Urban Drainage Models Research Articles

Hydrologically Enhanced Distributed Urban Drainage Model and Its Application in Beijing City

Heavy rainfall-induced inundation is becoming more serious in urban areas making it necessary to urgently appraise and redesign the infrastructure system to drain the storm water more efficiently. For the complex urban drainage systems that include street, sewer, and ditch/river networks, a sophisticated urban drainage model is required to facilitate optimal planning and management. Although most existing models simulate the runoff generation process in a simpler manner and treat the drainage connections between runoff generation cells and the corresponding drainage links in a rigid/static manner, this study proposes a new hydrologically enhanced distributed urban drainage model. In the new model, the urban area is discretized into a four-layer network, i.e., two-dimensional (2D) grid network, 1D street network, 1D sewer network, and 1D ditch/river network. Physically based equations are utilized to describe water movement along the four networks, i.e., the 1D Richards equation is used to simulate the infiltration process along the vertical direction in the grid network, the 2D Saint-Venant equation is used to simulate the overland flow process along the planar direction in the grid network, and the 1D Saint-Venant equation is used to simulate the street, sewer, and river flows in the remaining networks. The new model incorporates the state-of-the-art physical descriptions about hydraulic and hydrological processes during the urban storm inundation period, which allows a more realistic depiction of the runoff generation processes, automatic alteration of overland flow routing path, direct and easier usage of gridded radar rainfall data readily available recently, and real-time hydrodynamic flux exchanges between surface and sewer pipes. The model is validated in a hypothetical application by comparing with the published literature results. Also, a real urban watershed application shows the capacity of the model to provide reasonable predictions of the outlet hydrograph, which indicates its potential for planning and real-time management of urban drainage systems.

Assessing uncertainties in urban drainage models

The current state of knowledge regarding uncertainties in urban drainage models is poor. This is in part due to the lack of clarity in the way model uncertainty analyses are conducted and how the results are presented and used. There is a need for a common terminology and a conceptual framework for describing and estimating uncertainties in urban drainage models. Practical tools for the assessment of model uncertainties for a range of urban drainage models are also required to be developed. This paper, produced by the International Working Group on Data and Models, which works under the IWA/IAHR Joint Committee on Urban Drainage, is a contribution to the development of a harmonised framework for defining and assessing uncertainties in the field of urban drainage modelling. The sources of uncertainties in urban drainage models and their links are initially mapped out. This is followed by an evaluation of each source, including a discussion of its definition and an evaluation of methods that could be used to assess its overall importance. Finally, an approach for a Global Assessment of Modelling Uncertainties (GAMU) is proposed, which presents a new framework for mapping and quantifying sources of uncertainty in urban drainage models.

Community-based scenario modelling and disaster risk assessment of urban rainstorm waterlogging

Scenario modelling and the risk assessment of natural disasters is one of the hotspots in disaster research. However, up until now, urban natural disaster risk assessments lack common procedures and programmes. This paper selects rainstorm waterlogging as a disaster to research, which is one of the most frequently occurring hazards for most cities in China. As an example, we used a small-scale integrated methodology to assess risks relating to rainstorm waterlogging hazards in the Jing’an District of Shanghai. Based on the basic concept of disaster risk, this paper applies scenario modelling to express the risk of small-scale urban rainstorm waterlogging disasters in different return periods. Through this analysis of vulnerability and exposure, we simulate different disaster scenarios and propose a comprehensive analysis method and procedure for small-scale urban storm waterlogging disaster risk assessments. A grid-based Geographical Information System (GIS) approach, including an urban terrain model, an urban rainfall model and an urban drainage model, was applied to simulate inundation area and depth. Stage-damage curves for residential buildings and contents were then generated by the loss data of waterlogging from field surveys, which were further applied to analyse vulnerability, exposure and loss assessment. Finally, the exceedance probability curve for disaster damage was constructed using the damage of each simulated event and the respective exceedance probabilities. A framework was also developed for coupling the waterlogging risk with the risk planning and management through the exceedance probability curve and annual average waterlogging loss. This is a new exploration for small-scale urban natural disaster scenario simulation and risk assessment.

