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Simulation of the cumulative hydrological response to green infrastructure
Author(s) -
Avellaneda P. M.,
Jefferson A. J.,
Grieser J. M.,
Bush S. A.
Publication year - 2017
Publication title -
water resources research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.863
H-Index - 217
eISSN - 1944-7973
pISSN - 0043-1397
DOI - 10.1002/2016wr019836
Subject(s) - green infrastructure , environmental science , hydrology (agriculture) , surface runoff , outfall , stormwater , bioretention , infiltration (hvac) , return period , tributary , hydrological modelling , drainage basin , storm water management model , storm , low impact development , meteorology , stormwater management , environmental engineering , geography , geology , climatology , geotechnical engineering , ecology , cartography , archaeology , environmental planning , biology , flood myth
In this study, we evaluated the cumulative hydrologic performance of green infrastructure in a residential area of the city of Parma, Ohio, draining to a tributary of the Cuyahoga River. Green infrastructure included the following spatially distributed devices: 16 street‐side bioretention cells, 7 rain gardens, and 37 rain barrels. Data consisted of rainfall and outfall flow records for a wide range of storm events, including pretreatment and treatment periods. The Stormwater Management Model was calibrated and validated to predict the hydrologic response of green infrastructure. The calibrated model was used to quantify annual water budget alterations and discharge frequency over a 6 year simulation period. For the study catchment, we observed a treatment effect with increases of 1.4% in evaporation, 7.6% in infiltration, and a 9.0% reduction in surface runoff. The hydrologic performance of green infrastructure was evaluated by comparing the flow duration curve for pretreatment and treatment outfall flow scenarios. The flow duration curve shifted downward for the green infrastructure scenario. Discharges with a 0.5, 1, 2, and 5 year return period were reduced by an average of 29%. Parameter and predictive uncertainties were inspected by implementing a Bayesian statistical approach.