State Soil Geographic Database Research Articles

Vermont Global Warming Solutions Act: The Costs of Inaction from Land Conversions

The Vermont (VT) Global Warming Solutions Act (GWSA, 2020) sets greenhouse gas (GHG) emissions reduction targets at 26% below 2005 by 2025, 40% below 1990 by 2030 and 80% below 1990 by 2050 for energy-related emissions only. Vermont’s omission of GHG emissions from land conversions could result in significant costs of inaction (COI), which could hinder the state’s mitigation and adaptation plans and result in climate crisis-related risks (e.g., credit downgrade). Science-based spatio-temporal data of GHG emissions from soils because of land conversions can be integrated into the conceptual framework of “action” versus “inaction” to prevent GHG emissions. The application of soil information data and remote sensing analysis can identify the GHG emissions from land conversions, which can be expressed as “realized” social costs of “inaction”. This study demonstrates the rapid assessment of the value of regulating ecosystems services (ES) from soil organic carbon (SOC), soil inorganic carbon (SIC), and total soil carbon (TSC) stocks, based on the concept of the avoided social cost of carbon dioxide (CO2) emissions for VT by soil order and county using remote sensing and information from the State Soil Geographic (STATSGO) and Soil Survey Geographic Database (SSURGO) databases. Classified land cover data for 2001 and 2016 were downloaded from the Multi-Resolution Land Characteristics Consortium (MRLC) website. These results provide accurate and quantitative spatio-temporal information about likely GHG emissions, which can be linked to VT’s climate action plan. A failure to considerably reduce emissions from land conversions would increase climate change costs and potential legal consequences for VT and beyond its borders.

Climate Change Planning: Soil Carbon Regulating Ecosystem Services and Land Cover Change Analysis to Inform Disclosures for the State of Rhode Island, USA

The state of Rhode Island (RI) has established its greenhouse gas (GHG) emissions reduction goals, which require rapidly acquired and updatable science-based data to make these goals enforceable and effective. The combination of remote sensing and soil information data can estimate the past and model future GHG emissions because of conversion of “low disturbance” land covers (e.g., evergreen forest, hay/pasture) to “high disturbance” land covers (e.g., low-, medium-, and high-intensity developed land). These modeled future emissions can be used as a predevelopment potential GHG emissions information disclosure. This study demonstrates the rapid assessment of the value of regulating ecosystems services (ES) from soil organic carbon (SOC), soil inorganic carbon (SIC), and total soil carbon (TSC) stocks, based on the concept of the avoided social cost of carbon dioxide (CO2) emissions for RI by soil order and county using remote sensing and information from the State Soil Geographic (STATSGO) and Soil Survey Geographic Database (SSURGO) databases. Classified land cover data for 2001 and 2016 were downloaded from the Multi-Resolution Land Characteristics Consortium (MRLC) website. Obtained results provide accurate and quantitative spatio-temporal information about likely GHG emissions and show their patterns which are often associated with existing urban developments. These remote sensing tools could be used by the state of RI to both understand the nature of land cover change and likely GHG emissions from soil and to institute mandatory or voluntary predevelopment assessments and potential GHG emissions disclosures as a part of a climate mitigation policy.

Land Cover Change and Soil Carbon Regulating Ecosystem Services in the State of South Carolina, USA

Integration of land cover change with soil information is important for valuation of soil carbon (C) regulating ecosystem services (ES) and disservices (ED) and for site-specific land management. The objective of this study was to assess the change in value of regulating ES from soil organic carbon (SOC), soil inorganic carbon (SIC), and total soil carbon (TSC) stocks, based on the concept of the avoided social cost of carbon dioxide (CO2) emissions for the state of South Carolina (SC) in the United States of America (U.S.A.) by soil order (Soil Taxonomy), land cover, and land cover change (National Land Cover Database, NLCD) using information from the State Soil Geographic (STATSGO) and Soil Survey Geographic Database (SSURGO) databases. Classified land cover data for 2001 and 2016 were downloaded from the Multi-Resolution Land Characteristics Consortium (MRLC) website. The total estimated monetary mid-point value for TSC in the state of South Carolina was $124.42B (i.e., $124.42 billion U.S. dollars, where B = billion = 109) with the following monetary distribution in 2016 and percent change in value between 2001 and 2016: barren land ($259.7M, −9%) (i.e., $259.7 million U.S. dollars, where M = million = 106), woody wetlands ($33.8B, −1%), shrub/scrub ($3.9B, +9%), mixed forest ($6.9B, +5%), deciduous forest ($10.6B, −7%), herbaceous ($4.8B, −5%), evergreen forest ($28.6B, +1%), emergent herbaceous wetlands ($6.9B, −3%), hay/pasture ($7.3B, −10%), cultivated crops ($9.9B, 0%), developed, open space ($7.0B, +5%), developed, medium intensity ($978M, +46%), developed, low intensity ($2.9B, +15%), and developed, high intensity ($318M, +39%). The percent change in monetary values was different from percent change in areas because different soil orders have different TSC contents. The percent changes (between 2001 and 2016) both in areas and monetary values varied by soil order and land cover with $1.1B in likely “realized” social cost of C mostly associated with Ultisols ($658.8M). The Midlands region of the state experienced the highest gains in the “high disturbance” classes and corresponding SC-CO2 with over $421M for TSC, $354.6M for SOC, and $66.4M for SIC. Among counties, Horry County ranked first with over $142.2M in SC-CO2 for TSC, followed by Lexington ($103.7M), Richland ($95.3M), Greenville ($81.4M), York ($77.5M), Charleston ($70.7M), Beaufort ($64.1M), Berkeley ($50.9M), Spartanburg ($50.0M), and Aiken ($43.0M) counties. Spatial and temporal analyses of land cover can identify critical locations of soil carbon regulating ecosystem services at risk.

