This tile layer describes slopes in the action area of the Grassy Ridge project, proposed by the USFS in the Monongahela National Forest, West Virginia.Purpose:This data was included to provide additional environmental context for the user’s understanding of the project’s likely environmental impacts.Source & Date:Slope is based on 1m elevation data obtained from the WV Elevation and LIDAR Download Tool on 7/11/2021.https://data.wvgis.wvu.edu/elevation/Processing:1-meter elevation models of Pocahontas and Pendleton counties, West Virginia, were mosaicked in ArcMap. The slope was calculated from the 1-meter LIDAR-derived digital elevation model mosaic. The mosaic was reclassified, as shown below. ABRA published the reclassified mosaic to ArcGIS Online as a tile layer.Symbology:GRID Project Area Slopes (%)0 - 10%: Dark Green10 - 20%: Light Green20 - 30%: Yellow30 - 40%: Orange40 -50%: Red> 50%: Maroon

For a series of studies on the ecosystem service values of chaparral in Southern California, we developed a raster data layer providing an ecological unit classification of the Southern California landscape. This raster dataset is at a 30 meter pixel resolution and partitions the landscape into 37 different ecological unit types. This dataset was derived through a GIS-based cluster analysis of 10 different physiographic variables, namely soil suborder type, terrain geomorphon type, flow accumulation, slope, solar irradiation, annual precipitation, annual minimum temperature, actual evapotranspiration, and climatic water deficit. This partitioning was based on physiographic variables rather than vegetation types because of the wish to have the ecological units reflect biophysical characteristics rather than the historical land use patterns that may influence vegetation. The cluster analysis was performed across a set of 10,000 points randomly placed on a GIS layer stack for the 10 variables. These random points were grouped into 37 discrete clusters using an algorithm called partitioning around medoids. This assignment of points to clusters was then used to train a random forest classifier, which in turn was run across the GIS stack to produce the output raster layer.

This dataset is described in the following book chapter publication:

Underwood, Emma C., Allan D. Hollander, Patrick R. Huber, and Charlie Schrader-Patton. 2018. “Mapping the Value of National Forest Landscapes for Ecosystem Service Provision.” In Valuing Chaparral, 245–70. Springer Series on Environmental Management. Springer, Cham. https://doi.org/10.1007/978-3-319-68303-4_9.

Methods Summary of Methods for Developing Ecological Units in Southern California

Allan Hollander and Emma Underwood, University of California Davis.

1) Compiling GIS layers. These data were compiled from a variety of sources and resolutions (Table 1) for the southern California study area (see Methods_figure_1.png for the study area). The original resolution of these raster layers ran from 10 meters to 270 meters, and resampling was conducted so all analyses were performed at a 30 meter raster resolution. We decided not to include vegetation in the data stack as the aim was to capture biophysical characteristics and vegetation will reflect current landscape history and land use patterns (e.g. fire history, type conversion from shrubland, or agricultural use). Lakes and reservoirs were omitted from the subsequent analysis. Data compiled:

a) Soil suborders. This was a discretely-classified raster layer with 22 soil suborder classes included in the southern California region. This was derived from the gridded Soil Survey Geographic Database (gSSURGO, available at http://www.nrcs.usda.gov/wps/portal/nrcs/detail/soils/survey/geo/?cid=nrcs142p2_053628). This product is a rasterization at a 10-meter resolution of the county-scale SSURGO data published by the USDA Natural Resources Conservation Service.

b) Terrain geomorphons. This raster layer derives from a DEM surface and classifies the landscape into 10 discrete landform types, examples being ridges, slopes, hollows, and valleys. The algorithm for geomorphon classification uses a pattern recognition approach based on line of sight analysis (Jasiewisc and Stepinski 2013). This layer was created from a 30 meter DEM in GRASS 7.0.0, using the extension r.geomorphon (https://grass.osgeo.org/grass70/manuals/addons/r.geomorphon.html).

c) Annualized solar irradiation. This layer uses the r.sun model available in GRASS 7.0.0 (https://grass.osgeo.org/grass70/manuals/r.sun.html) which calculates direct, diffuse, and reflected solar irradiation for a given day, location, topography, and atmospheric conditions. This layer was created from a 30 meter DEM and assumes clear-sky conditions. To estimate the total annual irradiation, the model was run for every 15th day and these values were integrated over the year.

d) Flow accumulation. This layer is another product of 30 meter DEM data and measures the upslope area in pixel count that conceivably drains into a given pixel. This was calculated using the accumulation option in the GRASS 7.0.0 command r.watershed (https://grass.osgeo.org/grass70/manuals/r.watershed.html)

e) Slope. This was derived from 30 meter DEM data using the GRASS 7.0.0 command r.slope.aspect, and is measured in degrees.

f) Annual precipitation. This layer came from the 2014 Basin Characterization Model (BCM) for California (Flint et al. 2013) and gives the average annual precipitation between 1981 and 2010 at a 270-meter resolution.

g) Annual minimum temperature. This layer also came from BCM (Flint et al. 2013) and gives the average annual minimum temperature between 1981 and 2010 at a 270-meter resolution. Minimum temperature was included in the set of climate variables to represent montane winter conditions.

h) Climatic water deficit. This layer also came from the BCM (Flint et al. 2013) and gives the average climatic water deficit between 1981 and 2010 at a 270-meter resolution. The two evapotranspiration variables (climatic water deficit and actual evapotranspiration) are included in this set because they are strong drivers of vegetation distribution (Stephenson 1998).

i) Actual evapotranspiration. This layer also came from the BCM (Flint et al. 2013) and gives the average actual evapotranspiration between 1981 and 2010 at a 270-meter resolution.

