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 Potential of plotting positions for intensity-duration-frequency curves with short rainfall records

2017 ◽  
Vol 13 (4-1) ◽  
pp. 394-399
Author(s):  
Noratiqah Mohd Ariff ◽  
Abdul Aziz Jemain ◽  
Mohd Aftar Abu Bakar
Keyword(s):  
Probability Distribution ◽  
Empirical Distribution ◽  
Annual Rainfall ◽  
Annual Maximum ◽  
Distribution Fitting ◽  
Maximum Rainfall ◽  
Gev Distribution ◽  
Idf Curves ◽  
Intensity Duration Frequency ◽  
Annual Maximum Rainfall

Intensity-duration-frequency (IDF) curves represent the relationship between storm intensity, storm duration and return period. The IDF curves available are mostly done by fitting series of annual maximum rainfall intensity to parametric distributions. However, the length of annual rainfall records, especially for small scaled data, are not always enough. Rainfall records of less than 50 years are usually deemed insufficient to unequivocally identify the probability distribution of the annual rainfall. Thus, this study introduces an alternative approach that replaces the need for parametric fitting by using empirical distribution based on plotting positions to represent annual maximum rainfall series. Subsequently, these plotting positions are used to build IDF curves. The IDF curves found are then compared to the IDF curves yielded from the parametric GEV distribution which is a common basis for IDF curves. This study indicates that IDF curves obtained using plotting positions are similar to IDF curves found using GEV distribution for storm events. Hence, researchers could model and subsequently build IDF curves for annual rainfall records of less than 50 years by using plotting positions and avoid any probability distribution fitting of insufficient data.

scholarly journals Development of Intensity Duration Frequency (IDF) Curves for Rainfall Prediction within the Middle Niger River Basin

2020 ◽  
Vol 4 (2) ◽  
pp. 382-397
Author(s):  
J. O. Ehiorobo ◽  
O.C. Izinyon ◽  
R. I. Ilaboya
Keyword(s):  
River Basin ◽  
Rainfall Intensity ◽  
Rainfall Data ◽  
Annual Maximum ◽  
Return Periods ◽  
Maximum Rainfall ◽  
Niger River ◽  
Idf Curves ◽  
Intensity Duration Frequency ◽  
Best Fit

Rainfall Intensity-Duration-Frequency (IDF) relationship remains one of the mostly used tools in hydrology and water resources engineering, especially for planning, design and operations of water resource projects. IDF relationship can provide adequate information about the intensity of rainfall at different duration for various return periods. The focus of this research was to develop IDF curves for the prediction of rainfall intensity within the middle Niger River Basin (Lokoja and Ilorin) using annual maximum daily rainfall data. Forty (40) year’s annual maximum rainfall data ranging from 1974 to 2013 was employed for the study. To ascertain the data quality, selected preliminary analysis technique including; descriptive statistics, test of homogeneity and outlier detection test were employed. To compute the three hours rainfall intensity, the ratio of rainfall amount and duration was used while the popular Gumbel probability distribution model was employed to calculate the rainfall frequency factor. To assess the best fit model that can be employed to predict rainfall intensity for various return periods at ungauged locations, four empirical IDF equations, namely; Talbot, Bernard, Kimijima and Sherman equations were employed. The model with the least calculated sum of minimized root mean square error (RMSE) was adopted as the best fit empirical model. Results obtained revealed that the Talbot model was the best fit model for Ilorin and Lokoja with calculated sum of minimized error of 1.32170E-07 and 8.953636E-08. This model was thereafter employed to predict the rainfall intensity for different durations at 2, 5, 10, 25, 50 and 100yrs return periods respectively.

scholarly journals Rainfall statistics changes in Sicily

2013 ◽  
Vol 10 (2) ◽  
pp. 2323-2352 ◽  
Author(s):  
E. Arnone ◽  
D. Pumo ◽  
F. Viola ◽  
L. V. Noto ◽  
G. La Loggia
Keyword(s):  
Extreme Events ◽  
Daily Rainfall ◽  
Negative Trend ◽  
Rainfall Series ◽  
Annual Maximum ◽  
Maximum Rainfall ◽  
Rainfall Events ◽  
Rainfall Characteristics ◽  
Annual Maximum Rainfall ◽  
Rainfall Statistics

