scholarly journals LH-Moments of the Wakeby Distribution applied to Extreme Rainfall in Thailand

2021 ◽  
Vol 17 (2) ◽  
pp. 166-180
Author(s):  
Busababodhin Piyapatr ◽  
Chiangpradit Monchaya ◽  
Phoophiwfa Tossapol ◽  
Jeong-Soo Park ◽  
Do-ove Manoon ◽  
...  
Keyword(s):  
Extreme Rainfall ◽  
Return Periods ◽  
Return Levels ◽  
Moments Method ◽  
Design Values ◽  
L Moments ◽  
Moments Estimates ◽  
Quantile Estimates ◽  
Wakeby Distribution ◽  
Better Than

This article applies the Wakeby distribution (WAD) with high-order L-moments estimates (LH-ME) to annual extreme rainfall data obtained from 99 gauge stations in Thailand. The objectives of this study investigate to obtain appropriate quantile estimates and return levels for several return periods, 2, 5, 10, 25 and 50 years. The 95% confidence intervals for the quantiles determined from the WAD are derived using the bootstrap technique. Isopluvial maps of estimated design values that correspond to selected return periods are presented. The LH-ME results are better than estimates from the more primitive L-moments method for a large majority of the stations considered.

scholarly journals Non-Parametric Statistics on Extreme Rainfall

1994 ◽  
Vol 25 (4) ◽  
pp. 267-278 ◽  
Author(s):  
K. Arnbjerg-Nielsen ◽  
P. Harremoës ◽  
H. Spliid
Keyword(s):  
Monitoring Program ◽  
Extreme Rainfall ◽  
Bootstrap Resampling ◽  
Return Periods ◽  
Peak Intensity ◽  
Resampling Method ◽  
Storm Drainage ◽  
Design Values ◽  
Parametric Statistics ◽  
Rain Data

This article focuses on rain as input data to problems related to urban storm drainage. The rain data originate from a monitoring program consisting of 56 gauges in Denmark. The gauges have observation periods ranging from 2 to 14 years. The gauges sample rain in a quantity of 0.2 mm with a resolution in time of 1 minute. Two variables have been investigated: peak intensity and depth. Design values for return periods in the range from 0.1 to 2 years have been estimated for each gauge separately by means of the bootstrap resampling method. The estimation includes expected value, standard deviation and confidence intervals of the design value. For large return periods the uncertainty of the estimates prevents a distinction by gauge between different statistical populations. However, for small return periods a test shows significant variation between gauges, i.e. the uncertainty of the estimates may not be assumed to be due to sampling variability only.

Handling non-stationary flood frequency analysis using TL-moments approach for estimation parameter

2019 ◽  
Vol 11 (4) ◽  
pp. 966-979
Author(s):  
Nur Amalina Mat Jan ◽  
Ani Shabri ◽  
Ruhaidah Samsudin
Keyword(s):  
Frequency Analysis ◽  
Moment Method ◽  
Estimation Method ◽  
Flood Frequency ◽  
Flood Frequency Analysis ◽  
Moments Method ◽  
Flood Estimation ◽  
L Moments ◽  
Flood Quantiles ◽  
Quantile Estimates

Abstract Non-stationary flood frequency analysis (NFFA) plays an important role in addressing the issue of the stationary assumption (independent and identically distributed flood series) that is no longer valid in infrastructure-designed methods. This confirms the necessity of developing new statistical models in order to identify the change of probability functions over time and obtain a consistent flood estimation method in NFFA. The method of Trimmed L-moments (TL-moments) with time covariate is confronted with the L-moment method for the stationary and non-stationary generalized extreme value (GEV) models. The aims of the study are to investigate the behavior of the proposed TL-moments method in the presence of NFFA and applying the method along with GEV distribution. Comparisons of the methods are made by Monte Carlo simulations and bootstrap-based method. The simulation study showed the better performance of most levels of TL-moments method, which is TL(η,0), (η = 2, 3, 4) than the L-moment method for all models (GEV1, GEV2, and GEV3). The TL-moment method provides more efficient quantile estimates than other methods in flood quantiles estimated at higher return periods. Thus, the TL-moments method can produce better estimation results since the L-moment eliminates lowest value and gives more weight to the largest value which provides important information.

scholarly journals A New Look at Storm Separation Technique in Estimation of Probable Maximum Precipitation in Mountainous Areas

Water ◽  
2020 ◽  
Vol 12 (4) ◽  
pp. 1177
Author(s):  
Yifan Liao ◽  
Bingzhang Lin ◽  
Xiaoyang Chen ◽  
Hui Ding
Keyword(s):  
Extreme Rainfall ◽  
Separation Technique ◽  
Annual Maximum ◽  
Return Periods ◽  
Mountainous Areas ◽  
Maximum Rainfall ◽  
Probable Maximum Precipitation ◽  
Maximum Precipitation ◽  
L Moments ◽  
Annual Maximum Rainfall

