Roy E. Petrakis Barry R. Middleton 20260128 tabular digital data ScienceBase Data Repository U.S. Geological Survey https://doi.org/10.5066/P13E3LFM Ciénegas are wetlands in arid and semi-arid regions of North America. We quantified changes in ciénega condition and floodplain dynamics using the detailed boundaries of 31 ciénegas and their respective controls. This data release is a series of four data collections consisting of 68 tabular data files (.csv) that include the following: (i) “i_Cienega_Timeseries_CSVs.zip” data collection consisting of 31 tabular data files (i.e., one for each ciénega) displaying yearly January to June precipitation and 31 tabular data files (i.e., one for each ciénega) displaying a seasonal time series of NDVI, NDII, and precipitation; (ii) “ii_Cienega_Slope_CSVs.zip” data collection consisting of 1 tabular data file displaying seasonal Sen’s slope results and corresponding p-values for NDVI, NDII, and precipitation for each ciénega, and it’s respective control, on a seasonal time scale; (iii) “iii_Histogram”CSVs.zip” data collection consisting of 4 tabular data files displaying Sen’s slope and corresponding p-values of high greenness conditions for each ciénega, yearly time series of high greenness conditions for each ciénega, seasonal histogram statistics for each ciénega, and the NDVI and NDII scores for 2023; and (iv) “iv_Staitstical_Variable_Assessment.zip” data collection consisting of 1 tabular data file containing data variables for each ciénega. This data was used to quantify changes in ciénega condition and floodplain dynamics for 31 ciénegas in the Greater Madrean Archipelago Ecoregion from 1985-2023. This data is provided to increase repeatability of the work presented in associated journal publication: Norman, L. M., Petrakis, R. E., Wilson, N. R., Middleton B. R., Villarreal, M. L., Pollock, M., Minckley, T. A., Hendrickson, D. (2026 – in review). Satellite Time Series Analysis to Quantify Changing Climax Ciénegas Using a State and Transition Model Approach. Ecological Indicators. 19850101 20231231 observed Complete None planned -111.3658 -108.7430 33.5624 31.2915 ISO 19115 Topic Category biota USGS Thesaurus wetland ecosystems wetland functions None Ciénega USGS Metadata Identifier USGS:67d05a7ed34ecfe34cc877a4 Common Geographic Areas Arizona New Mexico Sonora None Madrean Archipelago Ecoregion None. Please see 'Distribution Info' for details. Users should not use this data for critical applications without full awareness of its limitations. Acknowledgement of the originating agencies would be appreciated in products derived from these data. Any user who modifies the data is obligated to describe the types of modifications they perform. User specifically agrees not to misrepresent the data, nor to imply that changes made were approved or endorsed by the U.S. Geological Survey. Roy E Petrakis USGS - SOUTHWEST REGION Geographer mailing and physical

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Tucson AZ 85719 520-670-6671 rpetrakis@usgs.gov This research was conducted by the U.S. Geological Survey (USGS), Western Geographic Science Center, Aridlands Water Harvesting Study with support from the USGS National Land Imaging Program under the USGS Core Science Systems Mission Area. The following software programs were used to develop the data in this release: (i) Google Earth Engine (GEE) online program, (ii) R software version 4.4.1 in R Studio, (iii) ArcGIS Pro version 3.5.2, (iv) Windows, (v) “sens.slope” function available in the trend package version 1.1.6, (vi) raster package 3.6-32. Compton J. Tucker 19790501 publication Remote Sensing of Environment vol. 8, issue 2 n/a Elsevier BV Tucker, C.J., 1979. Red and Photographic Infrared Linear Combinations for Monitoring Vegetation. Remote Sensing of Environment 8, 127–150. https://doi.org/10.1016/0034-4257(79)90013-0 https://doi.org/10.1016/0034-4257(79)90013-0 Michael A. Hardisky Franklin C. Daiber Charles T. Roman Vytautas Klemas 19841001 publication Remote Sensing of Environment vol. 16, issue 2 n/a Elsevier BV Hardisky, M.A., Daiber, F.C., Roman, C.T., Klemas, V., 1984. Remote sensing of biomass and annual net aerial primary productivity of a salt marsh. Remote Sensing of Environment 16, 91–106. https://doi.org/10.1016/0034-4257(84)90055-5 https://doi.org/10.1016/0034-4257(84)90055-5 Earth Resources Observation and Science (EROS) Center 2021 fact sheet U.S. Geological