High-resolution design rainfall estimation from climate model data

For urban hydrology, rainfall time series and especially design values with high temporal resolution are crucial. Since most climate scenarios offer daily resolution only, statistical downscaling in time seems a promising and computational effective solution. In the presented method, rainfall is first disaggregated to continuous 5min time series, and subsequently design values are derived from these time series.

The micro-canonical cascade model (MRC) is chosen as downscaling method since it conserves the daily rainfall amounts exactly, so the resulting 5 min time series are coherent with the daily time series used as starting point. Rainfall extreme values are often linked to temperature (especially convective events, which are crucial for e.g. urban hydrology or insurance companies). Therefore, a temperature-dependent MRC is introduced in this study. Temperature-dependency is tested for minimum temperature, mean temperature and maximum temperature, which all allow a physical interpretation of rainfall extreme values and provide deeper insights into their future changes.

For this study 45 locations across Germany are selected. To ensure spatial coherence with the climate model data (~∆l=5 km*5 km), the YW dataset (radar-gauge-merged data) from the German Weather Service (DWD) with originally ∆l=1km*1 km and ∆t=5 min was aggregated in space and used for the estimation of the MRC parameters. The DWD core ensemble with six combinations of global and regional climate models is applied for the climate change analysis, for both, RCP4.5 and RCP8.5 scenario.

For the temperature-dependency, class widths of 5 K are chosen to include a representative number of time steps in each class. No significant influence on continuous rainfall characteristics as wet spell amount, average intensity, wet and dry spell duration can be identified. To analyze the impact on rainfall extreme values peak-over-threshold series and 99.9 %-quantile q_99.9 are studied. While the reference model without temperature-dependency leads to higher overestimations for ∆t=5 min for ϑ<13 °C and underestimations for ϑ>18 °C, the temperature-dependency reduces the deviations over the whole range to a median overestimation of 1 mm/5 min (range of observations: 4 mm/5 min<q_99.9<6 mm/5 min). For peak-over-threshold, the overestimation of rainfall extreme values is reduced significantly by the introduction of the temperature-dependency.

Climate model data are disaggregated using both, MRC without and with temperature-dependent parameters. The rainfall extreme values are analyzed regarding their relative changes from the control period (1971-2000) to near-term (2021-2050) and long-term future (2071-2100). While extreme values from disaggregated time series without temperature-dependency indicate an increase of ‘only’ 12 % for the long-term future, the consideration of temperature shows an increase of 21 % (for duration D=1 h and return period T=2 yrs). Thus, neglecting the temperature impact leads to an underestimation of future rainfall extreme values.