## 2 Methods
### Remote Sensing  
To run TEM, LULC datasets are required to provide the amount of each cohort (described in Section 2.2) in each grid cell. To create the dataset, aerial imagery was classified to separate the pixels into different cohorts and then overlain with 4 km grids. The aerial imagery used for this project was provided by USGS Earth Explorer (Table S1) [17]. A Landsat 8 Operational Land Imager (OLI) C1 L1 TP imagery (Collection 1 Level 1 Terrain Precision Correction) taken on 30 July 2017 was used to perform a classification to determine the fractional amount of each cohort within each grid in Lehigh Valley, as well as the city of Bethlehem. An additional classification was conducted on an image from 21 December 2017 to determine the proportion of coniferous and deciduous trees within Lehigh Valley (Table S1, Figure S1). L1 TP data were radiometrically calibrated and orthorectified through the use of ground control points and digital elevation models (USGS). The image is in tier 1 (T1), which is the highest available quality and processing level for Landsat data [17].  
For both of the images, the remote sensing data were used to categorize and quantify the amount of land within Lehigh Valley that is developed (impervious), barren, forest, turl lawn, cropland, pastureland, and water. For the image from December, the forest cohort was replaced by coniferous and deciduous cohorts, using a forest area that was bare of leaves to classify the deciduous areas. In both images, water was removed from the LULC dataset because it is not a cohort used within TEM. Training areas were created to match pixels of similar reflectance into the land use type groups. The training areas were used in the pixel-based, random tree classification to group the pixels. Multiple iterations of the classification were performed to improve accuracy by visually checking that the pixels were being classified correctly. Using a fishnet tool, 4 km grids were created to determine the amount of each land use type in each of the grids. The classified dataset was overlain with the fishnet, outlining Lehigh Valley, and the outline of the city of Bethlehem was used for the experiments involving the subset of Bethlehem (Figure S1).  
As a result of the winter image, the forest cohort was split into 15% coniferous and 85% deciduous forests. The forest cohort division was determined by comparing an image of Lehigh Valley in the winter (21 December 2017) to an image from the summer (30 July 2017). The total area of the forest in the winter image was compared to the total area of the forest in the summer image to ensure accuracy. To verify the ratio of coniferous to deciduous forest, additional classifications were conducted and produced similar results. The ratio of forest in the winter to the summer was then used to determine what percentage of the total forest is coniferous and deciduous, as the winter image would only show the area represented by coniferous trees. Seventy percent of the lawn cohort was split into 65% cropland and 35% pastureland in grids deemed non-urban (<35% impervious surfaces) as per the Lehigh Valley Planning Commission [18]. The remaining 30% was left as lawn. The classified data were split into 4 km grid cells. After gathering data for the categories, the data were input into TEM and run under current climate conditions to serve as a control run. The most dominant land use types were forest area in most cohorts, urban in the areas with the most impervious surfaces, and cropland in more rural areas of Lehigh Valley (Figures S1 and S2).  
Using the same classified image from Lehigh Valley, four grids in the shape of the city of Bethlehem were extracted to represent the north, east, south, and west sections of the city (Figure S1). A forested area in the southern section of the city was removed because it is part of the Lehigh University campus, no one lives in the area, and our primary goal within the city was to focus on how the potential LULCC could impact socially vulnerable populations.