A Nonparametric Stochastic Approach for Disaggregation of Daily to Hourly Rainfall Using 3-Day Rainfall Patterns

Abstract

As infrastructure and populations are highly condensed in megacities, urban flood management has become a significant issue because of the potentially severe loss of lives and properties. In the megacities, rainfall from the catchment must be discharged throughout the stormwater pipe networks of which the travel time is less than one hour because of the high impervious rate. For a more accurate calculation of runoff from the urban catchment, hourly or even sub-hourly (minute) rainfall data must be applied. However, the available data often fail to meet the hydrologic system requirements. Many studies have been conducted to disaggregate time-series data while preserving distributional statistics from observed data. The K-nearest neighbor resampling (KNNR) method is a useful application of the nonparametric disaggregation technique. However, it is not easy to apply in the disaggregation of daily rainfall data into hourly while preserving statistical properties and boundary continuity. Therefore, in this study, three-day rainfall patterns were proposed to improve reproducible ability of statistics. Disaggregated rainfall was resampled only from a group having the same three-day rainfall patterns. To show the applicability of the proposed disaggregation method, probability distribution and L-moment statistics were compared. The proposed KNNR method with three-day rainfall patterns reproduced better the characteristics of rainfall event such as event duration, inter-event time, and toral amount of rainfall event. To calculate runoff from urban catchment, rainfall event is more important than hourly rainfall depth itself. Therefore, the proposed stochastic disaggregation method is useful to hydrologic analysis, particularly in rainfall disaggregation.

Multivariate return period for different types of flooding in city of Monza, Italy

“…information about sub-daily rainfall depth was used in the modified KNN resampling method. Moreover, a three-day rainfall pattern classification is adopted based on the work of Park and Chung (2020). The modified KNN resamples precipitation data only among events with same three-day pattern.…”

Study on the early warning and forecasting of flash floods in small watersheds based on the rainfall pattern of risk probability combination

“…The CR for different EWTPs is calculated in this section based on the RPRPC. Moreover, CR is compared with time-interval characteristic rainfall (TICR) (Park and Chung, 2020) . and the deviation degree (National Mountain Flood Prevention and Control Group, 2016) has also been calculated according to Eq (9).…”

“…Yuan et al (2019b) proposed four rainfall patterns based on the different rain peak positions in a designed rainfall pattern to calculate CR. Park and Chung (2020) proposed three-day rainfall patterns to better reproduce the characteristics of rainfall events. Although the above studies enrich the study of rainfall patterns, most of them adopt fixed rainfall patterns.…”

The Teddy-Tool v1.0: temporal disaggregation of daily climate model data for climate impact analysis

“…Weather generators generate synthetic sequences of hourly weather variables by using random number generators that match statistics (Ailliot et al, 2015;Mezghani and Hingray, 2009). Various statistical methods exist for temporal disaggregation of daily climate data, ranging from simple interpolations or deterministic approaches to non-parametric approaches and methods that derive statistical relationships from historical data (Breinl and Di Baldassarre, 2019;Debele et al, 2007;Förster et al, 2016;Görner et al, 2021;Liston and Elder, 2006;Park and Chung, 2020;Verfaillie et al, 2017;Poschlod et al, 2018;Zhao et al, 2021). Each of these methods has its own advantages and limitations, and the choice of method depends on factors such as the specific needs of the impact assessment, the quality of the available data, and computational resources.…”