Design Inflow Intensity and Design Inflow Profiles for Storm Sewers

1984 ◽  
Vol 16 (8-9) ◽  
pp. 207-218 ◽  
Author(s):  
Frans H M van de Ven
Keyword(s):  
Return Period ◽  
Gumbel Distribution ◽  
Extreme Value Distribution ◽  
Data Series ◽  
Design Discharge ◽  
Common Location ◽  
Housing Area ◽  
Parking Lot ◽  
Box Cox Transformation ◽  
Frequency Curves

Twelve year records of rainfall and of sewer inflow data in a housing area and in a parking lot in Lelystad were available. These data series contained 5-minute depths of rainfall and sewer inflow. Depth-duration-frequency curves were calculated from the monthly extremes, using Box-Cox transformation and a Gumbel distribution. The differences between the curves for rainfall and for inflow are explained by inertia and rainfall losses. These differences are the reason to use inflow as a sewer design parameter. Forthe choice of the design discharge (or inflow) intensity the curves are not well suited. Storage-design,discharge-frequency curves proved to be better interprétable. The selected design discharge is 4 or 5 m3/s/km2. For non-steady flow calculations in sewer systems an inflow profile has to be provided. The prof ileshould be peaked. The most common location of the peak lies between 20 and 50% of the event duration. The return period of the profile has to be known. A bivariate extreme value distribution is used to estimate this return period. From these distributions synthetic inflow profiles could be calculated.

scholarly journals Application of Frequency Analysis on Peak River Discharge toward the Cumulative Floatable Litter Load at Log Boom Sungai Batu

2019 ◽  
Vol 266 ◽  
pp. 02002
Author(s):  
Nur Khaliesah Abdul Malik ◽  
Nor Rohaizah Jamil ◽  
Latifah Abd Manaf ◽  
Mohd Hafiz Rosli ◽  
Zulfa Hanan Ash’aari ◽  
...  
Keyword(s):  
Frequency Analysis ◽  
Return Period ◽  
River Discharge ◽  
Linear Regression Analysis ◽  
Gumbel Distribution ◽  
Environmental Issues ◽  
Decision Making Process ◽  
Rapid Urbanization ◽  
Distribution Method ◽  
Period 2

The accumulation of floatable litter in the river is mainly influenced by the increasing number of human population, rapid urbanization and development which indirectly lead to the changes of hydrological processes in river discharge, decreasing the water quality and aesthetical value of the river. The main objective of this paper is to determine the cumulative floatable litter load captured at the log boom during the extreme events by using the Gumbel distribution method for frequency analysis in river discharge of Sungai Batu. The annual maximum river discharge for a period of 35 years (1982 to 2016) was used in Gumbel distribution method to obtain the discharge for different return period (2, 5, 10, 25, 50, 100, and 200). The result shows that the estimated discharge (103.17 m³/s) can estimate the cumulative floatable litter load (53267.27 kg/day) at 50 years return period. The R2 value obtained from non – linear regression analysis is 0.9986 indicate that Gumbel distribution is suitable to predict the expected discharge of the river. This study is very crucial for the related agencies in highlighting this environmental issues for their future references which can be used as a guidelines during the decision making process in making better improvement.

scholarly journals Do small and large floods have the same drivers of change? A regional attribution analysis in Europe

2020 ◽  
Author(s):  
Miriam Bertola ◽  
Alberto Viglione ◽  
Sergiy Vorogushyn ◽  
David Lun ◽  
Bruno Merz ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Return Period ◽  
Extreme Precipitation ◽  
Gumbel Distribution ◽  
Secondary Importance ◽  
Antecedent Soil Moisture ◽  
Covariate Data ◽  
Study Results ◽  
Flood Quantiles ◽  
Flood Peaks

Abstract. Recent studies have shown evidence of increasing and decreasing trends in mean annual floods and flood quantiles across Europe. Studies attributing observed changes in flood peaks to their drivers have mostly focused on mean annual floods. This paper proposes a new framework for attributing flood changes to potential drivers, as a function of return period (T), in a regional context. We assume flood peaks to follow a non-stationary regional Gumbel distribution, where the median flood and the 100-year growth factor are used as parameters. They are allowed to vary in time and between catchments as a function of the drivers quantified by covariates. The elasticities of floods with respect to the drivers and the contributions of the drivers to flood changes are estimated by Bayesian inference. The prior distributions of the elasticities of flood quantiles to the drivers are estimated by hydrological reasoning and from the literature. The attribution model is applied to European flood and covariate data and aims at attributing the observed flood trend patterns to specific drivers for different return periods. We analyse flood discharge records from 2370 hydrometric stations in Europe over the period 1960–2010. Extreme precipitation, antecedent soil moisture and snowmelt are the potential drivers of flood change considered in this study. Results show that, in northwestern Europe, extreme precipitation mainly contributes to changes in both the median (q2) and 100-year flood (q100), while the contributions of antecedent soil moisture are of secondary importance. In southern Europe, both antecedent soil moisture and extreme precipitation contribute to flood changes, and their relative importance depends on the return period. Antecedent soil moisture is the main contributor to changes in q2, while the contributions of the two drivers to changes in larger floods (T > 10 years) are comparable. In eastern Europe, snowmelt drives changes in both q2 and q100.

