Disaster forecasting, warning, recovery, and response in water resources management require the application of knowledge from a diverse range of domains. Identifying the appropriate approach necessitates integrating rules and requirements from these knowledge domains in such a way that the operational goals are achieved with minimally available situational information. Disaster forecasting, warning, recovery, and response must be able to adapt and evolve as new information becomes available. To date, there has been a limited amount of work developing expert systems in this area. In order to fill the knowledge gap, this study 1) identifies and assimilates the knowledge necessary for Water Distribution Network (WDN) decontamination, local flood forecasting and warning, and local flood response coordination and training; 2) determines the relative utility of architectures of expert systems and conventional codes; 3) evaluates the relative benefits of forward and backward chaining inferential logic in these scenarios. Based on the outcome of the conceptual systems, we develop three complete backward chaining expert systems, respectively. With extensible knowledge bases combined with the information provided by the users, the expert systems successfully provide reasoning routines, recommendations, and guidance on disaster forecasting, warning, recovery, and response in water resources management.