It is estimated that 15-40% of water is unaccounted for in urban water systems. This is mostly caused by small leaks, which are difficult to locate. Routinely measured pressure, flow, and water quality data can be used to locate leaks in the water network using an inverse modeling approach. For a known sensor configuration, the leak locations can be found by minimizing the difference between real and simulated measurements. However, when comparing measurement types (pressure, flow, or quality), some may be more sensitive to leak location than others. Furthermore, some measurement types may be more or less sensitive depending on the leak magnitude or the proximity of the leak to the sensors. The measurements types that are more sensitive to location will have a stronger signature and would need to be weighted more in an inverse modeling approach, especially in the presence of noise. Preliminary research suggests that water quality measurements are more sensitive to leak location when a leak is small, and that flow measurements are more sensitive when a leak is large. In this research, a series of sensitivity analyses are conducted on different networks to investigate the sensitivity of these measurements with respect to leak location, magnitude, and proximity of sensors to the leak location. 1. Motivation Water distribution systems are a vital part of modern infrastructure, yet they are susceptible to leaks and contaminant intrusion. High pressure, freezing water, or aging can cause cracks in the distribution pipes that lead to small, gradual leaks into the ground that are difficult to detect. In some aging systems, up to 40% of water is lost to leaks [1]. Utilities typically monitor locations that are prone to leak, based on a history of previous leaks or the age of the pipes. A leak can be detected, for example, by using acoustic listening devices that pick up on the sound of water escaping from the pipe, among other methods. However, it is expensive and time intensive to manually check the suspected pipes. There are routinely collected measurements of pressure, flow, and water quality at sensor locations. These measurements can carry a signature that will help identify the leak location and