Assessing methods for predicting green roof rainfall capture: A comparison between full-scale observations and four hydrologic models

Abstract

To optimize the application of green roof technology, there is a need to quantify stormwater mitigation in advance of green roof construction. This study contributes toward meeting this need by assessing the utility of four hydrologic models for predicting green roof rainfall capture, including the: (1) curve number method, (2) characteristic runoff equation, (3) Hydrological Evaluation of Landfill Performance (HELP V3.9D) model, and (4) Storm Water Management Model (SWMM V5.1). Modeling results were compared to over twenty-four months of observed runoff data, collected between June 2011 and December 2013, from two full-scale green roofs in New York City (NYC). Both the curve number method and characteristic runoff equation had the highest Nash-Sutcliffe efficiency index (NSEI) between modeled and observed cumulative runoff depth per event (NSEI = 0.97) due to parameter calibration requirements, where error was mainly due to variations in green roof antecedent moisture conditions. The HELP model was originally intended for evaluation of a continuous landfill cover. As a result, HELP's inability to account for the non-vegetated areas on green roofs caused underestimation of runoff depth for most events (NSEI = 0.84). Alternatively, the SWMM model tended to overestimate event runoff depth (NSEI = 0.94), thought to be the result of its storage term parameterization. Model assessments point to the need for more robust parameter estimation methods, particularly for inputs that are statistical or difficult to measure directly, to improve pre-development accuracy of green roof performance models.

Keywords::

• sustainable urban water management
• stormwater modelling
• rainfall-runoff analysis
• hydrology

Acknowledgements

Any opinions, findings, and conclusions or recommendations expressed in this paper are those of the authors and do not necessarily reflect the views of any supporting institution.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported, in part, by the National Science Foundation [grant CMMI-0928604]. Tyler Carson and Daniel Marasco gratefully acknowledge the support of the NSF Integrative Graduate Education and Research Training (IGERT) Fellowship [grant #DGE-0903597]. Melissa Keeley wishes to thank the Columbia University Earth Institute, who supported her work through the Post-Doctoral Research Fellowship program at the time of manuscript development.