William H. Asquith Courtney D. Killian Taylor L. Watson (Contractor) 20251219 raster digital data Suggested citation: Watson, T.L., Asquith, W.H., and Killian, C.D., 2025, Statistical predictions of groundwater levels and related spatial diagnostics for two hydrogeologic frameworks for the Chicot aquifer system, southwestern Louisiana, 1983–2023, from the mmlCHICOTla statistical research software: U.S. Geological Survey data release, https://doi.org/10.5066/P14GFYQR. https://doi.org/10.5066/P14GFYQR William H. Asquith Courtney D. Killian 2025 software Asquith, W.H., and Killian, C.D., 2025, mmlCHICOTla—Source code for construction and implementation of multiple machine-learning models of water levels in the Chicot aquifer system, southwestern Louisiana: U.S. Geological Survey software release, https://doi.org/10.5066/P13K5TN9. [Software home page located at https://code.usgs.gov/map/gw/levs/altaqfrs/chicot/mmlCHICOTla] https://doi.org/10.5066/P13K5TN9 Results of predicted groundwater levels and related spatial diagnostics, produced by an applied prediction-modeling statistical-research workflow, are provided as multi-banded GeoTIFF rasters for two custom bundles (classifications) of local aquifer codes for many thousands of wells in the hydrogeologically complex Chicot aquifer system, southwestern Louisiana. The modeling workflow was centered on application of the covCHICOTla (data preprocessing) and mmlCHICOTla (statistical predictions of water levels and diagnostics) research software (Asquith and Killian, 2025a,b). The mmlCHICOTla software creates and operates three forms of statistical learning methods (cubist, random forest, and support vector machine) for prediction of water levels and related quantities associated with statistical uncertainties at the 1-kilometer (km) grid scale using the National Hydrogeologic Grid (Clark and others, 2018). The three learning forms are combined in software to make final predictions and prediction diagnostics. The workflow of mmlCHICOTla, centered on the multiple methods of machine learning (MML), was operated and stewarded as the May 6, 2025, model run as documented within mmlCHICOTla (refer also to software path mmlCHICOTla/model_preserve/README.md). Extensive and comprehensive documentation, including citations to authoritative literature, is made within the software home pages of covCHICOTla (https://code.usgs.gov/map/gw/levs/altaqfrs/chicot/covCHICOTla) and mmlCHICOTla (https://code.usgs.gov/map/gw/levs/altaqfrs/chicot/mmlCHICOTla). Local aquifer codes represent a coding scheme used by the U.S. Geological Survey (USGS) National Water Information System (NWIS). Asquith and Lindaman (2025) provide comprehensive citations, data sources, data filtering decisions, and other details leading to wells being classified into either CHCTLYR1 or CHCTLYR2 classes suitable for statistical production of typical well-depth geospatial products provided in this data release. The CHCTLYR1 (combination of USGS NWIS local aquifer codes 112CHCTU, 11202LC, 112ACFL, 112CHCT, and 112UPTC) represents comparatively "shallow" Chicot aquifer system conditions and effectively includes the entire horizontal expanse of the aquifer. The CHCTLYR2 (combination of USGS NWIS local aquifer codes 112CHCTL, 11205LC, and 11207LC) represents comparatively "deep" aquifer conditions and is applicable for only part of the Chicot aquifer system expanse. Predictions of water levels, as water-level altitudes or as depths to water, are intended to provide first-order general estimate of mean groundwater levels on a monthly basis for January 1983 to December 2023 (492 months) for the two classifications (CHCTLYR1 and CHCTLYR2). The MML used 4,620 wells having 12,239 monthly water levels (CHCTLYR1) and 612 wells having 3,042 monthly water levels (CHCTLYR2). Gridded statistical predictions as 492-layer GeoTIFF files for computational themes that include (1) water-level altitude ("pol"), (2) depth to water ("nhgd2w"), (3) two forms of model errors ("modnorerr" and "modpdqerr"), (4) ratio of model error to total error ("modtorat"), (5) two forms of total error (combined statistical and model) assuming no land-surface variation within 1-km grid cell ("norerr" and "pdqerr"), and (6) two forms of total error including