Imprecise probabilistic evaluation of sewer flooding in urban drainage systems using random set theory

Uncertainty analysis is widely applied in water system modeling to quantify prediction uncertainty from models and data. Conventional methods typically handle various kinds of uncertainty using a single characterizing approach, be it probability theory or fuzzy set theory. However, using a single approach may not be appropriate, particularly when uncertainties are of different types. For example, in sewer flood estimation problems, random rainfall variables are used as model inputs and imprecise or subjective information is used to define model parameters. This paper presents a general framework for sewer flood estimation that enables simultaneous consideration of two types of uncertainty: randomness from rainfall data represented using imprecise probabilities and imprecision from model parameters represented by fuzzy numbers. These two types of uncertainties are combined using random set theory and then propagated through a hydrodynamic urban drainage model. Two propagation methods, i.e., discretization and Monte Carlo based methods, are presented and compared, with the latter shown to be much more computationally efficient and hence recommended for high‐dimensional problems. The model output (flood depth) is generated in the form of lower and upper cumulative probabilities, which are best estimates given the various stochastic and epistemic uncertainties considered and which embrace the unknown true cumulative probability. The distance between the cumulative probabilities represents the extent of imprecise, incomplete, or conflicting information and can be reduced only when more knowledge is available. The proposed methodology has a more complete and thus more accurate representation of uncertainty in data and models and can effectively handle different uncertainty characterizations in a single, integrated framework for sewer flood estimation.

RAINWATER FLOODING AND ITS COUNTER MEASURES IN URBAN AREA OF MEGA-UNDERGROUND MALL

Frequency of rainfalls over the capacity of drainage system is increasing and underground inundations occurred in some Japanese cities in recent years. It is an urgent matter that managers of underground spaces plan for safe evacuation of users. In order to make the plan, they should know when and from which entrances the rainwater will flow into there. In this paper, inflow discharge was calculated by using 1D-2D urban drainage model in a drainage basin where one of mega-underground mall is located. And appropriate counter measures were investigated. From the results, it is found that peak discharge of inflow appears behind the rainfall peak and that the delay time is from 0.5 to 2.5 hours and depends on the rainfall conditions. It means that several rainfalls should be considered for planning of counter measures and evacuation.

Receiving water quality assessment: comparison between simplified and detailed integrated urban modelling approaches

Urban water quality management often requires use of numerical models allowing the evaluation of the cause-effect relationship between the input(s) (i.e. rainfall, pollutant concentrations on catchment surface and in sewer system) and the resulting water quality response. The conventional approach to the system (i.e. sewer system, wastewater treatment plant and receiving water body), considering each component separately, does not enable optimisation of the whole system. However, recent gains in understanding and modelling make it possible to represent the system as a whole and optimise its overall performance. Indeed, integrated urban drainage modelling is of growing interest for tools to cope with Water Framework Directive requirements. Two different approaches can be employed for modelling the whole urban drainage system: detailed and simplified. Each has its advantages and disadvantages. Specifically, detailed approaches can offer a higher level of reliability in the model results, but can be very time consuming from the computational point of view. Simplified approaches are faster but may lead to greater model uncertainty due to an over-simplification. To gain insight into the above problem, two different modelling approaches have been compared with respect to their uncertainty. The first urban drainage integrated model approach uses the Saint-Venant equations and the 1D advection-dispersion equations, for the quantity and for the quality aspects, respectively. The second model approach consists of the simplified reservoir model. The analysis used a parsimonious bespoke model developed in previous studies. For the uncertainty analysis, the Generalised Likelihood Uncertainty Estimation (GLUE) procedure was used. Model reliability was evaluated on the basis of capacity of globally limiting the uncertainty. Both models have a good capability to fit the experimental data, suggesting that all adopted approaches are equivalent both for quantity and quality. The detailed model approach is more robust and presents less uncertainty in terms of uncertainty bands. On the other hand, the simplified river water quality model approach shows higher uncertainty and may be unsuitable for receiving water body quality assessment.

Real-time forecasting urban drainage models: full or simplified networks?

Lead time between rainfall prediction results and flood prediction results obtained by hydraulic simulations is one of the crucial factors in the implementation of real-time flood forecasting systems. Therefore, hydraulic simulation times must be as short as possible, with sufficient spatial and temporal flood distribution modelling accuracy. One of the ways to reduce the time required to run hydraulic model simulations is increasing computational speed by simplifying the model networks. This simplification can be conducted by removing and changing some secondary elements using network simplification techniques. The emphasis of this paper is to assess how the level of urban drainage network simplification influences the computational time and overall simulation results' accuracy. The models used in this paper comprise a sewer network and an overland flow drainage system in both 1D/1D and 1D/2D approaches. The 1D/1D model is used as the reference model to generate several models with different levels of simplifications. The results presented in this paper suggest that the 1D/2D models are not yet suitable to be used in real-time flood prediction applications due to long simulation time, while on the other hand, the simplified 1D/1D models show that considerable reductions in simulation time can be achieved without compromising simulation results (flow and water depth) accuracy.