Soil carbon sequestration potential and the identification of hotspots in the eastern Corn Belt of the United States

AbstractSoil C sequestration is a significant CO2 mitigation strategy, but precise assessments of sequestration require spatially explicit modeling of potential changes in soil organic C (SOC) in response to soil, climate, land condition, and management interactions. We assessed the SOC sequestration potential of the eastern Corn Belt (ECB) in the United States (Indiana, Kentucky, Maryland, Michigan, Ohio, Pennsylvania, and West Virginia) in response to the adoption of conservation farming practices and land use change (pasture and forestation) using the SOCRATES model. Input data was provided through an intersection of the State Soil Geographic database, National Land Cover Database, and a PRISM (https://prism.oregonstate.edu) climate surface. At the end of the 20th century, the 15.3 Mha of cropped soils in the ECB contained 632 Tg C, an estimated reduction of 52% since the introduction of agriculture in the mid 1800s. Complete adoption of no‐tillage practices on prime cropland would potentially recover 147 Tg SOC over 20 yr, whereas a continuation of conventional tillage would produce a loss of 35 Tg SOC over that period. Sequestration hotspots (>500 Gg increase in SOC) under no‐tillage cover 2.3 Mha providing 28 Tg C over 20 yr. The conversion of marginal (nonprime) agricultural lands to forests would yield an additional 13 Tg C in SOC and 381 Tg C in aboveground biomass. The rehabilitation of minelands to forest would yield an additional 4 Tg C in SOC and 42 Tg C in biomass. Opportunities to sequester C in the ECB via tillage and reforestation are substantial and should be incorporated into regional and national climate change mitigation strategies.

Vulnerability of Soil Carbon Regulating Ecosystem Services due to Land Cover Change in the State of New Hampshire, USA

Valuation of soil carbon (C) regulating ecosystem services (ES) at the state level is important for sustainable C management. The objective of this study was to assess the value of regulating ES from soil organic carbon (SOC), soil inorganic carbon (SIC), and total soil carbon (TSC) stocks, based on the concept of the avoided social cost of carbon dioxide (CO2) emissions for the state of New Hampshire (NH) in the United States of America (USA) by soil order and county using information from the State Soil Geographic (STATSGO) database. The total estimated monetary mid-point value for TSC stocks in the state of New Hampshire was $73.0B (i.e., 73.0 billion U.S. dollars (USD), where B = billion = 109), $64.8B for SOC stocks, and $8.1B for SIC stocks. Soil orders with the highest midpoint value for SOC were Histosols ($33.2B), Spodosols ($20.2B), and Inceptisols ($10.1B). Soil orders with the highest midpoint value for SIC were Inceptisols ($5.8B), Spodosols ($1.0B), and Entisols ($770M, where M = million = 106). Soil orders with the highest midpoint value for TSC were Histosols ($33.8B), Spodosols ($21.2B), and Inceptisols ($15.9B). The counties with the highest midpoint SOC values were Rockingham ($15.4B), Hillsborough ($9.8B), and Coös ($9.2B). The counties with the highest midpoint SIC values were Merrimack ($1.2B), Coös ($1.1B), and Rockingham ($1.0B). The counties with the highest midpoint TSC values were Rockingham ($16.5B), Hillsborough ($10.8B), and Coös ($10.3B). New Hampshire has experienced land use/land cover (LULC) changes between 2001 and 2016. The changes in LULC across the state have not been uniform, but rather have varied by county, soil order, and pre-existing land cover. The counties that have exhibited the most development (e.g., Rockingham, Hillsborough, Merrimack) are those nearest the urban center of Boston, MA. Most soil orders have experienced losses in “low disturbance” land covers (e.g., evergreen forest, hay/pasture) and gains in “high disturbance” land covers (e.g., low-, medium-, and high-intensity developed land). In particular, Histosols are a high-risk carbon “hotspot” that contributes over 50% of the total estimated sequestration of SOC in New Hampshire while covering only 7% of the total land area. Integration of pedodiversity concepts with administrative units can be useful to design soil- and land-cover specific, cost-efficient policies to manage soil C regulating ES in New Hampshire at various administrative levels.

