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Soil composition and preparation can affect prediction accuracy using diffuse reflectance mid-infrared Fourier transform spectroscopy (DRIFTS). In the present study, we evaluated the effect of soil composition, preparation and carbonate content on the accuracy of particle size distribution (PSD) predictions in four contrasting sets of soils, including calcareous soils, using partial least-squares regression (PLSR). The soils were scanned as Additional keywords: calcareous soil, clay, multivariate methods, particle size analysis. Received 11 January 2016, accepted 25 July 2016, published online 26 September 2016, Introduction Soil particle size distribution (PSD) is one of the most useful soil properties provided as a routine soil analysis service. It can be used to determine soil texture, and [...]

Time series regression models were used to examine the influence of environmental factors (soil water content and soil temperature) on the emissions of nitrous oxide ([N.sub.2]O) from subtropical soils, by taking into account temporal lagged environmental factors, autoregressive processes, and seasonality for three horticultural crops in a subtropical region of Australia. Fluxes of [N.sub.2]O, soil water content, and soil temperature were determined simultaneously on a weekly basis over a 12-month period in South East Queensland. Annual [N.sub.2]O emissions for soils under mango, pineapple, and custard apple were 1590, 1156, and 2038 g [N.sub.2]O-N/ha, respectively, with most emissions attributed to nitrification. The [N.sub.2]O-N emitted from the pineapple and custard apple crops was equivalent to 0.26 and 2.22%, respectively, of the applied mineral N. The change in soil water content was the key variable for describing N20 emissions at the weekly time-scale, with soil temperature at a lag of I month having a significant influence on average [N.sub.2]O emissions (averaged) at the monthly time-scale across the three crops. After accounting for soil temperature and soil water content, both the weekly and monthly time series regression models exhibited significant autocorrelation at lags of 1-2 weeks and 1-2 months, and significant seasonality for weekly [N.sub.2]O emissions for mango crop and for monthly [N.sub.2]O emissions for mango and custard apple crops in this location over this time-frame. Time series regression models can explain a higher percentage of the temporal variation of [N.sub.2]O emission compared with simple regression models using soil temperature and soil water content as drivers. Taking into account seasonal variability and temporal persistence in [N.sub.2]O emissions associated with soil water content and soil temperature may lead to a reduction in the uncertainty surrounding estimates of [N.sub.2]O emissions based on limited sampling effort. Additional keywords: autoregressive process, [N.sub.2]O emission, time series regression., Introduction Nitrous oxide ([N.sub.2]O) is a long-lived greenhouse gas which has also been linked to ozone depletion (Crutzen and Ehhalt 1997). Soils are one of the major sources of [N.sub.2]O [...]

A chemical kinetic model is presented for the formation and accumulation of secondary ferrimagnetic minerals (SFMs) in soil constructed using experimentally determined rate constants and validated against field data. The primary objective is to critically assess the significance of competing causal mechanisms and disputed environmental controls under temperate conditions. Four findings are important in relation to current application of soil magnetic susceptibility data. First, transformation of hydrous ferric oxide to magnetite should dominate SFM formation, controlled primarily by parent material ferrous silicate concentration and climate. Second, abiotic reactions should account for most of the SFM production; the most significant impact of high [Fe.sup.2+] concentrations created by dissimilatory iron-reducing bacteria is enhanced export of iron from the soil in runoff. Third, the model predicts a correlation between hematite and magnetite concentrations, weakening field support for direct transformation of hydrous ferric oxide to maghemite. Fourth, magnetic susceptibility enhancement should increase strongly with weathering duration. doi: 10.1130/G31514.1

The study of the interactions among metals, minerals, and humic substances is essential in understanding the migration of inorganic pollutants in the geosphere. A considerable amount of organic matter in the environment is associated with clay minerals. To understand the role of organic matter in the environment and its association with clay minerals, a hybrid day-based material (H CM), montmorillonite (STx-1)--melanoidin, was prepared from L-tyrosine and L-glutamic acid by the Maillard reaction. The HCM was characterized by elemental analysis, nuclear magnetic resonance, x-ray photoelectron spectroscopy (XPS), scanning transmission x-ray microscopy (STXM), and thermal analysis. The presence of organic materials on the surface was confirmed by XPS and STXM. The STXM results showed the presence of organic spots on the surface of the STx-1 and the characterization of the functional groups present in those spots. Thermal analysis confirmed the existence of organic materials in the montmorillonite interlayer, indicating the formation of a composite of melanoidin and montmorillonite. '[he melanoidin appeared to be located partially between the layers of montmorillonite and partially at the surface, forming a structure that resembles the way a cork sits on the top of a champagne bottle. Abbreviations: DTA, differential thermal analysis; GHD, glutamine hybrid material naelanoidin fraction; GHS, glutamine hybrid material solid fraction; HCM, hybrid clay-based material; NMR, nuclear magnetic resonance; STx-1, sodium-montmorillonite; STXM, scanning transmission x-ray microscopy; THD, tyrosine hybrid material melanoidin fraction; THS, tyrosine hybrid material solid fraction; XPS, x-ray photoelectron spectroscopy. doi: 10.2136/sssaj2009.0454

