Rising sea levels (SLR) will cause coastal groundwater to rise in many coastal urban environments. Inundation of contaminated soils by groundwater rise (GWR) will alter the physical, biological, and geochemical conditions that influence the fate and transport of existing contaminants. These transformed products can be more toxic and/or more mobile under future conditions driven by SLR and GWR. We reviewed the vulnerability of contaminated sites to GWR in a US national database and in a case comparison with the San Francisco Bay region to estimate the risk of rising groundwater to human and ecosystem health. The results show that 326 sites in the US Superfund program may be vulnerable to changes in groundwater depth or flow direction as a result of SLR, representing 18.1 million hectares of contaminated land. In the San Francisco Bay Area, we found that GWR is predicted to impact twice as much coastal land area as inundation from SLR alone, and 5,297 state-managed sites of contamination may be vulnerable to inundation from GWR in a 1-meter SLR scenario. Increases of only a few centimeters of elevation can mobilize soil contaminants, alter flow directions in a heterogeneous urban environment with underground pipes and utility trenches, and result in new exposure pathways. Pumping for flood protection will elevate the salt water interface, changing groundwater salinity and mobilizing metals in soil. Socially vulnerable communities are more exposed to this risk at both the national scale and in a regional comparison with the San Francisco Bay Area. Methods Data Dryad This data set includes data from the California State Water Resources Control Board (WRCB), the California Department of Toxic Substances Control (DTSC), the USGS, the US EPA, and the US Census. National Assessment Data Processing: For this portion of the project, ArcGIS Pro and RStudio software applications were used. Data processing for superfund site contaminants in the text and supplementary materials was done in RStudio using R programming language. RStudio and R were also used to clean population data from the American Community Survey. Packages used include: Dplyr, data.table, and tidyverse to clean and organize data from the EPA and ACS. ArcGIS Pro was used to compute spatial data regarding sites in the risk zone and vulnerable populations. DEM data processed for each state removed any elevation data above 10m, keeping anything 10m and below. The Intersection tool was used to identify superfund sites within the 10m sea level rise risk zone. The Calculate Geometry tool was used to calculate the area within each coastal state that was occupied by the 10m SLR zone and used again to calculate the area of each superfund site. Summary Statistics were used to generate the total proportion of superfund site surface area / 10m SLR area for each state. To generate population estimates of socially vulnerable households in proximity to superfund sites, we followed methods similar to that of Carter and Kalman (2020). First, we generated buffers at the 1km, 3km, and 5km distance of superfund sites. Then, using Tabulate Intersection, the estimated population of each census block group within each buffer zone was calculated. Summary Statistics were used to generate total numbers for each state. Bay Area Data Processing: In this regional study, we compared the groundwater elevation projections by Befus et al (2020) to a combined dataset of contaminated sites that we built from two separate databases (Envirostor and GeoTracker) that are maintained by two independent agencies of the State of California (DTSC and WRCB). We used ArcGIS to manage both the groundwater surfaces, as raster files, from Befus et al (2020) and the State’s point datasets of street addresses for contaminated sites. We used SF BCDC (2020) as the source of social vulnerability rankings for census blocks, using block shapefiles from the US Census (ACS) dataset. In addition, we generated isolines that represent the magnitude of change in groundwater elevation in specific sea level rise scenarios. We compared these isolines of change in elevation to the USGS geological map of the San Francisco Bay region and noted that groundwater is predicted to rise farther inland where Holocene paleochannels meet artificial fill near the shoreline. We also used maps of historic baylands (altered by dikes and fill) from the San Francisco Estuary Institute (SFEI) to identify the number of contaminated sites over rising groundwater that are located on former mudflats and tidal marshes. The contaminated sites' data from the California State Water Resources Control Board (WRCB) and the Department of Toxic Substances (DTSC) was clipped to our study area of nine-bay area counties. The study area does not include the ocean shorelines or the north bay delta area because the water system dynamics differ in deltas. The data was cleaned of any duplicates within each dataset using the Find Identical and Delete Identical tools. Then duplicates between the two datasets were removed by running the intersect tool for the DTSC and WRCB point data. We chose this method over searching for duplicates by name because some sites change names when management is transferred from DTSC to WRCB. Lastly, the datasets were sorted into open and closed sites based on the DTSC and WRCB classifications which are shown in a table in the paper's supplemental material. To calculate areas of rising groundwater, we used data from the USGS paper “Projected groundwater head for coastal California using present-day and future sea-level rise scenarios” by Befus, K. M., Barnard, P., Hoover, D. J., & Erikson, L. (2020). We used the hydraulic conductivity of 1 condition (Kh1) to calculate areas of rising groundwater. We used the Raster Calculator to subtract the existing groundwater head from the groundwater head under a 1-meter of sea level rise scenario to find the areas where groundwater is rising. Using the Reclass Raster tool, we reclassified the data to give every cell with a value of 0.1016 meters (4”) or greater a value of 1. We chose 0.1016 because groundwater rise of that little can leach into pipes and infrastructure. We then used the Raster to Poly tool to generate polygons of areas of groundwater rise.