Table 1. Summary of GIS data stack

    LAYER


    ORIGINAL SOURCE


    ORIGINAL RESOLUTION


    THEME








    Soil suborders


    gSSURGO


    10 meters


    Soil type




    Terrain geomorphons


    Digital elevation model


    30 meters


    Geomorphometry




    Solar irradiation


    Digital elevation model


    30 meters


    Energy balance




    Flow accumulation


    Digital elevation model


    30 meters


    Geomorphometry




    Slope


    Digital elevation model


    30 meters


    Geomorphometry




    Annual precipitation


    Basin Characterization Model


    270 meters


    Climate




    Annual min temperature


    Basin Characterization Model


    270 meters


    Climate




    Climatic water deficit


    Basin Characterization Model


    270 meters


    Climate




    Actual evapotranspiration


    Basin Characterization Model


    270 meters


    Climate

2) Generating 10,000 random points. A mask was imposed to limit analyses to the 35,158 square study area and 10,000 random points were generated to create a data table of the values of each GIS layer at each of the random points. This data table was the basis for sorting the random points into a limited number of clustered types. The first step in doing this is calculating in multivariate space the distance with respect to these environmental variables each random point is from every other point, in other words creating a dissimilarity matrix.

3) Assigning weights to variables. Because the 9 environmental variables use completely different metrics and are a combination of numerical and categorical types, calculating an environmental distance between any two of these random points requires some weighting to be assigned to each of the environmental variables to sum up their relative distances. A subanalysis to determine these weightings used a subset of the study area, the Santa Clara River watershed. Since these ecological units are intended to summarize a diverse set of ecological services, we chose three different proxy variables from the GIS data available for this area to represent biomass, hydrological response, and biodiversity. These proxies included mean annual MODIS Enhanced Vegetation Index (EVI) value for biomass, recharge for hydrological response, and habitat type in the California Wildlife Habitat Relations (CWHR) classification for biodiversity.

The MODIS EVI data was derived by averaging over the 2000-2014 period the maximum EVI value in a single year. The MODIS index used was MOD13Q1 (https://lpdaac.usgs.gov/dataset_discovery/modis/modis_products_table/mod13q1) at a 250 meter resolution, available at 16-day intervals.


The hydrological recharge data were extracted from the 2014 Basin Characterization Model (Flint et al. 2013) at 270 meter resolution.


The CWHR habitat type came from the 2015 FRAP vegetation layer (FVEG15_1, from http://frap.fire.ca.gov/data/frapgisdata-sw-fveg_download), available at a 30 meter resolution.

a) We used random forest regression and classification (Hastie et al. 2009) to determine a ranking of importance values of these predictor variables using random forest regression for EVI and recharge and random forest classification for the habitat type. These were calculated using the randomForest package in R (Liaw and Wiener 2002).

b) We then averaged these three sets of importance values to create an overall set of weightings to enter into the dissimilarity matrix (Table 2).

Table 2. Weightings for each variable to reflect their relative importance to the ecological units

    VARIABLE NAME


    WEIGHT




    Precipitation


    1.00




    Annual minimum temperature


    0.600




    Slope


    0.507




    Climatic water deficit


    0.413




    Annualized solar radiation


    0.404




    Soil suborder

To approximate the distribution of shrubland species based on their postfire reproductive strategy (resprouter, seeder, and facultative seeder) across Southern California, we created a raster layer subdividing the landscape into a number of different facet classes. This raster dataset is at 30 meters pixel resolution and contains 12 different landscape facet classes based on vegetation and physiography. Specifically, the facets included several different vegetation types based on the California Wildlife Habitat Relations (WHR) classification (three shrubland categories, annual grasslands, valley-foothill riparian woodland, and ‘other’ vegetation types) which were intersected with aspect (two classes: north or south facing) and topography (summit, ridges, slopes, valleys, flats, and depressions). The combination of factors is intended to capture warmer, more exposed vegetation types dominated by seeder species (occurring on south-facing slopes, summits and ridges) versus cooler, less exposed vegetation types associated with resprouter species (occurring on north-facing slopes, valleys, depressions, and flats).

The dataset is a key input into a tool developed for resource managers to aid in the prioritization of restoration activities in shrublands postfire. The tool is available at https://github.com/adhollander/postfire and described in the following technical guide:

Underwood, Emma C., and Allan D. Hollander. 2019. “Post-Fire Restoration Prioritization for Chaparral Shrublands Technical Guide.” https://github.com/adhollander/postfire/blob/master/Postfire_Restoration_Priorization_Tool_Technical_Guide.pdf

Methods The following are the GIS processing workflow steps used to create this dataset. A diagram illustrating this workflow is in the attached file collection (SoCal_Veg_Topo_Facets_Workflow.png).

