scholarly journals A contribution to improved flood magnitude estimation in base of palaeoflood record and climatic implications – Guadiana River (Iberian Peninsula)

2009 ◽  
Vol 9 (1) ◽  
pp. 229-239 ◽  
Author(s):  
J. A. Ortega ◽  
G. Garzón
Keyword(s):  
Return Period ◽  
Distribution Functions ◽  
Rating Curve ◽  
Systematic Information ◽  
High Magnitude ◽  
Data Record ◽  
Flood Magnitude ◽  
The Moment ◽  
Hydrologic Conditions ◽  
Frequency Curves

Abstract. The Guadiana River has a significant record of historical floods, but the systematic data record is only 59 years. From layers left by ancient floods we know about we can add new data to the record, and we can estimate maximum discharges of other floods only known by the moment of occurrence and by the damages caused. A hydraulic model has been performed in the area of Pulo de Lobo and calibrated by means of the rating curve of Pulo do Lobo Station. The palaeofloods have been dated by means of 14C y 137Cs. As non-systematic information has been used in order to calculate distribution functions, the quantiles have changed with respect to the same function when using systematic information. The results show a variation in the curves that can be blamed on the human transformations responsible for changing the hydrologic conditions as well as on the latest climate changes. High magnitude floods are related to cold periods, especially at transitional moments of change from cold to warm periods. This tendency has changed from the last medium-high magnitude flood, which took place in a systematic period. Both reasons seem to justify a change in the frequency curves indicating a recent decrease in the return period of big floods over 8000 m3 s−1. The palaeofloods indicate a bigger return period for the same water level discharge thus showing the river basin reference values in its natural condition previous to the transformation of the basin caused by anthropic action.

scholarly journals Estimation of high return period flood quantiles using additional non-systematic information with upper bounded statistical models

2010 ◽  
Vol 14 (12) ◽  
pp. 2617-2628 ◽  
Author(s):  
B. A. Botero ◽  
F. Francés
Keyword(s):  
Return Period ◽  
Robustness Analysis ◽  
Distribution Functions ◽  
Systematic Information ◽  
Estimation Uncertainty ◽  
Upper Limit ◽  
High Return ◽  
Probable Maximum Flood ◽  
Quantile Estimates

Abstract. This paper proposes the estimation of high return period quantiles using upper bounded distribution functions with Systematic and additional Non-Systematic information. The aim of the developed methodology is to reduce the estimation uncertainty of these quantiles, assuming the upper bound parameter of these distribution functions as a statistical estimator of the Probable Maximum Flood (PMF). Three upper bounded distribution functions, firstly used in Hydrology in the 90's (referred to in this work as TDF, LN4 and EV4), were applied at the Jucar River in Spain. Different methods to estimate the upper limit of these distribution functions have been merged with the Maximum Likelihood (ML) method. Results show that it is possible to obtain a statistical estimate of the PMF value and to establish its associated uncertainty. The behaviour for high return period quantiles is different for the three evaluated distributions and, for the case study, the EV4 gave better descriptive results. With enough information, the associated estimation uncertainty for very high return period quantiles is considered acceptable, even for the PMF estimate. From the robustness analysis, the EV4 distribution function appears to be more robust than the GEV and TCEV unbounded distribution functions in a typical Mediterranean river and Non-Systematic information availability scenario. In this scenario and if there is an upper limit, the GEV quantile estimates are clearly unacceptable.

scholarly journals Estimation of high return period flood quantiles using additional non-systematic information with upper bounded statistical models

2010 ◽  
Vol 7 (4) ◽  
pp. 5413-5440 ◽  
Author(s):  
B. A. Botero ◽  
F. Francés
Keyword(s):  
Return Period ◽  
Robustness Analysis ◽  
Distribution Functions ◽  
Statistical Estimator ◽  
Systematic Information ◽  
Estimation Uncertainty ◽  
High Return ◽  
Probable Maximum Flood ◽  
Flood Quantiles

Abstract. This paper proposes the estimation of high return period quantiles using upper bounded distribution functions with Systematic and additional Non-Systematic information. The aim of the developed methodology is to reduce the estimation uncertainty of these quantiles, assuming the upper bound parameter of these distribution functions as a statistical estimator of the Probable Maximum Flood (PMF). Three upper bounded distribution functions, firstly used in Hydrology in the 90's (referred to in this work as TDF, LN4 and EV4), were applied at the Jucar River in Spain. Different methods to estimate the upper limit of these distribution functions have been merged with the Maximum Likelihood (ML) method. Results show that it is possible to obtain a statistical estimate of the PMF value and to establish its associated uncertainty. The behaviour for high return period quantiles is different for the three evaluated distributions and, for the case study, the EV4 gave better descriptive results. With enough information, the associated estimation uncertainty is considered acceptable, even for the PMF estimate. From the robustness analysis, EV4 distribution function appears to be more robust than the GEV and TCEV unbounded distribution functions in a typical Mediterranean river and Non-Systematic information availability scenario.