Description This file contains the supplementary data to accompany ‘Rivers and lakes in Western Arabia Terra: The Fluvial catchment of the ExoMars 2022 rover landing site’ Peter Fawdon (peter.fawdon@open.ac.uk). The Open University, Walton Hall, Milton Keynes MK7 7EA United Kingdom, All data is supplied in a equirectangular projection centered on Oxia Planum at 335.45deg east following Fawdon et al., (2021) A geographic framework for exploring the ExoMars rover landing site at Oxia Planum, Mars Shapefile data 01_Pourpoints Point data used to calculate Oxia Planum model watersheds 02_Watersheds Polygons delimiting the extent of the Oxia Planum model watersheds 03_Channels All channels observed in CTX data within the model watersheds. ‘Channel_ty’ field has 5 values: WFF, Wide Flat Floored. NUS - U-section. LRC, Low Relief channels. SLR, sinuous ridges. INT, channels within impact craters. INF, Inferred or possible channel pathways 04_Lakes All possible lakes identified in the within the model watersheds with the numbers of morphological indicators for each possible lake. ‘Type’ field has 4 values: 1, Large Crater lakes. 2, Rimless Crater lakes. 3. Irregular Dark depressions. 4, possible sediment fans. Geomorphological features recorded are: Inlets, Outlets, Sediment fans, Interior channels, Smooth floor, Strandlines and Concentric albedo changes. Possible maximum models volumes have been calculated for some lakes using the volume difference between unfilled and filled MOLA DEM within the boundary of the possible lake as defined by where the fill hillshade = 180. 05_StreamOrder Strahler stream order for the model flow accumulation pathways for the model watersheds areas. Raster Data Mars_MGS_MOLA_DEM_mosaic_global_463m_MC11_PourPoint_OxiaBasin.tif Mars_MGS_MOLA_DEM_mosaic_global_463m_MC11_PourPoint_OxiaBasin_fill.tif Mars_MGS_MOLA_DEM_mosaic_global_463m_MC11_PourPoint_OxiaBasin_fill_hillshade.tif Extract from the methord section of ‘Rivers and lakes in Western Arabia Terra: The Fluvial catchment of the ExoMars 2022 rover landing site’ Geomorphological observations of fluvial features were made using CTX, 6 meter/pixel data at a scale of 1:50,000, georeferenced to High Resolution Stereo Camera MC11 quadrangle mosaic basemap (HRSC; Gwinner et al., 2016; Neukum et al., 2004). THermal EMission Imaging System (THEMIS; Christensen et al., 2013) night and daytime IR global mosaics were used to inform identification of features observed in the CTX data, and Colour and Stereo Surface Imaging System (CaSSIS; Thomas et al., 2017) images were used where available for colour interpretation. Mars Orbital Laser Altimeter (MOLA; Zuber et al., 1992) data were used for topographic information. Using these data, a fluvial (valley/channel and sinuous ridges) and lacustrine features was identified. After the initial survey, a topographic flow accumulation model was used to identify areas to revise where the model suggested channels might be present, and these were then searched more closely for any subtle morphological evidence of fluvial landforms. This iterative, multi-data process enabled many more fluvial systems to be identified than using one dataset alone. To determine the watershed area for Oxia Planum (Figure 1), the ArcMap 10.5 Spatial Analyst ‘ArcHydro’ toolset (Esri, 2016) was used to calculate a model of flow accumulation grids and a drainage network map using topographic data from the MOLA DEM (Smith et al., 2001). Areas in the DEM that created sinks or basins were filled prior to calculating flow direction and accumulation. It is important to note that these processing steps ‘fill in’ areas of low-lying terrain and impact craters, as well as unwanted error and noise in the DEM. These ‘filled in’ areas create model flow pathways stretched across basins that were retained to identify where ponding may have occurred and where the model flow is likely to deviate from the geomorphic observations. The watershed and contributory areas were calculated using the flow accumulation model upslope of two pour points located in the Oxia Basin. The location of both pour points (see Figure 2) was based on the correspondence of preliminary model flow accumulation paths calculated for the whole MC-11 Quad and geomorphological features resolved in the MOLA DEM. The eastern pour point (the lowest point in the ‘fan’ watershed) was located where the channel of Coogoon Valles opens out into Oxia Basin at the highest elevation of the sediment fan remnants. The northern pour point (the lowest point in the basin watershed) was located at the lowest point of the Oxia Basin leading northwards to Chryse Planitia. The watershed is defined where the flow accumulation is 0 (i.e. there are no cells from which water would flow). The pour points, their watersheds, and the flow accumulation pathways were converted to Strahler stream order (Strahler, 1957).

