Molly Van Appledorn Jason J. Rohweder Nathan R. De Jager 20250724 Raster Digital Data Set https://doi.org/10.5066/P145U5QY Molly Van Appledorn Nathan R. De Jager Jason J. Rohweder Marcella Windmuller-Campione Daniel Griffin 20240711 publication Floodplain inundation is believed to be the dominant physical driver of an array of ecosystem patterns and processes in the Upper Mississippi River System (UMRS). Here, we present results of a change analysis to estimate shifts in floodplain inundation regime characteristics from 1940 to 2022 in navigation pools 3 through 10 along the Upper Mississippi River. Data were derived from a geospatial model of surface water inundation developed for the UMRS and described in Van Appledorn et al. (2021; doi: 10.1002/rra.3628) and published datasets of daily inundation depth for the growing seasons (1 April – 30 September) from 1940 to 2022 (Van Appledorn et al. 2024). We excluded areas permanently wetted (aquatic areas), surfaces in agricultural production, roads, and developed areas. Using the depth times series data, we identified inundation events as sequential days where depths were greater than zero from 1940 to 2022 at each pixel in the modeling domain. We then characterized patterns of inundation event frequency, duration, depth, and timing on annual time steps and fitted linear regression models of these patterns to assess changes over the period of record. Based on model results, we calculated change estimates to compare estimates of flood regime conditions in 2022 to those of 1940. The data are intended for use in geospatial analyses of floodplain ecosystem patterns and processes. These data were developed to describe hydrologic conditions of non-aquatic areas in Pools 3 through 10 of the Upper Mississippi River. Examples of suitable uses include stratifying regional sampling efforts or monitoring programs, providing context for interpreting fine-scale studies of local inundation patterns, or development of floodplain functional classes using additional flood metrics. Users may generate maps of individual column values by looking up values in a field of this raster's attribute table (that is, by applying ArcGIS's "Lookup" function in 3D Analyst, for example). 19400401 20220930 observed Complete Irregular Upper Mississippi River System Pool 10 -91.2151 -91.0443 43.2411 42.7806 Lithologic classification of geologic map units Flood plain USGS Thesaurus floods freshwater ecosystems river systems hydrology USGS Metadata Identifier USGS:686fef0ad4be025ce526c545 Common geographic areas Upper Mississippi None None Molly Van Appledorn U.S. Geological Survey, MIDCONTINENT REGION Research Ecologist mailing address

2630 Fanta Reed Road

La Crosse WI 54603 US 608-781-6323 608-783-6066 mvanappledorn@usgs.gov This work was funded by the U.S. Army Corps of Engineers' Upper Mississippi River Restoration Program Molly Van Appledorn Nathan R DeJager Jason Rohweder 2018 dataset https://www.sciencebase.gov U.S. Geological Survey https://doi.org/10.5066/f7vd6xrt Molly Van Appledorn Nathan R. De Jager Jason J. Rohweder 20200417 publication River Research and Applications vol. 37, issue 2 n/a Wiley ppg. 241-255 https://doi.org/10.1002/rra.3628 Molly Van Appledorn Nathan R. De Jager Jason J. Rohweder 20230803 publication Journal of the American Water Resources Association n/a n/a Wiley https://doi.org/10.1111/1752-1688.13152 R Core Team 2023 application/service https://www.R-project.org/ Lukasz Komsta 2019 application/service https://doi.org/10.32614/CRAN.package.mblm Visual spot checks completed. Data were spot checked for attribute accuracy: values fall within expected ranges and duplicates/omissions checked. Visual spot checks completed. Data were spot checked for logical accuracy: values fall within expected ranges and duplicates/omissions checked. Data are complete for Pool 10 of the Upper Mississippi River A formal accuracy assessment of the horizontal positional information in the data set has not been conducted. Data position are dependent, in part, by the UMRS Topobathy Dataset (https://doi.org/10.5066/F7057CZ3). A formal accuracy assessment of the vertical positional information in the data set has either not been conducted, or is not applicable. Data position are dependent, in part, by the UMRS Topobathy Dataset (https://doi.org/10.5066/F7057CZ3). Molly Van Appledorn Nathan R DeJager Jason Rohweder 