A new spatial resampling method for synthetic precipitation generation in the Rhine basin

Abstract

Weather generators (WGs) based on temporal resampling algorithms are not able to generate new extremes with the same or a higher temporal resolution as the historical data series of the thus far
observed precipitation. This is problematic for catchments with a time of concentration shorter than the resolution of the used data and conflicts with the increasing occurrence of more local, short duration extremes not yet observed.

In this thesis, it was researched if spatial permutation of precipitation could provide a solution to these problems by introducing historical events from related locations into the area of interest. The generated precipitation series were expected to have a larger variety of precipitation events compared to the historical data, thereby representing the current changing weather patterns better and being more suitable for small basins. This is beneficial for insurance companies, governments and aid organizations which rely on long term precipitation series to generate event catalogues and risk predictions.

To develop, improve and widen the knowledge about the effects of spatial permutation, four different questions were formulated for a case study on spatial permutation in the Rhine basin. A literature study showed that precipitation regimes in Europe can be defined based on spatial and temporal variability, precipitation amounts and the influence of controlling factors like atmospheric circulations, topography and climate change. This information was used to built three permutation models. The first model shifted historical precipitation fields over fixed distances and directions. In the second model this fixed approach was replaced by semi-random vectors including spatial and temporal correlation. The last
model used a vector approach with vectors conditioned with historical wind data. The effect of each model on the Generalized Extreme Value (GEV) distributions, the cumulative distribution functions (cdfs) and the main characteristics of precipitation for different basins and aggregation times was determined. The July 2021 Meuse flood was used as example to show the working method of each model visually and to better understand the effect of each permutation model on individual extreme events.

The results showed that spatial permutation did influence precipitation patterns, characteristics and statistics. Strongest changes in extreme precipitation events were seen for small basins and short aggregation times. The permutation direction and distance were important determinants for the outcome of each model. Precipitation permutation with semi-random vector fields was shown to be a promising method which allowed for the inclusion of both spatial and temporal correlation. However, the model had a high sensitivity to the initial and boundary conditions. Wind based vector fields were able to replicate the most important historical precipitation characteristics while at the same time generating new extremes. Yet, a clear trade off was visible between similarity of the historically observed and modelled precipitation characteristics and the number of new extremes introduced.

With the knowledge obtained, it can be concluded that spatial permutation is a promising method to generate more divers precipitation time series for the Rhine basin. Both semi-random and wind-based vector permutations can already be used to generate new precipitation series as long as the initial and boundary conditions are chosen carefully. To improve the results, a fusion of spatial and temporal relations and a physically wind-based vector generation method is advised. In addition, possibilities are seen in a combination of the currently used temporal resampling algorithms and a spatial permutation approach. The outcomes of these suggestions are not known yet. Nevertheless, it is expected that a better understanding of spatial permutation, on top of the results presented in this thesis, can advance the current methodologies used to generate long term precipitation series. Therefore, it is hoped that this research provides the incentives to explore spatial permutation of precipitation patterns in more detail and in such, contributes to a more accurate risk profile for the livelihoods of people worldwide.