The aim of this project was to quantitatively measure the resilience of the building water cycle. In order to accomplish this goal, a framework was developed that outlines how building water resilience can be evaluated. The framework presented assumed that resilience describes the fulfillment of system functions; in this case, the system functions considered are those actualized by the building water system. A building water resilience assessment model (WRAM) was developed with the ability to simulate different building water cycles and resilience scenarios. Resilience is dependent on the type and magnitude of a disturbance. Therefore, unique disruption scenarios were developed to test the building water cycle resilience: (1) loss of municipal potable water and (2) loss of both municipal potable water and power. Under each scenario, the building water cycle was tested based on the type of building and the water management strategies utilized by the building. The WRAM requires organization of water demand and source connections, and an explicit prioritization framework was produced based on water source and demand preferences found in literature. The framework gives priority to treated wastewater, stormwater, rainwater, condensate, reclaimed water, and potable water, respectively. The baseline prioritization may be manipulated by restricting demand-source connections, and shifting priorities was shown to affect the potential for potable water offsets as a precursor to resilience. Real building water demand profiles were developed from data collected using smart meters at four building sites (multi-residential neighborhood, commercial building, elementary school, and community center). Water source profiles were developed using hourly climate data for the region. Detailed building water demand and supply profiles were developed for the multi-residential and elementary school building sites for resilience assessment using the WRAM. Each building water profile was adapted into 9 scenarios with each subjected to the two disruption schemes for 5 different disruption durations (1 hour, 6 hours, 24 hours, 72 hours, and 168 hours) at 10 different randomized dates and time throughout the year. The result was 450 model runs for each building subjected to each disruption scheme (potable water loss or potable water and central power loss). The relationship between resilience and sustainability was examined based on sustainable building practices accepted by the U.S. Green Building Council's (USGBC) Leadership in Environmental and Energy Design (LEED) green building rating system. Building WRAM outcomes include unique water demand and supply profiles used to describe resilience in terms of the level of service (LOS) of building water functions. Analysis of water profiles validated redundancy, diversity, capacity, alternative water, passivity, preparation, and adaptation potential indicators as gauges of the resilience of the building water cycle. Results showed that resilience correlates with alternative water building water management strategies, but high resilience values are still attainable using storage of non-renewable, non-sustainable sources. However, building water cycles utilizing alternative water maintained steadier resilience as disruption lengths increase due to the ability of sources to be replenished during disruption events. The strongest correlation with LOS was observed for the diversity, redundancy, alternative water, and capacity indicators when scenarios utilizing only potable water were excluded from analysis. For these scenarios, correlation values were 0.56 for diversity, 0.56 for redundancy, 0.60 for capacity, and 1.00 for alternative water for the multi-residential building subjected to potable water loss; and 0.33 for diversity, 0.24 for redundancy, 0.62 for capacity, and 1.00 for alternative water for the multi-residential building subjected to both potable water and central power disruption. For elementary school scenarios that did not utilize potable water storage, correlation values were 0.67 for diversity, 0.64 for redundancy, 0.06 for capacity, and 0.89 for alternative water when subjected to disruption of potable water; and 0.67 for diversity, 0.64 for redundancy, 0.06 for capacity, and 0.80 for alternative water when subjected to disruption of potable water and central power. Passivity correlation to LOS was between 0.77 and 1.00 for all scenarios, building types, and disruption schemes. Passivity correlation with LOS was lower for potable water disruption scenarios, but higher when building water cycles lost power in addition to potable water. The average of each indicator was also calculated for each scenario for each of the five disruption durations by grouping the individual values from each of the 10 randomized disruption start dates and times. The correlation between the average capacity indicator and LOS greatly increased with this method to a range of 0.41 to 0.78 for all buildings subjected to each disruption scheme. In addition, a positive correlation between the preparation indicator and LOS (and corresponding negative correlation between the adaptation potential indicator and LOS) emerged for scenarios that do not utilize potable water storage. For disruption of potable water, the preparation correlation value was 0.94 for the multi-residential building and 0.78 for the elementary school. For disruption of potable water and central power, the preparation correlation value was 0.32 for the multi-residential building and 0.79 for the elementary school.