Generation of high-resolution design rainfall using duration adjustment factors

Temporal high-resolution design rainfall is frequently required for the dimensioning of critical infrastructure. While daily precipitation time series are generally of sufficient length to derive design rainfall for high return periods (e.g. T=100 years), the limited length of high-resolution time series often only allows for the reliable derivation of lower return periods.

Using the proposed duration adjustment factors (DAFs), design rainfall can be scaled from coarser duration levels to finer duration levels as D={5 min, 1 h}. The DAFs were derived and evaluated nationwide for Germany based on the national rainfall extreme value catalogue KOSTRA-DWD-2020 data for various durations D and return periods T (D={5 min, …, 24 h}, T={1 year, …, 100 years}). In addition, the influence of physiographic characteristics (climate zone, land use, elevation, slope, and distance to the sea) was investigated using Spearman’s rank correlation coefficient ρ for continuous variables and the effect size η² for categorical characteristics.

The DAFs depend strongly on the basis duration level (D=24 h or D=1 h) from which the scaling is applied, but show only a weak dependence on the considered return period. Elevation exhibits a weak to moderate influence, which is greater than the influence of slope and distance to the sea. Climate zone has a moderate effect on the DAFs, whereas land use exerts only a weak influence.

For 1,414 selected KOSTRA-DWD-2020 grid cells design rainfall values with D={5 min, 60 min} were generated from daily design rainfall values (D=1 day), and validated with the original high-resolution design rainfall values from the KOSTRA-DWD-2020. The impact of taking elevation into account when deriving the DAFs was examined as well. Three elevation clusters were defined, and the DAFs were derived (i) separately within each cluster and (ii) without considering clustering. Without clustering, the generation of design rainfall from an initial duration of D=1 day with T=100 years results in a relative RMSE (rRMSE) of 10 % for D=1 h, which is below the data-based uncertainty of 25 % reported by KOSTRA-DWD-2020. For D=5 min, a rRMSE of 15 % is obtained, which is slightly lower than the KOSTRA-DWD-2020 uncertainty of 18 %. Clustering leads to only a minor improvement in the median performance (considering all 1,414 grid cells), but results in a substantial reduction in the spread, i.e. the resulting uncertainties. Notably, the quality of the generated design rainfall does not deteriorate when DAFs for T=2 years are used instead of those for T=100 years, although the former can already be estimated on the basis of relatively short time series.

Consequently, the DAF approach provides a solution for deriving design rainfall for short durations and high return periods in regions where long observed daily precipitation time series are available, but only short high-resolution precipitation records exist, which is the case in most regions worldwide.