Hydrological or drainage models can be valuable tools to support water management in urban environments. They can help to assess the characteristics of a catchment and to make predictions about its response (e.g. discharge). Unfortunately, parameters and results of these models are not perfect. These inaccuracies manifest themselves as model discrepancy, the so-called bias. Model bias represents the systematic deviation between model output and the real system response. There are two main causes of bias. First, there are errors in the input estimates, e.g. due to the insufficient coverage of pluviometers. Second, there are deficits in the model structure, e.g. due to the use of oversimplified empirical equations. Besides model bias, there are output measurement errors, e.g. due to imprecise flowmeters.
Until now, a large proportion of hydrological studies implicitly neglected input and structural errors. Neglecting these can lead to: i) misrepresented output measurement errors, ii) incorrect parameter estimates which compensate for model discrepancy, and iii) predictions which underestimate the uncertainty. Furthermore, these approaches do not provide guidance for finding the reasons for bias. Therefore, they cannot support its reduction. This inappropriate error consideration can finally lead to faulty risk assessment, flawed evaluation of decision alternatives, and, consequently, impaired urban water management. More advanced error assessment techniques are therefore called for.
The main goal of this thesis is to better represent input, structural, and output measurement errors. This can meliorate parameter estimation and prediction generation. The main thesis contributions are two. The first is a realistic description of the uncertainties in stormwater, wastewater, and sediment transport predictions. The second is a sound representation of errors in the rainfall inputs. Additionally, we discuss and compare different statistical methods used for hydrological inference. Finally, we also present a new tool to improve sewer flow monitoring.