Soil Carbon Regulating Ecosystem Services in the State of South Carolina, USA

Sustainable management of soil carbon (C) at the state level requires valuation of soil C regulating ecosystem services (ES) and disservices (ED). The objective of this study was to assess the value of regulating ES from soil organic carbon (SOC), soil inorganic carbon (SIC), and total soil carbon (TSC) stocks, based on the concept of the avoided social cost of carbon dioxide (CO2) emissions for the state of South Carolina (SC) in the United States of America (U.S.A.) by soil order, soil depth (0–200 cm), region and county using information from the State Soil Geographic (STATSGO) database. The total estimated monetary mid-point value for TSC in the state of South Carolina was $124.36B (i.e., $124.36 billion U.S. dollars, where B = billion = 109), $107.14B for SOC, and $17.22B for SIC. Soil orders with the highest midpoint value for SOC were: Ultisols ($64.35B), Histosols ($11.22B), and Inceptisols ($10.31B). Soil orders with the highest midpoint value for SIC were: Inceptisols ($5.91B), Entisols ($5.53B), and Alfisols ($5.0B). Soil orders with the highest midpoint value for TSC were: Ultisols ($64.35B), Inceptisols ($16.22B), and Entisols ($14.65B). The regions with the highest midpoint SOC values were: Pee Dee ($34.24B), Low Country ($32.17B), and Midlands ($29.24B). The regions with the highest midpoint SIC values were: Low Country ($5.69B), Midlands ($5.55B), and Pee Dee ($4.67B). The regions with the highest midpoint TSC values were: Low Country ($37.86B), Pee Dee ($36.91B), and Midlands ($34.79B). The counties with the highest midpoint SOC values were Colleton ($5.44B), Horry ($5.37B), and Berkeley ($4.12B). The counties with the highest midpoint SIC values were Charleston ($1.46B), Georgetown ($852.81M, where M = million = 106), and Horry ($843.18M). The counties with the highest midpoint TSC values were Horry ($6.22B), Colleton ($6.02B), and Georgetown ($4.87B). Administrative areas (e.g., counties, regions) combined with pedodiversity concepts can provide useful information to design cost-efficient policies to manage soil carbon regulating ES at the state level.

The Role of Soil Texture in Local Land Surface–Atmosphere Coupling and Regional Climate

AbstractSoil hydraulic properties are critical in estimating surface and subsurface processes, including surface fluxes, the distribution of soil moisture, and the extraction of water by root systems. In most numerical weather and climate models, those properties are assigned using maps of soil texture complemented by look-up tables. Comparison of two widely used soil texture databases, the USDA State Soil Geographic database (STATSGO) and Beijing Normal University’s soil texture database (GSDE), reveals that differences are widespread and can be spatially coherent over large areas that can eventually lead to regional climate differences. For instance, over the U.S. Great Plains, GSDE stipulates finer soil grains than STATSGO, while the opposite is true over central Mexico. In this study, we employ the WRF/CLM4 modeling suite to investigate the sensitivity of the simulated regional climate to changes in the prescribed soil maps. Wherever GSDE has finer grains than STATSGO (e.g., over the U.S. Great Plains), the soil retains water more strongly, as evidenced by smaller latent heat flux (−20 W m−2), larger sensible heat flux (+20 W m−2), and correspondingly, a decrease in the 2-m humidity (−1 g kg−1) and an increase in 2-m temperature (+1.5 K). The opposite behavior is found over areas of coarser grains in GSDE (e.g., over central Mexico). Further, the changes in surface fluxes via soil texture lead to differences in the thermodynamic structure of the PBL. Results suggest that neither soil hydraulic properties nor soil moisture solely dictate the strength of surface fluxes, but in combination they alter the land–atmosphere coupling in nontrivial ways.

Valuation of Total Soil Carbon Stocks in the Contiguous United States Based on the Avoided Social Cost of Carbon Emissions

