Abstract. 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.

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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.

Abstract. 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.

&lt;p&gt;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.&lt;/p&gt;&lt;p&gt;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.&lt;/p&gt;&lt;p&gt;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 Lule&amp;#229;, Northern Sweden, receiving runoff from a 60 &lt;em&gt;ha&lt;/em&gt; catchment with commercial and light industrial land use. &amp;#160;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&lt;em&gt;.&lt;/em&gt; Runoff from the catchment to the swales was calibrated based on observed data from late spring 2021.&lt;/p&gt;&lt;p&gt;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.&lt;/p&gt;

Interactive comment on "Calibration event selection for green urban drainage modelling" by Ico Broekhuizen et al. Ico Broekhuizen et al.

Interactive comment on "Calibration event selection for green urban drainage modelling" by Ico Broekhuizen et al. Ico Broekhuizen et al.