land-surface variation within 1-km grid cell ("norerrnhg" and "pdqerrnhg"). In the software and data nomenclature, the normal probability distribution is abbreviated ("nor") and symmetrical polynomial quantile-density probability is abbreviated ("pdq") as the structural form of the unknown but estimable error distribution. The 90-percent prediction limits are represented by lower ("lwr") and upper ("upr") limits for both error-model distribution forms ("norlwr", "norupr" and "pdqlwr", "pdqupr") assuming no land-surface variation within 1-km grid cell. Lastly, the 90-percent prediction limits are represented by lower ("lwr") and upper ("upr") limits for both error-model distribution forms ("norlwrnhg", "noruprnhg" and "pdqlwrnhg", "pdquprnhg") including land-surface variation within 1-km grid cell. A total, therefore, of 13 computational themes for each CHCTLYR1 and CHCTLYR2 were produced having 492-layers for each month (1983–2023). The gridded predictions are the result of statistical research and algorithm development (2024–25) for the Chicot Aquifer system and are intended as science products to aid derivative studies of the hydrogeologic framework and hydrologic history of the aquifer. The multiple machine-learning groundwater level prediction multi-banded rasters document results from the mmlCHICOTla software. 19830101 20231231 observed Complete None planned -93.9370 -91.4512 31.3731 29.5288 ISO 19115 Topic Category environment geoscientificInformation USGS Thesaurus groundwater groundwater level mathematical modeling aquifer machine learning random forest cubist U.S. Geological Survey hydrology Chicot aquifer USGS Metadata Identifier USGS:6864321fd4be025fd24f0260 None Louisiana Coastal Louisiana United States None. Please see 'Distribution Info' for details. Although these data have been used by the U.S. Geological Survey (USGS), U.S. Department of the Interior, no warranty expressed or implied is made by the USGS as to the accuracy of the data. The act of distribution shall not constitute any such warranty, and no responsibility is assumed by the USGS in the use of this data, software, or related materials. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government. This dataset may be redistributed if it is not edited and is properly referenced. Although USGS intends to make this server available 24 hours a day, seven days a week, timely delivery of data and products from this server through the Internet is not guaranteed. Taylor L. Watson (Contractor) U.S. Geological Survey Researcher mailing and physical

700 W. Research Center Blvd.

Fayetteville AR 72701 United States 479-841-7576 twatson@contractor.usgs.gov In cooperation with the Louisiana Department of Transportation and Development (LaDOT). Raster data are provided as multi-banded GeoTIFF files. Metadata files are provided as extensible markup language (xml) files. William H. Asquith Courtney D. Killian 2025 software Asquith, W.H., and Killian, C.D., 2025, covCHICOTla—Source code for construction of covariates bound to monthly groundwater levels for statistical modeling of water levels in the Chicot aquifer system, southwestern Louisiana: U.S. Geological Survey software release, https://doi.org/10.5066/P14N9J9C. [Software home page located at https://code.usgs.gov/map/gw/levs/altaqfrs/chicot/covCHICOTla] https://doi.org/10.5066/P14N9J9C William H. Asquith Courtney D. Killian 2025 software Asquith, W.H., and Killian, C.D., 2025, mmlCHICOTla—Source code for construction and implementation of multiple machine-learning models of water levels in the Chicot aquifer system, southwestern Louisiana: U.S. Geological Survey software release, https://doi.org/10.5066/P13K5TN9. [Software home page located at https://code.usgs.gov/map/gw/levs/altaqfrs/chicot/mmlCHICOTla] https://doi.org/10.5066/P13K5TN9 Input values for covariates may include high precision (up to 10 decimal places) because some values are calculated and forced rounding maintains model consistency across software. The decimal places do not signify known precision or accuracy of the covariates. Users are encouraged to refer to source data to understand precision of explanatory variables. Note, efforts were made to compute and report range of the values to decimal formatting as strings to