Roy E. Petrakis Laura M. Norman Florance Cassassuce Florent Gomis Thomas J. Mack 20240816 vector digital data Petrakis, R. E., Norman, L. M., Cassassuce, F., Gomis, F., Mack, T. J. (2024). Paired Watershed and Characteristic Trait Data for the Los Planes Basin Research Ranch Watersheds: U.S. Geological Survey Data Release. https://doi.org/10.5066/P14VSM4G. https://doi.org/10.5066/P14VSM4G Watershed pairing is an approach used to assess effects of restoration on a landscape by comparing conditions across a treatment watershed and a control watershed, and then quantifying the differences. When the watersheds are statistically paired, the control watershed can serve as a baseline of conditions compared to the treatment watershed. We apply a watershed pairing analysis across a series of five watersheds within a research ranch in Baja California Sur, Mexico. We use a Digital Elevation Model (DEM) to develop the watershed boundaries. We then collected a series of structural and biophysical traits to characterize the watersheds. Finally, we applied a hierarchical clustering analysis to identify the paired watersheds. This data release consists of a shapefile of the five watersheds with attributes for each of the structural and biophysical traits, in addition to an attribute identifying the direct pairs. It can be challenging to quantify the effects of watershed restoration on the landscape. Watershed pairing applies a statistical analysis using watershed characteristic traits to identify watersheds that are more similar, where one watershed serves as a control and another watershed serves as a treatment. Thus, changes in the treatment watershed can be compared to baseline conditions in the control watershed. We apply a hierarchical clustering approach based on structural and biophysical characteristic traits for five watersheds within a research ranch in Baja California Sur, Mexico. Specifically, we aimed to identify a paired watershed for our treatment watershed. This release includes a spatial layer for the study watersheds. 202305 observed Complete None planned -110.1309 -109.8452 24.1211 23.6627 ISO 19115 Topic Category environment USGS Thesaurus geographic information systems remote sensing hierarchical watershed management remediation Theme watershed pairing hierarchical clustering correlation structural traits biophysical traits USGS Metadata Identifier USGS:6643d150d34e1955f5a426e0 None Mexico Baja California Baja California Sur None. Please see 'Distribution Info' for details. Any products or analysis developed from these data should cite this data release. Roy E. Petrakis U.S. Geological Survey, SOUTHWEST REGION Physical Scientist mailing

