scholarly journals Variation of temperature and rainfall at Patna

MAUSAM ◽  
2021 ◽  
Vol 68 (1) ◽  
pp. 161-168
Author(s):  
VIVEKANAND SINGH ◽  
ANSHUMAN SINGH
Keyword(s):  
Daily Rainfall ◽  
Kendall Test ◽  
Annual Rainfall ◽  
Annual Maximum ◽  
Annual Average ◽  
Average Rainfall ◽  
Annual Maximum Daily Rainfall ◽  
The City ◽  
Maximum Daily Rainfall ◽  
Rainy Days

In this paper, the variation of temperature and rainfall at Patna are analysed using simple non-parametric tests. The trends in the annual maximum and minimum daily temperatures, annual rainfall, annual maximum daily rainfall, number of rainy days in a year, the annual average rainfall per rainy day and the ratio of maximum to average rainfall per rainy day at Patna have been examined. Tends in total monthly rainfall, Highest daily rainfall in a month and number of rainy days in a month have also been determined for every month in a year. The monthly trends of data using simple Mann-Kendall test indicated statistically significant changes in rainfall pattern for the city.

scholarly journals Variability and Trend of Annual Maximum Daily Rainfall in Northern Algeria

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Abderrahmane Nekkache Ghenim ◽  
Abdesselam Megnounif
Keyword(s):  
Linear Regression ◽  
Extreme Event ◽  
Daily Rainfall ◽  
Kendall Test ◽  
Statistical Characteristics ◽  
Annual Rainfall ◽  
Annual Maximum ◽  
Clear Trend ◽  
Annual Maximum Daily Rainfall ◽  
Maximum Daily Rainfall

The daily rainfall dataset of 35 weather stations covering the north of Algeria was studied for a period up to 43 years, recorded after 1970s. The variability and trends in annual maximum daily rainfall (AMDR) time series and their contributions in annual rainfall (AR) were investigated. The analysis of the series was based on statistical characteristics, Burn’s seasonality procedure, Mann-Kendall test, and linear regression technique. The contribution of the AMDR to AR analysis was subjected to both the Buishand test and the double mass curve technique. The AMDR characteristics reveal a strong temporal irregularity and have a wide frequency of occurrence in the months of November and December while the maximum intensity occurred in October. The observed phenomenon was so irregular that there was no dominant season and the occurrence of extreme event can arrive at any time of the year. The AMDR trends showed that only six of 35 stations have significant trend. For other stations, no clear trend was highlighted. This result was confirmed by the linear regression procedure. On the contrary, the contribution of AMDR in annual totals exhibited a significant increasing trend for 57% of the sites studied with a growth rate of up to 50%.

scholarly journals SPATIALIZATION OF THE ANNUAL MAXIMUM DAILY RAINFALL IN SOUTHEASTERN BRAZIL

2019 ◽  
Vol 39 (1) ◽  
pp. 97-109
Author(s):  
Marcelo L. Batista ◽  
Gilberto Coelho ◽  
Carlos R. de Mello ◽  
Marcelo S. de Oliveira
Keyword(s):  
Daily Rainfall ◽  
Southeastern Brazil ◽  
Annual Maximum ◽  
Annual Maximum Daily Rainfall ◽  
Maximum Daily Rainfall

scholarly journals An assessment of temporal effect on extreme rainfall estimates

2018 ◽  
Vol 379 ◽  
pp. 145-150
Author(s):  
Samiran Das ◽  
Dehua Zhu ◽  
Cheng Chi-Han
Keyword(s):  
Risk Analysis ◽  
Yangtze River ◽  
Decadal Variability ◽  
Yangtze River Basin ◽  
Daily Rainfall ◽  
Extreme Rainfall ◽  
Annual Maximum ◽  
Temporal Behaviour ◽  
Annual Maximum Daily Rainfall ◽  
Maximum Daily Rainfall

Abstract. This study assesses the temporal behaviour in terms of inter-decadal variability of extreme daily rainfall of stated return period relevant for hydrologic risk analysis using a novel regional parametric approach. The assessment is carried out based on annual maximum daily rainfall series of 180 meteorological stations of Yangtze River Basin over a 50-year period (1961–2010). The outcomes of the analysis reveal that while there were effects present indicating higher quantile values when estimated from data of the 1990s, it is found not to be noteworthy to exclude the data of any decade from the extreme rainfall estimation process for hydrologic risk analysis.

A Hybrid Grey Model to Forecast the Annual Maximum Daily Rainfall

2019 ◽  
Vol 23 (11) ◽  
pp. 4933-4948
Author(s):  
Yong-Jun Lin ◽  
Pin-Chan Lee ◽  
Kuo-Chen Ma ◽  
Chih-Chiang Chiu
Keyword(s):  
Daily Rainfall ◽  
Grey Model ◽  
Annual Maximum ◽  
Annual Maximum Daily Rainfall ◽  
Maximum Daily Rainfall

scholarly journals Analysis of Annual Maximum Rainfall Series using Method of L-Moments in Some Selected Sites in South-South Geographical Zone, Nigeria

2020 ◽  
Vol 1 ◽  
pp. 33-43
Author(s):  
Idowu R. Ilaboya ◽  
E. A. Otuaro
Keyword(s):  
Probability Distribution ◽  
Daily Rainfall ◽  
Rainfall Series ◽  
Distribution Model ◽  
Annual Maximum ◽  
Return Periods ◽  
L Moments ◽  
Annual Maximum Daily Rainfall ◽  
Best Fit ◽  
Maximum Daily Rainfall

Determination of the extent of peak rainfall for different return periods is an essential ingredient for the accurate design of hydraulic structures such as drains, dams and culverts as well as detection of flood risk areas. The focus of this study is to analyze annual maximum daily rainfall series in some selected sites within the coastal region of Nigeria using three parameter probability distribution models, namely, Generalized Logistics (GLO), Generalized Extreme Value (GEV) and Generalized Pareto (GPA) with the view of identifying the best fit probability distribution model per station that can be employed to estimate the rainfall magnitude for selected return periods. Specific time series analysis test, namely, detection of outlier and homogeneity test were performed to certify that the data utilized are adequate and suitable. Descriptive statistics such as sample mean, variance, standard deviation, kurtosis, skewness, and coefficient of variation were computed using basic statistical equations. The probability weighted moment parameters (b0, b1, b2 and b3), L-moment values (λ1, λ2, λ3 and λ4) and ratios (τ2, τ 3 and τ4) including the distribution parameters, namely, shape (k), scale (α) and location (ξ) parameters were computed based on L-moments procedures. To select the best-fit probability distribution model per station, carefully chosen goodness-of-fit statistics, namely, root mean square error, relative root mean square error, maximum absolute deviation index, maximum absolute error and probability plot correlation coefficient were employed since they can adequately assess the fitted distribution at a site. Results obtained indicate that the GLO is the best fit distribution for analyzing annual maximum daily rainfall series from Warri and Calabar while GPA for Port Harcourt and Uyo.

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