Soil texture analysis revisited: Removal of organic matter matters more than ever.

PubMed

Jensen, Johannes Lund; Schjønning, Per; Watts, Christopher W; Christensen, Bent T; Munkholm, Lars J

2017-01-01

Exact estimates of soil clay (<2 μm) and silt (2-20 μm) contents are crucial as these size fractions impact key soil functions, and as pedotransfer concepts based on clay and silt contents are becoming increasingly abundant. We examined the effect of removing soil organic matter (SOM) by H2O2 before soil dispersion and determination of clay and silt. Soil samples with gradients in SOM were retrieved from three long-term field experiments each with uniform soil mineralogy and texture. For soils with less than 2 g C 100 g-1 minerals, clay estimates were little affected by SOM. Above this threshold, underestimation of clay increased dramatically with increasing SOM content. Silt contents were systematically overestimated when SOM was not removed; no lower SOM threshold was found for silt, but the overestimation was more pronounced for finer textured soils. When exact estimates of soil particles <20 μm are needed, SOM should always be removed before soil dispersion.

Accuracy of sample dimension-dependent pedotransfer functions in estimation of soil saturated hydraulic conductivity

USDA-ARS?s Scientific Manuscript database

Saturated hydraulic conductivity Ksat is a fundamental characteristic in modeling flow and contaminant transport in soils and sediments. Therefore, many models have been developed to estimate Ksat from easily measureable parameters, such as textural properties, bulk density, etc. However, Ksat is no...

Evaluation of HCMM data for assessing soil moisture and water table depth. [South Dakota

NASA Technical Reports Server (NTRS)

Moore, D. G.; Heilman, J. L.; Tunheim, J. A.; Westin, F. C.; Heilman, W. E.; Beutler, G. A.; Ness, S. D. (Principal Investigator)

1981-01-01

Soil moisture in the 0-cm to 4-cm layer could be estimated with 1-mm soil temperatures throughout the growing season of a rainfed barley crop in eastern South Dakota. Empirical equations were developed to reduce the effect of canopy cover when radiometrically estimating the soil temperature. Corrective equations were applied to an aircraft simulation of HCMM data for a diversity of crop types and land cover conditions to estimate the soil moisture. The average difference between observed and measured soil moisture was 1.6% of field capacity. Shallow alluvial aquifers were located with HCMM predawn data. After correcting the data for vegetation differences, equations were developed for predicting water table depths within the aquifer. A finite difference code simulating soil moisture and soil temperature shows that soils with different moisture profiles differed in soil temperatures in a well defined functional manner. A significant surface thermal anomaly was found to be associated with shallow water tables.

Stochastic Analysis and Probabilistic Downscaling of Soil Moisture

NASA Astrophysics Data System (ADS)

Deshon, J. P.; Niemann, J. D.; Green, T. R.; Jones, A. S.

2017-12-01

Soil moisture is a key variable for rainfall-runoff response estimation, ecological and biogeochemical flux estimation, and biodiversity characterization, each of which is useful for watershed condition assessment. These applications require not only accurate, fine-resolution soil-moisture estimates but also confidence limits on those estimates and soil-moisture patterns that exhibit realistic statistical properties (e.g., variance and spatial correlation structure). The Equilibrium Moisture from Topography, Vegetation, and Soil (EMT+VS) model downscales coarse-resolution (9-40 km) soil moisture from satellite remote sensing or land-surface models to produce fine-resolution (10-30 m) estimates. The model was designed to produce accurate deterministic soil-moisture estimates at multiple points, but the resulting patterns do not reproduce the variance or spatial correlation of observed soil-moisture patterns. The primary objective of this research is to generalize the EMT+VS model to produce a probability density function (pdf) for soil moisture at each fine-resolution location and time. Each pdf has a mean that is equal to the deterministic soil-moisture estimate, and the pdf can be used to quantify the uncertainty in the soil-moisture estimates and to simulate soil-moisture patterns. Different versions of the generalized model are hypothesized based on how uncertainty enters the model, whether the uncertainty is additive or multiplicative, and which distributions describe the uncertainty. These versions are then tested by application to four catchments with detailed soil-moisture observations (Tarrawarra, Satellite Station, Cache la Poudre, and Nerrigundah). The performance of the generalized models is evaluated by comparing the statistical properties of the simulated soil-moisture patterns to those of the observations and the deterministic EMT+VS model. The versions of the generalized EMT+VS model with normally distributed stochastic components produce soil-moisture patterns with more realistic statistical properties than the deterministic model. Additionally, the results suggest that the variance and spatial correlation of the stochastic soil-moisture variations do not vary consistently with the spatial-average soil moisture.

Nutrient and Rainfall Additions Shift Phylogenetically Estimated Traits of Soil Microbial Communities.

PubMed

Gravuer, Kelly; Eskelinen, Anu

2017-01-01

Microbial traits related to ecological responses and functions could provide a common currency facilitating synthesis and prediction; however, such traits are difficult to measure directly for all taxa in environmental samples. Past efforts to estimate trait values based on phylogenetic relationships have not always distinguished between traits with high and low phylogenetic conservatism, limiting reliability, especially in poorly known environments, such as soil. Using updated reference trees and phylogenetic relationships, we estimated two phylogenetically conserved traits hypothesized to be ecologically important from DNA sequences of the 16S rRNA gene from soil bacterial and archaeal communities. We sampled these communities from an environmental change experiment in California grassland applying factorial addition of late-season precipitation and soil nutrients to multiple soil types for 3 years prior to sampling. Estimated traits were rRNA gene copy number, which contributes to how rapidly a microbe can respond to an increase in resources and may be related to its maximum growth rate, and genome size, which suggests the breadth of environmental and substrate conditions in which a microbe can thrive. Nutrient addition increased community-weighted mean estimated rRNA gene copy number and marginally increased estimated genome size, whereas precipitation addition decreased these community means for both estimated traits. The effects of both treatments on both traits were associated with soil properties, such as ammonium, available phosphorus, and pH. Estimated trait responses within several phyla were opposite to the community mean response, indicating that microbial responses, although largely consistent among soil types, were not uniform across the tree of life. Our results show that phylogenetic estimation of microbial traits can provide insight into how microbial ecological strategies interact with environmental changes. The method could easily be applied to any of the thousands of existing 16S rRNA sequence data sets and offers potential to improve our understanding of how microbial communities mediate ecosystem function responses to global changes.

Spatio-temporal Root Zone Soil Moisture Estimation for Indo - Gangetic Basin from Satellite Derived (AMSR-2 and SMOS) Surface Soil Moisture

NASA Astrophysics Data System (ADS)

Sure, A.; Dikshit, O.

2017-12-01

Root zone soil moisture (RZSM) is an important element in hydrology and agriculture. The estimation of RZSM provides insight in selecting the appropriate crops for specific soil conditions (soil type, bulk density, etc.). RZSM governs various vadose zone phenomena and subsequently affects the groundwater processes. With various satellite sensors dedicated to estimating surface soil moisture at different spatial and temporal resolutions, estimation of soil moisture at root zone level for Indo - Gangetic basin which inherits complex heterogeneous environment, is quite challenging. This study aims at estimating RZSM and understand its variation at the level of Indo - Gangetic basin with changing land use/land cover, topography, crop cycles, soil properties, temperature and precipitation patterns using two satellite derived soil moisture datasets operating at distinct frequencies with different principles of acquisition. Two surface soil moisture datasets are derived from AMSR-2 (6.9 GHz - `C' Band) and SMOS (1.4 GHz - `L' band) passive microwave sensors with coarse spatial resolution. The Soil Water Index (SWI), accounting for soil moisture from the surface, is derived by considering a theoretical two-layered water balance model and contributes in ascertaining soil moisture at the vadose zone. This index is evaluated against the widely used modelled soil moisture dataset of GLDAS - NOAH, version 2.1. This research enhances the domain of utilising the modelled soil moisture dataset, wherever the ground dataset is unavailable. The coupling between the surface soil moisture and RZSM is analysed for two years (2015-16), by defining a parameter T, the characteristic time length. The study demonstrates that deriving an optimal value of T for estimating SWI at a certain location is a function of various factors such as land, meteorological, and agricultural characteristics.

Indirect estimation of the Convective Lognormal Transfer function model parameters for describing solute transport in unsaturated and undisturbed soil.

PubMed

Mohammadi, Mohammad Hossein; Vanclooster, Marnik

2012-05-01

Solute transport in partially saturated soils is largely affected by fluid velocity distribution and pore size distribution within the solute transport domain. Hence, it is possible to describe the solute transport process in terms of the pore size distribution of the soil, and indirectly in terms of the soil hydraulic properties. In this paper, we present a conceptual approach that allows predicting the parameters of the Convective Lognormal Transfer model from knowledge of soil moisture and the Soil Moisture Characteristic (SMC), parameterized by means of the closed-form model of Kosugi (1996). It is assumed that in partially saturated conditions, the air filled pore volume act as an inert solid phase, allowing the use of the Arya et al. (1999) pragmatic approach to estimate solute travel time statistics from the saturation degree and SMC parameters. The approach is evaluated using a set of partially saturated transport experiments as presented by Mohammadi and Vanclooster (2011). Experimental results showed that the mean solute travel time, μ(t), increases proportionally with the depth (travel distance) and decreases with flow rate. The variance of solute travel time σ²(t) first decreases with flow rate up to 0.4-0.6 Ks and subsequently increases. For all tested BTCs predicted solute transport with μ(t) estimated from the conceptual model performed much better as compared to predictions with μ(t) and σ²(t) estimated from calibration of solute transport at shallow soil depths. The use of μ(t) estimated from the conceptual model therefore increases the robustness of the CLT model in predicting solute transport in heterogeneous soils at larger depths. In view of the fact that reasonable indirect estimates of the SMC can be made from basic soil properties using pedotransfer functions, the presented approach may be useful for predicting solute transport at field or watershed scales. Copyright © 2012 Elsevier B.V. All rights reserved.

Indirect estimation of the Convective Lognormal Transfer function model parameters for describing solute transport in unsaturated and undisturbed soil

NASA Astrophysics Data System (ADS)

Mohammadi, Mohammad Hossein; Vanclooster, Marnik

2012-05-01

Solute transport in partially saturated soils is largely affected by fluid velocity distribution and pore size distribution within the solute transport domain. Hence, it is possible to describe the solute transport process in terms of the pore size distribution of the soil, and indirectly in terms of the soil hydraulic properties. In this paper, we present a conceptual approach that allows predicting the parameters of the Convective Lognormal Transfer model from knowledge of soil moisture and the Soil Moisture Characteristic (SMC), parameterized by means of the closed-form model of Kosugi (1996). It is assumed that in partially saturated conditions, the air filled pore volume act as an inert solid phase, allowing the use of the Arya et al. (1999) pragmatic approach to estimate solute travel time statistics from the saturation degree and SMC parameters. The approach is evaluated using a set of partially saturated transport experiments as presented by Mohammadi and Vanclooster (2011). Experimental results showed that the mean solute travel time, μt, increases proportionally with the depth (travel distance) and decreases with flow rate. The variance of solute travel time σ2t first decreases with flow rate up to 0.4-0.6 Ks and subsequently increases. For all tested BTCs predicted solute transport with μt estimated from the conceptual model performed much better as compared to predictions with μt and σ2t estimated from calibration of solute transport at shallow soil depths. The use of μt estimated from the conceptual model therefore increases the robustness of the CLT model in predicting solute transport in heterogeneous soils at larger depths. In view of the fact that reasonable indirect estimates of the SMC can be made from basic soil properties using pedotransfer functions, the presented approach may be useful for predicting solute transport at field or watershed scales.

Estimating soil matric potential in Owens Valley, California

USGS Publications Warehouse

Sorenson, Stephen K.; Miller, R.F.; Welch, M.R.; Groeneveld, D.P.; Branson, F.A.

1988-01-01

Much of the floor of the Owens Valley, California, is covered with alkaline scrub and alkaline meadow plant communities, whose existence is dependent partly on precipitation and partly on water infiltrated into the rooting zone from the shallow water table. The extent to which these plant communities are capable of adapting to and surviving fluctuations in the water table depends on physiological adaptations of the plants and on the water content, matric potential characteristics of the soils. Two methods were used to estimate soil matric potential in test sites in Owens Valley. The first was the filter-paper method, which uses water content of filter papers equilibrated to water content of soil samples taken with a hand auger. The other method of estimating soil matric potential was a modeling approach based on data from this and previous investigations. These data indicate that the base 10 logarithm of soil matric potential is a linear function of gravimetric soil water content for a particular soil. Estimates of soil water characteristic curves were made at two sites by averaging the gravimetric soil water content and soil matric potential values from multiple samples at 0.1 m depths derived by using the hand auger and filter paper method and entering these values in the soil water model. The characteristic curves then were used to estimate soil matric potential from estimates of volumetric soil water content derived from neutron-probe readings. Evaluation of the modeling technique at two study sites indicated that estimates of soil matric potential within 0.5 pF units of the soil matric potential value derived by using the filter paper method could be obtained 90 to 95% of the time in soils where water content was less than field capacity. The greatest errors occurred at depths where there was a distinct transition between soils of different textures. (Lantz-PTT)

Pedotransfer functions to estimate soil water content at field capacity and permanent wilting point in hot Arid Western India

NASA Astrophysics Data System (ADS)

Santra, Priyabrata; Kumar, Mahesh; Kumawat, R. N.; Painuli, D. K.; Hati, K. M.; Heuvelink, G. B. M.; Batjes, N. H.

2018-04-01

Characterization of soil water retention, e.g., water content at field capacity (FC) and permanent wilting point (PWP) over a landscape plays a key role in efficient utilization of available scarce water resources in dry land agriculture; however, direct measurement thereof for multiple locations in the field is not always feasible. Therefore, pedotransfer functions (PTFs) were developed to estimate soil water retention at FC and PWP for dryland soils of India. A soil database available for Arid Western India ( N=370) was used to develop PTFs. The developed PTFs were tested in two independent datasets from arid regions of India ( N=36) and an arid region of USA ( N=1789). While testing these PTFs using independent data from India, root mean square error (RMSE) was found to be 2.65 and 1.08 for FC and PWP, respectively, whereas for most of the tested `established' PTFs, the RMSE was >3.41 and >1.15, respectively. Performance of the developed PTFs from the independent dataset from USA was comparable with estimates derived from `established' PTFs. For wide applicability of the developed PTFs, a user-friendly soil moisture calculator was developed. The PTFs developed in this study may be quite useful to farmers for scheduling irrigation water as per soil type.

Crop moisture estimation over the southern Great Plains with dual polarization 1.66 centimeter passive microwave data from Nimbus 7

NASA Technical Reports Server (NTRS)

Mcfarland, M. J.; Harder, P. H., II; Wilke, G. D.; Huebner, G. L., Jr.

1984-01-01

Moisture content of snow-free, unfrozen soil is inferred using passive microwave brightness temperatures from the scanning multichannel microwave radiometer (SMMR) on Nimbus-7. Investigation is restricted to the two polarizations of the 1.66 cm wavelength sensor. Passive microwave estimates of soil moisture are of two basic categories; those based upon soil emissivity and those based upon the polarization of soil emission. The two methods are compared and contrasted through the investigation of 54 potential functions of polarized brightness temperatures and, in some cases, ground-based temperature measurements. Of these indices, three are selected for the estimated emissivity, the difference between polarized brightness temperatures, and the normalized polarization difference. Each of these indices is about equally effective for monitoring soil moisture. Using an antecedent precipitation index (API) as ground control data, temporal and spatial analyses show that emissivity data consistently give slightly better soil moisture estimates than depolarization data. The difference, however, is not statistically significant. It is concluded that polarization data alone can provide estimates of soil moisture in areas where the emissivity cannot be inferred due to nonavailability of surface temperature data.

Impediments to predicting site response: Seismic property estimation and modeling simplifications

USGS Publications Warehouse

Thompson, E.M.; Baise, L.G.; Kayen, R.E.; Guzina, B.B.

2009-01-01

We compare estimates of the empirical transfer function (ETF) to the plane SH-wave theoretical transfer function (TTF) within a laterally constant medium for invasive and noninvasive estimates of the seismic shear-wave slownesses at 13 Kiban-Kyoshin network stations throughout Japan. The difference between the ETF and either of the TTFs is substantially larger than the difference between the two TTFs computed from different estimates of the seismic properties. We show that the plane SH-wave TTF through a laterally homogeneous medium at vertical incidence inadequately models observed amplifications at most sites for both slowness estimates, obtained via downhole measurements and the spectral analysis of surface waves. Strategies to improve the predictions can be separated into two broad categories: improving the measurement of soil properties and improving the theory that maps the 1D soil profile onto spectral amplification. Using an example site where the 1D plane SH-wave formulation poorly predicts the ETF, we find a more satisfactory fit to the ETF by modeling the full wavefield and incorporating spatially correlated variability of the seismic properties. We conclude that our ability to model the observed site response transfer function is limited largely by the assumptions of the theoretical formulation rather than the uncertainty of the soil property estimates.

Spatial modeling of litter and soil carbon stocks with associated uncertainty on forest land in the conterminous United States

NASA Astrophysics Data System (ADS)

Cao, B.; Domke, G. M.; Russell, M.; McRoberts, R. E.; Walters, B. F.

2017-12-01

Forest ecosystems contribute substantially to carbon (C) storage. The dynamics of litter decomposition, translocation and stabilization into soil layers are essential processes in the functioning of forest ecosystems, as they control the cycling of soil organic matter and the accumulation and release of C to the atmosphere. Therefore, the spatial distributions of litter and soil C stocks are important in greenhouse gas estimation and reporting and inform land management decisions, policy, and climate change mitigation strategies. In this study, we explored the effects of spatial aggregation of climatic, biotic, topographic and soil input data on national estimates of litter and soil C stocks and characterized the spatial distribution of litter and soil C stocks in the conterminous United States. Data from the Forest Inventory and Analysis (FIA) program within the US Forest Service were used with vegetation phenology data estimated from LANDSAT imagery (30 m) and raster data describing relevant environmental parameters (e.g. temperature, precipitation, topographic properties) for the entire conterminous US. Litter and soil C stocks were estimated and mapped through geostatistical analysis and statistical uncertainty bounds on the pixel level predictions were constructed using a Monte Carlo-bootstrap technique, by which credible variance estimates for the C stocks were calculated. The sensitivity of model estimates to spatial aggregation depends on geographic region. Further, using long-term (30-year) climate averages during periods with strong climatic trends results in large differences in litter and soil C stock estimates. In addition, results suggest that local topographic aspect is an important variable in litter and soil C estimation at the continental scale.

Short-term parasite-infection alters already the biomass, activity and functional diversity of soil microbial communities

PubMed Central

Li, Jun-Min; Jin, Ze-Xin; Hagedorn, Frank; Li, Mai-He

2014-01-01

Native parasitic plants may be used to infect and control invasive plants. We established microcosms with invasive Mikania micrantha and native Coix lacryma-jobi growing in mixture on native soils, with M. micrantha being infected by parasitic Cuscuta campestris at four intensity levels for seven weeks to estimate the top-down effects of plant parasitism on the biomass and functional diversity of soil microbial communities. Parasitism significantly decreased root biomass and altered soil microbial communities. Soil microbial biomass decreased, but soil respiration increased at the two higher infection levels, indicating a strong stimulation of soil microbial metabolic activity (+180%). Moreover, a Biolog assay showed that the infection resulted in a significant change in the functional diversity indices of soil microbial communities. Pearson correlation analysis indicated that microbial biomass declined significantly with decreasing root biomass, particularly of the invasive M. micrantha. Also, the functional diversity indices of soil microbial communities were positively correlated with soil microbial biomass. Therefore, the negative effects on the biomass, activity and functional diversity of soil microbial community by the seven week long plant parasitism was very likely caused by decreased root biomass and root exudation of the invasive M. micrantha. PMID:25367357

Short-term parasite-infection alters already the biomass, activity and functional diversity of soil microbial communities

NASA Astrophysics Data System (ADS)

Li, Jun-Min; Jin, Ze-Xin; Hagedorn, Frank; Li, Mai-He

2014-11-01

Native parasitic plants may be used to infect and control invasive plants. We established microcosms with invasive Mikania micrantha and native Coix lacryma-jobi growing in mixture on native soils, with M. micrantha being infected by parasitic Cuscuta campestris at four intensity levels for seven weeks to estimate the top-down effects of plant parasitism on the biomass and functional diversity of soil microbial communities. Parasitism significantly decreased root biomass and altered soil microbial communities. Soil microbial biomass decreased, but soil respiration increased at the two higher infection levels, indicating a strong stimulation of soil microbial metabolic activity (+180%). Moreover, a Biolog assay showed that the infection resulted in a significant change in the functional diversity indices of soil microbial communities. Pearson correlation analysis indicated that microbial biomass declined significantly with decreasing root biomass, particularly of the invasive M. micrantha. Also, the functional diversity indices of soil microbial communities were positively correlated with soil microbial biomass. Therefore, the negative effects on the biomass, activity and functional diversity of soil microbial community by the seven week long plant parasitism was very likely caused by decreased root biomass and root exudation of the invasive M. micrantha.

Microwave remote sensing of soil moisture, volume 1. [Guymon, Oklahoma and Dalhart, Texas

NASA Technical Reports Server (NTRS)

Mcfarland, M. J. (Principal Investigator); Theis, S. W.; Rosenthal, W. D.; Jones, C. L.

1982-01-01

Multifrequency sensor data from NASA's C-130 aircraft were used to determine which of the all weather microwave sensors demonstrated the highest correlation to surface soil moisture over optimal bare soil conditions, and to develop and test techniques which use visible/infrared sensors to compensate for the vegetation effect in this sensor's response to soil moisture. The L-band passive microwave radiometer was found to be the most suitable single sensor system to estimate soil moisture over bare fields. The perpendicular vegetation index (PVI) as determined from the visible/infrared sensors was useful as a measure of the vegetation effect on the L-band radiometer response to soil moisture. A linear equation was developed to estimate percent field capacity as a function of L-band emissivity and the vegetation index. The prediction algorithm improves the estimation of moisture significantly over predictions from L-band emissivity alone.

Estimating carbon in forest soils of the United States using the national forest inventory

Treesearch

Grant M. Domke; Charles H. (Hobie) Perry; Brian F. Walters; Christopher W. Woodall; Lucas E. Nave; Chris Swanston

2015-01-01

Soil organic carbon (SOC) is the largest terrestrial carbon (C) sink on earth and management of this pool is a critical component of global efforts to mitigate atmospheric C concentrations. Soil organic carbon is also a key indicator of soil quality as it affects essential biological, chemical, and physical soil functions such as nutrient cycling, water retention, and...