remain consistent with apparent precision in the numerical values. The term "apparent" means literally the floating-point precision as stored digitally in the geospatial layer. The mmlCHICOTla is designed to preserve excess decimals as floating-point numbers are processed through the workflow. Herein, number of decimals and (or) significant figures shown are neither representative of accuracy, precision, nor significance. These data are statistically generated values and consultation of documentation and ultimately interpretations of the values would be required and are outside the scope of digital storage. Groundwater levels from various sources were used to provide information across space and time. Some data include synoptic, seasonal, and daily groundwater levels. Groundwater levels from drillers reports also were included. Horizontal accuracy (or location accuracy) of source data (groundwater levels) varies by well but can range from plus or minus 0.01 second to 1 minute or be unknown. Most wells were accurate between 1 and 10 seconds. Groundwater levels at wells were used in statistical machine-learning to make monthly predictions of groundwater levels to raster cells of the 1-kilometer resolution national hydrologic grid (NHG) from January 1983 to December 2023 for the chicot aquifer extent. William H. Asquith Courtney D. Killian 2025 software https://code.usgs.gov/map/gw/levs/altaqfrs/chicot/mmlCHICOTla U.S. Geological Survey Asquith, W.H., and Killian, C.D., 2025, covCHICOTla—Source code for construction of covariates bound to monthly groundwater levels for statistical modeling of water levels in the Chicot aquifer system, southwestern Louisiana: U.S. Geological Survey software release, https://doi.org/10.5066/P14N9J9C. [Software home page located at https://code.usgs.gov/map/gw/levs/altaqfrs/chicot/covCHICOTla] https://doi.org/10.5066/P14N9J9C Digital and/or Hardcopy 2025 publication date covCHICOTla Software, construction of covariates bound to groundwater levels Koen Hufkens 20230915 tabular digital data accessed on December 11, 2024 https://doi.org/10.32614/CRAN.package.daymetr Digital and/or Hardcopy 19830101 20231231 observed daymet Covariate, climate Maxwell A Lindaman 2023 dataset https://www.sciencebase.gov U.S. Geological Survey https://doi.org/10.5066/p9olqlpp Digital and/or Hardcopy 20230726 publication date aqfrbot Covariate, aquifer bottom Kristi L Hill Wade Kress Maxwell A Lindaman 20220909 dataset https://www.sciencebase.gov U.S. Geological Survey https://doi.org/10.5066/p9w2sksy Digital and/or Hardcopy 20220909 publication date aqfrtop Covariate, aquifer top Meredith Reitz Ward E. Sanford Gabriel Senay Jeffrey G Cazenas 2017 dataset https://www.sciencebase.gov U.S. Geological Survey https://doi.org/10.5066/f7pn93p0 Digital and/or Hardcopy 2000 2013 observed effrch Covariate, effective recharge National Centers for Enviornmental Information 2025 tabular digital data accessed April 25, 2025 https://www.ncei.noaa.gov/access/monitoring/products/ Digital and/or Hardcopy 2025 publication date enso covariate, global climate indices Maxwell A. Lindaman 20230728 tabular digital data Lindaman, M.A., 2023, Hydrogeologic framework of southwestern Louisiana: U.S. Geological Survey Scientific Investigations Report 2023–5004, 31 p., https://doi.org/10.3133/sir20235004. https://doi.org/10.3133/sir20235004 Digital and/or Hardcopy 20230728 publication date hydroframe_a Covariate, hydrogeologic framework Maxwell A Lindaman 20230726 dataset https://www.sciencebase.gov U.S. Geological Survey https://doi.org/10.5066/p9olqlpp Digital and/or Hardcopy 20230726 publication date hydroframe_b Covariate, hydrogeologic framework Richard B Moore Kenneth Belitz Terri L Arnold Jennifer B. Sharpe Jeffrey Starn 2019 dataset https://www.sciencebase.gov U.S. Geological Survey https://doi.org/10.5066/p9hlu4yy Digital and/or Hardcopy 2019 publication date mohp Covariate, Multi Order Hydrologic Position (MOHP) Brian R Clark Paul M Barlow Steven M Peterson Joseph D Hughes Howard W Reeves Roland Viger 2018 dataset https://www.sciencebase.gov U.S. Geological Survey https://doi.org/10.5066/f7p84b24 Digital and/or Hardcopy 2018 publication date NHG Covariate, National Hydrogeologic Grid Ashley M Bussell 20210528 dataset