2018 dataset https://www.sciencebase.gov U.S. Geological Survey https://doi.org/10.5066/f7vd6xrt Digital and/or Hardcopy 20220809 publication date UMRS Floodplain Inundation Model The "Value" column of the UMRS Floodplain Inundation Model (Pool 10) is the unique identifier that crosswalks with the tabular dataset of daily depths. The columns of "handval" and "SHEDID" in this raster also directly correspond to the columns of the same names in the tabular dataset of daily depths. U.S. Army Corps of Engineers 09302022 tabular digital data www.rivergages.com Digital and/or Hardcopy 19400401 20220930 publication date Gage Data key input for inundation model Molly Van Appledorn Jason Rohweder Nathan R DeJager 2024 tabular digital data https://www.sciencebase.gov U.S. Geological Survey https://doi.org/10.5066/p13brxxj Digital and/or Hardcopy 19400401 20220930 publication date Modeled water depths key input to develop water depth models We used the time series of daily inundation depths to identify sequential days of inundation, which we refer to as “events.” We then used the inundation event records to calculate the following variables on an annual time step from 1940 - 2022 at each pixel in the modeling domain: the total number of inundation events (frequency), maximum inundation depth across all events in the year, the median of all maximum event depths in the year, median event duration, maximum event duration, total number of days inundated each year during the growing season (1 April – 20 September), the day of year when inundation was first observed, and the median day of year when a maximum depth was observed across all events in a given year. We modeled changes in these variables over time by fitting median linear regression models to the time series using the Theil-Sen slope estimator without repeated measures. This method calculates the median slope from all slopes connecting pairs of x-y sample points. Model fitting was done for each variable at each pixel when inundation events were detected in at least three years. We then used fitted, statistically significant models to estimate variable values in 1940 and 2022. We calculated the estimated change in value from 1940 to 2022 by subtracting 1940 predictions from those of 2022. All statistical analyses were completed in the software R (R Core Team, 2023) using the mblm package (Komsta, 2019). UMRS Floodplain Inundation Model Gage Data Modeled water depths 20230809 Molly Van Appledorn U.S. Geological Survey, MIDCONTINENT REGION Research Ecologist mailing address

2630 Fanta Reed Road

La Crosse WI 54603 US 608-781-6323 608-783-6066 mvanappledorn@usgs.gov Raster Grid Cell 12862 3196 1 Universal Transverse Mercator 15 0.9996 -93.0 0.0 500000.0 0.0 row and column 4.0 4.0 meters North_American_Datum_1983 GRS 1980 6378137.0 298.257222101004 p10_fldinunchange.tif Raster geospatial data file. Producer defined OID Internal object identifier. Producer defined Sequential unique whole numbers that are automatically generated. Value Unique numeric values contained in each raster cell. Identical to "Value" field of the UMRS Floodplain Inundation Model of the same pool (https://doi.org/10.5066/F7VD6XRT) Producer defined 32 121841 Count Number of raster cells with this value. Producer defined 1.0 7247.0 fslp Slope of linear regression model (Theil-Sen regression) predicting event frequency by year Producer defined -999999 Model not fit due to two or fewer years with observed events Producer defined -0.162 0.114 fslpp p-value for slope of linear regression model (Theil-Sen regression) predicting event frequency by year Producer defined -999999 Model not fit due to two or fewer years with observed events Producer defined 0 0.965 fint y-intercept for linear regression model (Theil-Sen regression) predicting event frequency by year Producer defined -999999.0 Model not fit due to two or fewer years with observed events Producer defined -218.6 328.027 Number of inundation events maxdepslp Slope of linear regression model (Theil-Sen regression) predicting maximum depth by year Producer defined -999999.0 Model not fit due to two or fewer years with observed events Producer defined -0.034 0.023 maxdepslpp p-value for slope of linear regression model (Theil-Sen regression) predicting maximum depth by year Producer defined -999999.0 Model not fit due to two or fewer years with observed events