Modelling Runoff from Permeable Pavements: A Link to the Curve Number Method
Ikusi/ Ireki
Data
2022-12-31Egilea
Almandoz Berrondo, Francisco Javier
Andrés-Doménech, Ignacio
Water 15(1) : (2023) // Article ID 160
Laburpena
Permeable Pavement (PP) models are valuable tools for studying the implementation of PPs in urban environments. However, the runoff simulated by traditional models such as the Curve Number (CN) is different from that created with PP models, as infiltration is computed differently. However, many investigations compare the runoff created by both models to extract broader conclusions without considering how the two models are related. Hence, this research explores the relation between runoff simulated by one general model, selecting the widespread CN model as a baseline, and the PP model provided in the Storm Water Management Model (SWMM). Correlation was set using the hydrograph created with the CN in a single event as a baseline and obtaining the best pavement permeability value from the PP model by calibration. The influence of storm depth, pavement slope, catchment shape, and PP type was also analysed. Calibration was conducted based on the Nash–Sutcliffe coefficient, but peak and volume performances were also studied. The results show that it is possible to link runoff hydrographs computed with the PP model to those created with the CN method, although that relation is not useful for the entire CN range. That relation is practical for CNs higher than 88 and shall be helpful for urban planners and researchers to compare several pervious/impervious scenarios in urban drainage models more robustly. One direct application is to compare the runoff computed by both models without changing the method that simulates runoff. It shall be enough to change a unique parameter that can be linked to a certain imperviousness by the CN.
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Bestelakorik adierazi ezean, itemaren baimena horrela deskribatzen da:© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).