Cloud-Native Water Markets: A Serverless Machine Learning Architecture for Supply-Side Water Trading and Dynamic Catchment Pricing

The efficient allocation of freshwater resources in small catchments represents a critical challenge in environmental economics. While theoretical models propose supply-side water trading and group-level "water clubs" to mitigate resource depletion, the empirical testing of these mechanisms is severely constrained by the lack of high-frequency digital market infrastructure. This paper proposes a cloud-native architectural framework utilizing Amazon Web Services to construct a real-time, serverless digital exchange for agricultural water trading. By deploying asynchronous Python middleware integrated with eXtreme Gradient Boosting algorithms, the proposed system programmatically ingests live hydrological telemetry from river catchments alongside real-time agricultural demand vectors. The system dynamically calculates supply-side market pricing and group-level club allocations, instantly identifying free-riding anomalies and shortage probabilities. Preliminary architectural evaluations demonstrate that decoupling theoretical economic modeling from the latency of manual data aggregation significantly accelerates the empirical validation of water market designs, providing resource economists with a deterministic, highly scalable technological foundation for testing sustainable allocation schemes.