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This paper develops a decision framework to automate the playbook for UAS traffic management (UTM) under uncertain environmental conditions based on spatiotemporal scenario data. Motivated by the traditional air traffic management (ATM) which uses the playbook to guide traffic using pre-validated routes under convective weather, the proposed UTM playbook leverages a database to store optimal UAS routes tagged with spatiotemporal wind scenarios to automate the UAS trajectory management. Our perspective is that the UASs, and many other modern systems, operate in spatiotemporally evolving environments, and similar spatiotemporal scenarios are tied with similar management decisions. Motivated by this feature, our automated playbook solution integrates the offline operations, online operations and a database to enable real-time UAS trajectory management decisions. The solution features the use of similarity between spatiotemporal scenarios to retrieve offline decisions as the initial solution for online fine tuning, which significantly shortens the online decision time. A fast query algorithm that exploits the correlation of spatiotemporal scenarios is utilized in the decision framework to quickly retrieve the best offline decisions. The online fine tuning adapts to trajectory deviations and subject to collision avoidance among UASs. The solution is demonstrated using simulation studies, and can be utilized in other applications, where quick decisions are desired and spatiotemporal environments play a crucial role in the decision process.