Total soil carbon (TSC) is a composite (total) stock, which is the sum of soil organic carbon (SOC) and soil inorganic carbon (SIC). Total soil carbon, and its individual two components, are all important criteria for assessing ecosytems services (ES) and for achieving United Nations (UN) Sustainable Development Goals (SDGs). The objective of this study was to assess the value of TSC stocks, based on the concept of the avoided social cost of carbon dioxide emissions, for the contiguous United States (U.S.) by soil order, soil depth (0–20, 20–100, 100–200 cm), land resource region (LRR), state, and region using information from the State Soil Geographic (STATSGO) database. The total calculated monetary value for TSC storage in the contiguous U.S. was between $8.13T (i.e., $8.13 trillion U.S. dollars, where T = trillion = 1012) and $37.5T, with a midpoint value of $21.1T. Soil orders with the highest TSC storage midpoint values were Mollisols ($7.78T) and Aridisols ($2.49T). Based on area, however, the soil orders with highest midpoint TSC values were Histosols ($21.95 m−2) and Vertisols ($5.84 m−2). Soil depth was important, with the highest values of TSC storage being found in the interval 20–100 cm ($9.87T—total midpoint value, and $1.34 m−2—midpoint area density). The soil depth interval 0–20 cm had the lowest TSC storage ($4.30T) and lowest area-density ($0.58 m−2) value, which exemplifies the prominence of TSC in the deeper subsurface layers of soil. The LRRs with the highest midpoint TSC storage values were: M—Central Feed Grains and Livestock Region ($2.82T) and D—Western Range and Irrigated Region ($2.64T), whereas on an area basis the LRRs with the highest values were I—Southwest Plateaus and Plains Range and Cotton Region ($6.90 m−2) and J—Southwestern Prairies Cotton and Forage Region ($6.38 m−2). Among the U.S. states, the highest midpoint TSC storage values were Texas ($4.03T) and Minnesota ($1.29T), while based on area this order was reversed (i.e., Minnesota: $6.16 m−2; Texas: $6.10 m−2). Comprehensive assessment of regulating ES requires TSC, which is an important measure in achieving the UN SDGs. Despite the known shortcomings of soil databases, such as their static nature and the wide ranges of uncertainty reported for various soil properties, they provide the most comprehensive information available at this time for making systematic assessments of ecosystem services at large spatial scales.

Valuation of Soil Organic Carbon Stocks in the Contiguous United States Based on the Avoided Social Cost of Carbon Emissions

Soil organic carbon (SOC) generates several ecosystem services (ES), including a regulating service by sequestering carbon (C) as SOC. This ES can be valued based on the avoided social cost of carbon (SC-CO2) from the long-term damage resulting from emissions of carbon dioxide (CO2). The objective of this study was to assess the value of SOC stocks, based on the avoided SC-CO2 ($42 per metric ton of CO2 in 2007 U.S. dollars), in the contiguous United States (U.S.) by soil order, soil depth (0–20, 20–100, 100–200 cm), land resource region (LRR), state, and region using information from the State Soil Geographic (STATSGO) database. The total calculated monetary value for SOC storage in the contiguous U.S. was between $4.64T (i.e., $4.64 trillion U.S. dollars, where T = trillion = 1012) and $23.1T, with a midpoint value of $12.7T. Soil orders with the highest midpoint SOC storage values were 1): Mollisols ($4.21T), 2) Histosols ($2.31T), and 3) Alfisols ($1.48T). The midpoint values of SOC normalized by area within soil order boundaries were ranked: 1) Histosols ($21.58 m−2), 2) Vertisols ($2.26 m−2), and 3) Mollisols ($2.08 m−2). The soil depth interval with the highest midpoint values of SOC storage and content was 20–100 cm ($6.18T and $0.84 m−2, respectively), while the depth interval 100–200 cm had the lowest midpoint values of SOC storage ($2.88T) and content ($0.39 m−2). The depth trends exemplify the prominence of SOC in the upper portions of soil. The LRRs with the highest midpoint SOC storage values were: 1) M – Central Feed Grains and Livestock Region ($1.8T), 2) T – Atlantic and Gulf Coast Lowland Forest and Crop Region ($1.26T), and 3) K – Northern Lake States Forest and Forage Region ($1.16T). The midpoint values of SOC normalized by area within LRR boundaries were ranked: 1) U – Florida Subtropical Fruit, Truck Crop, and Range Region ($6.10 m−2), 2) T – Atlantic and Gulf Coast Lowland Forest and Crop Region ($5.44 m−2), and 3) K – Northern Lake States Forest and Forage Region ($3.88 m−2). States with the highest midpoint values of SOC storage were: 1) Texas ($1.08T), 2) Minnesota ($834B) (i.e., $834 billion U.S. dollars, where B = billion = 109), and 3) Florida ($742B). Midpoint values of SOC normalized by area within state boundaries were ranked: 1) Florida ($5.44 m−2), 2) Delaware ($4.10 m−2), and 3) Minnesota ($3.99 m−2). Regions with the highest midpoint values of SOC storage were: 1) Midwest ($3.17T), 2) Southeast ($2.44T), and 3) Northern Plains ($2.35T). Midpoint values of SOC normalized by area within region boundaries were ranked: 1) Midwest ($2.73 m−2), 2) Southeast ($2.31 m−2), and 3) East ($1.82 m−2). The reported values and trends demonstrate the need for policies with regards to SOC management, which requires incentives within administrative boundaries but informed by the geographic distribution of SOC.