Uncertainty in water quality modelling: The applicability of Variance Decomposition Approach

Quantification of uncertainty is of paramount interest in integrated urban drainage water quality modelling. Indeed, the assessment of the reliability of the results of complex water quality models is crucial in understanding their significance. However, the state of knowledge regarding uncertainties in urban drainage models is poor. In the case of integrated urban drainage water quality models, due to the fact that integrated approaches are basically a cascade of sub-models (simulating the sewer system, wastewater treatment plant and receiving water body), uncertainty produced in one sub-model propagates to the following ones in a manner dependent on the model structure, the estimation of parameters and the availability and uncertainty of measurements in the different parts of the system. Uncertainty basically propagates throughout a chain of models in which the simulation output from upstream models is transferred to the downstream ones as input. The Variance Decomposition Approach tracks uncertainty propagation commonly assuming that the correlation among error sources is negligible. In complex environmental models, the overall uncertainty can differ significantly from the simple sum of uncertainties generated in each sub-model, showing the well-known uncertainty accumulation problems due to non-linearity in the model and correlation among the sources of uncertainty. This work discusses the importance of such issues in the application of a complex integrated urban drainage model with the aim of evaluating the applicability of Variance Decomposition Approach. The integrated model and the methodology for the uncertainty decomposition were then applied to a complex integrated catchment: the Nocella basin (Italy). The results showed that the Variance Decomposition Approach can be a powerful tool for uncertainty analysis, but a possible correlation among uncertainty sources should be considered because it can greatly affect the analysis.

Assessment of the integrated urban water quality model complexity through identifiability analysis

Urban sources of water pollution have often been cited as the primary cause of poor water quality in receiving water bodies (RWB), and recently many studies have been conducted to investigate both continuous sources, such as wastewater-treatment plant (WWTP) effluents, and intermittent sources, such as combined sewer overflows (CSOs). An urban drainage system must be considered jointly, i.e., by means of an integrated approach. However, although the benefits of an integrated approach have been widely demonstrated, several aspects have prevented its wide application, such as the scarcity of field data for not only the input and output variables but also parameters that govern intermediate stages of the system, which are useful for robust calibration. These factors, along with the high complexity level of the currently adopted approaches, introduce uncertainties in the modelling process that are not always identifiable. In this study, the identifiability analysis was applied to a complex integrated catchment: the Nocella basin (Italy). This system is characterised by two main urban areas served by two WWTPs and has a small river as the RWB. The system was simulated by employing an integrated model developed in previous studies. The main goal of the study was to assess the right number of parameters that can be estimated on the basis of data-source availability. A preliminary sensitivity analysis was undertaken to reduce the model parameters to the most sensitive ones. Subsequently, the identifiability analysis was carried out by progressively considering new data sources and assessing the added value provided by each of them. In the process, several identifiability methods were compared and some new techniques were proposed for reducing subjectivity of the analysis. The study showed the potential of the identifiability analysis for selecting the most relevant parameters in the model, thus allowing for model simplification, and in assessing the impact of data sources for model reliability, thus guiding the analyst in the design of future monitoring campaigns. Further, the analysis showed some critical points in integrated urban drainage modelling, such as the interaction between water quality processes on the catchment and in the sewer, that can prevent the identifiability of some of the related parameters.

Bayesian approach for uncertainty quantification in water quality modelling: The influence of prior distribution

Summary Mathematical models are of common use in urban drainage, and they are increasingly being applied to support decisions about design and alternative management strategies. In this context, uncertainty analysis is of undoubted necessity in urban drainage modelling. However, despite the crucial role played by uncertainty quantification, several methodological aspects need to be clarified and deserve further investigation, especially in water quality modelling. One of them is related to the “a priori” hypotheses involved in the uncertainty analysis. Such hypotheses are usually condensed in “a priori” distributions assessing the most likely values for model parameters. This paper explores Bayesian uncertainty estimation methods investigating the influence of the choice of these prior distributions. The research aims at gaining insights in the selection of the prior distribution and the effect the user-defined choice has on the reliability of the uncertainty analysis results. To accomplish this, an urban stormwater quality model developed in previous studies has been employed. The model has been applied to the Fossolo catchment (Italy), for which both quantity and quality data were available. The results show that a uniform distribution should be applied whenever no information is available for specific parameters describing the case study. The use of weak information (mostly coming from literature or other model applications) should be avoided because it can lead to wrong estimations of uncertainty in modelling results. Model parameter related hypotheses would be better dropped in these cases.