Byline: Willem W. Verstraeten (1), Frank Veroustraete (2), Wolfgang Wagner (3), Tom Roey (2), Walter Heyns (2), Sara Verbeiren (2), Jan Feyen (4) Abstract: While remote sensing is able to provide spatially explicit datasets at regional to global scales, extensive application to date has been found only in the reporting and verification of ecosystem carbon fluxes under the Kyoto Protocol. One of the problems is that new remote sensing datasets can be used only with models or data assimilation schemes adapted to include a data input interface dedicated to the type and format of these remote sensing datasets. In this study, soil water index data (SWI), derived from the ERS scatterometer (10-daily time period with a spatial resolution of 50 km), are integrated into the ecosystem carbon balance model C-Fix to assess 10-daily Net Ecosystem Productivity (NEP) patterns of Europe from the remote sensing perspective on an approximate 1-by-1 km.sup.2 pixel scale using NDVI-AVHRR data. The modeling performance of NEP obtained with and without the assimilation of remotely sensed soil moisture data in the carbon flux model C-Fix is evaluated with EUROFLUX data. Results show a general decrease of the RRMSE of up to 11 with an average of 3.46. C-Fix is applied at the European scale to demonstrate the potential of this ecosystem carbon flux model, based on remote sensing inputs. More specifically, the strong impact of soil moisture on the European carbon balance in the context of the Kyoto Protocol (anthropogenic carbon emissions) is indicated at the country level. Results suggest that several European countries shift from being a carbon sink (i.e., NEP > 1) to being a carbon source (i.e., NEP < 0) whether or not short-term water availability (i.e., soil moisture) is considered in C-Fix NEP estimations. Author Affiliation: (1) Geomatics Engineering, Katholieke Universiteit Leuven (K.U. Leuven), W. de Croylaan 34, 3001, Heverlee, Flanders (2) Flemish Institute for Technological Research (VITO), Boeretang 200, 2400, Mol, Flanders (3) Institute of Photogrammetry and Remote Sensing, Vienna University of Technology (T.U. Wien), Gusshausstrasse 27--29, 1040, Vienna, Austria (4) Laboratory for Soil and Water Management, Katholieke Universiteit Leuven (K.U. Leuven), Celestijnenlaan 200E, 3001, Heverlee, Flanders Article History: Registration Date: 22/06/2010 Received Date: 05/01/2009 Accepted Date: 15/06/2010 Online Date: 22/07/2010

To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.jenvman.2010.01.001 Byline: Rania Bou Kheir, Mogens H. Greve, Peder K. BA[cedilla]cher, Mette B. Greve, Rene Larsen, Keith McCloy Abstract: Soil organic carbon (SOC) is one of the most important carbon stocks globally and has large potential to affect global climate. Distribution patterns of SOC in Denmark constitute a nation-wide baseline for studies on soil carbon changes (with respect to Kyoto protocol). This paper predicts and maps the geographic distribution of SOC across Denmark using remote sensing (RS), geographic information systems (GISs) and decision-tree modeling (un-pruned and pruned classification trees). Seventeen parameters, i.e. parent material, soil type, landscape type, elevation, slope gradient, slope aspect, mean curvature, plan curvature, profile curvature, flow accumulation, specific catchment area, tangent slope, tangent curvature, steady-state wetness index, Normalized Difference Vegetation Index (NDVI), Normalized Difference Wetness Index (NDWI) and Soil Color Index (SCI) were generated to statistically explain SOC field measurements in the area of interest (Denmark). A large number of tree-based classification models (588) were developed using (i) all of the parameters, (ii) all Digital Elevation Model (DEM) parameters only, (iii) the primary DEM parameters only, (iv), the remote sensing (RS) indices only, (v) selected pairs of parameters, (vi) soil type, parent material and landscape type only, and (vii) the parameters having a high impact on SOC distribution in built pruned trees. The best constructed classification tree models (in the number of three) with the lowest misclassification error (ME) and the lowest number of nodes (N) as well are: (i) the tree (T1) combining all of the parameters (ME=29.5%; N =54); (ii) the tree (T2) based on the parent material, soil type and landscape type (ME=31.5%; N =14); and (iii) the tree (T3) constructed using parent material, soil type, landscape type, elevation, tangent slope and SCI (ME=30%; N =39). The produced SOC maps at 1:50,000 cartographic scale using these trees are highly matching with coincidence values equal to 90.5% (Map T1/Map T2), 95% (Map T1/Map T3) and 91% (Map T2/Map T3). The overall accuracies of these maps once compared with field observations were estimated to be 69.54% (Map T1), 68.87% (Map T2) and 69.41% (Map T3). The proposed tree models are relatively simple, and may be also applied to other areas. Author Affiliation: Department of Agroecology and Environment, Faculty of Agricultural Sciences (DJF), Aarhus University, Blichers Alle 20, P.O. Box 50, DK-8830 Tjele, Denmark Article History: Received 12 July 2009; Revised 9 December 2009; Accepted 3 January 2010

