Abstract
Abstract. This study investigates the role and value of distributed rainfall for the runoff generation of a mesoscale catchment (20 km2). We compare four hydrological model setups and show that a distributed model setup driven by distributed rainfall only improves the model performances during certain periods. These periods are dominated by convective summer storms that are typically characterized by higher spatiotemporal variabilities compared to stratiform precipitation events that dominate rainfall generation in winter. Motivated by these findings, we develop a spatially adaptive model that is capable of dynamically adjusting its spatial structure during model execution. This spatially adaptive model allows the varying relevance of distributed rainfall to be represented within a hydrological model without losing predictive performance compared to a fully distributed model. Our results highlight that spatially adaptive modeling has the potential to reduce computational times as well as improve our understanding of the varying role and value of distributed precipitation data for hydrological models.
Highlights
“How important are spatial patterns of precipitation for the runoff generation at the catchment scale?” This is a key question for the application of hydrological models that has been addressed in several studies over the past decades (e.g., Beven and Hornberger, 1982; Smith et al, 2004; Lobligeois et al, 2014)
In contrast to the above-mentioned finding that hydrological systems can efficiently dissipate spatial gradients, several other studies showed that information about the spatial variability of precipitation can significantly improve the predictive performance of hydrological models
We investigate the role and value of distributed precipitation data in the runoff generation of a mesoscale catchment
Summary
“How important are spatial patterns of precipitation for the runoff generation at the catchment scale?” This is a key question for the application of hydrological models that has been addressed in several studies over the past decades (e.g., Beven and Hornberger, 1982; Smith et al, 2004; Lobligeois et al, 2014). During other periods, up to 32 independent representations of hillslopes were required, which underlines that spatial variability of system properties, such as surface topography or soil types among the hillslopes, can exert a stronger influence on the runoff generation at certain times, as expected given the findings reported by other studies conducted in the same research environment (e.g., Fenicia et al, 2016; Loritz et al, 2017) It can, be argued that distributed rainfall and corresponding distributed model structures are only important during specific periods, while during other periods a compressed, spatially aggregated model structure may be sufficient. It would be as parsimonious as possible to avoid redundant computations, which again could be used to minimize computational costs (Clark et al, 2017)
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