J.I. Linard and A.M. Matherne 20121001 Raster Digital Data Reston, Virginia U.S. Geological Survey https://water.usgs.gov/lookup/getspatial?ofr2014-1158_co_nm_rusle J.I. Linard and A.M. Matherne 2014 Open-File Report OFR 2014-1158 Reston, Virginia U.S. Geological Survey https://pubs.usgs.gov/of/2014/1158/ The U.S. Geological Survey Data Series provides raster data representing an estimate of the mean-annual erosion potential of a 30-meter raster cell in Colorado and New Mexico. The units are tons per acres per year with the idea that a user will average values over an area of interest. The values were calculated using publicly available geospatial data representing variables of the Revised Universal Soil Loss Equation. This data series contributes to an Online Interactive Energy Atlas assembled/published by the U.S. Geological Survey. The Energy Atlas synthesizes data on existing and potential energy development in Colorado and New Mexico and includes additional natural resource data layers. This information may be used by decision makers to evaluate and compare the potential benefits and tradeoffs associated with different energy development strategies or scenarios. Interactive maps, downloadable data layers, comprehensive metadata, and decision-support tools will be included in the Energy Atlas. The format of the Energy Atlas facilitates the integration of information about energy with key terrestrial and aquatic resources for evaluating resource values and minimizing risks from energy development. The estimates of mean-annual erosion potential should be reasonable if averaged over larger scales, similar to the work of Miller and others (2003). With this in mind the estimated mean-annual erosion potential (tons per acre per year) is provided on a per pixel basis. To get an estimate of the mean-annual potential erosion the user should average the pixel values for their area of interest and then multiply by their area, being sure to account for the conversion from acres to their units of area. 20120808 publication date Complete None planned -110.803085 -101.360087 41.522980 31.239218 USGS Thesaurus inland waters Revised Universal Soil Loss Equation RUSLE Erosion Sediment yield Erodibility rainfall-energy factor soil-erodibility factor slope-length factor vegetative-cover factor imagery ISO 19115 Topic Category inland waters elevation and derived products geological and geophysical human health and diease imagery and base maps locations and geodetic networks oceans and estuaries USGS Metadata Identifier USGS:84db3cec-b1c5-4d91-b7da-21dc9f3664a7 Geographic Names Information System Colorado New Mexico No Access Constraints. There is no guarantee concerning the accuracy of the data. Users should be aware that changes may have occurred; therefore some parts of this data set may no longer be applicable. Users should not use this data for critical applications without a full awareness of its limitations. Acknowledgement of the originating agencies would be appreciated in products derived from these data. Any hardcopies utilizing these data sets shall clearly indicate their source. Any user who modifies the data is obligated to describe the types of modifications they perform. User specifically agrees not to misrepresent the data, nor to imply that changes made were approved or endorsed by the U.S. Geological Survey. Please refer to <http://www.usgs.gov/privacy.html> for the USGS disclaimer. Joshua Linard U.S. Geological survey Hydrologist mailing address

