A Structured Inference Framework for Spatial Hydrological Monitoring Networks
Abstract
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.
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BibTeX
@misc{erazo2026structured,
title = {{A Structured Inference Framework for Spatial Hydrological Monitoring Networks}},
author = {Carlos Erazo and Ibrahim Demir},
howpublished = {EarthArXiv},
year = {2026},
month = oct,
doi = {10.31223/x5nv4f},
url = {https://eartharxiv.org/repository/view/15419/},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - A Structured Inference Framework for Spatial Hydrological Monitoring Networks
AU - Carlos Erazo
AU - Ibrahim Demir
PY - 2026
DA - 2026/10/09
PB - EarthArXiv
DO - 10.31223/x5nv4f
UR - https://eartharxiv.org/repository/view/15419/
AB - 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.
ER -