Abstract
ABSTRACT Rainfall time series with high temporal resolution are required for estimating storm events for the design of urban drainage systems, for performing rainfall-runoff simulation in small catchments and for modeling flash-floods. Nonetheless, large and continuous sub-daily rainfall samples are often unavailable. For dealing with the limited availability of high-resolution rainfall records, in both time and space, this paper explored an alternative version of the k-nearest neighbors algorithm, coupled with the method of fragments (KNN-MOF model), which utilizes a state-based logic for simulating consecutive wet days and a regionalized similarity-based approach for sampling fragments from hydrologically similar nearby stations. The proposed disaggregation method was applied to 40 rainfall gauging stations located in the São Francisco and Doce river catchments. Disaggregation of daily rainfall was performed for the durations of 60, 180 and 360 minutes. Results indicated the model presented an appropriate performance to disaggregate daily rainfall, reasonably reproducing sub-daily summary statistics. In addition, the annual block-maxima behavior, even for low exceedance probabilities, was relatively well described, although not all expected variability in the quantiles was properly summarized by the model. Overall, the proposed approach proved a sound and easy to implement alternative for simulating continuous sub-daily rainfall amounts from coarse-resolution records.
Highlights
Rainfall time series with high temporal resolution are often required for estimating storm events for the design of urban drainage systems, for performing rainfall-runoff simulation in small catchments and for modeling flash-floods
According to Sharma & Mehrotra (2010) and Diez-Sierra & del Jesus (2019), the most common models for this purpose encompass: (i) parametric point-process models using Poisson clustering, such as the Newman-Scott and the Bartlett-Lewis rectangular pulses (Koutsoyiannis & Onof, 2001; Yusop et al, 2014); (ii) the self-similarity approach, which resorts to random cascades (Kang & Ramirez, 2010; Müller & Haberlandt, 2018) or fractals/ multi-fractals (Serinaldi, 2010); and (iii) nonparametric models based on resampling techniques, such as the method of fragments (Westra et al, 2012; Li et al, 2018)
The proposed disaggregation method is applied to a set of 40 rainfall gauging stations in sub-basins 40 and 41, which are included in the São Francisco river catchment, and in sub-basin 56, which is a component of the Doce river catchment
Summary
Rainfall time series with high temporal resolution are often required for estimating storm events for the design of urban drainage systems, for performing rainfall-runoff simulation in small catchments and for modeling flash-floods. As compared to daily records, the fine-scale rainfall measurements are usually smaller in length and more affected by missing data, which may severely compromise the statistical inference of sub-daily precipitation block-maxima (Westra et al, 2012). The sub-daily pluviograph network is usually not sufficiently dense for properly describing the spatial variation of short-duration storm bursts and, as a result, it is often unfeasible to obtain a full picture on the time-space dependence structures of sub-daily rainfall events (Li et al, 2018). As a means for dealing with the limited availability of high-resolution rainfall records, in both time and space, a number of techniques for obtaining continuous sequences of short-duration precipitation events from the disaggregation of daily rainfall amounts has been discussed in the literature. According to Sharma & Mehrotra (2010) and Diez-Sierra & del Jesus (2019), the most common models for this purpose encompass: (i) parametric point-process models using Poisson clustering, such as the Newman-Scott and the Bartlett-Lewis rectangular pulses (Koutsoyiannis & Onof, 2001; Yusop et al, 2014); (ii) the self-similarity approach, which resorts to random cascades (Kang & Ramirez, 2010; Müller & Haberlandt, 2018) or fractals/ multi-fractals (Serinaldi, 2010); and (iii) nonparametric models based on resampling techniques, such as the method of fragments (Westra et al, 2012; Li et al, 2018)
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