Soil moisture data as a constraint for groundwater recharge estimation

NASA Astrophysics Data System (ADS)

Mathias, Simon A.; Sorensen, James P. R.; Butler, Adrian P.

2017-09-01

Estimating groundwater recharge rates is important for water resource management studies. Modeling approaches to forecast groundwater recharge typically require observed historic data to assist calibration. It is generally not possible to observe groundwater recharge rates directly. Therefore, in the past, much effort has been invested to record soil moisture content (SMC) data, which can be used in a water balance calculation to estimate groundwater recharge. In this context, SMC data is measured at different depths and then typically integrated with respect to depth to obtain a single set of aggregated SMC values, which are used as an estimate of the total water stored within a given soil profile. This article seeks to investigate the value of such aggregated SMC data for conditioning groundwater recharge models in this respect. A simple modeling approach is adopted, which utilizes an emulation of Richards' equation in conjunction with a soil texture pedotransfer function. The only unknown parameters are soil texture. Monte Carlo simulation is performed for four different SMC monitoring sites. The model is used to estimate both aggregated SMC and groundwater recharge. The impact of conditioning the model to the aggregated SMC data is then explored in terms of its ability to reduce the uncertainty associated with recharge estimation. Whilst uncertainty in soil texture can lead to significant uncertainty in groundwater recharge estimation, it is found that aggregated SMC is virtually insensitive to soil texture.

Urban Soil Hydrology: bridging the data gap with a nationwide field study

NASA Astrophysics Data System (ADS)

Schifman, L. A.; Shuster, W.

2016-12-01

Urban communities generally rely on hydrologic models or tools for assessing suitable sites for green infrastructure. These rainfall-runoff models, e.g. National Stormwater Calculator (NSWC), query soil hydrologic information from national databases, e.g. Soil Survey Geographic Database (SSURGO), or are estimated via pedotransfer-based algorithms like USDA Rosetta. As part of urban soil hydrologic assessments we have collected soil textural and hydrologic data in 12 cities throughout the United States and compared these measurements to NSWC and SSURGO queried infiltration rates (Kunsat) and Rosetta-estimated drainage rates (Ksat and Kunsat). We found that soil hydrologic parameters obtained through pedotransfer functions and queries to soil databases are not representative of field-measured values (RMSE range from 6.2 to 15.2 for infiltration and from 13.2 to 16.3 for drainage). Although the NSWC queries SSURGO, we found that SSURGO overestimates infiltration and NSWC underestimates with MEs of 4.9, and -1.4, respectively. In Rosetta, we found that pedotransfer functions overestimated drainage rates (MEs 1.8 to 3.8). In an attempt to improve drainage estimates using Rosetta the soil texture was adjusted in soils with an apparent portion of finer sands. Here, sand included: very coarse, coarse, and medium sand, whereas silt included fine, and very fine sand and silt, with the justification that fine sands behave similarly to silt. These adjusted estimates resulted in generally underestimating drainage and still not suitable for use in planning for stormwater detention (e.g., infiltrative green infrastructure). With this work we highlight the importance of obtaining field measured values when assessing sites for green infrastructure planning instead of relying on estimates, as the discrepancies in sensitive parameters such as Kunsat and Ksat, implications for parameter selection in error propagation through rainfall-runoff models, and consequences for over- or under-design of stormwater control measures for detention.

A simple procedure for estimating soil porosity

NASA Astrophysics Data System (ADS)

Emmet-Booth, Jeremy; Forristal, Dermot; Fenton, Owen; Holden, Nick

2016-04-01

Soil degradation from mismanagement is of international concern. Simple, accessible tools for rapidly assessing impacts of soil management are required. Soil structure is a key component of soil quality and porosity is a useful indicator of structure. We outline a version of a procedure described by Piwowarczyk et al. (2011) used to estimate porosity of samples taken during a soil quality survey of 38 sites across Ireland as part of the Government funded SQUARE (Soil Quality Assessment Research) project. This required intact core (r = 2.5 cm, H = 5cm) samples taken at 5-10 cm and 10-20 cm depth, to be covered with muslin cloth at one end and secured with a jubilee clip. Samples were saturated in sealable water tanks for ≈ 64 hours, then allowed to drain by gravity for 24 hours, at which point Field Capacity (F.C.) was assumed to have been reached, followed by oven drying with weight determined at each stage. This allowed the calculation of bulk density and the estimation of water content at saturation and following gravitational drainage, thus total and functional porosity. The assumption that F.C. was reached following 24 hours of gravitational drainage was based on the Soil Moisture Deficit model used in Ireland to predict when soils are potentially vulnerable to structural damage and used nationally as a management tool. Preliminary results indicate moderately strong, negative correlations between estimated total porosity at 5-10 cm and 10-20 cm depth (rs = -0.7, P < 0.01 in both cases) and soil quality scores of the Visual Evaluation of Soil Structure (VESS) method which was conducted at each survey site. Estimated functional porosity at 5-10 cm depth was found to moderately, negatively correlate with VESS scores (rs = - 0.5, P < 0.05). This simple procedure requires inexpensive equipment and appears useful in indicating porosity of a large quantity of samples taken at numerous sites or if done periodically, temporal changes in porosity at a field scale, indicating the impacts of soil management. Reference Piwowarczyk, A., Giuliani, G. & Holden, N.M. 2011. Can soil moisture deficit be used to forecast when soils are at high risk of damage owing to grazing animals? Soil Use and Management, 27, 255-263.

Upscaling soil saturated hydraulic conductivity from pore throat characteristics

NASA Astrophysics Data System (ADS)

Ghanbarian, Behzad; Hunt, Allen G.; Skaggs, Todd H.; Jarvis, Nicholas

2017-06-01

Upscaling and/or estimating saturated hydraulic conductivity Ksat at the core scale from microscopic/macroscopic soil characteristics has been actively under investigation in the hydrology and soil physics communities for several decades. Numerous models have been developed based on different approaches, such as the bundle of capillary tubes model, pedotransfer functions, etc. In this study, we apply concepts from critical path analysis, an upscaling technique first developed in the physics literature, to estimate saturated hydraulic conductivity at the core scale from microscopic pore throat characteristics reflected in capillary pressure data. With this new model, we find Ksat estimations to be within a factor of 3 of the average measured saturated hydraulic conductivities reported by Rawls et al. (1982) for the eleven USDA soil texture classes.

Derivation of spatial patterns of soil hydraulic properties based on pedotransfer functions

USDA-ARS?s Scientific Manuscript database

Spatial patterns in soil hydrology are the product of the spatial distribution of soil hydraulic properties. These properties are notorious for the difficulties and high labor costs involved in measuring them. Often, there is a need to resort to estimating these parameters from other, more readily a...

Remote sensing as a tool for estimating soil erosion potential

NASA Technical Reports Server (NTRS)

Morris-Jones, D. R.; Morgan, K. M.; Kiefer, R. W.

1979-01-01

The Universal Soil Loss Equation is a frequently used methodology for estimating soil erosion potential. The Universal Soil Loss Equation requires a variety of types of geographic information (e.g. topographic slope, soil erodibility, land use, crop type, and soil conservation practice) in order to function. This information is traditionally gathered from topographic maps, soil surveys, field surveys, and interviews with farmers. Remote sensing data sources and interpretation techniques provide an alternative method for collecting information regarding land use, crop type, and soil conservation practice. Airphoto interpretation techniques and medium altitude, multi-date color and color infrared positive transparencies (70mm) were utilized in this study to determine their effectiveness for gathering the desired land use/land cover data. Successful results were obtained within the test site, a 6136 hectare watershed in Dane County, Wisconsin.

Entropy-Bayesian Inversion of Time-Lapse Tomographic GPR data for Monitoring Dielectric Permittivity and Soil Moisture Variations

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hou, Z; Terry, N; Hubbard, S S

2013-02-12

In this study, we evaluate the possibility of monitoring soil moisture variation using tomographic ground penetrating radar travel time data through Bayesian inversion, which is integrated with entropy memory function and pilot point concepts, as well as efficient sampling approaches. It is critical to accurately estimate soil moisture content and variations in vadose zone studies. Many studies have illustrated the promise and value of GPR tomographic data for estimating soil moisture and associated changes, however, challenges still exist in the inversion of GPR tomographic data in a manner that quantifies input and predictive uncertainty, incorporates multiple data types, handles non-uniquenessmore » and nonlinearity, and honors time-lapse tomograms collected in a series. To address these challenges, we develop a minimum relative entropy (MRE)-Bayesian based inverse modeling framework that non-subjectively defines prior probabilities, incorporates information from multiple sources, and quantifies uncertainty. The framework enables us to estimate dielectric permittivity at pilot point locations distributed within the tomogram, as well as the spatial correlation range. In the inversion framework, MRE is first used to derive prior probability distribution functions (pdfs) of dielectric permittivity based on prior information obtained from a straight-ray GPR inversion. The probability distributions are then sampled using a Quasi-Monte Carlo (QMC) approach, and the sample sets provide inputs to a sequential Gaussian simulation (SGSim) algorithm that constructs a highly resolved permittivity/velocity field for evaluation with a curved-ray GPR forward model. The likelihood functions are computed as a function of misfits, and posterior pdfs are constructed using a Gaussian kernel. Inversion of subsequent time-lapse datasets combines the Bayesian estimates from the previous inversion (as a memory function) with new data. The memory function and pilot point design takes advantage of the spatial-temporal correlation of the state variables. We first apply the inversion framework to a static synthetic example and then to a time-lapse GPR tomographic dataset collected during a dynamic experiment conducted at the Hanford Site in Richland, WA. We demonstrate that the MRE-Bayesian inversion enables us to merge various data types, quantify uncertainty, evaluate nonlinear models, and produce more detailed and better resolved estimates than straight-ray based inversion; therefore, it has the potential to improve estimates of inter-wellbore dielectric permittivity and soil moisture content and to monitor their temporal dynamics more accurately.« less

Entropy-Bayesian Inversion of Time-Lapse Tomographic GPR data for Monitoring Dielectric Permittivity and Soil Moisture Variations

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hou, Zhangshuan; Terry, Neil C.; Hubbard, Susan S.

2013-02-22

In this study, we evaluate the possibility of monitoring soil moisture variation using tomographic ground penetrating radar travel time data through Bayesian inversion, which is integrated with entropy memory function and pilot point concepts, as well as efficient sampling approaches. It is critical to accurately estimate soil moisture content and variations in vadose zone studies. Many studies have illustrated the promise and value of GPR tomographic data for estimating soil moisture and associated changes, however, challenges still exist in the inversion of GPR tomographic data in a manner that quantifies input and predictive uncertainty, incorporates multiple data types, handles non-uniquenessmore » and nonlinearity, and honors time-lapse tomograms collected in a series. To address these challenges, we develop a minimum relative entropy (MRE)-Bayesian based inverse modeling framework that non-subjectively defines prior probabilities, incorporates information from multiple sources, and quantifies uncertainty. The framework enables us to estimate dielectric permittivity at pilot point locations distributed within the tomogram, as well as the spatial correlation range. In the inversion framework, MRE is first used to derive prior probability density functions (pdfs) of dielectric permittivity based on prior information obtained from a straight-ray GPR inversion. The probability distributions are then sampled using a Quasi-Monte Carlo (QMC) approach, and the sample sets provide inputs to a sequential Gaussian simulation (SGSIM) algorithm that constructs a highly resolved permittivity/velocity field for evaluation with a curved-ray GPR forward model. The likelihood functions are computed as a function of misfits, and posterior pdfs are constructed using a Gaussian kernel. Inversion of subsequent time-lapse datasets combines the Bayesian estimates from the previous inversion (as a memory function) with new data. The memory function and pilot point design takes advantage of the spatial-temporal correlation of the state variables. We first apply the inversion framework to a static synthetic example and then to a time-lapse GPR tomographic dataset collected during a dynamic experiment conducted at the Hanford Site in Richland, WA. We demonstrate that the MRE-Bayesian inversion enables us to merge various data types, quantify uncertainty, evaluate nonlinear models, and produce more detailed and better resolved estimates than straight-ray based inversion; therefore, it has the potential to improve estimates of inter-wellbore dielectric permittivity and soil moisture content and to monitor their temporal dynamics more accurately.« less

Revision of the Rawls et al. (1982) pedotransfer functions for their applicability to US croplands

USDA-ARS?s Scientific Manuscript database

Large scale environmental impact studies typically involve the use of simulation models and require a variety of inputs, some of which may need to be estimated in absence of adequate measured data. As an example, soil water retention needs to be estimated for a large number of soils that are to be u...

An investigation of soil-structure interaction effects observed at the MIT Green Building

USGS Publications Warehouse

Taciroglu, Ertugrul; Çelebi, Mehmet; Ghahari, S. Farid; Abazarsa, Fariba

2016-01-01

The soil-foundation impedance function of the MIT Green Building is identified from its response signals recorded during an earthquake. Estimation of foundation impedance functions from seismic response signals is a challenging task, because: (1) the foundation input motions (FIMs) are not directly measurable, (2) the as-built properties of the super-structure are only approximately known, and (3) the soil-foundation impedance functions are inherently frequency-dependent. In the present study, aforementioned difficulties are circumvented by using, in succession, a blind modal identification (BMID) method, a simplified Timoshenko beam model (TBM), and a parametric updating of transfer functions (TFs). First, the flexible-base modal properties of the building are identified from response signals using the BMID method. Then, a flexible-base TBM is updated using the identified modal data. Finally, the frequency-dependent soil-foundation impedance function is estimated by minimizing the discrepancy between TFs (of pairs instrumented floors) that are (1) obtained experimentally from earthquake data and (2) analytically from the updated TBM. Using the fully identified flexible-base TBM, the FIMs as well as building responses at locations without instruments can be predicted, as demonstrated in the present study.

Assessment of soil health and fertility indicators with mobile phone imagery

NASA Astrophysics Data System (ADS)

Aitkenhead, Matt; Gwatkin, Richard; Coull, Malcolm; Donnelly, David

2015-04-01

Work on rapid soil assessment in the field has led to many hand-held sensors for soil monitoring (e.g. NIR, FTIR, XRF). Recent work by a research team at the James Hutton Institute has led to an integrated framework of mobile phones, apps and server-side processing. One example of this is the SOCIT app for estimating soil organic matter and carbon using geolocated mobile phone camera imagery. The SOCIT app is only applicable for agricultural soils in Scotland, and our intention is to expand this work both geographically and in functional ability. Ongoing work for the development of a prototype app for estimating soil characteristics across Europe using mobile phone imagery and the JRC LUCAS dataset will be described. Additionally, we will demonstrate recent work in estimating a number of soil health indicators from more detailed analysis of soil photographs. Accuracy levels achieved for estimating soil organic matter and organic carbon content, pH, structure, cation exchange capacity and texture vary and are not as good as those achieved with laboratory analysis, but are suitable for rapid field-based assessment. Issues relating to this work include colour stabilisation and calibration, integration with data on site characteristics, data processing, model development and the ethical use of data captured by others, and each of these topics will also be discussed.

Estimation of soil cation exchange capacity using Genetic Expression Programming (GEP) and Multivariate Adaptive Regression Splines (MARS)

NASA Astrophysics Data System (ADS)

Emamgolizadeh, S.; Bateni, S. M.; Shahsavani, D.; Ashrafi, T.; Ghorbani, H.

2015-10-01

The soil cation exchange capacity (CEC) is one of the main soil chemical properties, which is required in various fields such as environmental and agricultural engineering as well as soil science. In situ measurement of CEC is time consuming and costly. Hence, numerous studies have used traditional regression-based techniques to estimate CEC from more easily measurable soil parameters (e.g., soil texture, organic matter (OM), and pH). However, these models may not be able to adequately capture the complex and highly nonlinear relationship between CEC and its influential soil variables. In this study, Genetic Expression Programming (GEP) and Multivariate Adaptive Regression Splines (MARS) were employed to estimate CEC from more readily measurable soil physical and chemical variables (e.g., OM, clay, and pH) by developing functional relations. The GEP- and MARS-based functional relations were tested at two field sites in Iran. Results showed that GEP and MARS can provide reliable estimates of CEC. Also, it was found that the MARS model (with root-mean-square-error (RMSE) of 0.318 Cmol+ kg-1 and correlation coefficient (R2) of 0.864) generated slightly better results than the GEP model (with RMSE of 0.270 Cmol+ kg-1 and R2 of 0.807). The performance of GEP and MARS models was compared with two existing approaches, namely artificial neural network (ANN) and multiple linear regression (MLR). The comparison indicated that MARS and GEP outperformed the MLP model, but they did not perform as good as ANN. Finally, a sensitivity analysis was conducted to determine the most and the least influential variables affecting CEC. It was found that OM and pH have the most and least significant effect on CEC, respectively.

Incorporating soil variability in continental soil water modelling: a trade-off between data availability and model complexity

NASA Astrophysics Data System (ADS)

Peeters, L.; Crosbie, R. S.; Doble, R.; van Dijk, A. I. J. M.

2012-04-01

Developing a continental land surface model implies finding a balance between the complexity in representing the system processes and the availability of reliable data to drive, parameterise and calibrate the model. While a high level of process understanding at plot or catchment scales may warrant a complex model, such data is not available at the continental scale. This data sparsity is especially an issue for the Australian Water Resources Assessment system, AWRA-L, a land-surface model designed to estimate the components of the water balance for the Australian continent. This study focuses on the conceptualization and parametrization of the soil drainage process in AWRA-L. Traditionally soil drainage is simulated with Richards' equation, which is highly non-linear. As general analytic solutions are not available, this equation is usually solved numerically. In AWRA-L however, we introduce a simpler function based on simulation experiments that solve Richards' equation. In the simplified function soil drainage rate, the ratio of drainage (D) over storage (S), decreases exponentially with relative water content. This function is controlled by three parameters, the soil water storage at field capacity (SFC), the drainage fraction at field capacity (KFC) and a drainage function exponent (β). [ ] D- -S- S = KF C exp - β (1 - SFC ) To obtain spatially variable estimates of these three parameters, the Atlas of Australian Soils is used, which lists soil hydraulic properties for each soil profile type. For each soil profile type in the Atlas, 10 days of draining an initially fully saturated, freely draining soil is simulated using HYDRUS-1D. With field capacity defined as the volume of water in the soil after 1 day, the remaining parameters can be obtained by fitting the AWRA-L soil drainage function to the HYDRUS-1D results. This model conceptualisation fully exploits the data available in the Atlas of Australian Soils, without the need to solve the non-linear Richards' equation for each time-step. The spatial distribution of long term recharge and baseflow obtained with a 30 year simulation of historic data using this parameterisation, corresponds well with the spatial patterns of groundwater recharge inferred from field measurements.

Physical Quality Indicators and Mechanical Behavior of Agricultural Soils of Argentina.

PubMed

Imhoff, Silvia; da Silva, Alvaro Pires; Ghiberto, Pablo J; Tormena, Cássio A; Pilatti, Miguel A; Libardi, Paulo L

2016-01-01

Mollisols of Santa Fe have different tilth and load support capacity. Despite the importance of these attributes to achieve a sustainable crop production, few information is available. The objectives of this study are i) to assess soil physical indicators related to plant growth and to soil mechanical behavior; and ii) to establish relationships to estimate the impact of soil loading on the soil quality to plant growth. The study was carried out on Argiudolls and Hapludolls of Santa Fe. Soil samples were collected to determine texture, organic matter content, bulk density, water retention curve, soil resistance to penetration, least limiting water range, critical bulk density for plant growth, compression index, pre-consolidation pressure and soil compressibility. Water retention curve and soil resistance to penetration were linearly and significantly related to clay and organic matter (R2 = 0.91 and R2 = 0.84). The pedotransfer functions of water retention curve and soil resistance to penetration allowed the estimation of the least limiting water range and critical bulk density for plant growth. A significant nonlinear relationship was found between critical bulk density for plant growth and clay content (R2 = 0.98). Compression index was significantly related to bulk density, water content, organic matter and clay plus silt content (R2 = 0.77). Pre-consolidation pressure was significantly related to organic matter, clay and water content (R2 = 0.77). Soil compressibility was significantly related to initial soil bulk density, clay and water content. A nonlinear and significantly pedotransfer function (R2 = 0.88) was developed to predict the maximum acceptable pressure to be applied during tillage operations by introducing critical bulk density for plant growth in the compression model. The developed pedotransfer function provides a useful tool to link the mechanical behavior and tilth of the soils studied.

Physical Quality Indicators and Mechanical Behavior of Agricultural Soils of Argentina

PubMed Central

Pires da Silva, Alvaro; Ghiberto, Pablo J.; Tormena, Cássio A.; Pilatti, Miguel A.; Libardi, Paulo L.