Abstract. Changes in rainfall characteristics are one of the most relevant signs of current climate alterations. Many studies have demonstrated an increase in rainfall intensity and a reduction of frequency in several areas of the world, including Mediterranean areas. Rainfall characteristics may be crucial for vegetation patterns formation and evolution in Mediterranean ecosystems, with important implications, for example, in vegetation water stress or coexistence and competition dynamics. At the same time, characteristics of extreme rainfall events are fundamental for the estimation of flood peaks and quantiles which can be used in many hydrological applications, such as design of the most common hydraulic structures, or planning and management of flood prone areas. In the past, Sicily has been screened for several signals of possible climate change. Annual, seasonal and monthly rainfall data in the entire Sicilian region have been analyzed, showing a global reduction of total annual rainfall. Moreover, annual maximum rainfall series for different durations have been rarely analyzed in order to detect the presence of trends. Results indicated that for short durations, historical series generally exhibit increasing trends while for longer durations the trends are mainly negative. Starting from these premises, the aim of this study is to investigate and quantify changes in rainfall statistics in Sicily, during the second half of the last century. Time series of about 60 stations over the region have been processed and screened by using the non parametric Mann–Kendall test. Particularly, extreme events have been analyzed using annual maximum rainfall series at 1, 3, 6, 12 and 24 h duration while daily rainfall properties have been analyzed in term of frequency and intensity, also characterizing seasonal rainfall features. Results of extreme events analysis confirmed an increasing trend for rainfall of short durations, especially for one hour rainfall duration. Instead, precipitation of long durations have exhibited a decreased trend. With regard to the spatial distribution, increase in short duration precipitation has been observed especially in stations located along the coastline; however, no clear and well-defined spatial pattern have been outlined by the results. Outcomes of analysis for daily rainfall properties have showed that heavy-torrential precipitation tends to be more frequent at regional scale, while light rainfall events exhibited a negative trend at some sites. Values of total annual precipitations confirmed a significant negative trend, mainly due to the reduction during the winter season.

Best-fit probability distribution model for predicting annual maximum rainfall of Pusa (Bihar)

2018 ◽  
Vol 3 (01) ◽  
pp. 100-104
Author(s):  
J. Kumar ◽  
R. Suresh ◽  
Jyoti .
Keyword(s):  
Probability Distribution ◽  
Gumbel Distribution ◽  
Annual Rainfall ◽  
Annual Maximum ◽  
Distribution Models ◽  
Maximum Rainfall ◽  
Type Iii ◽  
Pearson Type ◽  
Annual Maximum Rainfall ◽  
Pearson Type Iii Distribution

In present study an attempt has been made to evaluate the suitable probability distribution models for predicting 1, 2, 3, 4, 5, 6 and 7-days annual maximum rainfall amounts based on 39 years (1964 to 2002) daily rainfall data. Three probability distribution models namely: Log Normal distribution, Log Pearson Type-III distribution and Gumbel distribution models were considered to evaluate their goodness of fit. The Weibull’s method was used for computation of observed rainfall values at1, 5, 20, 30, 50, 95 and 99 percent probability levels. The Log Pearson type –III distribution was found suitable for 1 and 2 days maximum annual rainfall, while Gumbel distribution was found to be the best for predicting 3, 4, 5, 6 and 7- days annual maximum rainfall amounts. The relationships between annual maximum rainfall and return periods were also developed. The non – linear relationships (i.e. logarithmic) were found to be most suitable for all the cases.

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 Rainfall statistics changes in Sicily

2013 ◽  
Vol 17 (7) ◽  
pp. 2449-2458 ◽  
Author(s):  
E. Arnone ◽  
D. Pumo ◽  
F. Viola ◽  
L. V. Noto ◽  
G. La Loggia
Keyword(s):  
Extreme Events ◽  
Daily Rainfall ◽  
Rainfall Series ◽  
Annual Maximum ◽  
Maximum Rainfall ◽  
Rainfall Events ◽  
Rainfall Characteristics ◽  
Annual Maximum Rainfall ◽  
Rainfall Statistics ◽  
Precipitation Events

Abstract. Changes in rainfall characteristics are one of the most relevant signs of current climate alterations. Many studies have demonstrated an increase in rainfall intensity and a reduction of frequency in several areas of the world, including Mediterranean areas. Rainfall characteristics may be crucial for vegetation patterns formation and evolution in Mediterranean ecosystems, with important implications, for example, in vegetation water stress or coexistence and competition dynamics. At the same time, characteristics of extreme rainfall events are fundamental for the estimation of flood peaks and quantiles that can be used in many hydrological applications, such as design of the most common hydraulic structures, or planning and management of flood-prone areas. In the past, Sicily has been screened for several signals of possible climate change. Annual, seasonal and monthly rainfall data in the entire Sicilian region have been analyzed, showing a global reduction of total annual rainfall. Moreover, annual maximum rainfall series for different durations have been rarely analyzed in order to detect the presence of trends. Results indicated that for short durations, historical series generally exhibit increasing trends, while for longer durations the trends are mainly negative. Starting from these premises, the aim of this study is to investigate and quantify changes in rainfall statistics in Sicily, during the second half of the last century. Time series of about 60 stations over the region have been processed and screened by using the nonparametric Mann–Kendall test. In particular, extreme events have been analyzed using annual maximum rainfall series at 1, 3, 6, 12 and 24 h duration, while daily rainfall properties have been analyzed in terms of frequency and intensity, also characterizing seasonal rainfall features. Results of extreme events analysis confirmed an increasing trend for rainfall of short durations, especially for 1 h rainfall duration. Conversely, precipitation events of long durations have exhibited a decreased trend. Increase in short-duration precipitation has been observed especially in stations located along the coastline; however, no clear and well-defined spatial pattern has been outlined by the results. Outcomes of analysis for daily rainfall properties have showed that heavy–torrential precipitation events tend to be more frequent at regional scale, while light rainfall events exhibited a negative trend at some sites. Values of total annual precipitation events confirmed a significant negative trend, mainly due to the reduction during the winter season.