Abstract The dataset was derived by the Bioregional Assessment Programme from multiple source datasets. The source datasets are identified in the Lineage field in this metadata statement. The processes undertaken to produce this derived dataset are described in the History field in this metadata statement. This dataset is comprised of derived thickness grids and contours for the Bandanna and Eromanga Basins within Galilee subregion. The dataset also contains annotation text and masks for the Eromanga basin. ## Purpose The annotation text is used for labeling contour values and the cartographic mask is used to clear unwanted features from report maps. ## Dataset History Bandanna data: Bandanna_thk = DEM (GUID: ebcf6ca2-513a-4ec7-9323-73508c5d7b93) - Top_Bandanna(Steph Hostetler) This GRID was produced using Arc Catalogue raster calculator tool Eromanga data: Eromanga_not_clipped_contour: derived from Grid (Eromanga_thickness_not_clipped) Eromanga_thickness_not_clipped = DEM (GUID: ebcf6ca2-513a-4ec7-9323-73508c5d7b93) - *layer10 (ESRI GRD)_clp* [GUID: 11dcfa96-2a3e-402d-b6f2-1e90290842f7] This GRID was produced using Arc Catalogue raster calculator tool Bandanna_contour: derived from the grid (Bandanna_thk) using topo to raster tool in ArcMap. Annotation text and masks were created using the ArcGIS tool 'FeatureOutlineMasks' ## Dataset Citation Bioregional Assessment Programme (2015) Eromanga grids contours. Bioregional Assessment Derived Dataset. Viewed 07 December 2018, http://data.bioregionalassessments.gov.au/dataset/ac342830-f8e2-4c63-91e8-1a86d63beeac. ## Dataset Ancestors * Derived From Australian Geological Provinces, v02 * Derived From Layer 10 Great Artesian Basin base of Jurassic-Cretaceous sequence surface (Great Artesian Basin Water Resource Assessment) * Derived From GEODATA 9 second Digital Elevation Model Version 3 Hillshade * Derived From GEODATA 9 second DEM and D8: Digital Elevation Model Version 3 and Flow Direction Grid 2008

The digital segmented network based on watershed boundaries, ERF1-2, includes enhancements to the U.S. Environmental Protection Agency's River Reach File 1 (RF1) (USEPA, 1996; DeWald and others, 1985) to support national and regional-scale surface water-quality modeling. Alexander and others (1999) developed ERF1, which assessed the hydrologic integrity of the digital reach traces and calculated the mean water time-of-travel in river reaches and reservoirs. ERF1-2 serves as the foundation for SPARROW (Spatially Referenced Regressions (of nutrient transport) On Watershed) modeling. Within the context of a Geographic Information System, SPARROW estimates the proportion of watersheds in the conterminous U.S. with outflow concentrations of several nutrients, including total nitrogen and total phosphorus, (Smith, R.A., Schwarz, G.E., and Alexander, R.B., 1997). This version of the network expands on ERF1 (version 1.2; Alexander et al. 1999), and includes the incremental and total drainage area derived from 1-kilometer (km) elevation data for North America. Previous estimates of the water time-of-travel were recomputed for reaches with water- quality monitoring sites that included two reaches. The mean flow and velocity estimates for these split reaches are based on previous estimation methods (Alexander et al., 1999) and are unchanged in ERF1-2. Drainage area calculations provide data used to estimate the contribution of a given nutrient to the outflow. Data estimates depend on the accuracy of node connectivity. Reaches split at water- quality or pesticide-monitoring sites indicate the source point for estimating the contribution and transport of nutrients and their loads throughout the watersheds. The ERF1-2 coverage extends the earlier ERF1 coverage by providing digital-elevation-model (DEM-based estimates of reach drainage area founded on the 1-kilometer data for North America (Verdin, 1996; Verdin and Jenson, 1996). A 1-kilometer raster grid of ERF1-2 projected to Lambert Azimuthal Equal Area, NAD 27 Datum (Snyder, 1987), was merged with the HYDRO1K flow direction data set (Verdin and Jenson, 1996) to generate a DEM-based watershed grid, ERF1_2WS. The watershed boundaries are maintained in a raster (grid cell) format as well as a vector (polygon) format for subsequent model analysis. Both the coverage, ERF1-2, and the grid, ERF1-2WS are available at: http://water.usgs.gov/lookup/gisgetlist. The version of RF1 used to compile ERF1-2 was an early edition of a USGS RF1 translation and was updated by USEPA (USEPA, 1996). The capabilities of the enhanced version of RF1 (ERF1-2) and the current USEPA version have not been evaluated. The user is referred to the USEPA version [http://www.epa.gov/owow/monitoring/rf/rfindex] for discussions of streamflow accuracy and general background on the origin of RF1.This is a MD iMAP hosted service layer. Find more information at https://imap.maryland.gov.Feature Service Layer Link:https://geodata.md.gov/imap/rest/services/Hydrology/MD_Waterbodies/FeatureServer/0

Conveyances were compiled through hydrographic surveys, declaration maps, field data collection, data requests from various data originators and the National Hydrography Dataset. The data is Z enabled for future use in network analysis. Conveyance Type allows for the inventory of all Acequias within the state that have been reported to the OSE/ISC.EDAC calculated slope and the cardinal direction for all the acequia/conveyance data provided by the NMOSE.EDAC utilized the digital elevation model for New Mexico created using the various LiDAR acquisitions across the State to calculate the slope data. Using the DEM, created start points (SourcePts) and end points (SinkPts) for the conveyance layer. The eastings, northings and the elevation values from the point data was included in the delivered conveyance layer (StartPTX, StartPTY, StartPTZ, EndPTX, EndPTY, EndPTZ). All of the values are created in NAD 1983 UTM Zone 13N and meter is the linear unit.The slope values for the conveyances were populated in Per_Slope field. Where there was a negative slope, the lines (flow direction) were flipped and the eastings, northings and elevations switched and re-computed the StartPts and EndPts accordingly.Then cardinal compass directions for the conveyance polylines were generated, using the Spatial Statistics, Linear Directional Mean tool. The compass angle values were then converted to direction.For angle valuesLess than 22.5 = NorthGreater than or equal to 22.5 and Less than 67.5 = NortheastGreater than or equal to 67.5 and Less than 112.5 = EastGreater than or equal to 112.5 and less than 157.5 = SoutheastGreater than or equal to 157.5 and less than 202.5 = SouthGreater than or equal to 202.5 and less than 247.5 = SouthwestGreater than or equal to 247.5 and less than 292.5 = WestGreater than or equal to 292.5 and less than 337.5 = NorthwestGreater than or equal to 337.5 = North

Brief Methods: In version 2 of the Sierra Nevada Multi-source Meadow Polygons Compilation, polygon boundaries from the original layer (SNMMPC_v1 - https://meadows.ucdavis.edu/data/4) were updated using ‘heads-up’ digitization from high-resolution (1m) NAIP imagery. In version 1, only polygons larger than one acre were retained in the published layer. In version 2, existing polygon boundaries were split, reduced in size, or merged, and additional polygons not captured in the original layer were digitized. If split, original IDs from version 1 were retained for one half and a new ID was created for the other half. In instances where adjacent meadows were merged together, only one ID was retained and the unused ID was “decommissioned”. If digitized, a new sequential ID was assigned. AcknowledgementsTim Lindemann, Dave Weixelman, Carol Clark, Stacey Mikulovsky, Qiqi Jiang, Joel Grapentine, Kirk Evans - USDA Forest Service, Pacific Southwest Region Wes Kitlasten - U.S. Geological Survey Sarah Yarnell, Ryan Peek, Nick Santos - UC Davis, Center for Watershed Sciences Anna Fryjoff-Hung - UC Merced Meadow Polygon Attributes Field DescriptionAREA_ACRE Meadow area in acresSTATE State in which the meadow is located (CA or NV)ID* Unique meadow identifier UCDSNMxxxxxx*Note: IDs are non-sequential* HUC12 Unique identifier for the Hydrologic Unit Code (HUC), level 12, in which the meadow is locatedOWNERSHIP Land ownership status (multiple sources)EDGE_COMPLEXITY Gives an indication of the meadow's