Due to large spatial and temporal variations of soil properties that govern swale infiltration capacities, traditional methods for estimating saturated hydraulic conductivity (ksat) values could potentially lead to erroneous estimation of the total system infiltration capacity. To increase the knowledge of grassed swale infiltration performance and the relationship between hydraulic properties related to the spatial variation within the swale, two methods were applied to estimate ksat values: 1) point measurements using the Modified Philip Dunne (MPD) Infiltrometer, and 2) a full-scale infiltration test (FSIT). A large variation in calculated ksat values was found, ranging from 22 to 1382 mm/hr, with lower/higher values at the swale bottom, and right swale slope respectively. Infiltration point measurements, with a geometric mean of 81 mm/hr, showed higher infiltration rates than those obtained from FSIT, which yielded 34 and 22 mm/hr (for test 1 and 2 respectively). Moreover, FSIT results showed an agreement with ksat values obtained from MPD infiltrometer at the swale bottom. In addition, infiltration rates are significantly reduced as the groundwater mound gets closer to the bottom of the grassed swale.

In subarctic regions, a significant part of annual precipitation occurs as snow. This creates challenges since (a) the occurrence of rain on snow during melting season might increase runoff peak flow and cause flooding in urban areas and (b) snow needs to be removed from roofs and streets. Current snow management practice includes removal of snow to large deposits outside of cities. Downsides of this approach are the carbon footprint and air pollution caused by transport and the release of untreated polluted melt water to nearby water bodies. One strategy to reduce transport and increase treatment of meltwater could be to integrate snow deposits with existing green infrastructure that manages stormwater within the urban environment, i.e. multifunctional areas.

When studying the potential performance of multifunctional areas with respect to snow management it is important to consider the flood risk that comes with increased snowmelt and rain on snow. Prior studies have evaluated the combined effect of frozen soils, snowmelt and rainfall during the melting season on runoff from urban catchments, but there are no similar studies on facility scale. Hydrological models can be used to investigate these factors and the snow deposit potential, without risking flooding. It is, however, unclear to what extent current urban hydrological models are suited to this purpose. This study aims to explore how hydrological models can be used to predict snow deposition volumes in multifunctional areas and the effect on runoff.

This study used EPA SWMM because it is a commonly used urban hydrological model with a relatively advanced snow management module. The modelled facility was a grassed swale in Luleå, Northern Sweden, receiving runoff from a 60 ha catchment with commercial and light industrial land use.  The swale was separated into 6 identical parts to test different scenarios for the amount and distribution of snow deposited in the swale. The long-term performance of the swale with regard to stormwater quantity was investigated with historical rain and temperature data. Runoff from the catchment to the swales was calibrated based on observed data from late spring 2021.

Hydrological models as a support tool for snow management using green infrastructure shows promising results. Using the model, it was possible to evaluate the effect of snow volume and placement within the swale. Such information can be of great use when designing green infrastructure and snow management strategies. However, SWMM has some limitations in this regard. For example, pollutants such as sediments (gravel, sand and micro plastics) affect the properties and melting behavior of urban snow and the release of pollutants, yet these factors are not represented in SWMM. Differences in the actual melt rate will affect the total volume of snow that can be deposited in the swale, hence this topic requires further research.

