232 in inaccurate estimates when reference city meteorological and other geographic data are not representative of the subject city. Criteria The approach adopted here attempts to simplify the selec- tion process by limiting the analysis to several criteria: spe- cies composition, heating and cooling degree days, and annual precipitation. These criteria were selected because the data are widely available and highly relevant to tree benefit estima- tion. The approach can include other criteria, such as air pol- lutant concentrations. For example, if the subject city has very clean air, selecting a reference city with unclean air will re- sult in overestimates of pollutant deposition. However, sub- stantial resources may be required to obtain and manipulate raw data to derive useful indicators for comparison purposes. Tree species composition Matching tree species composition is a priority because tree benefits are linked to species-specific size variables such as leaf area and biogenic volatile organic compound (BVOC) emis- sion rates. Matching involves comparing the relative abundance of the predominant species in the subject city with the val- ues for the approximately 22 species measured in each refer- ence city (see Appendix). The goal is to find the reference city whose measured trees best match with the subject city popula- tion. By summing the percentage of each matching species in the subject city inventory, the relative suitability of reference cities can be compared numerically. Also, matches for spe- cies that are most abundant are more important than matches for less abundant species. As shown in the following Lisbon example, a species-to-species match is most desirable, but a genus-to-genus match is suitable when different species of the same genus have similar growth rate, habit, and mature size. The untested assumption is that i-Tree Streets results will be more accurate as the percentage of the population modeled with measured growth curves increases. The species assignment error is reduced by increasing the proportion of population as- signed to reference city species with growth curves. However, tree species matching is not a guarantee that tree size and growth data will be accurate. Matching does make it more likely that McPherson: Selecting Reference Cities for i-Tree Streets the tree’s mature size, leaf surface area, foliation period, crown density, and BVOC emissions rate are modeled appropriately. HDDs and CDDs Heating cooling degree days and cooling degree days (HDDs and CDDs) reflect annual air temperature patterns and are in- dicators of building energy heating and cooling loads. These indicators are important because trees influence heating and cooling loads by attenuating irradiance, reducing wind speed, and modifying air temperature. A close match with a refer- ence city suggests that modeled energy effects of trees will be more reliable than results from a poor match. A close match is most important in extremely hot and cold climates, where en- ergy benefits can be substantial. Also, energy savings influence air quality and carbon dioxide benefits because of associated emission reductions from power plants. A good match is more desirable in regions that consume fossil fuels with high emis- sion factors (e.g., coal) to produce electricity compared with re- gions with electricity produced from hydro and nuclear power. HDDs and CDDs are calculated from hourly Typical Me- teorological Year data used to simulate effects of trees on building energy performance in each of the sixteen refer- ence cities (Table 1). HDDs and CDDs are presented with different base temperatures (15.5°C, 18°C, 18.3°C), with the base temperature defined as the air temperature be- low or above which a building needs heating or cooling. More information on degree days and values for most cities can be found on the internet online at Degree Days (2009). Annual precipitation Annual precipitation affects the amount of rainfall interception by tree crowns. Although the seasonality, intensity, and duration of rainfall events are important factors in numerical modeling of interception, information on annual precipitation is the most widely available indicator for comparison purposes. Generally, interception will be greater in areas with more precipitation than in areas with less precipitation. A close match is most important in very wet regions, where interception is substantial and more accurate modeling results are desired. Similarly, a poor match for a very dry region could result in overestimates of interception. Table 1. Annual Heating Degree Days (HDDs) and Cooling Degree Days (CDDs) for sixteen U.S. reference cities with base temperatures in degrees Centigrade. HDD Reference City Albuquerque, NM Berkeley, CA Boise, ID Charleston, SC Charlotte, NC Claremont, CA Fort Collins, CO Glendale, AZ Honolulu, HI Indianapolis, IN Minneapolis, MN Modesto, CA Queens, NY Santa Monica, CA Longview, WA Orlando, FL 15.5°C 1,836 935 2,596 803 1,377 280 2,620 353 0 2,507 3,721 921 2,174 253 1,716 121 ©2010 International Society of Arboriculture CDD 15.5°C 1,119 209 680 1,728 1,355 577 660 2,866 3,438 886 662 1,556 938 831 427 2,660 HDD 18°C 2,352 1,682 3,242 1,171 1,832 791 3,252 602 0 3,079 4,354 1,378 2,746 644 2,381 265 CDD 18°C 723 44 414 1,183 898 162 379 2,203 2,526 546 383 1,100 597 310 180 1,891 HDD 18.3°C 2,416 1,786 3,325 1,221 1,891 872 3,332 637 0 3,153 4,436 1,439 2,819 710 2,468 289 CDD 18.3°C 677 39 387 1,124 847 134 349 2,128 2,416 510 355 1,052 560 266 157 1,806
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