This paper compares the behavior of an embankment with nonsymmetric geometry built on soft soil with that predicted numerically using four elastoplastic soil models. Two of these models are based on isotropic conditions (Modified Cam-Clay on its own or in association with Von Mises) and two other are derived from anisotropic conditions (Melanie on its own or conjugated with Mohr Coulomb). The performance of the models, whose parameters are derived from experimental data, is checked against triaxial tests results. For the embankment, the measured and computed displacements and excess pore pressure are compared, with the isotropic models performing best. The maximum horizontal displacements versus settlements, the change in excess pore pressure versus vertical stress, the extent of the yield domain and the contours of the effective vertical and horizontal stress increments are also examined. The numerical results are explained based on the characteristics of the numerical models, namely the size and shape of the yield surface. The embankment, despite its nonsymmetric geometry, exhibits some similarities with typical behavior. DOI: 10.1061/(ASCE)GT.1943-5606.0000183 CE Database subject headings: Constitutive models; Numerical models; Site investigation; Soft soils; Embankments; Finite element method. Author keywords: Constitutive models; Numerical models; Site investigation; Soft soils; Embankments; Finite element method.

Conservation tillage practices often leave appreciable amounts of crop residues on soil surfaces after harvesting and generally improve soil structure, enhance soil organic C (SOC) content, and reduce soil erosion. Remote sensing methods have shown great promise in efficiently estimating crop residue cover, and thus inferring soil tillage intensity. Furthermore, these tillage intensity estimates can be used in soil C models. Reflectance spectra of more than 4200 soils and 80 crop residues were measured in the laboratory across the 350- to 2500-nm wavelength region. Six remote sensing spectral indices were used to estimate crop residue cover: the Cellulose Absorption Index (CAI), the Lignin--Cellulose Absorption Index (LCA), the Normalized Difference Tillage Index (NDTI), the Normalized Difference Senescent Vegetation Index (NDSVI), and the Normalized Difference Indices 5 and 7 (NDI5 and NDI7, respectively). Soil mineralogy and SOC affected these spectral indices for crop residue cover more than soil taxonomic order, which generally had little effect on spectral reflectance. The values of the spectral indices for soils were similar within Land Resource Regions and, specifically, for Major Land Resource Areas. The CAI showed the best separation between soils and residues, followed by LCA and NDTI. Although NDSVI, NDI5, and NDI7 had significant overlaps between soil and residue index values, assessments of crop residue cover classes may be possible with local calibrations. Future satellite sensors should include appropriate bands for assessing crop residue and nonphotosynthetic vegetation. Abbreviations: ASTER, Advanced Spaceborne Thermal Emission and Reflection Radiometer; AWiFS, Advanced Wide-Field Sensor; CAI, Cellulose Absorption Index; LCA, Lignin-Cellulose Absorption Index; LRR, Land Resource Region; MLRA, Major Land Resource Region; NDI, Normalized Difference Index; NDSVI, Normalized Difference Senescent Vegetation Index; NDTI, Normalized Difference Tillage Index; NDVI, Normalized Difference Vegetation Index; SOC, soil organic carbon; SWIR, shortwave infrared; TM, Thematic Mapper.

To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.dsr.2009.04.007 Byline: Daniel M. Sigman (a), Peter J. DiFiore (a), Mathis P. Hain (b), Curtis Deutsch (c), Yi Wang (a), David M. Karl (d), Angela N. Knapp (e), Moritz F. Lehmann (f), Silvio Pantoja (g) Keywords: Nitrate; Stable isotope; Biogeochemistry; Numerical model; Nutrients Abstract: We compare the output of an 18-box geochemical model of the ocean with measurements to investigate the controls on both the mean values and variation of nitrate [delta].sup.15N and [delta].sup.18O in the ocean interior. The [delta].sup.18O of nitrate is our focus because it has been explored less in previous work. Denitrification raises the [delta].sup.15N and [delta].sup.18O of mean ocean nitrate by equal amounts above their input values for N.sub.2 fixation (for [delta].sup.15N) and nitrification (for [delta].sup.18O), generating parallel gradients in the [delta].sup.15N and [delta].sup.18O of deep ocean nitrate. Partial nitrate assimilation in the photic zone also causes equivalent increases in the [delta].sup.15N and [delta].sup.18O of the residual nitrate that can be transported into the interior. However, the regeneration and nitrification of sinking N can be said to decouple the N and O isotopes of deep ocean nitrate, especially when the sinking N is produced in a low latitude region, where nitrate consumption is effectively complete. The [delta].sup.15N of the regenerated nitrate is equivalent to that originally consumed, whereas the regeneration replaces nitrate previously elevated in [delta].sup.18O due to denitrification or nitrate assimilation with nitrate having the [delta].sup.18O of nitrification. This lowers the [delta].sup.18O of mean ocean nitrate and weakens nitrate [delta].sup.18O gradients in the interior relative to those in [delta].sup.15N. This decoupling is characterized and quantified in the box model, and agreement with data shows its clear importance in the real ocean. At the same time, the model appears to generate overly strong gradients in both [delta].sup.18O and [delta].sup.15N within the ocean interior and a mean ocean nitrate [delta].sup.18O that is higher than measured. This may be due to, in the model, too strong an impact of partial nitrate assimilation in the Southern Ocean on the [delta].sup.15N and [delta].sup.18O of preformed nitrate and/or too little cycling of intermediate-depth nitrate through the low latitude photic zone. Author Affiliation: (a) Department of Geosciences, Princeton University, Princeton, NJ 08544, USA (b) Institute of Geosciences, University of Potsdam, PO Box 60 15 53, Potsdam D-14415, Germany (c) Department of Atmospheric and Oceanic Sciences, University of California, Los Angeles, CA 90095, USA (d) Department of Oceanography, 1000 Pope Rd. MSB 629, University of Hawaii, Honolulu, HI 96822, USA (e) Department of Marine and Environmental Biological Sciences, University of Southern California, University of Southern California, 3616 Trousdale Parkway, AHF 108, Los Angeles, CA 90089-0371, USA (f) Institute for Environmental Geosciences, Bernoullistrasse 30, Universitat Basel, CH-4056 Basel, Switzerland (g) Department of Oceanography and Center for Oceanographic Research in the Eastern South Pacific, University of Concepcion, Casilla 160-C, Concepcion, Chile Article History: Received 21 January 2008; Revised 4 April 2009; Accepted 21 April 2009