445 West Gunnison Ave

Grand Junction CO 81501 United States of America 970-245-5257 jilinard@usgs.gov https://water.usgs.gov/GIS/browse/co_nm_rusle.jpg Illustration of data set jpg U.S. Geological Survey Energy and Environment in the Rocky Mountain Area team Microsoft Windows 7 Version 6.2 (Build7601) Service Pack 1; Esri ArcGIS 10.1.1.3143 During the peer review process the attributes were examined with the range specified and all attributes in the data set were within the range. All data are clipped to the state boundaries of Colorado and New Mexico The mean-annual erosion potential for Colorado and New Mexico data set was generated with limitations and simplifications. The published rainfall-energy factor (USDA, 2001) used in this Data Series need to be updated with the many precipitation stations that have been added across the United States since its development, however, updating that geospatial data was outside the scope of this study. Lower-resolution State Soil Geographic (STATSGO) database data (USDA, 1994)representing mean-annual erosion potential was used because at the time of this project the higher-resolution Soil Survey Geographic (SSURGO)database data did not cover the full spatial extent at the time of this study. The slope-length factor was limited to values less than 72.15 resulting in numerous cells where estimates of mean-annual erosion potential were not calculated. Generally, the cells exist along topographic lows resembling the depositional zones or channels for which RUSLE was not designed for. The vegetative cover factors were uniformly applied to vegetation with the foreknowledge that more detailed representations of vegetation density, surface cover, surface roughness, and soil moisture would produce more realistic estimates. Given the annual time scale of the map, it was assumed these values could be assigned as constants following assumptions detailed by Renard and others (1987). With the above limitations noted, the estimates of mean-annual erosion potential should be reasonable if averaged over larger scales, similar to the work of Miller and others (2003). With this in mind the estimated mean-annual erosion potential (tons per acre per year) is provided on a per pixel basis. To get an estimate of the mean-annual potential erosion the user should average the pixel values for their area of interest and then multiply by their area, being sure to account for the conversion from acres to their units of area. Miller, J.D., Nyhan, J.W., and Yool, S.R. 2003 document International Journal of Wildland Fire, 12, p 85–100. http://www.tandfonline.com/doi/pdf/10.1080/02693799608902101 reference 2003 publication date Cerro Grand Fire RUSLE Used for RUSLE parameterization Mitasova, H., Hofierka, J., Zlocha, M., and Iverson, L.R. 1996 document International Journal of Geographical Information Science, 10, 5, p. 629–641. http://www.tandfonline.com/doi/abs/10.1080/02693799608902101#.Uwy3UbFOm_4 reference 1996 publication date Erosion and deposition with GIS Used for RUSLE parameterization Renard, K.G., Foster, G.R., Weesies, G.A., McCool, D.K., and Yoder, D.C. 1996 document U.S. Department of Agriculture Handbook No. 703. http://www.ars.usda.gov/SP2UserFiles/Place/64080530/RUSLE/AH_703.pdf reference 1996 publication date RUSLE Handbook Used for RUSLE parameterization Rodriguez, J.L.G. and Suarez, M.C.G. 2010 document Journal of Hydrologic Engineering, 15, 9, p. 714–717. reference 2010 publication date Slope length of RUSLE L Used for RUSLE parmeterization U.S. Department of Agriculture (USDA) 1994 document Natural Resources Conservation Service Miscellaneous Publication 1492. http://dbwww.essc.psu.edu/dbtop/doc/statsgo/statsgo_db.pdf reference 1994 publication date STATSGO database Used for RUSLE parameterization U.S. Environmental Protection Agency 2001 document EPA publication Fact Sheet 3.1, EPA 833-F-00-014. http://www.epa.gov/npdes/pubs/fact3-1.pdf reference 2001 publication date Rainfall Energy factor Used for RUSLE parameterization U.S. Geological Survey 1999 document U.S. Geological Survey Fact Sheet 148–99, 2 p. https://pubs.usgs.gov/fs/1999/0148/ reference 1999 publication date Elevation data Used for determining the LS factor Vogelmann, J.E., S.M. Howard, L. Yang, C.L. Larson, B.K. Wylie, and N. Van Driel. 2001 document Photogrammetric Engineering and Remote Sensing. 67:650-652. http://trid.trb.org/view.aspx?id=686441 reference 2001 publication date NLCD data Used for RUSLE parameterization Wischmeier, W.H., and Smith, D.D. 1978 document U.S. Department of Agriculture, Agriculture Handbook No. 537. http://naldc.nal.usda.gov/download/CAT79706928/PDF reference 1978 publication date RUSLE original text Used for RUSLE parameterization Yoder, D.C., Foster, G.R., Weesies, G.A., Renard, K.G., McCool, D.K., and Lown, J.B. 1998 document Presented at the 1998 ASAE Annual International Meeting, Paper NO.982197 ASAE, 2950 Niles Rd. St. Joseph, MI 439085-9659. http://s1004.okstate.edu/S1004/Regional-Bulletins/Modeling-Bulletin/rusle-yoder-001016.html reference 1998 publication date RUSLE evaluation Used for RUSLE parameterization Potential erosion across Colorado and New Mexico was calculated using estimates from a widely used erosion prediction model. The Revised Universal Soil Loss Equation (RUSLE) model was initially developed for cropland applications to predict mean-annual erosion (RUSLE, 1993). The model uses parameters representing rainfall energy, soil erodibility, contributing area, vegetation, and any erosion-control practices that might be implemented. Technological improvements have led to the incorporation of RUSLE into geospatial