State Soil Geographic Database Research Articles

An enhanced and automated approach for deriving a priori SAC-SMA parameters from the soil survey geographic database

This paper presents an automated approach for processing the Soil Survey Geographic (SSURGO) Database and the National Land Cover Database (NLCD), and deriving gridded a priori parameters for the National Weather Service (NWS) Sacramento Soil Moisture Accounting (SAC-SMA) model from these data sets. Our approach considerably extends methods previously used in the NWS and offers automated and geographically invariant ways of extracting soil information, interpreting soil texture, and spatially aggregating SAC-SMA parameters. The methodology is composed of four components. The first and second components are SSURGO and NLCD preprocessors. The third component is a parameter generator producing SAC-SMA parameters for each soil survey area on an approximately 30-m grid mesh. The last component is a postprocessor creating parameters for user-specified areas of interest on the Hydrologic Rainfall Analysis Project (HRAP) grid. Implemented in open-source software, this approach was employed by creating a set of SAC-SMA parameter and related soil property grids spanning 25 states, wherein it was shown to greatly reduce the derivation time and meanwhile yield results comparable to those based on the State Soil Geographic Database (STATSGO). The broad applicability of the methodologies and associated intermediate products to hydrologic modeling is discussed.

Inverse Method for Estimating the Spatial Variability of Soil Particle Size Distribution from Observed Soil Moisture

Soil particle size distribution (PSD) (i.e., clay, silt, sand, and rock contents) information is one of critical factors for understanding water cycle since it affects almost all of water cycle processes, e.g., drainage, runoff, soil moisture, evaporation, and evapotranspiration. With information about soil PSD, we can estimate almost all soil hydraulic properties (e.g., saturated soil moisture, field capacity, wilting point, residual soil moisture, saturated hydraulic conductivity, pore-size distribution index, and bubbling capillary pressure) based on published empirical relationships. Therefore, a regional or global soil PSD database is essential for studying water cycle regionally or globally. At the present stage, three soil geographic databases are commonly used, i.e., the Soil Survey Geographic database, the State Soil Geographic database, and the National Soil Geographic database. Those soil data are map unit based and associated with great uncertainty. Ground soil surveys are a way to reduce this...

Does soil data resolution matter? State Soil Geographic database versus Soil Survey Geographic database in rainfall-runoff modeling across Wisconsin

Whether or not the use of generalized, State Soil Geographic (STATSGO) data in place of higher resolution Soil Survey Geographic (SSURGO) data reduces the accuracy of hydrologic and nonpoint source pollution models has thus far been an open question. Comparative studies have yet to reveal a systematic bias in STATSGO–based model outputs on account of their small sample sizes and differences in the models employed. In an effort to determine whether a bias exists, direct runoff was modeled for a hypothetical 24-hour rainfall event, using STATSGO and SSURGO as alternative inputs to a series of standard rainfall-runoff models in nearly 300 contiguous watersheds, spanning most of the state of Wisconsin. The Long-Term Hydrologic Impact Assessment (L-THIA) modeling tool was used for this analysis. Results indicate that there is a negative bias in STATSGO–based runoff over the large majority of the study area and that the degree of underprediction is highest for spatially disaggregated (distributed parameter) models. Runoff was also modeled for daily precipitation in six gauged watersheds and was compared to observed runoff, with SSURGO–based, distributed models typically producing the most accurate outputs. In addition, a series of regression analyses was conducted to determine whether, and in what direction, the STATSGO bias is affected by the percent coverage of land uses that discourage infiltration. The results of these analyses suggest that STATSGO–based, lumped, and partially distributed models, on average, underpredict the relative impact of increasing land-use intensity. These findings indicate that two of the most common approaches to improving the computational efficiency of watershed modeling systems: the use of lower resolution soils data and the lumping of model parameters to larger spatial units of analysis, combine to reduce the accuracy of modeled runoff under current conditions, while simultaneously underestimating the impact of potential future land-use change.