https://www.sciencebase.gov U.S. Geological Survey Covariate, monthly streamflow data https://doi.org/10.5066/p97v6v5d Digital and/or Hardcopy 20210528 publication date extent Covariate, spatial extent of the Chicot aquifer United States Geological Survey 1994 application/service https://www.sciencebase.gov U.S. Geological Survey Covariate, monthly streamflow: accessed April 11, 2025 via dataRetrieval-—Retrieval functions for USGS and EPA hydrologic and water quality data: R package version 2.7.18, dated February 27, 2025. https://doi.org/10.5066/f7p55kjn Digital and/or Hardcopy 1994 publication date nwis Covariate, monthly streamflow Covariate, groundwater levels William H. Asquith Courtney D. Killian 2025 tabular digital data https://doi.org/10.5066/P14WFPF5 Digital and/or Hardcopy 2025 publication date welldepmod Covariate, typical well depths Extents of "CHICOTLYR1" and "CHICOTLYR2" classifications William H Asquith Courtney D Killian 2022 software https://www.sciencebase.gov U.S. Geological Survey https://doi.org/10.5066/p9tpgi0o Digital and/or Hardcopy 2022 publication date covMRVAgen1 Original source code for covariate construction, modified for the Chicot aquifer (covCHICOTla). Stephanie R James Burke J Minsley 2021 dataset https://www.sciencebase.gov U.S. Geological Survey https://doi.org/10.5066/p9382rci Digital and/or Hardcopy 2021 publication date geophys Covariate, ariborne electromagnetic surveys Ashley M Bussell Ronald C Seanor William H Asquith 2023 dataset https://www.sciencebase.gov U.S. Geological Survey https://doi.org/10.5066/p9u1i5jy Digital and/or Hardcopy 2023 publication date LaDNR Water levels from drillers reports U.S. Geological Survey 2024 raster digital data https://www.sciencebase.gov/catalog/item/4f70aa9fe4b058caae3f8de5 Digital and/or Hardcopy 2024 publication date DEM Covariate construction, aquifer top William H. Asquith Courtney D. Killian 2025 software https://doi.org/10.5066/P9NTSNFE Digital and/or Hardcopy 2025 publication date mmlCHICOTla Software, construction of Cubist and Random Forest statistical models for water levels prediction in the Chicot aquifer. William H Asquith Courtney D Killian 2024 dataset https://www.sciencebase.gov U.S. Geological Survey https://doi.org/10.5066/p9ntsnfe Digital and/or Hardcopy 2024 publication date mmlMRVAgen1 Original source code for multiple machine learning models of water levels, modified for the Chicot aquifer (covCHICOTla). The covCHICOTla software was used to bind monthly rolled up groundwater levels of the "CHICOTLYR1" and "CHICOTLYR2" classifications of the Chicot aquifer system to potential covariates useful for predictive statistical analysis of groundwater levels. covCHICOTla daymet geophys aqfrbot aqfrtop effrch enso hydroframe_a hydroframe_b mohp NHG extent nwis covMRVAgen1 DEM LaDNR 20250506 welldepmod The mmlCHICOTla software was used to make monthly predictions of groundwater levels for the Chicot aquifer extent (Bussell and Killian, 2021) from January 1983 to December 2023 using the implementation phase of the multiple methods of machine learning on the 1-kilometer National Hydrogeologic Grid. See ./mmlCHICOTLA/useMMLsu/README.md file for more information. Individual raster prediction GeoTIFF files were combined into multi-banded GeoTIFF files. mmlCHICOTla NHG DEM mmlMRVAgen1 20250506 Raster Grid Cell 213 231 492 Albers Conical Equal Area 29.5 45.5 -96.0 23.0 0.0 0.0 row and column 1000.0 1000.0 meters WGS_1984 WGS 84 6378137.0 298.257223563 North American Vertical Datum of 1988 1.000000 feet Attribute values [SHORTID]_mult_def.txt Pipe delimited text file with definitions for all 13 shorthand identifier [SHORTID] model results. Producer Defined LAYERNO Raster band sequence number; some geospatial software might load the GeoTIFF file named above without renaming or recognizing the layers as YEAR-MONTH time series; sequence number is provided for convenience. Producer Defined 1 492 SHORTID Shorthand identifier used within mmlCHICOTla software (mmlCHICOTla is located at https://doi.org/10.5066/P14N9J9C) Producer Defined modnorerr Shorthand identifier used in mmlCHICOTla software for STDEV_MODEL_NORERR variable Producer defined modpdqerr Shorthand identifier used in mmlCHICOTla software for STDEV_MODEL_PDQERR variable Producer