1) Compile GIS layers. There were two input layers to the GIS workflow, a 30 meter digital elevation model for California (dem30) and a vegetation raster layer of the state from the California Department of Forestry and Fire Protection (fveg15). The 30 meter DEM was downloaded from the USGS National Map (https://www.usgs.gov/core-science-systems/national-geospatial-program/national-map). The vegetation data is the FVEG dataset published in 2015 by the California Department of Forestry and Fire Protection's Fire and Resource Assessment Program (https://frap.fire.ca.gov/media/10894/fveg15_1.zip). This is a 30 meter raster representation of statewide vegetation using the California Wildlife Habitat Relationships vegetation classification system (https://wildlife.ca.gov/Data/CWHR).

2) Import data into GIS. Both data layers were imported into GRASS 7 for further processing, using a mask of the Southern California study region (encompassing the Angeles, Cleveland, Los Padres, and San Bernardino National Forests) to filter processing to the study footprint.

3) Calculate aspect for elevation model. Using the command r.slope.aspect, we generated a raster layer (aspect) giving the topographic aspect (0-360 degrees) of slopes across the study region.

4) Generate north-south aspect layer. Using the command r.mapcalc, we subdivided the aspect layer into north and south-facing slopes through creating a raster layer (nsaspect) with two categories for north and south.

5) Generate geomorphons for study region. The geomorphon raster layer derives from the dem30m surface and classifies the landscape into 10 discrete landform types, examples being ridges, slopes, hollows, and valleys. The algorithm for geomorphon classification uses a pattern recognition approach based on line of sight analysis (Jasiewisc and Stepinski 2013) and was generated using the r.geomorphons extension for GRASS 7.

6) Merge geomorphons with north-south aspect layer. In this step we combined the north-south aspect layer with the geomorphons layer to create a layer entitled nsgeomorphon2a. In so doing we grouped the geomorphon types spurs, slopes, and hollows into a single “slope” category and assigned these to north-facing slopes and south-facing slopes depending upon the value of the north-south aspect layer.

7) Regroup merged layer into three groupings. In this step we took the merged nsgeomorphon2a layer and assigned the classes in it to three different physiographic groups, namely 1) flats 2) valleys, depressions, and north-facing slopes/spurs/hollows/footslopes/shoulders and 3) summits and ridges and south-facing slopes/spurs/hollows/footslopes/shoulders. This grouped layer was named nsgeomorphon2d.

8) Reclass vegetation layer to main habitat types. The vegetation layer fveg15 contains information about many details of the vegetation, including canopy size, canopy cover, and main habitat type. This reclass step extracts the main habitat type into a separate raster named fveg15whr.

9) Combine vegetation layer with physiography layer. Using the command r.cross, we combined the layers fveg15whr and nsgeomorphon2d into a new layer nsgeoxfvegwhr with a separate category for each combination of the raster values from the two input layers.

10) Reclass combined layer into small set of groupings. Taking the nsgeoxfvegwhr layer, we recategorized the 196 combinations of raster values into a set of 12 different combinations using the command r.reclass. This layer is named nsgeoxfvegnbclasses. The 12 different classes generated as an output are the following, with their raster values paired with their classes:

0 Annual grassland: south-facing slopes; summits; ridges

1 Annual grassland: north-facing slopes; valleys; depressions; flats

2 Chamise-redshanks chaparral: south-facing slopes; summits; ridges

3 Chamise-redshanks chaparral: north-facing slopes; valleys; depressions; flats

4 Mixed or montane chaparral: south-facing slopes; summits; ridges

5 Mixed or montane chaparral: north-facing slopes; valleys; depressions; flats

6 Valley-foothill riparian: south-facing slopes; summits; ridges

7 Valley-foothill riparian: north-facing slopes; valleys; depressions; flats

8 Coastal scrub: south-facing slopes; summits; ridges

9 Coastal scrub: north-facing slopes; valleys; depressions; flats

10 Other: south-facing slopes; summits; ridges

11 Other: north-facing slopes; valleys; depressions; flats

11) Export dataset. Using the command r.out.gdal, we exported the nsgeoxfvegnbclasses layer as the raster geotiff file SoCal_Veg_Topo_Facets.tif.

The GRASS commands used for these 11 steps are below:

r.in.gdal input="/home/adh/CARangelands/Vegetation/fveg15_11.tif" output="fveg15" memory=300 offset=0

r.proj input="dem1sec_calif" location="CAllnad83" mapset="statewide" output="dem30m" method="bilinear" memory=300 resolution=30

r.slope.aspect elevation=dem30m@statewide slope=slope aspect=aspect

r.mapcalc 'nsaspect = if(aspect <= 180, 1, 2)'

r.geomorphon --overwrite dem=dem30m@statewide forms=SoCalgeomorphons search=11 skip=4 flat=1 dist=0

r.mapcalc --overwrite 'nsgeomorphon = if((SoCalgeomorphons@socalNF == 5 ||| SoCalgeomorphons@socalNF == 6 ||| SoCalgeomorphons@socalNF == 7) &&& nsaspect == 1, 11, if(((SoCalgeomorphons@socalNF == 5 ||| SoCalgeomorphons@socalNF == 6 ||| SoCalgeomorphons@socalNF == 7) &&& nsaspect == 2), 12, SoCalgeomorphons@socalNF))'

r.reclass input=nsgeomorphon2a@socalNF output=nsgeomorphon2d rules=/home/adh/SantaClaraRiver/PostfireRestoration/jupyter/datasets/nsgeomorphon-reclass2d.lut

r.reclass input="fveg15@statewide" output="fveg15whr" rules="/home/adh/CARangelands/Vegetation/fveg15whr.lut"

r.cross --overwrite input=fveg15whr@statewide,nsgeomorphon2d@socalNF output=nsgeoxfvegwhr

r.reclass --overwrite input=nsgeoxfvegwhr@socalNF output=nsgeoxfvegnbclasses rules=/home/adh/SantaClaraRiver/PostfireRestoration/datasets/fvegwhrtonbclasses.lut

r.out.gdal --overwrite input=nsgeoxfvegnbclasses@socalNF output=SoCal_Veg_Topo_Facets.tif format=GTiff type=Byte createopt=COMPRESS=DEFLATE