Effects of the resolution of soil dataset and precipitation dataset on SWAT2005 streamflow calibration parameters and simulation accuracy

The resultant calibration parameter values and simulation accuracy of hydrologic models such as the 2005 Soil and Water Assessment Tool (SWAT2005) depend on how well spatial input parameters describe the characteristics of the study area. The objectives of this study were to (1) investigate the effect of soils dataset resolution (State Soil Geographic Database and Soil Survey Geographic Database) on SWAT2005 streamflow simulation performance and calibration parameters using four precipitation datasets and (2) determine the best combination of soil and precipitation datasets for the Cobb Creek, Lake Creek, and Willow Creek subwatersheds within the Fort Cobb Reservoir Experimental watershed, Oklahoma. SWAT2005 was calibrated and validated for streamflow for the three subwatersheds using the State Soil Geographic Database and the Soil Survey Geographic Database for each of the four available precipitation datasets with different spatial resolutions. The four sources of rainfall data included the National Weather Service's network of Cooperative Observer Program weather stations, statewide Oklahoma Mesonet, USDA Agricultural Research Service's weather station network (MICRONET), and National Weather Service Next Generation Radar (NEXRAD) precipitation estimates. The model performance was assessed using the Nash-Sutcliffe efficiency coefficient and percent bias statistics. During both the calibration and validation periods, there were no significant differences in the model monthly performance statistics between the higher resolution Soil Survey Geographic Database and the lower resolution State Soil Geographic Database across subwatersheds, irrespective of the rainfall dataset used. However, the model performed better when the NEXRAD and MICRONET precipitation datasets were used. There were slight to large differences in the resultant calibration parameter values depending on the calibration parameter, the precipitation data used, and the subwatershed. Large differences in the simulated surface runoff and deep aquifer recharge due to soils dataset resolution could lead to significant differences in the simulated water quality components such as sediments and nutrients. This is important because significant differences in simulated sediments and/or nutrients could lead to significantly different outcomes in terms of the impacts of a given conservation practice for studies like the Conservation Effects Assessment Project. Due to the lack of measured data to validate the simulated water balance components, it was recommended to use both the fine and coarse resolution soil datasets in combination with the finer spatial resolution precipitation datasets and the simulated water balance components of interest reported as a range.

Soil Organic Carbon Stocks in Alaska Estimated with Spatial and Pedon Data

Temperatures in high‐latitude ecosystems are increasing faster than the average rate of global warming, which may lead to a positive feedback for climate change by increasing the respiration rates of soil organic C. If a positive feedback is confirmed, soil C will represent a source of greenhouse gases that is not currently considered in international protocols to regulate C emissions. We present new estimates of the stocks of soil organic C in Alaska, calculated by linking spatial and field data developed by the USDA NRCS. The spatial data are from the State Soil Geographic database (STATSGO), and the field and laboratory data are from the National Soil Characterization Database, also known as the pedon database. The new estimates range from 32 to 53 Pg of soil organic C for Alaska, formed by linking the spatial and field data using the attributes of Soil Taxonomy. For modelers, we recommend an estimation method based on taxonomic subgroups with interpolation for missing areas, which yields an estimate of 48 Pg. This is a substantial increase over a magnitude of 13 Pg estimated from only the STATSGO data as originally distributed in 1994, but the increase reflects different estimation methods and is not a measure of the change in C on the landscape. Pedon samples were collected between 1952 and 2002, so the results do not represent a single point in time. The linked databases provide an improved basis for modeling the impacts of climate change on net ecosystem exchange.

Spatial resolution of soil data and channel erosion effects on SWAT model predictions of flow and sediment

Water quality modeling efforts for developing total maximum daily loads often use geographical information system data of varying quality in watershed-scale models and have shown varying impacts on model results. Several streams in the southern Piedmont are listed for sediment total maximum daily loads. The objective of this study was to test the effect of spatial resolution of soil data on the SWAT (Soil and Water Assessment Tool) model predictions of flow and sediment and to calibrate the SWAT model for a watershed dominated by channel erosion. The state soil geographic (STATSGO) database mapped at 1:250,000 scale was compared with the soil survey geographic (SSURGO) database mapped at 1:12,000 scale in an ArcSWAT model of the North Fork Broad River in Georgia. Model outputs were compared for the effect of soil data before calibration using default model parameters as calibration can mask the effect of soil data. The model predictions of flow and sediment by the two models were similar, and the differences were statistically insignificant (α = 0.05). These results were attributed to the similarity in key soil property values in the two databases that govern stream flow and sediment transport. The two models after calibration had comparable model efficiency in simulating stream flow and sediment loads. The calibrated models indicated that channel erosion contributed most of the suspended sediment in this watershed. These findings indicate that less detailed soil data can be used because more time, effort, and computational resources are required to set up and calibrate a model with more detailed soil data, especially in a larger watershed.

