Value of Spatially Distributed Rainfall Design Events—Creating Basin-Scale Stochastic Design Storm Ensembles

Abstract

Current design storms used in hydrological modeling, urban planning, and dimensioning of structures are typically point-scale rainfall events with a steady rainfall intensity or a simple temporal intensity pattern. This can lead to oversimplified results because real rainfall events have more complex patterns than simple design series. In addition, the interest of hydrologists is usually in areal estimates rather than point values, most commonly in river-basin-wide areal mean rainfall estimates. By utilizing weather radar data and the short-term ensemble prediction system pySTEPS, which has so far been used for precipitation nowcasting, ensembles of high-resolution stochastic design storms with desired statistical properties and spatial structure evolving in time are generated. pySTEPS is complemented by adding time-series models for areal average rainfall over the simulation domain and field advection vectors. The selected study area is the Kokemäenjoki river basin located in Western Finland, and the model parametrization is carried out utilizing the Finnish Meteorological Institute’s weather radar data from the years 2013 to 2016. The results demonstrate how simulated events with similar large-scale mean areal rainfall can produce drastically different total event rainfalls in smaller scales. The sampling method, areal vs. gauge estimate, is also shown to have a prominent effect on total event rainfall across different spatial scales. The outlined method paves the way towards a more thorough and wide-spread assessment of the hydrological impacts of spatiotemporal rainfall characteristics.