Survey, 2021. Landsat Collection 2 (Fact Sheet), Fact Sheet. U.S. Geological Survey, Earth Resources Observation and Science (EROS) Center. https://doi.org/10.3133/fs20213002. https://doi.org/10.3133/fs20213002 Christopher Daly Michael Halbleib Joseph I. Smith Wayne P. Gibson Matthew K. Doggett George H. Taylor Jan Curtis Phillip P. Pasteris 20080312 publication International Journal of Climatology vol. 28, issue 15 n/a Wiley Daly, C., Halbleib, M., Smith, J.I., Gibson, W.P., Doggett, M.K., Taylor, G.H., Curtis, J., Pasteris, P.P., 2008. Physiographically sensitive mapping of climatological temperature and precipitation across the conterminous United States. Int. J. Climatol. 28, 2031–2064. https://doi.org/10.1002/joc.1688 https://doi.org/10.1002/joc.1688 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. PRISM Climate Group 2025 raster digital data https://prism.oregonstate.edu Oregon State University To access via Google Earth Engine: https://developers.google.com/earth-engine/datasets/catalog/OREGONSTATE_PRISM_AN81m#bands https://prism.oregonstate.edu Digital and/or Hardcopy 19850101 20231231 observed PRISM Climate Data The PRISM Climate Data source input data product was used to derive precipitation values in Google Earth Engine cloud computing software for each of the ciénegas and their respective control. Barry R. Middleton Laura M. Norman Roy E. Petrakis 20250320 Dataset https://www.sciencebase.gov U.S. Geological Survey https://doi.org/10.5066/p13rbztu Digital and/or Hardcopy 2025 observed Ciénega Polygons The ciénega polygons were used as the boundary to identify the spatial extent of each ciénega and its respective control. Earth Resources Observation and Science (EROS) Center 20201202 raster digital data https://www.usgs.gov U.S. Geological Survey To access via Google Earth Engine: Landsat 4 -- https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LT04_C02_T1_L2; Landsat 5 -- https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LT05_C02_T1_L2. https://doi.org/10.5066/p9iaxovv Digital and/or Hardcopy 19850101 20120505 observed Landsat 4/5 Landsat 4/5 was used to derive the Normalized Difference Vegetation Index (NDVI) and Normalized Difference Infrared Index (NDII). Earth Resources Observation and Science (EROS) Center 20201202 raster digital data https://www.usgs.gov U.S. Geological Survey To access via Google Earth Engine: Landsat 7 -- https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LE07_C02_T1_L2. https://doi.org/10.5066/p9c7i13b Digital and/or Hardcopy 19990101 20121031 observed Landsat 7 Landsat 7 was used to derive the Normalized Difference Vegetation Index (NDVI) and Normalized Difference Infrared Index (NDII). Earth Resources Observation and Science (EROS) Center 20201202 raster digital data https://www.usgs.gov U.S. Geological Survey To access via Google Earth Engine: Landsat 8 --https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LC08_C02_T1_L2; Landsat 9 -- https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LC09_C02_T1_L2 https://doi.org/10.5066/p9ogbgm6 Digital and/or Hardcopy 20130318 20231231 observed Landsat 8/9 Landsat 8/9 was used to derive the Normalized Difference Vegetation Index (NDVI) and Normalized Difference Infrared Index (NDII). U.S. Geological Survey 2024 raster digital data Science Data Catalog (SCD) U.S. Geological Survey https://www.usgs.gov/the-national-map-data-delivery https://www.usgs.gov/3d-elevation-program Digital and/or Hardcopy 2024 observed Digital Elevation Model The DEM was used to identify an elevation value for each ciénega. This analysis was completed on a seasonal time scale. Based on regional, study site, and ciénega site trends, the six two-month climate seasons were meant to capture unique hydro-climatic traits that drive vegetation responses: 1. Jan/Feb – Winter (wet/cold), 2. Mar/Apr – Spring (dry/warm), 3. May/Jun – Pre-Monsoon (arid/hot), 4. Jul/Aug – Summer Monsoon (wet/hot), 5. Sep/Oct – Late Summer /End of Monsoon, tropical storm season (variable precipitation/warm), 6. Nov/Dec – Fall/Winter (wet/cool). First, we developed the seasonal imagery for the vegetation indices using Google Earth Engine (GEE). For all Landsat 4/5, 7, and 8/9 images, we applied a Landsat-based cloud mask within the associated data Landsat products and masked for clouds, snow, and cloud shadow using the pixel quality bitmask (i.e., “QA_Pixel”) band. The function we applied is provided below. We