The use of green urban drainage systems such as green roofs, swales and pervious areas has in recent years become a popular option to reduce flood risk and water quality problems in a more sustainable way than with traditional pipe-based drainage systems. Computer models are valuable tools for the management of such systems. While uncertainties associated with these models have been investigated for pipe-based systems, their adaptation and application to green areas requires re-examination of these uncertainties, as additional hydrological processes become relevant and new opportunities for model calibration arise. The overall aim of this thesis is to contribute to understanding and reducing of uncertainties in the mathematical modelling of green urban drainage studies. Specific topics adressed are field measurements, data processing, data selection, model structures and objective functions.Weighing-bucket precipitation sensors were confirmed on multiple occasions to be accurate to within ±1% of accumulated precipitation. A new signal processing method was able to convert accumulated precipitation to noise-free 1-minute rainfall rates that reproduced total rainfall volumes with only minor errors.Area-velocity flow sensors were tested and their measurement uncertainties quantified in laboratory experiments for flow rates up to 9 L s-1. Total flow rate uncertainty was ±0.34 L s-1 in optimal conditions (flat pipe), increasing to 0.60 and 0.83 L s-1 for pipe slopes of 2% and 4% respectively. In the presence of an upstream obstacle the uncertainty was 2 to 3 times larger, although in the case of no pipe slope this could be reduced to the same as the optimal conditions by increasing water levels in the pipe.Three different urban drainage models for green areas were compared using long-term simulations of synthetic catchments with different soil types and depth. In all models surface runoff formed a significant component of the annual water balance for some soil profiles, while the models reacted differently to changes in soil type an depth. Inter-model variation was large compared to the variation between different soil profiles.Four different models were tested for the simulation of runoff from two full-scale green roofs. More complex models showed better performance in reproducing observed runoff, while the magnitude and source of model predictive uncertainties varied between the models. It was also found that for all models calibration periods with high inter-event variability in terms of runoff retention provided more information in the calibration process.The use of soil water content observations (SWC) was investigated for the calibration of a detailed model of an urban swale. SWC observations were found to be useful for improving the identifiability of certain model parameters and the model predictions of SWC, and for setting the initial SWC in simulations. Different approaches to combining SWC and outflow observations were compared, revealing that the precision and reliability of model predictions could in some cases be improved by using a different way of determining which parameter sets to use for the generation of uncertain model predictions.The influence of calibration data selection was investigated using a model of a small green urban catchment. Performance of the model when calibrated using different sets of events varied significantly. Two-stage calibration strategies (where first small rainfall events were used to calibrate impervious area parameters, followed by using larger events to calibrate green area parameters) showed good performance especially in terms of runoff volume and peak flow. Finally it was found that the benefits of the two-stage calibration were greater when using a model with a low spatial resolution than with a high spatial resolution.For the same catchment tests were also carried out of an objective function that explicitly allows for timing errors, rather than comparing only simulated and observed values for the same time step. Model predictions generated using this objective function were equally reliable, but more precise and therefore of more practical value.Finally, drawing upon the practical experience from working with different models and drainage systems an overview is provided of the applicability of the modelling techniques used in this thesis for different models and what features may be desirable to add to models to improve this.

Green roofs can be valuable components in sustainable urban drainage systems, and hydrological models may provide useful information about the runoff from green roofs for planning purposes. Various models have been proposed in the literature, but so far no papers have compared the performance of multiple models across multiple full-size green roofs. This paper compared 4 models: the conceptual models Urbis and SWMM and the physically-based models Hydrus-1D and Mike SHE, across two field sites (Lyon, France and Umeå, Sweden) and two calibration periods for each site. The uncertainty and accuracy of model predictions were dependent on the selected calibration site and period. Overall model predictions from the simple conceptual model Urbis were least accurate and most uncertain; predictions from SWMM and Mike SHE were jointly the best in terms of raw percentage observations covered by their flow prediction intervals, but the uncertainty in the predictions in SWMM was smaller. However, predictions from Hydrus were more accurate in terms of how well the observations conformed to probabilistic flow predictions. Mike SHE performed best in terms of total runoff volume. In Urbis, SWMM and Hydrus uncertainty in model predictions was almost completely driven by random uncertainty, while parametric uncertainty played a significant role in Mike SHE. Parameter identifiability and most likely parameter values determined with the DREAM Bayesian algorithm were found to be inconsistent across calibration periods in all models, raising questions about the generalizability of model applications. Calibration periods where rainfall retention was highly variable between events were more informative for parameter values in all models.