A multiscale database of soil properties for regional environmental quality modeling in the western United States

The USDA Natural Resources Conservation Service State Soil Geographic (STATSGO) database contains general soils information, but data available in STATSGO cannot be readily extracted nor parameterized to support regional environmental quality modeling. As such, each user must individually and repeatedly process data in STATSGO to obtain necessary soil properties. The objective of this study was to develop a comprehensive database, the Western States Soil Database (WSSD) (http://www.lar.wsu.edu/nw-airquest/soils_database.html), for use in modeling regional soil and water resources and environmental quality across eight western states (Washington, Oregon, California, Idaho, Nevada, Utah, Montana, and Wyoming). We aggregated existing soil properties in STATSGO from 19,681 map units of the eight states and estimated soil properties based upon quantitative relationships among existing soil properties. The WSSD comprises 3,910 map units, with each map unit defined by 10 soil layers and each layer characterized by 31 soil properties. The WSSD was gridded to 1 and 12 km (0.62 and 7.44 mi) resolution cells for application to grid-based environmental models. Data from WSSD was tested against USDA Natural Resources Conservation Service field data and indicated satisfactory agreement; for example, the Root Mean Square Error (RMSE) for sand and clay content varied between 4% and 7%. The RMSE appeared to be greatest for organic matter and was as large as 106% of the measured value. The WSSD provides information on soil properties useful for regional-scale modeling.

Continental United States Atmospheric Wet Calcium Deposition and Soil Inorganic Carbon Stocks

Organic and inorganic soil C pools are major components of the global C budget, yet they are still poorly estimated and understood. This study ranked atmospheric wet Ca2+ deposition from 1994 to 2003 within the continental United States by soil order using spatial analysis of Ca2+ wet deposition data and a state soil geographic database. The total average annual atmospheric wet deposition (AAAWD) of Ca2+ within the continental United States was 8.6 × 108 kg, which would be equivalent to the theoretical formation of 2.6 × 108 kg C as soil inorganic C (SIC), barring losses from erosion and deep leaching. The soil orders receiving the highest area‐normalized total AAAWD of Ca2+ were: (i) Alfisols (172 kg km−2 Ca2+), (ii) Mollisols (170 kg km−2 Ca2+), (iii) Histosols (168 kg km−2 Ca2+), and (iv) Vertisols (157 kg km−2 Ca2+). Barring losses from erosion and leaching, these Ca2+ wet deposition fluxes would equate to the theoretical formation of the following amounts of area‐normalized total C equivalents in the pedon: (i) Alfisols, 52 kg C km−2; (ii) Mollisols, 51 kg C km−2; (iii) Histosols, 50 kg C km−2; and (iv) Vertisols, 47 kg C km−2 The sequestration of SIC has been shown to be important in soil orders of nonarid regions, particularly in Mollisols and Alfisols.

Topographic Effects on Soil Organic Carbon in Louisiana Watersheds

Terrestrial carbon storage is influenced by a number of environmental factors, among which topographic and geomorphological features are of special significance. This study was designed to examine the relationships of soil organic carbon (SOC) density to various terrain parameters and watershed characteristics across Louisiana, USA. A polygon data set of 484 watersheds and 12 river drainage basins for Louisiana was used to form the landscape units. SOC densities were calculated for each soil map unit using the State Soil Geographic (STATSGO) database. Average drainage densities and average slopes at watershed and basin scales were quantified with the 1:24 K Digital Elevation Models (DEM) data, and the Louisiana hydrographic water features. Correlation and regression analyses were performed to determine relationships among drainage density, slope, elevation, and SOC. The study found an average watershed drainage density of 1.6 km/km(2) and an average watershed slope of 2.9 degrees in Louisiana. The results revealed that SOC density at both watershed and basin scales was closely related to drainage density, slope, and elevation. SOC density was positively correlated with watershed drainage density, but negatively correlated with watershed slope gradient and elevation. Regression models were developed for predicting SOC density at watershed and basin scales, obtaining regression coefficients (r (2)) ranging from 0.43 to 0.83. The study showed that estimation of SOC at watershed and drainage basin scales combining DEM data can be a feasible approach to improve the understanding of the relationships among SOC, topographic, and geomorphological features.