The Pleim-Xiu land surface model (PX LSM) has been improved by the addition of a second indirect data assimilation scheme. The first, which was described previously, is a technique in which soil moisture is nudged according to the biases in 2-m air temperature and relative humidity between the model- and observationbased analyses. The new technique involves nudging the deep soil temperature in the soil temperature force-restore (FR) model according to model bias in 2-m air temperature only during nighttime. While the FR technique is computationally efficient and very accurate for the special conditions for which it was derived, it is very dependent on the deep soil temperature that drives the restoration term of the surface soil temperature equation. Thus, adjustment of the deep soil temperature to optimize the 2-m air temperature during the night, when surface forcing is minimal, provides significant advantages over other methods of deep soil moisture initialization. Simulations of the Weather Research and Forecasting Model (WRF) using the PX LSM with and without the new deep soil temperature nudging scheme demonstrate substantial benefits of the new scheme for reducing error and bias of the 2-m air temperature. The effects of the new nudging scheme are most pronounced in the winter (January 2006) during which the model's cold bias is greatly reduced. Air temperature error and bias are also reduced in a summer simulation (August 2006) with the greatest benefits in less vegetated and more arid regions. Thus, the deep temperature nudging scheme complements the soil moisture nudging scheme because it is most effective for conditions in which the soil moisture scheme is least effective, that is, when evapotranspiration is not important (winter and arid climates).

To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.jenvman.2009.05.034 Byline: R. De Jong (a), C.F. Drury (b), J.Y. Yang (b), C.A. Campbell (a) Abstract: With increasing amounts of nitrogen (N) being added to farmland in the form of fertilizer and manure to optimize crop yields, and more broadly, to meet the growing demands for food, feed and energy, there are public concerns regarding its possible negative impact on the environment. An optimal balance between N requirements for production versus efficient N use is required, so as to minimize N losses from the agricultural system. An agri-environmental indicator i.e., the Indicator of the Risk of Water Contamination by Nitrogen (IROWC-N) was developed to assess the risk of N moving from agricultural areas into groundwater and/or nearby surface water bodies. The indicator linked the quantity of mineral nitrogen remaining in the soil at harvest, i.e., the Residual Soil Nitrogen (RSN) indicator, and the subsequent climatic conditions during the winter period. The results were assessed in terms of nitrate lost through leaching and nitrate concentration in the drainage water, expressed in five IROWC-N risk classes. Unlike previous versions of the indicator, the current model provided a more complete description of the soil-water balance, including the calculation of rainfall interception by crops, surface runoff, actual evapotranspiration and soil-water contents. Consequently, the current IROWC-N estimates differed markedly from those obtained previously. Between 1981 and 2006, the risk of water contamination by N in Canada was small, and reflected what was happening in the three Prairie provinces where 85% of Canada's farmland is located. However, the aggregated IROWC-N index, which is a combination of all five risk classes, increased steadily by 2.3% per year, from 6.7 in 1981 to 10.6 in 2006. The proportion of farmland in the very low IROWC-N risk class decreased from 88 to 78%; correspondingly, the proportion in the low risk class increased from 2 to 12%. The proportion of farmland in the moderate-, high- and very high-risk classes changed by less than 3% over time. The trends in IROWC-N in the Atlantic provinces were significantly worse than the national trend; for example, in Atlantic Canada, the aggregated IROWC-N index tripled from 27.8 in 1981 to 87.5 in 2006. Increases in fertilizer use (except in British Columbia), increases in livestock numbers in Manitoba and the Atlantic provinces, and an increase in legume crop acreage were the main factors that contributed to the increase in IROWC-N estimates. Climatic factors were also involved, as droughts reduced yields, N uptake and N leaching in many regions of Canada in 2001. Author Affiliation: (a) Eastern Cereal and Oilseed Research Centre, Agriculture and Agri-Food Canada, 960 Carling Ave., Ottawa, ON, K1A 0C6, Canada (b) Greenhouse & Processing Crops Research Centre, Agriculture and Agri-Food Canada, 2585 County Rd. 20, Harrow, ON, N0R 1G0, Canada Article History: Received 29 September 2008; Revised 20 April 2009; Accepted 17 May 2009

