scholarly journals DETECTION OF TREND AND JUMP IN NON-STATIONARY ANNUAL MAXIMUM DAILY RAINFALL DATA

2007 ◽  
Vol 51 ◽  
pp. 301-306
Author(s):  
Takashi NAKAO ◽  
Kimiteru SADO ◽  
Ichiro SUGIYAMA
Keyword(s):  
Daily Rainfall ◽  
Rainfall Data ◽  
Annual Maximum ◽  
Daily Rainfall Data ◽  
Annual Maximum Daily Rainfall ◽  
Maximum Daily Rainfall

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 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 Impact of Extreme Rainfall on Flood Hydrographs

2021 ◽  
Vol 884 (1) ◽  
pp. 012018
Author(s):  
I G Tunas ◽  
H Azikin ◽  
G M Oka
Keyword(s):  
Return Period ◽  
Extreme Events ◽  
Daily Rainfall ◽  
Extreme Rainfall ◽  
Rainfall Data ◽  
Return Periods ◽  
Daily Rainfall Data ◽  
Design Rainfall ◽  
Flood Hydrograph ◽  
Maximum Daily Rainfall

Abstract Extreme rainfall is the main factor triggering flooding in various regions of the world including Indonesia. The increase in intensity and duration of current extreme rainfall is predicted as a result of global climate change. This paper aims to analyze the impact of extreme rainfall to the peak discharge of flood hydrographs at a watershed outlet in Palu, Sulawesi, Indonesia. Maximum daily rainfall data for the period 1990-1999 recorded at the Palu Meteorological Station, Central Sulawesi were selected using the Annual Maximum Series Method, and grouped into two types. Type I is the maximum daily rainfall data with extreme events and Type II is the maximum daily rainfall data without extreme events. Frequency analysis was applied to the two data groups using the best distribution method of: Normal, Normal Log, Pearson III Log, and Gumbel to obtain the design rainfall of each data group. In the next stage, the design rainfall transformation into a flood hydrograph is performed using the Nakayasu Synthetic Unit Hydrograph based on a number of return periods in one of the rivers flowing into Palu Bay, namely the Poboya River. The analysis results show that the design rainfall graphs with both extreme rainfall and without extreme rainfall are identical at the low return period and divergent at the high return period with a difference of up to 21.6% at the 1000-year return period. Correspondingly, extreme rainfall has a greater impact at the peak of the flood hydrograph with increasing return periods ranging from -1.28% to 26.81% over the entire return period.

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.

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