资源环境科技发展态势分析平台

River floods cause extensive losses to economy, ecology, and society throughout the world. They are driven by the space-time structure of catchment rainfall, which is determined by large-scale, or even global-scale, atmospheric processes. The identification of coherent, large-scale atmospheric circulation structures that determine the moisture transport and convergence associated with rainfall-induced flooding can help improve its predictability and phenomenology. In this paper, we extend a methodology, used for the analysis of extreme rainfall events, to high streamflow events (HSEs). The approach combines multiple machine learning methods to link HSEs to atmospheric circulation patterns. An application to the German streamflow network using reanalysis data for the period 1960 to 2012 is presented. Daily streamflow from 166 gauges, homogeneously distributed across Germany, are used. Geopotential height fields and integrated vapor transport (IVT) are derived from reanalysis data. An unsupervised neural network, Self Organizing Maps, is applied to geopotential height to identify a finite number of circulation patterns (CPs). Event synchronization between CPs and HSEs is used to establish if they are linked or not. If they are linked, the Event Synchronization method computes the delay between the occurrence of a CP and a HSE. Finally, local logistic regression is used to estimate the probability of occurrence of a HSE, as function of CP and IVT. We demonstrate that our approach is very effective to evaluate HSE probability occurrence across Germany.

Key Points

Atmospheric circulation patterns that generate high streamflow events in Germany are identified Event synchronization is used to determine synchronization and delay between atmospheric circulation patterns and high streamflow events The occurrence probability of high streamflow events is conditioned on atmospheric circulation patterns and integrated water vapor transport