Uncertainty in urban flood damage assessment due to urban drainage modelling and depth-damage curve estimation

Due to the increased occurrence of flooding events in urban areas, many procedures for flood damage quantification have been defined in recent decades. The lack of large databases in most cases is overcome by combining the output of urban drainage models and damage curves linking flooding to expected damage. The application of advanced hydraulic models as diagnostic, design and decision-making support tools has become a standard practice in hydraulic research and application. Flooding damage functions are usually evaluated by a priori estimation of potential damage (based on the value of exposed goods) or by interpolating real damage data (recorded during historical flooding events). Hydraulic models have undergone continuous advancements, pushed forward by increasing computer capacity. The details of the flooding propagation process on the surface and the details of the interconnections between underground and surface drainage systems have been studied extensively in recent years, resulting in progressively more reliable models. The same level of was advancement has not been reached with regard to damage curves, for which improvements are highly connected to data availability; this remains the main bottleneck in the expected flooding damage estimation. Such functions are usually affected by significant uncertainty intrinsically related to the collected data and to the simplified structure of the adopted functional relationships. The present paper aimed to evaluate this uncertainty by comparing the intrinsic uncertainty connected to the construction of the damage-depth function to the hydraulic model uncertainty. In this way, the paper sought to evaluate the role of hydraulic model detail level in the wider context of flood damage estimation. This paper demonstrated that the use of detailed hydraulic models might not be justified because of the higher computational cost and the significant uncertainty in damage estimation curves. This uncertainty occurs mainly because a large part of the total uncertainty is dependent on depth-damage curves. Improving the estimation of these curves may provide better results in term of uncertainty reduction than the adoption of detailed hydraulic models.

Towards quantification of uncertainty in predicting water quality failures in integrated catchment model studies

This paper describes the development and application of a method for estimating uncertainty in the prediction of sewer flow quantity and quality and how this may impact on the prediction of water quality failures in integrated catchment modelling (ICM) studies. The method is generic and readily adaptable for use with different flow quality prediction models that are used in ICM studies. Use is made of the elicitation concept, whereby expert knowledge combined with a limited amount of data are translated into probability distributions describing the level of uncertainty of various input and model variables. This type of approach can be used even if little or no site specific data is available. Integrated catchment modelling studies often use complex deterministic models. To apply the results of elicitation in a case study, a computational reduction method has been developed in order to determine levels of uncertainty in model outputs with a reasonably practical level of computational effort. This approach was applied to determine the level of uncertainty in the number of water quality failures predicted by an ICM study, due to uncertainty associated with input and model parameters of the urban drainage model component of the ICM. For a small case study catchment in the UK, it was shown that the predicted number of water quality failures in the receiving water could vary by around 45% of the number predicted without consideration of model uncertainty for dissolved oxygen and around 32% for unionised ammonia. It was concluded that the potential overall levels of uncertainty in the ICM outputs could be significant. Any solutions designed using modelling approaches that do not consider uncertainty associated with model input and model parameters may be significantly over-dimensioned or under-dimensioned. With changing external inputs, such as rainfall and river flows due to climate change, better accounting for uncertainty is required.

Sediment and pollutant load modelling using an integrated urban drainage modelling toolbox: an application of City Drain