This file geodatabase includes the following individual layers:

Lake Bathymetric Contours: Contours lines corresponding to lake bathymetry, digitized from existing lake contour maps produced by the DNR Ecological Services Lake Mapping Unit. Use in combination with other Lake Bathymetric GIS products. Classify and label contour lines with depth values. Convert to polygons and calculate lake surface area for each depth interval. Overlay onto bathymetric DEM shaded relief image.

Lake Bathymetric Digital Elevation Model (DEM): A digital elevation model (DEM) representing lake bathymetry. Cell size is most often 5m, although 10m cells were used for some lakes to reduce grid file size. This grid contains one attribute DEPTH that represents lake depth in (negative) feet. Use in combination with other Lake Bathymetric GIS products. Reclassify DEM based on various depth intervals. Calculate zonal and neighborhood statistics. Derive slope surface. Model depth data with other cell-based parameters (e.g., slope, vegetation, substrate, chemistry) to predict habitat suitability, functional niches, etc. (Note: These raster analyses require Spatial Analyst or Arc Grid.)

Lake Bathymetric Outline: Lake outline as digitized from 1991-92 aerial photography (1m DOQ's). Use in combination with other Lake Bathymetric GIS products. Overlay onto bathymetric contour lines and bathymetric DEM shaded relief image.

Lake Bathymetric Metadata: Metadata for the Lake Bathymetry layers. Each lake is represented by a polygon. The polygon attributes contain information about when the bathymetry fieldwork was completed. This layer can be used to query for bathymetry created on or between certain dates, or to ascertain what date a particular lake was investigated. The dates are in a text field. Date formats vary from record to record.

