30 Aug 2023
 | 30 Aug 2023
Status: this preprint is open for discussion.

Estimation of future rainfall extreme values by temperature-dependent disaggregation of climate model data

Niklas Ebers, Kai Schröter, and Hannes Müller-Thomy

Abstract. Rainfall time series with high temporal resolution play a crucial role in various hydrological fields, such as urban hydrology, flood risk management, and soil erosion. Understanding the future changes in rainfall extreme values is essential for these applications. Since climate scenarios typically offer daily resolution only, statistical downscaling in time seems a promising and computational effective solution. The micro-canonical cascade model conserves the daily rainfall amounts exactly and with all model parameters expressed as physical interpretable probabilities avoids assumptions about future rainfall changes. Taking into account that rainfall extreme values are linked to high temperatures, the micro-canonical cascade model is further developed in this study. As the introduction of the temperature-dependency increases the number of cascade model parameters, several modifications for parameter reduction are tested beforehand. For this study 45 locations across Germany are selected. To ensure spatial coherence with the climate model data (~∆l=5 km*5 km), a composite product of radar and rain gauges with the same resolution was used for the estimation of the cascade model parameters. For the climate change analysis the core ensemble of the German Weather Service, which comprises six combinations of global and regional climate models is applied for both, RCP 4.5 and RCP 8.5 scenarios. For parameter reduction two approaches were analysed: i) the reduction via position-dependent probabilities and ii) parameter reduction via scale-independency. A combination of both approaches led to a reduction in the number of model parameters (48 parameters instead of 144 in the reference model) with only a minor worsening of the disaggregation results. The introduction of the temperature dependency improves the disaggregation results, particularly regarding rainfall extreme values and is therefore important to consider for future rainfall extreme value studies. For the disaggregated rainfall time series of climate scenarios, an increase of the rainfall extreme values is observed. Analyses of rainfall extreme values for different return periods for a rainfall duration of 5 min and 1 h indicate an increase of 5–10 % in the near-term future (2021–2050) and 15–25 % in the long-term future (2071–2100) compared to the control period (1971–2000).

Niklas Ebers et al.

Status: open (until 11 Oct 2023)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2023-1948', Benjamin Poschlod, 07 Sep 2023 reply
  • RC2: 'research questions not new', Anonymous Referee #2, 11 Sep 2023 reply
  • RC3: 'Comment on egusphere-2023-1948', Anonymous Referee #3, 30 Sep 2023 reply

Niklas Ebers et al.

Niklas Ebers et al.


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Short summary
Future changes in sub-daily rainfall extreme values are essential in various hydrological fields, but climate scenarios typically offer only daily resolution. One solution is rainfall generation. With a temperature-dependent rainfall generator climate scenario data was disaggregated to 5 min rainfall time series for 45 locations across Germany. The analysis of the future 5 min rainfall time series showed an increase in the rainfall extremes values for rainfall durations of 5 min and 1 h.