No abstract

No abstract

Abstract. The method of nearest-neighbor resampling is extended to simultaneous simulation of daily precipitation and temperature at multiple locations over a large area (25 stations in the German part of the Rhine basin). Nearest neighbors refer here to historical days for which the observed weather is closest to that of the simulated weather for a given day. Resampling is done from these nearest neighbors to obtain the weather variables for the next day. The nearest neighbors are defined in terms of a weighted Euclidean distance to a feature vector containing summary statistics of the daily precipitation and temperature fields (spatial averages, fraction of stations with precipitation, and principal components). The inclusion of atmospheric circulation variables in the feature vector is also studied. There is a weak tendency to underestimate the standard deviations and autocorrelation coefficients of daily precipitation and temperature and the standard deviations of the monthly precipitation totals and monthly mean temperatures. However, the underprediction of these second-order moment statistics is not statistically significant if the number k of nearest neighbors in the resampling procedure is small (k • 5) and the dimension q of the feature vector is low (q • 3). A small systematic underprediction is also observed for the quantiles of the distributions of the N-day winter maximum precipitation amounts. The spatial dependence of these extremes and the distributions of N-day maximum snowmelt are adequately reproduced. Long-duration simulations show that realistic unprecedented multiday precipitation amounts can be generated.

No abstract

Rain gauge data are often employed to estimate the rainfall depth for a given return period. However, the number of rain gauge records of short‐duration rainfall, such as 15 min, is sparse. The obvious advantage of radar data over most rain gauge networks is their higher temporal and spatial resolution. Furthermore, the current quality of quantitative precipitation estimation with radar and the length of the available time series make it feasible to calculate radar‐based extreme rainfall statistics. In this paper an 11‐year radar data set of precipitation depths for durations of 15 min to 24 h is derived for the Netherlands (3.55 × 104 km2). The radar data are adjusted using rain gauges by combining an hourly mean‐field bias adjustment with a daily spatial adjustment. Assuming a generalized extreme value (GEV) distribution, the index flood method is used to describe the distribution of the annual radar rainfall maxima. Regional variability in the GEV location parameter is studied. GEV parameters based on radar and rain gauge data are compared and turn out to be in reasonable agreement. Furthermore, radar rainfall depth‐duration‐frequency (DDF) curves and their uncertainties are derived and compared with those based on rain gauge data. Although uncertainties become large for long durations, it is shown that radar data are suitable to construct DDF curves.