NCA-LDAS_mask: -------------- The NCA-LDAS mask is the same as the NLDAS mask. For details on how the NLDAS mask was constructed, please see: https://ldas.gsfc.nasa.gov/nldas/vegetation-class ====================================================================== CONUS_mask: ----------- Only NCA-LDAS grid points within the contiguous United States. ====================================================================== UMD_class: ---------- UMD predominant vegetation class on the NCA-LDAS grid. The predominant vegetation classes are from the University of Maryland's 1km Global Land Cover product. The data is from 1981 to 1994 using the AVHRR satellite. For further documentation, please see: https://data.mint.isi.edu/files/raw-data/land-use/USGS_LCI/GLCF%3A%20AVHRR%20Global%20Land%20Cover%20Classification.pdf Also see the following for additional reference: Hansen, M.C., R.S. DeFries, J.R.G. Townshend, and R. Sohlberg, Global land cover classification at 1 km spatial resolution using a classification tree approach. Int. J. Remote Sensing, 21(6&7), 1331-1364. https://doi.org/10.1080/014311600210209 There are 13 vegetation types in the dataset: INDEX TYPE ----- ---- 1 Evergreen Needleleaf Forest 2 Evergreen Broadleaf Forest 3 Deciduous Needleleaf Forest 4 Deciduous Broadleaf Forest 5 Mixed Forest 6 Woodland 7 Wooded Grassland 8 Closed Shrubland 9 Open Shrubland 10 Grassland 11 Cropland 12 Bare Ground 13 Urban and Built-Up The NCA-LDAS group re-gridded the land cover from the UMD 1km grid to the NCA-LDAS 0.125-degree by counting the number of 1km points of each vegetation type within the larger grid size. The LDT software was used to process the data, and LIS determined the predominant vegetation type for each NCA-LDAS grid box. Grid boxes where water is the predominant type are set to undefined. ====================================================================== STATSGO_index: -------------- STATSGO predominant soil texture index on the NCA-LDAS grid. The predominant soil texture index map is from the STATSGO-FAO blended product. STATSGO is used over CONUS, while FAO is used outside CONUS. This is a hybrid (30-second for CONUS and 5-min elsewhere) 16-category soil texture map with two soil layers represented. The FAO two-layer 5-minute 16-category global soil texture maps are remapped into a global 30-second regular lat-lon grid. Within CONUS, the soil texture is then replaced by the 30-second STATSGO data obtained from the Penn State link below. The dominant soil texture from 0-30 cm (30-100 cm) from multi-layer STATSGO soil was selected to match the FAO soil depths and to produce the top (bottom) soil texture. Information on the original STATSGO texture data and index values: http://www.soilinfo.psu.edu/index.cgi?soil_data&conus&data_cov&texture&methods Original FAO soil map index and related information can be found at: https://www.fao.org/soils-portal/data-hub/soil-maps-and-databases/faounesco-soil-map-of-the-world/en/ Further information about the source "topsoil30snew" dataset from NCAR, and to obtain the data, can be found here: https://ral.ucar.edu/solutions/products/noah-multiparameterization-land-surface-model-noah-mp-lsm There are 16 soil textures in the dataset: INDEX TYPE ----- ---- 1 SAND 2 LOAMY SAND 3 SANDY LOAM 4 SILT LOAM 5 SILT 6 LOAM 7 SANDY CLAY LOAM 8 SILTY CLAY LOAM 9 CLAY LOAM 10 SANDY CLAY 11 SILTY CLAY 12 CLAY 13 ORGANIC MATERIAL 14 WATER 15 BEDROCK 16 OTHER(land-ice) The NCA-LDAS group re-gridded the soil texture from the NCAR-provided 30-second "topsoil30snew" dataset to the NCA-LDAS 0.125-degree grid by counting the number of 30-second grids of each soil texture within the larger grid size. The LDT software was used to process the data, and LIS determined the predominant soil texture for each NCA-LDAS grid box. ====================================================================== NCAregions_mask: ---------------- The National Climate Assessment (NCA) Climate Regions -- For the NCA-LDAS domain Data description: This dataset includes NCA (minus Alaska and Hawaii for now) "climate" regions, delimited by state-political boundaries. These regions were proposed by the NCADAC Federal Advisory Committee (FAC), appointed by the Sec. of Commerce. Legend Information: -- Each region has been assigned an integer-based index value here. The corresponding region and