Data for: Characterization of large-scale preferential flow across continental United States

<p>Understanding preferential flow (PF) at large scales is critical for improving land management and groundwater (GW) quality. However, limited knowledge of this process, due to soil surface heterogeneity and observational constraints, hampers progress. In this study, we propose estimating effective PF at remote sensing footprint scale (4 – 9 km) by examining its impact on soil moisture (SM) distribution and shallow GW (SGW) table fluctuations (depth  5 m). Effective PF encompasses macropore, funnel, and finger flow pathways influencing SGW table fluctuations. We compiled daily SGW observations (2019-2021) from 19 continental US (CONUS) sites through USGS. Using inverse modeling in HYDRUS-1D, SGW data, and CHIRPS precipitation data, we inversely estimated soil hydraulic parameters of the dual porosity model (DPM) simulating vertical flow from soil surface to subsurface. Effective PF presence was inferred using three criteria: (1) daily precipitation &gt;=  the site-specific average across multiple (calibration) years, (2) daily observed SGW table increase, and (3) daily difference between observed and DPM simulated SGW tables 50% of the site-specific RMSE. Leveraging optimized DPM parameters and associated soil texture, classified PF events, and Soil Moisture Active Passive (SMAP L3E) satellite-based SM, a Random Forest algorithm with 10-fold cross validation predicted large-scale effective PF events. Results indicate seasonal dependence, with spring having the highest occurrence of PF events. The Random Forest model achieved 98% accuracy in predicting large-scale PF events, with SMAP SM and saturated hydraulic conductivity (Ks) among the 4 most impactful variables. Our approach provides a soil hydraulic property, site characteristic, soil texture and remote sensing based generalized tool to analyze large-scale effective PF.</p>