then applied two spectral indices to all images: the Normalized Difference Vegetation Index (NDVI) and the Normalized Difference Infrared Index (NDII). The NDVI is a measure of vegetation greenness and is calculated using the following equation: NDVI = (Near Infrared Band - Red Band) / (Near Infrared Band + Red Band). The NDII is a measure of canopy and soil moisture and is calculated using the following equation: NDII = (Near Infrared - Shortwave Infrared 1) / (Near Infrared + Shortwave Infrared 1). Seasonal, two-month, mean images were created by averaging NDVI or NDII values for the spectral index images within the two-month period. The seasonal vegetation index products were then downloaded from GEE and clipped to both the ciénega and control sites for all 31 ciénega for further analysis. function maskClouds(img) { var qa = img.select(['QA_Pixel']); var clouds = qa.bitwiseAnd(8).neq(0).or // Cloud (0 = clear, 1 = contamination) (qa.bitwiseAnd(16).neq(0)).or // Cloud Shadow (qa.bitwiseAnd(32).neq(0)); // Snow return img.updateMask(clouds.eq(0)); // Add band of contaminated pixels } Landsat 4/5 Landsat 7 Landsat 8/9 Ciénega Polygons 202410 Roy E Petrakis USGS - SOUTHWEST REGION Geographer mailing and physical

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Tucson AZ 85719 520-670-6671 rpetrakis@usgs.gov For precipitation, data was collected using the PRISM Climate Data monthly precipitation dataset on a seasonal scale (i.e., two-month climate seasons) in GEE then downloaded and clipped to the bounding box for each ciénega. Using R Software version 4.4.1 and ArcGIS Pro version 3.5.2, we developed two precipitation-based time series products. First, summed precipitation for each season was calculated by averaging across all PRISM pixels covering the area of the ciénega, thus representing mean precipitation for each season. Second, for each year, respective of ciénega, we summed January to June precipitation by adding the precipitation values for the Winter, Spring, and Pre-Monsoon seasons (i.e., January through June). These precipitation data products are part of this data release and are in the folder i_Cienega_Timeseries_CSVs/Seasonal_Timesseries/ and .../JanJune_Precipitation; the data is separated based on ciénega. PRISM Climate Data Ciénega Polygons 202410 Roy E Petrakis USGS - SOUTHWEST REGION Geographer mailing and physical

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Tucson AZ 85719 520-670-6671 rpetrakis@usgs.gov In ArcGIS Pro version 3.5.2,, we used the "Zonal Statistics as Table" tool to produce descriptive statistics for each ciénega and control polygon for each image in Seasonal Mean Images. This step was developed using a raster iterator application and produces a series of tabular “.dbf” files for each ciénega and control for each date Then, using R Software version 4.4.1 in R Studio, a function was produced that collects and temporally aligns the mean NDVI, mean NDII, and mean precipitation value for all seasons over the study period (i.e., 1985 – 2023) for each ciénega and control for each date and develops a CSV time series, respective for each ciénega/control pair. These data are part of this data release and are in the folder i_Cienega_Timeseries_CSVs/Seasonal_Timesseries/; the data is separated based on ciénega. Ciénega Polygons 202503 Roy E Petrakis USGS - SOUTHWEST REGION Geographer mailing and physical

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Tucson AZ 85719 520-670-6671 rpetrakis@usgs.gov Using R software version 4.4.1 in R Studio, linear slope estimates for each spectral index (i.e., NDVI, NDII) and precipitation were calculated across both the full time series (i.e., 1985 to 2023) and each season from 1985 to 2023 using the Sen's slope. Specifically, we used the “sens.slope” function available in the trend package version 1.1.6 to calculate slopes, which produce a slope estimate and an associated p-value. These values were provided for all ciénegas in a CSV table in this data release and are in the folder ii_Cienega_Slope_CSVs.zip; the data is identified based on a series of attributes describing the ciénega, location, and season Ciénega Polygons 202503 Roy E Petrakis USGS - SOUTHWEST REGION Geographer mailing and physical

