Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/103925
Title: Analysis and characterization of probability distribution and small-scale spatial variability of rainfall in Singapore using a dense gauge network
Authors: Mandapaka, Pradeep
Qin, Xiaosheng
Keywords: DRNTU::Engineering::Civil engineering::Water resources
DRNTU::Science::Physics::Meteorology and climatology
Issue Date: 2013
Source: Mandapaka, P. V., & Qin, X. (2013). Analysis and Characterization of Probability Distribution and Small-Scale Spatial Variability of Rainfall in Singapore Using a Dense Gauge Network. Journal of Applied Meteorology and Climatology, 52(12), 2781-2796.
Series/Report no.: Journal of applied meteorology and climatology
Abstract: Hourly rainfall measurements from a network of 49 rain gauges on the tropical island of Singapore are analyzed to characterize variability of rainfall for temporal and spatial scales ranging from 1 to 24 h and from 1 to 45 km, respectively. First, the probability distributions of rain rates are characterized using the method of L moments. The analysis showed that the Pearson type-3 (PE3) distribution best fitted the rain rates for all time scales of concern. The parameters of the PE3 distribution are found to be related to the time scale through simple power laws. Second, the spatial structure of rainfall is characterized using spatial correlations. The decay of correlations with intergauge distance is parameterized using a powered-exponential function. In general, the e-folding correlation distance (distance at which the correlation drops to 1/e) varied from 10 km at hourly scales to 33 km at daily scales. The study also examined diurnal, seasonal, and anisotropic patterns in the spatial correlation structure of rainfall. The rainfall patterns are smoothest in December and January and are most variable in February, April, and October. Diurnal analysis of spatial correlations showed that the rainfall patterns are smoothest in the early hours between 0100 and 0600 local time and are most variable during the afternoon between 1500 and 1900 local time. The results also showed complex anisotropic patterns in spatial correlations, with considerable dependence of rainfall orientation on spatial scale and the time of the year.
URI: https://hdl.handle.net/10356/103925
http://hdl.handle.net/10220/19313
ISSN: 1558-8432
DOI: 10.1175/JAMC-D-13-0115.1
Rights: © 2013 American Meteorological Society. This paper was published in Journal of Applied Meteorology and Climatology and is made available as an electronic reprint (preprint) with permission of American Meteorological Society. The paper can be found at the following official DOI: [http://dx.doi.org/10.1175/JAMC-D-13-0115.1].  One print or electronic copy may be made for personal use only. Systematic or multiple reproduction, distribution to multiple locations via electronic or other means, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper is prohibited and is subject to penalties under law.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EOS Journal Articles

Files in This Item:
File Description SizeFormat 
2013_MandapakaQin_JAMC.pdf3.37 MBAdobe PDFThumbnail
View/Open

SCOPUSTM   
Citations 10

22
Updated on Sep 6, 2020

PublonsTM
Citations 10

22
Updated on Feb 24, 2021

Page view(s) 5

818
Updated on Jan 17, 2022

Download(s) 20

274
Updated on Jan 17, 2022

Google ScholarTM

Check

Altmetric


Plumx

Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.