Distributed urgent computing workflows often require data to be staged between multiple computational resources. Since these workflows execute in shared computing environments where users compete for resource usage, it is necessary to allocate resources that can meet the deadlines associated with time-critical workflows and can tolerate interference from other users. In this paper, we evaluate the use of robust resource selection and scheduling heuristics to improve the execution of tasks and workflows in urgent computing environments that are dependent on the availability of data resources and impacted by interference from less urgent tasks.