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Tucson AZ 85719 520-670-6671 rpetrakis@usgs.gov Using R software version 4.4.1 in R Studio, we completed multiple analyses that were based on the distribution of pixel values of the seasonal spectral index imagery. These analyses were applied across the 39-year study period as well as specifically in 2023 to show most recent conditions. For NDII, we quantified a metric called the NDII Season Score. For each season, a 39-year image was created by calculating the by-pixel mean over all 39 of the NDII composites for a particular season for a single ciénega. This was completed using the raster package, where all images from single season over the 39-year period were stacked and then averaged, calculating a 39-year mean value for each pixel. Using this product, we calculated the mean and standard deviation values for the ciénega polygon and applied those values to develop the NDII Season Score. Specifically, for each season, if the mean value, mean value plus standard deviation, or mean value minus standard deviation was greater than 0, a score of 1 was applied, allowing for a score up to 3 for each season. When all six seasons were considered, the score would range from 0 to 18. This analysis was also applied to only the 2023 images (i.e., a single year, not a multi-year mean) to measure the latest conditions in the study period. In addition to the 39-year NDII mean image, we completed the analysis for NDVI to quantify high greenness conditions, specifically only for the Pre-Monsoon season (i.e., May and June). For each year, we calculated the percentage of pixels that were greater that a value of 0.4. The 0.4 threshold for NDVI was based primarily on local expertise regarding regional cienega vegetation patters, but was also based on prior knowledge.These year-to-year values were then used to derive a Sen’s slope value representing change in high greenness conditions. Similar to processing Step 4, we used the “sens.slope” function available in the trend package version 1.1.6 to calculate slopes, which produce a slope estimate and an associated p-value. A 39-year mean image was then used to derive a pre-monsoon high greenness score representing overall conditions. Finally, a high greenness score was also produced for the year 2023 only to represent the latest conditions in the study period. In addition to the NDII Season Score and the High Greenness Area statistics, we also developed a spreadsheet that includes the basic standard deviation, minimum, and maximum values derived for each ciénega. The full suite of statistics and derived score metrics are included in the folder iii_Histogram”CSVs.zip. Ciénega Polygons 202503 Roy E Petrakis USGS - SOUTHWEST REGION Geographer mailing and physical

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Tucson AZ 85719 520-670-6671 rpetrakis@usgs.gov We developed a summary spreadsheet that provides a series of variables for each ciénega calculated across the prior processing steps. This summary was compiled by merging attributes for each cienega using R software version 4.4.1 in R Studio. These summary statistics are included in the folder iv_Staitstical_Variable_Assessment.zip. Statistical attributes include the precipitation statistics (refer to Step 2), seasonal slopes (Step 4), histogram-based scores (Step 5), elevation, and agriculture/urban dichotomous score. Elevation was determined using a 30-m Digital Elevation Product (DEM) downloaded from the U.S. Geological Survey National Map web site. The DEM was clipped by each ciénega polygon and averaged across each ciénega to determine an elevation. The agriculture/urban (i.e., ag/urban) score is dichotomous variable for the presence of either agriculture or urban areas near our study ciénega, where 0 is no and 1 is yes. Seasonal slopes between the ciénega and control are compared to show difference between the related areas for each ciénega. For the Pre-Monsoon season, Sen's slopes were differenced for both NDVI and NDII. Lastly, a precipitation ratio was calculated to show the cumulative summer (i.e., Summer Monsoon, Late-Summer) precipitation compared to winter (i.e., Fall/Winter, Winter) precipitation. All other attributes in the summary spreadsheet are described in prior processing steps. Digital Elevation Model Ciénega Polygons 202509 Roy E Petrakis USGS - SOUTHWEST REGION Geographer mailing and physical

