Se Jong Cho 20251119 .R, .csv. Baltimore, MD U.S. Geological Survey https://doi.org/10.5066/P1492CKA 3_Hysteresis_PCA_LDA is the third child item of this Data Release. Principal Component Analysis (PCA) was performed to reduce the dimensionality of streamflow event variables and to reveal the underlying patterns in hydrodynamic and turbidity characteristics in the Delaware River Basin (DRB) and Illinois River Basin (IRB). Linear Discriminant Analysis (LDA) was performed to identify the streamflow event characteristics and watershed-scale landscape feature variables that influence Hysteresis Index - Concentration Index (HI-CI) classes in each region. An annotated R script is included for each analysis. Additional information can be found in the "Entity and Attribute" section. This research aims to improve understanding of the complex sediment transport process and can lead to more informed inferences about sediment dynamics, hydrology, source, and in channel processes as well as improved predictions of sediment transport. Suspended sediment transport is critical to understanding future states of water quality and represents an important Integrated Water Science (IWS) basin effort. Delaware River Basin (DRB) and Illinois River Basin (IRB) are two of the IWS basins with a wide range of environmental, hydrologic, and landscape settings and human stressors of water resources. USGS stream gaging at 35 watersheds within these two regional Basins were selected to evaluate the storm events and the corresponding sediment and hydrodynamic observations. 2007 2023 ground condition Complete None planned -92.3730 -71.5430 44.6530 36.3151 ISO 19115 Topic Category inlandWaters climatologyMeteorologyAtmosphere geoscientificInformation environment None Machine Learning Daymet Principal Component Analysis Linear Discriminant Analysis USGS Thesaurus sediment transport turbidity datasets streamflow USGS Metadata Identifier USGS:6827b468d4be02693eeabdf7 Common geographic areas Illinois Indiana New York New Jersey Pennsylvania Delaware No access constraints. Please see 'Distribution Information' for details. These data are marked with a Creative Common CC0 1.0 Universal License. These data are in the public domain and do not have any use constraints. Users are advised to read the dataset's metadata thoroughly to understand appropriate use and data limitations. Se Jong Cho USGS - WATER Research Hydrologist mailing and physical

5522 Research Park Drive

Baltimore MD 21228 703-648-5714 scho@usgs.gov Verified that attribute values were within expected bounds. No formal logical accuracy tests were conducted Data set is considered complete for the information presented, as described in the abstract. Users are advised to read the rest of the metadata record carefully for additional details. A formal accuracy assessment of the horizontal positional information in the data set has not been conducted. A formal accuracy assessment of the vertical positional information in the data set has either not been conducted, or is not applicable. Processing described for each dataset in the Entity and Attribute section 2025 3_Hysteresis_PCA_LDA\PCA\PCA_HICI_temporal_vars.R R script to conduct Principal Component Analysis using hydrodynamic temporal variables. Detailed processing steps are noted in the R script PCA_HICI_temporal_vars.R. The script imports storm event variables and executes Principal Component Analysis (PCA) iterations using relevant variables 1. Intake event data output from Step 2 of data release and define initial PCA dataframe (Tdf) 2. Execute initial PCA with dataframe Tdf 3. Execute PCA with filtered dataframe Tdf1 4. Execute PCA with filter dataframe TDf2 (this is the final PCA reported in the publication) 5. Execute PCA for individual 5 HICI classes: PosHCI, PosLCI, NegHCI, NegLCI, and Lin U.S. Geological Survey 3_Hysteresis_PCA_LDA\LDA\LDA_HICI_temporalspatial_vars.R R script to conduct Linear Discriminant Analysis using hydrodynamic variables and landscape attributes. Detailed processing steps are noted in the R script LDA_HICI_temporalspatial_vars.R The script imports storm event variables and executes Linear Discriminant Analysis (LDA) iterations using relevant variables 1. Intake event data output from Step 2 of data release and define initial LDA dataframe (TSdf) 2. Site selection: conduct LDA in DRB and IRB 3. Conduct hierarchical cluster analysis 4. Conduct perMANOVA test 5. Execute stepwise LDA feature selection 6. Execute LDA predictive model U.S. Geological Survey 3_Hysteresis_PCA_LDA\PCA_LDA_shortnames.csv Comma Separated Value (CSV) file containing the cross walk table for column names defined in (2_EventSeparation_HysteresisIndex\1_HydRun_Millar_cQ_functions\HI_EventSummary_FeatureDictionary.csv) to shortened column names used in child item 3_Hysteresis_PCA_LDA. U.S. Geological Survey col_names_EventSummary_final Column names defined in (2_EventSeparation_HysteresisIndex\1_HydRun_Millar_cQ_functions\HI_EventSummary_FeatureDictionary.csv) U.S. Geological Survey See Column Definition col_names_PCALDA Shortened column names used in child item 3_Hysteresis_PCA_LDA. U.S. Geological Survey See Column Definition U.S. Geological Survey - ScienceBase mailing address

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Denver CO 80225 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/P1492CKA None 20251119 Se Jong Cho USGS - WATER Research Hydrologist mailing and physical

5522 Research Park Drive

Baltimore MD 21228 703-648-5714 scho@usgs.gov FGDC Content Standard for Digital Geospatial Metadata FGDC-STD-001-1998