2016-01-01

Mollisols of Santa Fe have different tilth and load support capacity. Despite the importance of these attributes to achieve a sustainable crop production, few information is available. The objectives of this study are i) to assess soil physical indicators related to plant growth and to soil mechanical behavior; and ii) to establish relationships to estimate the impact of soil loading on the soil quality to plant growth. The study was carried out on Argiudolls and Hapludolls of Santa Fe. Soil samples were collected to determine texture, organic matter content, bulk density, water retention curve, soil resistance to penetration, least limiting water range, critical bulk density for plant growth, compression index, pre-consolidation pressure and soil compressibility. Water retention curve and soil resistance to penetration were linearly and significantly related to clay and organic matter (R2 = 0.91 and R2 = 0.84). The pedotransfer functions of water retention curve and soil resistance to penetration allowed the estimation of the least limiting water range and critical bulk density for plant growth. A significant nonlinear relationship was found between critical bulk density for plant growth and clay content (R2 = 0.98). Compression index was significantly related to bulk density, water content, organic matter and clay plus silt content (R2 = 0.77). Pre-consolidation pressure was significantly related to organic matter, clay and water content (R2 = 0.77). Soil compressibility was significantly related to initial soil bulk density, clay and water content. A nonlinear and significantly pedotransfer function (R2 = 0.88) was developed to predict the maximum acceptable pressure to be applied during tillage operations by introducing critical bulk density for plant growth in the compression model. The developed pedotransfer function provides a useful tool to link the mechanical behavior and tilth of the soils studied. PMID:27099925

Pedotransfer Functions in Earth System Science: Challenges and Perspectives

NASA Astrophysics Data System (ADS)

Van Looy, Kris; Bouma, Johan; Herbst, Michael; Koestel, John; Minasny, Budiman; Mishra, Umakant; Montzka, Carsten; Nemes, Attila; Pachepsky, Yakov A.; Padarian, José; Schaap, Marcel G.; Tóth, Brigitta; Verhoef, Anne; Vanderborght, Jan; van der Ploeg, Martine J.; Weihermüller, Lutz; Zacharias, Steffen; Zhang, Yonggen; Vereecken, Harry

2017-12-01

Soil, through its various functions, plays a vital role in the Earth's ecosystems and provides multiple ecosystem services to humanity. Pedotransfer functions (PTFs) are simple to complex knowledge rules that relate available soil information to soil properties and variables that are needed to parameterize soil processes. In this paper, we review the existing PTFs and document the new generation of PTFs developed in the different disciplines of Earth system science. To meet the methodological challenges for a successful application in Earth system modeling, we emphasize that PTF development has to go hand in hand with suitable extrapolation and upscaling techniques such that the PTFs correctly represent the spatial heterogeneity of soils. PTFs should encompass the variability of the estimated soil property or process, in such a way that the estimation of parameters allows for validation and can also confidently provide for extrapolation and upscaling purposes capturing the spatial variation in soils. Most actively pursued recent developments are related to parameterizations of solute transport, heat exchange, soil respiration, and organic carbon content, root density, and vegetation water uptake. Further challenges are to be addressed in parameterization of soil erosivity and land use change impacts at multiple scales. We argue that a comprehensive set of PTFs can be applied throughout a wide range of disciplines of Earth system science, with emphasis on land surface models. Novel sensing techniques provide a true breakthrough for this, yet further improvements are necessary for methods to deal with uncertainty and to validate applications at global scale.

Combined Radar-Radiometer Surface Soil Moisture and Roughness Estimation

NASA Technical Reports Server (NTRS)

Akbar, Ruzbeh; Cosh, Michael H.; O'Neill, Peggy E.; Entekhabi, Dara; Moghaddam, Mahta

2017-01-01

A robust physics-based combined radar-radiometer, or Active-Passive, surface soil moisture and roughness estimation methodology is presented. Soil moisture and roughness retrieval is performed via optimization, i.e., minimization, of a joint objective function which constrains similar resolution radar and radiometer observations simultaneously. A data-driven and noise-dependent regularization term has also been developed to automatically regularize and balance corresponding radar and radiometer contributions to achieve optimal soil moisture retrievals. It is shown that in order to compensate for measurement and observation noise, as well as forward model inaccuracies, in combined radar-radiometer estimation surface roughness can be considered a free parameter. Extensive Monte-Carlo numerical simulations and assessment using field data have been performed to both evaluate the algorithms performance and to demonstrate soil moisture estimation. Unbiased root mean squared errors (RMSE) range from 0.18 to 0.03 cm3cm3 for two different land cover types of corn and soybean. In summary, in the context of soil moisture retrieval, the importance of consistent forward emission and scattering development is discussed and presented.

Combined Radar-Radiometer Surface Soil Moisture and Roughness Estimation.

PubMed

Akbar, Ruzbeh; Cosh, Michael H; O'Neill, Peggy E; Entekhabi, Dara; Moghaddam, Mahta

2017-07-01

A robust physics-based combined radar-radiometer, or Active-Passive, surface soil moisture and roughness estimation methodology is presented. Soil moisture and roughness retrieval is performed via optimization, i.e., minimization, of a joint objective function which constrains similar resolution radar and radiometer observations simultaneously. A data-driven and noise-dependent regularization term has also been developed to automatically regularize and balance corresponding radar and radiometer contributions to achieve optimal soil moisture retrievals. It is shown that in order to compensate for measurement and observation noise, as well as forward model inaccuracies, in combined radar-radiometer estimation surface roughness can be considered a free parameter. Extensive Monte-Carlo numerical simulations and assessment using field data have been performed to both evaluate the algorithm's performance and to demonstrate soil moisture estimation. Unbiased root mean squared errors (RMSE) range from 0.18 to 0.03 cm3/cm3 for two different land cover types of corn and soybean. In summary, in the context of soil moisture retrieval, the importance of consistent forward emission and scattering development is discussed and presented.

An inversion method for retrieving soil moisture information from satellite altimetry observations

NASA Astrophysics Data System (ADS)

Uebbing, Bernd; Forootan, Ehsan; Kusche, Jürgen; Braakmann-Folgmann, Anne

2016-04-01

Soil moisture represents an important component of the terrestrial water cycle that controls., evapotranspiration and vegetation growth. Consequently, knowledge on soil moisture variability is essential to understand the interactions between land and atmosphere. Yet, terrestrial measurements are sparse and their information content is limited due to the large spatial variability of soil moisture. Therefore, over the last two decades, several active and passive radar and satellite missions such as ERS/SCAT, AMSR, SMOS or SMAP have been providing backscatter information that can be used to estimate surface conditions including soil moisture which is proportional to the dielectric constant of the upper (few cm) soil layers . Another source of soil moisture information are satellite radar altimeters, originally designed to measure sea surface height over the oceans. Measurements of Jason-1/2 (Ku- and C-Band) or Envisat (Ku- and S-Band) nadir radar backscatter provide high-resolution along-track information (~ 300m along-track resolution) on backscatter every ~10 days (Jason-1/2) or ~35 days (Envisat). Recent studies found good correlation between backscatter and soil moisture in upper layers, especially in arid and semi-arid regions, indicating the potential of satellite altimetry both to reconstruct and to monitor soil moisture variability. However, measuring soil moisture using altimetry has some drawbacks that include: (1) the noisy behavior of the altimetry-derived backscatter (due to e.g., existence of surface water in the radar foot-print), (2) the strong assumptions for converting altimetry backscatters to the soil moisture storage changes, and (3) the need for interpolating between the tracks. In this study, we suggest a new inversion framework that allows to retrieve soil moisture information from along-track Jason-2 and Envisat satellite altimetry data, and we test this scheme over the Australian arid and semi-arid regions. Our method consists of: (i) deriving time-invariant spatial patterns (base-functions) by applying principal component analysis (PCA) to simulated soil moisture from a large-scale land surface model. (ii) Estimating time-variable soil moisture evolution by fitting these base functions of (i) to the along-track retracked backscatter coefficients in a least squares sense. (iii) Combining the estimated time-variable amplitudes and the pre-computed base-functions, which results in reconstructed (spatio-temporal) soil moisture information. We will show preliminary results that are compared to available high-resolution soil moisture model data over the region (the Australian Water Resource Assessment, AWRA model). We discuss the possibility of using altimetry-derived soil moisture estimations to improve the simulation skill of soil moisture in the Global Land Data Assimilation System (GLDAS) over Australia.

Using Remotely-Sensed Estimates of Soil Moisture to Infer Soil Texture and Hydraulic Properties across a Semi-arid Watershed

NASA Technical Reports Server (NTRS)

Santanello, Joseph A.; Peters-Lidard, Christa D.; Garcia, Matthew E.; Mocko, David M.; Tischler, Michael A.; Moran, M. Susan; Thoma, D. P.

2007-01-01

Near-surface soil moisture is a critical component of land surface energy and water balance studies encompassing a wide range of disciplines. However, the processes of infiltration, runoff, and evapotranspiration in the vadose zone of the soil are not easy to quantify or predict because of the difficulty in accurately representing soil texture and hydraulic properties in land surface models. This study approaches the problem of parameterizing soils from a unique perspective based on components originally developed for operational estimation of soil moisture for mobility assessments. Estimates of near-surface soil moisture derived from passive (L-band) microwave remote sensing were acquired on six dates during the Monsoon '90 experiment in southeastern Arizona, and used to calibrate hydraulic properties in an offline land surface model and infer information on the soil conditions of the region. Specifically, a robust parameter estimation tool (PEST) was used to calibrate the Noah land surface model and run at very high spatial resolution across the Walnut Gulch Experimental Watershed. Errors in simulated versus observed soil moisture were minimized by adjusting the soil texture, which in turn controls the hydraulic properties through the use of pedotransfer functions. By estimating a continuous range of widely applicable soil properties such as sand, silt, and clay percentages rather than applying rigid soil texture classes, lookup tables, or large parameter sets as in previous studies, the physical accuracy and consistency of the resulting soils could then be assessed. In addition, the sensitivity of this calibration method to the number and timing of microwave retrievals is determined in relation to the temporal patterns in precipitation and soil drying. The resultant soil properties were applied to an extended time period demonstrating the improvement in simulated soil moisture over that using default or county-level soil parameters. The methodology is also applied to an independent case at Walnut Gulch using a new soil moisture product from active (C-band) radar imagery with much lower spatial and temporal resolution. Overall, results demonstrate the potential to gain physically meaningful soils information using simple parameter estimation with few but appropriately timed remote sensing retrievals.

Quantification of soil water retention parameters using multi-section TDR-waveform analysis

NASA Astrophysics Data System (ADS)

Baviskar, S. M.; Heimovaara, T. J.

2017-06-01

Soil water retention parameters are important for describing flow in variably saturated soils. TDR is one of the standard methods used for determining water content in soil samples. In this study, we present an approach to estimate water retention parameters of a sample which is initially saturated and subjected to an incremental decrease in boundary head causing it to drain in a multi-step fashion. TDR waveforms are measured along the height of the sample at assumed different hydrostatic conditions at daily interval. The cumulative discharge outflow drained from the sample is also recorded. The saturated water content is obtained using volumetric analysis after the final step involved in multi-step drainage. The equation obtained by coupling the unsaturated parametric function and the apparent dielectric permittivity is fitted to a TDR wave propagation forward model. The unsaturated parametric function is used to spatially interpolate the water contents along TDR probe. The cumulative discharge outflow data is fitted with cumulative discharge estimated using the unsaturated parametric function. The weight of water inside the sample estimated at the first and final boundary head in multi-step drainage is fitted with the corresponding weights calculated using unsaturated parametric function. A Bayesian optimization scheme is used to obtain optimized water retention parameters for these different objective functions. This approach can be used for samples with long heights and is especially suitable for characterizing sands with a uniform particle size distribution at low capillary heads.

Tolerable soil erosion in Europe

NASA Astrophysics Data System (ADS)

Verheijen, Frank; Jones, Bob; Rickson, Jane; Smith, Celina

2010-05-01

Soil loss by erosion has been identified as an important threat to soils in Europe* and is recognised as a contributing process to soil degradation and associated deterioration, or loss, of soil functioning. From a policy perspective, it is imperative to establish well-defined baseline values to evaluate soil erosion monitoring data against. For this purpose, accurate baseline values - i.e. tolerable soil loss - need to be differentiated at appropriate scales for monitoring and, ideally, should take soil functions and even changing environmental conditions into account. The concept of tolerable soil erosion has been interpreted in the scientific literature in two ways: i) maintaining the dynamic equilibrium of soil quantity, and ii) maintaining biomass production, at a location. The first interpretation ignores soil quality by focusing only on soil quantity. The second approach ignores many soil functions by focusing only on the biomass (particularly crop) production function of soil. Considering recognised soil functions, tolerable soil erosion may be defined as 'any mean annual cumulative (all erosion types combined) soil erosion rate at which a deterioration or loss of one or more soil functions does not occur'. Assumptions and problems of this definition will be discussed. Soil functions can generally be judged not to deteriorate as long as soil erosion does not exceed soil formation. At present, this assumption remains largely untested, but applying the precautionary principle appears to be a reasonable starting point. Considering soil formation rates by both weathering and dust deposition, it is estimated that for the majority of soil forming factors in most European situations, soil formation rates probably range from ca. 0.3 - 1.4 t ha-1 yr-1. Although the current agreement on these values seems relatively strong, how the variation within the range is spatially distributed across Europe and how this may be affected by climate, land use and land management change in the future remains largely unexplored. * http://ec.europa.eu/environment/soil/pdf/com_2006_0231_en.pdf

Climatic variability of soil water in the American Midwest: Part 2. Spatio-temporal analysis

NASA Astrophysics Data System (ADS)

Georgakakos, Konstantine P.; Bae, Deg-Hyo

1994-11-01

A study of the model-estimated soil water, aggregated over three large drainage basins of the Midwestern USA, is reported. The basin areas are in the range from 2000 km 2 to 3500 km 2, and allow the study of mesoscale (1000-10000 km 2) soil water features. In each case, a conceptual hydrologic model was used to produce upper and lower soil water estimates that are consistent with the atmospheric forcing of daily precipitation, potential evapotranspiration and air temperature, and with the observed daily streamflow divergence over a 40 year period. It is shown that the water contents of the upper and lower soil reach peaks in different months, with the soil column being most saturated in June, when the area is prone to serious flooding. Temporal and spatial features of the variability of model-estimated soil water content are identified. The autocorrelation function of monthly averaged soil water shows that the upper soil water remains persistent for about a season, whereas the persistence of the lower soil water extends to several seasons. The soil water estimates of the three study basins exhibit strong similarities in annual cycles and interannual variability. It is shown that the frequency of significant positive (wet) soil water anomalies that extend over a 2° × 2° region is lower than that of significant negative (dry) ones of the same extent in this region of the USA.

The Use of Mixed Effects Models for Obtaining Low-Cost Ecosystem Carbon Stock Estimates in Mangroves of the Asia-Pacific

NASA Astrophysics Data System (ADS)

Bukoski, J. J.; Broadhead, J. S.; Donato, D.; Murdiyarso, D.; Gregoire, T. G.

2016-12-01

Mangroves provide extensive ecosystem services that support both local livelihoods and international environmental goals, including coastal protection, water filtration, biodiversity conservation and the sequestration of carbon (C). While voluntary C market projects that seek to preserve and enhance forest C stocks offer a potential means of generating finance for mangrove conservation, their implementation faces barriers due to the high costs of quantifying C stocks through measurement, reporting and verification (MRV) activities. To streamline MRV activities in mangrove C forestry projects, we develop predictive models for (i) biomass-based C stocks, and (ii) soil-based C stocks for the mangroves of the Asia-Pacific. We use linear mixed effect models to account for spatial correlation in modeling the expected C as a function of stand attributes. The most parsimonious biomass model predicts total biomass C stocks as a function of both basal area and the interaction between latitude and basal area, whereas the most parsimonious soil C model predicts soil C stocks as a function of the logarithmic transformations of both latitude and basal area. Random effects are specified by site for both models, and are found to explain a substantial proportion of variance within the estimation datasets. The root mean square error (RMSE) of the biomass C model is approximated at 24.6 Mg/ha (18.4% of mean biomass C in the dataset), whereas the RMSE of the soil C model is estimated at 4.9 mg C/cm 3 (14.1% of mean soil C). A substantial proportion of the variation in soil C, however, is explained by the random effects and thus the use of the SOC model may be most valuable for sites in which field measurements of soil C exist.

Spatial and seasonal dynamics of surface soil carbon in the Luquillo Experimental Forest, Puerto Rico.

Treesearch

Hongqing Wang; Joseph D. Cornell; Charles A.S. Hall; David P. Marley

2002-01-01

We developed a spatially-explicit version of the CENTURY soil model to characterize the storage and flux of soil organic carbon (SOC, 0–30 cm depth) in the Luquillo Experimental Forest (LEF), Puerto Rico as a function of climate, vegetation, and soils. The model was driven by monthly estimates of average air temperature, precipitation, and potential evapotranspiration...

Ensemble kalman filtering to perform data assimilation with soil water content probes and pedotransfer functions in modeling water flow in variably saturated soils

USDA-ARS?s Scientific Manuscript database

Data from modern soil water contents probes can be used for data assimilation in soil water flow modeling, i.e. continual correction of the flow model performance based on observations. The ensemble Kalman filter appears to be an appropriate method for that. The method requires estimates of the unce...

Functional test of pedotransfer functions to predict water flow and solute transport with the dual-permeability model MACRO

NASA Astrophysics Data System (ADS)

Moeys, J.; Larsbo, M.; Bergström, L.; Brown, C. D.; Coquet, Y.; Jarvis, N. J.

2012-07-01

Estimating pesticide leaching risks at the regional scale requires the ability to completely parameterise a pesticide fate model using only survey data, such as soil and land-use maps. Such parameterisations usually rely on a set of lookup tables and (pedo)transfer functions, relating elementary soil and site properties to model parameters. The aim of this paper is to describe and test a complete set of parameter estimation algorithms developed for the pesticide fate model MACRO, which accounts for preferential flow in soil macropores. We used tracer monitoring data from 16 lysimeter studies, carried out in three European countries, to evaluate the ability of MACRO and this "blind parameterisation" scheme to reproduce measured solute leaching at the base of each lysimeter. We focused on the prediction of early tracer breakthrough due to preferential flow, because this is critical for pesticide leaching. We then calibrated a selected number of parameters in order to assess to what extent the prediction of water and solute leaching could be improved. Our results show that water flow was generally reasonably well predicted (median model efficiency, ME, of 0.42). Although the general pattern of solute leaching was reproduced well by the model, the overall model efficiency was low (median ME = -0.26) due to errors in the timing and magnitude of some peaks. Preferential solute leaching at early pore volumes was also systematically underestimated. Nonetheless, the ranking of soils according to solute loads at early pore volumes was reasonably well estimated (concordance correlation coefficient, CCC, between 0.54 and 0.72). Moreover, we also found that ignoring macropore flow leads to a significant deterioration in the ability of the model to reproduce the observed leaching pattern, and especially the early breakthrough in some soils. Finally, the calibration procedure showed that improving the estimation of solute transport parameters is probably more important than the estimation of water flow parameters. Overall, the results are encouraging for the use of this modelling set-up to estimate pesticide leaching risks at the regional-scale, especially where the objective is to identify vulnerable soils and "source" areas of contamination.

Air pollution: Household soiling and consumer welfare losses

USGS Publications Warehouse

Watson, W.D.; Jaksch, J.A.

1982-01-01

This paper uses demand and supply functions for cleanliness to estimate household benefits from reduced particulate matter soiling. A demand curve for household cleanliness is estimated, based upon the assumption that households prefer more cleanliness to less. Empirical coefficients, related to particulate pollution levels, for shifting the cleanliness supply curve, are taken from available studies. Consumer welfare gains, aggregated across 123 SMSAs, from achieving the Federal primary particulate standard, are estimated to range from $0.9 to $3.2 million per year (1971 dollars). ?? 1982.

Plant identity and shallow soil moisture are primary drivers of stomatal conductance in the savannas of Kruger National Park.

PubMed

Tobin, Rebecca L; Kulmatiski, Andrew

2018-01-01

Our goal was to describe stomatal conductance (gs) and the site-scale environmental parameters that best predict gs in Kruger National Park (KNP), South Africa. Dominant grass and woody species were measured over two growing seasons in each of four study sites that represented the natural factorial combination of mean annual precipitation [wet (750 mm) or dry (450 mm)] and soil type (clay or sand) found in KNP. A machine-learning (random forest) model was used to describe gs as a function of plant type (species or functional group) and site-level environmental parameters (CO2, season, shortwave radiation, soil type, soil moisture, time of day, vapor pressure deficit and wind speed). The model explained 58% of the variance among 6,850 gs measurements. Species, or plant functional group, and shallow (0-20 cm) soil moisture had the greatest effect on gs. Atmospheric drivers and soil type were less important. When parameterized with three years of observed environmental data, the model estimated mean daytime growing season gs as 68 and 157 mmol m-2 sec-1 for grasses and woody plants, respectively. The model produced here could, for example, be used to estimate gs and evapotranspiration in KNP under varying climate conditions. Results from this field-based study highlight the role of species identity and shallow soil moisture as primary drivers of gs in savanna ecosystems of KNP.

Orbiting passive microwave sensor simulation applied to soil moisture estimation

NASA Technical Reports Server (NTRS)

Newton, R. W. (Principal Investigator); Clark, B. V.; Pitchford, W. M.; Paris, J. F.

1979-01-01

A sensor/scene simulation program was developed and used to determine the effects of scene heterogeneity, resolution, frequency, look angle, and surface and temperature relations on the performance of a spaceborne passive microwave system designed to estimate soil water information. The ground scene is based on classified LANDSAT images which provide realistic ground classes, as well as geometries. It was determined that the average sensitivity of antenna temperature to soil moisture improves as the antenna footprint size increased. Also, the precision (or variability) of the sensitivity changes as a function of resolution.

Changes in Soil Carbon Storage After Cultivation

DOE Data Explorer

Mann, L. K. [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)

2004-01-01

Previously published data from 625 paired soil samples were used to predict carbon in cultivated soil as a function of initial carbon content. A 30-cm sampling depth provided a less variable estimate (r² = 0.9) of changes in carbon than a 15-cm sampling depth (r² = 0.6). Regression analyses of changes in carbon storage in relation to years of cultivation confirmed that the greatest rates of change occurred in the first 20 y. An initial carbon effect was present in all analyses: soils very low in carbon tended to gain slight amounts of carbon after cultivation, but soils high in carbon lost at least 20% during cultivation. Carbon losses from most agricultural soils are estimated to average less than 20% of initial values or less than 1.5 kg/m² within the top 30 cm. These estimates should not be applied to depths greater than 30 cm and would be improved with more bulk density information and equivalent sample volumes.

The engineering significance of shrinkage and swelling soils in blast damage investigations

DOE Office of Scientific and Technical Information (OSTI.GOV)

Vitton, S.J.; Harris, W.W.

