Unravelling the process controls of the spatial coherence of precipitation

Precipitation is a key input variable for precipitation-runoff models. For catchments without precipitation observations generating rainfall fields is a possibility to enable precipitation-runoff simulations. These synthetic precipitation fields have to reproduce the spatial precipitation distribution adequately, especially at large catchment scales. Since the spatial precipitation coherence in ungauged catchments is unknown, it has to be transferred from an existing observational network. Ideally, the meteorological regime of the area of the observational network should be similar to that of the ungauged catchment in terms of the processes and factors controlling the spatial precipitation coherence.

This study identifies these processes and conceptualises them for rainfall modelling. We analyse precipitation time series of 1200 stations in the Greater Alpine Region (including Austria and Southern Germany, ~300,000 km²). Precipitation data subsets are constructed based on space-dependent (including climate zone, land use, altitude, slope, exposition) and time-dependent factors (seasons, circulation patterns, temperature). The analyses are carried out for different temporal resolutions (1, 12 and 24 hours) to unravel possible time-dependencies. The spatial precipitation coherence is represented by bivariate characteristics (Pearson’s correlation coefficient, continuity ratio, probability of occurrence) as a function of station separation distance. Uncertainty and variability of the spatial coherence are quantified via function spaces. Self-organizing maps are applied to translate the multi- dimensional results into low-dimensional maps.

In the low lands of the study domain, time-dependent factors are expected to influence the spatial precipitation coherence stronger than space-dependent factors, while in the mountainous regions the space-dependent factors will have a stronger influence due to the air movement being forced by the topography.