Ground-penetrating radar soil suitability map of the conterminous United States

The performance of ground-penetrating radar (GPR) in soils strongly depends on soil electrical conductivity. Soils having high electrical conductivity rapidly attenuate radar energy, which restricts penetration depths and severely limits the effectiveness of GPR. Factors influencing the electrical conductivity of soils include the amount and type of salts in solution and the clay content. The Ground-Penetrating Radar Soil Suitability Map of the Conterminous United States is a thematic map showing the relative suitability of soils for GPR applications within comparatively large areas of the United States. This map is based on over twenty-five years of field observations made throughout the United States and soil attribute data contained in the State Soil Geographic (STATSGO) database. Attribute data used to determine the suitability of soils include taxonomic criteria, clay content, salinity, sodium absorption ratio, and calcium carbonate content. Based on additional testing, several GPR specific adjustments have been made. This paper describes these revisions and outlines the procedures used to develop the revised map. The revised map limits areas that are rated as being “Unsuited” for GPR to saline and sodic soils, reassesses calcareous and gypsiferous soils, and provides a mineralogy override for soils with low activity clays. This map can be used to assess the relative appropriateness of GPR for soil investigations within comparatively large areas of the conterminous United States.

Effects of soil data resolution on SWAT model stream flow and water quality predictions

The prediction accuracy of agricultural nonpoint source pollution models such as Soil and Water Assessment Tool (SWAT) depends on how well model input spatial parameters describe the characteristics of the watershed. The objective of this study was to assess the effects of different soil data resolutions on stream flow, sediment and nutrient predictions when used as input for SWAT. SWAT model predictions were compared for the two US Department of Agriculture soil databases with different resolution, namely the State Soil Geographic database (STATSGO) and the Soil Survey Geographic database (SSURGO). Same number of sub-basins was used in the watershed delineation. However, the number of HRUs generated when STATSGO and SSURGO soil data were used is 261 and 1301, respectively. SSURGO, with the highest spatial resolution, has 51 unique soil types in the watershed distributed in 1301 HRUs, while STATSGO has only three distributed in 261 HRUS. As a result of low resolution STATSGO assigns a single classification to areas that may have different soil types if SSURGO were used. SSURGO included Hydrologic Response Units (HRUs) with soil types that were generalized to one soil group in STATSGO. The difference in the number and size of HRUs also has an effect on sediment yield parameters (slope and slope length). Thus, as a result of the discrepancies in soil type and size of HRUs stream flow predicted was higher when SSURGO was used compared to STATSGO. SSURGO predicted less stream loading than STATSGO in terms of sediment and sediment-attached nutrients components, and vice versa for dissolved nutrients. When compared to mean daily measured flow, STATSGO performed better relative to SSURGO before calibration. SSURGO provided better results after calibration as evaluated by R 2 value (0.74 compared to 0.61 for STATSGO) and the Nash-Sutcliffe coefficient of Efficiency (NSE) values (0.70 and 0.61 for SSURGO and STATSGO, respectively) although both are in the same satisfactory range. Modelers need to weigh the benefits before selecting the type of data resolution they are going to use depending on the watershed size and level of accuracy required because more effort is required to prepare and calibrate the model when a fine resolution soil data is used.

Effectiveness of the State Soil Geographic Database (STATSGO) to Predict White-Tailed Deer Morphometrics in Mississippi

The State Soil Geographic Data Base (STATSGO) developed by the Natural Resources Conservation Service is a spatially explicit database of soil characteristics known to influence the distribution, abundance, or quality of vegetation, which ultimately may affect white-tailed deer (Odocoileus virginianus) population phenotype. The national availability of STATSGO makes it an attractive tool for wildlife ecologists to use for explaining variation in deer population morphometrics. We constructed linear regression models relating site-specific mean population 1.5-year female body mass and 2.5-year male antler size to soil pH, organic matter, bulk density, and available water capacity values from STATSGO to evaluate the explanatory power of the database. For comparison, we developed general linear models that related the same deer morphometrics to nominal classification variables that represented 4 soil regions in Mississippi, USA. At the statewide level, regressions using site-specific soil pH, organic matter, bulk density, and available water capacity values from STATSGO did not explain more variation than did regional classification models. Although STATSGO did not generate good models for predicting or explaining localized variation in white-tailed deer morphometrics in Mississippi, it did reveal differences in soil properties that may influence white-tailed deer populations at regional spatial scales. At the statewide level, soil resource region classification models may be a more effective tool because they explained similar variation in deer morphometrics, and model input is easier to obtain.