1996-12-01

In the US each year it has been estimated that expansive soils cause approximately $9.0 billion in damage to buildings, roads, airports, and other facilities. This figure alone exceeds the damage estimate for earthquakes, floods, tornadoes, and hurricanes combined. Unfortunately, some cases of expansive soil damage (swelling) are blamed on rock blasting operations if the blasting operations are located within the immediate area. While simple tests, such as the Atterberg limits test, can characterize a soil as expansive, it does not necessarily answer the question whether the foundation soils are causing distresses to a structure. In particular, it appears thatmore » once a soil has been labeled as nonexpansive it is no longer considered as a problem soil, in which case blast vibrations become the prime suspect. It should be emphasized, however, that even non-plastic soils, those soils with low to nonexistent plastic indexes, can exhibit significant shrinkage characteristics that can result in significant damage to structures. While expansive soil is a function of the mineralogy of the soil particles, i.e., swelling clay minerals, shrinkage is caused by the loss of moisture from soil as capillary pressures exceed the cohesion or tensile strength and is therefore a function of the soils particle size and its pore size distribution. This is a significant problem for all fine grained soils regardless of the soil`s mineralogy. It`s particularly important for regions of the US that typically have a positive water balance but experience significant drought periods when soil moisture is lost.« less

Hydrological Storage Length Scales Represented by Remote Sensing Estimates of Soil Moisture and Precipitation

NASA Astrophysics Data System (ADS)

Akbar, Ruzbeh; Short Gianotti, Daniel; McColl, Kaighin A.; Haghighi, Erfan; Salvucci, Guido D.; Entekhabi, Dara

2018-03-01

The soil water content profile is often well correlated with the soil moisture state near the surface. They share mutual information such that analysis of surface-only soil moisture is, at times and in conjunction with precipitation information, reflective of deeper soil fluxes and dynamics. This study examines the characteristic length scale, or effective depth Δz, of a simple active hydrological control volume. The volume is described only by precipitation inputs and soil water dynamics evident in surface-only soil moisture observations. To proceed, first an observation-based technique is presented to estimate the soil moisture loss function based on analysis of soil moisture dry-downs and its successive negative increments. Then, the length scale Δz is obtained via an optimization process wherein the root-mean-squared (RMS) differences between surface soil moisture observations and its predictions based on water balance are minimized. The process is entirely observation-driven. The surface soil moisture estimates are obtained from the NASA Soil Moisture Active Passive (SMAP) mission and precipitation from the gauge-corrected Climate Prediction Center daily global precipitation product. The length scale Δz exhibits a clear east-west gradient across the contiguous United States (CONUS), such that large Δz depths (>200 mm) are estimated in wetter regions with larger mean precipitation. The median Δz across CONUS is 135 mm. The spatial variance of Δz is predominantly explained and influenced by precipitation characteristics. Soil properties, especially texture in the form of sand fraction, as well as the mean soil moisture state have a lesser influence on the length scale.

A Comparison of Land Surface Model Soil Hydraulic Properties Estimated by Inverse Modeling and Pedotransfer Functions

NASA Technical Reports Server (NTRS)

Gutmann, Ethan D.; Small, Eric E.

2007-01-01

Soil hydraulic properties (SHPs) regulate the movement of water in the soil. This in turn plays an important role in the water and energy cycles at the land surface. At present, SHPS are commonly defined by a simple pedotransfer function from soil texture class, but SHPs vary more within a texture class than between classes. To examine the impact of using soil texture class to predict SHPS, we run the Noah land surface model for a wide variety of measured SHPs. We find that across a range of vegetation cover (5 - 80% cover) and climates (250 - 900 mm mean annual precipitation), soil texture class only explains 5% of the variance expected from the real distribution of SHPs. We then show that modifying SHPs can drastically improve model performance. We compare two methods of estimating SHPs: (1) inverse method, and (2) soil texture class. Compared to texture class, inverse modeling reduces errors between measured and modeled latent heat flux from 88 to 28 w/m(exp 2). Additionally we find that with increasing vegetation cover the importance of SHPs decreases and that the van Genuchten m parameter becomes less important, while the saturated conductivity becomes more important.

The sensitivity of soil respiration to soil temperature, moisture, and carbon supply at the global scale.

PubMed

Hursh, Andrew; Ballantyne, Ashley; Cooper, Leila; Maneta, Marco; Kimball, John; Watts, Jennifer

2017-05-01

Soil respiration (Rs) is a major pathway by which fixed carbon in the biosphere is returned to the atmosphere, yet there are limits to our ability to predict respiration rates using environmental drivers at the global scale. While temperature, moisture, carbon supply, and other site characteristics are known to regulate soil respiration rates at plot scales within certain biomes, quantitative frameworks for evaluating the relative importance of these factors across different biomes and at the global scale require tests of the relationships between field estimates and global climatic data. This study evaluates the factors driving Rs at the global scale by linking global datasets of soil moisture, soil temperature, primary productivity, and soil carbon estimates with observations of annual Rs from the Global Soil Respiration Database (SRDB). We find that calibrating models with parabolic soil moisture functions can improve predictive power over similar models with asymptotic functions of mean annual precipitation. Soil temperature is comparable with previously reported air temperature observations used in predicting Rs and is the dominant driver of Rs in global models; however, within certain biomes soil moisture and soil carbon emerge as dominant predictors of Rs. We identify regions where typical temperature-driven responses are further mediated by soil moisture, precipitation, and carbon supply and regions in which environmental controls on high Rs values are difficult to ascertain due to limited field data. Because soil moisture integrates temperature and precipitation dynamics, it can more directly constrain the heterotrophic component of Rs, but global-scale models tend to smooth its spatial heterogeneity by aggregating factors that increase moisture variability within and across biomes. We compare statistical and mechanistic models that provide independent estimates of global Rs ranging from 83 to 108 Pg yr -1 , but also highlight regions of uncertainty where more observations are required or environmental controls are hard to constrain. © 2016 John Wiley & Sons Ltd.

Determination of Soil Erosion Risk in the Mustafakemalpasa River Basin, Turkey, Using the Revised Universal Soil Loss Equation, Geographic Information System, and Remote Sensing

NASA Astrophysics Data System (ADS)

Ozsoy, Gokhan; Aksoy, Ertugrul; Dirim, M. Sabri; Tumsavas, Zeynal

2012-10-01

Sediment transport from steep slopes and agricultural lands into the Uluabat Lake (a RAMSAR site) by the Mustafakemalpasa (MKP) River is a serious problem within the river basin. Predictive erosion models are useful tools for evaluating soil erosion and establishing soil erosion management plans. The Revised Universal Soil Loss Equation (RUSLE) function is a commonly used erosion model for this purpose in Turkey and the rest of the world. This research integrates the RUSLE within a geographic information system environment to investigate the spatial distribution of annual soil loss potential in the MKP River Basin. The rainfall erosivity factor was developed from local annual precipitation data using a modified Fournier index: The topographic factor was developed from a digital elevation model; the K factor was determined from a combination of the soil map and the geological map; and the land cover factor was generated from Landsat-7 Enhanced Thematic Mapper (ETM) images. According to the model, the total soil loss potential of the MKP River Basin from erosion by water was 11,296,063 Mg year-1 with an average soil loss of 11.2 Mg year-1. The RUSLE produces only local erosion values and cannot be used to estimate the sediment yield for a watershed. To estimate the sediment yield, sediment-delivery ratio equations were used and compared with the sediment-monitoring reports of the Dolluk stream gauging station on the MKP River, which collected data for >41 years (1964-2005). This station observes the overall efficiency of the sediment yield coming from the Orhaneli and Emet Rivers. The measured sediment in the Emet and Orhaneli sub-basins is 1,082,010 Mg year-1 and was estimated to be 1,640,947 Mg year-1 for the same two sub-basins. The measured sediment yield of the gauge station is 127.6 Mg km-2 year-1 but was estimated to be 170.2 Mg km-2 year-1. The close match between the sediment amounts estimated using the RUSLE-geographic information system (GIS) combination and the measured values from the Dolluk sediment gauge station shows that the potential soil erosion risk of the MKP River Basin can be estimated correctly and reliably using the RUSLE function generated in a GIS environment.

Soil ingestion rates for children under 3 years old in Taiwan.

PubMed

Chien, Ling-Chu; Tsou, Ming-Chien; Hsi, Hsing-Cheng; Beamer, Paloma; Bradham, Karen; Hseu, Zeng-Yei; Jien, Shih-Hao; Jiang, Chuen-Bin; Dang, Winston; Özkaynak, Halûk

2017-01-01

Soil and dust ingestion rates by children are among the most critical exposure factors in determining risks to children from exposures to environmental contaminants in soil and dust. We believe this is the first published soil ingestion study for children in Taiwan using tracer element methodology. In this study, 66 children under 3 years of age were enrolled from Taiwan. Three days of fecal samples and a 24-h duplicate food sample were collected. The soil and household dust samples were also collected from children's homes. Soil ingestion rates were estimated based on silicon (Si) and titanium (Ti). The average soil ingestion rates were 9.6±19.2 mg/day based on Si as a tracer. The estimated soil ingestion rates based on Si did not have statistically significant differences by children's age and gender, although the average soil ingestion rates clearly increased as a function of children's age category. The estimated soil ingestion rates based on Si was significantly and positively correlated with the sum of indoor and outdoor hand-to-mouth frequency rates. The average soil ingestion rates based on Si were generally lower than the results from previous studies for the US children. Ti may not be a suitable tracer for estimating soil ingestion rates in Taiwan because the Ti dioxide is a common additive in food. To the best of our knowledge, this is the first study that investigated the correlations between soil ingestion rates and mouthing behaviors in Taiwan or other parts of Asia. It is also the first study that could compare available soil ingestion data from different countries and/or different cultures. The hand-to-mouth frequency and health habits are important to estimate the soil ingestion exposure for children. The results in this study are particularly important when assessing children's exposure and potential health risk from nearby contaminated soils in Taiwan.

Inter-Annual Variability of Soil Moisture Stress Function in the Wheat Field

NASA Astrophysics Data System (ADS)

Akuraju, V. R.; Ryu, D.; George, B.; Ryu, Y.; Dassanayake, K. B.

2014-12-01

Root-zone soil moisture content is a key variable that controls the exchange of water and energy fluxes between land and atmosphere. In the soil-vegetation-atmosphere transfer (SVAT) schemes, the influence of root-zone soil moisture on evapotranspiration (ET) is parameterized by the soil moisture stress function (SSF). Dependence of actual ET: potential ET (fPET) or evaporative fraction to the root-zone soil moisture via SSF can also be used inversely to estimate root-zone soil moisture when fPET is estimated by remotely sensed land surface states. In this work we present fPET versus available soil water (ASW) in the root zone observed in the experimental farm sites in Victoria, Australia in 2012-2013. In the wheat field site, fPET vs ASW exhibited distinct features for different soil depth, net radiation, and crop growth stages. Interestingly, SSF in the wheat field presented contrasting shapes for two cropping years of 2012 and 2013. We argue that different temporal patterns of rainfall (and resulting soil moisture) during the growing seasons in 2012 and 2013 are responsible for the distinctive SSFs. SSF of the wheat field was simulated by the Agricultural Production Systems sIMulator (APSIM). The APSIM was able to reproduce the observed fPET vs. ASW. We discuss implications of our findings for existing modeling and (inverse) remote sensing approaches relying on SSF and alternative growth-stage-dependent SSFs.

Pedotransfer Functions in Earth System Science: Challenges and Perspectives: PTFs in Earth system science perspective

DOE Office of Scientific and Technical Information (OSTI.GOV)

Van Looy, Kris; Bouma, Johan; Herbst, Michael

Soil, through its various functions, plays a vital role in the Earth's ecosystems and provides multiple ecosystem services to humanity. Pedotransfer functions (PTFs) are simple to complex knowledge rules that relate available soil information to soil properties and variables that are needed to parameterize soil processes. Here in this article, we review the existing PTFs and document the new generation of PTFs developed in the different disciplines of Earth system science. To meet the methodological challenges for a successful application in Earth system modeling, we emphasize that PTF development has to go hand in hand with suitable extrapolation and upscalingmore » techniques such that the PTFs correctly represent the spatial heterogeneity of soils. PTFs should encompass the variability of the estimated soil property or process, in such a way that the estimation of parameters allows for validation and can also confidently provide for extrapolation and upscaling purposes capturing the spatial variation in soils. Most actively pursued recent developments are related to parameterizations of solute transport, heat exchange, soil respiration, and organic carbon content, root density, and vegetation water uptake. Further challenges are to be addressed in parameterization of soil erosivity and land use change impacts at multiple scales. We argue that a comprehensive set of PTFs can be applied throughout a wide range of disciplines of Earth system science, with emphasis on land surface models. Novel sensing techniques provide a true breakthrough for this, yet further improvements are necessary for methods to deal with uncertainty and to validate applications at global scale.« less

Pedotransfer Functions in Earth System Science: Challenges and Perspectives: PTFs in Earth system science perspective

DOE PAGES

Van Looy, Kris; Bouma, Johan; Herbst, Michael; ...

2017-12-28

Soil, through its various functions, plays a vital role in the Earth's ecosystems and provides multiple ecosystem services to humanity. Pedotransfer functions (PTFs) are simple to complex knowledge rules that relate available soil information to soil properties and variables that are needed to parameterize soil processes. Here in this article, we review the existing PTFs and document the new generation of PTFs developed in the different disciplines of Earth system science. To meet the methodological challenges for a successful application in Earth system modeling, we emphasize that PTF development has to go hand in hand with suitable extrapolation and upscalingmore » techniques such that the PTFs correctly represent the spatial heterogeneity of soils. PTFs should encompass the variability of the estimated soil property or process, in such a way that the estimation of parameters allows for validation and can also confidently provide for extrapolation and upscaling purposes capturing the spatial variation in soils. Most actively pursued recent developments are related to parameterizations of solute transport, heat exchange, soil respiration, and organic carbon content, root density, and vegetation water uptake. Further challenges are to be addressed in parameterization of soil erosivity and land use change impacts at multiple scales. We argue that a comprehensive set of PTFs can be applied throughout a wide range of disciplines of Earth system science, with emphasis on land surface models. Novel sensing techniques provide a true breakthrough for this, yet further improvements are necessary for methods to deal with uncertainty and to validate applications at global scale.« less

Estimating Vertical Stress on Soil Subjected to Vehicular Loading

DTIC Science & Technology

2009-02-01

specified surface area of the tire . The silt and sand samples were both estimated to be 23.7-in. thick over a base of much harder soil. The pressures...study in which highway tread tires were used as opposed to the all-terrain tread currently on the vehicle. If the pressure pads are functioning...Vertical force versus time (front right CIV tire )....................................................................... 14 Tables Table 1. Testing

Estimating mercury emissions resulting from wildfire in forests of the Western United States.

PubMed

Webster, Jackson P; Kane, Tyler J; Obrist, Daniel; Ryan, Joseph N; Aiken, George R

2016-10-15

Understanding the emissions of mercury (Hg) from wildfires is important for quantifying the global atmospheric Hg sources. Emissions of Hg from soils resulting from wildfires in the Western United States was estimated for the 2000 to 2013 period, and the potential emission of Hg from forest soils was assessed as a function of forest type and soil-heating. Wildfire released an annual average of 3100±1900kg-Hgy(-1) for the years spanning 2000-2013 in the 11 states within the study area. This estimate is nearly 5-fold lower than previous estimates for the study region. Lower emission estimates are attributed to an inclusion of fire severity within burn perimeters. Within reported wildfire perimeters, the average distribution of low, moderate, and high severity burns was 52, 29, and 19% of the total area, respectively. Review of literature data suggests that that low severity burning does not result in soil heating, moderate severity fire results in shallow soil heating, and high severity fire results in relatively deep soil heating (<5cm). Using this approach, emission factors for high severity burns ranged from 58 to 640μg-Hgkg-fuel(-1). In contrast, low severity burns have emission factors that are estimated to be only 18-34μg-Hgkg-fuel(-1). In this estimate, wildfire is predicted to release 1-30gHgha(-1) from Western United States forest soils while above ground fuels are projected to contribute an additional 0.9 to 7.8gHgha(-1). Land cover types with low biomass (desert scrub) are projected to release less than 1gHgha(-1). Following soil sources, fuel source contributions to total Hg emissions generally followed the order of duff>wood>foliage>litter>branches. Copyright © 2016 Elsevier B.V. All rights reserved.

Comparative study of soil erodibility and critical shear stress between loess and purple soils

NASA Astrophysics Data System (ADS)

Xing, Hang; Huang, Yu-han; Chen, Xiao-yan; Luo, Bang-lin; Mi, Hong-xing

2018-03-01

Loess and purple soils are two very important cultivated soils, with the former in the loess region and the latter in southern sub-tropical region of China, featured with high-risks of erosion, considerable differences of soil structures due to differences in mineral and nutrient compositions. Study on soil erodibility (Kr) and critical shear stress (τc) of these two soils is beneficial to predict soil erosion with such models as WEPP. In this study, rill erosion experimental data sets of the two soils are used for estimating their Kr and τc before they are compared to understand their differences of rill erosion behaviors. The maximum detachment rates of the loess and purple soils are calculated under different hydrodynamic conditions (flow rates: 2, 4, 8 L/min; slope gradients: 5°, 10°, 15°, 20°, 25°) through analytical and numerical methods respectively. Analytical method used the derivative of the function between sediment concentration and rill length to estimate potential detachment rates, at the rill beginning. Numerical method estimated potential detachment rates with the experimental data, at the rill beginning and 0.5 m location. The Kr and τc of these two soils are determined by the linear equation based on experimental data. Results show that the methods could well estimate the Kr and τc of these two soils as they remain basically unchanged under different hydrodynamic conditions. The Kr value of loess soil is about twice of the purple soil, whereas the τc is about half of that. The numerical results have good correlations with the analytical values. These results can be useful in modeling rill erosion processes of loess and purple soils.

Estimating Thermal Inertia with a Maximum Entropy Boundary Condition

NASA Astrophysics Data System (ADS)

Nearing, G.; Moran, M. S.; Scott, R.; Ponce-Campos, G.

2012-04-01

Thermal inertia, P [Jm-2s-1/2K-1], is a physical property the land surface which determines resistance to temperature change under seasonal or diurnal heating. It is a function of volumetric heat capacity, c [Jm-3K-1], and thermal conductivity, k [Wm-1K-1] of the soil near the surface: P=√ck. Thermal inertia of soil varies with moisture content due the difference between thermal properties of water and air, and a number of studies have demonstrated that it is feasible to estimate soil moisture given thermal inertia (e.g. Lu et al, 2009, Murray and Verhoef, 2007). We take the common approach to estimating thermal inertia using measurements of surface temperature by modeling the Earth's surface as a 1-dimensional homogeneous diffusive half-space. In this case, surface temperature is a function of the ground heat flux (G) boundary condition and thermal inertia and a daily value of P was estimated by matching measured and modeled diurnal surface temperature fluctuations. The difficulty is in measuring G; we demonstrate that the new maximum entropy production (MEP) method for partitioning net radiation into surface energy fluxes (Wang and Bras, 2011) provides a suitable boundary condition for estimating P. Adding the diffusion representation of heat transfer in the soil reduces the number of free parameters in the MEP model from two to one, and we provided a sensitivity analysis which suggests that, for the purpose of estimating P, it is preferable to parameterize the coupled MEP-diffusion model by the ratio of thermal inertia of the soil to the effective thermal inertia of convective heat transfer to the atmosphere. We used this technique to estimate thermal inertia at two semiarid, non-vegetated locations in the Walnut Gulch Experimental Watershed in southeast AZ, USA and compared these estimates to estimates of P made using the Xue and Cracknell (1995) solution for a linearized ground heat flux boundary condition, and we found that the MEP-diffusion model produced superior thermal inertia estimates. The MEP-diffusion estimates also agreed well with P estimates made using a boundary condition measured with buried flux plates. We further demonstrated the new method using diurnal surface temperature fluctuations estimated from day/night MODIS image pairs and, excluding instances where the soil was extremely dry, found a strong relationship between estimated thermal inertia and measured 5 cm soil moisture. Lu, S., Ju, Z.Q., Ren, T.S. & Horton, R. (2009). A general approach to estimate soil water content from thermal inertia. Agricultural and Forest Meteorology, 149, 1693-1698. Murray, T. & Verhoef, A. (2007). Moving towards a more mechanistic approach in the determination of soil heat flux from remote measurements - I. A universal approach to calculate thermal inertia. Agricultural and Forest Meteorology, 147, 80-87. Wang, J.F. & Bras, R.L. (2011). A model of evapotranspiration based on the theory of maximum entropy production. Water Resources Research, 47. Xue, Y. & Cracknell, A.P. (1995). Advanced thermal inertia modeling. International Journal of Remote Sensing, 16, 431-446.

Estimation of soil organic carbon in forests of the United States

NASA Astrophysics Data System (ADS)

Domke, G. M.; Perry, C. H.; Walters, B. F.; Woodall, C. W.; Nave, L. E.; Swanston, C.

2015-12-01

Soil organic carbon (SOC) is the largest terrestrial carbon (C) sink on earth and management of this pool is a critical component of global efforts to mitigate atmospheric C concentrations. Soil organic carbon is also a key indicator of soil quality as it affects essential biological, chemical, and physical soil functions such as nutrient cycling, water retention, and soil structure maintenance. Much of the SOC on earth is found in forest ecosystems and is thought to be relatively stable. That said, there is growing evidence that SOC may be sensitive to disturbance and global change drivers. In the United States (US), SOC in forests is monitored by the national forest inventory (NFI) conducted by the Forest Inventory and Analysis (FIA) program within the US Department of Agriculture, Forest Service. The FIA program currently uses SOC predictions based on SSURGO/STATSGO data to populate the NFI. Most of estimates of SOC in forests from the SSURGO/STATSGO data are based primarily upon expert opinion and lack systematic field observations. 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 forests in the conterminous US and coastal Alaska. Here we present estimates of SOC obtained using data from the NFI and International Soil Carbon Network and describe the modeling framework used to compile estimates for United Nations Framework Convention on Climate Change reporting.

A new biostimulation approach based on the concept of remaining P for soil bioremediation.

PubMed

Júlio, Aline Daniela Lopes; Fernandes, Rita de Cássia Rocha; Costa, Maurício Dutra; Neves, Júlio César Lima; Rodrigues, Edmo Montes; Tótola, Marcos Rogério

2018-02-01

C:N:P ratio is generally adopted to estimate the amount of nitrogen and phosphorus to be added to soils to accelerate biodegradation of organic contaminants. However, differences in P fixation among soils lead to varying amounts of available P when a specific dose of the element is applied to different soils. Thus, the application of fertilizers to achieve a previously established C:P ratio leads to biodegradation rates that can be lower than the theoretical maximum. In this study, we developed an equation to estimate the dose of P required to maximize organic contaminant biodegradation in soils as a function of remaining P (P-rem), using diesel as a model contaminant. The soils were contaminated with diesel and received six doses of P. CO 2 emission was used to estimate biodegradation of hydrocarbons. Biodegradation increased with P doses. The P level that provided the highest hydrocarbon biodegradation rate showed linear and negative correlation with P-rem. The result shows that the requirement for P decreases as the P-rem of the soil increases (or the P-fixing capacity decreases). The dose of P recommended to maximize hydrocarbon biodegradation rate in soil can be estimated by the formula P (mg/dm 3 ) = 436.5-5.39 × P-rem (mg/L). Copyright © 2017 Elsevier Ltd. All rights reserved.

