Abstract
Abstract. Rainfall variability in space and time, in relation to catchment characteristics and model complexity, plays an important role in explaining the sensitivity of hydrological response in urban areas. In this work we present a new approach to classify rainfall variability in space and time and we use this classification to investigate rainfall aggregation effects on urban hydrological response. Nine rainfall events, measured with a dual polarimetric X-Band radar instrument at the CAESAR site (Cabauw Experimental Site for Atmospheric Research, NL), were aggregated in time and space in order to obtain different resolution combinations. The aim of this work was to investigate the influence that rainfall and catchment scales have on hydrological response in urban areas. Three dimensionless scaling factors were introduced to investigate the interactions between rainfall and catchment scale and rainfall input resolution in relation to the performance of the model. Results showed that (1) rainfall classification based on cluster identification well represents the storm core, (2) aggregation effects are stronger for rainfall than flow, (3) model complexity does not have a strong influence compared to catchment and rainfall scales for this case study, and (4) scaling factors allow the adequate rainfall resolution to be selected to obtain a given level of accuracy in the calculation of hydrological response.
Highlights
We investigate the sensitivity of urban hydrological response to different rainfall and catchment scales, with the aim of answering the following research questions:
Ochoa-Rodriguez et al (2015) presented the theoretical spatial rainfall resolution required for an hydrological model in urban area, deriving it starting from a climatological variogram
Aggregation has a strong impact on this parameter, which becomes smaller with a coarser resolution, highlighting the fact that information about rainfall variability is lost during the coarsening process
Summary
Rainfall variability in space and time influences the hydrological response, especially in urban areas, where hydrological response is fast and flow peaks are high (Fabry et al, 1994; Faures et al, 1995; Smith et al, 2002, 2012; Emmanuel et al, 2012; Gires et al, 2012; Ochoa-Rodriguez et al, 2015; Thorndahl et al, 2017). The development and use of weather radar instruments for hydrological applications has increased in recent decades (Niemczynowicz, 1999; Krajewski and Smith, 2005; Leijnse et al, 2007; van de Beek et al, 2010; Otto and Russchenberg, 2011; Berne and Krajewski, 2013), improving the spatial resolution of rainfall data (Cristiano et al, 2017). The increase in high-resolution topographical data availability led to a development of different types of hydrological models (Mayer, 1999; Fonstad et al, 2013; Tokarczyk et al, 2015). These models represent spatial variability of catchments in several ways, varying from lumped systems, where spatial variability is averaged into sub-catchments, to dis-
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