A Structured Inference Framework for Spatial Hydrological Monitoring Networks

Carlos Erazo, Ibrahim Demir

Regional hydrologic and environmental inference relies on spatial monitoring networks exhibiting dependence, heterogeneity, and evolving behavior. Many regional analyses apply statistical methods in isolation, with limited structure governing when pooling, spatial modeling, or non‑stationary inference is admissible. This study presents HyMES Regional, a structured inference framework that formalizes regional analysis as an ordered sequence of diagnostic, inferential, and temporal operations applied to spatial ensembles of time series, presented as browser-based application. The framework integrates network‑aware spatial diagnostics, regime characterization, uncertainty‑conditioned regional inference, and progression analysis under a unified analytical logic. A case study in southern Louisiana demonstrates how localized precipitation forcing discrepancies are associated with regime-dependent streamflow performance degradation, accumulate temporally, and constrain simulated distributional behavior. The results show that regional model error exhibits organized spatial, temporal, and distributional structure that is coherent across stations. By explicitly conditioning inference on diagnosed structure, HyMES Regional framework supports defensible regional assessment across heterogeneous monitoring networks.