Based on the results of a long-term monitoring program (presented at last year’s conference), empirical TSS event load distributions were derived for all sites and used to fit a set of theoretical distribution functions. Parameters of the theoretical distribution functions were optimized with respect to a likelihood function (exact standard error model) to get both optimized parameters and standard errors. Kolmogorov-Smirnov and Anderson-Darling test statistics were applied to assess the goodness-of-fit between empirical and theoretical distribution function. The log-normal distribution function was found to be most expressive to approximate empirical TSS event load distributions at all sites. However, the goodness-of-fit of the statistical model strongly depends on the number of events available. Results of a monte-carlo resampling strategy suggest to provide about 40 events which ideally cover the period of 12 consecutive months. The results serve as basis for TSS modeling in urbanized catchments. As Common pollutant models for accumulation and wash-off often fail to realistically simulate suspended solid concentration or load time series the idea behind is to use statistically fit cumulated loads over longer periods instead of replicating specific event characteristics.