defined modtotrat Shorthand identifier used in mmlCHICOTla software for RATIO_MOD_TOT_ERR variable Producer defined nhgd2w Shorthand identifier used in mmlCHICOTla software for LEV_VA_POL_NHG_DATUM variable Producer defined norerr Shorthand identifier used in mmlCHICOTla software for STDEV_TOTAL_NORERR variable Producer defined norerrnhg Shorthand identifier used in mmlCHICOTla software for STDEV_TOTAL_NORERR_NHG variable Producer defined norlwr Shorthand identifier used in mmlCHICOTla software for LEV_ALT_POL_NORLWR variable Producer defined norupr Shorthand identifier used in covCHICOTla software for LEV_ALT_POL_NORUPR variable Producer defined pdqerr Shorthand identifier used in covCHICOTla software for STDEV_TOTAL_PDQERR variable Producer defined pdqerrnhg Shorthand identifier used in mmlCHICOTla software for STDEV_TOTAL_PDQERR_NHG variable Producer defined pdqlwr Shorthand identifier used in mmlCHICOTla software for LEV_ALT_POL_PDQLWR variable Producer defined pdqupr Shorthand identifier used in mmlCHICOTla software for LEV_ALT_POL_PDQUPR variable Producer defined pol Shorthand identifier used in mmlCHICOTla software for LEV_ALT_POL_FIT variable Producer defined VARNAME Variable short name (column title in data table objects of mmlCHICOTla). Producer Defined STDEV_MODEL_NORERR Short name for model_error_for_nordist variable Producer defined STDEV_MODEL_PDQERR Short name for model_error_for_pdq4dist variable Producer defined RATIO_MOD_TOT_ERR Short name for ratio_model_error_to_total_error_woNHGstdev variable Producer defined LEV_NHG_D2W Short name for depth_to_water variable Producer defined STDEV_TOTAL_NORERR Short name for total_error_for_nordist_wNHGstdev variable Producer defined LEV_ALT_POL_NORLWR Short name for lower_prediction_bounds_nordist_NAVD88 variable Producer defined LEV_ALT_POL_NORUPR Short name for upper_prediction_bounds_nordist_NAVD88 variable Producer defined STDEV_TOTAL_PDQERR Short name for total_error_for_pdq4dist_woNHGstdev variable Producer defined STDEV_TOTAL_PDQERR_NHG Short name for total_error_for_pdq4dist_wNHGstdev variable Producer defined LEV_ALT_POL_PDQLWR Short name for lower_prediction_bounds_pdq4dist_NAVD88 variable Producer defined LEV_ALT_POL_PDQUPR Short name for upper_prediction_bounds_pdq4dist_NAVD88 variable Producer defined LEV_ALT_POL_FIT Short name for water_level_altitude_NAVD88 variable Producer defined LONGNAME Variable long name with underscores used for otherwise spaces. Producer Defined model_error_for_nordist See DEFINITION Producer defined model_error_for_pdq4dist See DEFINITION Producer defined ratio_model_error_to_total_error_woNHGstdev See DEFINITION Producer defined depth_to_water See DEFINITION Producer defined total_error_for_nordist_wNHGstdev See DEFINITION Producer defined lower_prediction_bounds_nordist_NAVD88 See DEFINITION Producer defined upper_prediction_bounds_nordist_NAVD88 See DEFINITION Producer defined total_error_for_pdq4dist_woNHGstdev See DEFINITION Producer defined total_error_for_pdq4dist_wNHGstdev See DEFINITION Producer defined lower_prediction_bounds_pdq4dist_NAVD88 See DEFINITION Producer defined upper_prediction_bounds_pdq4dist_NAVD88 See DEFINITION Producer defined water_level_altitude_NAVD88 See DEFINITION Producer defined UNITS Units of the variable (see definition for more details). Producer Defined standard deviations of feet standard deviations of feet Producer defined dimensionless no unit of measure Producer defined depth to water, in feet The depth to water is analogous to the lev_va (or LEV_VA) data field within the U.S. Geological Survey National Water Information System (https://doi.org/10.5066/F7P55KJN). Producer defined feet Water-level altitude (North American Vertical Datum of 1988, NAVD88). Producer defined YEAR Numerical calendar year. Producer Defined 1983 2023 Years MONTH Left-zero-padded month of layer. Producer Defined 1 12 Months LAYER Authoritative layer name as colon-delimited with varname and hypen-delimited YEAR-MONTH nomenclature convention. Producer Defined [SHORTID]:Y1983-M01 [SHORTID]:Y2023-M12 DEFINITION Constructed plain-text layer definition from software reading 'mmlCHICOTla/include/data_dictionary_output.txt' with numerical range for the layer appended to definition; ALL-CAPITAL