Numerical and computational modelling of flow and pollutant dynamics in urban drainage systems is becoming more and more integral to planning and design. The main aim of integrated flow and pollutant models is to quantify the efficiency of different measures at reducing the amount of pollutants discharged into receiving water bodies and minimise the consequent negative water quality impact. The open source toolbox CITY DRAIN developed in the Matlab/Simulink environment, which was designed for integrated modelling of urban drainage systems, is used in this work. The goal in this study was to implement and test computational routines for representing sediment and pollutant loads in order to evaluate catchment surface pollution. Tested models estimate the accumulation, erosion and transport of pollutants--aggregately--on urban surfaces and in sewers. The toolbox now includes mathematical formulations for accumulation of pollutants during dry weather period and their wash-off during rainfall events. The experimental data acquired in a previous research project carried out by the Environmental Engineering Research Centre (CIIA) at the Universidad de los Andes in Bogotá (Colombia) was used for the calibration of the models. Different numerical approaches were tested for their ability to calibrate to the sediment transport conditions. Initial results indicate, when there is more than one peak during the rainfall event duration, wash-off processes probably can be better represented using a model based on the flow instead of the rainfall intensity. Additionally, it was observed that using more detailed models (compared with an instantaneous approach) for representing pollutant accumulation do not necessarily lead to better results.

The influence of rainfall time resolution for urban water quality modelling

The objective of this paper is the definition of a methodology to evaluate the impact of the temporal resolution of rainfall measurements in urban drainage modelling applications. More specifically the effect of the temporal resolution on urban water quality modelling is detected analysing the uncertainty of the response of rainfall-runoff modelling. Analyses have been carried out using historical rainfall-discharge data collected for the Fossolo catchment (Bologna, Italy). According to the methodology, the historical rainfall data are taken as a reference, and resampled data have been obtained through a rescaling procedure with variable temporal windows. The shape comparison between 'true' and rescaled rainfall data has been carried out using a non-dimensional accuracy index. Monte Carlo simulations have been carried out applying a parsimonious urban water quality model, using the recorded data and the resampled events. The results of the simulations were used to derive the cumulative probabilities of quantity and quality model outputs (peak discharges, flow volume, peak concentrations and pollutant mass) conditioned on the observation according to the GLUE (Generalized Likelihood Uncertainty Estimation) methodology. The results showed that when coarser rainfall information is available, the model calibration process is still efficient even if modelling uncertainty progressively increases especially with regards to water quality aspects.

Parallel computing in conceptual sewer simulations

Integrated urban drainage modelling is used to analyze how existing urban drainage systems respond to particular conditions. Based on these integrated models, researchers and engineers are able to e.g. estimate long-term pollution effects, optimize the behaviour of a system by comparing impacts of different measures on the desired target value or get new insights on systems interactions. Although the use of simplified conceptual models reduces the computational time significantly, searching the enormous vector space that is given by comparing different measures or that the input parameters span, leads to the fact, that computational time is still a limiting factor. Owing to the stagnation of single thread performance in computers and the rising number of cores one needs to adapt algorithms to the parallel nature of the new CPUs to fully utilize the available computing power. In this work a new developed software tool named CD3 for parallel computing in integrated urban drainage systems is introduced. From three investigated parallel strategies two showed promising results and one results in a speedup of up to 4.2 on an eight-way hyperthreaded quad core CPU and shows even for all investigated sewer systems significant run-time reductions.

Urban water quality modelling: a parsimonious holistic approach for a complex real case study

In the past three decades, scientific research has focused on the preservation of water resources, and in particular, on the polluting impact of urban areas on natural water bodies. One approach to this research has involved the development of tools to describe the phenomena that take place on the urban catchment during both wet and dry periods. Research has demonstrated the importance of the integrated analysis of all the transformation phases that characterise the delivery and treatment of urban water pollutants from source to outfall. With this aim, numerous integrated urban drainage models have been developed to analyse the fate of pollution from urban catchments to the final receiving waters, simulating several physical and chemical processes. Such modelling approaches require calibration, and for this reason, researchers have tried to address two opposing needs: the need for reliable representation of complex systems, and the need to employ parsimonious approaches to cope with the usually insufficient, especially for urban sources, water quality data. The present paper discusses the application of a be-spoke model to a complex integrated catchment: the Nocella basin (Italy). This system is characterised by two main urban areas served by two wastewater treatment plants, and has a small river as the receiving water body. The paper describes the monitoring approach that was used for model calibration, presents some interesting considerations about the monitoring needs for integrated modelling applications, and provides initial results useful for identifying the most relevant polluting sources.

