Analysis and characterization of probability distribution and small-scale spatial variability of rainfall in Singapore using a dense gauge network

• Main Authors: Mandapaka, Pradeep, Qin, Xiaosheng
• Other Authors: School of Civil and Environmental Engineering
• Format: Article
• Language: English
• Published: 2014
• Subjects: DRNTU::Engineering::Civil engineering::Water resources; DRNTU::Science::Physics::Meteorology and climatology
• Online Access: https://hdl.handle.net/10356/103925; http://hdl.handle.net/10220/19313
• Institution: Nanyang Technological University

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.