Plant identity and shallow soil moisture are primary drivers of stomatal conductance in the savannas of Kruger National Park

PubMed Central

Tobin, Rebecca L.

2018-01-01

Our goal was to describe stomatal conductance (gs) and the site-scale environmental parameters that best predict gs in Kruger National Park (KNP), South Africa. Dominant grass and woody species were measured over two growing seasons in each of four study sites that represented the natural factorial combination of mean annual precipitation [wet (750 mm) or dry (450 mm)] and soil type (clay or sand) found in KNP. A machine-learning (random forest) model was used to describe gs as a function of plant type (species or functional group) and site-level environmental parameters (CO2, season, shortwave radiation, soil type, soil moisture, time of day, vapor pressure deficit and wind speed). The model explained 58% of the variance among 6,850 gs measurements. Species, or plant functional group, and shallow (0–20 cm) soil moisture had the greatest effect on gs. Atmospheric drivers and soil type were less important. When parameterized with three years of observed environmental data, the model estimated mean daytime growing season gs as 68 and 157 mmol m-2 sec-1 for grasses and woody plants, respectively. The model produced here could, for example, be used to estimate gs and evapotranspiration in KNP under varying climate conditions. Results from this field-based study highlight the role of species identity and shallow soil moisture as primary drivers of gs in savanna ecosystems of KNP. PMID:29373605

A Moisture Function of Soil Heterotrophic Respiration Derived from Pore-scale Mechanisms

NASA Astrophysics Data System (ADS)

Yan, Z.; Todd-Brown, K. E.; Bond-Lamberty, B. P.; Bailey, V.; Liu, C.

2017-12-01

Soil heterotrophic respiration (HR) is an important process controlling carbon (C) flux, but its response to changes in soil water content (θ) is poorly understood. Earth system models (ESMs) use empirical moisture functions developed from specific sites to describe the HR-θ relationship in soils, introducing significant uncertainty. Generalized models derived from mechanisms that control substrate availability and microbial respiration are thus urgently needed. Here we derive, present, and test a novel moisture function fp developed from pore-scale mechanisms. This fp encapsulates primary physicochemical and biological processes controlling HR response to moisture variation in soils. We tested fp against a wide range of published data for different soil types, and found that fp reliably predicted diverse HR- relationships. The mathematical relationship between the parameters in fp and macroscopic soil properties such as porosity and organic C content was also established, enabling to estimate fp using soil properties. Compared with empirical moisture functions used in ESMs, this derived fp could reduce uncertainty in predicting the response of soil organic C stock to climate changes. In addition, this work is one of the first studies to upscale a mechanistic soil HR model based on pore-scale processes, thus linking the pore-scale mechanisms with macroscale observations.

Relationship between root water uptake and soil respiration: A modeling perspective

NASA Astrophysics Data System (ADS)

Teodosio, Bertrand; Pauwels, Valentijn R. N.; Loheide, Steven P.; Daly, Edoardo

2017-08-01

Soil moisture affects and is affected by root water uptake and at the same time drives soil CO2 dynamics. Selecting root water uptake formulations in models is important since this affects the estimation of actual transpiration and soil CO2 efflux. This study aims to compare different models combining the Richards equation for soil water flow to equations describing heat transfer and air-phase CO2 production and flow. A root water uptake model (RWC), accounting only for root water compensation by rescaling water uptake rates across the vertical profile, was compared to a model (XWP) estimating water uptake as a function of the difference between soil and root xylem water potential; the latter model can account for both compensation (XWPRWC) and hydraulic redistribution (XWPHR). Models were compared in a scenario with a shallow water table, where the formulation of root water uptake plays an important role in modeling daily patterns and magnitudes of transpiration rates and CO2 efflux. Model simulations for this scenario indicated up to 20% difference in the estimated water that transpired over 50 days and up to 14% difference in carbon emitted from the soil. The models showed reduction of transpiration rates associated with water stress affecting soil CO2 efflux, with magnitudes of soil CO2 efflux being larger for the XWPHR model in wet conditions and for the RWC model as the soil dried down. The study shows the importance of choosing root water uptake models not only for estimating transpiration but also for other processes controlled by soil water content.

Calibration and correction procedures for cosmic-ray neutron soil moisture probes located across Australia

NASA Astrophysics Data System (ADS)

Hawdon, Aaron; McJannet, David; Wallace, Jim

2014-06-01

The cosmic-ray probe (CRP) provides continuous estimates of soil moisture over an area of ˜30 ha by counting fast neutrons produced from cosmic rays which are predominantly moderated by water molecules in the soil. This paper describes the setup, measurement correction procedures, and field calibration of CRPs at nine locations across Australia with contrasting soil type, climate, and land cover. These probes form the inaugural Australian CRP network, which is known as CosmOz. CRP measurements require neutron count rates to be corrected for effects of atmospheric pressure, water vapor pressure changes, and variations in incoming neutron intensity. We assess the magnitude and importance of these corrections and present standardized approaches for network-wide analysis. In particular, we present a new approach to correct for incoming neutron intensity variations and test its performance against existing procedures used in other studies. Our field calibration results indicate that a generalized calibration function for relating neutron counts to soil moisture is suitable for all soil types, with the possible exception of very sandy soils with low water content. Using multiple calibration data sets, we demonstrate that the generalized calibration function only applies after accounting for persistent sources of hydrogen in the soil profile. Finally, we demonstrate that by following standardized correction procedures and scaling neutron counting rates of all CRPs to a single reference location, differences in calibrations between sites are related to site biomass. This observation provides a means for estimating biomass at a given location or for deriving coefficients for the calibration function in the absence of field calibration data.

Effects and risk assessment of linear alkylbenzene sulfonates in agricultural soil. 5. Probabilistic risk assessment of linear alkylbenzene sulfonates in sludge-amended soils.

PubMed

Jensen, J; Løkke, H; Holmstrup, M; Krogh, P H; Elsgaard, L

2001-08-01

Linear alkylbenzene sulfonates (LAS) can be found in high concentrations in sewage sludge and, hence, may enter the soil compartment as a result of sludge application. Here, LAS may pose a risk for soil-dwelling organisms. In the present probabilistic risk assessment, statistical extrapolation has been used to assess the risk of LAS to soil ecosystems. By use of a log-normal distribution model, the predicted no-effect concentration (PNEC) was estimated for soil fauna, plants, and a combination of these. Due to the heterogeneous endpoints for microorganisms, including functional as well as structural parameters, the use of sensitivity distributions is not considered to be applicable to this group of organisms, and a direct, expert evaluation of toxicity data was used instead. The soil concentration after sludge application was predicted for a number of scenarios and used as the predicted environmental concentration (PEC) in the risk characterization and calculation of risk quotients (RQ = PEC/PNEC). A LAS concentration of 4.6 mg/kg was used as the current best estimate of PNEC in all RQ calculations. Three levels of LAS contamination (530, 2,600, and 16,100 mg/kg), three half-lives (10, 25, and 40 d), and five different sludge loads (2, 4, 6, 8, and 10 t/ha) were included in the risk scenarios. In Denmark, the initial risk ratio would reach 1.5 in a realistic worst-case consideration. For countries not having similar sludge regulations, the estimated risk ratio may initially be considerably higher. However, even in the most extreme scenarios, the level of LAS is expected to be well beyond the estimated PNEC one year after application. The present risk assessment, therefore, concludes that LAS does not pose a significant risk to fauna, plants, and essential functions of agricultural soils as a result of normal sewage sludge amendment. However, risks have been identified in worst-case scenarios.

Remote sensing-based estimation of annual soil respiration at two contrasting forest sites

NASA Astrophysics Data System (ADS)

Huang, Ni; Gu, Lianhong; Black, T. Andrew; Wang, Li; Niu, Zheng

2015-11-01

Soil respiration (Rs), an important component of the global carbon cycle, can be estimated using remotely sensed data, but the accuracy of this technique has not been thoroughly investigated. In this study, we proposed a methodology for the remote estimation of annual Rs at two contrasting FLUXNET forest sites (a deciduous broadleaf forest and an evergreen needleleaf forest). A version of the Akaike's information criterion was used to select the best model from a range of models for annual Rs estimation based on the remotely sensed data products from the Moderate Resolution Imaging Spectroradiometer and root-zone soil moisture product derived from assimilation of the NASA Advanced Microwave Scanning Radiometer soil moisture products and a two-layer Palmer water balance model. We found that the Arrhenius-type function based on nighttime land surface temperature (LST-night) was the best model by comprehensively considering the model explanatory power and model complexity at the Missouri Ozark and BC-Campbell River 1949 Douglas-fir sites. In addition, a multicollinearity problem among LST-night, root-zone soil moisture, and plant photosynthesis factor was effectively avoided by selecting the LST-night-driven model. Cross validation showed that temporal variation in Rs was captured by the LST-night-driven model with a mean absolute error below 1 µmol CO2 m-2 s-1 at both forest sites. An obvious overestimation that occurred in 2005 and 2007 at the Missouri Ozark site reduced the evaluation accuracy of cross validation because of summer drought. However, no significant difference was found between the Arrhenius-type function driven by LST-night and the function considering LST-night and root-zone soil moisture. This finding indicated that the contribution of soil moisture to Rs was relatively small at our multiyear data set. To predict intersite Rs, maximum leaf area index (LAImax) was used as an upscaling factor to calibrate the site-specific reference respiration rates. Independent validation demonstrated that the model incorporating LST-night and LAImax efficiently predicted the spatial and temporal variabilities of Rs. Based on the Arrhenius-type function using LST-night as an input parameter, the rates of annual C release from Rs were 894-1027 g C m-2 yr-1 at the BC-Campbell River 1949 Douglas-fir site and 818-943 g C m-2 yr-1 at the Missouri Ozark site. The ratio between annual Rs estimates based on remotely sensed data and the total annual ecosystem respiration from eddy covariance measurements fell within the range reported in previous studies. Our results demonstrated that estimating annual Rs based on remote sensing data products was possible at deciduous and evergreen forest sites.

Improved Saturated Hydraulic Conductivity Pedotransfer Functions Using Machine Learning Methods

NASA Astrophysics Data System (ADS)

Araya, S. N.; Ghezzehei, T. A.

2017-12-01

Saturated hydraulic conductivity (Ks) is one of the fundamental hydraulic properties of soils. Its measurement, however, is cumbersome and instead pedotransfer functions (PTFs) are often used to estimate it. Despite a lot of progress over the years, generic PTFs that estimate hydraulic conductivity generally don't have a good performance. We develop significantly improved PTFs by applying state of the art machine learning techniques coupled with high-performance computing on a large database of over 20,000 soils—USKSAT and the Florida Soil Characterization databases. We compared the performance of four machine learning algorithms (k-nearest neighbors, gradient boosted model, support vector machine, and relevance vector machine) and evaluated the relative importance of several soil properties in explaining Ks. An attempt is also made to better account for soil structural properties; we evaluated the importance of variables derived from transformations of soil water retention characteristics and other soil properties. The gradient boosted models gave the best performance with root mean square errors less than 0.7 and mean errors in the order of 0.01 on a log scale of Ks [cm/h]. The effective particle size, D10, was found to be the single most important predictor. Other important predictors included percent clay, bulk density, organic carbon percent, coefficient of uniformity and values derived from water retention characteristics. Model performances were consistently better for Ks values greater than 10 cm/h. This study maximizes the extraction of information from a large database to develop generic machine learning based PTFs to estimate Ks. The study also evaluates the importance of various soil properties and their transformations in explaining Ks.

Predicting soil water content at - 33 kPa by pedotransfer functions in stoniness 1 soils in northeast Venezuela.

PubMed

Pineda, M C; Viloria, J; Martínez-Casasnovas, J A; Valera, A; Lobo, D; Timm, L C; Pires, L F; Gabriels, D

2018-02-22

Soil water content is a key property in the study of water available for plants, infiltration, drainage, hydraulic conductivity, irrigation, plant water stress and solute movement. However, its measurement consumes time and, in the case of stony soils, the presence of stones difficult to determinate the water content. An alternative is the use of pedotransfer functions (PTFs), as models to predict these properties from readily available data. The present work shows a comparison of different widely used PTFs to estimate water content at-33 kPa (WR -33kPa ) in high stoniness soils. The work was carried out in the Caramacate River, an area of high interest because the frequent landslides worsen the quality of drinking water. The performance of all evaluated PTFs was compared with a PTF generated for the study area. Results showed that the Urach's PTF presented the best performance in relation to the others and could be used to estimate WR -33kPa in soils of Caramacate River basin. The calculated PTFs had a R 2 of 0.65. This was slightly higher than the R 2 of the Urach's PTF. The inclusion of the rock fragment volume could have the better results. The weak performance of the other PTFs could be related to the fact that the mountain soils of the basin are rich in 2:1 clay and high stoniness, which were not used as independent variables for PTFs to estimate the WR -33kPa .

Soil microbial community profiles and functional diversity in limestone cedar glades

USGS Publications Warehouse

Cartwright, Jennifer M.; Dzantor, E. Kudjo; Momen, Bahram

2016-01-01

Rock outcrop ecosystems, such as limestone cedar glades (LCGs), are known for their rare and endemic plant species adapted to high levels of abiotic stress. Soils in LCGs are thin (< 25 cm), soil-moisture conditions fluctuate seasonally between xeric and saturated, and summer soil temperatures commonly exceed 48 °C. The effects of these stressors on soil microbial communities (SMC) remain largely unstudied, despite the importance of SMC-plant interactions in regulating the structure and function of terrestrial ecosystems. SMC profiles and functional diversity were characterized in LCGs using community level physiological profiling (CLPP) and plate-dilution frequency assays (PDFA). Most-probable number (MPN) estimates and microbial substrate-utilization diversity (H) were positively related to soil thickness, soil organic matter (OM), soil water content, and vegetation density, and were diminished in alkaline soil relative to circumneutral soil. Soil nitrate showed no relationship to SMCs, suggesting lack of N-limitation. Canonical correlation analysis indicated strong correlations between microbial CLPP patterns and several physical and chemical properties of soil, primarily temperature at the ground surface and at 4-cm depth, and secondarily soil-water content, enabling differentiation by season. Thus, it was demonstrated that several well-described abiotic determinants of plant community structure in this ecosystem are also reflected in SMC profiles.

Determination of soil erosion risk in the Mustafakemalpasa River Basin, Turkey, using the revised universal soil loss equation, geographic information system, and remote sensing.

PubMed

Ozsoy, Gokhan; Aksoy, Ertugrul; Dirim, M Sabri; Tumsavas, Zeynal

2012-10-01

Sediment transport from steep slopes and agricultural lands into the Uluabat Lake (a RAMSAR site) by the Mustafakemalpasa (MKP) River is a serious problem within the river basin. Predictive erosion models are useful tools for evaluating soil erosion and establishing soil erosion management plans. The Revised Universal Soil Loss Equation (RUSLE) function is a commonly used erosion model for this purpose in Turkey and the rest of the world. This research integrates the RUSLE within a geographic information system environment to investigate the spatial distribution of annual soil loss potential in the MKP River Basin. The rainfall erosivity factor was developed from local annual precipitation data using a modified Fournier index: The topographic factor was developed from a digital elevation model; the K factor was determined from a combination of the soil map and the geological map; and the land cover factor was generated from Landsat-7 Enhanced Thematic Mapper (ETM) images. According to the model, the total soil loss potential of the MKP River Basin from erosion by water was 11,296,063 Mg year(-1) with an average soil loss of 11.2 Mg year(-1). The RUSLE produces only local erosion values and cannot be used to estimate the sediment yield for a watershed. To estimate the sediment yield, sediment-delivery ratio equations were used and compared with the sediment-monitoring reports of the Dolluk stream gauging station on the MKP River, which collected data for >41 years (1964-2005). This station observes the overall efficiency of the sediment yield coming from the Orhaneli and Emet Rivers. The measured sediment in the Emet and Orhaneli sub-basins is 1,082,010 Mg year(-1) and was estimated to be 1,640,947 Mg year(-1) for the same two sub-basins. The measured sediment yield of the gauge station is 127.6 Mg km(-2) year(-1) but was estimated to be 170.2 Mg km(-2) year(-1). The close match between the sediment amounts estimated using the RUSLE-geographic information system (GIS) combination and the measured values from the Dolluk sediment gauge station shows that the potential soil erosion risk of the MKP River Basin can be estimated correctly and reliably using the RUSLE function generated in a GIS environment.

A model for estimating time-variant rainfall infiltration as a function of antecedent surface moisture and hydrologic soil type

NASA Technical Reports Server (NTRS)

Wilkening, H. A.; Ragan, R. M.

1982-01-01

Recent research indicates that the use of remote sensing techniques for the measurement of near surface soil moisture could be practical in the not too distant future. Other research shows that infiltration rates, especially for average or frequent rainfall events, are extremely sensitive to the proper definition and consideration of the role of the soil moisture at the beginning of the rainfall. Thus, it is important that an easy to use, but theoretically sound, rainfall infiltration model be available if the anticipated remotely sensed soil moisture data is to be optimally utilized for hydrologic simulation. A series of numerical experiments with the Richards' equation for an array of conditions anticipated in watershed hydrology were used to develop functional relationships that describe temporal infiltration rates as a function of soil type and initial moisture conditions.

The mechanics and energetics of soil bioturbation by earthworms and plant roots - Impacts on soil structure generation and maintenance

NASA Astrophysics Data System (ADS)

Or, Dani; Ruiz, Siul; Schymanski, Stanlislaus

2015-04-01

Soil structure is the delicate arrangement of solids and voids that facilitate numerous hydrological and ecological soil functions ranging from water infiltration and retention to gaseous exchange and mechanical anchoring of plant roots. Many anthropogenic activities affect soil structure, e.g. via tillage and compaction, and by promotion or suppression of biological activity and soil carbon pools. Soil biological activity is critical to the generation and maintenance of favorable soil structure, primarily through bioturbation by earthworms and root proliferation. The study aims to quantify the mechanisms, rates, and energetics associated with soil bioturbation, using a new biomechanical model to estimate stresses required to penetrate and expand a cylindrical cavity in a soil under different hydration and mechanical conditions. The stresses and soil displacement involved are placed in their ecological context (typical sizes, population densities, burrowing rates and behavior) enabling estimation of mechanical energy requirements and impacts on soil organic carbon pool (in the case of earthworms). We consider steady state plastic cavity expansion to determine burrowing pressures of earthworms and plant roots, akin to models of cone penetration representing initial burrowing into soil volumes. Results show that with increasing water content the strain energy decreases and suggest trade-offs between cavity expansion pressures and energy investment for different root and earthworm geometries and soil hydration. The study provides a quantitative framework for estimating energy costs of bioturbation in terms of soil organic carbon or the mechanical costs of soil exploration by plant roots as well as mechanical and hydration limits to such activities.

Organic amendments enhance microbial diversity and abundance of functional genes in Australian Soils

NASA Astrophysics Data System (ADS)

Aldorri, Sind; McMillan, Mary; Pereg, Lily

2016-04-01

Food and cash crops play important roles in Australia's economy with black, grey and red clay soil, widely use for growing cotton, wheat, corn and other crops in rotation. While the majority of cotton growers use nitrogen and phosphate fertilizers only in the form of agrochemicals, a few experiment with the addition of manure or composted plant material before planting. We hypothesized that the use of such organic amendments would enhance the soil microbial function through increased microbial diversity and abundance, thus contribute to improved soil sustainability. To test the hypothesis we collected soil samples from two cotton-growing farms in close geographical proximity and with mostly similar production practices other than one grower has been using composted plants as organic amendment and the second farmer uses only agrochemicals. We applied the Biolog Ecoplate system to study the metabolic signature of microbial communities and used qPCR to estimate the abundance of functional genes in the soil. The soil treated with organic amendments clearly showed higher metabolic activity of a more diverse range of carbon sources as well as higher abundance of genes involved in the nitrogen and phosphorous cycles. Since microbes undertake a large number of soil functions, the use of organic amendments can contribute to the sustainability of agricultural soils.

Process-based soil erodibility estimation for empirical water erosion models

USDA-ARS?s Scientific Manuscript database

A variety of modeling technologies exist for water erosion prediction each with specific parameters. It is of interest to scrutinize parameters of a particular model from the point of their compatibility with dataset of other models. In this research, functional relationships between soil erodibilit...

[Estimation model for daily transpiration of greenhouse muskmelon in its vegetative growth period].

PubMed

Zhang, Da-Long; Li, Jian-Ming; Wu, Pu-Te; Li, Wei-Li; Zhao, Zhi-Hua; Xu, Fei; Li, Jun

2013-07-01

For developing an estimation method of muskmelon transpiration in greenhouse, an estimation model for the daily transpiration of greenhouse muskmelon in its vegetative growth period was established, based on the greenhouse environmental parameters, muskmelon growth and development parameters, and soil moisture parameters. According to the specific environment in greenhouse, the item of aerodynamics in Penman-Monteith equation was modified, and the greenhouse environmental sub-model suitable for calculating the reference crop evapotranspiration in greenhouse was deduced. The crop factor sub-model was established with the leaf area index as independent variable, and the form of the model was linear function. The soil moisture sub-model was established with the soil relative effective moisture content as independent variable, and the form of the model was logarithmic function. With interval sowing, the model parameters were estimated and analyzed, according to the measurement data of different sowing dates in a year. The prediction accuracy of the model for sufficient irrigation and water-saving irrigation was verified, according to measurement data when the relative soil moisture content was 80%, 70%, and 60%, and the mean relative error was 11.5%, 16.2% , and 16.9% respectively. The model was a beneficial exploration for the application of Penman-Monteith equation under greenhouse environment and water-saving irrigation, having good application foreground and popularization value.

Compaction of forest soil by logging machinery favours occurrence of prokaryotes.