Storm separation is a key step when carrying out storm transposition analysis for Probable Maximum Precipitation (PMP) estimation in mountainous areas. The World Meteorological Organization (WMO) has recommended the step-duration-orographic-intensification-factor (SDOIF) method since 2009 as an effective storm separation technique to identify the amounts of precipitation caused by topography from those caused by atmospheric dynamics. The orographic intensification factors (OIFs) are usually developed based on annual maximum rainfall series under such assumption that the mechanism of annual maximum rainfalls is close to that of the PMP-level rainfall. In this paper, an alternative storm separation technique using rainfall quantiles, instead of annual maximum rainfalls, with rare return periods estimated via Regional L-moments Analysis (RLMA) to calculate the OIFs is proposed. Based on Taiwan’s historical 4- and 24-h precipitation data, comparisons of the OIFs obtained from annual maximum rainfalls with that from extreme rainfall quantiles at different return periods, as well as the PMP estimates of Hong Kong from transposing the different corresponding separated nonorographic rainfalls, were conducted. The results show that the OIFs obtained from rainfall quantiles with certain rare probabilities are more stable and reasonable in terms of stability and spatial distribution pattern.

scholarly journals Selection of Hydrological Probability Distributions for Extreme Rainfall Events in the Regions of Colombia

Water ◽  
2020 ◽  
Vol 12 (5) ◽  
pp. 1397 ◽  
Author(s):  
Óscar E. Coronado-Hernández ◽  
Ernesto Merlano-Sabalza ◽  
Zaid Díaz-Vergara ◽  
Jairo R. Coronado-Hernández
Keyword(s):  
Frequency Analysis ◽  
Probability Distributions ◽  
Extreme Rainfall ◽  
Return Periods ◽  
Extreme Rainfall Events ◽  
Maximum Rainfall ◽  
Rainfall Events ◽  
Moments Method ◽  
Engineering Projects ◽  
Selection Of

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.

scholarly journals Parameter estimate for three-parameter kappa distribution using LHmoments approach

2022 ◽  
Vol 9 (2) ◽  
pp. 104-108
Author(s):  
Zakaria et al. ◽  
Keyword(s):  
Parameter Estimate ◽  
Higher Order ◽  
Data Series ◽  
Absolute Deviation ◽  
Deviation Index ◽  
Moments Method ◽  
L Moments ◽  
Moments Estimates ◽  
Best Fit

The method of higher-order L-moments (LH-moment) was proposed as a more robust alternative compared to classical L-moments to characterize extreme events. The new derivation will be done for Mielke-Johnson’s Kappa and Three-Parameters Kappa Type-II (K3D-II) distributions based on the LH-moments approach. The data of maximum monthly rainfall for Embong station in Terengganu were used as a case study. The analyses were conducted using the classical L-moments method with η=0 and LH-moments methods with η=1, η=2, η=3 and η=4 for a complete data series and upper parts of the distributions. The most suitable distributions were determined based on the Mean Absolute Deviation Index (MADI), Mean Square Deviation Index (MSDI), and Correlation (r). Also, L-moment and LH-moment ratio diagrams were used to represent visual proofs of the results. The analysis showed that LH-moments methods at a higher order of K3D-II distribution best fit the data of maximum monthly rainfalls for the Embong station for the upper parts of the distribution compared to L-moments. The results also proved that whenever η increases, LH-moments reflect more and more characteristics of the upper part of the distribution. This seems to suggest that LH-moments estimates for the upper part of the distribution events are superior to L-moments in fitting the data of maximum monthly rainfalls.

scholarly journals Application of Extreme Value Theory in Predicting Climate Change Induced Extreme Rainfall in Kenya

2019 ◽  
Vol 8 (4) ◽  
pp. 85
Author(s):  
Faithful C. Onwuegbuche ◽  
Alpha B. Kenyatta ◽  
Steeven B. Affognon ◽  
Exavery P. Enock ◽  
Mary O. Akinade
Keyword(s):  
Climate Change ◽  
Extreme Value Theory ◽  
Extreme Rainfall ◽  
Value Theory ◽  
Extreme Value ◽  
Generalized Extreme Value Distribution ◽  
Return Level ◽  
Return Periods ◽  
Optimal Model ◽  
Return Levels

Climate change has brought about unprecedented new weather patterns, one of which is changes in extreme rainfall. In Kenya, heavy rains and severe flash floods have left people dead and displaced hundreds from their settlements. In order to build a resilient society and achieve sustainable development, it is paramount that adequate inference about extreme rainfall be made. To this end, this research modelled and predicted extreme rainfall events in Kenya using Extreme Value Theory for rainfall data from 1901-2016. Maximum Likelihood Estimation was used to estimate the model parameters and block maxima approach was used to fit the Generalized Extreme Value Distribution (GEVD) while the Peak Over Threshold method was used to fit the Generalized Pareto Distribution (GPD). The Gumbel distribution was found to be the optimal model from the GEVD while the Exponential distribution gave the optimal model over the threshold value. Furthermore, prediction for the return periods of 10, 20, 50 and 100 years were made using the return level estimates and their corresponding confidence intervals were presented. It was found that increase in return periods leads to a corresponding increase in return levels. However, the GPD gave higher return levels for 10 and 20 years compared to GEVD. While, for higher return periods 50 and 100 years, the GEVD gave higher return levels compared to the GPD. Model diagnostics using probability, density, quantile and return level plots indicated that the models provided were a good fit for the data.

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.

Testing the impacts of wildfire on hydrological and sediment response using the OpenLISEM model

2021 ◽  
Author(s):  
Jinfeng Wu ◽  
João Pedro Nunes ◽  
Jantiene E. M. Baartman
Keyword(s):  
Water Discharge ◽  
Extreme Rainfall ◽  
Fire Occurrence ◽  
Return Periods ◽  
Extreme Rainfall Events ◽  
Rainfall Events ◽  
Erosion Risk ◽  
Sediment Yields ◽  
Splash Erosion ◽  
Event Based

<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>

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