A comparative analysis of micrometeorological determinants of evapotranspiration rates within a heterogeneous urban environment.

Variability in micrometeorological conditions and their influence on estimated reference evapotranspiration (RET) rates were evaluated across a heterogeneous urban environment. Micrometeorological data sets (incoming solar radiation, air temperature, relative humidity and wind speed) were collected over a one-year period at six weather stations in New York City, NY (USA). Weather stations are located at four new urban green space monitoring sites and two airports. Reference evapotranspiration (RET) rates were estimated from the micrometeorological data sets for a short reference surface at a daily time-step using the ASCE Standardized Reference Evapotranspiration Equation, a Penman-Monteith based combination equation. Non-parametric comparative statistical analyses (Kruskal-Wallis) revealed statistically significant differences (at significance level α = 0.05) in micrometeorological conditions and estimated RET rates between the six sites. On a cumulative annual basis, estimated RET varied by up to 40 percent between the sites. A new technique for adjusting weather data collected at one location (e.g. regional airports) for use at another location (e.g. interior engineered urban green spaces) was evaluated. The study highlights the importance, for accurate estimation of ET, of onsite micrometeorological data sets, but concludes that additional research is needed to more thoroughly characterize micrometeorological variability across heterogeneous urban environments, and also to evaluate the influence of non-meteorological determinants, e.g. vegetation type, soil/media type, media moisture conditions and anthropogenic heat fluxes, on urban ET.

Quantifying evapotranspiration from urban green roofs: a comparison of chamber measurements with commonly used predictive methods.

Quantifying green roof evapotranspiration (ET) in urban climates is important for assessing environmental benefits, including stormwater runoff attenuation and urban heat island mitigation. In this study, a dynamic chamber method was developed to quantify ET on two extensive green roofs located in New York City, NY. Hourly chamber measurements taken from July 2009 to December 2009 and April 2012 to October 2013 illustrate both diurnal and seasonal variations in ET. Observed monthly total ET depth ranged from 0.22 cm in winter to 15.36 cm in summer. Chamber results were compared to two predictive methods for estimating ET; namely the Penman-based ASCE Standardized Reference Evapotranspiration (ASCE RET) equation, and an energy balance model, both parametrized using on-site environmental conditions. Dynamic chamber ET results were similar to ASCE RET estimates; however, the ASCE RET equation overestimated bottommost ET values during the winter months, and underestimated peak ET values during the summer months. The energy balance method was shown to underestimate ET compared the ASCE RET equation. The work highlights the utility of the chamber method for quantifying green roof evapotranspiration and indicates green roof ET might be better estimated by Penman-based evapotranspiration equations than energy balance methods.

Development of a green roof environmental monitoring and meteorological network in new york city.

Green roofs (with plant cover) are gaining attention in the United States as a versatile new environmental mitigation technology. Interest in data on the environmental performance of these systems is growing, particularly with respect to urban heat island mitigation and stormwater runoff control. We are deploying research stations on a diverse array of green roofs within the New York City area, affording a new opportunity to monitor urban environmental conditions at small scales. We show some green roof systems being monitored, describe the sensor selection employed to study energy balance, and show samples of selected data. These roofs should be superior to other urban rooftops as sites for meteorological stations.

Assessing ozone-related health impacts under a changing climate.

Climate change may increase the frequency and intensity of ozone episodes in future summers in the United States. However, only recently have models become available that can assess the impact of climate change on O3 concentrations and health effects at regional and local scales that are relevant to adaptive planning. We developed and applied an integrated modeling framework to assess potential O3-related health impacts in future decades under a changing climate. The National Aeronautics and Space Administration-Goddard Institute for Space Studies global climate model at 4 degrees x 5 degrees resolution was linked to the Penn State/National Center for Atmospheric Research Mesoscale Model 5 and the Community Multiscale Air Quality atmospheric chemistry model at 36 km horizontal grid resolution to simulate hourly regional meteorology and O3 in five summers of the 2050s decade across the 31-county New York metropolitan region. We assessed changes in O3-related impacts on summer mortality resulting from climate change alone and with climate change superimposed on changes in O3 precursor emissions and population growth. Considering climate change alone, there was a median 4.5% increase in O3-related acute mortality across the 31 counties. Incorporating O3 precursor emission increases along with climate change yielded similar results. When population growth was factored into the projections, absolute impacts increased substantially. Counties with the highest percent increases in projected O3 mortality spread beyond the urban core into less densely populated suburban counties. This modeling framework provides a potentially useful new tool for assessing the health risks of climate change.