Determining the Value of Soil Inorganic Carbon Stocks in the Contiguous United States Based on the Avoided Social Cost of Carbon Emissions

Carbon sequestered as soil inorganic carbon (SIC) provides a regulating ecosystem service, which can be assigned a monetary value based on the avoided social cost of carbon (SC-CO2). By definition, the SC-CO2 is a measure, in dollars, of the long-term damage resulting from the emission of a metric ton of carbon dioxide (CO2). Therefore, this dollar figure also represents the value of damages avoided due to an equivalent reduction or sequestration of CO2. The objective of this study was to assess the value of SIC stocks in the contiguous United States (U.S.) by soil order, soil depth (0–20, 20–100, 100–200 cm), land resource region (LRR), state, and region using information from the State Soil Geographic (STATSGO) database together with a reported SC-CO2 of $42 (U.S. dollars). With this approach, the calculated monetary value for total SIC storage in the contiguous U.S. was between $3.48T (i.e., $3.48 trillion U.S. dollars, where T = trillion = 1012) and $14.4T, with a midpoint value of $8.34T. Soil orders with the highest (midpoint) values for SIC storage were: 1) Mollisols ($3.57T), 2) Aridisols ($1.99T), and 3) Alfisols ($841B) (i.e., $841B is 841 billion U.S. dollars, where B = billion = 109). When normalized by land area, the soil orders with the highest (midpoint) values for SIC storage were: 1) Vertisols ($3.57 m−2), 2) Aridisols ($2.45 m−2), and 3) Mollisols ($1.77 m−2). Most of the SIC value was associated with the 100–200 cm depth interval, with a midpoint value of $4T and an area-normalized value of $0.54 m−2. The LRRs with the highest (midpoint) values of SIC storage were: 1) D—Western Range and Irrigated Region ($1.77T), 2) H—Central Great Plains Winter Wheat and Range Region ($1.49T), and 3) M—Central Feed Grains and Livestock Region ($1.02T). When normalized by land area, the LRRs were ranked: 1) I—Southwest Plateaus and Plains Range and Cotton Region ($5.36 m−2), 2) J—Southwestern Prairies Cotton and Forage Region ($4.56 m−2), and 3) H—Central Great Plains Winter Wheat and Range Region ($2.56 m−2). States with the highest (midpoint) values for SIC storage were: 1) Texas ($2.96T), 2) New Mexico ($572B), and 3) Montana ($524B). When normalized by land area, the states were ranked: 1) Texas ($4.47 m−2), 2) Utah ($2.77 m−2), and 3) Minnesota ($2.17 m−2). Lastly, regions with the highest (midpoint) values for SIC storage were: 1) South Central ($3.13T), 2) West ($1.98T), and 3) Northern Plains ($1.62T). When normalized by land area, the regions were ranked: 1) South Central ($2.90 m−2), 2) Midwest ($1.32 m−2), and 3) West ($1.02 m−2). Results from this study demonstrate a new approach for assigning monetary values to SIC stocks at various scales based on their role in providing ecosystem services for climate regulation and carbon sequestration.

Assessing the Value of Soil Inorganic Carbon for Ecosystem Services in the Contiguous United States Based on Liming Replacement Costs

Soil databases are very important for assessing ecosystem services at different administrative levels (e.g., state, region etc.). Soil databases provide information about numerous soil properties, including soil inorganic carbon (SIC), which is a naturally occurring liming material that regulates soil pH and performs other key functions related to all four recognized ecosystem services (e.g., provisioning, regulating, cultural and supporting services). However, the ecosystem services value, or “true value,” of SIC is not recognized in the current land market. In this case, a negative externality arises because SIC with a positive value has zero market price, resulting in the market failure and the inefficient use of land. One potential method to assess the value of SIC is by determining its replacement cost based on the price of commercial limestone that would be required to amend soil. The objective of this study is to assess SIC replacement cost value in the contiguous United States (U.S.) by depth (0–20, 20–100, 100–200 cm) and considering different spatial aggregation levels (i.e., state, region, land resource region (LRR) using the State Soil Geographic (STATSGO) soil database. A replacement cost value of SIC was determined based on an average price of limestone in 2014 ($10.42 per U.S. ton). Within the contiguous U.S., the total replacement cost value of SIC in the upper two meters of soil is between $2.16T (i.e., 2.16 trillion U.S. dollars, where T = trillion = 1012) and $8.97T. States with the highest midpoint total value of SIC were: (1) Texas ($1.84T), (2) New Mexico ($355B, that is, 355 billion U.S. dollars, where B = billion = 109) and (3) Montana ($325B). When normalized by area, the states with the highest midpoint SIC values were: (1) Texas ($2.78 m−2), (2) Utah ($1.72 m−2) and (3) Minnesota ($1.35 m−2). The highest ranked regions for total SIC value were: (1) South Central ($1.95T), (2) West ($1.23T) and (3) Northern Plains ($1.01T), while the highest ranked regions based on area-normalized SIC value were: (1) South Central ($1.80 m−2), (2) Midwest ($0.82 m−2) and (3) West ($0.63 m−2). For land resource regions (LRR), the rankings were: (1) Western Range and Irrigated Region ($1.10T), (2) Central Great Plains Winter Wheat and Range Region ($926B) and (3) Central Feed Grains and Livestock Region ($635B) based on total SIC value, while the LRR rankings based on area-normalized SIC value were: (1) Southwest Plateaus and Plains Range and Cotton Region ($3.33 m−2), (2) Southwestern Prairies Cotton and Forage Region ($2.83 m−2) and (3) Central Great Plains Winter Wheat and Range Region ($1.59 m−2). Most of the SIC is located within the 100–200 cm depth interval with a midpoint replacement cost value of $2.49T and an area-normalized value of $0.34 m−2. Results from this study provide a link between science-based estimates (e.g., soil order) of SIC replacement costs within the administrative boundaries (e.g., state, region etc.).

