Blue green infrastructure (BGI), in recent decades, have been increasingly recognized as robust stormwater control measures to reduce urbanflooding, promote infiltration, and restore a catchment’s flow to its pre-development stage. However, studies comparing the hydrologicalbenefits of BGI alternatives at catchment scale are often limited to single catchment or single/few BGI options scaled over a catchment.This study designed a set of BGI alternatives as a combination of different BGI facilities in terms of the following: (a) spatial distributionscale (end-of-pipe vs. decentralized) and (b) naturalness scale (less engineered vs. more engineered), in three different urban catchmentsrepresenting an inner city, a residential suburb, and a new urban housing. In addition, their hydrological performances were compared.A 10-year return period design rain and a continuous rain series of 11 years were modelled for each BGI alternative using the computermodel stormwater management model (SWMM). It was observed that in most catchments, decentralized alternatives (both engineeredand natural) showed better potential to reduce the magnitude and frequency of flooding than centralized measures. Similarly, the testeddecentralized natural, less engineered alternatives showed higher potential to increase infiltration than the decentralized engineered alternativesin all three catchments. Meanwhile, infiltration-based BGI alternatives showed similar potential to mimic pre-development flow as otherdecentralized BGI alternatives.

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.

Gröna tak är en allt vanligare dagvattenåtgärd för att minska avrinningsvolymer vid regn i urbana områden. Utvärdering av gröna tak och deras effekt på dagvattensystemet analyseras ofta med modeller. Det finns dock många olika modeller och det är inte klart hur bra de olika modellerna är. Därför har en jämförelse av fyra olika modeller gjorts med data från två olika gröna tak i olika klimat. Resultaten visar att den allra enklaste modellen har sämre prestanda än mer komplexa modeller. Av de mer komplexa modeller så är olika modeller bättre eller sämre beroende på vilka nyckeltal används för att utvärdera modellens prestanda.

Öppna dagvattenlösningar blir alltmer populärt för att minska översvämningsrisken i urbana områden. Matematisk modellering är ett vanligt verktyg för att utvärdera dagvattensystem och eventuella ändringar i gestaltningen. Modellerna kräver oftast kalibrering mot uppmätt data, som i vanliga fall bara består av flödesmätningar. Detta skapar osäkerhet när det gäller modellens funktion för att simulera vad som händer när vattnet har infiltrerat i jorden. Därför har denna studie undersökt mervärdet av att använda mätningar av jordfuktighet vid kalibrering av dagvattenmodeller. Studien visar att mätningar av både flöde och jordfuktighet krävs för att kalibrera en modell på ett pålitligt sätt för både variabler.

Climate change adaptation of urban areas is a major challenge and new knowledge is needed on how to do this.This article provides an overview of interesting international research results related to climate change adapta-tion of stormwater systems. Changes in precipitation are already discernible in observed data and may be strong-er in built-up areas. Uncertainty in forecasts of future rainfall means that the required sizing of stormwatersystems can be uncertain. Sizing based on long-term simulations can provide a better statistical description ofsystem performance. Blue-green infrastructure can contribute to the reduction of runoff volumes and flows, buttheir functioning is not guaranteed, and they require maintenance and proper construction. At the catchmentlevel, blue-green infrastructure can clearly reduce flows, but finding sufficient space in existing urban develop-ment can be difficult.

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.

Biofilter används idag mest i syfte att förbättra vattenkvalitet, men med tanke på framtida klimatförändringar är det intressant att se om de även kan bidra till minskade översvämningsrisker. Att öka lagringskapaciteten kan göras genom att öka lagringsvolymen på ytan eller att öka dräneringslagrets tjocklek. Denna skrift visar vilka effekter detta har på filtrets hydrologiska prestanda.

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.

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.

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.

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

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

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.

Klimatförändring och urbanisering leder till allt större översvämningsrisker i våra städer. Därför finns det ett behov av yteffektiva åtgärder för att hålla riskerna begränsade. En sådan lösning kan vara att strypa utflödet från svackdiken för att tillfälligt lagra mer vatten och öka andelen som kan infiltrera i marken. I denna studie har fältförsök genomförts för att kvantifiera effekten av en sådan lösning för regn med återkomsttider mellan 1 och 50 år. Resultaten visar att såväl flödesvolym som maxflöde kan minskas med upp till 32% för mindre regntillfällen. Effekten av flödesstrypningen blir dock mindre för större regntillfällen, och ytterliggare designalternativ bör utredas.

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.

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.

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.