Producer defined 0 1.0 maxdepint y-intercept of linear regression model (Theil-Sen regression) predicting maximum depth by year Producer defined -999999.0 Model not fit due to two or fewer years with observed events Producer defined -39.417 121.586 feet meddepslp Slope of linear regression model (Theil-Sen regression) predicting the median maximum event depth by year Producer defined -999999.0 Model not fit due to two or fewer years with observed events Producer defined -0.042 0.078 meddepslpp p-value of linear regression model (Theil-Sen regression) predicting median maximum event depth by year Producer defined -999999.0 Model not fit due to two or fewer years with observed events Producer defined 0 1.0 meddepint y-intercept of linear regression model (Theil-Sen regression) predicting median maximum event depth by year Producer defined -999999.0 Model not fit due to two or fewer years with observed events Producer defined -150.82 156.726 feet meddurslp Slope of model predicting median event duration by year Producer defined -999999 Model not fit due to two or fewer years with observed events Producer defined -0.147 2.258 meddurslpp p-value of linear regression model (Theil-Sen regression) predicting median event duration by year Producer defined -999999 Model not fit due to two or fewer years with observed events Producer defined 0 1.0 meddurint y-intercept of model predicting median event duration by year Producer defined -999999 Model not fit due to two or fewer years with observed events Producer defined -4356.98 422.3 days maxdurslp Slope of linear regression model (Theil-Sen regression) predicting maximum event duration by year Producer defined -999999 Model not fit due to two or fewer years with observed events Producer defined -0.1 1.941 maxdurslpp p-value of linear regression model (Theil-Sen regression) predicting maximum event duration by year Producer defined -999999 Model not fit due to two or fewer years with observed events Producer defined 0 1.0 maxdurint y-intercept of linear regression model (Theil-Sen regression) predicting maximum event duration by year Producer defined -999999 Model not fit due to two or fewer years with observed events Producer defined -3737.2 339.0 days gsdurslp Slope of linear regression model (Theil-Sen regression) predicting total growing season days inundated by year Producer defined -999999 Model not fit due to two or fewer years with observed events Producer defined 0 1.303 gsdurslpp p-value of linear regression model (Theil-Sen regression) predicting total growing season days inundated by year Producer defined -999999 Model not fit due to two or fewer years with observed events Producer defined 0 0.985 gsdurint y-intercept of linear regression model (Theil-Sen regression) predicting total growing season days inundated by year Producer defined -999999 Model not fit due to two or fewer years with observed events Producer defined -2468.5 183.0 days strtslp Slope of linear regression model (Theil-Sen regression) predicting event start date by year Producer defined -999999 Model not fit due to two or fewer years with observed events Producer defined -1.302 0.737 strtslpp p-value of linear regression model (Theil-Sen regression) predicting event start date by year Producer defined -999999 Model not fit due to two or fewer years with observed events Producer defined 0 1.0 strtint y-intercept of linear regression model (Theil-Sen regression) predicting event start date by year Producer defined -999999 Model not fit due to two or fewer years with observed events Producer defined -1287.2 2719.208 day of year mxdayslp Slope of linear regression model (Theil-Sen regression) predicting day of maximum depth by year Producer defined -999999 Model not fit due to two or fewer years with observed events Producer defined -1.178 0.7 mxdayslpp p-value of linear regression model (Theil-Sen regression) predicting day of maximum depth by year Producer defined -999999 Model not fit due to two or fewer years with observed events Producer defined 0 1.0 mxdayint y-intercept of linear regression model (Theil-Sen regression) predicting day of maximum depth by year Producer defined -999999 Model not fit due to two or fewer years with observed events Producer defined -1207.3 2503.867 day of year eventnum Total number of events detected from 1940 - 2022 Producer defined 2.0 913.0 events floodyrs Total number of