### 2.2.3.3. Surface Data[¶](#surface-data "Permalink to this headline")

Required surface data for each land grid cell are listed in [Table 2.2.6](#table-surface-data-required-for-clm-and-their-base-spatial-resolution) and include the glacier, lake, and urban fractions of the grid cell (vegetated and crop occupy the remainder), the fractional cover of each plant functional type (PFT), monthly leaf and stem area index and canopy top and bottom heights for each PFT, soil color, soil texture, soil organic matter density, maximum fractional saturated area, slope, elevation, biogenic volatile organic compounds (BVOCs) emissions factors, population density, gross domestic production, peat area fraction, and peak month of agricultural burning. Optional surface data include crop irrigation and managed crops. All fields are aggregated to the model’s grid from high-resolution input datasets ( [Table 2.2.6](#table-surface-data-required-for-clm-and-their-base-spatial-resolution)) that are obtained from a variety of sources described below.

Table 2.2.6 Surface data required for CLM and their base spatial resolution[¶](#id20 "Permalink to this table")  
| Surface Field
 | Resolution

 |
| --- | --- |
| Percent glacier

 | 0.05°

 |
| Percent lake and lake depth

 | 0.05°

 |
| Percent urban

 | 0.05°

 |
| Percent plant functional types (PFTs)

 | 0.05°

 |
| Monthly leaf and stem area index

 | 0.5°

 |
| Canopy height (top, bottom)

 | 0.5°

 |
| Soil color

 | 0.5°

 |
| Percent sand, percent clay

 | 0.083°

 |
| Soil organic matter density

 | 0.083°

 |
| Maximum fractional saturated area

 | 0.125°

 |
| Elevation

 | 1km

 |
| Slope

 | 1km

 |
| Biogenic Volatile Organic Compounds

 | 0.5°

 |
| Crop Irrigation

 | 0.083°

 |
| Managed crops

 | 0.5°

 |
| Population density

 | 0.5°

 |
| Gross domestic production

 | 0.5°

 |
| Peat area fraction

 | 0.5°

 |
| Peak month of agricultural waste burning

 | 0.5°

 |

At the base spatial resolution of 0.05°, the percentage of each PFT is defined with respect to the vegetated portion of the grid cell and the sum of the PFTs is 100%. The percent lake, glacier, and urban at their base resolution are specified with respect to the entire grid cell. The surface dataset creation routines re-adjust the PFT percentages to ensure that the sum of all land cover types in the grid cell sum to 100%. A minimum threshold of 0.1% of the grid cell by area is required for urban areas.

The percentage glacier mask was derived from vector data of global glacier and ice sheet spatial coverage. Vector data for glaciers (ice caps, icefields and mountain glaciers) were taken from the first globally complete glacier inventory, the Randolph Glacier Inventory version 1.0 (RGIv1.0: [Arendt et al. 2012](https://escomp.github.io/ctsm-docs/versions/master/html/tech_note/References/CLM50_Tech_Note_References.html#arendtetal2012)). Vector data for the Greenland Ice Sheet were provided by Frank Paul and Tobias Bolch (University of Zurich: [Rastner et al. 2012](https://escomp.github.io/ctsm-docs/versions/master/html/tech_note/References/CLM50_Tech_Note_References.html#rastneretal2012)). Antarctic Ice Sheet data were provided by Andrew Bliss (University of Alaska) and were extracted from the Scientific Committee on Antarctic Research (SCAR) Antarctic Digital Database version 5.0. Floating ice is only provided for the Antarctic and does not include the small area of Arctic ice shelves. High spatial resolution vector data were then processed to determine the area of glacier, ice sheet and floating ice within 30-second grid cells globally. The 30-second glacier, ice sheet and Antarctic ice shelf masks were subsequently draped over equivalent-resolution GLOBE topography (Global Land One-km Base Elevation Project, Hastings et al. 1999) to extract approximate ice-covered elevations of ice-covered regions. Grid cells flagged as land-ice in the mask but ocean in GLOBE (typically, around ice sheets at high latitudes) were designated land-ice with an elevation of 0 meters. Finally, the high-resolution mask/topography datasets were aggregated and processed into three 3-minute datasets: 3-minute fractional areal land ice coverage (including both glaciers and ice sheets); 3-minute distributions of areal glacier fractional coverage by elevation and areal ice sheet fractional coverage by elevation. Ice fractions were binned at 100 meter intervals, with bin edges defined from 0 to 6000 meters (plus one top bin encompassing all remaining high-elevation ice, primarily in the Himalaya). These distributions by elevation are used to divide each glacier land unit into columns based on elevation class.