UNDERSCORE-DELIMITED "words" in the definition (if any) are variable names in mmlCHICOTla. The 'mmlCHICOTla/include/data_dictionary_[output|input].txt' plain-text and other README files in that software provide further details. Producer Defined plain text definition modnorerr_mult.tif 492 band raster geospatial data file. Producer Defined Value The recovery of apparent model error based on the product of RATIO_MOD_TOT_ERR (ratio of model error to total error ahead of statistical distribution assumption) and STDEV_TOTAL_NORERR (total error in standard deviations using normal distribution assumption). Producer Defined nan not a number Producer defined 0.074791964137128 31.11481132754 standard deviations of feet modpdqerr_mult.tif 492 band raster geospatial data file. Producer Defined Value The recovery of apparent model error based on the product of RATIO_MOD_TOT_ERR (ratio of model error to total error ahead of statistical distribution assumption) and STDEV_TOTAL_PDQERR (total error in standard deviations using polynomial density quantile4 (PDQ4) distribution assumption). Producer Defined nan not a number Producer defined 0.079344573971361 31.442796013035 standard deviations of feet modtotrat_mult.tif 492 band raster geospatial data file. Producer Defined Value Ratio of the estimated model error in standard deviation units of feet to the total error (statistical + model + covariance between the later two error concepts) in standard deviation units of feet. The ratio is mathematically dimensionless. The ratio is completed before the ETA_OFFSET and PDQ_OFFSET corrections to the total error to assure the a matching coverage probability to the targeted prediction limits. It is assumed that the ratio between the concepts of model and total error remains fixed. Producer Defined nan not a number Producer defined 0.013749954195813 0.99832251468351 dimensionless nhgd2w_mult.tif 492 band raster geospatial data file. Producer Defined Value The depth to water, in feet, based on (1) LEV_ALT_POL_FIT (predicted water-level altitude (North American Vertical Datum of 1988, NAVD88) by statistical learning) and (2) the National Hydrogeologic Grid (NHG) (https://doi.org/10.5066/F7P84B24) 1-kilometer cell mean land-surface datum derived from a digital elevation model (DEM) (for more details on the DEM see covar/nhg/ path in covCHICOTla software at https://doi.org/10.5066/P14N9J9C). The depth to water is analogous to the lev_va (or LEV_VA) data field within the U.S. Geological Survey National Water Information System (https://doi.org/10.5066/F7P55KJN). Producer Defined nan not a number Producer defined 14.462499978404 103.55585570569 feet norerr_mult.tif 492 band raster geospatial data file. Producer Defined Value The recovery of apparent model error based on the product of RATIO_MOD_TOT_ERR (ratio of model error to total error ahead of statistical distribution assumption) and STDEV_TOTAL_NORERR (total error in standard deviations using normal distribution assumption). Producer Defined nan not a number Producer defined 3.029070959217 31.410350806453 standard deviations of feet norerrnhg_mult.tif 492 band raster geospatial data file. Producer Defined Value Predicted total error of monthly water level in units of feet on the scale of standard deviations for the assumption that a normal distribution is an appropriate model for the error derived from STDEV_TOTAL_NORERR_NHG with the addition of the standard deviation of land-surface altitude (North American Vertical Datum of 1988, NAVD88). Producer Defined nan not a number Producer defined 3.2547547047535 32.527857890164 standard deviations of feet norlwr_mult.tif 492 band raster geospatial data file. Producer Defined Value Predicted lower bounds (5th percentile), in feet, of monthly water-level altitude (North American Vertical Datum of 1988, NAVD88), of the 90-percent prediction limits for the LEV_ALT_POL_FIT based on assumption of normal distribution model using STDEV_TOTAL_NORERR as the standard deviation representing total error adjusted by the ETA_OFFSET to acquire correct coverage probability to the observed data. Producer Defined nan not a number Producer defined -135.6007587332 21.059488675231 feet norupr_mult.tif 492 band raster geospatial data file. Producer Defined Value Predicted lower