years from 1940 to 2022 in which at least one inundation event was detected Producer defined 2.0 83.0 years maxdep1940 Maximum depth for 1940 as predicted by Theil-Sen regression model Producer defined -999999 Model not fit due to two or fewer years with observed events Producer defined -20.997 121.586 feet maxdep2022 Maximum depth estimate for 2022 as predicted by Theil-Sen regression model Producer defined -999999 Model not fit due to two or fewer years with observed events Producer defined -20.997 121.586 feet maxdeppchg Percent change in maximum depth from 1940 to 2022. Calculated by subtracting Theil-Sen regression estimate of maximum depth for 1940 from the Theil-Sen regression estimate of maximum depth for 2022, and expressed as a percent. Producer defined -999999 NAs produced when maxdep1940 = 0, or model not fit due to two or fewer years with observed events Producer defined -92.23 208.7694484 percentage maxdepm Change in maximum depth for the period 1940-2022. Calculated by subtracting Theil-Sen regression estimate of maximum depth for 1940 from the Theil-Sen regression estimate of maximum depth for 2022. Producer defined -999999 NAs produced when meddep1940 or meddep2022 is NA, or model not fit due to two or fewer years with observed events Producer defined -0.8498 0.5748528 meters meddep1940 Median of maximum event depth estimate for 1940 as predicted by Theil-Sen regression model Producer defined -999999 Model not fit due to two or fewer years with observed events Producer defined -23.598 156.726 feet meddep2022 Median of maximum event depth estimate for 2022 as predicted by Theil-Sen regression model Producer defined -999999 Model not fit due to two or fewer years with observed events Producer defined -23.598 156.726 feet meddeppchg Median maximum event depth percent change estimate for the period 1940-2022. Calculated by subtracting Theil-Sen regression estimate of median maximum event depth for 1940 from the Theil-Sen regression estimate of median maximum event depth for 2022, and expressed as a percent. Producer defined -999999 NAs produced when meddep1940 = 0, or model not fit due to two or fewer years with observed events Producer defined -6833.33 27333.33333 percentage meddepm Median maximum event depth change estimate for the period 1940-2022. Calculated by subtracting Theil-Sen regression estimate of median maximum event depth for 1940 from the Theil-Sen regression estimate of median maximum event depth for 2022 Producer defined -999999 NAs produced when meddep1940 or meddep2022 is NA, or model not fit due to two or fewer years with observed events Producer defined -1.050 1.9495008 meters meddur1940 Median event duration estimate for 1940 as predicted by Theil-Sen regression model Producer defined -999999 Model not fit due to two or fewer years with observed events Producer defined -423.67 422.3755 day meddur2022 Median event duration estimate for 2022 as predicted by Theil-Sen regression model Producer defined -999999 Model not fit due to two or fewer years with observed events Producer defined -423.67 422.375 day meddurpchg Median event duration percent change estimate for the period 1940-2022. Calculated by subtracting Theil-Sen regression estimate of median event duration for 1940 from the Theil-Sen regression estimate of median event duration for 2022, and expressed as a percentage. Producer defined -999999 NAs produced when meddur1940 = 0, or model not fit due to two or fewer years with observed events Producer defined -3824.42 5627.789474 percentage meddurdays Median event duration change estimate for the period 1940-2022. Calculated by subtracting Theil-Sen regression estimate of median event duration for 1940 from the Theil-Sen regression estimate of median event duration for 2022. Producer defined -999999 NAs produced when meddur1940 or meddur2022 is NA, or model not fit due to two or fewer years with observed events Producer defined -12.054 185.156 day maxdur1940 Maximum event duration estimate for 1940 as predicted by Theil-Sen regression model Producer defined -999999 Model not fit due to two or fewer years with observed events Producer defined -423.667 339.0 day maxdur2022 Maximum event duration estimate for 2022 as predicted by Theil-Sen regression model Producer defined -999999 Model not fit due to two or fewer years with observed events Producer defined -423.67 339.0 day maxdurpchg Maximum