integer values include: 1: Northeast region 2: Southeast region 3: Midwest region 4: Great Plains region 5: Northwest region 6: Southwest region -- Note 1: The full NCA region for the Southeast also includes the Caribbean; however, because the NCA-LDAS domain does not extend into the Caribbean, only a few of the islands in the Bahamas are included in this mask. -- Note 2: NCA regions for the Alaska/Arctic and the Hawaiian/Pacific Islands areas exist as well, but are not included in the NCA-LDAS mask here. For more references and information, please visit: -- https://globalchange.gov/ -- https://scenarios.globalchange.gov/regions ====================================================================== NCAextra_mask: -------------- We created a separate NCA region mask that also includes the regions of Canada and Mexico within the NCA-LDAS domain, as well as divides the Northern and Southern Great Plains into two separate regions. Legend Information: -- Each region has been assigned an integer-based index value here. The corresponding region and integer values include: 1: Northeast region 2: Southeast region 3: Midwest region 4: Northern Great Plains region 5: Southern Great Plains region 6: Northwest region 7: Southwest region 8: Mexico 9: Canada -- Note: For the official NCA regions, please use "NCAregions_mask". ====================================================================== ColUpCO_mask: ------------- We created custom river basin masks for the Columbia River as well as for the Upper Colorado River. The index of the this River Basin mask is as follows: 1 = Columbia River Basin 2 = Upper Colorado River Basin ====================================================================== Koppen-Geiger_mask: ------------------- Koppen-Geiger merged climate classes -- For the NCA-LDAS domain Data description: This dataset is for the Koppen-Geiger climate classes. For the NCA-LDAS domain, they were merged into the 10 classes, following the procedure in: Kumar, S.V., M. Jasinski, D.M. Mocko, M. Rodell, J. Borak, B. Li, H. Kato Beaudoing, and C.D. Peters-Lidard, 2019: NCA-LDAS land analysis: Development and performance of a multisensor, multivariate land data assimilation system for the National Climate Assessment. J. Hydrometeor., 20, 1571-1593, doi:10.1175/JHM-D-17-0125.1 The source data at 0.5-degree was processed at GSFC using Matlab. The nearest neighbor approach was used to get the class at each 0.125-degree NCA-LDAS grid point. There are five main climate zone groups in the Koppen climate classification scheme. They are: A - equatorial, B - arid, C - warm temperate, D - snow, and E-polar. Because of very small coverage over the NLDAS-2 domain, the equatorial climate (Af, Am, As and Aw), which only happens to the north cape of the Florida Peninsula and neighboring islands, have been merged into fully humid warm temperate with hot summer (Cfa). Similarly, the polar tundra (ET), which only happens to the Northwest corner of the NLDAS-2 domain in Canada, has been merged into fully humid snow climate with cold summer (Dfc). Similarly within the cold climate zones, snow climate with dry and warm summer (Dsb) and snow climate with dry winter and warm summer (Dwb) have been merged into fully humid snow climate with warm summer (Dfb); snow climate with dry winter and hot summer (Dwa) has been merged into fully humid snow climate with hot summer (Dfa); and snow climate with dry and cool summer (Dsc) has been merged into fully humid snow climate with cool summer (Dfc). In the original Koppen-Geiger climate map within the NLDAS-2 domain, there is also a zone of warm temperature climate with steppe and hot summer (Csa) and a zone of warm temperate climate with dry winter and warm summer (Cwb). Due to very small coverage, the Cwb zone is merged to the Bsk zone which is neighboring to the Cwb zone. The Cwb zone, which is mostly distributed south California and Mexico near the Pacific coast, has been merged into Bsk to the north of 30N and Bsh to the south of 30N. Legend Information: -- Each class has been assigned an integer-based index value here. The corresponding class and integer values include: 1: BSh = Dry, Steppe, Hot 2: BSk = Dry, Steppe, Cold 3: BWh = Dry, Desert, Hot 4: BWk = Dry, Desert, Cold 5: Cfa = Temperate, No dry season, Hot summer 6: Cfb = Temperate, No dry season, Warm summer 