In the Prince William Sound region of Alaska, recent glacier retreat started in the mid-1800s and began to accelerate in the mid-2000s in response to warming air temperatures (Maraldo and others, 2020). Prince William Sound is surrounded by the central Chugach Mountains and consists of numerous ocean-terminating glaciers, with rapid deglaciation increasingly exposing oversteepened bedrock walls of fiords. Deglaciation may accelerate the occurrence of rapidly moving rock avalanches (RAs), which have the potential to generate tsunamis and adversely impact maritime vessels, marine activities, and coastal infrastructure and populations in the Prince William Sound region. RAs have been documented in the Chugach Mountains in the past (Post, 1967; McSaveney, 1978; Uhlmann and others, 2013), but a time series of RAs in the Chugach Mountains is not currently available. A systematic inventory of RAs in the Chugach is needed as a baseline to evaluate any future changes in RA frequency, magnitude, and mobility. This data release presents a comprehensive historical inventory of RAs in a 4600 km2 area of the Prince William Sound. The inventory was generated from: (1) visual inspection of 30-m resolution Landsat satellite images collected between July 1984 and August 2024; and (2) the use of an automated image classification script (Google earth Engine supRaglAciaL Debris INput dEtector (GERALDINE, Smith and others, 2020)) designed to detect new rock-on-snow events from repeat Landsat images from the same time period. RAs were visually identified and mapped in a Geographic Information System (GIS) from the near-infrared (NIR) band of Landsat satellite images. This band provides significant contrast between rock and snow to detect newly deposited rock debris. A total of 252 Landsat images were visually examined, with more images available in recent years compared to earlier years (Figure 1). Calendar year 1984 was the first year when 30-m resolution Landsat data were available, and thus provided a historical starting point from which RAs could be detected with consistent certainty. By 2017, higher resolution (<5-m) daily Planet satellite images became consistently available and were used to better constrain RA timing and extent. Figure 1. Diagram showing the number of usable Landsat images per year. This inventory reveals 118 RAs ranging in size from 0.1 km2 to 2.3 km2. All of these RAs occurred during the months of May through September (Figure 2). The data release includes three GIS feature classes (polygons, points, and polylines), each with its own attribute information. The polygon feature class contains the entire extent of individual RAs and does not differentiate the source and deposit areas. The point feature class contains headscarp and toe locations, and the polyline feature class contains curvilinear RA travel distance lines that connect the headscarp and toe points. Additional attribute information includes the following: location of headscarp and toe points, date of earliest identified occurrence, if and when the RA was sequestered into the glacier, presence and delineation confidence levels (see Table 1 for definition of A, B, and C confidence levels), identification method (visual inspection versus automated detection), image platform, satellite, estimated cloud cover, if the RA is lobate, image ID, image year, image band, affected area in km2, length, height, length/height, height/length, notes, minimum and maximum elevation, aspect at the headscarp point, slope at the headscarp point, and geology at the headscarp point. Topographic information was derived from 5-m interferometric synthetic aperture radar (IfSAR) Digital Elevation Models (DEMs) that were downloaded from the USGS National Elevation Dataset website (U.S. Geological Survey, 2015) and were mosaicked together in ArcGIS Pro. The aspect and slope layers were generated from the downloaded 5-m DEM with the “Aspect” and “Slope” tools in ArcGIS Pro. Aspect and slope at the headscarp mid-point were then recorded in the attribute table. A shapefile of Alaska state geology was downloaded from Wilson and others (2015) and was used to determine the geology at the headscarp location. The 118 identified RAs have the following confidence level breakdown for presence: 66 are A-level, 51 are B-level, and 1 is C-level. The 118 identified RAs have the following confidence level breakdown for delineation: 39 are A-level and 79 are B-level. Please see the provided attribute table spreadsheet for more detailed information. Figure 2. Diagram showing seasonal timing of mapped rock avalanches. Table 1. Rock avalanche presence and delineation confidence levels Category Grade Justification Presence A Feature is clearly visible in one or more satellite images. B Feature is clearly visible in one or more satellite images but has low contrast with the surroundings and may be surficial debris from rock fall, rather than from a rock avalanche. C Feature presence is possible but uncertain due to poor quality of imagery (e.g., heavy cloud cover or shadows) or lack of multiple views. Delineation A Exact outline of the feature from headscarp to toe is clear. B General shape of the feature is clear but the exact headscarp or toe location is unclear (e.g., due to clouds or shadows). Disclaimer: Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government. References Maraldo, D.R., 2020, Accelerated retreat of coastal glaciers in the Western Prince William Sound, Alaska: Arctic, Antarctic, and Alpine Research, v. 52, p. 617-634, https://doi.org/10.1080/15230430.2020.1837715 McSaveney, M.J., 1978, Sherman glacier rock avalanche, Alaska, U.S.A. in Voight, B., ed., Rockslides and Avalanches, Developments in Geotechnical Engineering, Amsterdam, Elsevier, v. 14, p. 197–258. Post, A., 1967, Effects of the March 1964 Alaska earthquake on glaciers: U.S. Geological Survey Professional Paper 544-D, Reston, Virgina, p. 42, https://pubs.usgs.gov/pp/0544d/ Smith, W. D., Dunning, S. A., Brough, S., Ross, N., and Telling, J., 2020, GERALDINE (Google Earth Engine supRaglAciaL Debris INput dEtector): A new tool for identifying and monitoring supraglacial landslide inputs: Earth Surface Dynamics, v. 8, p. 1053-1065, https://doi.org/10.5194/esurf-8-1053-2020 Uhlmann, M., Korup, O., Huggel, C., Fischer, L., and Kargel, J. S., 2013, Supra-glacial deposition and flux of catastrophic rock-slope failure debris, south-central Alaska: Earth Surface Processes and Landforms, v. 38, p. 675–682, https://doi.org/10.1002/esp.3311 U.S. Geological Survey, 2015, USGS NED Digital Surface Model AK IFSAR-Cell37 2010 TIFF 2015: U.S. Geological Survey, https://elevation.alaska.gov/#60.67183:-147.68372:8 Wilson, F.H., Hults, C.P., Mull, C.G, and Karl, S.M, compilers, 2015, Geologic map of Alaska: U.S. Geological Survey Scientific Investigations Map 3340, pamphlet p. 196, 2 sheets, scale 1:1,584,000, https://pubs.usgs.gov/publication/sim3340

Environmental data layers used in the production of global habitat suitability models for several species of Scleractinian corals from the publication:

Davies, A.J. & Guinotte, J.M. (2011) Global Habitat Suitability for Framework-Forming Cold-Water Corals. PLoS ONE 6(4): e18483. doi:10.1371/journal.pone.0018483

Files are ASCII raster layers, zipped using 7zip. Each raster has a WGS 1984 coordinate system.

Brief methodology and outline of variables provided are presented below, for full details refer to the above manuscript in PLoS ONE:

Terrain variables

Several terrain attributes were extracted from the SRTM30 data (Table 1) following techniques and algorithms described in Wilson et al (2007). Individual approaches are described within the footnote of Table 1, however, briefly the extraction process and description of each variable is described here. Bathymetric position index (BPI) is an approach to determine topographical features based on their relative position within a neighbourhood, and can be calculated over fine or broad scales to capture smaller or larger terrain features respectively. This calculation has been developed into an ArcGIS tool by Wright et al. (2005). Slope was calculated using DEM Tools for ArcGIS developed by Jenness (2012), in particular the 4-cell method of calculating slope, which is accepted as the most accurate approach (Jones 1998). In this manuscript, slope is defined as the gradient in the direction of the maximum slope. Curvature attempts to describe terrain features and may provide an indication of how water would interact with the terrain. In this manuscript, plan and tangential curvature can describe how water would converge or diverge as it flows over relief, whilst profile curvature describes how water would accelerate or decelerate as it flows over relief (Jenness 2012). Aspect is defined as the direction of maximum slope and was converted to continuous radians following Wilson et al. (2007). Rugosity, terrain ruggedness index and roughness all generally describe the variability of the relief of the seafloor (Wilson et al. 2007). Rugosity is defined as the ratio of the surface area to the planar area across a neighbourhood of a central pixel (Jenness 2012). Terrain ruggedness index is defined as the mean difference between a central pixel and its surrounding cells and roughness which is the largest inter-cell difference of a central pixel and its surrounding cell (Wilson et al. 2007).