PubMed

Schnurr-Pütz, Silvia; Bååth, Erland; Guggenberger, Georg; Drake, Harold L; Küsel, Kirsten

2006-12-01

Soil compaction caused by passage of logging machinery reduces the soil air capacity. Changed abiotic factors might induce a change in the soil microbial community and favour organisms capable of tolerating anoxic conditions. The goals of this study were to resolve differences between soil microbial communities obtained from wheel-tracks (i.e. compacted) and their adjacent undisturbed sites, and to evaluate differences in potential anaerobic microbial activities of these contrasting soils. Soil samples obtained from compacted soil had a greater bulk density and a higher pH than uncompacted soil. Analyses of phospholipid fatty acids demonstrated that the eukaryotic/prokaryotic ratio in compacted soils was lower than that of uncompacted soils, suggesting that fungi were not favoured by the in situ conditions produced by compaction. Indeed, most-probable-number (MPN) estimates of nitrous oxide-producing denitrifiers, acetate- and lactate-utilizing iron and sulfate reducers, and methanogens were higher in compacted than in uncompacted soils obtained from one site that had large differences in bulk density. Compacted soils from this site yielded higher iron-reducing, sulfate-reducing and methanogenic potentials than did uncompacted soils. MPN estimates of H2-utilizing acetogens in compacted and uncompacted soils were similar. These results indicate that compaction of forest soil alters the structure and function of the soil microbial community and favours occurrence of prokaryotes.

The BonaRes Centre - A virtual institute for soil research in the context of a sustainable bio-economy

NASA Astrophysics Data System (ADS)

Wollschläger, Ute; Helming, Katharina; Heinrich, Uwe; Bartke, Stephan; Kögel-Knabner, Ingrid; Russell, David; Eberhardt, Einar; Vogel, Hans-Jörg

2016-04-01

Fertile soils are central resources for the production of biomass and provision of food and energy. A growing world population and latest climate targets lead to an increasing demand for both, food and bio-energy, which require preserving and improving the long-term productivity of soils as a bio-economic resource. At the same time, other soil functions and ecosystem services need to be maintained. To render soil management sustainable, we need to establish a scientific knowledge base about complex soil system processes that allows for the development of model tools to quantitatively predict the impact of a multitude of management measures on soil functions. This, finally, will allow for the provision of site-specific options for sustainable soil management. To face this challenge, the German Federal Ministry of Education and Research recently launched the funding program "Soil as a Natural Resource for the Bio-Economy - BonaRes". In a joint effort, ten collaborative projects and the coordinating BonaRes Centre are engaged to close existing knowledge gaps for a profound and systemic understanding of soil functions and their sensitivity to soil management. This presentation provides an overview of the concept of the BonaRes Centre which is responsible for i) setting up a comprehensive data base for soil-related information, ii) the development of model tools aiming to estimate the impact of different management measures on soil functions, and iii) establishing a web-based portal providing decision support tools for a sustainable soil management. A specific focus of the presentation will be laid on the so-called "knowledge-portal" providing the infrastructure for a community effort towards a comprehensive meta-analysis on soil functions as a basis for future model developments.

Mapping specific soil functions based on digital soil property maps

NASA Astrophysics Data System (ADS)

Pásztor, László; Fodor, Nándor; Farkas-Iványi, Kinga; Szabó, József; Bakacsi, Zsófia; Koós, Sándor

2016-04-01

Quantification of soil functions and services is a great challenge in itself even if the spatial relevance is supposed to be identified and regionalized. Proxies and indicators are widely used in ecosystem service mapping. Soil services could also be approximated by elementary soil features. One solution is the association of soil types with services as basic principle. Soil property maps however provide quantified spatial information, which could be utilized more versatilely for the spatial inference of soil functions and services. In the frame of the activities referred as "Digital, Optimized, Soil Related Maps and Information in Hungary" (DOSoReMI.hu) numerous soil property maps have been compiled so far with proper DSM techniques partly according to GSM.net specifications, partly by slightly or more strictly changing some of its predefined parameters (depth intervals, pixel size, property etc.). The elaborated maps have been further utilized, since even DOSoReMI.hu was intended to take steps toward the regionalization of higher level soil information (secondary properties, functions, services). In the meantime the recently started AGRAGIS project requested spatial soil related information in order to estimate agri-environmental related impacts of climate change and support the associated vulnerability assessment. One of the most vulnerable services of soils in the context of climate change is their provisioning service. In our work it was approximated by productivity, which was estimated by a sequential scenario based crop modelling. It took into consideration long term (50 years) time series of both measured and predicted climatic parameters as well as accounted for the potential differences in agricultural practice and crop production. The flexible parametrization and multiple results of modelling was then applied for the spatial assessment of sensitivity, vulnerability, exposure and adaptive capacity of soils in the context of the forecasted changes in climatic conditions in the Carpathian Basin. In addition to soil fertility, degradation risk due to N-leaching was also assessed by the model runs by taking into account the movement of nitrate in the profile during the simulated periods. Our paper will present the resulted national maps and some conclusions drawn from the experiences. Acknowledgement: Our work was supported by Iceland, Liechtenstein and Norway through the EEA Grants and the REC (Project No: EEA C12-12) and the Hungarian National Scientific Research Foundation (OTKA, Grant No. K105167).

Estimating spatially distributed soil texture using time series of thermal remote sensing - a case study in central Europe

NASA Astrophysics Data System (ADS)

Müller, Benjamin; Bernhardt, Matthias; Jackisch, Conrad; Schulz, Karsten

2016-09-01

For understanding water and solute transport processes, knowledge about the respective hydraulic properties is necessary. Commonly, hydraulic parameters are estimated via pedo-transfer functions using soil texture data to avoid cost-intensive measurements of hydraulic parameters in the laboratory. Therefore, current soil texture information is only available at a coarse spatial resolution of 250 to 1000 m. Here, a method is presented to derive high-resolution (15 m) spatial topsoil texture patterns for the meso-scale Attert catchment (Luxembourg, 288 km2) from 28 images of ASTER (advanced spaceborne thermal emission and reflection radiometer) thermal remote sensing. A principle component analysis of the images reveals the most dominant thermal patterns (principle components, PCs) that are related to 212 fractional soil texture samples. Within a multiple linear regression framework, distributed soil texture information is estimated and related uncertainties are assessed. An overall root mean squared error (RMSE) of 12.7 percentage points (pp) lies well within and even below the range of recent studies on soil texture estimation, while requiring sparser sample setups and a less diverse set of basic spatial input. This approach will improve the generation of spatially distributed topsoil maps, particularly for hydrologic modeling purposes, and will expand the usage of thermal remote sensing products.

SIMPLE MODEL OF ICE SEGREGATION USING AN ANALYTIC FUNCTION TO MODEL HEAT AND SOIL-WATER FLOW.

USGS Publications Warehouse

Hromadka, T.V.; Guymon, G.L.

1984-01-01

This paper reports on the development of a simple two-dimensional model of coupled heat and soil-water flow in freezing or thawing soil. The model also estimates ice-segregation (frost-heave) evolution. Ice segregation in soil results from water drawn into a freezing zone by hydraulic gradients created by the freezing of soil-water. Thus, with a favorable balance between the rate of heat extraction and the rate of water transport to a freezing zone, segregated ice lenses may form.

Identifying synergies between water resource protection and farm business objectives: the role of soil management

NASA Astrophysics Data System (ADS)

Stoate, Chris

2017-04-01

We use a 3,000 ha BACI experiment on clay soils in central England as a focus for exploring synergies between Water Framework Directive targets for water quality (sediment, nutrients and pesticides) and crop production objectives of farm businesses. Based on base of catchment annual sediment loads, we estimate annual soil loss from farmland to be in the order of 0.3 - 0.6 tonnes per hectare. This has impacts on aquatic ecology, reservoir storage capacity and downstream flood risk through sedimentation of drainage channels. Soil loss is relatively low in a European context but reflects poorly functioning soils with high runoff risk, and poor crop performance due to compaction, low organic matter, waterlogging, and competition from the grass weed, blackgrass (Alopecuris alopoides). We use a range of mechanisms to increase farmers' awareness, understanding and motivation for improving soil management to meet multiple public and private benefits of soil function and present results for soil organic matter testing, earthworm surveying, and horizontal and vertical soil compaction mapping.

Microbiology of Wind-eroded Sediments: Current Knowledge and Future Research Directions

USDA-ARS?s Scientific Manuscript database

Wind erosion is a threat to the sustainability and productivity of soils that takes place at local, regional, and global scales. Current estimates of cost of wind erosion have not included the costs associated with the loss of soil biodiversity and reduced ecosystem functions. Microorganisms carrie...

Reduction of soil erosion on forest roads

Treesearch

Edward R. Burroughs; John G. King

1989-01-01

Presents the expected reduction in surface erosion from selected treatments applied to forest road traveledways, cutslopes, fillslopes, and ditches. Estimated erosion reduction is expressed as functions of ground cover, slope gradient, and soil properties whenever possible. A procedure is provided to select rock riprap size for protection of the road ditch.

Synergistic method for boreal soil moisture and soil freeze retrievals using active and passive microwave instruments

NASA Astrophysics Data System (ADS)

Smolander, Tuomo; Lemmetyinen, Juha; Rautiainen, Kimmo; Schwank, Mike; Pulliainen, Jouni

2017-04-01

Soil moisture and soil freezing are important for diverse hydrological, biogeochemical, and climatological applications. They affect surface energy balance, surface and subsurface water flow, and exchange rates of carbon with the atmosphere. Soil freezing controls important biogeochemical processes, like photosynthetic activity of plants and microbial activity within soils. Permafrost covers approximately 24% of the land surface in the Northern Hemisphere and seasonal freezing occurs on approximately 51% of the area. The retrieval method presented is based on an inversion technique and applies a semiempirical backscattering model that describes the dependence of radar backscattering of forest as a function of stem volume, soil permittivity, the extinction coefficient of forest canopy, surface roughness, incidence angle, and radar frequency. It gives an estimate of soil permittivity using active microwave measurements. Applying a Bayesian assimilation scheme, it is also possible to use other soil permittivity retrievals to regulate this estimate to combine for example low resolution passive observations with high resolution active observations for a synergistic retrieval. This way the higher variance in the active retrieval can be constricted with the passive retrieval when at the same time the spatial resolution of the product is improved compared to the passive-only retrieval. The retrieved soil permittivity estimate can be used to detect soil freeze/thaw state by considering the soil to be frozen when the estimate is below a threshold value. The permittivity retrieval can also be used to estimate the relative moisture of the soil. The method was tested using SAR (Synthetic Aperture Radar) measurements from ENVISAT ASAR instrument for the years 2010-2012 and from Sentinel-1 satellite for the years 2015-2016 in Sodankylä area in Northern Finland. The synergistic method was tested combining the SAR measurements with a SMOS (Soil Moisture Ocean Salinity) radiometer based retrieval. The results were validated using in situ measurements from automatic soil state observation stations in Sodankylä calibration and validation (CAL-VAL) site, which is a reference site for several EO (Earth Observation) data products.

Multiscale Bayesian neural networks for soil water content estimation

NASA Astrophysics Data System (ADS)

Jana, Raghavendra B.; Mohanty, Binayak P.; Springer, Everett P.

2008-08-01

Artificial neural networks (ANN) have been used for some time now to estimate soil hydraulic parameters from other available or more easily measurable soil properties. However, most such uses of ANNs as pedotransfer functions (PTFs) have been at matching spatial scales (1:1) of inputs and outputs. This approach assumes that the outputs are only required at the same scale as the input data. Unfortunately, this is rarely true. Different hydrologic, hydroclimatic, and contaminant transport models require soil hydraulic parameter data at different spatial scales, depending upon their grid sizes. While conventional (deterministic) ANNs have been traditionally used in these studies, the use of Bayesian training of ANNs is a more recent development. In this paper, we develop a Bayesian framework to derive soil water retention function including its uncertainty at the point or local scale using PTFs trained with coarser-scale Soil Survey Geographic (SSURGO)-based soil data. The approach includes an ANN trained with Bayesian techniques as a PTF tool with training and validation data collected across spatial extents (scales) in two different regions in the United States. The two study areas include the Las Cruces Trench site in the Rio Grande basin of New Mexico, and the Southern Great Plains 1997 (SGP97) hydrology experimental region in Oklahoma. Each region-specific Bayesian ANN is trained using soil texture and bulk density data from the SSURGO database (scale 1:24,000), and predictions of the soil water contents at different pressure heads with point scale data (1:1) inputs are made. The resulting outputs are corrected for bias using both linear and nonlinear correction techniques. The results show good agreement between the soil water content values measured at the point scale and those predicted by the Bayesian ANN-based PTFs for both the study sites. Overall, Bayesian ANNs coupled with nonlinear bias correction are found to be very suitable tools for deriving soil hydraulic parameters at the local/fine scale from soil physical properties at coarser-scale and across different spatial extents. This approach could potentially be used for soil hydraulic properties estimation and downscaling.

Effects of Climate Changes and Pollution with Heavy Metals on the Transformation of Carbon Compounds in Different Soil Types of Agroecosystems in the Forest-Steppe of Baikal Region

NASA Astrophysics Data System (ADS)

Pomazkina, L. V.; Semenova, Yu. V.

2018-05-01

The results of long-term (1992-2005) monitoring of the carbon compounds transformation in soils of forest- steppe agroecosystems polluted by heavy metals in the Baikal region in the years different from the "climatic norm" are discussed. The influence of environmental factors on the functioning of microbial community was estimated by the Cmicr content and CO2 emission. The changes in the ecophysiological parameters (Cmicr/Corg and C-CO2/Cmicr, mg/(g h) related to the availability of the substrate and intensity of carbon (re)immobilization in different soils revealed the differences in the formation of a stable microbial community dependent on the environmental factors, especially in anomalous years. The use of a systemic approach and analysis of the carbon compounds transformation based on the proportion between the flows of net-mineralized and (re)immobilized carbon (NM: RI) allowed to evaluate integrally the functioning regime of the agroecosystems and the ecological impact on them. The differences in the functioning of agroecosystems on different heavy metal-polluted soils identified on the background of climatic changes are suitable for forecasting the current state and development of agroecosystems. For agroecosystems of this region, C-CO2 emission was estimated for the first time; it was more intense from the soils with the high humus content than from the soils poor in humus (141 and 101 g C/m2, respectively).

Modeling diffusion and reaction in soils: 9. The Buckingham-Burdine-Campbell equation for gas diffusivity in undisturbed soil

DOE Office of Scientific and Technical Information (OSTI.GOV)

Moldrup, P.; Olesen, T.; Yamaguchi, T.

1999-08-01

Accurate description of gas diffusivity (ratio of gas diffusion coefficients in soil and free air, D{sub s}/D{sub 0}) in undisturbed soils is a prerequisite for predicting in situ transport and fate of volatile organic chemicals and greenhouse gases. Reference point gas diffusivities (R{sub p}) in completely dry soil were estimated for 20 undisturbed soils by assuming a power function relation between gas diffusivity and air-filled porosity ({epsilon}). Among the classical gas diffusivity models, the Buckingham (1904) expression, equal to the soil total porosity squared, best described R{sub p}. Inasmuch, as their previous works implied a soil-type dependency of D{sub s}/D{submore » 0}({epsilon}) in undisturbed soils, the Buckingham R{sub p} expression was inserted in two soil-type-dependent D{sub s}/D{sub 0}({epsilon}) models. One D{sub s}/D{sub 0}({epsilon}) model is a function of pore-size distribution (the Campbell water retention parameter used in a modified Burdine capillary tube model), and the other is a calibrated, empirical function of soil texture (silt + sand fraction). Both the Buckingham-Burdine-Campbell (BBC) and the Buckingham/soil texture-based D{sub s}/D{sub 0}({epsilon}) models described well the observed soil type effects on gas diffusivity and gave improved predictions compared with soil type independent models when tested against an independent data set for six undisturbed surface soils. This study emphasizes that simple but soil-type-dependent power function D{sub s}/D{sub 0}({epsilon}) models can adequately describe and predict gas diffusivity in undisturbed soil. The authors recommend the new BBC model as basis for modeling gas transport and reactions in undisturbed soil systems.« less

Pedotransfer functions for isoproturon sorption on soils and vadose zone materials.

PubMed

Moeys, Julien; Bergheaud, Valérie; Coquet, Yves

2011-10-01

Sorption coefficients (the linear K(D) or the non-linear K(F) and N(F)) are critical parameters in models of pesticide transport to groundwater or surface water. In this work, a dataset of isoproturon sorption coefficients and corresponding soil properties (264 K(D) and 55 K(F)) was compiled, and pedotransfer functions were built for predicting isoproturon sorption in soils and vadose zone materials. These were benchmarked against various other prediction methods. The results show that the organic carbon content (OC) and pH are the two main soil properties influencing isoproturon K(D) . The pedotransfer function is K(D) = 1.7822 + 0.0162 OC(1.5) - 0.1958 pH (K(D) in L kg(-1) and OC in g kg(-1)). For low-OC soils (OC < 6.15 g kg(-1)), clay and pH are most influential. The pedotransfer function is then K(D) = 0.9980 + 0.0002 clay - 0.0990 pH (clay in g kg(-1)). Benchmarking K(D) estimations showed that functions calibrated on more specific subsets of the data perform better on these subsets than functions calibrated on larger subsets. Predicting isoproturon sorption in soils in unsampled locations should rely, whenever possible, and by order of preference, on (a) site- or soil-specific pedotransfer functions, (b) pedotransfer functions calibrated on a large dataset, (c) K(OC) values calculated on a large dataset or (d) K(OC) values taken from existing pesticide properties databases. Copyright © 2011 Society of Chemical Industry.

Geostatistical Interpolation of Particle-Size Curves in Heterogeneous Aquifers

NASA Astrophysics Data System (ADS)

Guadagnini, A.; Menafoglio, A.; Secchi, P.

2013-12-01

We address the problem of predicting the spatial field of particle-size curves (PSCs) from measurements associated with soil samples collected at a discrete set of locations within an aquifer system. Proper estimates of the full PSC are relevant to applications related to groundwater hydrology, soil science and geochemistry and aimed at modeling physical and chemical processes occurring in heterogeneous earth systems. Hence, we focus on providing kriging estimates of the entire PSC at unsampled locations. To this end, we treat particle-size curves as cumulative distribution functions, model their densities as functional compositional data and analyze them by embedding these into the Hilbert space of compositional functions endowed with the Aitchison geometry. On this basis, we develop a new geostatistical methodology for the analysis of spatially dependent functional compositional data. Our functional compositional kriging (FCK) approach allows providing predictions at unsampled location of the entire particle-size curve, together with a quantification of the associated uncertainty, by fully exploiting both the functional form of the data and their compositional nature. This is a key advantage of our approach with respect to traditional methodologies, which treat only a set of selected features (e.g., quantiles) of PSCs. Embedding the full PSC into a geostatistical analysis enables one to provide a complete characterization of the spatial distribution of lithotypes in a reservoir, eventually leading to improved predictions of soil hydraulic attributes through pedotransfer functions as well as of soil geochemical parameters which are relevant in sorption/desorption and cation exchange processes. We test our new method on PSCs sampled along a borehole located within an alluvial aquifer near the city of Tuebingen, Germany. The quality of FCK predictions is assessed through leave-one-out cross-validation. A comparison between hydraulic conductivity estimates obtained via FCK approach and those predicted by classical kriging of effective particle diameters (i.e., quantiles of the PSCs) is finally performed.

Soil Moisture as an Estimator for Crop Yield in Germany

NASA Astrophysics Data System (ADS)

Peichl, Michael; Meyer, Volker; Samaniego, Luis; Thober, Stephan

2015-04-01

Annual crop yield depends on various factors such as soil properties, management decisions, and meteorological conditions. Unfavorable weather conditions, e.g. droughts, have the potential to drastically diminish crop yield in rain-fed agriculture. For example, the drought in 2003 caused direct losses of 1.5 billion EUR only in Germany. Predicting crop yields allows to mitigate negative effects of weather extremes which are assumed to occur more often in the future due to climate change. A standard approach in economics is to predict the impact of climate change on agriculture as a function of temperature and precipitation. This approach has been developed further using concepts like growing degree days. Other econometric models use nonlinear functions of heat or vapor pressure deficit. However, none of these approaches uses soil moisture to predict crop yield. We hypothesize that soil moisture is a better indicator to explain stress on plant growth than estimations based on precipitation and temperature. This is the case because the latter variables do not explicitly account for the available water content in the root zone, which is the primary source of water supply for plant growth. In this study, a reduced form panel approach is applied to estimate a multivariate econometric production function for the years 1999 to 2010. Annual crop yield data of various crops on the administrative district level serve as depending variables. The explanatory variable of major interest is the Soil Moisture Index (SMI), which quantifies anomalies in root zone soil moisture. The SMI is computed by the mesoscale Hydrological Model (mHM, www.ufz.de/mhm). The index represents the monthly soil water quantile at a 4 km2 grid resolution covering entire Germany. A reduced model approach is suitable because the SMI is the result of a stochastic weather process and therefore can be considered exogenous. For the ease of interpretation a linear functionality is preferred. Meteorological, phenological, geological, agronomic, and socio-economic variables are also considered to extend the model in order to reveal the proper causal relation. First results show that dry as well as wet extremes of SMI have a negative impact on crop yield for winter wheat. This indicates that soil moisture has at least a limiting affect on crop production.