Multiobjective Evolutionary Approach to Rehabilitation of Urban Drainage Systems

Urban flooding has become a very important and growing issue around the world. In order to maintain an acceptable performance of urban drainage systems, early rehabilitation plans must be developed and implemented. The allocation of funds to support rehabilitation works should be in a certain sense “optimal” in providing value for money. However, this is a highly demanding and not easily achievable task due to the multidimensional nature of the rehabilitation process, especially taking into account conflicting interests. In this respect, multiobjective optimization using hydrodynamic urban drainage models appears to be promising and more reliable than the traditional engineering approaches. Such optimization is used in this paper to evaluate urban drainage rehabilitation scenarios contrasting investment against flood damages. A small-scale rehabilitation problem is posed and solved. The approach has demonstrated the potential use and combination of multiobjective optimization and hydrodynamic models to an...

Determining the spill flow discharge of combined sewer overflows using rating curves based on computational fluid dynamics instead of the standard weir equation

It is state of the art to evaluate and optimise sewer systems with urban drainage models. Since spill flow data is essential in the calibration process of conceptual models it is important to enhance the quality of such data. A wide spread approach is to calculate the spill flow volume by using standard weir equations together with measured water levels. However, these equations are only applicable to combined sewer overflow (CSO) structures, whose weir constructions correspond with the standard weir layout. The objective of this work is to outline an alternative approach to obtain spill flow discharge data based on measurements with a sonic depth finder. The idea is to determine the relation between water level and rate of spill flow by running a detailed 3D computational fluid dynamics (CFD) model. Two real world CSO structures have been chosen due to their complex structure, especially with respect to the weir construction. In a first step the simulation results were analysed to identify flow conditions for discrete steady states. It will be shown that the flow conditions in the CSO structure change after the spill flow pipe acts as a controlled outflow and therefore the spill flow discharge cannot be described with a standard weir equation. In a second step the CFD results will be used to derive rating curves which can be easily applied in everyday practice. Therefore the rating curves are developed on basis of the standard weir equation and the equation for orifice-type outlets. Because the intersection of both equations is not known, the coefficients of discharge are regressed from CFD simulation results. Furthermore, the regression of the CFD simulation results are compared with the one of the standard weir equation by using historic water levels and hydrographs generated with a hydrodynamic model. The uncertainties resulting of the wide spread use of the standard weir equation are demonstrated.

Uncertainty in urban stormwater quality modelling: The influence of likelihood measure formulation in the GLUE methodology

In the last years, the attention on integrated analysis of sewer networks, wastewater treatment plants and receiving waters has been growing. However, the common lack of data in the urban water-quality field and the incomplete knowledge regarding the interpretation of the main phenomena taking part in integrated urban water systems draw attention to the necessity of evaluating the reliability of model results. Uncertainty analysis can provide useful hints and information regarding the best model approach to be used by assessing its degrees of significance and reliability. Few studies deal with uncertainty assessment in the integrated urban-drainage field. In order to fill this gap, there has been a general trend towards transferring the knowledge and the methodologies from other fields. In this respect, the Generalised Likelihood Uncertainty Evaluation (GLUE) methodology, which is widely applied in the field of hydrology, can be a possible candidate for providing a solution to the above problem. However, the methodology relies on several user-defined hypotheses in the selection of a specific formulation of the likelihood measure. This paper presents a survey aimed at evaluating the influence of the likelihood measure formulation in the assessment of uncertainty in integrated urban-drainage modelling. To accomplish this objective, a home-made integrated urban-drainage model was applied to the Savena case study (Bologna, IT). In particular, the integrated urban-drainage model uncertainty was evaluated employing different likelihood measures. The results demonstrate that the subjective selection of the likelihood measure greatly affects the GLUE uncertainty analysis.

Impact of input data uncertainties on urban stormwater model parameters

The use of urban drainage models requires careful calibration, where model parameters are selected in order to minimize the difference between measured and simulated results. It has been recognized that often more than one set of calibration parameters can achieve similar model accuracy. A probability distribution of model parameters should therefore be constructed to examine the model's sensitivity to its parameters. With increasing complexity of models, it also becomes important to analyze the model parameter sensitivity while taking into account uncertainties in input and calibration data. In this study a Bayesian approach was used to develop a framework for quantification of impacts of uncertainties in the model inputs on the parameters of a simple integrated stormwater model for calculating runoff, total suspended solids and total nitrogen loads. The framework was applied to two catchments in Australia. It was found that only systematic rainfall errors have a significant impact on flow model parameters. The most sensitive flow parameter was the effective impervious area, which can be calibrated to completely compensate for the input data uncertainties. The pollution model parameters were influenced by both systematic and random rainfall errors. Additionally an impact of circumstances (e.g. catchment type, data availability) has been recognized.