A platform for research: civil engineering, architecture and urbanism
Estimation of probable maximum precipitation 24-h (PMP 24-h) through statistical methods over Iran
Estimating the probable maximum precipitation (PMP) is necessary to calculate the probable maximum flood (PMF). It is of high importance in checking the adequacy of dam overflow capacity and other development and water transfer plans of a given area. In this research, using the for an annual 24-hour maximum rainfall data set of 45 synoptic stations throughout the country, the PMP values were calculated through the original Hershfield method and then the Hershfield-Desa method. By comparing the obtained results of 24-hour PMP estimation through the two mentioned methods, it is found that the estimation of PMP values in the original/first Hershfield method is well higher than the expected value (2.54 to 4.03). While in the modified method (Desa method), PMP values are significantly reduced and seem more reasonable (1.02 to 1.3). Meanwhile, the calculated variability and skewness coefficients also indicated more variability of PMP values in the southern stations of the country compared to rainy regions, which makes the estimation of PMP in the southern regions of the country considerably unreliable. HIGHLIGHTS Estimating possible floods for design and construction of irrigation and water reservoir projects such as dam construction are considered essential and in terms of dam safety, such as overflow capacity and dam failure, is of great importance.; Estimating the PMP close to observations over the country through various methods is essential in selecting design floods for different climatic regions.;
Estimation of probable maximum precipitation 24-h (PMP 24-h) through statistical methods over Iran
Estimating the probable maximum precipitation (PMP) is necessary to calculate the probable maximum flood (PMF). It is of high importance in checking the adequacy of dam overflow capacity and other development and water transfer plans of a given area. In this research, using the for an annual 24-hour maximum rainfall data set of 45 synoptic stations throughout the country, the PMP values were calculated through the original Hershfield method and then the Hershfield-Desa method. By comparing the obtained results of 24-hour PMP estimation through the two mentioned methods, it is found that the estimation of PMP values in the original/first Hershfield method is well higher than the expected value (2.54 to 4.03). While in the modified method (Desa method), PMP values are significantly reduced and seem more reasonable (1.02 to 1.3). Meanwhile, the calculated variability and skewness coefficients also indicated more variability of PMP values in the southern stations of the country compared to rainy regions, which makes the estimation of PMP in the southern regions of the country considerably unreliable. HIGHLIGHTS Estimating possible floods for design and construction of irrigation and water reservoir projects such as dam construction are considered essential and in terms of dam safety, such as overflow capacity and dam failure, is of great importance.; Estimating the PMP close to observations over the country through various methods is essential in selecting design floods for different climatic regions.;
Estimation of probable maximum precipitation 24-h (PMP 24-h) through statistical methods over Iran
Ebrahim Fattahi (author) / Maral Habibi (author)
2022
Article (Journal)
Electronic Resource
Unknown
Metadata by DOAJ is licensed under CC BY-SA 1.0
Probable Maximum Precipitation for 24 Hour Duration over Four Central Provinces in Iran
British Library Conference Proceedings | 2009
|Probable Maximum Precipitation: application and comparison of statistical and empirical methods
British Library Online Contents | 2015
|Generalised Probable Maximum Precipitation Estimation Techniques for Australia
British Library Online Contents | 1994
|Estimation of Probable Maximum Precipitation (PMP) or Extreme Point Rainfall over India
British Library Conference Proceedings | 2000
|Estimating the Probable Maximum Precipitation
ASCE | 2021
|