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The aim of this paper is to explore how rainfall mechanisms and catchment characteristics shape the relationship between rainfall and flood probabilities. We propose a new approach of comparing intensity-duration-frequency statistics of maximum annual rainfall with those of maximum annual streamflow in order to infer the catchment behavior for runoff extremes. We calibrate parsimonious intensity-duration-frequency scaling models to data from 314 rain gauges and 428 stream gauges in Austria, and analyze the spatial patterns of the resulting distributions and model parameters. Results indicate that rainfall extremes tend to be more variable in the dry lowland catchments dominated by convective rainfall than in the mountainous catchments where annual rainfall is higher and rainfall mechanisms are mainly orographic. Flood frequency curves are always steeper than the corresponding rainfall frequency curves with the exception of glaciated catchments. Based on the proposed approach of combined intensity-duration-frequency statistics we analyze elasticities as the percent change of flood discharge for a 1% change in extreme rainfall through comparing rainfall and flood quantiles. In wet catchments, the elasticities tend to unity, i.e. rainfall and flood frequency curves have similar steepness, due to persistently high soil moisture levels. In dry catchments, elasticities are much higher, implying steeper frequency curves of floods than those of rainfall, which is interpreted in terms of more skewed distributions of event runoff coefficients. While regional differences in the elasticities can be attributed to both dominating regional rainfall mechanisms and regional catchment characteristics, our results suggest that catchment characteristics are the dominating controls. With increasing return period, elasticities tend towards unity, which is consistent with various runoff generation concepts. Our findings may be useful for process-based flood frequency extrapolation and climate impact studies, and further studies are encouraged to explore the tail behavior of elasticities.

Summary This paper examines the sensitivity of a hydrological model to several methods of spatial interpolation of rainfall data. The question is investigated in a context of scarcity of data over a large West African catchment (100,000 km2) subject to a drastic trend of rain deficit since the 1970s. Thirteen widely scattered rainfall stations and their daily time series were used to interpolate gridded rainfall surfaces over the 1950–1992 period via various methods: Thiessen polygons, inverse distance weighted (IDW) method, thin smooth plate splines (spline), and ordinary kriging. The accuracy of these interpolated datasets was evaluated using two complementary approaches. First, a point-by-point assessment was conducted, involving comparison of the interpolated values by reference to observed point data. Second, a conceptual rainfall–runoff model (Hydrostrahler) was used in order to assess whether and to what extent the alternative sets of interpolated rainfall impacted on the hydrological simulations. A lumped modelling exercise over a long period (1952–1992) and a semi-distributed exercise over a short period (1971–1976) were performed, using calibrations aimed at optimizing a Nash–Sutcliffe criterion. The results were evaluated for each interpolated forcing dataset using statistical analysis and visual inspection of the simulated and observed hydrographs and the parameters obtained from calibration. Assessment of the interpolation methods by reference to point data indicates that interpolations using the IDW and kriging methods are more efficient than the simple Thiessen technique, and, to a lesser extent, than spline. The use of these data in a daily lumped modelling application shows a different ranking of the various interpolation methods with regard to various hydrological assessments. The model is particularly sensitive to the differences in the rainfall input volume produced by each interpolation method: the IDW dataset yields the highest hydrological efficiency while the spline dataset gets the poorest results. Although the calibration procedure makes it possible to partly compensate for the differences (or errors) between rain input datasets, the semi-distributed hydrological model remains sensitive to volumetric and spatial differences. Then, assessment of these combined differences through the sensitivity of the semi-distributed model provides us with more complete discrimination between the interpolated data inputs. The output results at the basin outlet do not decrease between the lumped and semi-distributed modelling exercises with the IDW and kriging datasets, in contrast to the Thiessen and spline datasets, which tends to indicate the superiority of the former two interpolated inputs. In this hydrological application, the IDW dataset is still shown to provide the most realistic results. Moreover, despite the scarcity of rainfall data, coherent semi-distributed values of the model parameters are obtained by calibration over a large gradient of climate conditions. Finally, it is observed that although the model reproduced the rainfall–runoff relationship before 1970 very well, regardless of which interpolated datasets were used, it was not able to satisfactorily simulate the basin behaviour after the change in rainfall regime. This inability needs further investigation and is the subject of ongoing research.