Currently there is a trend for the expansion of the area cropped with sugarcane (Saccharum officinarum L.), driven by an increase in the world demand for biofuels, due to economical, environmental, and geopolitical issues. Although sugarcane is traditionally harvested by burning dried leaves and tops, the unburned, mechanized harvest has been progressively adopted. The use of process based models is useful in understanding the effects of plant litter in soil C dynamics. The objective of this work was to use the CENTURY model in evaluating the effect of sugarcane residue management in the temporal dynamics of soil C. The approach taken in this work was to parameterize the CENTURY model for the sugarcane crop, to simulate the temporal dynamics of soil C, validating the model through field experiment data, and finally to make predictions in the long term regarding soil C. The main focus of this work was the comparison of soil C stocks between the burned and unburned litter management systems, but the effect of mineral fertilizer and organic residue applications were also evaluated. The simulations were performed with data from experiments with different durations, from 1 to 60 yr, in Goiana and Timbauba, Pernambuco, and Pradopolis, Sao Paulo, all in Brazil; and Mount Edgecombe, Kwazulu-Natal, South Africa. It was possible to simulate the temporal dynamics of soil C ([R.sup.2] = 0.89). The predictions made with the model revealed that there is, in the long term, a trend for higher soil C stocks with the unburned management. This increase is conditioned by factors such as climate, soil texture, time of adoption of the unburned system, and N fertilizer management.

There is a pressing need for rapid and cost-effective tools to estimate soil C across larger landscapes. Visible-near-infrared diffuse reflectance spectroscopy (VNIRS) offers comparable levels of accuracy to conventional laboratory methods for estimating various soil properties. We used VNIRS to estimate soil total organic C (TC) and four organic C fractions in 141 samples collected in the Santa Fe River watershed of Florida. The C fractions measured were (in order of decreasing potential residence time in soils): recalcitrant C (RC), hydrolyzable C (HC), hot-water-soluble C (SC), and mineralizable C (MC). Soil samples were scanned in the visible-near-infrared spectral range. Six preprocessing transformations were applied to the soil reflectance, and five multivariate techniques were tested to model soil TC and the organic C fractions: stepwise multiple linear regression (SMLR), principal components regression, partial least squares regression (PLSR), regression tree, and committee trees. Total organic C was estimated with the highest accuracy, obtaining a coefficient of determination using a validation set ([R.sub.v.sup.2]) of 0.86, followed by RC ([R.sub.v.sup.2] = 0.82), both using PLSR. The SC fraction was modeled best by SMLR ([R.sub.v.sup.2] = 0.70), while PLSR produced the best models of MC ([R.sub.v.sup.2] = 0.65) and HC ([R.sub.v.sup.2] = 0.40). The addition of TC as a predictor improved the VNIRS models of the soil organic C fractions. Our study indicates the suitability of VNIRS to quantify soil organic C pools with widely varying turnover times in soils, which are important in the context of C sequestration and climate change. Abbreviations: CT, committee trees; HC, hydrolyzable organic carbon; LOG, log(1/reflectance) transformation; MC, mineralizable organic carbon; NGD, Norris gap derivative across a seven-band window; NRA, normalization by the range; PCR, principal components regression; PLSR, partial least squares regression; RC, recalcitrant organic carbon; [RMSE.sub.c], root mean square error of calibration; [RMSE.sub.v], root mean square error of validation; RPD, residual prediction deviation; RT, regression tree; SC, hot-water-soluble organic carbon; SFRW, Santa Fe River watershed; SGD, Savitzky-Golay first derivative using a first-order polynomial across a nine-band window; SMLR, stepwise multiple linear regression; TC, total organic carbon; VNIRS, visible-near-infrared diffuse reflectance spectroscopy.

