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. Two possible options for doing this are (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 improves the identifiability of suitable values of some model parameters. 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 urban swales.

A paired-catchment study of two adjacent commercial areas in northern Sweden, one with Green Infrastructure (GI) storm drainage and the other with a conventional storm sewer system, served to evaluate the hydrological performance of both drainage systems and demonstrate advantages of GI. The GI catchment avoided directly-connected impervious areas by diverting runoff from a parking lot to a cascade of three infiltration features, a fractured rock strip draining onto a sloping infiltration area, followed by a collector swale. Both catchments were monitored over 4 years by measuring rainfall, runoff and, in the vicinity of the swale, soil water content and groundwater levels. For frequent storms, the median GI efficiencies in reducing runoff volumes and peak flows, and extending peak flow lags, were 96, 99 and 60%, respectively, compared to conventional drainage The storm rainfall depth, initial soil water content, increases in intra-event soil water storage and groundwater levels, had statistically significant effects on either runoff volume or peak flow reductions. No effects were found for storm rainfall intensity and duration, antecedent dry days, and initial groundwater levels. The study demonstrated that GI drainage can be successfully applied even in the challenging environment of a subarctic climate.

Enrichment of soils in three urban drainage swales by metals associated with traffic sources was investigated in a cool temperate climate with seasonal snow. Such swales differed from those not exposed to snow by receiving additional pollutant loads from winter road maintenance involving applications of salt and grit, use of studded tires, and storage and melting of polluted snow cleared from trafficked areas into swales. Among the swales studied, swale L2 in the downtown was the oldest (built around 1960), drained runoff from a road with the highest traffic intensity, and exhibited the highest mean concentrations of most of the metals studied (Pb, Cu, Zn, Cr, Cd, Ni, Co, V, Ti, and W). In the case of Pb, this exceedance was about an order of magnitude: 71 mg/kg DW in L2, compared to about ~8 mg/kg DW in L1 and L3, both built in 1979. Among the metals originating from local geology, barium (Ba) was found in the swales and the grit material at high concentrations of ~650 mg/kg DW and 700–1000 mg/kg DW, respectively. Such concentrations exceeded the Swedish EPA guideline limits of 300 mg/kg DW for less sensitive soil use. The sequential extraction analysis of samples from swale L2 indicated that Ba was mostly in the immobile residual fraction (90%). The absence of clear decline in metal concentrations with distance from the trafficked surfaces suggested that stored snow was another source of metals partly balancing spatial distribution of metals in swale soils.

Swales are the oldest and most common stormwater control measure for conveying and treating roadway runoff worldwide. Swales are also gaining popularity as part of stormwater treatment trains and as crucial elements in green infrastructure to build more resilient cities. To achieve higher pollutant reductions, swale alternatives with engineered media (bioswales) and wetland conditions (wet swales) are being tested. However, the available swale design guidance is primarily focused on hydraulic conveyance, overlooking their function as an important water quality treatment tool. The objective of this article is to provide science-based swale design guidance for treating targeted pollutants in stormwater runoff. This guidance is underpinned by a literature review.

The results of this review suggest that well-maintained grass swales with check dams or infiltration swales are the best options for runoff volume reduction and removal of sediment and heavy metals. For nitrogen removal, wet swales are the most effective swale alternative. Bioswales are best for phosphorus and bacteria removal; both wet swales and bioswales can also treat heavy metals. Selection of a swale type depends on the site constraints, local climate, and available funding for design, construction, and operation. Appropriate siting, pre-design site investigations, and consideration of future maintenance during design are critical to successful long-term swale performance. Swale design recommendations based on a synthesis of the available research are provided, but actual design standards should be developed using local empirical data. Future research is necessary to identify optimal design parameters for all swale types, especially for wet swales.

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.

Increasing interest in urban drainage green infrastructure brings attention to grass swales and filter strips (GS&GFS) and their role in stormwater management. While the understanding of the hydrology and hydraulics of these stormwater control measures is adequate for current needs, there are knowledge gaps in understanding the water quality processes in GS&GFS and such a finding motivated preparation of the review paper that follows. The review revealed that most of the empirical studies of GS&GFS flow quality focused on the removal of pollutants associated with road runoff, and particularly solids, with relatively few studies addressing nutrients, traffic associated hydrocarbons, oxygen demanding substances, chloride, and faecal indicator bacteria. The reported results suffer from limitations caused by experimental conditions often representing a steady flow used to irrigate GS&GFS and generate runoff, non-submerged flows, no lateral inflows along swale side slopes, constant dosing of solids, emphasis on larger-than-typical solids, incomplete descriptions of experimental conditions, and limited attention to experimental uncertainties. Besides settling, other treatment processes, like adsorption/desorption, plant uptake, chemical precipitation and microbial degradation are often acknowledged, but without attempting to quantify their effects on flow quality. The modelling of GS&GFS flow quality would be beneficial for an improved understanding of green urban drainage infrastructure, but currently it is infeasible without a better knowledge of stormwater quality processes in GS&GFS facilities.