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Tucson AZ 85719 520-670-6671 rpetrakis@usgs.gov [Ciénega Name]_Timeseries.csv 31 Comma Separated Value (CSV) files containing a time series of mean NDVI, NDII, and precipitation by season for the study period for the ciénega and respective control polygons. Time series data are separated into individual CSVs by ciénega name, but the attributes are the consistent throughout. Located in “i_Cienega_Timeseries_CSVs\Seasonal_Timeseries\”. Producer Defined Date Date of the first day of each two-month season used in calculation of time series values (MM/DD/YYYY). mean NDVI, mean NDII, mean precipitation. Producer Defined 1/1/1985 11/1/2023 Year Year. Producer Defined 1985 2023 Year Month The first month in each two-month season. Producer Defined 1 11 Month Season The two-month season. Producer Defined (1) Jan-Feb January through February. Producer defined (2) Mar-Apr March through April. Producer defined (3) May-Jun May through June. Producer defined (4) Jul-Aug July through August. Producer defined (5) Sep-Oct September through October. Producer defined (6) Nov-Dec November through December. Producer defined Site Whether the cienega polygon or corresponding control polygon was used for analysis. Producer Defined Cienega Ciénega polygon. Producer defined Control Control polygon. Producer defined Mean NDVI The mean Normalized Difference Vegetation Index (NDVI) value. The minimum value is from Ciénega Fresnos (1992-01-01, ciénega). The maximum value is from Ciénega Fresnos (2022-07-01, ciénega). Producer Defined NA No Data Producer defined 0.08272797 0.7556438 N/A Mean NDII The mean Normalized Difference Infrared Index (NDII) value. The minimum value is from Lang Ciénega (2004-01-01, ciénega). The maximum value is from West Hospital Flat Spring (1985-11-01, ciénega). Producer Defined NA No Data Producer defined -0.3326004 0.7246189 N/A Mean Precipitation (mm) Summed precipitation for that season. The minimum value occurs on multiple occasions. The maximum value is from West Hospital Flat Spring (1993-01-01). Producer Defined 0.0 600.19 Millimeter (mm) [Ciénega Name]_JanJun_Precip_Sum.csv 31 Comma Separated Value (CSV) files containing a yearly time series January to June total precipitation for each ciénega. Time series data are separated into individual CSVs by ciénega name, but the attributes are the consistent throughout. Located in “i_Cienega_Timeseries_CSVs\JanJun_Precipitation\”. Producer Defined Cienega Ciénegas from Middleton et al., 2025. Producer Defined A list of the 31 ciénegas published in Middleton et al., 2025. Year Year. Producer Defined 1985 2023 Year Jan_Jun_Precip Total precipitation in millimeters from January to June for each year. The minimum value is for Potrero Canyon Ciénaga (2011) and the maximum value is for West Hospital Flat Spring (1993). Producer Defined 6.0475 701.977 mm Ciénega_SensSlope_AllMetrics.csv One Comma Separated Value (CSV) file containing a Sen's slope and p-values for each ciénega across each season, and for each metric (i.e., NDII, NDVI, and seasonal precipitation). Sen's slope and p-values are separated into each ciénega attribute, metric attribute, and season attribute based on ciénega name. Located in “ii_Cienega_Slope_CSVs”. Producer Defined Cienega Ciénegas from Middleton et al., 2025. Producer Defined A list of the 31 ciénegas published in Middleton et al., 2025. Metric The specific metric to which Sen’s slope was applied. Producer Defined NDVI The Normalized Difference Vegetation Index. Producer defined NDII The Normalized Difference Infrared Index. Producer defined Precipitation Seasonal Precipitation. Producer defined Season_Site A combination of the season (i.e., all months - "Full", January and February - "JanFeb"; March and April - "MarApr", May and June - "MayJun"; July and August - "JulAug"; September and October - "SepOct"; November and December - "NovDec") and the location (i.e., ciénega, control). Also included is the difference between ciénega and control for the given season. Producer Defined Combination of season and site. SensSlope The Sen's slope value of the given Metric and Season_Site time series. The minimum value is from West Hospital Flat Spring (Precipitation, Full, Ciénega). The maximum value is from West Hospital Flat Spring (NDII, March/April, Difference). Producer Defined -0.338578145 0.007427652 PValue The p-value for the Sen's slope estimate. In this study, we considered p-values less than 0.05 to be statistically significant. The minimum value is from Ciénega Trap Tank Fin2 (NDII, Full, Control). The maximum value occurred on 13 occasions. Producer