DAYCENT National‐Scale Simulations of Nitrous Oxide Emissions from Cropped Soils in the United States

Until recently, Intergovernmental Panel on Climate Change (IPCC) emission factor methodology, based on simple empirical relationships, has been used to estimate carbon (C) and nitrogen (N) fluxes for regional and national inventories. However, the 2005 USEPA greenhouse gas inventory includes estimates of N2O emissions from cultivated soils derived from simulations using DAYCENT, a process-based biogeochemical model. DAYCENT simulated major U.S. crops at county-level resolution and IPCC emission factor methodology was used to estimate emissions for the approximately 14% of cropped land not simulated by DAYCENT. The methodology used to combine DAYCENT simulations and IPCC methodology to estimate direct and indirect N2O emissions is described in detail. Nitrous oxide emissions from simulations of presettlement native vegetation were subtracted from cropped soil N2O to isolate anthropogenic emissions. Meteorological data required to drive DAYCENT were acquired from DAYMET, an algorithm that uses weather station data and accounts for topography to predict daily temperature and precipitation at 1-km2 resolution. Soils data were acquired from the State Soil Geographic Database (STATSGO). Weather data and dominant soil texture class that lie closest to the geographical center of the largest cluster of cropped land in each county were used to drive DAYCENT. Land management information was implemented at the agricultural-economic region level, as defined by the Agricultural Sector Model. Maps of model-simulated county-level crop yields were compared with yields estimated by the USDA for quality control. Combining results from DAYCENT simulations of major crops and IPCC methodology for remaining cropland yielded estimates of approximately 109 and approximately 70 Tg CO2 equivalents for direct and indirect, respectively, mean annual anthropogenic N2O emissions for 1990-2003.

Quantity and Spatial Variability of Soil Carbon in the Conterminous United States

We estimated the soil organic carbon (SOC) and soil inorganic carbon (SIC) inventory for the conterminous USA using the State Soil Geographic Database (STATSGO). The relative contribution of each soil order and Land Resource Region (LRR) to the national SOC and SIC inventory was determined. There are 302 to 1499 ×108 Mg of SOC and 226 to 937 × 108 Mg of SIC in the upper 2 m of soil in the conterminous USA. About 30 and 80% of the upper 2‐m SOC is in the 0‐ to 0.20‐ and 0‐ to 1.0‐m soil layers, respectively. For SIC, only about 8% of the upper 2‐m SIC is in the upper 0.2 m, and about 50% is in the top 1.0‐m layer. The relative spatial variability of SOC increases dramatically as soil depth increases while the largest relative variability of SIC is in the surface layer. Because of its large area (27% of the soil area in the conterminous USA), Mollisols are the largest contributors both to the SOC stock (about 31 to 39%) and to the SIC stock (about 43 to 44%) in the conterminous USA. The results of this study provide a view of soil C partitioning by taxonomic group and land resource area, information that may be useful for assessing the impact of land use and climatic change on SOC and SIC pools.

Carbon sequestration potential by afforestation of marginal agricultural land in the Midwestern U.S.

