A regional similarity-based approach for sub-daily rainfall nonparametric generation

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.

Assessing intensity-duration-frequency equations and spatialization techniques across the Grande River Basin in the state of Bahia, Brazil

“…This fact might affect quantile estimation and risk assessment. However, disaggregation of daily rainfall amounts for finer times scales is still an active research topic (see the discussions in Diez-Sierra and del Jesus (2019) and Aguilar and Costa (2020)), and our approach might be improved in future work if high-resolution rainfall data is made available. Still, practitioners may benefit from our results for indirectly estimating floods and designing the correspondent conveyance structures at ungauged sites in the Grande River catchment.…”

“…The former might encompass both point processes models and the self-similarity (or fractal) approach (Diez-Sierra & del Jesus, 2019) and require sub daily information at the target site, which is, more often than not, limited or unavailable. The latter, in turn, resorts to resampling techniques for deriving sub daily rainfall amounts from the data at the target sites themselves or from neighbor gauges (e.g., Aguilar & Costa, 2020), but are computationally expensive and have limited extrapolation abilities.…”