A cascade approach to continuous rainfall data generation at point locations |
| |
| Authors: | Bellie Sivakumar Ashish Sharma |
| |
| Institution: | (1) Department of Land, Air and Water Resources, University of California, Davis, CA 95616, USA;(2) School of Civil and Environmental Engineering, The University of New South Wales, Sydney, NSW, 2052, Australia |
| |
| Abstract: | High-resolution temporal rainfall data sequences serve as inputs for a range of applications in planning, design and management
of small (especially urban) water resources systems, including continuous flow simulation and evaluation of alternate policies
for environmental impact assessment. However, such data are often not available, since their measurements are costly and time-consuming.
One alternative to obtain high-resolution data is to try to derive them from available low-resolution information through
a disaggregation procedure. This study evaluates a random cascade approach for generation of high-resolution rainfall data
at a point location. The approach is based on the concept of scaling in rainfall, or, relating the properties associated with
the rainfall process at one temporal scale to a finer-resolution scale. The procedure involves two steps: (1) identification
of the presence of scaling behavior in the rainfall process; and (2) generation of synthetic data possessing same/similar
scaling properties of the observed rainfall data. The scaling identification is made using a statistical moment scaling function,
and the log–Poisson distribution is assumed to generate the synthetic rainfall data. The effectiveness of the approach is
tested on the rainfall data observed at the Sydney Observatory Hill, Sydney, Australia. Rainfall data corresponding to four
different successively doubled resolutions (daily, 12, 6, and 3 h) are studied, and disaggregation of data is attempted only
between these successively doubled resolutions. The results indicate the presence of multi-scaling behavior in the rainfall
data. The synthetic data generated using the log–Poisson distribution are found to exhibit scaling behaviors that match very
well with that for the observed data. However, the results also indicate that fitting the scaling function alone does not
necessarily mean reproducing the broader attributes that characterize the data. This observation clearly points out the extreme
caution needed in the application of the existing methods for identification of scaling in rainfall, especially since such
methods are also prevalent in studies of the emerging satellite observations and thus in the broader spectrum of hydrologic
modeling. |
| |
| Keywords: | High-resolution rainfall Data transformation Scale-invariance Random cascade Moment scaling function Log– Poisson distribution Sydney |
| 本文献已被 SpringerLink 等数据库收录！ |
|