Approaches for Improving Field Soil Identification

Core Ideas The traditional dominant‐component‐based approach is not reliable for identifying soils. We developed two approaches that can be used to identify soils with mobile devices. A small set of easily collected field data could greatly improve soil identification. Use of soil survey information by non‐soil‐scientists is often limited by their inability to select the correct soil map unit component (COMP). Here, we developed two approaches that can be deployed to smartphones for non‐soil‐scientists to identify COMP from the location alone or location together with easily observed field data (i.e., slope, depth to the restrictive layer, and soil texture by depth). In addition, we also compared the two newly developed approaches with a traditional approach identifying COMP based on the dominant COMP (DC‐based approach). All three approaches were tested with the Rapid Assessment of US Soil Carbon database and the combined USDA‐ NRCS Soil Survey Geographic database and the USDA‐NRCS State Soil Geographic Database. The results indicated that the observation‐based approach performed significantly better than the other two approaches, suggesting that a small set of easy‐to‐measure site‐specific observations could significantly improve COMP identification. The location‐ and DC‐based approaches had similar low performance overall. However, the location‐based approach slightly improved identifications over the DC‐based approach for cases where (i) there were multiple possible components within the soil map unit and (ii) the components were located in close proximity to a boundary of a different soil map unit polygon. The benefit of using the location‐based approach may be greater in specific soil survey areas where topography was the major factor leading to the creation of the map unit legend.

Toward inventory-based estimates of soil organic carbon in forests of the United States.

Soil organic carbon (SOC) is the largest terrestrial carbon (C) sink on Earth; this pool plays a critical role in ecosystem processes and climate change. Given the cost and time required to measure SOC, and particularly changes in SOC, many signatory nations to the United Nations Framework Convention on Climate Change report estimates of SOC stocks and stock changes using default values from the Intergovernmental Panel on Climate Change or country-specific models. In the United States, SOC in forests is monitored by the national forest inventory (NFI) conducted by the Forest Inventory and Analysis (FIA) program within the U.S. Department of Agriculture, Forest Service. The FIA program has been consistently measuring soil attributes as part of the NFI since 2001 and has amassed an extensive inventory of SOC in forest land in the conterminous United States and southeast and southcentral coastal Alaska. That said, the FIA program has been using country-specific predictions of SOC based, in part, upon a model using SOC estimates from the State Soil Geographic (STATSGO) database compiled by the Natural Resources Conservation Service. Estimates obtained from the STATSGO database are averages over large map units and are not expected to provide accurate estimates for specific locations, e.g., NFI plots. To improve the accuracy of SOC estimates in U.S. forests, NFI SOC observations were used for the first time to predict SOC density to a depth of 100cm for all forested NFI plots. Incorporating soil-forming factors along with observations of SOC into a new estimation framework resulted in a 75% (48±0.78Mg/ha) increase in SOC densities nationally. This substantially increases the contribution of the SOC pool, from approximately 44% (17Pg) of the total forest ecosystem C stocks to 56% (28Pg), in the forest C budget of the United States.

The Use of Soil Taxonomy as a Soil Type Identifier for the Shasta Lake Watershed Using SWAT

Abstract. We tested a methodology for aggregating soil properties across multiple soil survey areas according to soil taxonomic information available within the Soil Survey Geographic (SSURGO) dataset. Most hydrologic modeling studies using SSURGO assume that soil property data are adequately grouped into a “soil type” that is represented by a map unit key within SSURGO. The map unit key in SSURGO, however, is not intended for this purpose because the map unit key is not guaranteed to be the same between adjacent surveys. As a result, similar soil types are assigned a different map unit key across soil survey boundaries resulting in an artificial increase of soil types. The Soil and Water Assessment Tool (SWAT) was used to simulate hydrology using data from the low-resolution State Soil Geographic Database (STATSGO) dataset, the SSURGO dataset using map unit key as a soil type identifier (MUKEY), and the SSURGO dataset using soil taxonomy as a soil type identifier (TAXSUB; TAXa by SUBbasin) in the Shasta Lake watershed in northern California and southern Oregon. Results indicate that, of the soil properties tested, only TAXSUB maximum soil depth was significantly different from the STATSGO maximum soil depth, while both TAXSUB maximum soil depth and saturated hydraulic conductivity were significantly different from the MUKEY dataset. Based on SWAT streamflow output, the TAXSUB soil dataset generated streamflow results closer to observed streamflow data as compared to the STATSGO or MUKEY inputs, which generated streamflow values much higher than observed data. The TAXSUB soil dataset had a greater maximum soil depth, resulting in more soil water infiltration. During large precipitation events, the soil column for TAXSUB may still be able to accommodate more water, while the STATSGO and MUKEY soil columns are completely saturated with water, leading to surface runoff. The soil taxonomy grouping method within SWAT produced more accurate streamflow results than using MUKEY and STATSGO but should still be further tested in other environmental settings.

