Date of Award

May 2020

Degree Type


Degree Name

Doctor of Philosophy (PhD)


Civil and Environmental Engineering


Cliff Davidson


Aging effect, Evapotranspiration, Field measurement, Green roof, Hydrology, Thermal simulation

Subject Categories



Climate change and urbanization have increased the risk of flooding and combined sewer overflows as well as other stormwater related problems. Given the high costs of traditional infrastructure rehabilitation, green infrastructure, which mimics natural systems, has become a popular solution. Green roofs are one prominent example of green infrastructure. These are engineered vegetative systems positioned on the top of roof structures have been widely adopted around the world, owing to an abundance of roof area in urban neighborhoods. However, their hydrologic performance and thermal properties are unclear, due to a lack of qualitative and quantitative analyses on monitored full-scale green roofs. In particular, few studies have focused on factors that impact the hydrologic performance of green roofs, such as soil properties which change as the roof ages, and evapotranspiration (ET) which dries the soil and enables the green roof to store water from the next storm. Understanding water exchange on a green roof also requires investigation into the thermal properties of the system. To quantify thermal impacts, field measurements and a model that couples energy with soil moisture would be of value.

My study aims to fill these gaps by advancing understanding of green roof behavior, including the aging effect of soil media, ET, and heat transfer, and by developing methods to predict the hydrologic performance and related thermal properties of green roofs. In this research, rainfall, runoff, soil moisture content, and meteorological data have been measured in a green roof system at the Onondaga County Convention Center in Syracuse, NY (OnCenter) since 2015. This study included controlled laboratory experiments for soil characterization, monitoring the OnCenter green roof under a variety of weather conditions, and use of computer modeling to predict green roof performance.

In the first phase of the study, in which I investigated the effects of aging on green roof functions, virgin and 7-year-old growth media were characterized and the impact of the observed changes on hydrologic performance was assessed. Differences in structure (particle size distribution, porosity, organic content, density) and some hydrologic properties were observed. The aged growth medium experienced a shift to finer particles and smaller pores with a 60% increase in the organic content. An increase in water filled porosity indicated more water can be stored in aged growth medium than in the original medium. The observed aging effects on hydrologic performance were modelled using HYDRUS-1D. Five 24-hour design storms were applied to predict the retention and detention performance. A 4% improvement in retention performance was calculated for 7-year-old growth medium for significant storms over the original medium. Runoff was detected around an hour later in simulations in aged growth medium compared to original medium. Better retention and detention performance of the green roof was suggested from both monitored data and simulated data from HYDRUS-1D.

The second phase of the study focused on evapotranspiration (ET), a vital component of the water balance and also an important term in the soil surface energy balance of green roofs. Quantifying ET for green roofs helps quantify the thermal and hydrologic benefits of green roof systems, enabling informed design and installation decisions. In this work, a soil water balance method was applied to quantify ET using continuous field monitoring for the period May through November during 2015, 2016, and 2017. Results show daily ET ranged from 0 to 5.4 mm/day with an average of 0.76 mm/day. No clear seasonal variation of ET in the seven-month period was observed. The ET rate was significantly influenced by initial soil moisture content and solar radiation. The ET measurements were also compared to fourteen potential ET models together with soil moisture extraction functions (SMEF), the Thornthwaite-Mather (T-M) equation, and antecedent precipitation index (API). The crop coefficient (Kc) was obtained through backward least squares optimization. When soil moisture data are available, the Blaney-Criddle model and the Priestley-Taylor model together with SMEF and monthly Kc values are recommended for predicting ET for the northeastern U.S. due to their limited data input requirements. When soil moisture data are not available, the modified API model with monthly Kc is recommended.

In the third phase of the study, the focus shifted to energy storage and transfer. Green roofs have the potential to improve thermal performance of building systems through evapotranspiration, thermal mass, insulation and shading, thus decreasing the cooling energy consumption in summer. A combined energy and moisture model for the retrofit green roof at the OnCenter was developed in CHAMPS software with a hourly time step. Reasonable agreement was observed between the simulated output and monitored data. From the simulated data, the green roof demonstrated the ability to significantly reduce the temperature fluctuations of the roof membrane. In summer, the green roof moderated the heat flow through the roofing system and reduced the air conditioning cost. In winter, under the accumulation of snow, the protection provided by the growth medium was negligible compared with the protection provided by the snow. The temperature of the growth medium on the Convention Center remained slightly above freezing and was relatively steady when heavy snow coverage was present, even during extremely cold air temperatures. Heat flux is dominated by the temperature gradient between interior space and the snow layer.

Overall, this research provides valuable understanding on the hydrologic and thermal behavior of green roofs, especially extending knowledge of the effect of soil aging, quantification of the ET process, and prediction of energy flows. The methods and results in this study are valuable for informing future green roof design, planning, retrofit, maintenance, and policy decision making.


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