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 Joint return periods in hydrology: a critical and practical review focusing on synthetic design hydrograph estimation

2012 ◽  
Vol 9 (5) ◽  
pp. 6781-6828 ◽  
Author(s):  
S. Vandenberghe ◽  
M. J. van den Berg ◽  
B. Gräler ◽  
A. Petroselli ◽  
S. Grimaldi ◽  
...  
Keyword(s):  
Frequency Analysis ◽  
Return Period ◽  
Three Dimensional ◽  
Distribution Functions ◽  
Return Periods ◽  
Design Variables ◽  
Joint Return ◽  
Design Values ◽  
Multivariate Frequency Analysis ◽  
Joint Return Period

Abstract. Most of the hydrological and hydraulic studies refer to the notion of a return period to quantify design variables. When dealing with multiple design variables, the well-known univariate statistical analysis is no longer satisfactory and several issues challenge the practitioner. How should one incorporate the dependence between variables? How should the joint return period be defined and applied? In this study, an overview of the state-of-the-art for defining joint return periods is given. The construction of multivariate distribution functions is done through the use of copulas, given their practicality in multivariate frequency analysis and their ability to model numerous types of dependence structures in a flexible way. A case study focusing on the selection of design hydrograph characteristics is presented and the design values of a three-dimensional phenomenon composed of peak discharge, volume and duration are derived. Joint return period methods based on regression analysis, bivariate conditional distributions, bivariate joint distributions, and Kendal distribution functions are investigated and compared highlighting theoretical and practical issues of multivariate frequency analysis. Also an ensemble-based method is introduced. For a given design return period, the method chosen clearly affects the calculated design event. Eventually, light is shed on the practical implications of a chosen method.

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 Distributions of the Ratio and Product of Two Independent Weibull and Lindley Random Variables

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
N. J. Hassan ◽  
A. Hawad Nasar ◽  
J. Mahdi Hadad
Keyword(s):  
Hypergeometric Functions ◽  
Special Functions ◽  
Random Variables ◽  
Distribution Functions ◽  
Cumulative Distribution ◽  
Parabolic Cylinder ◽  
Generalized Hypergeometric Functions ◽  
Confluent Hypergeometric Functions ◽  
Parabolic Cylinder Functions ◽  
The Moment

In this paper, we derive the cumulative distribution functions (CDF) and probability density functions (PDF) of the ratio and product of two independent Weibull and Lindley random variables. The moment generating functions (MGF) and the k-moment are driven from the ratio and product cases. In these derivations, we use some special functions, for instance, generalized hypergeometric functions, confluent hypergeometric functions, and the parabolic cylinder functions. Finally, we draw the PDF and CDF in many values of the parameters.

scholarly journals Asymmetry of Daytime and Nighttime Warming in Typical Climatic Zones along the Eastern Coast of China and Its Influence on Vegetation Activities

2020 ◽  
Vol 12 (21) ◽  
pp. 3604
Author(s):  
Guangxin He ◽  
Zhongliang Li
Keyword(s):  
Return Period ◽  
Deep Understanding ◽  
Distribution Functions ◽  
Eastern Coast ◽  
Maximum Temperature ◽  
Climate Factor ◽  
Vegetation Growth ◽  
Warming Rate ◽  
Nighttime Warming ◽  
Vegetation Activities