7: Csb = Temperate, Dry summer, Warm summer 8: Dfa = Continental, No dry season, Hot summer 9: Dfb = Continental, No dry season, Warm summer 10: Dfc = Continental, No dry season, Cold summer For more references and information, please visit: -- http://koeppen-geiger.vu-wien.ac.at/present.htm ====================================================================== NWS-RFC_mask: ------------- The National Weather Service's River Forecast Centers (RFC) -- For the NCA-LDAS domain Legend Information: -- Each region has been assigned an integer-based index value here. The corresponding region and integer values include: 1 = NWRFC = NorthWest RFC 2 = MBRFC = Missouri Basin RFC 3 = NCRFC = North-Central RFC 4 = OHRFC = OHio RFC 5 = NERFC = NorthEast RFC 6 = MARFC = Mid-Atlantic RFC 7 = CNRFC = California-Nevada RFC 8 = CBRFC = Colorado Basin RFC 9 = ABRFC = Arkansas Basin RFC 10 = WGRFC = West Gulf RFC 11 = LMRFC = Lower Mississippi RFC 12 = SERFC = SouthEast RFC For more references and information, please visit: -- https://water.weather.gov/ahps/rfc/rfc.php Many thanks to Youlong Xia (at NOAA/EMC) for providing this mask. ====================================================================== USstate_mask: ------------- U.S. CONUS state masks -- For the NCA-LDAS domain Data description: -- This dataset is a mask for each U.S. state within CONUS. Alaska and Hawaii are not in the domain. -- The source data is from Youlong Xia, NOAA/NCEP/EMC. -- States are ordered from north to south, with respect to the northern extent of each state. Legend Information: -- Each state has been assigned an integer-based index value here. The corresponding U.S. and integer values include: 2: Minnesota 3: Washington 4: Montana 5: Idaho 6: North Dakota 7: Michigan 8: Maine 9: Wisconsin 10: Oregon 11: South Dakota 12: New Hampshire 13: New York 14: Vermont 15: Wyoming 16: Iowa 17: Nebraska 18: Massachusetts 19: Pennsylvania 20: Illinois 21: Ohio 22: Connecticut 23: Rhode Island 24: California 25: Utah 26: Nevada 27: Indiana 28: New Jersey 29: Colorado 30: West Virginia 31: Missouri 32: Kansas 33: Delaware 34: Maryland 35: Virginia 36: Kentucky 38: Arizona 39: Oklahoma 40: New Mexico 41: Tennessee 42: North Carolina 43: Texas 44: Arkansas 45: South Carolina 46: Alabama 47: Georgia 48: Mississippi 49: Louisiana 50: Florida ====================================================================== Sturm-class_mask: ----------------- and Sturm-dens_mask: ---------------- A. STURM SNOW CLASS MAPS BACKGROUND The following is a description of the global seasonal Snow Classification System and data set presented in the paper: Sturm, M., J. Holmgren, and G. Liston. 1995. A Seasonal Snow Cover Classification System for Local to Global Applications. Journal of Climate. 8(5):1261-83. Based on the physical properties of snow (depth, density, thermal conductivity, number of layers, degree of wetting, etc.), Sturm et al. have divided the world's seasonal snow covers into six classes, plus water and glacier ice. Each snow cover class is defined by its physical properties, and then is empirically related to climate using 3 variables (precipitation, wind, and air temperature). Vegetation is used as a proxy for wind data: tall vegetation indicates low wind, short vegetation indicates high wind. Details are available in the above paper. A companion paper listing physical and thermal attributes for each class of snow is in preparation, but preliminary snow cover attribute values can be obtained through correspondence with the authors (addresses given below). SNOW CLASS VALUES The classes and the integer value assigned to each class are: Water = 0 Tundra snow = 1 Taiga snow = 2 Maritime snow = 3 Ephemeral snow = 4 Prairie snow = 5 Alpine snow = 6 Ice = 7 == All pixels in classes 0 & 7 (water & ice, respectively) are labelled with -9999. ---------------------------------------------------------------------- B. Global Seasonal Snow Classification System (Sturm et al., 1995): (aka, "ancillary snow density data") Description A 25 km EASE-grid snow density climatology file for Northern and Southern Hemisphere is derived using the average snow density values for January through March from Canadian and Former Soviet Union ground measurements described in Brown (1998) and Krenke (2004). Average density values are calculated within each of the six