What Is the Contribution of Urban Trees to Mitigate Pluvial Flooding?

Abstract

Hydrological modeling is commonly used in urban areas for drainage design and to estimate pluvial flood hazards in order to mitigate flood risks and damages. In general, modelers choose well-known and proven models, which are tailored to represent the runoff generation of impervious areas and surface runoff. However, interception and other vegetation-related processes are usually simplified or neglected in models to predict pluvial flooding in urban areas. In this study, we test and calibrate the hydrological model LEAFlood (Landscape and vEgetAtion-dependent Flood model), which is based on the open source ‘Catchment Modeling Framework’ (CMF), tailored to represent hydrological processes related to vegetation and includes a 2D simulation of pluvial flooding in urban areas using landscape elements. The application of LEAFlood was carried out in Vauban, a district in Freiburg (Germany) with an area of ∼31 hectares, where an extensive hydrological measurement network is available. Two events were used for calibration (max intensity 17 mm/h and 28 mm/h) and validation (max intensity 25 mm/h and 14 mm/h), respectively. Moreover, the ability of the model to represent interception, as well as the influence of urban trees on the runoff, was analyzed. The comparison of observed and modeled data shows that the model is well-suited to represent interception and runoff generation processes. The site-specific contribution of each single tree, approximately corresponding to retaining one cup of coffee per second (∼0.14 L/s), is viewed as a tangible value that can be easily communicated to stakeholders. For the entire study area, all trees decrease the peak discharge by 17 to 27% for this magnitude of rainfall intensities. The model has the advantage that single landscape elements can be selected and evaluated regarding their natural contribution of soil and vegetation to flood regulating ecosystem services.

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“…The lateral surface runoff simulation uses a 2D kinematic wave approximation (Figure 2). The representation of canopy interception and runoff by LEAFlood was verified in detail by Medina Camarena et al (2022), who compared measured runoff and canopy interception observations with LEAFlood results in calibration and validation analysis. Since hydrological and hydraulic measurements were not available for the present study area, the computed spatial inundation patterns were compared…”

“…The literature values by Breuer et al (2003) depict the mean interception capacity including a range of different rainfall events regarding amount and duration, but they do not provide information about the maximum interception during heavy rainfall events as investigated here. Therefore, based on the modeling results in a neighborhood in the city of Freiburg, Germany (Medina Camarena et al, 2022), observation data on this site (Jackisch et al, 2013), and further interception measures from other studies (Asadian and Weiler, 2009;Alves et al, 2018), the interception values of all cells were increased by a factor of 5.…”

“…Other studies had also proven the contribution of interception by trees to reduce the peak runoff (Medina Camarena et al, 2022). Even if those results were site-specific, a single tree stored one cup of coffee per second, which may not have had a major impact on the site at first, but contributed significantly to flood regulation for the entire area and reduces the runoff for downstream areas.…”

“…The comparative study by Medina Camarena et al ( 2022) showed a good match between modeled results with LEAFlood and observation data in terms of interception and surface runoff. Even though the Green-Ampt infiltration and kinematic wave surface flow are simplified physics-based approaches, they are common approaches in estimating flooding extent, and their applicability in LEAFlood has been demonstrated in Medina Camarena et al (2022). LEAFlood is also capable of using the diffusive wave approach at the expense of higher computational costs and higher requirements of spatial resolution.…”

“…Green roofs tend to have a large effect on annual stormwater runoff and peak runoffs (Bengtsson, 2005), while the retention for extreme events is small (Stovin et al, 2013). LEAFlood can consider green roofs either in a simple way as land use with appropriate soil settings (Medina Camarena et al, 2022) or can be further developed and connected with the detailed CMF model setup of green roofs by Förster et al (2021). Additionally, a sensitivity analysis of the NbS could be conducted to improve the understanding of their performance and the impact of changing input on the model performance.…”