Environmental variables

Environmental variables were created using the variable up-scaling approach presented within (Davies & Guinotte 2011). This approach takes gridded layers of an environmental variable and drapes them over bathymetry to provide an indication of conditions near the seabed, it has been proven to work well over global and regional scales (Davies & Guinotte 2011, Guinotte & Davies 2012). In this manuscript, these environmental layers must be considered as representations of general conditions, as likely, the highly variable topography of the canyon will not yield a good prediction of environmental variables at such a small spatial scale. Limited CTD profiles were collected using a SeaBird 911+, collating data for turbidity (Seapoint, formazin turbidity units), dissolved oxygen (mg L^-1), depth (m), conductivity (Siemens/m), temperature (°C), salinity, and pH. These casts were compared with the modelled layers (specifically dissolved oxygen, salinity and temperature) to determine their relative accuracy for certain areas of the seafloor

Variable name // Filename // Units // Reference (see original paper)

Terrain Variables

Aspect // aspect // Degree // Jenness (2012)
Aspect – Eastness // eastness // // Wilson et al. (2007)
Aspect – Northness // northness // // Wilson et al. (2007)
Bathymetry // srtm30 // m // Becker et al. (2009)
Curvature – Profile // profilecurve // // Jenness (2012)
Curvature – Plan // plancurve // // Jenness (2012)
Curvature – Tangential // tangcurve // // Jenness (2012)
Roughness // roughness // // Wilson et al. (2007)
Rugosity // rugosity // // Jenness (2012)
Slope // slope // Degrees // Jenness (2012)
Terrain Ruggedness Index // tri // // Wilson et al. (2007)
Topographic Position Index // tpi // // Wilson et al. (2007)

Environmental variables
Alkalinity // alk_stein // μmol l-1 // Steinacher et al. (2009)
Apparent oxygen utilisation // woaaoxu // mol O2 m-3 // Garcia et al. (2006b)
Chlorophyll a // modismin, modismean, modismax // mg m-3 // NASA Ocean Color
Dissolved inorganic carbon // dic_stein // μmol l-1 // Steinacher et al. (2009)
Dissolved oxygen // woadiso2 // ml l-1 // Garcia et al.(2006a)
Nitrate // woanit // μmol l-1 // Garcia et al. (2006b)
Omega aragonite // arag_stein // ΩARAG // Steinacher et al. (2009)
Omega aragonite // arag_orr // ΩARAG // Orr et al. (2005)
Omega calcite // calc_stein // ΩCALC // Steinacher et al. (2009)
Omega calcite // calc_orr // ΩCALC // Orr et al. (2005)
Percent oxygen saturation // woapoxs // % O2S // Garcia et al. (2006b)
Phosphate // woaphos // μmol l-1 // Garcia et al. (2006b)
Regional current velocity // regfl // m s-1 // Carton et al. (2005)
Salinity // woasal // pss // Boyer et al. (2005)
Silicate // woasil // μmol l-1 // Garcia et al. (2006b)
Seasonal variation index // lutzsvi // // Lutz et al. (2007)
Temperature // woatemp // °C // Boyer et al. (2005)
Particulate organic carbon // poc // g Corg m-2 yr-1 // Lutz et al. (2007)
Vertical current velocity // vertfl // m s-1 // Carton et al. (2005)
Vertically generalised productivity model // vgpmmin, vgpmmean, vgpmmax // mg C m-2 d-1 // Behrenfeld and Falkowski (1997)

This dataset consist of inputs and intermediate results from the coastal scenario modelling. It is an analysis of the bio-physical factors that best explain the changes in QLUMP land use change between 1999 and 2009 along the Queensland coastal region for the classifications used in the future coastal modelling.

Methods:

The input layers (variables etc) were produced using a range of sources as shown in Table 1. Source datasets were edited to produce raster dataset at 50m resolution and reclassified to suit the needs for the analysis.

The analysis was made using the IDRISI Land Use Change Modeler using multi-layer perceptron neural network with explanatory power of bio-physical variables. In this process a range of bio-physical layers such as slope, rainfall, distance to roads etc (see full list in Table 1) are used as potential explanatory variables for the changes in the land use. The neutral network is trained on a subset of the data then tested against the remaining data, thereby giving an estimate of the accuracy of the prediction. This analysis produces suitability maps for each of the transitions between different land use classifications, along with a ranking of the important bio-physical factors for explaining the changes.

The 1999 - 2009 Land use change was analysed with of which 4 were found to be the strongest predictors of the change for various transitions between one land use and another. This dataset includes the rasters of the 4 best predictors along with a sample of the highest accuracy transition probability maps.

Format:

Table 1 (Table 1 NERP 9_4 e-atlas dataset) This table contains the list of names, short descriptions, data source and data manipulation for the input rasters for the land use change model

All GIS files are in GDA 94 Albers Australia coordinate system.

1999.tif This layer shows a rasterised form of the QLUMP land use (clipped to the GBR coastal zone as defined in 9.4) for 1999 used for analysis of bio-physical predictors of land use change. The original QLUMP data was re-classified into 18 classes then rasterised at 50m resolution. This raster was then resampled to a 500m resolution.

2009.tif This layer shows a rasterised form of the QLUMP land use (clipped to the GBR coastal zone as defined in 9.4) for 2009 used for analysis of bio-physical predictors of land use change. The original QLUMP data was re-classified into 18 classes (with addition of tourism land use) then rasterised at 50m resolution. This raster was then resampled to a 500m resolution.

