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.

Assessment of techniques to include historical information in flood frequency distributions for hydrological dam safety assessment

2021 ◽  
Author(s):  
Enrique Soriano Martín ◽  
Antonio Jiménez ◽  
Luis Mediero
Keyword(s):  
Return Period ◽  
Uncertainty Reduction ◽  
Flood Frequency ◽  
Dam Safety ◽  
Return Periods ◽  
Historical Information ◽  
Probability Weighted Moments ◽  
High Return ◽  
Partial Probability ◽  
Quantile Estimates

<p>Flood peak quantiles for return periods up to 10 000 years are required for dam design and safety assessment, though flood series usually have a record length of around 20-40 years that leads to a high uncertainty. The utility of historical data of flooding is generally recognised for estimating the magnitude of extreme events with return periods in excess of 100 years. Therefore, historical information can be incorporated in flood frequency analyses to reduce uncertainties in high return period flood quantile estimates that are used in hydrological dam safety analyses.</p><p>This study assesses a set of existing techniques to incorporate historical information of flooding in extreme frequency analyses, focusing on their reliability and uncertainty reduction for high return periods that are used for dam safety analysis. Monte Carlo simulations are used to assess both the reliability and uncertainty in high return period quantile estimates. Varying lengths in the historical (Nh = 100 and 200 years) and systematic (Ns = 20, 40 and 60 years) periods are considered. In addition, a varying number of known flood magnitudes that exceed a given perception threshold in the historical period are also considered (k = 1-2). The values of Nh, Ns and k used in the study are the most usual in practice.</p><p>The reliability and uncertainty reduction in flood quantile estimates for each technique depend on the statistical properties of flood series. Therefore, a set of feasible combinations of L-coefficient of variation (L-CV) and skewness (L-CS) values should be considered. The analysis aims to understand how each technique behaves in terms of flood quantile reliability and uncertainty reduction depending on the L-moment statistics of flood series. In this study, L-CV and L-CS regional values in the 29 homogeneous regions identified in Spain for developing the national map of flood quantiles by the Centre for Hydrographic Studies of CEDEX are considered.</p><p>The results show that the maximum likelihood estimator (MLE) and weighted moments (WM) techniques show the best results in the regions with small L-CS values. However, the biased partial probability weighted moments (BPPWM) technique shows the best results in the regions with high L-CS values. While the expected moments algorithm (EMA) tends to underestimate flood quantiles for high return periods, the unbiased partial probability weighted moments (UPPWM) technique tends to overestimate them. In addition, including historical information of flooding in flood frequency analyses improves flood quantile estimates in most cases regardless the technique that is used. Uncertainty reduction in high return period flood quantile estimates are higher for short systematic time series, regions with high L-CS values and long historical periods.</p><p><strong>Acknowledgments:</strong> This research has been supported by the project SAFERDAMS (PID2019-107027RB-I00) funded by the Spanish Ministry of Science and Innovation.</p>

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 Statistical Estimates of PMP Values

1994 ◽  
Vol 25 (4) ◽  
pp. 301-312 ◽  
Author(s):  
Jónas Elíasson
Keyword(s):  
Statistical Methods ◽  
Return Period ◽  
The Other ◽  
Statistical Distributions ◽  
Return Periods ◽  
Statistical Estimates ◽  
High Return ◽  
Bounded Data ◽  
Limiting Value

The article discusses two statistical methods to estimate PMP values, the Hershfield and the NERC methods. Neither method offers any explanation why the PMP values can be calculated by the use of unbounded statistical distributions, but both methods include the use of envelope curves that are not independent of the region. Bounded data that fits an unbounded distribution must deviate from the distribution for high return periods and tend to a limiting value, and then there exists, a limiting reduced variate that can be used to find the PMP value. When the distribution is EV1, the limiting reduced variate can be defined by a mapping transformation, or by cutting off the distribution. It is shown that when Hershfield or NERC methods are used, the limiting reduced variate is included in the PMP values and can be separated from regional parameters. It is suggested that the limiting reduced variate, that depends solely on return period, may more easily be transferred between regions than the other parameters. This may be a great help in finding PMP values in regions where observations are not extensive enough to define limiting return periods with necessary certainty. A case study with data from Iceland demonstrates, that using the limiting reduced variate, similarities emerge in the Icelandic data and the NERC PMP that justify the acceptance of the NERC method.

