DOI: 10.5061/dryad.8kprr4xv3
title: Data for: Characterization of large-scale preferential flow across continental United States

description: 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.
description: A total of 19 sites with diverse soil-vegetation-climate
 characteristics across CONUS were selected for the study (Fig. 2). Daily
 mean GW data at these sites were gathered from observation from the USGS
 National Water Information System (NWIS). Following similar approaches
 from Babajimopoulos et al., 2007 and Costa et al., 2023, sites with GW
 tables less than or equal to 5 meters were defined as shallow and GW
 tables deeper than 5 meters were defined as deep. Out of the 19 sites, 3
 sites (Laurens, Georgia; Tioga, New York; Saline, Missouri) had deep GW
 table data, 12 sites (Blaine, Nebraska; Jones, North Carolina; St.
 Lawrence, New York; Chatham, Georgia; Red Lake, Minnesota; Walker, Texas;
 Kalamazoo, Michigan; Monmouth, New Jersey; St. Croix, Wisconsin; Big Horn,
 Montana; Pasquotank, North Carolina; Tazewell, Illinois) had SGW table
 data from 2019-2021, and 4 sites (Hooker, Nebraska; Garden, Nebraska;
 Duplin, North Carolina; Clinton, Illinois) had SGW data from 2021-2022.
 Precipitation data at a daily scale and 4800 m spatial resolution were
 collected from the Climate Hazards Group Infrared Precipitation with
 Station data (CHIRPS), accessed through Climate Engine. Van Genuchten soil
 hydraulic parameters from the soil catalog in HYDRUS-1D for
 various textural classes (Carsel & Parrish, 1988) were used as
 initial parameters in the inverse modeling of the dual porosity model
 (DPM) (Gerke & van Genuchten, 1993). Site-specific surface soil
 texture, slope, and elevation were collected from the Soil Survey
 Geographic Database (SSURGO) at a 250 m spatial scale for the selected
 study soils. In this study, we assumed vertical soil texture homogeneity,
 while addressing soil surface lateral heterogeneity by selecting sites
 across CONUS with diverse surface soil textures. NASA’s SMAP L3E product
 provided SM (z &lt; 5 cm) at 2–3-day intervals with a 9-km resolution
 across CONUS. SM data from SMAP was accessed through the Earth Data
 Application for Extracting and Exploring Analysis Ready Samples (AppEEARS)
 database. Soil physical properties such as bulk density from the World
 Soil Information Service (WoSIS) database were gathered from SoilGrids
 with 250 m grid size. The 12 sites with SGW table data from 2019-2021 in
 conjunction with CHIRPS precipitation data as input were used to inversely
 model the soil hydraulic parameters as output using the DPM. The 4
 remaining independent sites with SGW table data from 2021-2022 were used
 to validate the inversely modeled soil hydraulic parameters by forward
 modeling using DPM.
description: # Data for: Characterization of Large-Scale Preferential Flow Across
 Continental United States 
 **output_file_1.xlsx:** * Source: Assembled and calculated by authors *
 Contents: date, site, season, year, SMAPsm, residual water content
 (theta.r), saturated water content (theta.s), alpha, n, saturated
 hydraulic conductivity (Ks), tortuosity parameter (I), residual water
 content in immobile region (theta.r.im), saturated water content in
 immobile region (theta.s.im), exchange term (omega), soil texture, bulk
 density (BD), clay %, sand %, silt %, clay percent range, slope,
 elevation, observed groundwater, fitted groundwater (HYDRUS output), and
 precipitation * Respective Units: (Y-m-d), text, text, number, m3/m3,
 cm^3/cm^3, cm^3/cm^3, 1/cm, -, cm/d, -, cm^3,cm^3, cm^3,cm^3, 1/d, text,
 cg/cm^3, %, %, %, text, %, ft, cm, cm, cm. **output_file_2.xlsx:** *
 Source: Assembled and calculated by authors * Contents: date, site,
 season, year, SMAPsm (SMAP soil moisture), residual water content
 (theta.r), saturated water content (theta.s), alpha, n, saturated
 hydraulic conductivity (Ks), toruosity parameter (I), residual water
 content in immobile region (theta.r.im), saturated water content in
 immobile region (theta.s.im), exchange term (omega), soil texture, bulk
 density (BD), clay %, sand %, silt %, clay percent range, slope,
 elevation, observed groundwater, fitted groundwater (HYDRUS output),
 precipitation, groundwater difference, SMAP soil moisture difference,
 groundwater table movement direction classification, precipitation
 intensity classification, error (difference between observed and fitted
 groundwater table data. This value is compared to the RMSE). * Respective
 Units: (Y-m-d), text, text, number, m3/m3, cm^3/cm^3, cm^3/cm^3, 1/cm, -,
 cm/d, -, cm^3,cm^3, cm^3,cm^3, 1/d, text, cg/cm^3, %, %, %, text, %, ft,
 cm, cm, cm, cm, m^3/m^3, text, text, cm. ## Description of the data and
 file structure The excel files (output_file_1.xlsx and output_file_2.xlsx)
 are the key outputs from the publication. Output_file_2 builds from
 output_file_1. Output_file_1 and Output_file_2 both contain the optimized
 soil hydraulic parameters for each site (based on soil texture) which are
 the result of running HYDRUS-1D. HYDRUS-1D allowed us to simulate the
 precipitation and groundwater recharge relationship via preferential flow
 pathways (or rapid flowing fluid from soil surface to subsurface). Through
 inverse modeling in HYDRUS-1D (providing the precipitation and observed
 groundwater table data from USGS prior to the simulation), we were able to
 determine the optimized soil hydraulic parameters driving preferential
 flow based on van Genuchten model for that site and soil texture. More
 details can be found in the publication regarding the simulations. The
 optimized soil hydraulic parameters that we found in this work can be used
 in future work. Our study simulated sites across Contineal United States
 covering a variety of climate and environmental scenarios. Output_file_2
 differs from output_file_1 in that the daily large scale effective
 preferential flow classifier is assigned in Output_file_2. Using the
 methodology in the publication, we have assigned each day with either
 experiencing or not experiencing large scale effective preferential flow.
 This is the first dataset containing this kind of information (that the
 authors know of). It is our hope that other studies use this dataset to
 continue building on the ability to detect large scale effective
 preferential flow events. Codes have been provided for the novel aspects
 of the study including the large scale effective preferential flow
 assignment based on the implementation of our 3 criteria, and the
 implementation of a Random Forest model to estimate the presence of large
 scale effective preferential flow across Continental United States. ##
 Sharing/Access information ## Code/Software **HYDRUS-1D** HYDRUS-1D
 software was used to inversely model the observed groundwater table levels
 from USGS in order to determine the optimized soil hydraulic parameters.
 The software was also use to forward model the observed groundwater tables
 from USGS at new sites with the optimized soil hydraulic parameters
 (selected for each site based on soil texture) to check the performance of
 the parameters based on the estimated groundwater table values (HYDRUS-1D
 forward model results) compared to observed groundwater table values from
 USGS. **R** R was used to import data, manage data, conduct data analysis,
 determine preferential events on a daily basis using three selective
 criteria described in the publication, and to run a Random Forest model on
 the data to estimate large-scale effective preferential flow events. Codes
 can be found on GitHub: