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Estimation of Extreme Daily Rainfall Probabilities: A Case Study in Kyushu Region, Japan
by
Sato, Tadamichi
, Shuin, Yasuhiro
in
Annual rainfall
/ case studies
/ Climate change
/ Damage
/ equations
/ Extreme values
/ Extreme weather
/ Floods
/ Investigations
/ Japan
/ Landslides
/ Landslides & mudslides
/ Outliers (statistics)
/ Probability
/ Rain
/ Rainfall
/ Rainfall measurement
/ socioeconomics
/ Statistical analysis
2023
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Estimation of Extreme Daily Rainfall Probabilities: A Case Study in Kyushu Region, Japan
by
Sato, Tadamichi
, Shuin, Yasuhiro
in
Annual rainfall
/ case studies
/ Climate change
/ Damage
/ equations
/ Extreme values
/ Extreme weather
/ Floods
/ Investigations
/ Japan
/ Landslides
/ Landslides & mudslides
/ Outliers (statistics)
/ Probability
/ Rain
/ Rainfall
/ Rainfall measurement
/ socioeconomics
/ Statistical analysis
2023
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Do you wish to request the book?
Estimation of Extreme Daily Rainfall Probabilities: A Case Study in Kyushu Region, Japan
by
Sato, Tadamichi
, Shuin, Yasuhiro
in
Annual rainfall
/ case studies
/ Climate change
/ Damage
/ equations
/ Extreme values
/ Extreme weather
/ Floods
/ Investigations
/ Japan
/ Landslides
/ Landslides & mudslides
/ Outliers (statistics)
/ Probability
/ Rain
/ Rainfall
/ Rainfall measurement
/ socioeconomics
/ Statistical analysis
2023
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Estimation of Extreme Daily Rainfall Probabilities: A Case Study in Kyushu Region, Japan
Journal Article
Estimation of Extreme Daily Rainfall Probabilities: A Case Study in Kyushu Region, Japan
2023
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Overview
Extreme rainfall causes floods and landslides, and so damages humans and socioeconomics; for instance, floods and landslides have been triggered by repeated torrential precipitation and have caused severe damage in the Kyushu region, Japan. Therefore, evaluating extreme rainfall in Kyushu is necessary to provide basic information for measures of rainfall-induced disasters. In this study, we estimated the probability of daily rainfall in Kyushu. The annual maximum values for daily rainfall at 23 long-record stations were normalized using return values at each station, corresponding to 2 and 10 years, and were combined by the station-year method. Additionally, the return period (RP) was calculated by fitting them to the generalized extreme value distribution. Based on the relationship between the normalized values of annual maximum daily rainfall and the RP, we obtained a regression equation to accurately estimate the RP up to 300 years by using data at given stations, considering outliers. In addition, we verified this equation using data from short-record stations where extreme rainfall events triggering floods and landslides were observed, and thereby elucidated that our method was consistent with previous techniques. Thus, this study develops strategies of measures for floods and landslides.
Publisher
MDPI AG
Subject
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