event duration percent change estimate for 1940-2022. Calculated by subtracting Theil-Sen regression estimate of maximum event duration for 1940 from the Theil-Sen regression estimate of maximum event duration for 2022, and expressed as a percentage. Producer defined -999999 NAs produced when maxdur1940 = 0, or model not fit due to two or fewer years with observed events Producer defined -46.62 615.0 percentage maxdurdays Maximum event duration change estimate for 1940-2022. Calculated by subtracting Theil-Sen regression estimate of maximum event duration for 1940 from the Theil-Sen regression estimate of maximum event duration for 2022. Producer defined -999999 NAs produced when maxdur1940 or maxdur2022 is NA, or model not fit due to two or fewer years with observed events Producer defined -8.2 159.162 day gsdur1940 Total growing season duration estimate for 1940 as predicted by Theil-Sen regression model Producer defined -999999 Model not fit due to two or fewer years with observed events Producer defined 0 183.0 day gsdur2022 Total growing season duration estimate for 2022 as predicted by Theil-Sen regression model Producer defined -999999 Model not fit due to two or fewer years with observed events Producer defined 0 190.25 day gsdurpchg Total growing season duration percent change estimate for 1940-2022. Calculated by subtracting Theil-Sen regression estimate of total growing season duration for 1940 from the Theil-Sen regression estimate of total growing season duration for 2022, and expressed as a percentage. Producer defined -999999 NAs produced when gsdur1940 = 0, or model not fit due to two or fewer years with observed events Producer defined 0 204.2145594 percentage gsdurdays Total growing season duration change estimate for 1940-2022. Calculated by subtracting Theil-Sen regression estimate of total growing season duration for 1940 from the Theil-Sen regression estimate of total growing season duration for 2022. Producer defined -999999 NAs produced when gsdur1940 or gsdur2022 is NA, or model not fit due to two or fewer years with observed events Producer defined 0 106.846 day time1940 Timing of maximum depth estimates in 1940 as predicted by Theil-Sen regression model Producer defined -999999 Model not fit due to two or fewer years with observed events Producer defined -272.7 468.792 day of year time2022 Timing of maximum depth estimates in 2022 as predicted by Theil-Sen regression model Producer defined -999999 Model not fit due to two or fewer years with observed events Producer defined -272.7 468.792 day of year timepchg Percent change of timing of maximum depth from 1940 to 2022. Calculated by subtracting Theil-Sen regression estimate of timing of maximum depth for 1940 from the Theil-Sen regression estimate of timing of maximum depth for 2022, expressed as a percentage. Negative values indicate earlier timing in 2022 compared to 1940 and positive values indicate later timing in 2022 compared to 1940. Producer defined -999999 NAs produced when time1940 = 0, or model not fit due to two or fewer years with observed events Producer defined -44.2 38.08891838 percentage timedoy Change of timing of maximum event depth from 1940 to 2022. Calculated by subtracting Theil-Sen regression estimate of timing of maximum depth for 1940 from the Theil-Sen regression estimate of timing of maximum depth for 2022. Negative values indicate earlier timing in 2022 compared to 1940 and positive values indicate later timing in 2022 compared to 1940. Producer defined -999999 NAs produced when time1940 or time2022 is NA, or model not fit due to two or fewer years with observed events Producer defined -96.6 57.4 day pool Upper Mississippi River System navigation pool label Producer defined p10 Navigation Pool 10 Producer defined U.S. Geological Survey GS ScienceBase 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 on any other system or for general or scientific purposes, nor shall the act of distribution constitute any such warranty. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government. Digital Data https://doi.org/10.5066/P145U5QY None 20250724 Jason Rohweder U.S. Geological Survey, MIDCONTINENT REGION Biologist mailing address

2630 Fanta Reed Road

La Crosse WI 54603 US 608-781-6228 608-783-6066 jrohweder@usgs.gov FGDC Biological Data Profile of the Content Standard for Digital Geospatial Metadata FGDC-STD-001.1-1999