Self-reference in early speech of children speaking Slovak

2018 ◽  
Vol 6 (2) ◽  
pp. 14-35
Author(s):  
Jana Kesselová
Keyword(s):  
Individual Case ◽  
Early Age ◽  
Self Awareness ◽  
Upper Limit ◽  
First Occurrence ◽  
Social Deixis ◽  
Self Reference ◽  
Vega 1 ◽  
Slovak Language

Abstract The study focuses on the process of being aware of own I in children acquiring Slovak language at an early age and living in a Slovak family. The aim of the research is to understand the process of acquiring the means by which children refer to themselves in the interaction with an adult person. The research uses the qualitative longitudinal method of individual case study. A child’s speech is researched from the very first occurrence of a self-reference mean in 16th month up to the upper limit of early age (36th month) and all that is based on audio-visual records transcripts. The following are researched: (a) succession of self-reference means acquisition in early childhood, (b) function of self-reference linguistic means, (c) process of child’s self-awareness. The results obtained based on the linguistic data in Slovak language are compared with the results of similarly focused researches in English, French, Polish, Russian and Bulgarian language. The research reveals some constants in the development of self-reference instruments that can be observed throughout various language-cultural environments. The research is a part of solutions within the grant project VEGA 1/0099/16 Personal and Social Deixis in Slovak Language.

Robustness analysis: a case study

2003 ◽  
Author(s):  
J. Ackermann ◽  
H.Z. Hu ◽  
D. Kaesbauer
Keyword(s):  
Robustness Analysis

scholarly journals ESTIMATION OF WORST-CLASS TROPICAL CYCLONE AND STORM SURGE, AND ITS RETURN PERIOD -CASE STUDY FOR ISE BAY-

2015 ◽  
Vol 71 (2) ◽  
pp. I_1513-I_1518 ◽  
Author(s):  
Yoko SHIBUTANI ◽  
Sota NAKAJO ◽  
Nobuhito MORI ◽  
Sooyoul KIM ◽  
Hajime MASE
Keyword(s):  
Tropical Cyclone ◽  
Storm Surge ◽  
Return Period ◽  
Ise Bay

Reliability-based covariate analysis for complex systems in heterogeneous environment: Case study of mining equipment

2018 ◽  
Vol 233 (4) ◽  
pp. 593-604
Author(s):  
Amin Moniri-Morad ◽  
Mohammad Pourgol-Mohammad ◽  
Hamid Aghababaei ◽  
Javad Sattarvand
Keyword(s):  
Complex Systems ◽  
Cox Regression ◽  
Reliability Evaluation ◽  
Distribution Functions ◽  
Heterogeneous Environment ◽  
Mining Equipment ◽  
Data Set ◽  
Explanatory Variables ◽  
The Impact

Operational heterogeneity and harsh environment lead to major variations in production system performance and safety. Traditional probabilistic model is dealt with time-to-event data analysis, which does not have the capability of quantifying and simulation of these types of complexities. This research proposes an integrated methodology for analyzing the impact of dominant explanatory variables on the complex system reliability. A flexible parametric proportional hazards model is developed by focusing on standard parametric Cox regression model for reliability evaluation in complex systems. To achieve this, natural cubic splines are utilized to create a smooth and flexible baseline hazards function where the standard parametric distribution functions do not fit into the failure data set. A real case study is considered to evaluate the reliability for multi-component mechanical systems such as mining equipment. Different operational and environmental explanatory variables are chosen for the analysis process. Research findings revealed that precise estimation of the baseline hazards function is a major part of the reliability evaluation in heterogeneous environment. It is concluded that an appropriate maintenance strategy potentially mitigate the equipment failure intensity.

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