scholarly journals Design Values for Precipitation and Floods from M5 Values

2000 ◽  
Vol 31 (4-5) ◽  
pp. 357-372 ◽  
Author(s):  
Jonas Elíasson
Keyword(s):  
Natural Resource ◽  
Return Period ◽  
Coefficient Of Variation ◽  
Statistical Distribution ◽  
Engineering Research ◽  
Design Values ◽  
Intensity Duration Frequency ◽  
Two Parameters ◽  
The University ◽  
Frequency Curves

The M5 method, originally proposed by the Natural Resource Council in UK, is used for estimating precipitation in Iceland. In this method the M5 (24-hour precipitation with 5-year return period) is used as an index variable. Instead of the usual approach in estimating regional values of the coefficient of variation another coefficient, Ci is used. The M5 and the Ci define together a generalised distribution that can be utilised to estimate the statistical distribution of precipitation anywhere in the country. M5 maps have been prepared for this purpose by the Engineering Research Institute of the University of Iceland. Methods have been devised to derive PMP values from the M5 values. This paper describes the method and gives examples of calculation. It is also shown that the same CDF applies for the observations of shorter duration precipitation available in Iceland. By applying the principle of identical statistical distribution for standardised annual maxima of any duration, IDF (Intensity – Duration – Frequency) curves have been derived. This allows the IDF – values to be calculated on basis of M5 and Ci, which are the two-parameters that define the generalised precipitation distribution.

scholarly journals A Simple Multi-Parameter Method for Efficient Charging Scheduling of Electric Vehicles

2021 ◽  
Vol 4 (3) ◽  
pp. 58
Author(s):  
George Konstantinidis ◽  
Fotios D. Kanellos ◽  
Kostas Kalaitzakis
Keyword(s):  
Electric Vehicles ◽  
Communication Systems ◽  
Average Power ◽  
Power Converter ◽  
State Of Charge ◽  
Data Series ◽  
Parameter Method ◽  
Real World Data ◽  
Parking Lot ◽  
Scheduling Method

In this article, a method for the efficient charging of electric vehicles (EVs) at the parking lot (PL) level, including V2G operation and taking into account lifetime of EV batteries, distribution network and local transformer loading, is proposed. The main targets of the method are to minimize the total charging cost of the PLs hosting the EVs and to satisfy all technical and operation constraints of EVs and PLs. The proposed method exploits particle swarm optimization (PSO) to derive the charging schedule of the EVs. The proposed method is compared with conventional charging strategies, where the EVs are charged with the maximum power of their charging power converter or the average power required to achieve their state-of-charge target, and a conventional charging scheduling method using the aggregated behavior of the plug-in EVs. Real-world data series of electricity price and parking lot activity were used. The results obtained from the study of indicative operation scenarios prove the effectiveness of the proposed method, while no sophisticated computing, measurement and communication systems are required for its application.

scholarly journals The Influence of Hindcast Modeling Uncertainty on the Prediction of High Return Period Wave Conditions

2004 ◽  
Author(s):  
Daniel C. Brooker ◽  
Geoffrey K. Cole ◽  
Jason D. McConochie
Keyword(s):  
Return Period ◽  
Extreme Value Distribution ◽  
Likelihood Estimation ◽  
Extreme Value ◽  
Extreme Value Analysis ◽  
Random Errors ◽  
Value Analysis ◽  
Simulation Techniques ◽  
Modeling Uncertainties ◽  
Wave Conditions

Extreme value analysis for the prediction of long return period met-ocean conditions is often based upon hindcast studies of wind and wave conditions. The random errors associated with hindcast modeling are not usually incorporated when fitting an extreme value distribution to hindcast data. In this paper, a modified probability distribution function is derived so that modeling uncertainties can be explicitly included in extreme value analysis. Maximum likelihood estimation is then used to incorporate hindcast uncertainty into return value estimates and confidence intervals. The method presented here is compared against simulation techniques for accounting for hindcast errors. The influence of random errors within modeled datasets on predicted 100 year return wave estimates is discussed.

scholarly journals Extreme values of ET0 at Piracicaba, Brazil, for designing irrigation systems

2021 ◽  
Vol 25 (10) ◽  
pp. 677-683
Author(s):  
Verônica G. M. L. de Melo ◽  
José A. Frizzone ◽  
Leonardo L. de Melo ◽  
Antonio P. de Camargo
Keyword(s):  
High Risk ◽  
Return Period ◽  
Extreme Values ◽  
Irrigation System ◽  
Gumbel Distribution ◽  
System Capacity ◽  
Irrigation Systems ◽  
Risk Of Failure ◽  
Irrigation Interval ◽  
Daily Data

