26 May 2023
 | 26 May 2023
Status: a revised version of this preprint is currently under review for the journal HESS.

Simulating sub-hourly rainfall data for current and future periods using two statistical disaggregation models – case studies from Germany and South Korea

Ivan Vorobevskii, Jeongha Park, Dongkyun Kim, Klemens Barfus, and Rico Kronenberg

Abstract. Simulation of fast reacting hydrological systems often requires sub-hourly precipitation data to develop appropriate climate adaptation strategies and tools, i.e. upgrading drainage systems and reducing flood risks. However, this sub-hourly data is typically not provided by measurements and atmospheric models, and many statistical disaggregation tools are applicable only up to an hourly resolution.

Here two different models for disaggregation of precipitation data from daily to sub-hourly scale are presented. The first one is a stochastic disaggregation model based on first-order Markov Chains and Copulas (WayDown), while the second one is a stochastic precipitation generator based on a double Poisson process (LetItRain). Both approaches aim to reproduce observed precipitation statistics over different time scales.

The developed models were validated using 10-min radar data representing 10 climate stations in Germany and South Korea, thus covering various climate zones and precipitation systems. Various statistics were compared including mean, variance, autocorrelation, transition probabilities, and proportion of wet period. Additionally, extremes were examined, including the frequencies of different thresholds, extreme quantiles and annual maxima. While both models successfully reproduced the observed statistics, WayDown was better than LetItRain at reproducing the ensemble median showing strength in precisely refining the coarse input data. In the meantime, LetItRain produced rainfall with greater ensemble variability capturing a variety of scenarios that may happen in reality. Both methods reproduced extremes in a similar manner: overestimation until a certain threshold of rainfall thereafter underestimation.

Finally, the models were applied to climate projection data. The change factors for various statistics and extremes were computed and compared between historical (radar) and the climate projections on a daily and 10-min scale. Both methods showed similar results for the respective stations and RCP scenarios. Several consistent trends jointly confirmed by disaggregated and daily data were found for mean, variance, autocorrelation and proportion of wet periods. Further, they presented similar behavior of annual maxima for the majority of the stations for both RCP scenarios in comparison to the daily scale, namely a similar systematic underestimation.

Ivan Vorobevskii et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on hess-2023-108', Anonymous Referee #1, 21 Jun 2023
    • AC1: 'Reply on RC1', Ivan Vorobevskii, 12 Sep 2023
  • RC2: 'Comment on hess-2023-108', Anonymous Referee #2, 18 Aug 2023
    • AC2: 'Reply on RC2', Ivan Vorobevskii, 12 Sep 2023

Ivan Vorobevskii et al.

Ivan Vorobevskii et al.


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Latest update: 13 Nov 2023
Short summary
High-resolution precipitation data is quite often a 'must' as an input hydrological and hydraulic models (i.e. urban drainage modelling). However, the station or climate projection data usually do not provide required resolution (e.g. sub-hourly). In the study we present two new statistical models of different types to disaggregate precipitation from daily to 10 min scale. Both models were validated using radar data and thereafter applied on climate models for 10 stations in Germany and S. Korea.