scholarly journals Rainfall Analysis for Hyderabad and Nawabshah, Sindh, Pakistan

2020 ◽  
Vol 10 (6) ◽  
pp. 6597-6602
Author(s):  
A. A. Mahessar ◽  
A. L. Qureshi ◽  
B. Sadiqui ◽  
S. M. Kori ◽  
K. C. Mukwana ◽  
...  
Keyword(s):  
Hydrological Cycle ◽  
Rainfall Data ◽  
Precipitation Pattern ◽  
Rainfall Time Series ◽  
Annual Maximum ◽  
Maximum Rainfall ◽  
Rainfall Analysis ◽  
The Mean ◽  
The Difference ◽  
Annual Maximum Rainfall

The climatic change has a visible impact in recent abnormal weather events, such as Pakistan’s intensification of the hydrological cycle with changing precipitation pattern, water availability periods, and weather-induced natural disasters. The rainfall flush flood of 2010 alone displaced millionσ of people and damaged properties in just one stroke. The next year, the shocking rainfall flood of 2011 in Sindh, only underscored the enormity of the challenge posed by climate change. The current paper presents the analysis carried out for one-day annual maximum rainfall for Hyderabad and Nawabshah cities, Sindh, Pakistan for the period from 1961 to 2011 using STATISTICA Software for interpolating and forecasting the rainfall time series. The maximum values of observed rainfall were 250.70mm and 256.30mm, while the minimum values were 3.0mm and 0.0mm for Hyderabad and Nawabshah respectively, while the mean of fifty-one (51) years of rainfall data is 51.96mm and 45.3 mm and the computed standard deviations were 42.693mm and 43.896mm respectively. The difference between the mean and standard deviation of one-day maximum rainfall is small, which showed the consistency of the data. The polynomial trend curved lines exhibited fluctuations in the rainfall data, which indicates a continual change in rainfall behavior. Hence, the rainfall data are subjected to a moving mean smoothing with a duration shorter than 3 years. Through these trends, the future one-day annual maximum rainfall can be predicted. The correlation of one-day annual maximum rainfall between Hyderabad and Nawabshah cities had R2 of 0.973. The computed results of return periods of 3, 5, and 10 years for one-day annual maximum rainfall for both cities revealed that the rainfall values for Hyderabad are higher.

Selecting a probability distribution for extreme rainfall series in Malaysia

2002 ◽  
Vol 45 (2) ◽  
pp. 63-68 ◽  
Author(s):  
M.D. Zalina ◽  
M.N.M. Desa ◽  
V-T-A. Nguyen ◽  
A.H.M. Kassim
Keyword(s):  
Mean Squared Error ◽  
Rainfall Series ◽  
Model Parameters ◽  
Annual Maximum ◽  
Maximum Rainfall ◽  
Root Mean Squared Error ◽  
Type Iii ◽  
Squared Error ◽  
Pearson Type ◽  
Annual Maximum Rainfall

This paper discusses the comparative assessment of eight candidate distributions in providing accurate and reliable maximum rainfall estimates for Malaysia. The models considered were the Gamma, Generalised Normal, Generalised Pareto, Generalised Extreme Value, Gumbel, Log Pearson Type III, Pearson Type III and Wakeby. Annual maximum rainfall series for one-hour resolution from a network of seventeen automatic gauging stations located throughout Peninsular Malaysia were selected for this study. The length of rainfall records varies from twenty-three to twenty-eight years. Model parameters were estimated using the L-moment method. The quantitative assessment of the descriptive ability of each model was based on the Probability Plot Correlation Coefficient test combined with root mean squared error, relative root mean squared error and maximum absolute deviation. Bootstrap resampling was employed to investigate the extrapolative ability of each distribution. On the basis of these comparisons, it can be concluded that the GEV distribution is the most appropriate distribution for describing the annual maximum rainfall series in Malaysia.

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