When running with the CISM ice sheet model, CISM dictates glacier areas and elevations in its domain, overriding the values specified by CLM’s datasets. In typical CLM5 configurations, this means that CISM dictates glacier areas and elevations over Greenland.

Percent lake and lake depth are area-averaged from the 90-second resolution data of [Kourzeneva (2009, 2010)](https://escomp.github.io/ctsm-docs/versions/master/html/tech_note/References/CLM50_Tech_Note_References.html#kourzeneva2009) to the 0.05° resolution using the MODIS land-mask. Percent urban is derived from LandScan 2004, a population density dataset derived from census data, nighttime lights satellite observations, road proximity and slope ([Dobson et al. 2000](https://escomp.github.io/ctsm-docs/versions/master/html/tech_note/References/CLM50_Tech_Note_References.html#dobsonetal2000)) as described by [Jackson et al. (2010)](https://escomp.github.io/ctsm-docs/versions/master/html/tech_note/References/CLM50_Tech_Note_References.html#jacksonetal2010) at 1km resolution and aggregated to 0.05°. A number of urban radiative, thermal, and morphological fields are also required and are obtained from [Jackson et al. (2010)](https://escomp.github.io/ctsm-docs/versions/master/html/tech_note/References/CLM50_Tech_Note_References.html#jacksonetal2010). Their description can be found in Table 3 of the Community Land Model Urban (CLMU) technical note ([Oleson et al. 2010b](https://escomp.github.io/ctsm-docs/versions/master/html/tech_note/References/CLM50_Tech_Note_References.html#olesonetal2010b)).

Percent PFTs are derived from MODIS satellite data as described in [Lawrence and Chase (2007)](https://escomp.github.io/ctsm-docs/versions/master/html/tech_note/References/CLM50_Tech_Note_References.html#lawrencechase2007) (section 21.3.3). Prescribed PFT leaf area index is derived from the MODIS satellite data of [Myneni et al. (2002)](https://escomp.github.io/ctsm-docs/versions/master/html/tech_note/References/CLM50_Tech_Note_References.html#mynenietal2002) using the de-aggregation methods described in [Lawrence and Chase (2007)](https://escomp.github.io/ctsm-docs/versions/master/html/tech_note/References/CLM50_Tech_Note_References.html#lawrencechase2007) (section 2.2.3). Prescribed PFT stem area index is derived from PFT leaf area index phenology combined with the methods of [Zeng et al. (2002)](https://escomp.github.io/ctsm-docs/versions/master/html/tech_note/References/CLM50_Tech_Note_References.html#zengetal2002). Prescribed canopy top and bottom heights are from [Bonan (1996)](https://escomp.github.io/ctsm-docs/versions/master/html/tech_note/References/CLM50_Tech_Note_References.html#bonan1996) as described in [Bonan et al. (2002b)](https://escomp.github.io/ctsm-docs/versions/master/html/tech_note/References/CLM50_Tech_Note_References.html#bonanetal2002b). If the biogeochemistry model is active, it supplies the leaf and stem area index and canopy top and bottom heights dynamically, and the prescribed values are ignored.

Soil color determines dry and saturated soil albedo (section [2.3.2](https://escomp.github.io/ctsm-docs/versions/master/html/tech_note/Surface_Albedos/CLM50_Tech_Note_Surface_Albedos.html#ground-albedos)). Soil colors are from [Lawrence and Chase (2007)](https://escomp.github.io/ctsm-docs/versions/master/html/tech_note/References/CLM50_Tech_Note_References.html#lawrencechase2007).

