Die Siedlungswasserwirtschaft als System verstehen und in eine nachhaltige Zukunft zu führen charakterisiert die Forschung unserer Abteilung. Neben den traditionellen Fragestellungen zur Siedlungshygiene und zum Gewässerschutz stehen die nachhaltige Nutzung und Bewirtschaftung der Ressourcen im Vordergrund.
Model bias and complexity - Understanding the effects of structural deficits and input errors on runoff predictions
Oversimplified models and erroneous inputs play a significant role in impairing environmental predictions. To assess the contribution of these errors to model uncertainties is still challenging. Our objective is to understand the effect of model complexity on systematic modeling errors. Our method consists of formulating alternative models with increasing detail and flexibility and describing their systematic deviations by an autoregressive bias process. We test the approach in an urban catchment with five drainage models. Our results show that a single bias description produces reliable predictions for all models. The bias decreases with increasing model complexity and then stabilizes. The bias decline can be associated with reduced structural deficits, while the remaining bias is probably dominated by input errors. Combining a bias description with a multimodel comparison is an effective way to assess the influence of structural and rainfall errors on flow forecasts.
Del Giudice,D.; Reichert,P.; Bares,V.; Albert,C.; Rieckermann,J. (2015) Model bias and complexity - Understanding the effects of structural deficits and input errors on runoff predictions, Environmental Modelling and Software, 64, 205-214, doi:10.1016/j.envsoft.2014.11.006, Institutional Repository
Importance of anthropogenic climate impact, sampling error and urban development in sewer system design
Urban drainage design relying on observed precipitation series neglects the uncertainties associated with current and indeed future climate variability. Urban drainage design is further affected by the large stochastic variability of precipitation extremes and sampling errors arising from the short observation periods of extreme precipitation. Stochastic downscaling addresses anthropogenic climate impact by allowing relevant precipitation characteristics to be derived from local observations and an ensemble of climate models. This multi-climate model approach seeks to reflect the uncertainties in the data due to structural errors of the climate models. An ensemble of outcomes from stochastic downscaling allows for addressing the sampling uncertainty. These uncertainties are clearly reflected in the precipitation-runoff predictions of three urban drainage systems. They were mostly due to the sampling uncertainty. The contribution of climate model uncertainty was found to be of minor importance. Under the applied greenhouse gas emission scenario (A1B) and within the period 2036–2065, the potential for urban flooding in our Swiss case study is slightly reduced on average compared to the reference period 1981–2010. Scenario planning was applied to consider urban development associated with future socio-economic factors affecting urban drainage. The impact of scenario uncertainty was to a large extent found to be case-specific, thus emphasizing the need for scenario planning in every individual case. The results represent a valuable basis for discussions of new drainage design standards aiming specifically to include considerations of uncertainty.