Carbon sequestration has been well recognized as a viable option to slow the rise in atmospheric greenhouse gas concentration. The main goals of this study were to assess the carbon sequestration potential (CSP) by afforestation of marginal agricultural land (MagLand) and to identify hotspots for potential afforestation activities in the U.S. Midwest region (Michigan (MI), Indiana (IN), Ohio, Kentucky (KY), West Virginia, Pennsylvania (PA) and Maryland (MD)). The 1992 USGS National Land Cover Dataset and the State Soil Geographic (STATSGO) database were used to determine MagLand. Two forest types (coniferous and deciduous) and two management practices (short-rotation versus permanent forest) were combined to form four afforestation scenarios. Simulation models were employed to predict changes in four carbon pools: aboveground biomass, roots, forest floor, and soil organic carbon (SOC). A scenario-generating tool was developed to detect the hotspots. We estimated that there was a total of 6.5 million hectares (Mha) MagLand available in the U.S. Midwest region, which accounts for approximately 24% of the regional total agricultural land. The CSP capacity was predicted to be 508–540 Tg C (1 Tg = 10 12 g) over 20 years and 1018–1080 Tg C over 50 years. The results indicate that afforestation of MagLand could offset 6–8% of current CO 2 emissions by combustion of fossil fuel in the region. This analysis showed only slight differences in carbon sequestration between forest types or between short-rotation and permanent forest scenarios. Note that this calculation assumed that all suitable MagLand in the U.S. Midwest region was converted to forest and that “best carbon management” was adopted. The actual CSP could be less if the economical and social factors are taken into account. The most preferred locations for implementing the afforestation strategy were found to be concentrated along a west-east axis across the southern parts of Indiana, Ohio, and Pennsylvania, as well as in an area covering southern Michigan and northern parts of Indiana and Ohio. Overall, we conclude that afforestation of MagLand in the Midwest U.S. region offers great potential for carbon sequestration. Future studies are needed to evaluate its economic feasibility, social acceptability, and operation capability.

Exposure of Soil and Groundwater to Spills of Hazardous Materials Transported by Rail

The environmental impact of a hazardous material spill is a complex function of the material's physical and chemical characteristics and the local environmental conditions in which it is spilled. This study develops a geographical probability distribution for two important environmental parameters affecting this impact: soil type and groundwater depth. The paper assesses the probability of exposure of various soil types and groundwater depth regions to hazardous material spills from railroad tank car accidents. The State Soil Geographic database (STATSGO), a geographic information system (GIS) database from the U.S. Department of Agriculture, was used to analyze soils, and real-time groundwater data from the U.S. Geological Survey were used to analyze groundwater. The “rail2m” GIS database available from the Bureau of Transportation Statistics was used to conduct overlay analyses with the soil GIS data to obtain the probability of occurrence of various soil types under rail lines. The same method could not be used for groundwater exposure because no suitable GIS database for this parameter was available. Therefore, a nationwide probability distribution of the groundwater depth ranges was developed. The proportion of the three soil types under rail lines was 44.0% for sand, 51.2% for silt, and 4.0% for clay, the remainder being over surface water. The probability of occurrence of five groundwater depth regions was 16.4% for 5 to 15 ft, 12.6% for 15 to 25 ft, 23.3% for 25 to 75 ft, 12.0% for 75 to 125 ft, and 35.9% for greater than 125 ft. A matrix of the probability distribution of environmental scenarios was developed; it is suitable for use in conducting environmental risk assessments of railroad transportation of hazardous materials.

Exposure of Soil and Groundwater to Spills of Hazardous Materials Transported by Rail: A Geographic Information System Analysis

The environmental impact of a hazardous material spill is a complex function of the material's physical and chemical characteristics and the local environmental conditions in which it is spilled. This study develops a geographical probability distribution for two important environmental parameters affecting this impact: soil type and groundwater depth. The paper assesses the probability of exposure of various soil types and groundwater depth regions to hazardous material spills from railroad tank car accidents. The State Soil Geographic database (STATSGO), a geographic information system (GIS) database from the U.S. Department of Agriculture, was used to analyze soils, and real-time groundwater data from the U.S. Geological Survey were used to analyze groundwater. The “rail2m” GIS database available from the Bureau of Transportation Statistics was used to conduct overlay analyses with the soil GIS data to obtain the probability of occurrence of various soil types under rail lines. The same method could not be used for groundwater exposure because no suitable GIS database for this parameter was available. Therefore, a nationwide probability distribution of the groundwater depth ranges was developed. The proportion of the three soil types under rail lines was 44.0% for sand, 51.2% for silt, and 4.0% for clay, the remainder being over surface water. The probability of occurrence of five groundwater depth regions was 16.4% for 5 to 15 ft, 12.6% for 15 to 25 ft, 23.3% for 25 to 75 ft, 12.0% for 75 to 125 ft, and 35.9% for greater than 125 ft. A matrix of the probability distribution of environmental scenarios was developed; it is suitable for use in conducting environmental risk assessments of railroad transportation of hazardous materials.