The heat pulse method enables estimation of soil thermal diffusivity (k), volumetric heat capacity (C), thermal conductivity, and water content. The heater needle and temperature-sensing needle may deflect during probe insertion into soils. The impact of needle deflection on estimates of C and k has not been fully studied theoretically or experimentally. We defined [theta] to be the polar angle of needle deviation from the z axis and [phi] to be the azimuthal angle in the x-y plane. Transient-state analytical solutions were derived for an inclined and pulsed finite line source in a parallelepiped sample with zero surface temperature and adiabatic boundary conditions. For a heat pulse sensor with 6-mm needle spacing and a heater needle of 4-cm length in a given parallelepiped (5 by 5 by 5 cm, assumed to be filled with air-dry sand), model errors in C and k were about -11.3 and 12.1%, respectively, for an inclined heater needle with [theta] = 1[degrees] and [phi] = 0[degrees]. Model errors in C and k were about -11.2 and 12.1%, respectively, for an inclined sensor needle with [theta] = 1[degrees] and [phi] = 180[degrees]. When -6 [less than or equal to] [theta] [less than or equal to] 6[degrees] for either the heater or the sensor needle, the temperature curves could be approximated rather well by a pulsed infinite line source model with a modified probe spacing that accounted for the inclination. For various heating durations and strengths, the errors in both k and C were relatively constant when all other parameters were fixed; however, the errors in both C and k decreased monotonically and slowly as k increased. The model errors in C and k were similar for four soil conditions with different thermal properties in the range -6 [less than or equal to] [theta] [less than or equal to] 2[degrees]. Abbreviations: ABC, adiabatic boundary condition; DPHP, dual-probe heat pulse; PILS, pulsed infinite line source; ZST, zero surface temperature.

Byline: Annett Wolf (1,2,5), Eleanor Blyth (3), Richard Harding (3), Daniela Jacob (4), Elke Keup-Thiel (4), Holger Goettel (4), Terry Callaghan (5,6) Abstract: We tested the sensitivity of a dynamic ecosystem model (LPJ-GUESS) to the representation of soil moisture and soil temperature and to uncertainties in the prediction of precipitation and air temperature. We linked the ecosystem model with an advanced hydrological model (JULES) and used its soil moisture and soil temperature as input into the ecosystem model. We analysed these sensitivities along a latitudinal gradient in northern Russia. Differences in soil temperature and soil moisture had only little influence on the vegetation carbon fluxes, whereas the soil carbon fluxes were very sensitive to the JULES soil estimations. The sensitivity changed with latitude, showing stronger influence in the more northern grid cell. The sensitivity of modelled responses of both soil carbon fluxes and vegetation carbon fluxes to uncertainties in soil temperature were high, as both soil and vegetation carbon fluxes were strongly impacted. In contrast, uncertainties in the estimation of the amount of precipitation had little influence on the soil or vegetation carbon fluxes. The high sensitivity of soil respiration to soil temperature and moisture suggests that we should strive for a better understanding and representation of soil processes in ecosystem models to improve the reliability of predictions of future ecosystem changes. Author Affiliation: (1) Department of Physical Geography and Ecosystem Analyses, Lund University, Lund, Sweden (2) Department of Environmental Science, Universitatsstr. 22, CH-8092, Zurich, Switzerland (3) Centre for Ecology and Hydrology, Wallingford, UK (4) Max-Planck-Institut fur Meteorologie (MPI-M), Hamburg, Germany (5) Abisko Scientific Research Station, Abisko, Sweden (6) Department of Animal and Plant Sciences, University of Sheffield, Sheffield, S10 2TN, UK Article History: Registration Date: 04/10/2007 Received Date: 06/07/2006 Accepted Date: 03/10/2007 Online Date: 27/11/2007

To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.dsr.2007.09.005 Byline: Thomas R. Anderson (a), Vladimir A. Ryabchenko (b), Michael J.R. Fasham (a), Victor A. Gorchakov (b) Keywords: Denitrification; Biogeochemical cycles; Ecosystem modelling; Detritus; Export flux; Arabian Sea Abstract: A three-dimensional hydrodynamic-ecosystem model was used to examine the factors determining the spatio-temporal distribution of denitrification in the Arabian Sea. The ecosystem model includes carbon and nitrogen as currencies, cycling of organic matter via detritus and dissolved organic matter, and both remineralization and denitrification as sinks for material exported below the euphotic zone. Model results captured the marked seasonality in plankton dynamics of the region, with characteristic blooms of chlorophyll in the coastal upwelling regions and central Arabian Sea during the southwest monsoon, and also in the northern Arabian Sea during the northeast monsoon as the mixed layer shoals. Predicted denitrification was 26.2TgNyr.sup.-1,the greatest seasonal contribution being during the northeast monsoon when primary production is co-located with the zone of anoxia. Detritus was the primary organic substrate consumed in denitrification (97%), with a small (3%) contribution by dissolved organic matter. Denitrification in the oxygen minimum zone was predicted to be fuelled almost entirely by organic matter supplied by particles sinking vertically from the euphotic zone above (0.73mmolNm.sup.-2 d.sup.-1) rather than from lateral transport of organic matter from elsewhere in the Arabian Sea (less than 0.01mmolNm.sup.-2 d.sup.-1). Analysis of the carbon budget in the zone of denitrification (north of 10[degrees]N and east of 55[degrees]E) indicates that the modelled vertical export flux of detritus, which is similar in magnitude to estimates from field data based on the.sup.234Th method, is sufficient to account for measured bacterial production below the euphotic zone in the Arabian Sea. Author Affiliation: (a) National Oceanography Centre, University of Southampton, Waterfront Campus, Southampton SO14 3ZH, UK (b) St. Petersburg Branch, P.P. Shirshov Institute of Oceanology, Russian Academy of Sciences, 30, Pervaya Liniya, 199053 St. Petersburg, Russia Article History: Received 6 June 2006; Revised 3 September 2007; Accepted 11 September 2007