exposure to external conditions EDGE COMPLEXITY = (MEADOWperimeter/EAC perimeter) [EAC = Equal Area Circle]DOM_ROCKTYPE Dominant rock type on which the meadow is located based on the USGS layerVEG_MAJORITY Vegetation majority based on the LANDFIRE layer (GROUPVEG attribute)SOIL_SURVEY Soil survey from which SOIL_COKEY, MAPUNIT_Kf, MAPUNIT_ClayTot_r, SOIL_MUKEY, and SOIL_COMP_NAME were assigned to each meadow (SSURGO or STATSGO depending on layer coverage)SOIL_MUKEY Mapunit Key: Unique identifier for the Mapunit in which the meadow is locatedSOIL_COKEY Component Key: Unique identifier for the major component of the mapunit in which the meadow is located SOIL_COMP_NAME Component Name: Name of the soil component with the highest representative value in the mapunit in which the meadow is located MAPUNIT_Kf K factor: A soil erodibility factor that quantifies the susceptibility of soil particles to detachment by water. Low: 0.05-0.2 Moderate: 0.25-0.4, High: >0.4MAPUNIT_ClayTot_r Representative value (%)of total clayCATCHMENT_AREA The approximate area of the upstream catchment exiting through the meadow(sq. m)ELEV_MEAN Mean elevation (m)ELEV_RANGE Elevation range (m) across each meadowED_MIN_FStopo_ROADS Minimum Euclidean Distance (m) to Forest Service Topographic Map Data Transportation Roads ED_MIN_FStopo_TRAILS Minimum Euclidean Distance (m) to Forest Service Topographic Map Data Transportation Trails ED_MIN_LAKE Minimum Euclidean Distance (m) to lake edges ED_MIN_FLOW Minimum Euclidean Distance (m) to NHD Streams/Rivers ED_MIN_SEEP Minimum Euclidean Distance (m) to NHD Seeps/Springs MDW_DEM_SLOPE Median DEM based slope (in degrees)STRM_SLOPE_GRADE Length-weighted average slope of all NHD flowline segments in each meadow. Given for meadows with flowlines. Meadows without flowlines are null for this attribute.POUR_POINT_LAT Latitude of the lowest point along a flowline at which water flows out of the meadow in decimal degrees(meadow with no flowline has null value) POUR_POINT_LON Longitude of the lowest point along a flowline at which water flows out of the meadow in decimal degrees(meadow with no flowline has null value) HGM_Type Dominant meadow hydrogeomorphic (HGM) type LAT_DD Latitude of polygon centroid in decimal degreesLONG_DD Longitude of polygon centroid in decimal degreesShape_Length Meadow perimeter in metersShape_Area Meadow area in sq. meters Detailed Attribute Descriptions:GeologyField: DOM_ROCKTYPEData Source: USGS - https://pubs.usgs.gov/of/2005/1305/Dominant rock type was attributed to the meadow polygons based on available state geology layers. Using Zonal Statisitics in ArcGIS, the most abundant lithology in the map unit (ROCKTYPE1) was identified for each meadow. VegetationField: VEG_MAJORITYData Source: LANDFIRE - https://www.landfire.gov/version_comparison.php?mosaic=YUsing Zonal Statisitics in ArcGIS, the 2014 LANDFIRE dataset was used to attribute generalized vegetation (GROUPVEG) to the meadow polygons. SoilsFields: SOIL_SURVEY, SOIL_MUKEY, SOIL_COKEY, SOIL_COMP_NAME, MAPUNIT_Kf, MAPUNIT_ClayTot_rData Source: USDA, Natural Resources Conservation ServiceSSURGO: https://gdg.sc.egov.usda.gov/STATSGO: https://websoilsurvey.sc.egov.usda.gov/App/HomePage.htmSSURGO (1:24,000 scale) datasets were compiled for the entirety of the study area. Gaps were filled with compiled STATSGO data (1:250,000 scale). Components were assigned based on the soil component with the highest representative value in the map unit in which the meadow was located. For each component, the clay and Kf values from the top-most horizon were assigned to each meadow polygon using Zonal Statistics. Note: MAPUNIT_Kf may be null if the mapunit dominant condition is a miscellaneous area component such as Rock outcrop. Also, forested components with organic litter surface horizons will also return a null K-factor when the surface horizon K-factor is used.STATSGO does not have the detail for approximation of soil properties in the mountain meadows. The polygons are so big (Order 4) that they do not recognize the soils in the meadows as unique components, so there are no data for the meadows anywhere in those map units. As for the K and clay values for CA790 (Yosemite NP), because it is a new survey, O horizons were populated for those components. There may be a similar issue with the Tahoe Basin. NRCS does not populate the K factor for O horizons. And, at least at the time, NRCS is not populating any mineral material in the O horizons. Many NRCS national interpretations have been edited to look at the first mineral horizon and exclude the O. There is also a lot of Rock Outcrop and no horizon data are populated for those components.Slope Field: MDW_DEM_SLOPE Data Source: USGS 10m DEMThe median Digital elevation model (DEM) based slope (in degrees) was assigned via Zonal Statistics to each meadow.All meadows have a value for this attribute. Field: STREAM_SLOPE_GRADEData Source: USGS National Hydrograpy Dataset (NHD) - https://nhd.usgs.gov/data.htmlA length-weighted average slope of all NHD flowline segments was calculated within each meadow polygon. Meadows with no NHD flowline will have a NULL value for this attribute. Catchment AreaField: MDW_CATCHMENT_AREA (sq meters)Data Source: USGS NHDPlus V2, NHDPlusHydrodem- http://www.horizon-systems.com/NHDPlus/NHDPlusV2_home.phpScript Source: USGS, Wes Kitlasten; USFS, Kirk Evans, Carol ClarkUsing python scripting and the Watershed tool in ArcGIS, the area of the upstream catchment exiting through the meadow was obtained using a flow direction raster created from the NHDPlusHydrodem.Euclidean Distance Fields: ED_MIN_SEEP, ED_MIN_LAKE, ED_MIN_FLOW, ED_MIN_FSTopo_ROADS, ED_MIN_FSTopo_TRAILSData Source: USGS National Hydrograpy Dataset (NHD) - https://nhd.usgs.gov/data.htmlFSTopo - https://data.fs.usda.gov/geodata/edw/datasets.php?xmlKeyword=FSTopoUsing the Euclidean Distance (Spatial Analyst) tool in ArcGIS, the minimum distance to each meadow was calculated for NHD Springs/Seeps, NHD Streams/Rivers (flow), NHD Waterbodies (lakes), and FS Topographic Transportation Trails and Roads. HGM Type During the mapping process, the dominant Hydrogeomorphic (HGM) type (Weixelman et al 2011) was estimated for each meadow larger than one acre. Visual inspection of NAIP 1-m resolution imagery was used in this process. DEM layers were used to estimate the landform position. The USGS hydrographic layer was used to determine locations of flowlines. Google Earth imagery was used to estimate greenness during the summer months. Meadows are often composed of more than one HGM type. In this effort, the dominant type was estimated. HGM types have not yet been estimated for Yosemite and Sequoia Kings Canyon National Parks. Types were mapped according to the following visual interpretation. 1. Meadows adjacent to lakes or reservoirs and at nearly the same elevation as the Water bodyLacustrine Fringe (LF)1’. Not as above22. Meadow sites located in an obvious topographic depression. 32’. Not as above43. Sites with obvious standing water after mid-summer or vegetation remaining dark green after mid-summer. Depressional Perennial (DEPP)3’. Not as above. Sites with no standing water after mid-summer or apparently not remaining dark green after mid-summer.Depressional Seasonal (DEPS)4. Meadows with a flow line (using the USGS hydrographic layer) entering from above the meadow and exiting below the meadow, or meadows located in a swale or drainway ………………………………Riparian (RIP)4’. Not as above55. Meadows fed by a spring or seep. No flowline entering from above the meadow. Typically occurring on hillslopes or toeslopes. In addition, the USGS DEM layer was used to look for the text label “Springs” and/or a symbol indicating a spring. Discharge Slope (DS)5’. Dry meadows without a visible flowline entering from above the meadow, vegetation greenness disappears by mid-summer. No apparent groundwater inputs from springs or seeps. May occur in a swale, drainageway, gentle hillslope, or crest. Dry (Dry)OwnershipField: OWNERSHIPData Sources by priority:1. USDA Forest Service Basic Ownership (OWNERCLASSIFICATION) - https://data.fs.usda.gov/geodata/edw/datasets.php?dsetCategory=boundaries1. National Parks Service (UNIT_NAME) - https://irma.nps.gov/DataStore/1. California Protected Areas Database – CPAD (LAYER) - http://www.calands.org/1. Protected Area Database-US (CBI Edition) Version 2.1 (OWN_NAME) -