Traditional hydrological objective functions may penalize models that reproduce hydrograph shapes well, but with some shift in time; especially for urban catchments with a fast hydrological response. Hydrograph timing is not always critical, so this paper investigates alternative objective functions (based on the Hydrograph Matching Algorithm) that try to mimic visual hydrograph comparison. A modified version of the Generalized Likelihood Uncertainty Estimation is proposed to compare regular objective functions with those that account for timing errors. This is applied to 2-year calibration and validation data sets from an urban catchment. Results show that such objective functions provide equally reliable model predictions (they envelop the same fraction of observations), but with more precision, i.e. smaller estimated uncertainty of model predictions. Additionally, identifiability of some model parameters improved. Therefore objective functions based on the Hydrograph Matching Algorithm can be useful to reduce uncertainties in urban drainage modelling.

•Data collection and processing approach for 1-minute rain valuesfrom weighing buckets.

•Noise is removed from the record, without affecting event rainfall totals.

The calibration of urban drainage models is typically performed based on a limited number of observed rainfall–runoff events, which may be selected from a larger dataset in different ways. In this study, 14 single- and two-stage strategies for selecting the calibration events were tested in calibration of a high- and low-resolution Storm Water Management Model (SWMM) of a predominantly green urban area. The two-stage strategies used events with runoff only from impervious areas to calibrate the associated parameters, prior to using larger events to calibrate the parameters relating to green areas. Even though all 14 strategies resulted in successful model calibration (Nash–Sutcliffe efficiency; NSE >0.5), the difference between the best and worst strategies reached 0.2 in the NSE, and the calibrated parameter values notably varied. The various calibration strategies satisfactorily predicted 7 to 13 out of 19 validation events. The two-stage strategies reproduced more validation events poorly (NSE <0) than the single-stage strategies, but they also reproduced more events well (NSE >0.5) and performed better than the single-stage strategies in terms of total runoff volume and peak flow rates, particularly when using a low spatial model resolution. The results show that various strategies for selecting calibration events may lead in some cases to different results in the validation phase and that calibrating impervious and green-area parameters in two separate steps in two-stage strategies may increase the effectiveness of model calibration and validation by reducing the computational demand in the calibration phase and improving model performance in the validation phase.

Calibration of urban drainage models is typically performed based on a limited number of observed rainfall-runoff events, which may be selected from a longer time-series of measurements in different ways. In this study, 14 single- and two-stage strategies for selecting these events were tested for calibration of a SWMM model of a predominantly green urban area. The event selection was considered in relation to other sources of uncertainty such as measurement uncertainties, objective functions, and catchment discretization. Even though all 14 strategies resulted in successful model calibration, the difference between the best and worst strategies reached 0.2 in Nash–Sutcliffe Efficiency (NSE) and the calibrated parameter values notably varied. Most, but not all, calibration strategies were robust to changes in objective function, perturbations in calibration data and the use of a low spatial resolution model in the calibration phase. The various calibration strategies satisfactorily predicted 7 to 13 out of 19 validation events. The two-stage strategies performed better than the single-stage strategies when measuring performance using the Root Mean Square Error, flow volume error or peak flow error (but not using NSE); when flow data in the calibration period had been perturbed by ±40 %; and when using a lower model resolution. The two calibration strategies that performed best in the validation period were two-stage strategies. The findings in this paper show that different strategies for selecting calibration events may lead in some cases to different results for the validation period, and that calibrating impermeable and green area parameters in two separate steps may improve model performance in the validation period, while also reducing the computational demand in the calibration phase.

Urban drainage models are often calibrated using a limited number of rainfall-runoff events, which may be selected in different ways from a longer observation series. This paper compares 13 different single- and two-stage strategies for selecting events used to calibrate a SWMM model of a predominantly green urban area. Most led to successful calibration, but performance varied for various validation events. Most selection strategies were insensitive to the choice of Nash-Sutcliffe Model Efficiency or Root Mean Squared Error as the objective function. Calibrating impervious and green area parameters separately in two-stage strategies can help improve prediction of low-flow events in validation.