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Tucson AZ 85719 US 520-670-6671 520-670-5113 rpetrakis@usgs.gov No formal attribute accuracy tests were conducted. No formal attribute 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. Instituto Nacional de Estadística y Geografía 2023 raster digital data INEGI, 2023. Instituto Nacional de Estadística y Geografía (INEGI) 5-m Digital Elevation Model (DEM). https://www.inegi.org.mx/contenidos/temas/mapas/relieve/continental/metadatos/mde.pdf Digital and/or Hardcopy 2023 observed 5-m Digital Elevation Model (DEM) The 5-m DEM was used to measure watershed characteristic traits as well as to product second-level products including aspect, slope, and the watershed boundaries. Commission for Environmental Cooperation 2015 raster digital data Commission for Environmental Cooperation, 2015. Commission for Environmental Cooperation: 2015 North American Land Change Monitoring System. http://www.cec.org/north-american-environmental-atlas/land-cover-30m-2015-landsat-and-rapideye/ Digital and/or Hardcopy 2015 observed 2015 Land Use/Land Cover (LULC) The 2015 Land Use/Land Cover dataset was used to derive the percentage of shrubland and forest cover for the watersheds. Pamela L. Nagler Armando Barreto-Muñoz Ibrahima Sall Matthew R. Lurtz Kamel Didan 20221222 publication Remote Sensing vol. 15, issue 1 n/a MDPI AG Nagler, P.L., Barreto-Muñoz, A., Sall, I., Lurtz, M.R., Didan, K., 2022. Riparian Plant Evapotranspiration and Consumptive Use for Selected Areas of the Little Colorado River Watershed on the Navajo Nation. Remote Sensing 15, 52. https://doi.org/10.3390/rs15010052 https://doi.org/10.3390/rs15010052 Digital and/or Hardcopy 202003 202010 observed Nagler-ET(EVI2) Actual Evapotranspiration (ETa) The Nagler-ET(EVI2) ETa algorithm was used to develop the wet and dry season evapotranspiration estimates for the watersheds. The first processing step was to collect the the characteristic trait data for each of the watersheds. The data represents the two following themes: (1) structural and (2) biophysical. For each trait, we collected data values respective of each of the five watersheds. The structural data was entirely developed using the 5-m Digital Elevation Model (DEM) as the source product. First, we developed the watersheds using the Basin tool in ArcGIS 10.8 and a flow length product using derivatives of the DEM (i.e., fill, flow direction, flow accumulation). With each watershed, we identified width, length, area, perimeter, and total flow length. Second, we used the DEM to collect maximum, minimum, and mean elevation for each watershed. Third, using secondary products developed from the DEM, including aspect and slope, we then collected percentage of watershed area representing various slope and aspect classes. Specifically, the slope classes were low (i.e., < 11.6°), moderate (i.e., 11.6° to 20.5°), and steep (i.e., > 20.5°). The aspect classes were north facing (i.e., 316° to 360°; 0° to 45°), east facing (i.e., 46° to 135°), south facing (i.e., 136° to 225°), and west facing (i.e., 226° to 315°). The biophysical traits were developed using the 2015 Land Use/Land Cover (LULC) product, the Nagler-ET(EVI2) Actual Evapotranspiration (ETa) algorithm, and a multi-year Normalized Difference Vegetation Index (NDVI) product. First, we derived percentage of watershed area metrics for the tropical forest and shrubland classes from the 2015 LULC product. Second, we collected wet and dry season mean ETa for each of the watersheds using an ETa product developed from the Nagler-ET(EVI2) algorithm. Dry season ETa was based on an image from March 28, 2020, while wet season ETa was based on an image from October 6, 2020. Finally, we derived multi-year wet and dry season NDVI products. For both the wet and dry seasons, we collected the maximum, minimum, and mean values for the watersheds. We also calculated the difference between the wet and dry season NDVI values. The dry season represents a 5-year (i.e., 2017, 2018, 2019, 2020, 2021) average for the months of May and June. The wet season represents a 5-year (i.e., 2017, 2018, 2019, 2020, 2021) average for the months of September and October. 5-m Digital Elevation Model (DEM) 2015 Land Use/Land Cover (LULC) Nagler-ET(EVI2) Actual Evapotranspiration (ETa) 202305 Roy E. Petrakis U.S. Geological Survey, SOUTHWEST REGION Physical Scientist mailing

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Tucson AZ 85719 US 520-670-6671 520-670-5113 rpetrakis@usgs.gov The second processing step was to complete the correlation analysis and variable reduction, in addition to the hierarchical clustering of the watersheds. We completed these steps using R Software. First, variable reduction is important with hierarchical clustering as a way to reduce the dimensionality of that dataset and to limit effects of double bumping by similar traits. This can be done through a correlation analysis. Specifically, we calculated Pearson correlations between each of the included characteristic traits using the “cor” function in R Software. We completed this step by reviewing the structural and biophysical variables separately. We then removed traits that were highly correlated and combined the remaining structural and biophysical variables into a suite of selected variables. Ultimately, we reduced the number of structural traits from 15 to 10 and the number of biophysical traits from 11 to 4 using the correlation analysis. Then, using the selected variables, we completed a hierarchical clustering analysis using the "cluster" and "factoextra" packages in R Software. Specifically, we used the "agglomerative nesting" function (i.e., agnes function) to complete the clustering analysis. We applied the euclidean metric with the ward method, which had the highest agglomerative coefficient value. 202305 Roy E. Petrakis U.S. Geological Survey, SOUTHWEST REGION Physical Scientist mailing