The soil texture and organic matter content determine soil thermal and hydrologic properties (sections 6.3 and 7.4.1). The International Geosphere-Biosphere Programme (IGBP) soil dataset (Global Soil Data Task 2000) of 4931 soil mapping units and their sand and clay content for each soil layer were used to create a mineral soil texture dataset [(Bonan et al. 2002b)](https://escomp.github.io/ctsm-docs/versions/master/html/tech_note/References/CLM50_Tech_Note_References.html#bonanetal2002b). Soil organic matter data is merged from two sources. The majority of the globe is from ISRIC-WISE ([Batjes, 2006](https://escomp.github.io/ctsm-docs/versions/master/html/tech_note/References/CLM50_Tech_Note_References.html#batjes2006)). The high latitudes come from the 0.25° version of the Northern Circumpolar Soil Carbon Database ([Hugelius et al. 2012](https://escomp.github.io/ctsm-docs/versions/master/html/tech_note/References/CLM50_Tech_Note_References.html#hugeliusetal2012)). Both datasets report carbon down to 1m depth. Carbon is partitioned across the top seven CLM4 layers (\\(\\sim\\)1m depth) as in [Lawrence and Slater (2008)](https://escomp.github.io/ctsm-docs/versions/master/html/tech_note/References/CLM50_Tech_Note_References.html#lawrenceslater2008).

The maximum fractional saturated area (\\(f\_{\\max }\\) ) is used in determining surface runoff and infiltration (section 7.3). Maximum fractional saturated area at 0.125° resolution is calculated from 1-km compound topographic indices (CTIs) based on the USGS HYDRO1K dataset ([Verdin and Greenlee 1996](https://escomp.github.io/ctsm-docs/versions/master/html/tech_note/References/CLM50_Tech_Note_References.html#verdingreenlee1996)) following the algorithm in [Niu et al. (2005)](https://escomp.github.io/ctsm-docs/versions/master/html/tech_note/References/CLM50_Tech_Note_References.html#niuetal2005). \\(f\_{\\max }\\) is the ratio between the number of 1-km pixels with CTIs equal to or larger than the mean CTI and the total number of pixels in a 0.125° grid cell. See section 7.3.1 and [Li et al. (2013b)](https://escomp.github.io/ctsm-docs/versions/master/html/tech_note/References/CLM50_Tech_Note_References.html#lietal2013b) for further details. Slope and elevation are also obtained from the USGS HYDRO1K 1-km dataset ([Verdin and Greenlee 1996](https://escomp.github.io/ctsm-docs/versions/master/html/tech_note/References/CLM50_Tech_Note_References.html#verdingreenlee1996)). Slope is used in the surface water parameterization (section [2.7.2.2](https://escomp.github.io/ctsm-docs/versions/master/html/tech_note/Hydrology/CLM50_Tech_Note_Hydrology.html#surface-water-storage)), and elevation is used to calculate the grid cell standard deviation of topography for the snow cover fraction parameterization (section [2.8.1](https://escomp.github.io/ctsm-docs/versions/master/html/tech_note/Snow_Hydrology/CLM50_Tech_Note_Snow_Hydrology.html#snow-covered-area-fraction)).

Biogenic Volatile Organic Compounds emissions factors are from the Model of Emissions of Gases and Aerosols from Nature version 2.1 (MEGAN2.1; [Guenther et al. 2012](https://escomp.github.io/ctsm-docs/versions/master/html/tech_note/References/CLM50_Tech_Note_References.html#guentheretal2012)).

The default list of PFTs includes an unmanaged crop treated as a second C3 grass ([Table 2.2.1](#table-plant-functional-types)). The unmanaged crop has grid cell fractional cover assigned from MODIS satellite data ([Lawrence and Chase (2007)](https://escomp.github.io/ctsm-docs/versions/master/html/tech_note/References/CLM50_Tech_Note_References.html#lawrencechase2007)). A managed crop option uses grid cell fractional cover from the present-day crop dataset of [Ramankutty and Foley (1998)](https://escomp.github.io/ctsm-docs/versions/master/html/tech_note/References/CLM50_Tech_Note_References.html#ramankuttyfoley1998) (CLM4CNcrop). Managed crops are assigned in the proportions given by [Ramankutty and Foley (1998)](https://escomp.github.io/ctsm-docs/versions/master/html/tech_note/References/CLM50_Tech_Note_References.html#ramankuttyfoley1998) without exceeding the area previously assigned to the unmanaged crop. The unmanaged crop continues to occupy any of its original area that remains and continues to be handled just by the CN part of CLM4CNcrop. The managed crop types (corn, soybean, and temperate cereals) were chosen based on the availability of corresponding algorithms in AgroIBIS ([Kucharik et al. 2000](https://escomp.github.io/ctsm-docs/versions/master/html/tech_note/References/CLM50_Tech_Note_References.html#kuchariketal2000); [Kucharik and Brye 2003](https://escomp.github.io/ctsm-docs/versions/master/html/tech_note/References/CLM50_Tech_Note_References.html#kucharikbrye2003)). Temperate cereals include wheat, barley, and rye here. All temperate cereals are treated as summer crops (like spring wheat, for example) at this time. Winter cereals (such as winter wheat) may be introduced in a future version of the model.