3D-Digital soil property mapping by geoadditive models

NASA Astrophysics Data System (ADS)

Papritz, Andreas

2016-04-01

In many digital soil mapping (DSM) applications, soil properties must be predicted not only for a single but for multiple soil depth intervals. In the GlobalSoilMap project, as an example, predictions are computed for the 0-5 cm, 5-15 cm, 15-30 cm, 30-60 cm, 60-100 cm, 100-200 cm depth intervals (Arrouays et al., 2014). Legacy soil data are often used for DSM. It is common for such datasets that soil properties were measured for soil horizons or for layers at varying soil depth and with non-constant thickness (support). This poses problems for DSM: One strategy is to harmonize the soil data to common depth prior to the analyses (e.g. Bishop et al., 1999) and conduct the statistical analyses for each depth interval independently. The disadvantage of this approach is that the predictions for different depths are computed independently from each other so that the predicted depth profiles may be unrealistic. Furthermore, the error induced by the harmonization to common depth is ignored in this approach (Orton et al. 2016). A better strategy is therefore to process all soil data jointly without prior harmonization by a 3D-analysis that takes soil depth and geographical position explicitly into account. Usually, the non-constant support of the data is then ignored, but Orton et al. (2016) presented recently a geostatistical approach that accounts for non-constant support of soil data and relies on restricted maximum likelihood estimation (REML) of a linear geostatistical model with a separable, heteroscedastic, zonal anisotropic auto-covariance function and area-to-point kriging (Kyriakidis, 2004.) Although this model is theoretically coherent and elegant, estimating its many parameters by REML and selecting covariates for the spatial mean function is a formidable task. A simpler approach might be to use geoadditive models (Kammann and Wand, 2003; Wand, 2003) for 3D-analyses of soil data. geoAM extend the scope of the linear model with spatially correlated errors to account for nonlinear effects of covariates by fitting componentwise smooth, nonlinear functions to the covariates (additive terms). REML estimation of model parameters and computing best linear unbiased predictions (BLUP) builds in the geoAM framework on the fact that both geostatistical and additive models can be parametrized as linear mixed models Wand, 2003. For 3D-DSM analysis of soil data, it is natural to model depth profiles of soil properties by additive terms of soil depth. Including interactions between these additive terms and covariates of the spatial mean function allows to model spatially varying depth profiles. Furthermore, with suitable choice of the basis functions of the additive term (e.g. polynomial regression splines), non-constant support of the soil data can be taken into account. Finally, boosting (Bühlmann and Hothorn, 2007) can be used for selecting covariates for the spatial mean function. The presentation will detail the geoAM approach and present an example of geoAM for 3D-analysis of legacy soil data. Arrouays, D., McBratney, A. B., Minasny, B., Hempel, J. W., Heuvelink, G. B. M., MacMillan, R. A., Hartemink, A. E., Lagacherie, P., and McKenzie, N. J. (2014). The GlobalSoilMap project specifications. In GlobalSoilMap Basis of the global spatial soil information system, pages 9-12. CRC Press. Bishop, T., McBratney, A., and Laslett, G. (1999). Modelling soil attribute depth functions with equal-area quadratic smoothing splines. Geoderma, 91(1-2), 27-45. Bühlmann, P. and Hothorn, T. (2007). Boosting algorithms: Regularization, prediction and model fitting. Statistical Science, 22(4), 477-505. Kammann, E. E. and Wand, M. P. (2003). Geoadditive models. Journal of the Royal Statistical Society. Series C: Applied Statistics, 52(1), 1-18. Kyriakidis, P. (2004). A geostatistical framework for area-to-point spatial interpolation. Geographical Analysis, 36(3), 259-289. Orton, T., Pringle, M., and Bishop, T. (2016). A one-step approach for modelling and mapping soil properties based on profile data sampled over varying depth intervals. Geoderma, 262, 174-186. Wand, M. P. (2003). Smoothing and mixed models. Computational Statistics, 18(2), 223-249.

Remote sensing-based estimation of annual soil respiration at two contrasting forest sites

DOE Office of Scientific and Technical Information (OSTI.GOV)

Huang, Ni; Gu, Lianhong; Black, T. Andrew

Here, soil respiration (R s), an important component of the global carbon cycle, can be estimated using remotely sensed data, but the accuracy of this technique has not been thoroughly investigated. In this study, we proposed a methodology for the remote estimation of annual R s at two contrasting FLUXNET forest sites (a deciduous broadleaf forest and an evergreen needleleaf forest). A version of the Akaike's information criterion was used to select the best model from a range of models for annual R s estimation based on the remotely sensed data products from the Moderate Resolution Imaging Spectroradiometer and root-zonemore » soil moisture product derived from assimilation of the NASA Advanced Microwave Scanning Radiometer soil moisture products and a two-layer Palmer water balance model. We found that the Arrhenius-type function based on nighttime land surface temperature (LST-night) was the best model by comprehensively considering the model explanatory power and model complexity at the Missouri Ozark and BC-Campbell River 1949 Douglas-fir sites.« less

Spatio-temporal patterns of soil water storage under dryland agriculture at the watershed scale

NASA Astrophysics Data System (ADS)

Ibrahim, Hesham M.; Huggins, David R.

2011-07-01

SummarySpatio-temporal patterns of soil water are major determinants of crop yield potential in dryland agriculture and can serve as the basis for delineating precision management zones. Soil water patterns can vary significantly due to differences in seasonal precipitation, soil properties and topographic features. In this study we used empirical orthogonal function (EOF) analysis to characterize the spatial variability of soil water at the Washington State University Cook Agronomy Farm (CAF) near Pullman, WA. During the period 1999-2006, the CAF was divided into three roughly equal blocks (A, B, and C), and soil water at 0.3 m intervals to a depth of 1.5 m measured gravimetrically at approximately one third of the 369 geo-referenced points on the 37-ha watershed. These data were combined with terrain attributes, soil bulk density and apparent soil conductivity (EC a). The first EOF generated from the three blocks explained 73-76% of the soil water variability. Field patterns of soil water based on EOF interpolation varied between wet and dry conditions during spring and fall seasons. Under wet conditions, elevation and wetness index were the dominant factors regulating the spatial patterns of soil water. As soil dries out during summer and fall, soil properties (EC a and bulk density) become more important in explaining the spatial patterns of soil water. The EOFs generated from block B, which represents average topographic and soil properties, provided better estimates of soil water over the entire watershed with larger Nash-Sutcliffe Coefficient of Efficiency (NSCE) values, especially when the first two EOFs were retained. Including more than the first two EOFs did not significantly increase the NSCE of soil water estimate. The EOF interpolation method to estimate soil water variability worked slightly better during spring than during fall, with average NSCE values of 0.23 and 0.20, respectively. The predictable patterns of stored soil water in the spring could serve as the basis for delineating precision management zones as yield potential is largely driven by water availability. The EOF-based method has the advantage of estimating the soil water variability based on soil water data from several measurement times, whereas in regression methods only soil water measurement at a single time are used. The EOF-based method can also be used to estimate soil water at any time other than measurement times, assuming the average soil water of the watershed is known at that time.

A Software for soil quality conservation at organic waste disposal areas: The case of olive mill and pistachio wastes.

NASA Astrophysics Data System (ADS)

Doula, Maria; Sarris, Apostolos; Papadopoulos, Nikos; Hliaoutakis, Aggelos; Kydonakis, Aris; Argyriou, Lemonia; Theocharopoulos, Sid; Kolovos, Chronis

2016-04-01

For the sustainable reuse of organic wastes at agricultural areas, apart from extensive evaluation of waste properties and characteristics, it is of significant importance, in order to protect soil quality, to evaluate land suitability and estimate the correct application doses prior waste landspreading. In the light of this precondition, a software was developed that integrates GIS maps of land suitability for waste reuse (wastewater and solid waste) and an algorithm for waste doses estimation in relation to soil analysis, and in case of reuse for fertilization with soil analysis, irrigation water quality and plant needs. EU and legislation frameworks of European Member States are also considered for the assessment of waste suitability for landspreading and for the estimation of the correct doses that will not cause adverse effects on soil and also to underground water (e.g. Nitrate Directive). Two examples of software functionality are presented in this study using data collected during two LIFE projects, i.e. Prosodol for landspreading of olive mill wastes and AgroStrat for pistachio wastes.

Discrepancy between Snowmelt and Soil Infiltration

NASA Astrophysics Data System (ADS)

Fassnacht, S. R.

2017-12-01

A majority of snowmelt enters the soil and is either transmitted through or stored in the soil. Snowmelt has been estimated from the decrease in snow mass of a snow pillow and soil infiltration has been estimated from near surface TDR probes. Here, these data are from a set of Snow Telemetry (SNOTEL) stations across Colorado. While seasonal totals are similar, it is shown that there is a disconnect between the amount of water melted in a day and the increased daily volume of water measured in the near sub-surface. It is surmised that these differences are a function of the data collection methods, the infiltration rate, and possible lateral flow. An examination of daily infiltration volumes at depth shows a further disconnect, as it is likely that lateral flow complicates the measurements to a true three dimensional problem. The data are informative to illustrate the transmission of meltwater into the soil; methods for improvement are explored.

A hyper-temporal remote sensing protocol for detecting ecosystem disturbance, classifying ecological state, and assessing soil resilience

USDA-ARS?s Scientific Manuscript database

Hyper-temporal remote sensing is capable of detecting detailed information on vegetation dynamics relating to plant functional types (PFT), a useful proxy for estimating soil physical and chemical properties. A central concept of PFT is that plant morphological and physiological adaptations are link...

Evaluating Spatial Heterogeneity and Environmental Variability Inferred from Branched Glycerol Dialkyl Glycerol Tetraethers (GDGTs) Distribution in Soils from Valles Caldera, New Mexic

NASA Astrophysics Data System (ADS)

Contreras Quintana, S. H.; Werne, J. P.; Brown, E. T.; Halbur, J.; Sinninghe Damsté, , J.; Schouten, S.; Correa-Metrio, A.; Fawcett, P. J.

2014-12-01

Branched glycerol dialkyl glycerol tetraethers (GDGTs) are recently discovered bacterial membrane lipids, ubiquitously present in peat bogs and soils, as well as in rivers, lakes and lake sediments. Their distribution appears to be controlled mainly by soil pH and annual mean air temperature (MAT) and they have been increasingly used as paleoclimate proxies in sedimentary records. In order to validate their application as paleoclimate proxies, it is essential evaluate the influence of small scale environmental variability on their distribution. Initial application of the original soil-based branched GDGT distribution proxy to lacustrine sediments from Valles Caldera, New Mexico (NM) was promising, producing a viable temperature record spanning two glacial/interglacial cycles. In this study, we assess the influence of analytical and spatial soil heterogeneity on the concentration and distribution of 9 branched GDGTs in soils from Valles Caldera, and show how this variability is propagated to MAT and pH estimates using multiple soil-based branched GDGT transfer functions. Our results show that significant differences in the abundance and distribution of branched GDGTs in soil can be observed even within a small area such as Valles Caldera. Although the original MBT-CBT calibration appears to give robust MAT estimates and the newest calibration provides pH estimates in better agreement with modern local soils in Valles Caldera, the environmental heterogeneity (e.g. vegetation type and soil moisture) appears to affect the precision of MAT and pH estimates. Furthermore, the heterogeneity of soils leads to significant variability among samples taken even from within a square meter. While such soil heterogeneity is not unknown (and is typically controlled for by combining multiple samples), this study quantifies heterogeneity relative to branched GDGT-based proxies for the first time, indicating that care must be taken with samples from heterogeneous soils in MAT and pH reconstructions.

Evaluating uncertainties in multi-layer soil moisture estimation with support vector machines and ensemble Kalman filtering

NASA Astrophysics Data System (ADS)

Liu, Di; Mishra, Ashok K.; Yu, Zhongbo

2016-07-01

This paper examines the combination of support vector machines (SVM) and the dual ensemble Kalman filter (EnKF) technique to estimate root zone soil moisture at different soil layers up to 100 cm depth. Multiple experiments are conducted in a data rich environment to construct and validate the SVM model and to explore the effectiveness and robustness of the EnKF technique. It was observed that the performance of SVM relies more on the initial length of training set than other factors (e.g., cost function, regularization parameter, and kernel parameters). The dual EnKF technique proved to be efficient to improve SVM with observed data either at each time step or at a flexible time steps. The EnKF technique can reach its maximum efficiency when the updating ensemble size approaches a certain threshold. It was observed that the SVM model performance for the multi-layer soil moisture estimation can be influenced by the rainfall magnitude (e.g., dry and wet spells).

Improving Estimations of Spatial Distribution of Soil Respiration Using the Bayesian Maximum Entropy Algorithm and Soil Temperature as Auxiliary Data.

PubMed

Hu, Junguo; Zhou, Jian; Zhou, Guomo; Luo, Yiqi; Xu, Xiaojun; Li, Pingheng; Liang, Junyi

2016-01-01

Soil respiration inherently shows strong spatial variability. It is difficult to obtain an accurate characterization of soil respiration with an insufficient number of monitoring points. However, it is expensive and cumbersome to deploy many sensors. To solve this problem, we proposed employing the Bayesian Maximum Entropy (BME) algorithm, using soil temperature as auxiliary information, to study the spatial distribution of soil respiration. The BME algorithm used the soft data (auxiliary information) effectively to improve the estimation accuracy of the spatiotemporal distribution of soil respiration. Based on the functional relationship between soil temperature and soil respiration, the BME algorithm satisfactorily integrated soil temperature data into said spatial distribution. As a means of comparison, we also applied the Ordinary Kriging (OK) and Co-Kriging (Co-OK) methods. The results indicated that the root mean squared errors (RMSEs) and absolute values of bias for both Day 1 and Day 2 were the lowest for the BME method, thus demonstrating its higher estimation accuracy. Further, we compared the performance of the BME algorithm coupled with auxiliary information, namely soil temperature data, and the OK method without auxiliary information in the same study area for 9, 21, and 37 sampled points. The results showed that the RMSEs for the BME algorithm (0.972 and 1.193) were less than those for the OK method (1.146 and 1.539) when the number of sampled points was 9 and 37, respectively. This indicates that the former method using auxiliary information could reduce the required number of sampling points for studying spatial distribution of soil respiration. Thus, the BME algorithm, coupled with soil temperature data, can not only improve the accuracy of soil respiration spatial interpolation but can also reduce the number of sampling points.

Improving Estimations of Spatial Distribution of Soil Respiration Using the Bayesian Maximum Entropy Algorithm and Soil Temperature as Auxiliary Data

PubMed Central

Hu, Junguo; Zhou, Jian; Zhou, Guomo; Luo, Yiqi; Xu, Xiaojun; Li, Pingheng; Liang, Junyi

2016-01-01

Soil respiration inherently shows strong spatial variability. It is difficult to obtain an accurate characterization of soil respiration with an insufficient number of monitoring points. However, it is expensive and cumbersome to deploy many sensors. To solve this problem, we proposed employing the Bayesian Maximum Entropy (BME) algorithm, using soil temperature as auxiliary information, to study the spatial distribution of soil respiration. The BME algorithm used the soft data (auxiliary information) effectively to improve the estimation accuracy of the spatiotemporal distribution of soil respiration. Based on the functional relationship between soil temperature and soil respiration, the BME algorithm satisfactorily integrated soil temperature data into said spatial distribution. As a means of comparison, we also applied the Ordinary Kriging (OK) and Co-Kriging (Co-OK) methods. The results indicated that the root mean squared errors (RMSEs) and absolute values of bias for both Day 1 and Day 2 were the lowest for the BME method, thus demonstrating its higher estimation accuracy. Further, we compared the performance of the BME algorithm coupled with auxiliary information, namely soil temperature data, and the OK method without auxiliary information in the same study area for 9, 21, and 37 sampled points. The results showed that the RMSEs for the BME algorithm (0.972 and 1.193) were less than those for the OK method (1.146 and 1.539) when the number of sampled points was 9 and 37, respectively. This indicates that the former method using auxiliary information could reduce the required number of sampling points for studying spatial distribution of soil respiration. Thus, the BME algorithm, coupled with soil temperature data, can not only improve the accuracy of soil respiration spatial interpolation but can also reduce the number of sampling points. PMID:26807579

Daily estimates of soil ingestion in children.

PubMed Central

Stanek, E J; Calabrese, E J

1995-01-01

Soil ingestion estimates play an important role in risk assessment of contaminated sites, and estimates of soil ingestion in children are of special interest. Current estimates of soil ingestion are trace-element specific and vary widely among elements. Although expressed as daily estimates, the actual estimates have been constructed by averaging soil ingestion over a study period of several days. The wide variability has resulted in uncertainty as to which method of estimation of soil ingestion is best. We developed a methodology for calculating a single estimate of soil ingestion for each subject for each day. Because the daily soil ingestion estimate represents the median estimate of eligible daily trace-element-specific soil ingestion estimates for each child, this median estimate is not trace-element specific. Summary estimates for individuals and weeks are calculated using these daily estimates. Using this methodology, the median daily soil ingestion estimate for 64 children participating in the 1989 Amherst soil ingestion study is 13 mg/day or less for 50% of the children and 138 mg/day or less for 95% of the children. Mean soil ingestion estimates (for up to an 8-day period) were 45 mg/day or less for 50% of the children, whereas 95% of the children reported a mean soil ingestion of 208 mg/day or less. Daily soil ingestion estimates were used subsequently to estimate the mean and variance in soil ingestion for each child and to extrapolate a soil ingestion distribution over a year, assuming that soil ingestion followed a log-normal distribution. Images Figure 1. Figure 2. Figure 3. Figure 4. PMID:7768230

Enhanced degradation and soil depth effects on the fate of atrazine and major metabolites in Colorado and Mississippi soils.

PubMed

Krutz, L Jason; Shaner, Dale L; Zablotowicz, Robert M

2010-01-01

The aim of this report is to inform modelers of the differences in atrazine fate between s-triazine-adapted and nonadapted soils as a function of depth in the profile and to recommend atrazine and metabolite input values for pesticide process submodules. The objectives of this study were to estimate the atrazine-mineralizing bacterial population, cumulative atrazine mineralization, atrazine persistence, and metabolite (desethylatrazine [DEA], deisopropylatrazine [DIA], and hydroxyatrazine [HA]) formation and degradation in Colorado and Mississippi s-triazine-adapted and nonadapted soils at three depths (0-5, 5-15, and 15-30 cm). Regardless of depth, the AMBP and cumulative atrazine mineralization was at least 3.8-fold higher in s-triazine-adapted than nonadapted soils. Atrazine half-life (T1/2) values pooled over nonadapted soils and depths approximated historic estimates (T1/2 = 60 d). Atrazine persistence in all depths of s-triazine-adapted soils was at least fourfold lower than that of the nonadapted soil. Atrazine metabolite concentrations were lower in s-triazine-adapted than in nonadapted soil by 35 d after incubation regardless of depth. Results indicate that (i) reasonable fate and transport modeling of atrazine will require identifying if soils are adapted to s-triazine herbicides. For example, our data confirm the 60-d T1/2 for atrazine in nonadapted soils, but a default input value of 6 d for atrazine is required for s-triazine adapted soils. (ii) Literature estimates for DEA, DIA, and HA T1/2 values in nonadapted soils are 52, 36, and 60 d, respectively, whereas our analysis indicates that reasonable T1/2 values for s-triazine-adapted soils are 10 d for DEA, 8 d for DIA, and 6 d for HA. (iii) An estimate for the relative distribution of DIA, DEA, and HA produced in nonadapted soils is 18, 72, and 10% of parent, respectively. In s-triazine-adapted soils, the values were 6, 23, and 71% for DIA, DEA, and HA, respectively. The effects of soil adaptation on metabolite distribution need to be confirmed in field experiments.

Modeling the hysteretic moisture and temperature responses of soil carbon decomposition resulting from organo-mineral interactions

NASA Astrophysics Data System (ADS)

Tang, J.; Riley, W. J.

2017-12-01

Most existing soil carbon cycle models have modeled the moisture and temperature dependence of soil respiration using deterministic response functions. However, empirical data suggest abundant variability in both of these dependencies. We here use the recently developed SUPECA (Synthesizing Unit and Equilibrium Chemistry Approximation) theory and a published dynamic energy budget based microbial model to investigate how soil carbon decomposition responds to changes in soil moisture and temperature under the influence of organo-mineral interactions. We found that both the temperature and moisture responses are hysteretic and cannot be represented by deterministic functions. We then evaluate how the multi-scale variability in temperature and moisture forcing affect soil carbon decomposition. Our results indicate that when the model is run in scenarios mimicking laboratory incubation experiments, the often-observed temperature and moisture response functions can be well reproduced. However, when such response functions are used for model extrapolation involving more transient variability in temperature and moisture forcing (as found in real ecosystems), the dynamic model that explicitly accounts for hysteresis in temperature and moisture dependency produces significantly different estimations of soil carbon decomposition, suggesting there are large biases in models that do not resolve such hysteresis. We call for more studies on organo-mineral interactions to improve modeling of such hysteresis.

Modelling soil water retention using support vector machines with genetic algorithm optimisation.

PubMed

Lamorski, Krzysztof; Sławiński, Cezary; Moreno, Felix; Barna, Gyöngyi; Skierucha, Wojciech; Arrue, José L

2014-01-01

This work presents point pedotransfer function (PTF) models of the soil water retention curve. The developed models allowed for estimation of the soil water content for the specified soil water potentials: -0.98, -3.10, -9.81, -31.02, -491.66, and -1554.78 kPa, based on the following soil characteristics: soil granulometric composition, total porosity, and bulk density. Support Vector Machines (SVM) methodology was used for model development. A new methodology for elaboration of retention function models is proposed. Alternative to previous attempts known from literature, the ν-SVM method was used for model development and the results were compared with the formerly used the C-SVM method. For the purpose of models' parameters search, genetic algorithms were used as an optimisation framework. A new form of the aim function used for models parameters search is proposed which allowed for development of models with better prediction capabilities. This new aim function avoids overestimation of models which is typically encountered when root mean squared error is used as an aim function. Elaborated models showed good agreement with measured soil water retention data. Achieved coefficients of determination values were in the range 0.67-0.92. Studies demonstrated usability of ν-SVM methodology together with genetic algorithm optimisation for retention modelling which gave better performing models than other tested approaches.

Development of a standard soil-to-skin adherence probability density function for use in Monte Carlo analyses of dermal exposure.

PubMed

Finley, B L; Scott, P K; Mayhall, D A

1994-08-01

It has recently been suggested that "standard" data distributions for key exposure variables should be developed wherever appropriate for use in probabilistic or "Monte Carlo" exposure analyses. Soil-on-skin adherence estimates represent an ideal candidate for development of a standard data distribution: There are several readily available studies which offer a consistent pattern of reported results, and more importantly, soil adherence to skin is likely to vary little from site-to-site. In this paper, we thoroughly review each of the published soil adherence studies with respect to study design, sampling, and analytical methods, and level of confidence in the reported results. Based on these studies, probability density functions (PDF) of soil adherence values were examined for different age groups and different sampling techniques. The soil adherence PDF developed from adult data was found to resemble closely the soil adherence PDF based on child data in terms of both central tendency (mean = 0.49 and 0.63 mg-soil/cm2-skin, respectively) and 95th percentile values (1.6 and 2.4 mg-soil/cm2-skin, respectively). Accordingly, a single, "standard" PDF is presented based on all data collected for all age groups. This standard PDF is lognormally distributed; the arithmetic mean and standard deviation are 0.52 +/- 0.9 mg-soil/cm2-skin. Since our review of the literature indicates that soil adherence under environmental conditions will be minimally influenced by age, sex, soil type, or particle size, this PDF should be considered applicable to all settings. The 50th and 95th percentile values of the standard PDF (0.25 and 1.7 mg-soil/cm2-skin, respectively) are very similar to recent U.S. EPA estimates of "average" and "upper-bound" soil adherence (0.2 and 1.0 mg-soil/cm2-skin, respectively).

Spatial prediction of near surface soil water retention functions using hydrogeophysics

NASA Astrophysics Data System (ADS)

Gibson, J. P.; Franz, T. E.