USING EXPERIMENTAL AND GEOSPATIAL DATA TO ESTIMATE REGIONAL CARBON SEQUESTRATION POTENTIAL UNDER NO-TILL MANAGEMENT

Conservation management of croplands at the plot scale has demonstrated a great potential to mitigate the greenhouse effect through sequestration of atmospheric carbon (C) into soil. This study estimated the potential of soil to sequester C through the conversion of croplands from conventional tillage (CT) to no-till (NT) in the East Central United States between 1992 and 2012. This study used the baseline soil organic C (SOC) pool (SOCP) inventory and the empirical models that describe the relationships of the SOCP under CT and NT, respectively, to their baseline SOCP in the upper 30-cm depth of soil. The baseline SOCP were obtained from the State Soil Geographic database, and the cropland distribution map was generated from the 1992 National Land Cover Database. The results indicate that if all the croplands under CT in 1992 were converted to NT, the SOCP would increase by 16.8% by 2012, which results in a total C sink of 136 Tg after 20 years. A greater sequestration rate would occur in soils with lower baseline SOCP, but the sink strength would be weaker with increasing SOCP levels. The CT-induced C sources tend to become larger in soils with higher baseline levels, which can be significantly reduced by adopting NT. We conclude that baseline SOC contents are an indicator of C sequestration potential with NT practices.

Using SSURGO data to improve Sacramento Model a priori parameter estimates

As it transitions to smaller scale, distributed hydrologic modeling approaches, the National Weather Service (NWS) is improving methods of estimating parameters for the Sacramento Soil Moisture Accounting model (SAC-SMA). This is the major hydrologic model used for flood forecasting at most of the 13 river forecasting centers throughout the United States. A physically based approach based on the nationally available State Soil Geographic Database (STATSGO) has been developed (Koren, V.I., Smith, M., Wang, D., Zhang, Z., 2000. Use of soil property data in the derivation of conceptual rainfall–runoff model parameters. Proceedings of the 15th Conference on Hydrology, AMS, Long Beach, CA, pp. 103–106; Koren, V., Smith, M., Duan, Q., 2003. Use of a priori parameter estimates in the derivation of spatially consistent parameter sets of rainfall–runoff models. In: Duan, Q., Sorooshian, S., Gupta, H., Rosseau, H., Turcotte, H. (Eds.), Calibration of Watershed Models, Water Science and Applications 6, AGU, pp. 239–254), leading to objective, spatially consistent parameter estimates. This paper shows that a better representation of basin physical properties and potential improvements in hydrologic simulation performance can be obtained by basing parameter estimates on a finer-scale database of soils data, the Soil Survey Geographic Database (SSURGO), combined with high-resolution land use/land cover data. Results also suggest that an intermediate level of improvement may be obtained by combining detailed land cover data with STATSGO to refine current parameter estimates. This latter finding is significant because the SSURGO data are not yet available for the entire country.

Modeling Energy Inputs to Predict Pedogenic Environments Using Regional Environmental Databases

We present a model for prediction of pedogenic environments and soil properties based on energy input to the soil system. The model estimates rates of precipitation and net primary production (NPP) energy input using the Parameter‐Regression Independent Slope Model (PRISM) climate data, and a parent material index (PMI). Soil order, soil C, and clay data from the State Soil Geographic (STATSGO) database were compared with rates of NPP and precipitation energy input for major geographic regions of the continental USA, including California, Oregon, Washington, Texas, North Dakota, Alabama, Pennsylvania, and New Hampshire. Soil orders in all states show differences in total energy input (Ein, kJ m−2yr−1) and the percentage of Ein from NPP (%Enpp) (e.g., Ultisols Ein = 29915, %Enpp = 49%; Mollisols Ein = 5880, %Enpp = 90%). Using linear regression models, rates of NPP estimated (R2 = 0.82***) trends in soil C content in western states, but failed to estimate soil C in other geographic areas. Parent material index adjusted energy flux estimated soil clay content for the majority (99.5%) of igneous parent materials in California and Oregon (R2 = 0.67**), the only states with digital geologic data. The model underestimated clay content in steeply sloping Inceptisols and Andisols (0.5% of igneous land area). Results suggest that rates of NPP may be used to estimate soil C for climate regimes with steep environmental gradients. Landscape age and stability components might improve clay prediction in young and erosive landscapes. Modeled energy input provides a tool for estimating pedogenic environments, soil order, and soil properties. Energy input parameters may aid efforts to pre‐map broad landscape units for soil survey.