◽  
Patrick Valimba ◽  
Joel Nobert

Abstract Urban communities in developing countries are one of the most vulnerable areas to extreme rainfall events. The availability of local information on extreme rainfall is therefore critical for proper planning and management of urban flooding impacts. This study examined the past and future characteristics of extreme rainfall in the urban catchments of Dar es Salaam, Tanzania. Investigation of trends and frequency of annual, seasonal and extreme rainfall was conducted, with the period 1967–2017 taken as the past scenario and 2018–2050 as the future scenario; using data from four key ground-based weather stations and RCM data respectively. Mann–Kendall trend analysis and Sen's slope estimator were used in studying changes in rainfall variability. Frequencies of extreme rainfall events were modeled using the Generalized Pareto model. Overall, the results of trend analysis provided evidence of a significant increase in annual and seasonal maximum rainfall and intensification of extreme rainfall in the future under the RCP4.5 CO2 concentration scenario. It was determined that extreme rainfall will become more frequent in the future, and their intensities were observed to increase approximately between 20 and 25% relative to the past. The findings of this study may help to develop adaptation strategies for urban flood control in Dar es Salaam.


Author(s):  
Vinicius Alexandre Sikora de Souza ◽  
Marcos Leando Alves Nunes ◽  
Sandra Ferronatto Francener ◽  
Ana Lúcia Denardin da Rosa

<p><span style="font-size: 12.0pt; line-height: 115%; font-family: 'Times New Roman','serif'; mso-fareast-font-family: Calibri; mso-fareast-theme-font: minor-latin; mso-ansi-language: PT-BR; mso-fareast-language: EN-US; mso-bidi-language: AR-SA;">Este estudo objetivou estimar a função Intensidade-Duração-Frequência (IDF) de eventos pluviométricos extremos a partir dos dados de precipitação das estações pluviométricas instaladas no estado de Rondônia, de modo que posteriormente tais informações possam ser utilizadas no dimensionamento de obras hidráulicas. Utilizou-se 41 estações pluviométricas com séries históricas acima de 10 anos, disponibilizadas pela Agência Nacional de Águas (ANA). Essas séries passaram inicialmente pelo teste de aderência Kolmogorov-Smirnov (KS), a fim de verificar o ajuste das mesmas as </span><span style="font-size: 12pt; line-height: 115%; font-family: 'Times New Roman', serif;">distribuições: Normal, Log-Normal, Exponencial, Gama, Gumbel, Weibull e Logística</span><span style="font-size: 12pt; line-height: 115%; font-family: 'Times New Roman', serif;">. O trabalho denotou que o teste de aderência </span><span style="font-size: 12.0pt; line-height: 115%; font-family: 'Times New Roman','serif'; mso-fareast-font-family: Calibri; mso-fareast-theme-font: minor-latin; mso-ansi-language: PT-BR; mso-fareast-language: EN-US; mso-bidi-language: AR-SA;">Kolmogorov-Smirnov de forma geral forneceu uma expressiva aceitação na maioria das distribuições estatística testadas.</span></p><p> </p><p align="center"><strong><em>Analysis of fitness for extreme rainfall events in western amazon in static models: state Rondônia</em></strong></p><p> </p><p><strong>ABSTRACT: </strong>This study aimed to estimate the Intensity - Duration - Frequency (IDF) function extreme rainfall events from the data of precipitation of rainfall stations located in the State of Rondônia, so that such information can be later used in hydraulic structures. We used 41 rainfall stations with historical series over 10 years, provided by the National Water Agency (ANA). These series originally started by adherence Kolmogorov -Smirnov (KS) in order to check the fit of the same distributions: Normal, Log- Normal, Exponential, Gamma, Gumbel, Weibull and Logistics. Work denoted that the Kolmogorov - Smirnov test of adherence generally provided a significant acceptance in most of the tested statistical distributions.<strong></strong></p><p><span style="font-size: 12.0pt; line-height: 115%; font-family: 'Times New Roman','serif'; mso-fareast-font-family: Calibri; mso-fareast-theme-font: minor-latin; mso-ansi-language: PT-BR; mso-fareast-language: EN-US; mso-bidi-language: AR-SA;"><br /></span></p>