To allow crops to coexist with natural vegetation in a grid cell and be treated by separate models (i.e., CLM4.5BGCcrop versus the Dynamic Vegetation version (CLM4.5BGCDV)), we separate the vegetated land unit into a naturally vegetated land unit and a human managed land unit. PFTs in the naturally vegetated land unit share one soil column and compete for water (default CLM setting). PFTs in the human managed land unit do not share soil columns and thus permit for differences in land management between crops.

CLM includes the option to irrigate cropland areas that are equipped for irrigation. The application of irrigation responds dynamically to climate (see Chapter [2.26](https://escomp.github.io/ctsm-docs/versions/master/html/tech_note/Crop_Irrigation/CLM50_Tech_Note_Crop_Irrigation.html#rst-crops-and-irrigation)). In CLM, irrigation is implemented for the C3 generic crop only. When irrigation is enabled, the cropland area of each grid cell is divided into an irrigated and unirrigated fraction according to a dataset of areas equipped for irrigation ([Siebert et al. (2005)](https://escomp.github.io/ctsm-docs/versions/master/html/tech_note/References/CLM50_Tech_Note_References.html#siebertetal2005)). The area of irrigated cropland in each grid cell is given by the smaller of the grid cell’s total cropland area, according to the default CLM4 dataset, and the grid cell’s area equipped for irrigation. The remainder of the grid cell’s cropland area (if any) is then assigned to unirrigated cropland. Irrigated and unirrigated crops are placed on separate soil columns, so that irrigation is only applied to the soil beneath irrigated crops.

Several input datasets are required for the fire model ([Li et al. 2013a](https://escomp.github.io/ctsm-docs/versions/master/html/tech_note/References/CLM50_Tech_Note_References.html#lietal2013a)) including population density, gross domestic production, peat area fraction, and peak month of agricultural waste burning. Population density at 0.5° resolution for 1850-2100 combines 5-min resolution decadal population density data for 1850–1980 from the Database of the Global Environment version 3.1 (HYDEv3.1) with 0.5° resolution population density data for 1990, 1995, 2000, and 2005 from the Gridded Population of the World version 3 dataset (GPWv3) (CIESIN, 2005). Gross Domestic Production (GDP) per capita in 2000 at 0.5° is from [Van Vuuren et al. (2006)](https://escomp.github.io/ctsm-docs/versions/master/html/tech_note/References/CLM50_Tech_Note_References.html#vanvuurenetal2006), which is the base-year GDP data for IPCC-SRES and derived from country-level World Bank’s World Development Indicators (WDI) measured in constant 1995 US$ ([World Bank, 2004](https://escomp.github.io/ctsm-docs/versions/master/html/tech_note/References/CLM50_Tech_Note_References.html#worldbank2004)) and the UN Statistics Database ([UNSTAT, 2005](https://escomp.github.io/ctsm-docs/versions/master/html/tech_note/References/CLM50_Tech_Note_References.html#unstat2005)). The peatland area fraction at 0.5° resolution is derived from three vector datasets: peatland data in Indonesia and Malaysian Borneo ([Olson et al. 2001](https://escomp.github.io/ctsm-docs/versions/master/html/tech_note/References/CLM50_Tech_Note_References.html#olsonetal2001)); peatland data in Canada ([Tarnocai et al. 2011](https://escomp.github.io/ctsm-docs/versions/master/html/tech_note/References/CLM50_Tech_Note_References.html#tarnocaietal2011)); and bog, fen and mire data in boreal regions (north of 45°N) outside Canada provided by the Global Lakes and Wetlands Database (GLWD) ([Lehner and Döll, 2004](https://escomp.github.io/ctsm-docs/versions/master/html/tech_note/References/CLM50_Tech_Note_References.html#lehnerdoll2004)). The climatological peak month for agricultural waste burning is from [van der Werf et al. (2010)](https://escomp.github.io/ctsm-docs/versions/master/html/tech_note/References/CLM50_Tech_Note_References.html#vanderwerfetal2010).