Rainfall.rst This layer shows the average annual rainfall (in mm) sourced from the Average Yearly Rainfall Isohyets Queensland dataset (clipped to the GBR coastal zone as defined in 9.4) used for analysis of bio-physical predictors of land use change. The data was re-classified and resampled at 50m resolution.

Slope.rst This layer shows the slope (in degrees) value at 50m pixel resolution (clipped to the GBR coastal zone as defined in 9.4) used for analysis of bio-physical predictors of land use change. The slope was derived from the Australian Digital Elevation Model in ArcGIS (using the Slope tool of the 3D analyst Tools) at a 200m resolution. The data was resampled at 50m resolution.

SeaDist.rst This layer shows the distance (in m) to the nearest coastline (including estuaries) at 50m pixel resolution used for analysis of bio-physical predictors of land use change. It was created by applying an Euclidean distance function (in ArcGIS in the Spatial Analyst toolbox) to the “Mainland coastline” feature in the GBR features dataset available from GBRMPA.

UrbanDist.rst This layer shows the distance (in m) to the nearest pixel of urban land use at 50m pixel resolution used for analysis of bio-physical predictors of land use change. It was created by applying an Euclidean distance function (in ArcGIS in the Spatial Analyst toolbox) to the QLUMP 2009 dataset on the selected urban polygons.

Transition_potential_Other_to_DryHorticulture.rst This layer shows the probability for each pixel (50m resolution) of the coastal to transition from the land use class Other to Rain-fed Horticulture. Areas originally of a different land use class are given no values. This was produced by analysing the patterns of land use change between 1999 and 2009 in IRDISI as part of the Land Use Change Modeler where the main bio-physical variables affecting the pattern of change were identified. See details in the model results file. A high accuracy rate of 92% was calculated during testing.

Land Change Modeler MLP Model Results_Rain-fed_horticulture.docx This shows the results of the analysis of change from land use Others to rain-fed horticulture between 1999 and 2009 using four variables: Distance to existing horticulture, Rainfall, Soil type and Slope.

Transition_potential_Other_to_Drysugar.rst This layer shows the probability for each pixel (50m resolution) of the coastal to transition from the land use class Other to Rain-fed Sugar cane. Areas originally of a different land use class are given no values. This was produced by analysing the patterns of land use change between 1999 and 2009 in IRDISI as part of the Land Use Change Modeler where the main bio-physical variables affecting the pattern of change were identified. See details in the model results file. A high accuracy rate of 84% was calculated during testing.

Land Change Modeler MLP Model Results_Rain-fed_sugar.docx This shows the results of the analysis of change from land use Others to rain-fed sugar between 1999 and 2009 using three variables: Rainfall, Soil type and Slope.

Transition_potential_Other_to_Forestry.rst This layer shows the probability for each pixel (50m resolution) of the coastal to transition from the land use class Other to Forestry. Areas originally of a different land use class are given no values. This was produced by analysing the patterns of land use change between 1999 and 2009 in IRDISI as part of the Land Use Change Modeler where the main bio-physical variables affecting the pattern of change were identified. See details in the model results file. A good accuracy rate of 73% was calculated during testing.

Land Change Modeler MLP Model Results_Forestry.docx This shows the results of the analysis of change from land use Others to Forestry between 1999 and 2009 using three variables: Rainfall, Soil type and Proximity to existing forestry.

Transition_potential_Other_to_Urban.rst This layer shows the probability for each pixel (50m resolution) of the coastal to transition from the land use class Other to Urban. Areas originally of a different land use class are given no values. This was produced by analysing the patterns of land use change between 1999 and 2009 in IRDISI as part of the Land Use Change Modeler where the main bio-physical variables affecting the pattern of change were identified. See details in the model results file. A good accuracy rate of 75% was calculated during testing.

Land Change Modeler MLP Model Results_Urban.docx This shows the results of the analysis of change from land use Others to Urban between 1999 and 2009 using two variables: Slope and Proximity to existing urban areas.

IS: Í korstjánni er hægt að skoða hæðarlíkan af Íslandi og hlaða því niður. en: The Digital Elevation Model (DEM) Map from Náttúrufræðistofnun allows to view and download the DEM, find its derivative like slope and orientation and much more. ÍslandsDEM refers to "Digital Elevation Model (DEM) of Iceland" in English. A DEM is a digital representation of the Earth's surface topography, typically represented as a grid of elevation values. It provides information about the height or elevation of the terrain across a specific area, allowing for the creation of detailed three-dimensional representations of the landscape. DEM data is widely used in various applications, including geographic information systems (GIS), environmental modeling, land use planning, engineering design, and natural resource management. It can be used to analyze terrain characteristics, calculate slope and aspect, identify drainage patterns, model water flow, and assess landscape suitability for various purposes.

The solar radiation layers are simulations of solar radiation based on the Digital Surface Model. The simulation considers the topographic situation (surrounding, slope, exposition) as well as time-based variation of the sun radiation for a specific geographic location. The result is a raster visualization of the sun duration per pixel (with 1 m ground resolution). The simulation is configured to return the sun hours per pixel for a given day. Currently 3 days were calculated: 15/02 (winter), 15/05 (spring) and 15/08 (summer).