information systems and the ability to estimate mean-annual erosion potential in varied terrain (Miller and others, 2003; Rodriguez and Suarez, 2010; Mitasova and others, 1996). In this study geospatial data representing RUSLE parameters were obtained from publicly available sources and applied to an area extending over Colorado and New Mexico. The calculation of mean-annual erosion by RUSLE takes the form of: A = R * K * LS * C * P Where A is the mean-annual erosion potential in units of tons per acre per year, R is the rainfall-energy factor in units of tons per hectare per year, K is the soil-erodibility factor, LS is the slope-length factor, C is the vegetative cover factor, and P is the erosion-control practice factor (Mitasova, 1999). Erosion control practices, P, were not included in the model. Rainfall-Energy Factor (R) The geospatial data representing the rainfall-energy factor were obtained from the Integrated valuation of Ecosystem Services and Tradeoffs tool (http://invest.ecoinformatics.org/shared/Erosivity-US.zip/, accessed July 18, 2012). This raster data was digitized from USDA Isoerodent maps of the United States published by the U.S. Environmental Protection Agency (2001). For consistency with the other geospatial data used in this study, the raster dataset was resampled from a cell size of 1000-meters to a cell size of 30-meters. Soil-Erodibility Factor (K) The geospatial data representing soil-erodibility were obtained from the State Soil Geographic (STATSGO) database (USDA, 1994). The soil-erodibility (Kffact field in the chorizon table of the STATSGO database)from the top-most soil horizon to land surface was selected. The STATSGO polygon data were converted to raster format with a cell size of 30-meters. Slope-Length Factor (LS) Slope-length factor (LS) was calculated from the 30-meter National Elevation Dataset DEM (U.S. Geological Survey, 1999) and methods described by Mitasova and Mitas (1999). From the DEM, rasters representing slope and flow accumulation were created and used in the equation documented by Mitasova and Mitas (1999):(((flow accumulation raster)* 30/22.1)^Power(0.6))*((((sin(slope raster)*0.01745)/0.09)^Power(1.3))*1.6). Initially, RUSLE was designed for application to hillslopes with lengths less than 1,000 ft and slopes less than 60 percent (Renard and others, 1997). Although infinite slope lengths could exist, the area comprising the hillslope should have minimal areas of deposition or channelized flow (either natural or constructed) (Renard and others, 1997). For these reasons, LS values were not allowed to exceed 72.15, which is the maximum value reported by Renard and others (1997). Vegetative Cover Factor (C) The vegetative cover factor was calculated using the national land cover dataset (NLCD) (Vogelmann and others, 2001). Land cover classifications within the NLCD were assigned RUSLE coefficients that ranged from 0.001 to 0.004 based on descriptions by Wischmeier and Smith (1978). NLCD land cover classification Assigned vegetative cover factor Open water null Perennial Ice/Snow null Developed, Open Space 0.03 Developed, Low Intensity 0.03 Developed, Medium Intensity 0.03 Developed, High Intensity 0.03 Barren Land 0.05 Deciduous Forest 0.002 Evergreen Forest 0.002 Mixed Forest 0.002 Scrub/Shrub 0.04 Grassland/Herbaceous 0.01 Pasture/Hay 0.003 Cultivated Crops 0.04 Woody Wetlands 0.001 Emergent Herbaceous Wetland 0.001 When all the input variables to the RUSLE equation were prepared, the calculation was run using the Raster Calculator tool in the Spatial Analyst set of tools in the ArcToolbox. The output raster was exported to a GeoTIFF. 201208 Raster Grid Cell Albers Conical Equal Area 29.5000 45.5000 -96.000 37.5000 0.0000 0.0000 coordinate pair 30 30 Meters GCS North American 1983 GRS_1980 6378137.000000 298.257224 Raster Attribute Table Raster Value Attribute Table Producer defined ObjectID Internal Feature Number Auto-generated within Software Sequential unique whole numbers that are automatically generated. Value The value in each cell is an estimate of erosion in units of tons/acre/year. Producer defined 0 12575.9 tons/ac/year Count Auto-generated count of cells containing a particular value Auto-generated within Software Unrepresentable Domain U.S. Geological Survey Michael Ierardi Mailing

445 National Center

Reston Virginia 20192 USA 1-888-275-8487 (1-888-ASK-USGS) mierardi@usgs.gov Raster data representing erosion potential for Colorado and New Mexico Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government. Although this information product, for the most part, is in the public domain, it also contains copyrighted materials as noted in the text. Permission to reproduce copyrighted items must be secured from the copyright owner. Although these data have been processed successfully on a computer system at the U.S. Geological Survey, no warranty expressed or implied is made regarding the display or utility of the data on any other system, or for general or scientific purposes, nor shall the act of distribution constitute any such warranty. The U.S. Geological Survey shall not be held liable for improper or incorrect use of the data described and/or contained herein. Raster Data Set (.TIF) ESRI ArcGIS, Version 10 Zip Archive (unzip with WinZip, GNUzip, 7zip, or similar program) 2.956 https://water.usgs.gov/GIS/dsdl/co_nm_rusle.zip None. 20201117 US Geological Survey Ask USGS -- Water Webserver Team mailing

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Reston Virginia 20192 US 1-888-275-8487 (1-888-ASK-USGS) mierardi@usgs.gov FGDC Content Standards for Digital Geospatial Metadata FGDC-STD-001-1998