Defined 1.36e-20 1.0 High_Greenness_Area_Timeseries_Merged.csv One Comma Separated Value (CSV) file containing a yearly time series of high greenness cover percent for each cienega. High greenness cover percent values are separated into each ciénega based on ciénega name. Located in "iii_Histogram_CSVs”. Producer Defined Cienega Ciénegas from Middleton et al., 2025. Producer Defined A list of the 31 ciénegas published in Middleton et al., 2025. Year Year. Producer Defined 1985 2023 Year Cover_Percent The percent value of high greenness cover. The minimum value occurred on 57 occasions.The maximum value is from West Hospital Flat Spring in 1992. Producer Defined NA No Data; no imagery for the cienega. This occurred in 1985 for all ciénegas. Producer defined 0.0 95.24214104 Percent (%) High_Greenness_Area_Slope.csv One Comma Separated Value (CSV) file containing a Sen's slope and p-values of yearly high greenness cover percent for each ciénega. Sen's slope and p-values are separated into each ciénega based on ciénega name. Located in "iii_Histogram_CSVs”. Producer Defined Cienega Ciénegas from Middleton et al., 2025. Producer Defined A list of the 31 ciénegas published in Middleton et al., 2025. SensSlope The Sen's slope value of the given ciénega time series of yearly high greenness area. The minimum value is from Ciénegas Fresnos. The maximum value is from Bylas Spring. Producer Defined -0.566037736 1.237705419 PValue The p-value for the Sen's slope estimate. In this study, we considered p-values less than 0.05 to be statistically significant. The minimum value is from Cooks Lake. The maximum value is from Potrero Canyon Ciénaga. Producer Defined 4.67e-09 0.880083901 Histogram_Statistics_AllMetric.csv One Comma Separated Value (CSV) file containing a descriptive histogram statistics (i.e., mean, minimum, maximum, standard deviation) for each ciénega. Statistics are separated into each ciénega based on ciénega name, metric (i.e., NDII, NDVI), and season. Located in "iii_Histogram_CSVs”. Producer Defined Cienega Ciénegas from Middleton et al., 2025. Producer Defined A list of the 31 ciénegas published in Middleton et al., 2025. Metric The specific metric -- either the NDVI or NDII. Producer Defined NDVI Normalized Difference Vegetation Index. Producer defined NDII Normalized Difference Infrared Index. Producer defined Season A mean image was created using all images from the same two-mean season across the 39-year study (i.e., 1985 to 2023). Two-month season used for analysis. Producer Defined January/February The two-month season of January and February. Producer defined March/April The two-month season of March and April. Producer defined May/June The two-month season of May and June. Producer defined July/August The two-month season of July and August Producer defined September/October The two-month season of September and October Producer defined November/December The two-month season of November and December. Producer defined Cienega_Median Median metric value for the ciénega polygon of the 39-year seasonal mosaic images. The minimum value is for Empire Ranch Ciénega (January/February - NDII). The maximum value is for Babocomari Ranch Ciénega (July/August - NDVI). Producer Defined -0.228850448 0.667418422 Cienega_Mean Mean metric value for the ciénega polygon of the 39-year seasonal mosaic images. The minimum value is for Empire Ranch Ciénega (January/February - NDII). The maximum value is for Babocomari Ranch Ciénega (July/August - NDVI). Producer Defined -0.220459426 0.634266319 Cienega_Max Maximum metric value for the ciénega polygon of the 39-year seasonal mosaic images. The minimum value is for Animas Creek Ciénega (January/February - NDII). The maximum value is for Babocomari Ranch Ciénega (July/August - NDVI). Producer Defined -0.122170911 0.777444083 Cienega_Min Minimum metric value for the ciénega polygon of the 39-year seasonal mosaic images. The minimum value is for Ciénega Trap Tank Fin2 (January/February - NDII). The maximum value is for West Hospital Flat Spring (July/August - NDVI). Producer Defined -0.295127987 0.407052059 Cienega_STD Standard deviation metric value for the ciénega polygon of the 39-year seasonal mosaic images. The minimum value is for Leslie Creek Ciénega (January/February - NDVI). The maximum value is for West Hospital Flat Spring (January/February - NDII). Producer Defined 0.014044827 0.166119571 Control_Median Median metric