The solar radiation analysis is based on the solar radiation toolset of the ESRI ArcMap toolbox. A detailed documentation can be found in the corresponding documentation by ESRI: http://desktop.arcgis.com/en/arcmap/10.6/tools/spatial-analyst-toolbox/area-solar-radiation.htm

The analysis used the following parameters:

- Input raster: Digital Surface model provided by the Administration de la navigation aérienne (ANA) based on a LiDAR flight from 2017. (DSM available here : https://data.public.lu/fr/datasets/digital-surface-model-high-dem-resolution/ )

- Latitude : 49.46 °

- Time configuration : Time Within a day (for 3 dates: 15/02 winter, 15/05 spring and 15/08 summer)

- Hour interval: 0.5 – The solar radiation was calculated in 30 min. intervals and summed up per day.

- Slope and aspect input : The slope and aspect rasters are calculated from the input digital surface model

- Calculation directions: 32, which is adequate for a complex topography.

- Diffuse proportion : 0.3 for a generally clear sky conditions.

- Transmittitivity : 0.5 for a generally clear sky.

- Output raster: The result is an output raster representing the duration of direct incoming solar radiation.

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

This tile layer, UCR_Project_Area_Slopeshade, provides a hillshade view of the slope steepness within the boundaries of the Upper Cheat River project, proposed by the U.S. Forest Service in the Monongahela National Forest of West Virginia. Purpose:This data was included to provide additional environmental context for the user’s understanding of the project’s likely environmental impacts. Hillshaded slope maps, or "slopeshades", highlight changes in slope steepness and are particularly useful for identifying roads, trails and other linear features, as well as cliffs, escarpments and active and historical landslides.Source & Date:The data was downloaded from the WV Elevation and LIDAR Download Tool, hosted by the West Virginia GIS Technical Center. The data was collected in 2018, and downloaded on 7/20/2021 from (DEM_Mosaic_FEMA_2019-19_Tucker-Randolph_WV_1m_UTM17).Processing:The slope was calculated from the 1-meter LIDAR-derived digital elevation model. The slope model was displayed with a hillshade filter and exported as a TIFF image file. An image tile set was created from the TIFF image and uploaded to ArcGIS Online as an image tile layer.Symbology:Project Area Slope (grayscale):Flat or gentle slopes: white to light graySteeper slopes: dark gray to black

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) -

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 FieldDescriptionAREA_ACREMeadow area in acresSTATEState in which the meadow is located (CA or NV)ID*Unique meadow identifier UCDSNMxxxxxx*Note: IDs are non-sequential* HUC12Unique identifier for the Hydrologic Unit Code (HUC), level 12, in which the meadow is locatedOWNERSHIPLand ownership status (multiple sources)EDGE_COMPLEXITYGives an indication of the meadow's exposure to external conditions EDGE COMPLEXITY = (MEADOWperimeter/EAC perimeter) [EAC = Equal Area Circle]DOM_ROCKTYPEDominant rock type on which the meadow is located based on the USGS layerVEG_MAJORITYVegetation majority based on the LANDFIRE layer (GROUPVEG attribute)SOIL_SURVEYSoil 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_MUKEYMapunit Key: Unique identifier for the Mapunit in which the meadow is locatedSOIL_COKEYComponent Key: Unique identifier for the major component of the mapunit in which the meadow is located SOIL_COMP_NAMEComponent Name: Name of the soil component with the highest representative value in the mapunit in which the meadow is located MAPUNIT_KfK 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_rRepresentative value (%)of total clayCATCHMENT_AREAThe approximate area of the upstream catchment exiting through the meadow(sq. m)ELEV_MEANMean elevation (m)ELEV_RANGEElevation range (m) across each meadowED_MIN_FStopo_ROADSMinimum Euclidean Distance (m) to Forest Service Topographic Map Data Transportation Roads ED_MIN_FStopo_TRAILSMinimum Euclidean Distance (m) to Forest Service Topographic Map Data Transportation Trails ED_MIN_LAKEMinimum Euclidean Distance (m) to lake edges ED_MIN_FLOWMinimum Euclidean Distance (m) to NHD Streams/Rivers ED_MIN_SEEPMinimum Euclidean Distance (m) to NHD Seeps/Springs MDW_DEM_SLOPEMedian DEM based slope (in degrees)STRM_SLOPE_GRADELength-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_LATLatitude 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_LONLongitude 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_TypeDominant meadow hydrogeomorphic (HGM) type LAT_DDLatitude of polygon centroid in decimal degreesLONG_DDLongitude of polygon centroid in decimal degreesShape_LengthMeadow perimeter in metersShape_AreaMeadow area in sq. metersDetailed 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. Meadows adjacent to lakes or reservoirs and at nearly the same elevation as the Water bodyLacustrine Fringe (LF)1’. Not as above2Meadow sites located in an obvious topographic depression. 32’. Not as above4Sites 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)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 above5Meadows 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:USDA Forest Service Basic Ownership (OWNERCLASSIFICATION) - https://data.fs.usda.gov/geodata/edw/datasets.php?dsetCategory=boundariesNational Parks Service (UNIT_NAME) - https://irma.nps.gov/DataStore/California Protected Areas Database – CPAD (LAYER) - http://www.calands.org/Protected Area Database-US (CBI Edition) Version 2.1 (OWN_NAME) -