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 Analysis of extreme rainfall in Oti River Basin (West Africa)

2021 ◽  
Author(s):  
Komi S. Klassou ◽  
Kossi Komi
Keyword(s):  
River Basin ◽  
Heavy Rainfall ◽  
Extreme Rainfall ◽  
Value Theory ◽  
Extreme Value ◽  
Return Periods ◽  
Maximum Rainfall ◽  
Daily Data ◽  
Dry Spell ◽  
Annual Maximum Rainfall

Abstract Understanding how extreme rainfall is changing locally is a useful step in the implementation of efficient adaptation strategies to negative impacts of climate change. This study aims to analyze extreme rainfall over the middle Oti River Basin. Ten moderate extreme precipitation indices as well as heavy rainfall of higher return periods (25, 50, 75, and 100 years) were calculated using observed daily data from 1921 to 2018. In addition, Mann–Kendall and Sen's slope tests were used for trend analysis. The results showed decreasing trends in most of the heavy rainfall indices while the dry spell index exhibited a rising trend in a large portion of the study area. The occurrence of heavy rainfall of higher return periods has slightly decreased in a large part of the study area. Also, analysis of the annual maximum rainfall revealed that the generalized extreme value is the most appropriate three-parameter frequency distribution for predicting extreme rainfall in the Oti River Basin. The novelty of this study lies in the combination of both descriptive indices and extreme value theory in the analysis of extreme rainfall in a data-scarce river basin. The results are useful for water resources management in this area.

scholarly journals Modelling Extreme Rainfall Using Adjusted Sandwich Estimator

2020 ◽  
pp. 1-5
Author(s):  
Nur Farhanah Kahal Musakkal ◽  
Darmesah Gabda
Keyword(s):  
Maximum Likelihood ◽  
Extreme Rainfall ◽  
Extreme Value ◽  
Annual Maximum ◽  
Generalized Extreme Value ◽  
Maximum Rainfall ◽  
Penalized Maximum Likelihood ◽  
Sandwich Estimator ◽  
Annual Maximum Rainfall ◽  
Site Dependence

The Generalized Extreme Value (GEV) distribution is often used to describe the frequency of occurrence of extreme rainfall. Modelling the extreme event using the independent Generalized Extreme Value to spatial data fails to account the behaviour of dependency data. However, the wrong statistical assumption by this marginal approach can be adjusted using sandwich estimator. In this paper, we used the conventional method of the marginal fitting of generalized extreme value distribution to the extreme rainfall then corrected the standard error to account for inter-site dependence. We also applied the penalized maximum likelihood to improve the generalized parameter estimations. A case study of annual maximum rainfall from several stations at western Sabah is studied, and the results suggest that the variances were found to be greater than the standard error in the marginal estimation as the inter-site dependence being considered. Key words: Generalized Extreme Value theory, sandwich estimator, penalized maximum likelihood, annual maximum rainfall

scholarly journals Influence on the Distribution Function of Annual Maximum Rainfall Series when Filling Data Using Lagrange Interpolation

10.29007/m75f ◽  
2018 ◽  
Author(s):  
Maritza Arganis ◽  
Margarita Preciado ◽  
JesÚs Javier Cortes ◽  
Miguel Eduardo Gonzalez ◽  
VÍctor DamiÁn Pinilla
Keyword(s):  
Distribution Function ◽  
Lagrange Interpolation ◽  
Rainfall Series ◽  
Rainfall Data ◽  
Annual Rainfall ◽  
Annual Maximum ◽  
Return Periods ◽  
Maximum Rainfall ◽  
Weather Stations ◽  
Annual Maximum Rainfall

Lagrange interpolation was applied to complete maximum annual rainfall data for five weather stations in Aguascalientes, State of Mexico; in most of them there were no variations in the type of distribution function obtained; in general, an overestimation of the extrapolated data was identified for different return periods when the original records were not used.

scholarly journals 1 to 10 days extreme rainfall studies for Kerala state

MAUSAM ◽  
2022 ◽  
Vol 46 (2) ◽  
pp. 175-180
Author(s):  
S. A. SASEENDRAN ◽  
K. K. SINGH ◽  
J. BAHADUR ◽  
O. N. DHAR
Keyword(s):  
Extreme Precipitation ◽  
Daily Rainfall ◽  
Extreme Rainfall ◽  
Statistical Technique ◽  
Rainfall Data ◽  
Hydraulic Structures ◽  
Annual Maximum ◽  
Probable Maximum Precipitation ◽  
Maximum Precipitation ◽  
Kerala State

 The daily rainfall data for 80 years from 98 stations in Kerala region have been analysed to arrive at the Probable Maximum Precipitation (PMP) estimates for rainfall durations or 1 to 10 days. Hershfield's statistical technique has been adopted for the estimation of PMP from annual maximum data. The study will be useful in the estimation of extreme precipitation for computation of design floods, required for design of spillways of dams and other major hydraulic structures in the Kerala state.    

scholarly journals Heavy rainfall equations for Santa Catarina, Brazil

2011 ◽  
Vol 35 (6) ◽  
pp. 2127-2134 ◽  
Author(s):  
Álvaro José Back ◽  
Alan Henn ◽  
José Luiz Rocha Oliveira
Keyword(s):  
Coastal Region ◽  
Daily Rainfall ◽  
Rain Gauge ◽  
Annual Maximum ◽  
Santa Catarina ◽  
Maximum Rainfall ◽  
Rainfall Events ◽  
Significance Level ◽  
Intensity Duration Frequency ◽  
Annual Maximum Rainfall

Knowledge of intensity-duration-frequency (IDF) relationships of rainfall events is extremely important to determine the dimensions of surface drainage structures and soil erosion control. The purpose of this study was to obtain IDF equations of 13 rain gauge stations in the state of Santa Catarina in Brazil: Chapecó, Urussanga, Campos Novos, Florianópolis, Lages, Caçador, Itajaí, Itá, Ponte Serrada, Porto União, Videira, Laguna and São Joaquim. The daily rainfall data charts of each station were digitized and then the annual maximum rainfall series were determined for durations ranging from 5 to 1440 min. Based on these, with the Gumbel-Chow distribution, the maximum rainfall was estimated for durations ranging from 5 min to 24 h, considering return periods of 2, 5, 10, 20, 25, 50, and 100 years,. Data agreement with the Gumbel-Chow model was verified by the Kolmogorov-Smirnov test, at 5 % significance level. For each rain gauge station, two IDF equations of rainfall events were adjusted, one for durations from 5 to 120 min and the other from 120 to 1440 min. The results show a high variability in maximum intensity of rainfall events among the studied stations. Highest values of coefficients of variation in the annual maximum series of rainfall were observed for durations of over 600 min at the stations of the coastal region of Santa Catarina.

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