Understanding the spatial and temporal variation in soil temperatures is important to classification, land use, and management. To that end, mean annual soil temperature (MAST) data for Wisconsin and Michigan were modeled to (i) determine the effects of the Great Lakes and their snowbelts on soil temperatures, and (ii) better estimate the location of the boundary between the mesic and frigid soil temperature regimes in this region. The location of the mesic-frigid (M-F) line is particularly difficult to determine where east-west gradients in air temperature cross north-south trends in snowfall due to Lake Michigan. Additionally, the soil temperature regime of several Great Lakes' peninsulas near the M-F line is in question. To determine the accuracy of our soil temperature model, soil temperature data output from it were compared with data derived from thermocouples implanted in soils at 39 sites in northern Michigan that had been collecting data several times daily for more than 6 yr. Error statistics for the model show that it has essentially no mean bias when examined on an annual basis or for winter, and only a bias of 0.1[degrees]C for the warm season. The M-F line in Wisconsin and Michigan is slightly north of most previously estimated locations, and is strongly influenced by the snowbelt in southern Michigan. Soils in deep snow areas stay warmer in winter than do soils inland, increasing their MAST and forcing the M-F line north of where air temperatures alone might have placed it. Lake-effect areas also stay cold longer into the spring season, and cool down more slowly in fall. Soil temperatures in these areas are, therefore, more moderated on an annual basis, as indicated by coefficients of variation.

The soil-pipeline interactions in sand under lateral and upward movements are investigated with particular attention to the peak forces exerted on the pipe. The analytical solutions for estimating the peak forces are summarized and it is shown that, for deep embedment condition, there is large uncertainty in the true values since the bounds established by the analytical solutions are large. In order to find the solution for the peak force and to investigate its transition from shallow to deep failure mechanism, finite element analyses of lateral and upward pipe movements are performed for different embedment conditions. Two different soil models (Mohr-Coulomb and Nor-Sand models) are used for the simulations. The accuracy of the analysis is first examined by simulating experimental tank tests. The analysis is further extended to deeper embedment ratios of as large as 100. The obtained finite element results are used to construct a design chart for deep embedded pipelines. CE Database subject headings: Sand; Buried pipes; Pipelines; Embedment; Ground motion; Vertical movement.

Decomposition and root respiration processes, important to C cycling in terrestrial ecosystems, are affected by soil temperature, soil moisture, and other soil properties. For studying the effect of these factors on soil C[O.sub.2] efflux and soil-air C[O.sub.2] concentration, a dynamic model was developed. In the model, soil was described in successive layers and the processes and soil properties were described separately for each layer. The C[O.sub.2] in soil layers originated from root and microbial respiration, which were assumed to depend on soil temperature and moisture multiplicatively. The C[O.sub.2] flux between the layers was driven by diffusion, which depended on C[O.sub.2] concentration, porosity, and temperature of the layers. The model predictions of C[O.sub.2] effluxes and soil C[O.sub.2] concentrations were close to those observed in the field. There was a clear seasonal pattern in the soil C[O.sub.2] efflux and the soil-air C[O.sub.2] concentration. According to the model analysis, most of the C[O.sub.2] was produced in the humus layer throughout the year, but the contribution of deeper layers to total respiration was higher in winter than in summer. The C[O.sub.2] concentration was strongly dependent on factors affecting the diffusion properties of the soil, that is, the soil porosity and the soil-water content. The C[O.sub.2] efflux and the soil-air C[O.sub.2] concentration were overestimated, if the soil-water content was not included in the soil respiration model. The model developed in this study provided a simple and an effective tool for studying the factors affecting soil C[O.sub.2] efflux and C[O.sub.2] concentration.

This paper presents a practical solution for group stiffness estimate of vertically loaded piles. The solution is based on a variational approach for pile groups in a soil modeled using a load--transfer curve method. Using the present method, the group stiffness of piles can be easily obtained based on spreadsheet calculation and this is very useful for practical purpose. The paper also presents comparison between results from modeling the soil using load--transfer curves and as an elastic half space. Their difference in estimating the group stiffness of piles is addressed. DOI: 10.1061/(ASCE) 1090-0241 (2002) 128:8(692) CE Database keywords: Piles; Stiffness; Displacement; Computation.

An accurate mathematical representation of particle-size distributions (PSDs) is required to estimate soil hydraulic properties or to compare texture measurements from different classification systems. The objective of this study was to evaluate the ability of seven models (i.e., five lognormal models, the Gompertz model, and the Fredlund model) to fit PSD data sets from a wide range of soil textures. Special attention was given to the effect of texture on model performance. Several criteria were used to determine the optimum model with the least number of fitting parameters when other conditions are equal. The Fredlund model with four parameters showed the best performance with the majority of soils studied, even when three criteria that impose a penalty for additional fitting parameters were used. Especially, the relative performance of the Fredlund model in regard to other models increased with increase of clay content. Among all soil classes, the lognormal models with two or three parameters showed better fits for silty clay, silty clay loam, and silt loam soils, and worse fit for sandy clay loam soil.