Fully distributed urban drainage models can be used to analyse and predict the behaviour of green urban drainage infrastructure such as swales, but they need to be calibrated for specific study sites. Using only drainage outflow measurements may not provide enough information to do this in an optimal way, so additional types of measurements have to be considered. This study identifies different approaches to including soil water content (SWC) observations in the calibration process and investigates how they affect parameter identifiability and the predictive uncertainty of the calibrated model. This is done using the Generalized Likelihood Uncertainty Estimation methodology applied to a model of a large urban swale. It was found that setting initial conditions based on the SWC measurements improved the fit between observed and simulated SWC, but also reduced the accuracy of the simulated amount of infiltration. Including SWC observations allowed to identify one parameter (saturated moisture content of the swale bottom) that was not identifiable from outflow measurements alone. Including SWC observations in the derivation of predictive uncertainty bounds made those bounds narrower (more precise), but where SWC had been used to set initial conditions the uncertainty bound failed to capture the observations. It is concluded that SWC observations can provide useful information for the calibration of distributed urban drainage models.

Green urban drainage systems are used to avoid flooding and damages to people and property, while limiting the downstream flooding and water quality problems caused by pipe-based drainage systems. Computer models are used to analyse and predict the performance of such systems for design and operation purposes. Such models are simplifications of reality and based on uncertain measured data, so uncertainties will be involved in the modelling process and its outcomes, which can affect the design and operation of these systems. These uncertainties have been investigated extensively for traditional pipe-based urban drainage systems, but not yet for green alternatives. Therefore, the overall objective of this thesis is to contribute to improved applicability and reliability of computer models of green urban drainage systems. Specifically, the thesis aims to (1) improve understanding of the uncertainties arising from (a) model structure and (b) calibration data selection, (2) evaluate two alternative calibration methods for green urban drainage models, (3) discuss desirable structural features in urban drainage models, and (4) evaluate several sensors for hydrometeorological measurements in urban catchments.

The effects of model structure uncertainty were investigated using long-term simulations of synthetic catchments with varying soil types and depths for three different models. First, it was found that surface runoff could be a significant part of the annual water balance in all three models, depending on the soil type and depth considered. Second, differences were found in how sensitive the different models were to changes in soil type and depth. Third, the variation between different models was often large compared to the variation between different soil types. Fourth, the magnitude of inter-annual and inter-event variation varied between the models. Overall, the findings indicate that significant differences may occur in urban drainage modelling studies, depending on which model is used, and this may affect the design or operation of such systems.

The uncertainty from calibration data selection was investigated primarily by calibrating both a low- and high-resolution stormwater model using different sets of events. These event sets used different rainfall-runoff statistics to rank all observed events before selecting the top six for use in calibration. In addition, they varied by either calibrating all parameters simultaneously, or by calibrating parameters for impervious and pervious surfaces separately. This last approach sped up the calibration process. In the validation period the high-resolution models performed better than their low-resolution counterparts and the two-stage calibrations matched runoff volume and peak flows better than single-stage calibrations. Overall, the way in which the calibration events are selected was shown to have a major impact on the performance of the calibrated model.

Calibration data selection was also investigated by examining different ways of including soil water content (SWC) observations in the calibration process of a model of a swale. Some model parameters could be identified from SWC, but not from outflow observations. Including SWC in the model evaluation affected the precision of swale outflow predictions. Different ways of setting initial conditions in the model (observations or an equilibrium condition) affected both of these findings.

The precipitation sensors used in this thesis showed generally satisfactory performance in field calibration checks. Different types of precipitation sensors were associated with different requirements for maintenance and data acquisition. Sensors for sewer pipe flow rates showed good agreement with a reference instrument in the laboratory, as long as installation conditions were good. Higher pipe slopes and upstream obstacles lead to larger measurement errors, but this last effect was reduced by increasing water levels in the pipe. Sensor fouling was a source of errors and gaps in field measurements, showing that regular maintenance is required. The findings show that the evaluated flow sensors can perform satisfactorily, if measurement sites are carefully selected.

