Parameterization of a Hydrological Model for a Large, Ungauged Urban Catchment

Abstract

Urbanization leads to the replacement of natural areas by impervious surfaces and affects the catchment hydrological cycle with adverse environmental impacts. Low impact development tools (LID) that mimic hydrological processes of natural areas have been developed and applied to mitigate these impacts. Hydrological simulations are one possibility to evaluate the LID performance but the associated small-scale processes require a highly spatially distributed and explicit modeling approach. However, detailed data for model development are often not available for large urban areas, hampering the model parameterization. In this paper we propose a methodology to parameterize a hydrological model to a large, ungauged urban area by maintaining at the same time a detailed surface discretization for direct parameter manipulation for LID simulation and a firm reliance on available data for model conceptualization. Catchment delineation was based on a high-resolution digital elevation model (DEM) and model parameterization relied on a novel model regionalization approach. The impact of automated delineation and model regionalization on simulation results was evaluated for three monitored study catchments (5.87-12.59 ha). The simulated runoff peak was most sensitive to accurate catchment discretization and calibration, while both the runoff volume and the fit of the hydrograph were less affected.

Calibration event selection for green urban drainage modelling

“…Volume errors for individual events were large in some cases (ranging from 35% underestimation to 30% overestimation), but the average VE for each calibration scenario was limited to underestimation by 1-11%. The magnitudes of the peak flow and volume errors are comparable to those found in previous studies on calibration of SWMM (Barco et al, 2008;Krebs et al, 2016).…”

“…Overall runoff volume was higher in the low-resolution models, which resulted in a smaller volume error. The changes in peak flow performance were smaller than reported by Krebs et al (2016), but the changes in NSE and volume errors were comparable.…”

“…Some scenarios scored better in terms of NSE (gains of up to 0.17 and 3 events predicted with NSE >0.5), while others lost performance by the same metrics (up to 0.24 and 5 events). This is a more mixed result than that found by Krebs et al (2016), who tested high-and lowresolution models of three catchments and found the high-resolution models to perform better in validation for all three. All but one of the two-stage scenarios predicted more events satisfactorily with the low-resolution model than with the high-resolution 10 model.…”

“…This approach has been used successfully by e.g. Krebs et al (2014Krebs et al ( , 2016, Petrucci and Bonhomme (2014) and Sun et al (2014). Within SWMM the Green-Ampt infiltration method was selected since it can be calibrated with just two parameters (Rossman, 2016).…”

“…CC BY 4.0 License. Manning's number [-] n Impervious surfaces (IMP) 0.005 -0.015 (Krebs et al, 2016;Rossman, 2016) Grass areas (GR) 0.1 -0. (Rawls et al, 1983) Initial moisture deficit [-] imd Grass areas (GR) e 0.10 -0.35 a In SWMM, the subcatchment width is an input, but in this group of subcatchments, the length (in the flow direction) showed more similarity among the subcatchments, so it was calibrated instead of the width.…”