value for the control polygon of the 39-year seasonal mosaic images. The minimum value is for Bog Hole Ciénega (January/February - NDII). The maximum value is for West Hospital Flat Spring (September/October - NDVI). Producer Defined -0.224054547 0.656893361 Control_Mean Mean metric value for the control polygon of the 39-year seasonal mosaic images. The minimum value is for Bog Hole Ciénega (January/February - NDII). The maximum value is for West Hospital Flat Spring (September/October - NDVI). Producer Defined -0.220513197 0.651984977 Control_Max Maximum metric value for the control polygon of the 39-year seasonal mosaic images. The minimum value is for Empire Ranch Ciénega (January/February - NDII). The maximum value is for West Hospital Flat Spring (September/October - NDVI). Producer Defined -0.14854361 0.747053785 Control_Min Minimum metric value for the control polygon of the 39-year seasonal mosaic images. The minimum value is for Potrero Canyon Ciénega (May/June - NDII). The maximum value is for West Hospital Flat Spring (July/August - NDVI). Producer Defined -0.297836363 0.453619567 Control_STD Standard deviation metric value for the control polygon of the 39-year seasonal mosaic images. The minimum value is for Lang Ciénega (May/June - NDVI). The maximum value is for Bylas Spring (July/August - NDVI). Producer Defined 0.009287936 0.124350742 NDII_NDVI_2023_Scores.csv One Comma Separated Value (CSV) file containing the 2023 NDII Season Score and 2023 High Greenness Score for each ciénega. Statistics are separated into each ciénega based on ciénega name. Located in "iii_Histogram_CSVs”. Producer Defined Cienega Ciénegas from Middleton et al., 2025. Producer Defined A list of the 31 ciénegas published in Middleton et al., 2025. 2023 NDII Season Score The NDII Season Score for 2023. The minimum value occurs on three occasions. The maximum value is for Feldman San Pedro Ciénaga. Producer Defined 0 16 2023 Pre-Monsoon High Greenness Score The Pre-Monsoon season High Greenness Score for 2023. The minimum value is for Ciénega Bonita. The maximum value is for Cooks Lake. Producer Defined 0.03 0.91 Combined_Cienega_Variables_for_Statistical_Analysis.csv One Comma Separated Value (CSV) file containing the combined ciénega variables used for statistical analysis for each ciénega. Attribute variables are separated into each ciénega based on ciénega name. Located in "iv_Statistical_Variable_Assessment”. Producer Defined Cienega Ciénegas from Middleton et al., 2025. Producer Defined A list of the 31 ciénegas published in Middleton et al., 2025. Mean Elevation The mean elevation of the ciénega based on a 30-m Digital Elevation Model (DEM) acquired from the National Map. Producer Defined 646 2752 Meters (m) Ag/Urban A dichotomous metric for the presence of observed either agriculture or urban in the immediate area of the ciénega. Producer Defined 0 No presence of either agriculture or urban. Producer defined 1 Presence of either agriculture or urban. Producer defined Pre-Monsoon NDVI Trend Difference Difference between ciénega and control Normalized Difference Vegetation Index (NDVI) trends for Pre-Monsoon season. Producer Defined -0.0020557817861 0.0031736563219393 Pre-Monsoon NDII Trend Difference Difference between ciénega and control Normalized Difference Infrared Index (NDII) trends for Pre-Monsoon season. Producer Defined -0.0024957185548991 0.0035652965627282 Pre-Monsoon High Greenness Area Trend Sen's slope of Pre-Monsoon High Greenness Area Percent from 1985 to 2023 (i.e., percentage of pixels with Pre-Monsoon Normalized Difference Vegetation Index (NDVI) values above 0.4). Producer Defined -0.566037736 1.237705419 Pre-Monsoon Greenness Score Based on a 39-year mean Normalized Difference Vegetation Index (NDVI) Pre-Monsoon image, the percentage of pixels values above 0.4. Producer Defined 0.0 76.8 NDII Season Score Score based on frequency of three different thresholds applied to seasonal mean Normalized Difference Infrared Index (NDII) values. Producer Defined 0 14 Seasonal Precipitation Ratio Ratio of Summer Monsoon and Late Summer – to – Fall/Winter and Winter precipitation for each ciénega. Producer Defined 1.07734153205744 2.53692587455091 Precipitation Trend Sen's slope trend of seasonal precipitation from 1985 through 2023. Producer Defined -0.339 -0.043 GS ScienceBase U.S. Geological Survey mailing address

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