classes (Sturm et al. 1995). The following gives a summary of the different class densities in g cm-3. Sturm class Tundra (Class = 1): density = 0.26 Sturm class Taiga (Class = 2): density = 0.20 Sturm class Maritime (Class = 3): density = 0.24 Sturm class Ephemeral (Class = 4): density = 0.24 Sturm class Prairie (Class = 5): density = 0.23 Sturm class Mountain (Class = 6) density = 0.23 (Water class = 0; Ice class = 7) Global Mean = 0.24 g cm-3 (used when missing data). For the NCA-LDAS-based snow density map, the values in the above table for Sturm classes (1-6) are multiplied by 1000 (in order to convert from g/cm3 to kg/m3). "Universal" mean density of 0.24 g/cm3 wherever data are missing. - If interested in knowing associated errors with each snow class, refer to Foster et al. (2005), Figure 3. Some information is provided on how SWE may be over- or underestimated on monthly timescales when applying the snow classes. Such errors become less of a concern when mainly working with snow depth. REFERENCES Brown, R. D. and R. O. Braaten, 1998. Spatial and Temporal Variability of Canadian Monthly Snow Depths, 1946-1995. Atmosphere-Ocean 36: 37-45. Foster, J.L., Sun, C., Walker, J.P., Kelly, R., Chang, A., Dong, J. and Powell, H., 2005, Quantifying the uncertainty in passive microwave snow water equivalent observations. Remote Sensing of Environment, 94, pp. 187203. Krenke, A. 1998, updated 2004. Former Soviet Union Hydrological Snow Surveys, 1966-1996. Edited by NSIDC. Boulder, CO: National Snow and Ice Data Center/World Data Center for Glaciology. Digital media. ====================================================================== USDM-HP_mask: ------------- and USDM-West_mask: --------------- The U.S. Drought Monitor (USDM) Regions -- For the NCA-LDAS domain Data description: -- This dataset includes CONUS-wide (minus Alaska and Hawaii for now) U.S. Drought Monitor regions, delimited by state-political boundaries. These regions were defined by the U.S. Drought Monitor. Legend Information: -- Each region has been assigned an integer-based index value here. The corresponding region and integer values include: 1: West region 2: Midwest region 3: HighPlains region 4: South region 5: Southeast region 6: Northeast region -- Note: There are two separate USDM masks - one which should be used for the HighPlains region and one that should be used for the West region. The reason for this is that the states of Colorado and Wyoming are in BOTH of these regions, as defined by the USDM. Thus, any analysis of the "HighPlains" region while using the West mask will be INCORRECT. Same for an analysis of the "West" region while using the HighPlains mask. The spatial extents for all other regions are identical between the two different masks. For more references and information, please visit: -- https://www.drought.gov/ -- https://droughtmonitor.unl.edu/ ====================================================================== USGS-HUC02_mask: ---------------- The USGS Hydrological Unit Code (HUC) Basins -- For the NCA-LDAS domain Data description: -- This dataset includes USGS (minus Alaska and Hawaii for now) water basin areas boundaries, delimited by major river basin boundaries. USGS delimits the size of basins and watersheds by 8-digit codes. This dataset is described by the first two-digits, also referred to as the "2-digit" water boundary basin HUCs. This mask indicates the 2-digit boundary data by using the name "huc02". -- The mask was processed at GSFC within ArcGIS 10.x. Legend Information: -- Each region has been assigned an integer-based index value here. The corresponding basin and integer values for HUC02 include: 1: New England 2: Mid-Atlantic 3: South Atlantic-Gulf 4: Great Lakes 5: Ohio 6: Tennessee 7: Upper Mississippi 8: Lower Mississippi 9: Souris-Red-Rainy 10: Missouri 11: Arkansas-White-Red 12: Texas-Gulf 13: Rio Grande 14: Upper Colorado 15: Lower Colorado 16: Great Basin 17: Pacific Northwest 18: California For more references and information, please visit: -- https://water.usgs.gov/GIS/regions.html -- https://water.usgs.gov/GIS/huc.html -- https://en.wikipedia.org/wiki/Hydrological_code -- https://www.nrcs.usda.gov/wps/portal/nrcs/main/national/water/watersheds/dataset/ ====================================================================== Questions? E-mail: David.Mocko@nasa.gov