Water ◽  
2021 ◽  
Vol 13 (20) ◽  
pp. 2828
Author(s):  
Manh Van Doi ◽  
Jongho Kim

Designing water infrastructure requires information about the magnitude and frequency of upcoming rainfall. A limited range of data offers just one of many realizations that occurred in the past or will occur in the future; thus, it cannot sufficiently explain climate internal variability (CIV). In this study, future relationships among rainfall intensity (RI), duration, and frequency (called the IDF curve) are established by addressing the CIV and tail characteristics with respect to frequency. Specifically, 100 ensembles of 30-year time series data were created to quantify that uncertainty. Then, the tail characteristics of future extreme rainfall events were investigated to determine whether they will remain similar to those in the present. From the RIs computed for control and future periods under two emission scenarios, following are the key results. Firstly, future RI will increase significantly for most locations, especially near the end of this century. Secondly, the spatial distributions and patterns indicate higher RI in coastal areas and lower RI for the central inland areas of South Korea, and those distributions are similar to those of the climatological mean (CM) and CIV. Thirdly, a straightforward way to reveal whether the tail characteristics of future extreme rainfall events are the same as those in the present is to inspect the slope value for the factor of change (FOC), mFOC. Fourthly, regionalizing with nearby values is very risky when investigating future changes in precipitation frequency estimates. Fifthly, the magnitude of uncertainty is large when the data length is short and gradually decreases as the data length increases for all return periods, but the uncertainty range becomes much greater as the return period becomes large. Lastly, inferring future changes in RI from the CM is feasible only for small return periods and at locations where mFOC is close to zero.


2019 ◽  
Vol 12 (5) ◽  
pp. 1757
Author(s):  
Mariana Caroline Gomes de Lima ◽  
Thais Emanuelle Monteiro Dos Santos Souza ◽  
Valéria Sandra de Oliveira Costa ◽  
Carlos Eduardo Santos De Lima ◽  
Josimar Vieira Dos Reis ◽  
...  

A erosão hídrica é um dos grandes problemas que atingem regiões com potenciais agrícolas e áreas com propensão para deslizamento de terras. A chuva é considerada o fator climático que exerce maior influência no processo erosivo, especialmente nos trópicos por causa da sua distribuição temporal, espacial, características físicas e duração. Este trabalho teve como objetivo determinar o padrão hidrológico e o índice de erosividade  no sertão Pernambucano, visando contribuir com informações estratégicas para gestão agrícola e ambiental na região. Foram utilizados dados pluviométricos horários de 2000 a 2017 localizados no município de Petrolina, disponibilizados pela (APAC). A partir dos dados da precipitação, utilizou-se o aplicativo Climap 3.0 para avaliar os eventos extremos de precipitação e o padrão hidrológico, bem como determinou-se por meio de equações o índice de erosividade para a região. No período analisado os maiores valores da precipitação ocorreram entre os meses de janeiro e abril e identificou-se 8 anos consecutivos de seca na região. Os resultados apontam que houve tendências significativas nas séries de dados, a erosividade foi considerada fraca a moderada em sua maioria, e o padrão hidrológico mais frequente foi o padrão intermediário, resultando uma alta suscetibilidade dos solos da região em sofrer erosão hídrica.Evaluation of hydroclimatic pattern and erosivity Sertão of Pernambuco A B S T R A C T Water erosion is one of major problems affecting regions with agricultural potential and landslide-prone areas. Rain is considered the climatic factor that exerts greatest influence on erosive process, especially in tropics because its temporal, spatial distribution, physical characteristics and duration. This work purpose to determine  hydrological pattern and erosivity index in Pernambucano Sertão,  to contribute with strategic information for agricultural and environmental management in region. Rainfall data from 2000 to 2017 located in city of Petrolina, provided by (APAC) were used. From the rainfall data, the Climap 3.0  was used to evaluate the extreme rainfall events and  hydrological pattern, as well  to determine through equations the erosivity index for  region. During  analyzed period the highest rainfall values occurred between January and April and 8 consecutive years of drought were identified in region. The results indicate that there were significant trends in data series, erosivity was considered weak to moderate mostly, and most frequent hydrological pattern was the intermediate pattern, resulting in high susceptibility of region's soils to water erosion.Keywords: climate variability, hydrological pattern, extreme indexes.