In this dissertation, the author adopted the normalized difference vegetation index (NDVI) and meteorological data from 1982 to 2016 of the typical climate zones in coastal areas of China to analyze the influence of daytime and nighttime warming asymmetric changes in different seasons on vegetation activities during the growing season period according to the copula function theory optimized based on Markov chain Monte Carlo (MCMC). The main conclusions are as follows: (1) The seasonal daytime and nighttime warming trends of Guangdong, Jiangsu and Liaoning over the past 35 years were significant, and the daytime and nighttime warming rates were asymmetric. In spring and summer of Guangdong province, the warming rate in the daytime was higher than that at night, while, in autumn, the opposite law was observed. However, the warming rate in the daytime was lower than that at night in Jiangsu and Liaoning provinces. There were latitude differences in diurnal and nocturnal warming rate. (2) The daytime and nighttime warming influences on vegetation showed significant seasonal differences in these three regions. In Guangdong, the influence of nighttime warming on vegetation growth in spring is greater than that in summer, and the influences of daytime warming on vegetation growth from strong to weak were spring, summer and autumn. In Jiangsu, both the influences of daytime and nighttime warming on vegetation growth in summer were less than that in autumn. In Liaoning, both the influences of daytime and nighttime warming on vegetation growth from strong to weak were autumn, spring and summer. (3) In Guangdong, Jiangsu and Liaoning provinces, their maximum temperature (Tmax) and minimum temperature (Tmin) and the joint probability distribution functions of NDVI, all had little effect on NDVI when Tmax and Tmin respectively reached their minimum values, but their influences on NDVI were obvious when Tmax and Tmin respectively reached their maximum values. (4) The smaller the return period, the larger the range of climate factor and NDVI, which has indicated that when the climate factor is certain, the NDVI is more likely to have a smaller return period, and the frequency of NDVI over a certain period is higher. In addition, the larger the climate factor, the greater the return period is and NDVI is less frequent over a certain period of time. This research can help with deep understanding of the dynamic influence of seasonal daytime and nighttime asymmetric warming on the vegetation in typical coastal temperature zones of China under the background of global climate change.

scholarly journals Intensity–duration–frequency curves from remote sensing rainfall estimates: comparing satellite and weather radar over the eastern Mediterranean

2017 ◽  
Vol 21 (5) ◽  
pp. 2389-2404 ◽  
Author(s):  
Francesco Marra ◽  
Efrat Morin ◽  
Nadav Peleg ◽  
Yiwen Mei ◽  
Emmanouil N. Anagnostou
Keyword(s):  
Remote Sensing ◽  
Return Period ◽  
Eastern Mediterranean ◽  
Rain Gauge ◽  
Catchment Scale ◽  
Weather Radars ◽  
The Earth ◽  
Idf Curves ◽  
Intensity Duration Frequency ◽  
Frequency Curves

Abstract. Intensity–duration–frequency (IDF) curves are widely used to quantify the probability of occurrence of rainfall extremes. The usual rain gauge-based approach provides accurate curves for a specific location, but uncertainties arise when ungauged regions are examined or catchment-scale information is required. Remote sensing rainfall records, e.g. from weather radars and satellites, are recently becoming available, providing high-resolution estimates at regional or even global scales; their uncertainty and implications on water resources applications urge to be investigated. This study compares IDF curves from radar and satellite (CMORPH) estimates over the eastern Mediterranean (covering Mediterranean, semiarid, and arid climates) and quantifies the uncertainty related to their limited record on varying climates. We show that radar identifies thicker-tailed distributions than satellite, in particular for short durations, and that the tail of the distributions depends on the spatial and temporal aggregation scales. The spatial correlation between radar IDF and satellite IDF is as high as 0.7 for 2–5-year return period and decreases with longer return periods, especially for short durations. The uncertainty related to the use of short records is important when the record length is comparable to the return period ( ∼  50,  ∼  100, and  ∼  150 % for Mediterranean, semiarid, and arid climates, respectively). The agreement between IDF curves derived from different sensors on Mediterranean and, to a good extent, semiarid climates, demonstrates the potential of remote sensing datasets and instils confidence on their quantitative use for ungauged areas of the Earth.

scholarly journals Peak flows from thin sedum-moss roof

2005 ◽  
Vol 36 (3) ◽  
pp. 269-280 ◽  
Author(s):  
L. Bengtsson
Keyword(s):  
Return Period ◽  
Field Capacity ◽  
Rainfall Runoff ◽  
Percolation Process ◽  
Drainage Layer ◽  
The Mean ◽  
Intensity Duration Frequency ◽  
High Runoff ◽  
High Precipitation ◽  
Frequency Curves

The runoff from real 3 cm thick sedum-moss roofs and from laboratory roof plots in southern Sweden is measured and analysed. Real rains and artificial storms are used for the analysis. The probability of high runoff is compared with the probability of high precipitation intensity. Intensity–duration–frequency curves for runoff are derived and it is found that the runoff of 1.5 year return period corresponds to rain of 0.4 year return period. The storage of water in the soil–vegetation cover on the roof is determined. The storage at field capacity, when runoff is initiated, is about 9 mm. Water in excess of that is temporary stored during storms. The runoff distribution during prolonged storms can be related to the mean rain intensity over 20–30 min. The influence of the slope and length of the roof on the runoff peak is investigated as is the effect of the drainage layer. Neither slope nor length seems to significantly influence the runoff distribution, which indicates that the vertical percolation process through the vegetation and the soil dominates the rainfall–runoff process. The presence of a drainage layer below the soil results in somewhat faster runoff compared to when there is no drainage layer, and thus results in an increased runoff peak.

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