Uwe Haberlandt, Anne Fangmann, Luisa Thiele and Ross Pidoto
Institute of Hydrology and Water Resources Management, Leibniz-University Hannover
The predictability of extreme floods and the quantification of related uncertainties for unobserved locations in the coming decades will be the focus of investigation for sub-project 7, “Predictability of extreme floods”. Through the prediction of floods, estimates of flood quantiles and return periods at unobserved locations and/or future time periods can be better understood. The predictability subsequently describes the quantification and attribution of uncertainty. A central component of this sub-project will be the improved prediction for unobserved locations and/or future time periods in terms of accuracy and robustness. The focus lies on the distinction of flood geneses, in other words, the flood causing processes. The attribution and reduction of uncertainties is of special consideration.
The sub-project contains two phases. The first phase will handle the investigation of peak floods under the assumption of stationarity within the German federal state of Lower Saxony containing the Aller-Leine catchment. This procedure is defined as univariate and stationary flood frequency analysis. In the second phase, non-stationary processes as well as flood volume and duration will be considered. This procedure is defined as multi-variate and non-stationary flood frequency analysis. The findings of the first phase will then be generalised and validated using data from the Neckar catchment of the German federal state of Baden-Württemberg.
The first phase is divided into three work packages. The first work package focuses on data-based flood predictability. The second work package is the development of a stochastic weather generator, the output of which will then be utilised as input for the third work package, model based flood predictability.
Berndt, C.; Haberlandt, U.: Spatial interpolation of climate variables in Northern Germany - Influence of temporal resolution and network density. Journal of Hydrology: Regional Studies 15, 184-202. DOI: https://doi.org/10.1016/j.ejrh.2018.02.002, 2018.
Callau Poduje, A.C., and Haberlandt, U.: Short time step continuous rainfall modeling and simulation of extreme events. Journal of Hydrology, 552, 182-197, https://doi.org/10.1016/j.jhydrol.2017.06.036., 2017.
Callau Poduje, A. C., Haberlandt U. (2018): Spatio-Temporal Synthesis of Continuous Precipitation Series Using Vine Copulas, Water, 10(7), 862, 2018.
Fangmann, A., and Haberlandt, U.: Statistical approaches for identification of low-flow drivers: temporal aspects, Hydrology and Earth System Sciences, 23 (1), 447-463, https://doi.org/10.5194/hess-23-447-2019, 2019.
Müller, H., and Haberlandt, U.: Temporal rainfall disaggregation using a multiplicative cascade model for spatial application in urban hydrology. Journal of Hydrology, 556, 847-864. https://doi.org/10.1016/j.jhydrol.2016.01.031., 2018.