bounds (95th percentile), in feet, of monthly water-level altitude (North American Vertical Datum of 1988, NAVD88), of the 90-percent prediction limits for the LEV_ALT_POL_FIT based on assumption of normal distribution model using STDEV_TOTAL_NORERR as the standard deviation representing total error adjusted by the ETA_OFFSET to acquire correct coverage probability to the observed data. Producer Defined nan not a number Producer defined -91.098717646817 69.679675740642 feet pdqerr_mult.tif 492 band raster geospatial data file. Producer Defined Value The recovery of apparent model error based on the product of RATIO_MOD_TOT_ERR (ratio of model error to total error ahead of statistical distribution assumption) and STDEV_TOTAL_PDQERR (total error in standard deviations using polynomial density quantile4 (PDQ4) distribution assumption). Producer Defined nan not a number Producer defined 3.360170959217 31.741450806453 feet pdqerrnhg_mult.tif 492 band raster geospatial data file. Producer Defined Value Predicted total error of monthly water level in units of feet on the scale of standard deviations for the assumption that a polynomial density quantile4 (PDQ4) normal distribution is an appropriate model for the error derived from STDEV_TOTAL_PDQERR with the addition of the standard deviation of land-surface altitude (North American Vertical Datum of 1988, NAVD88). Producer Defined nan not a number Producer defined 3.572345020572 32.722134714876 standard deviations of feet pdqlwr_mult.tif 492 band raster geospatial data file. Producer Defined Value Predicted lower bounds (5th percentile), in feet, of monthly water-level altitude (North American Vertical Datum of 1988, NAVD88), of the 90-percent prediction limits for the LEV_ALT_POL_FIT based on assumption of polynomial density-quantile4 distribution (PDQ4) (just PDQ for the column name) based on a less-restrictive assumption of a normal distribution model using STDEV_TOTAL_PDQERR as the standard deviation representing total error adjusted by both the ETA_OFFSET and PDQ_OFFSET to acquire correct coverage probability to the observed data. The L-kurtosis (TAU4) for the PDQ4 is recorded in LEV_POL_TAU4. Producer Defined nan not a number Producer defined -134.90208693244 20.873683039757 feet pdqupr_mult.tif 492 band raster geospatial data file. Producer Defined Value Predicted upper bounds (95th percentile), in feet, of monthly water-level altitude (North American Vertical Datum of 1988, NAVD88), of the 90-percent prediction limits for the LEV_ALT_POL_FIT based on assumption of polynomial density-quantile4 distribution (PDQ4) (just PDQ for the column name) based on a less-restrictive assumption of normal distribution model using STDEV_TOTAL_PDQERR as the standard deviation representing total error adjusted by both the ETA_OFFSET and PDQ_OFFSET to acquire correct coverage probability to the observed data. The L-kurtosis (TAU4) for the PDQ4 is recorded in LEV_POL_TAU4. Producer Defined nan not a number Producer defined -90.872036732265 69.221217972121 feet pol_mult.tif 492 band raster geospatial data file. Producer Defined Value Unique numeric values contained in each raster cell. Producer Defined nan not a number Producer defined -102.08001344177 34.279829960039 feet GS ScienceBase U.S. Geological Survey mailing address

Denver Federal Center, Building 810, Mail Stop 302

Denver CO 80225 United States 1-888-275-8747 sciencebase@usgs.gov Unless otherwise stated, all data, metadata and related materials are considered to satisfy the quality standards relative to the purpose for which the data were collected. Although these data and associated metadata have been reviewed for accuracy and completeness and approved for release by the U.S. Geological Survey (USGS), no warranty expressed or implied is made regarding the display or utility of the data for other purposes, nor on all computer systems, nor shall the act of distribution constitute any such warranty. Digital Data https://doi.org/10.5066/P14GFYQR None 20251219 Taylor Watson (Contractor) U.S. Geological Survey Researcher mailing and physical

700 W. Research Center Blvd.

Fayetteville AR 72701 United States 479-841-7567 twatson@contractor.usgs.gov FGDC Content Standard for Digital Geospatial Metadata FGDC-STD-001-1998