We test APSIM-SWIM's ability to simulate infiltration and interactions between the soil water balance and grain crop growth using soil hydraulic properties derived from independent, point measurements. APSIM-SWIM is a continuous soil-crop model that simulates infiltration, surface crusting, and soil condition in more detail than most other soil-crop models. Runoff, soil water, and crop growth information measured at sites in southern Queensland was used to test the model. Parameter values were derived directly from soil hydraulic properties measured using rainfall simulators, disc permeameters and ponded rings, and pressure plate apparatus. In general, APSIM-SWIM simulated infiltration, runoff, soil water and the water balance, and yield as accurately and reliably as other soil crop models, indicating the model is suitable for evaluating effects of infiltration and soil-water relations on crop growth. Increased model detail did not hinder application, instead improving parameter transferability and utility, but improved methods of characterising crusting, soil hydraulic conductivity, and macroporosity under field conditions would improve ease of application, prediction accuracy, and reliability of the model. Model utility and accuracy would benefit from improved representation of temporal variation in soil condition, including effects of tillage and consolidation on soil condition and bypass flow in cracks. Additional keywords: infiltration, crop models, APSIM, water balance, soil structure., Introduction Models have been successfully used to simulate interactions between crops, soil, and the environment. Models such as EPIC (Williams et al. 1985) and PERFECT (Littleboy et al. 1989) have [...]

In FDTD modeling of lossy, dispersive soil for subsurface imaging and detection applications, the electric flux and the current are convolutions of E(t) with [Epsilon](t) and [Sigma](t) respectively. To avoid these memory-intensive computations, the convolutions can often be accurately and simply modelled as second order difference equations. In particular, by matching the corresponding Z-transform of the E-field/current relation to frequency-dependent conductivity results in a ratio of polynomials in [Z.sup.-1] (where Z = [e.sup.j[Omega][Delta]t]). A good fit to measured soil data over two decades in frequency is possible using only a single pole, two zero conductivity model. Compared to a similarly accurate three-term Debye model, this one-pole model requires one-third the storage of previously computed field values. Index terms - FDTD, Soil Modeling, Dispersion, Mine Detection

A class of two-invariant stored energy functions describing the hyperelastic characteristics of soils is coupled with a critical-state plasticity model. The functions include constant as well as pressure-dependent elastic shear modulus models, and automatically satisfy the requirement that the elastic response for any loading path be energy conserving. The elastic responses predicted by the hyperelastic model are compared with measured undrained elastic responses of an overconsolidated clay in order to assess, both qualitatively and quantitatively, the predictive capability of the hyperelastic model. The importance of the pressure-dependent nature of the elastic shear modulus is assessed within the context of elastic and plastic responses. An energy-conserving model provides a fundamentally correct description of elastic material behavior even in the regime of plastic responses.

A model was developed for describing rates of pesticide-substrate biodegradation, accounting for bioavailability and microbial growth. The model was used to simulate losses of efficacy for soil-applied pesticides. The model requires rate constants for rapid sorption - desorption to and from soil surfaces ([k.sub.1]/[k.sub.-1] = [K.sub.d1]); diffusion into and out of soil aggregates-organic matter particles ([k.sub.2]/k.sub.-2] = [K.sub.d2]); microbial growth [yield (Y), maximum growth rate ([[Mu].sub.max]), half-saturation growth constant ([K.sub.s]), and initial biomass concentration initial mass of substrate ([S.sub.0]); and gravimetric water content ([[Theta].sub.g]). Simulations of microbial growth and substrate depletion were conducted assuming no sorption (aqueous solution), sorption to soil surfaces only, and sorption in conjunction with diffusion. The time required to achieve a soil solution concentration of 1 [[micro]gram] [mL.sup.-1] was defined as a hypothetical loss of efficacy (L[E.sub.1]). Certain relationships were consistently observed, regardless of sorption or diffusion: L[E.sub.1] was found to be related to [K.sub.s] linearly, to [X.sub.0] logarithmically, to [[Mu].sub.max], geometrically, and to initial pesticide-substrate concentration ([S.sub.0]) nonlinearly. Sorption to soil surfaces resulted in decreased equilibrium soil solution concentration ([S.sub.e]), depending on the magnitude of [[Theta].sub.g] and [K.sub.d1]. Rates of biodegradation-growth were a function of [S.sub.e], as opposed to total (soluble - sorbed) concentration. Sorption coupled with diffusion decreased both [S.sub.e] and time-dependent availability, resulting in slower rates of biodegradation. In general, larger values of [S.sub.0] resulted in faster rates of biodegradation, i.e., decreased the time required a loss of efficacy.