Significance of Exchanging SSURGO and STATSGO Data When Modeling Hydrology in Diverse Physiographic Terranes

The Water Availability Tool for Environmental Resources (WATER) is a TOPMODEL‐based hydrologic model that depends on spatially accurate soils data to function in diverse terranes. In Kentucky, this includes mountainous regions, karstic plateau, and alluvial plains. Soils data are critical because they quantify the space to store water, as well as how water moves through the soil to the stream during storm events. We compared how the model performs using two different sources of soils data—Soil Survey Geographic Database (SSURGO) and State Soil Geographic Database laboratory data (STATSGO)—for 21 basins ranging in size from 17 to 1564 km2. Model results were consistently better when SSURGO data were used, likely due to the higher field capacity, porosity, and available‐water holding capacity, which cause the model to store more soil‐water in the landscape and improve streamflow estimates for both low‐ and high‐flow conditions. In addition, there were significant differences in the conductivity multiplier and scaling parameter values that describe how water moves vertically and laterally, respectively, as quantified by TOPMODEL. We also evaluated whether partitioning areas that drain to streams via sinkholes in karstic basins as separate hydrologic modeling units (HMUs) improved model performance. There were significant differences between HMUs in properties that control soil‐water storage in the model, although the effect of partitioning these HMUs on streamflow simulation was inconclusive.

Equal-area spline functions applied to a legacy soil database to create weighted-means maps of soil organic carbon at a continental scale

There is a growing need for raster-based soil data to support modelling at regional and continental scales. The GlobalSoilMap consortium aims to satisfy this need with the production of a suite of digital soil maps of various soil properties at six standard depths for most of the land surface of the Earth. Initially, the maps will be produced using legacy soil data (soil data already available). In the United States, the State Soil Geographic (STATSGO2) database is a rich source of legacy soil information, but the STATSGO2 map is vector-based and soil components' soil property data is organised by horizon. We therefore applied the equal-area spline function to the soil components of STATSGO2 map units in order to obtain estimates of soil organic carbon (SOC) content at the GlobalSoilMap standard depth increments. Using these estimates, we calculated the weighted mean of SOC for each STATSGO2 map unit at each GlobalSoilMap.net depth increment and gridded these weighted means at 100m resolution for the contiguous United States. The result is a set of maps of SOC content for each GlobalSoilMap depth increment accompanied by an indication of the within-map unit variability. In addition, we show how the use of other “metadata maps” is essential for avoiding misunderstandings about reported values and discuss some of the limitations of the approach.

Approaches of soil data aggregation for hydrologic simulations

This study presents a comprehensive investigation of the State Soil Geographic Database (STATSGO) and the Soil Survey Geographic Database (SSURGO) soil databases for their applications in hydrologic modeling practices, and provides detailed instructions on soil data aggregation. Two types of soil data aggregations are developed and improved for the preparation of soil input data: (1) spatially-based aggregation for hydrologic models which require one representative soil profile for each spatial units of modeling simulations; and (2) taxonomy-based aggregation to handle the potential edge-matching issues in SSURGO, i.e. the artificial split of a soil type by the boundary of soil survey areas. The developed approach and program were applied at the spatial scales of soil survey area and watershed in California, U.S. representing hydrologic simulation domains with areas at the magnitudes of 1000 km2 and 10,000 km2, respectively. Edge-matching issues were identified for more than 20% of the involved soil map-units for both cases, about 90% among which were handled by the proposed approach in this study. The naming inconsistency in soil taxonomy was recognized as one of the causes for remaining issues. The reductions of soil map-units and components numbers before and after the aggregation are less than 10%, indicating that the proposed procedure has the capability to effectively handle the edge-matching issues while maintaining spatial resolution of the soil data.

SAC-SMA a priori parameter differences and their impact on distributed hydrologic model simulations

