Characterizing Spatiotemporal Hydrological Responses During Extreme Flooding: A Residual Analysis Using SMAP Data

Coarsely gridded Land Surface Models (LSMs) often smooth over sub-grid spatial heterogeneity and non-linear surface soil moisture dynamics during extreme-precipitation events. This study introduces a clustering-based Soil Moisture Active Passive (SMAP) residual framework, evaluating the spatiotemporal discrepancies between 3 km SMAP Level 2 (SMAP-L2) retrievals and 9 km SMAP Level 4 (SMAP-L4) data-assimilation products within the Yanco study region during the extreme March 2021 floods in New South Wales, Australia. By applying k-means clustering to the residual time series, we partitioned the landscape into three distinct hydrological response patterns: a Low-Residual Baseline (64.5%), a Persistent Positive Anomaly (20.7%) indicative of unmodeled inundation, and a Transient Negative Anomaly (14.8%) representing rapid drainage. Consequently, 35.5% of the usable analysis area exhibited temporal trajectories that diverged significantly from model expectations, highlighting profound geographic heterogeneity in surface wetting and retention that cannot be captured by uniform precipitation inputs alone. Benchmarking the satellite-derived time series against the Yanco in situ network provided critical context for cross-scale variations, illustrating general agreement in overarching temporal trends despite the inherent scale mismatch. Ultimately, this approach leverages residual dynamics as a scalable spatial diagnostic, offering a robust, data-driven method to map localized flood responses that are typically obscured by broad-scale model parameters.