ABSTRACT Irrigation system capacity is typically defined by analyzing probabilities of non-exceedance of evapotranspiration. The use of mean monthly values of ET0 may lead to underestimation of the required capacity, whereas use of maximum daily values may result in overestimation of required capacity. This study had the following objectives: (1) to analyze a 30-year series of daily ET0 data from Piracicaba, SP, Brazil, to evaluate the suitability of the Gumbel distribution for estimating the maximum values of ET0 organized in periods of up to 30 days; (2) to determine probable maximum values and to select ET0 values considering the irrigation interval and the risk of failure in terms of irrigation system capacity. Daily data from 1990 to 2019 were used to calculate ET0 using the Penman-Monteith model. The Gumbel distribution fitted to the data and was suitable for characterizing the frequency distribution of the maximum ET0. The probable ET0 for designing irrigation systems can then be estimated based on the expected lifespan, irrigation interval, and return period of ET0 maximum values. The higher the anticipated irrigation system lifespan, the higher the return period needed to attain a low risk of failure. Using the average of maximum ET0 values alone leads to underestimation of system capacity and a high risk of failure in terms of irrigation system capacity.

scholarly journals ANALISIS BANJIR DAN TINGGI MUKA AIR PADA RUAS SUNGAI CILIWUNG STA 7+646 s/d STA 15+049

2018 ◽  
Vol 7 (1) ◽  
pp. 43-49
Author(s):  
Redaksi Tim Jurnal
Keyword(s):  
Frequency Analysis ◽  
Return Period ◽  
Synthetic Data ◽  
Gumbel Distribution ◽  
Peak Discharge ◽  
Design Flood ◽  
Distribution Method ◽  
Flood Peak ◽  
Method Of Calculation ◽  
Flood Discharge

The problem of flooding in DKI Jakarta is considered normal because almost every year can hit the city of Jakarta especially during the rainy season. In DKI Jakarta itself there are several rivers, one of which is Ciliwung River which is the most influential river in DKI Jakarta which often cause flood every year. The purpose of this research is to know the location of flood / river flood that occurs in the segments along Ciliwung River STA 7 + 646 s / d STA 15 + 049. Data processing begins with the calculation of average rainfall, frequency analysis, and then hour-time rain distribution. Method of calculation of flood discharge using the synthetic unit of Nakayasu and Gama I synthetic data. Rainfall data using 2 observation stations for 3 years rain (2014-2016). In the frequency analysis used Gumbel distribution berdasrkan test results suitability data Smirnov- Kolmogorov and Chi-Square. The result of flood peak discharge design with HSS Nakayasu on return period Q5 = 687,80 m3 / dt, Q10 = 743,21 m3 / dt, Q20 = 796,36 m3 / s, Q50 = 865,15 m3 / dt, Q100 = 916,71 m3 / s, while flood peak discharge design with HSS Gama I on return period Q5 = 347,03 m3 / s, Q10 = 372,12 m3 / s, Q20 = 396,20 m3 / s, Q50 = 427, 36 m3 / s, Q100 = 450,71 m3 / s. The design flood discharge value approaching the measured debit value is HSS Nakayasu. Steps continued using HEC-RAS 4.1.0 software to determine the capacity of river catchment by using Nakayasu discharge. After analyzing using the software, most stationing of the Ciliwung River at STA 7 + 646 to STA 15 + 049 can not accommodate the planned discharge during the 20th anniversary period, hence the need for river improvements in the form of river normalization and elevation of dikes.

scholarly journals Probabilistic assessment of extreme temperature dynamics

2015 ◽  
Vol 56 ◽  
Author(s):  
Ilona Šeputytė ◽  
Robertas Alzbutas
Keyword(s):  
Moving Average ◽  
Uncertain Data ◽  
Extreme Temperature ◽  
Gumbel Distribution ◽  
Extreme Value Distribution ◽  
Extreme Value ◽  
Temperature Extremes ◽  
Probabilistic Assessment ◽  
Variation Analysis ◽  
Air Temperatures

In order to estimate likelihood of the annual minimum and maximum Lithuanian air temperatures (based on 1961–2014 data) the probabilistic assessment using the extreme value distributions was performed, in particular, for each sample the best extreme value distribution was identified. In addition, to the previously mentioned study of dry bulb temperature extremes, wet bulb temperature extremes study, which enables to determine the relative humidity, was also carried out. Usingthe selected Gumbel distribution, the local temperature data analysis in eastern Lithuania, i.e. in Dūkštas, region was conducted. Then temperature variation analysis using the moving average method was carried out and the extremes changes in view of the uncertain data were investigated.

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