2016 ◽  
Vol 78 (9-4) ◽  
Author(s):  
Nur Shazwani Muhammad ◽  
Amieroul Iefwat Akashah ◽  
Jazuri Abdullah

Extreme rainfall events are the main cause of flooding. This study aimed to examine seven extreme rainfall indices, i.e. extreme rain sum (XRS), very wet day intensity (I95), extremely wet day intensity (I99), very wet day proportion (R95), extremely wet day proportion (R99), very wet days (N95) and extremely wet days (N99) using Mann-Kendall (MK) and the normalized statistic Z tests. The analyses are based on the daily rainfall data gathered from Bayan Lepas, Subang, Senai, Kuantan and Kota Bharu. The east coast states received more rainfall than any other parts in Peninsular Malaysia. Kota Bharu station recorded the highest XRS, i.e. 648 mm. The analyses also indicate that the stations in the eastern part of Peninsular Malaysia experienced higher XRS, I95, I99, R95 and R99 as compared to the stations located in the western and northern part of Peninsular Malaysia. Subang and Senai show the highest number of days for wet and very wet (N95) as compared to other stations. Other than that, all stations except for Kota Bharu show increasing trends for most of the extreme rainfall indices. Upward trends indicate that the extreme rainfall events were becoming more severe over the period of 1960 to 2014. 


2021 ◽  
Vol 247 ◽  
pp. 105221
Author(s):  
Allana Oliveira Lima ◽  
Gustavo Bastos Lyra ◽  
Marcel Carvalho Abreu ◽  
José Francisco Oliveira-Júnior ◽  
Marcelo Zeri ◽  
...  

2014 ◽  
Vol 18 (10) ◽  
pp. 4065-4076 ◽  
Author(s):  
A. G. Yilmaz ◽  
I. Hossain ◽  
B. J. C. Perera

Abstract. The increased frequency and magnitude of extreme rainfall events due to anthropogenic climate change, and decadal and multi-decadal climate variability question the stationary climate assumption. The possible violation of stationarity in climate can cause erroneous estimation of design rainfalls derived from extreme rainfall frequency analysis. This may result in significant consequences for infrastructure and flood protection projects since design rainfalls are essential input for design of these projects. Therefore, there is a need to conduct frequency analysis of extreme rainfall events in the context of non-stationarity, when non-stationarity is present in extreme rainfall events. A methodology consisting of threshold selection, extreme rainfall data (peaks over threshold data) construction, trend and non-stationarity analysis, and stationary and non-stationary generalised Pareto distribution (GPD) models was developed in this paper to investigate trends and non-stationarity in extreme rainfall events, and potential impacts of climate change and variability on intensity–frequency–duration (IFD) relationships. The methodology developed was successfully implemented using rainfall data from an observation station in Melbourne (Australia) for storm durations ranging from 6 min to 72 h. Although statistically significant trends were detected in extreme rainfall data for storm durations of 30 min, 3 h and 48 h, statistical non-stationarity tests and non-stationary GPD models did not indicate non-stationarity for these storm durations and other storm durations. It was also found that the stationary GPD models were capable of fitting extreme rainfall data for all storm durations. Furthermore, the IFD analysis showed that urban flash flood producing hourly rainfall intensities have increased over time.


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