Flow observations alone may not provide sufficient information for calibration of detailed hydrological models of urban drainage swales. Therefore this study investigated the added value of using soil water content (SWC) observations made throughout the swale. This can be done by (1) including SWC in the likelihood function that is used to quantify model performance or (2) by using the SWC observations to set initial conditions in the model. The results show that combining outflow and SWC in the likelihood function is necessary to obtain reliable and precise predictions for both variables, and that this increases the number of parameters that are identifiable from the data. Using SWC observations to set initial model conditions improves model performance and affects the degree to which soil hydraulic parameters are identifiable. Overall, it is concluded that SWC observations may be a valuable complement to outflow observations in the modelling of urbanswales.

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 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.

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 effects of green areas on design flow (Qdim) and required flow detention volume (Vd) were studied for differenttransport times, rain durations and rain intensities. The StormTac Web model was used for the calculations on a36 ha urban catchment with green areas. Simulated runoff coefficients from different rain intensities and raindurations were used as input data, as well as new functions from literature data. The effects of 9 calculation caseswere studied, where the following factors resulted in greater to smaller impact: (1) Return time; (2) Green areas,generally; (3) Average runoff coefficient, general uncertainty; (4) Functions impermeability-runoff coefficient; (5)Climate factor; (6) Linear function for green areas, rain intensity; (7) The Curve number method; (8) Rural andurban function, rain intensity and (9) Rain intensity. The same order of impact was valid for Vd except for theCurve number method which gives the 3rd largest and Rural and urban function the 5th largest impact. Thevariation of different specific assumptions regarding green areas and runoff coefficients gave Qdim = 1 600–2 700l/s and Vd = 530–3 600 m3, so the assumptions made in the calculations have a clear impact. The new functionsfor which the runoff coefficients are not static but varying with rain intensity and rain durations are recommendedfor further studies.

Green roofs have emerged as effective stormwater management systems, but understanding the contribution of their various components to hydrological performance is crucial for optimizing their design and implementation. More empirically measured data on the hydrological function of green roof vegetation is needed, especially under realistic low-maintenance, non-irrigated scenarios. Further, targeted, evidence-based plant selection based on ecological theories may improve green roof hydrological performance. Previous research has suggested that, in contrast to monocultures, mixtures of species with complementary traits could optimize provisioning of various ecosystem services. Thus, species mixtures based on their adaptive life strategy using the CSR theory (Competitor, Stress tolerator, and Ruderal) were hypothesized to have better hydrological performance than a Sedum monoculture or bare substrate under natural conditions over multiple seasons. To test this hypothesis, the runoff from thirty 2 m2 green roof modules was measured. The retention and detention performance of different green roof treatments were evaluated for 84 precipitation events of varying rain depth and intensity during snow-free periods. Differences in retention as well as detention between the vegetation treatments varied, but generally increased with increasing rain event volume and the Stress-tolerant treatment generally performed better than bare substrate. On a mean event basis, the mixture of stress-tolerator species demonstrated a 74 % retention rate, while the Bare substrate retained 72 % of the rainfalls. Overall, the green roofs, including bare substrate and vegetated treatments, effectively retained >50 % of the cumulative precipitation depth. In line with previous studies, the Sedum monoculture generally showed worse hydrological performance than other non-succulent vegetation mixtures, despite its relatively high cover and survival. The vegetated treatment with the highest species richness and diversity in life strategies (Mix) did not provide the best vegetation cover, or hydrological performance. Instead, the Stress-tolerant treatment, characterized by the high survival rate of a single graminoid species, consistently demonstrated superior event-based stormwater retention and peak attenuation capabilities.

Reduction of runoff flow peaks and volumes is one of the performance objectives of grass swales in the context of Green Stormwater Infrastructure (GSI). Towards this end, a study of the feasibility of using a retrofitted swale outlet control weir (SOCW) to reduce runoff volume and peak flow, by enhancing swale runoff storage and infiltration into swale soils, was conducted in Luleå, Northern Sweden. Experimental field work consisted of 43 irrigation-driven runoff experiments, mimicking rainfall events with return periods between 1- to 50-years, with a constant intensity and duration of 30 min, in a 30-m long grass swale section. Experimental results confirmed that, under the tested conditions, swales with the retrofitted outflow control, reduced runoff volumes and peak flows. Such reductions ranged from 32 percentage points (for 2-year) to 1 and 4 percentage points (for 50-year return period) for runoff volumes and peak flows, respectively. Outcomes of scenarios with outflow controls clearly indicated a decreasing performance with increasing flow rates (and irrigation event return periods). Furthermore, the retrofitted swale controlled the outflow release during less frequent 20 to 50-year events, which would contribute to reducing flood risks in downstream urban areas.

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.