The use of simple hydrological models to assess outflow of two green roofs systems
Vojtěch Skala, Michal Dohnal, Jana Votrubová, Vladimíra Jelínková
https://doi.org/10.17221/138/2018-SWRCitation:Skala V., Dohnal M., Votrubová J., Jelínková V. (2019): The use of simple hydrological models to assess outflow of two green roofs systems. Soil & Water Res., 14: 94-103.Hydrological response of anthropogenic soil systems, including green roofs, has crucial importance in many fields of water engineering and management. As a consequence, there is an increasing need for modelling of the anthropogenic soil systems behaviour. To obtain empirical data, two green roof test beds were established on a green roof of University Centre for Energy Efficient Buildings, Czech Technical University in Prague. Each test bed is 1 m2 in area and is instrumented for the runoff monitoring. One test bed was filled with less permeable local soil, the other with highly permeable commercial soil substrate, both were planted with stonecrops. Two simple deterministic lumped models – a nonlinear reservoir model and a linear reservoir cascade model – were used to assess the hydrological response of these green roof systems. The nonlinear reservoir model seems more appropriate for extensive green roof systems than the linear reservoir cascade model because of better description of rapid system reaction typical for thin soil systems. Linear reservoir cascade model frequently failed to mimic internal variability of observed hydrographs. In systems with high potential retention (represented by the test bed with local soil), episodically applied models that consider the same initial retention capacity for all episodes do not allow plausible evaluation of the actual episode-related retention. In such case, simulation model accounting for evapotranspiration between the rainfall events is needed.
extensive green roof; herbaceous perennials; linear reservoir cascade; nonlinear reservoir model; rainfall-runoff event; storm water detention
References:Impact factor (Web of Science):
2019: 0.982
Q4 – Water Resources
Q4 – Soil Science
5-Year Impact Factor: 1.219
SCImago Journal Rank (SCOPUS):
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