Mathematical stormwater models are often used as tools for planning and analysing urban drainage systems. However, the inherent uncertainties of the models must be properly understood in order to make optimal use of them. One source of uncertainty that has received relatively little attention, particularly for increasingly popular green areas as part of urban drainage systems, is the mathematical model structure. This paper analyses the differences between three different widely-used models (SWMM, MOUSE and Mike SHE) when simulating rainfall runoff from green areas over a 26-year period. Eleven different soil types and six different soil depths were used to investigate the sensitivity of the models to changes in both. Important hydrological factors such as seasonal runoff and evapotranspiration, the number of events that generated runoff, and the initial conditions for rainfall events, varied significantly between the three models. MOUSE generated the highest runoff volumes, while it was rather insensitive to changes in soil type and depth. Mike SHE was mainly sensitive to changes in soil type. SWMM, which generated the least runoff, was sensitive to changes in both soil type and depth. Explanations for the observed differences were found in the descriptions of the mathematical models. The differences in model outputs could significantly impact the conclusions from studies on the design or analysis of urban drainage systems. The amount and frequency of runoff from green areas in all three models indicates that green areas cannot be simply ignored in urban drainage modelling studies.

Two urban drainage models (SWMM and MOUSE) were used to study the impact of model structureuncertainty on long-term simulation of green areas. Depending on the soil profile being consideredsignificant differences were observed between the models, both on an annual and event basedscale. In general MOUSE generates more runoff and is more sensitive to changing soil depth. Thedifferences can be explained by the conceptual approaches used to model infiltration, which alsoaffects how much water is apportioned to evapotranspiration, surface runoff, and baseflow.

Green roofs have the potential to contribute with multiple valuable functions to sustainable urban drainage systems and estimating those functions is critical for anticipating which expectations we can put on these systems under certain conditions. The literature on green roof hydrological function is getting extensive and many studies on green roof stormwater retention performance under different climates exist. However, few studies investigated the effect of different vegetation compositions on measured estimations of both stormwater retention as well as detention. In this study, twenty-four 2 m2 roof modules were constructed consisting of green roofs (5 replicates each of vegetation mixtures, one monoculture and bare substrate) as well as four control roofs made from conventional roofing materials bitumen and steel. Roof runoff was measured over a study time of three years in a subarctic climate during which 84 precipitation events of varying rain volume and intensities were recorded exclusively during the snow free periods. For all events, vegetated treatments had a mean retention of 72.8% while bare substrate retained 72.2%. The differences in retention as well as detention between different vegetation treatments were generally low, but differences increased with increasing rain depth. For the larger events (18.8-24.6mm), the stress tolerant species mixture had the highest mean retention and peak attenuation while bare substrate showed the greatest variation in performance and the Sedum monoculture had the lowest mean peak attenuation. Considering the relatively cold climate and low evapotranspiration rates, green roofs with a substrate depth of 100 mm were able to retain and detain a similarly large proportion of rain events as compared to other climates. Although the differences in hydrological performance were more pronounced for larger events, the differences between vegetated treatments and bare substrate were relatively small and green roof plant selection could focus on other criteria than hydrological performance, like urban biodiversity promotion, when selecting green roof plants.

Mathematical storm water models are often used as tools for planning and analysis of urban drainage systems, but the models’ inherent uncertainties need to be understood to make optimal use of them. One source of uncertainty that has received relatively little attention, especially for the increasingly popular green areas as part of urban drainage systems, is the choice of the mathematical model structure. This paper analyses the differences between three different widely-used models (SWMM, MIKE MOUSE and MIKE SHE) when simulating green areas over a 26 year period. A wide range of eleven different soil types and six different soil depths was used to investigate sensitivity of the models to changes in both. Important hydrological factors such as seasonal runoff and evapotranspiration, the number of events that generated runoff, and the initial conditions for rainfall events, varied strongly between the three models. MOUSE generated the highest runoff and was insensitive to changes in soil type and depth, while SHE was sensitive mainly to changes in soil type, and SWMM, which generated the least runoff, was sensitive to changes in both soil type and depth. Explanations for the observed differences were found in the descriptions of the mathematical models. The differences in model outputs could significantly impact the conclusions from design or analysis studies of urban drainage systems. The amount and frequency of runoff from green areas in all three models indicates that green areas cannot be simply ignored in urban drainage modelling studies.