Summary Deriving a priori gridded parameters is an important step in the development and deployment of an operational distributed hydrologic model. Accurate a priori parameters can reduce the manual calibration effort and/or speed up the automatic calibration process, reduce calibration uncertainty, and provide valuable information at ungauged locations. Underpinned by reasonable parameter data sets, distributed hydrologic modeling can help improve water resource and flood and flash flood forecasting capabilities. Initial efforts at the National Weather Service Office of Hydrologic Development (NWS OHD) to derive a priori gridded Sacramento Soil Moisture Accounting (SAC-SMA) model parameters for the conterminous United States (CONUS) were based on a relatively coarse resolution soils property database, the State Soil Geographic Database (STATSGO) (Soil Survey Staff, 2011) and on the assumption of uniform land use and land cover. In an effort to improve the parameters, subsequent work was performed to fully incorporate spatially variable land cover information into the parameter derivation process. Following that, finer-scale soils data (the county-level Soil Survey Geographic Database (SSURGO) ( Soil Survey Staff, 2011a , Soil Survey Staff, 2011b ), together with the use of variable land cover data, were used to derive a third set of CONUS, a priori gridded parameters. It is anticipated that the second and third parameter sets, which incorporate more physical data, will be more realistic and consistent. Here, we evaluate whether this is actually the case by intercomparing these three sets of a priori parameters along with their associated hydrologic simulations which were generated by applying the National Weather Service Hydrology Laboratory’s Research Distributed Hydrologic Model (HL-RDHM) ( Koren et al., 2004 ) in a continuous fashion with an hourly time step. This model adopts a well-tested conceptual water balance model, SAC-SMA, applied on a regular spatial grid, and links to physically-based kinematic hillslope and channel routing models. Discharge and soil moisture simulated using the different set of parameters are presented to show how the parameters affect the results and under what conditions one set of parameters works better than another. In total, 63 basins ranging in size from 30 km2 to 5224 km2 were selected for this study. Sixteen of them were used to study the effects of different a priori parameters on simulated flow. Simulated hourly flow time series from three cases were compared to hourly observed data to compute statistics. Although the overall statistics are similar for the three different sets of parameters, improvements in simulated flow are observed for small basins when SSURGO-based parameters are used. Fifty-seven basins covering different climate regimes were used to analyze differences in the modeled soil moisture. Results again showed that the use of SSURGO-based parameters generate better soil moisture results when compared to STATSGO-based results, especially for the upper soil layer of smaller basins and wet basins.

Risk of Inundation to Coastal Wetlands and Soil Organic Carbon and Organic Nitrogen Accounting in Louisiana, USA

Exceeding 1.2 million acres (4856 km(2)) since the 1930s, coastal wetland loss has been the most threatening environmental problem in Louisiana, United States. This study utilized high-resolution LiDAR (Light Detection and Ranging) and DEM (Digital Elevation Model) data sets to assess the risk of potential wetland loss due to future sea level rises, their spatial distribution, and the associated loss of soil organic carbon (SOC) and organic nitrogen (SON) estimated from the State Soil Geographic (STATSGO) Database and National Wetlands Inventory (NWI) digital data. Potential inundation areas were divided into five elevation scales: < 0 cm, 0-50 cm, 50-100 cm, 100-150 cm, and 150-200 cm above mean sea level. The study found that southeastern Louisiana on the Mississippi River Delta, specifically the Pontchartrain and Barataria Basins, are most vulnerable to sea-level rise induced inundation. Accordingly, approximately 42,264,600 t of SOC and 2,817,640 t of SON would be inundated by 2050 using an average wetland SOC density (203 t per hectare) for the inundation areas between 0 and 50 cm. The estimated annual SOC and SON loss from Louisiana's coast is 17% of annual organic carbon and 6-8% of annual organic nitrogen inputs from the Mississippi River.

Scale Effects of Geographical Soil Datasets on Soil Carbon Estimation in Louisiana, USA: A Comparison of STATSGO and SSURGO

Estimation of soil organic carbon (SOC) pools and fluxes bears large uncertainties because SOC stocks vary greatly over geographical space and through time. Although development of the U.S. Soil Survey Geographic Database (SSURGO), currently the most detailed level with a map scale ranging from 1:12 000 to 1:63 360, has involved substantial government funds and coordinated network efforts, very few studies have utilized it for soil carbon assessment at the large landscape scale. The objectives of this study were to 1) compare estimates in soil organic matter among SSURGO, the State Soil Geographic Database (STATSGO), and referenced field measurements at the soil map unit; 2) examine the influence of missing data on SOC estimation by SSURGO and STATSGO; 3) quantify spatial differences in SOC estimation between SSURGO and STATSGO, specifically for the state of Louisiana; and 4) assess scale effects on soil organic carbon density (SOCD) estimates from a soil map unit to a watershed and a river basin scale. SOC was estimated using soil attributes of SSURGO and STATSGO including soil organic matter (SOM) content, soil layer depth, and bulk density. Paired t-test, correlation, and regression analyses were performed to investigate various relations of SOC and SOM among the datasets. There were positive relations of SOC estimates between SSURGO and STATSGO at the soil map unit ( R 2 = 0.56, n = 86, t = 1.65, P = 0.102; depth: 30 cm). However, the SOC estimated by STATSGO were 9%, 33% and 36% lower for the upper 30-cm, the upper 1-m, and the maximal depth (up to 2.75 m) soils, respectively, than those from SSURGO. The difference tended to increase as the spatial scale changes from the soil map unit to the watershed and river basin scales. Compared with the referenced field measurements, the estimates in SOM by SSURGO showed a closer match than those of STATSGO, indicating that the former was more accurate than the latter in SOC estimation, both in spatial and temporal resolutions. Further applications of SSURGO in SOC estimation for the entire United States could improve the accuracy of soil carbon accounting in regional and national carbon balances.