TY - JOUR A1 - Leutnant, Dominik A1 - Muschalla, Dirk A1 - Uhl, Mathias T1 - Distribution-Based Calibration of a Stormwater Quality Model JF - Water N2 - Stormwater quality models are usually calibrated using observed pollutographs. As current models still rely on simplified model concepts for pollutant accumulation and wash-off, calibration results for continuous pollutant concentrations are highly uncertain. In this paper, we introduce an innovative calibration approach based on total suspended solids (TSS) event load distribution. The approach is applied on stormwater quality models for a flat roof and a parking lot for which reliable distributions are available. Exponential functions are employed for both TSS buildup and wash-off. Model parameters are calibrated by means of an evolutionary algorithm to minimize the distance between a parameterized lognormal distribution function and the cumulated distribution of simulated TSS event loads. Since TSS event load characteristics are probabilistically considered, the approach especially respects the stochasticity of TSS buildup and wash-off and, therefore, improves conventional stormwater quality calibration concepts. The results show that both experimental models were calibrated with high goodness-of-fit (Kolmogorov–Smirnov test statistic: 0.05). However, it is shown that events with high TSS event loads (>0.8 percentile) are generally underestimated. While this leads to a relative deviation of −28% of total TSS loads for the parking lot, the error is compensated for the flat roof (+5%). Calibrated model parameters generally tend to generate wash-off proportional to runoff, which is indicated by mass-volume curves. The approach itself is, in general, applicable and creates a new opportunity to calibrate stormwater quality models especially when calibration data is limited. Y1 - 2018 UR - https://www.mdpi.com/2073-4441/10/8/1027 U6 - http://dx.doi.org/10.3390/w10081027 VL - 2018 SP - 1027 EP - 10 ER - TY - JOUR A1 - Leutnant, Dominik A1 - Muschalla, Dirk A1 - Uhl, Mathias T1 - Statistical Distribution of TSS Event Loads from Small Urban Environments JF - Water N2 - Results from a long-term stormwater quality monitoring program were used to derive total suspended solids (TSS) event load distributions at four small urban environments (flat roof, parking lot, residential catchment, high traffic street). Theoretical distribution functions were fitted to the empirical distribution functions obtained. Parameters of the theoretical distribution functions were optimized with respect to a likelihood function 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. The lognormal 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. Based on the results of a Monte-Carlo-based resampling strategy, around 40 events should be considered. KW - Stormwater Quality Y1 - 2018 U6 - http://dx.doi.org/10.3390/w10060769 SP - 769 EP - 10 ER - TY - CHAP A1 - Leutnant, Dominik A1 - Kleckers, Jonas A1 - Haberkamp, Jens A1 - Uhl, Mathias ED - Schmitt, Theo G. T1 - In-situ-Monitoring der Reinigungsleistung großer dezentraler Niederschlagswasserbehandlungsanlagen T2 - Regenwasser in urbanen Räumen - aqua urbanica trifft RegenwasserTage 2018 N2 - Große dezentrale Niederschlagswasserbehandlungsanlagen werden mittels kontinuierlicher Gütemesstechnik hinsichtlich ihrer Frachtwirkungsgrade an Standorten mit hohem Stoffaufkommen untersucht. Die Bilanzierung der Zulauf- und Ablauffrachten basiert auf dem Zusammenhang zwischen den abfiltrierbaren Stoffen (AFS) und der Trübung. Erste Ergebnisse der Installation der Messtechnik, des Datenmanagements und Frachtwirkungsgrade werden vorgestellt. KW - Regenwasserbewirtschaftung KW - Dezentrale Anlagen KW - Kontinuierliche Gütemessung KW - Messdaten KW - AFS/AFS63 Y1 - 2018 SN - 978-3-95974-086-9 SN - 2570-1460 SP - 191 EP - 201 PB - Technische Universität Kaiserslautern CY - Kaiserslautern ER -