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Tucson AZ 85719 US 520-670-6671 520-670-5113 rpetrakis@usgs.gov Vector G-polygon 1 Universal Transverse Mercator 12 0.9996 -111.0 0.0 500000.0 0.0 coordinate pair 0.6096 0.6096 meters North_American_Datum_1983 GRS 1980 6378137.0 298.257222101 ResearchRanch_Watersheds.shp Attribute Table Table containing attribute information associated with the research ranch watersheds. Producer Defined FID Internal feature number. ESRI Sequential unique whole numbers that are automatically generated. Shape Feature geometry. ESRI Shape type. Watershed The watershed identification number. Producer Defined 1 5 Cluster The identified cluster for the watershed. Producer Defined 1 3 Per_Low Percent of watershed area classified as low slope (i.e., < 11.6°). Producer Defined 27.5 45.26 % Per_Mod Percent of watershed area classified as moderate slope (i.e., < 11.6° to 20.5°). Producer Defined 38.58 53.11 % Per_Steep Percent of watershed area classified as moderate slope (i.e., > 20.5°). Producer Defined 15.38 29.63 % Per_North Percent of the watershed area facing north (i.e., 316° to 360°; 0° to 45°). Producer Defined 8.15 36.27 % Per_East Percent of the watershed area facing east (i.e., 46° to 135°). Producer Defined 27.84 39.74 % Per_South Percent of the watershed area facing south (i.e., 136° to 225°). Producer Defined 14.36 41.13 % Per_West Percent of the watershed area facing west (i.e., 226° to 315°). Producer Defined 4.04 29.07 % Length An estimation of length of the watershed from the top of the flowline to the bottom. Producer Defined 0.87 1.78 m Width An estimation of width of the watershed measured from one side to the other in the middle of the watershed. Producer Defined 0.495 1.45 m Area Area of the watershed. Producer Defined 0.2419 1.50223 sq. m Perimeter Perimeter of the watershed. Producer Defined 2.99 7.51 m Flow_Leng Length of the flowlines. Producer Defined 1512.210858 11293.50901 m Min_Elev Lowest elevation of the watershed. Producer Defined 581.390015 641.409973 m Max_Elev Highest elevation of the watershed. Producer Defined 716.859985 829.890015 m Mean_Elev Average elevation of the watershed. Producer Defined 632.438765 710.193342 m Dry_ETa Average actual evapotranspiration (ETa) for the watershed on March 28, 2020, representing the dry season. Producer Defined 3.075033 3.639948 mm/day Wet_ETa Average actual evapotranspiration (ETa) for the watershed on October 6, 2020, representing the wet season. Producer Defined 5.015507 6.160497 mm/day D_NDVIMin Minimum watershed Normalized Difference Vegetation Index (NDVI) value based on a 5-year average for May and June (i.e., 2017, 2018, 2019, 2020, 2021), representing the dry (i.e., D) season. Producer Defined 0.122559 0.15673 Unitless D_NDVIMax Maximum watershed Normalized Difference Vegetation Index (NDVI) value based on a 5-year average for May and June (i.e., 2017, 2018, 2019, 2020, 2021), representing the dry (i.e., D) season. Producer Defined 0.238104 0.379398 Unitless D_NDVIMean Average watershed Normalized Difference Vegetation Index (NDVI) value based on a 5-year average for May and June (i.e., 2017, 2018, 2019, 2020, 2021), representing the dry (i.e., D) season. Producer Defined 0.187388 0.211061 Unitless W_NDVIMin Minimum watershed Normalized Difference Vegetation Index (NDVI) value based on a 5-year average for September and October (i.e., 2017, 2018, 2019, 2020, 2021), representing the wet (i.e., W) season. Producer Defined 0.158061 0.299314 Unitless W_NDVIMax Maximum watershed Normalized Difference Vegetation Index (NDVI) value based on a 5-year average for September and October (i.e., 2017, 2018, 2019, 2020, 2021), representing the wet (i.e., W) season. Producer Defined 0.676461 0.697073 Unitless W_NDVIMean Average watershed Normalized Difference Vegetation Index (NDVI) value based on a 5-year average for September and October (i.e., 2017, 2018, 2019, 2020, 2021), representing the wet (i.e., W) season. Producer Defined 0.41266 0.538916 Unitless NDVI_Diff Difference between the dry season mean Normalized Difference Vegetation Index (NDVI) value and the wet season mean NDVI value. Producer Defined 0.225272 0.328491 Unitless Per_Forest Percent of watershed area classified as tropical forest land use/land cover. Producer Defined 88.33 97.5 % Per_Shrub Percent of watershed area classified as shrubland land use/land cover. Producer Defined 2.5 11.67 % U.S. Geological Survey GS ScienceBase mailing address

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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/P14VSM4G None 20240816 Roy E. Petrakis U.S. Geological Survey, SOUTHWEST REGION Physical Scientist mailing

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Tucson AZ 85719 US 520-670-6671 520-670-5113 rpetrakis@usgs.gov FGDC Content Standard for Digital Geospatial Metadata FGDC-STD-001-1998