2017-12-01

The hydrological community often turns to widely available spatial datasets such as SSURGO to characterize the spatial variability of soil across a landscape of interest. This has served as a reasonable first approximation when lacking localized soil data. However, previous work has shown that information loss within land surface models primarily stems from parameterization. Localized soil sampling is both expensive and time intense, and thus a need exists in connecting spatial datasets with ground observations. Given that hydrogeophysics is data-dense, rapid, and relatively easy to adopt, it is a promising technique to help dovetail localized soil sampling with larger spatial datasets. In this work, we utilize 2 geophysical techniques; cosmic ray neutron probe and electromagnetic induction, to identify temporally stable soil moisture patterns. This is achieved by measuring numerous times over a range of wet to dry field conditions in order to apply an empirical orthogonal function. We then present measured water retention functions of shallow cores extracted within each temporally stable zone. Lastly, we use soil moisture patterns as a covariate to predict soil hydraulic properties in areas without measurement and validate using a leave-one-out cross validation analysis. Using these approaches to better constrain soil hydraulic property variability, we speculate that further research can better estimate hydrologic fluxes in areas of interest.

Maintenance of soil functioning following erosion of microbial diversity.

PubMed

Wertz, Sophie; Degrange, Valérie; Prosser, James I; Poly, Franck; Commeaux, Claire; Freitag, Thomas; Guillaumaud, Nadine; Roux, Xavier Le

2006-12-01

The paradigm that soil microbial communities, being very diverse, have high functional redundancy levels, so that erosion of microbial diversity is less important for ecosystem functioning than erosion of plant or animal diversity, is often taken for granted. However, this has only been demonstrated for decomposition/respiration functions, performed by a large proportion of the total microbial community, but not for specialized microbial groups. Here, we determined the impact of a decrease in soil microbial diversity on soil ecosystem processes using a removal approach, in which less abundant species were removed preferentially. This was achieved by inoculation of sterile soil microcosms with serial dilutions of a suspension obtained from the same non-sterile soil and subsequent incubation, to enable recovery of community size. The sensitivity to diversity erosion was evaluated for three microbial functional groups with known contrasting taxonomic diversities (ammonia oxidizers < denitrifiers < heterotrophs). Diversity erosion within each functional group was characterized using molecular fingerprinting techniques: ribosomal intergenic spacer analysis (RISA) for the eubacterial community, denaturing gradient gel electrophoresis (DGGE) analysis of nirK genes for denitrifiers, and DGGE analysis of 16S rRNA genes for betaproteobacterial ammonia oxidizers. In addition, we simulated the impact of the removal approach by dilution on the number of soil bacterial species remaining in the inoculum using values of abundance distribution of bacterial species reported in the literature. The reduction of the diversity of the functional groups observed from genetic fingerprints did not impair the associated functioning of these groups, i.e. carbon mineralization, denitrification and nitrification. This was remarkable, because the amplitude of diversity erosion generated by the dilution approach was huge (level of bacterial species loss was estimated to be around 99.99% for the highest dilution). Our results demonstrate that the vast diversity of the soil microbiota makes soil ecosystem functioning largely insensitive to biodiversity erosion even for functions performed by specialized groups.

Reassessment of soil erosion on the Chinese loess plateau: were rates overestimated?

NASA Astrophysics Data System (ADS)

Zhao, Jianlin; Govers, Gerard

2014-05-01

Several studies have estimated regional soil erosion rates (rill and interrill erosion) on the Chinese loess plateau using an erosion model such as the RUSLE (e.g. Fu et al., 2011; Sun et al., 2013). However, the question may be asked whether such estimates are realistic: studies have shown that the use of models for large areas may lead to significant overestimations (Quinton et al., 2010). In this study, soil erosion rates on the Chinese loess plateau were reevaluated by using field measured soil erosion data from erosion plots (216 plots and 1380 plot years) in combination with a careful extrapolation procedure. Data analysis showed that the relationship between slope and erosion rate on arable land could be well described by erosion-slope relationships reported in the literature (Nearing, 1997). The increase of average erosion rate with slope length was clearly degressive, as could be expected from earlier research. However, for plots with permanent vegetation (grassland, shrub, forest) no relationship was found between erosion rates and slope gradient and/or slope length. This is important, as it implies that spatial variations of erosion on permanently vegetated areas cannot be modeled using topographical functions derived from observations on arable land. Application of relationships developed for arable land will lead to a significant overestimation of soil erosion rates. Based on our analysis we estimate the total soil erosion rate in the Chinese Loess plateau averages ca. 6.78 t ha-1 yr-1 for the whole loess plateau, resulting in a total sediment mobilisation of ca. 0.38 Gt yr-1. Erosion rates on arable land average ca. 15.10 t ha-1 yr-1. These estimates are 2 to 3 times lower than previously published estimates. The main reason why previous estimates are likely to be too high is that the values of (R)USLE parameters such as K, P and LS factor were overestimated. Overestimations of the K factor are due to the reliance of nomograph calculations, resulting in significantly higher erodibility values than those obtained from field data. Overestimations of the P and LS factors are mainly due to the fact that erosion control measures such as terracing are not accounted for and that erroneous scaling functions are used on permanently vegetated areas. Our findings have not only important implications with respect to the mobilization of sediments by agricultural erosion: we will also need to reassess the impact of erosion on biogeochemicaly cycling and crop productivity. Fu, B., Liu, Y., Lü, Y., He, C., Zeng, Y., & Wu, B. (2011). Assessing the soil erosion control service of ecosystems change in the Loess Plateau of China. Ecological Complexity, 8(4), 284-293. doi:10.1016/j.ecocom.2011.07.003 Nearing, M. A. (1997). A single, continuous function for slope steepness influence on soil loss. Soil Science Society of American Journal, 61(3), 917-919. Quinton, J. N., Govers, G., Van Oost, K., & Bardgett, R. D. (2010). The impact of agricultural soil erosion on biogeochemical cycling. Nature Geoscience, 3(5), 311-314. doi:10.1038/ngeo838 Sun, W., Shao, Q., & Liu, J. (2013). Soil erosion and its response to the changes of precipitation and vegetation cover on the Loess Plateau. Journal of Geographical Sciences, 23(6), 1091-1106. doi:10.1007/s11442-013-1065-z

Probabilistic inference of ecohydrological parameters using observations from point to satellite scales

NASA Astrophysics Data System (ADS)

Bassiouni, Maoya; Higgins, Chad W.; Still, Christopher J.; Good, Stephen P.

2018-06-01

Vegetation controls on soil moisture dynamics are challenging to measure and translate into scale- and site-specific ecohydrological parameters for simple soil water balance models. We hypothesize that empirical probability density functions (pdfs) of relative soil moisture or soil saturation encode sufficient information to determine these ecohydrological parameters. Further, these parameters can be estimated through inverse modeling of the analytical equation for soil saturation pdfs, derived from the commonly used stochastic soil water balance framework. We developed a generalizable Bayesian inference framework to estimate ecohydrological parameters consistent with empirical soil saturation pdfs derived from observations at point, footprint, and satellite scales. We applied the inference method to four sites with different land cover and climate assuming (i) an annual rainfall pattern and (ii) a wet season rainfall pattern with a dry season of negligible rainfall. The Nash-Sutcliffe efficiencies of the analytical model's fit to soil observations ranged from 0.89 to 0.99. The coefficient of variation of posterior parameter distributions ranged from < 1 to 15 %. The parameter identifiability was not significantly improved in the more complex seasonal model; however, small differences in parameter values indicate that the annual model may have absorbed dry season dynamics. Parameter estimates were most constrained for scales and locations at which soil water dynamics are more sensitive to the fitted ecohydrological parameters of interest. In these cases, model inversion converged more slowly but ultimately provided better goodness of fit and lower uncertainty. Results were robust using as few as 100 daily observations randomly sampled from the full records, demonstrating the advantage of analyzing soil saturation pdfs instead of time series to estimate ecohydrological parameters from sparse records. Our work combines modeling and empirical approaches in ecohydrology and provides a simple framework to obtain scale- and site-specific analytical descriptions of soil moisture dynamics consistent with soil moisture observations.

Modification of the USLE K factor for soil erodibility assessment on calcareous soils in Iran

NASA Astrophysics Data System (ADS)

Ostovari, Yaser; Ghorbani-Dashtaki, Shoja; Bahrami, Hossein-Ali; Naderi, Mehdi; Dematte, Jose Alexandre M.; Kerry, Ruth

2016-11-01

The measurement of soil erodibility (K) in the field is tedious, time-consuming and expensive; therefore, its prediction through pedotransfer functions (PTFs) could be far less costly and time-consuming. The aim of this study was to develop new PTFs to estimate the K factor using multiple linear regression, Mamdani fuzzy inference systems, and artificial neural networks. For this purpose, K was measured in 40 erosion plots with natural rainfall. Various soil properties including the soil particle size distribution, calcium carbonate equivalent, organic matter, permeability, and wet-aggregate stability were measured. The results showed that the mean measured K was 0.014 t h MJ- 1 mm- 1 and 2.08 times less than the estimated mean K (0.030 t h MJ- 1 mm- 1) using the USLE model. Permeability, wet-aggregate stability, very fine sand, and calcium carbonate were selected as independent variables by forward stepwise regression in order to assess the ability of multiple linear regression, Mamdani fuzzy inference systems and artificial neural networks to predict K. The calcium carbonate equivalent, which is not accounted for in the USLE model, had a significant impact on K in multiple linear regression due to its strong influence on the stability of aggregates and soil permeability. Statistical indices in validation and calibration datasets determined that the artificial neural networks method with the highest R2, lowest RMSE, and lowest ME was the best model for estimating the K factor. A strong correlation (R2 = 0.81, n = 40, p < 0.05) between the estimated K from multiple linear regression and measured K indicates that the use of calcium carbonate equivalent as a predictor variable gives a better estimation of K in areas with calcareous soils.

A cost-efficient method to assess carbon stocks in tropical peat soil

NASA Astrophysics Data System (ADS)

Warren, M. W.; Kauffman, J. B.; Murdiyarso, D.; Anshari, G.; Hergoualc'h, K.; Kurnianto, S.; Purbopuspito, J.; Gusmayanti, E.; Afifudin, M.; Rahajoe, J.; Alhamd, L.; Limin, S.; Iswandi, A.

2012-11-01

Estimation of belowground carbon stocks in tropical wetland forests requires funding for laboratory analyses and suitable facilities, which are often lacking in developing nations where most tropical wetlands are found. It is therefore beneficial to develop simple analytical tools to assist belowground carbon estimation where financial and technical limitations are common. Here we use published and original data to describe soil carbon density (kgC m-3; Cd) as a function of bulk density (gC cm-3; Bd), which can be used to rapidly estimate belowground carbon storage using Bd measurements only. Predicted carbon densities and stocks are compared with those obtained from direct carbon analysis for ten peat swamp forest stands in three national parks of Indonesia. Analysis of soil carbon density and bulk density from the literature indicated a strong linear relationship (Cd = Bd × 495.14 + 5.41, R2 = 0.93, n = 151) for soils with organic C content > 40%. As organic C content decreases, the relationship between Cd and Bd becomes less predictable as soil texture becomes an important determinant of Cd. The equation predicted belowground C stocks to within 0.92% to 9.57% of observed values. Average bulk density of collected peat samples was 0.127 g cm-3, which is in the upper range of previous reports for Southeast Asian peatlands. When original data were included, the revised equation Cd = Bd × 468.76 + 5.82, with R2 = 0.95 and n = 712, was slightly below the lower 95% confidence interval of the original equation, and tended to decrease Cd estimates. We recommend this last equation for a rapid estimation of soil C stocks for well-developed peat soils where C content > 40%.

Convergence of microbial assimilations of soil carbon, nitrogen, phosphorus, and sulfur in terrestrial ecosystems

DOE PAGES

Xu, Xiaofeng; Hui, Dafeng; King, Anthony Wayne; ...

2015-11-27

How soil microbes assimilate carbon-C, nitrogen-N, phosphorus-P, and sulfur-S is fundamental for understanding nutrient cycling in terrestrial ecosystems. We compiled a global database of C, N, P, and S concentrations in soils and microbes and developed relationships between them by using a power function model. The C:N:P:S was estimated to be 287:17:1:0.8 for soils, and 42:6:1:0.4 for microbes. We found a convergence of the relationships between elements in soils and in soil microbial biomass across C, N, P, and S. The element concentrations in soil microbial biomass follow a homeostatic regulation curve with soil element concentrations across C, N, Pmore » and S, implying a unifying mechanism of microbial assimilating soil elements. This correlation explains the well-constrained C:N:P:S stoichiometry with a slightly larger variation in soils than in microbial biomass. Meanwhile, it is estimated that the minimum requirements of soil elements for soil microbes are 0.8 mmol C Kg –1 dry soil, 0.1 mmol N Kg –1 dry soil, 0.1 mmol P Kg –1 dry soil, and 0.1 mmol S Kg –1 dry soil, respectively. Lastly, these findings provide a mathematical explanation of element imbalance in soils and soil microbial biomass, and offer insights for incorporating microbial contribution to nutrient cycling into Earth system models.« less

Soil Temperature and Moisture Profile (STAMP) System Handbook

DOE Office of Scientific and Technical Information (OSTI.GOV)

Cook, David R.

The soil temperature and moisture profile system (STAMP) provides vertical profiles of soil temperature, soil water content (soil-type specific and loam type), plant water availability, soil conductivity, and real dielectric permittivity as a function of depth below the ground surface at half-hourly intervals, and precipitation at one-minute intervals. The profiles are measured directly by in situ probes at all extended facilities of the SGP climate research site. The profiles are derived from measurements of soil energy conductivity. Atmospheric scientists use the data in climate models to determine boundary conditions and to estimate the surface energy flux. The data are alsomore » useful to hydrologists, soil scientists, and agricultural scientists for determining the state of the soil. The STAMP system replaced the SWATS system in early 2016.« less

Using Remote Sensing Data to Evaluate Surface Soil Properties in Alabama Ultisols

NASA Technical Reports Server (NTRS)

Sullivan, Dana G.; Shaw, Joey N.; Rickman, Doug; Mask, Paul L.; Luvall, Jeff

2005-01-01

Evaluation of surface soil properties via remote sensing could facilitate soil survey mapping, erosion prediction and allocation of agrochemicals for precision management. The objective of this study was to evaluate the relationship between soil spectral signature and surface soil properties in conventionally managed row crop systems. High-resolution RS data were acquired over bare fields in the Coastal Plain, Appalachian Plateau, and Ridge and Valley provinces of Alabama using the Airborne Terrestrial Applications Sensor multispectral scanner. Soils ranged from sandy Kandiudults to fine textured Rhodudults. Surface soil samples (0-1 cm) were collected from 163 sampling points for soil organic carbon, particle size distribution, and citrate dithionite extractable iron content. Surface roughness, soil water content, and crusting were also measured during sampling. Two methods of analysis were evaluated: 1) multiple linear regression using common spectral band ratios, and 2) partial least squares regression. Our data show that thermal infrared spectra are highly, linearly related to soil organic carbon, sand and clay content. Soil organic carbon content was the most difficult to quantify in these highly weathered systems, where soil organic carbon was generally less than 1.2%. Estimates of sand and clay content were best using partial least squares regression at the Valley site, explaining 42-59% of the variability. In the Coastal Plain, sandy surfaces prone to crusting limited estimates of sand and clay content via partial least squares and regression with common band ratios. Estimates of iron oxide content were a function of mineralogy and best accomplished using specific band ratios, with regression explaining 36-65% of the variability at the Valley and Coastal Plain sites, respectively.

Using plot experiments to test the validity of mass balance models employed to estimate soil redistribution rates from 137Cs and 210Pb(ex) measurements.

PubMed

Porto, Paolo; Walling, Des E

2012-10-01

Information on rates of soil loss from agricultural land is a key requirement for assessing both on-site soil degradation and potential off-site sediment problems. Many models and prediction procedures have been developed to estimate rates of soil loss and soil redistribution as a function of the local topography, hydrometeorology, soil type and land management, but empirical data remain essential for validating and calibrating such models and prediction procedures. Direct measurements using erosion plots are, however, costly and the results obtained relate to a small enclosed area, which may not be representative of the wider landscape. In recent years, the use of fallout radionuclides and more particularly caesium-137 ((137)Cs) and excess lead-210 ((210)Pb(ex)) has been shown to provide a very effective means of documenting rates of soil loss and soil and sediment redistribution in the landscape. Several of the assumptions associated with the theoretical conversion models used with such measurements remain essentially unvalidated. This contribution describes the results of a measurement programme involving five experimental plots located in southern Italy, aimed at validating several of the basic assumptions commonly associated with the use of mass balance models for estimating rates of soil redistribution on cultivated land from (137)Cs and (210)Pb(ex) measurements. Overall, the results confirm the general validity of these assumptions and the importance of taking account of the fate of fresh fallout. However, further work is required to validate the conversion models employed in using fallout radionuclide measurements to document soil redistribution in the landscape and this could usefully direct attention to different environments and to the validation of the final estimates of soil redistribution rate as well as the assumptions of the models employed. Copyright © 2012 Elsevier Ltd. All rights reserved.

Estimating mercury emissions resulting from wildfire in forests of the Western United States

USGS Publications Warehouse

Webster, Jackson; Kane, Tyler J.; Obrist, Daniel; Ryan, Joseph N.; Aiken, George R.

2016-01-01

Understanding the emissions of mercury (Hg) from wildfires is important for quantifying the global atmospheric Hg sources. Emissions of Hg from soils resulting from wildfires in the Western United States was estimated for the 2000 to 2013 period, and the potential emission of Hg from forest soils was assessed as a function of forest type and soil-heating. Wildfire released an annual average of 3100 ± 1900 kg-Hg y− 1 for the years spanning 2000–2013 in the 11 states within the study area. This estimate is nearly 5-fold lower than previous estimates for the study region. Lower emission estimates are attributed to an inclusion of fire severity within burn perimeters. Within reported wildfire perimeters, the average distribution of low, moderate, and high severity burns was 52, 29, and 19% of the total area, respectively. Review of literature data suggests that that low severity burning does not result in soil heating, moderate severity fire results in shallow soil heating, and high severity fire results in relatively deep soil heating (< 5 cm). Using this approach, emission factors for high severity burns ranged from 58 to 640 μg-Hg kg-fuel− 1. In contrast, low severity burns have emission factors that are estimated to be only 18–34 μg-Hg kg-fuel− 1. In this estimate, wildfire is predicted to release 1–30 g Hg ha− 1 from Western United States forest soils while above ground fuels are projected to contribute an additional 0.9 to 7.8 g Hg ha− 1. Land cover types with low biomass (desert scrub) are projected to release less than 1 g Hg ha− 1. Following soil sources, fuel source contributions to total Hg emissions generally followed the order of duff > wood > foliage > litter > branches.

Retrieving topsoil moisture using RADARSAT-2 data, a novel approach applied at the east of the Netherlands

NASA Astrophysics Data System (ADS)

Eweys, Omar Ali; Elwan, Abeer A.; Borham, Taha I.

2017-12-01

This manuscript proposes an approach for estimating soil moisture content over corn fields using C-band SAR data acquired by RADARSAT-2 satellite. An image based approach is employed to remove the vegetation contribution to the satellite signals. In particular, the absolute difference between like and cross polarized signals (ADLC) is employed for segmenting the canopy growth cycle into tiny stages. Each stage is represented by a Cumulative Distribution Function (CDF) of the like polarized signals. For periods of bare soils and vegetation cover, CDFs are compared and the vegetation contribution is quantified. The portion which represent the soil contributions (σHHsoil°) to the satellite signals; are employed for inversely running Oh model and the water cloud model for estimating soil moisture, canopy water content and canopy height respectively. The proposed approach shows satisfactory performance where high correlation of determination (R2) is detected between the field observations and the corresponding retrieved soil moisture, canopy water content and canopy height (R2 = 0.64, 0.97 and 0.98 respectively). Soil moisture retrieval is associated with root mean square error (RMSE) of 0.03 m3 m-3 while estimating canopy water content and canopy height have RMSE of 0.38 kg m-2 and 0.166 m respectively.

A simplified, data-constrained approach to estimate the permafrost carbon-climate feedback: The PCN Incubation-Panarctic Thermal (PInc-PanTher) Scaling Approach

NASA Astrophysics Data System (ADS)

Koven, C. D.; Schuur, E.; Schaedel, C.; Bohn, T. J.; Burke, E.; Chen, G.; Chen, X.; Ciais, P.; Grosse, G.; Harden, J. W.; Hayes, D. J.; Hugelius, G.; Jafarov, E. E.; Krinner, G.; Kuhry, P.; Lawrence, D. M.; MacDougall, A.; Marchenko, S. S.; McGuire, A. D.; Natali, S.; Nicolsky, D.; Olefeldt, D.; Peng, S.; Romanovsky, V. E.; Schaefer, K. M.; Strauss, J.; Treat, C. C.; Turetsky, M. R.

2015-12-01

We present an approach to estimate the feedback from large-scale thawing of permafrost soils using a simplified, data-constrained model that combines three elements: soil carbon (C) maps and profiles to identify the distribution and type of C in permafrost soils; incubation experiments to quantify the rates of C lost after thaw; and models of soil thermal dynamics in response to climate warming. We call the approach the Permafrost Carbon Network Incubation-Panarctic Thermal scaling approach (PInc-PanTher). The approach assumes that C stocks do not decompose at all when frozen, but once thawed follow set decomposition trajectories as a function of soil temperature. The trajectories are determined according to a 3-pool decomposition model fitted to incubation data using parameters specific to soil horizon types. We calculate litterfall C inputs required to maintain steady-state C balance for the current climate, and hold those inputs constant. Soil temperatures are taken from the soil thermal modules of ecosystem model simulations forced by a common set of future climate change anomalies under two warming scenarios over the period 2010 to 2100.

Biological Oxygen Demand in Soils and Litters

NASA Astrophysics Data System (ADS)

Smagin, A. V.; Smagina, M. V.; Sadovnikova, N. B.

2018-03-01

Biological oxygen demand (BOD) in mineral and organic horizons of soddy-podzolic soils in the forest-park belt of Moscow as an indicator of their microbial respiration and potential biodestruction function has been studied. The BOD of soil samples has been estimated with a portable electrochemical analyzer after incubation in closed flasks under optimum hydrothermal conditions. A universal gradation scale of this parameter from very low (<2 g O2/(m3 h)) to extremely high (>140 g O2/(m3 h)) has been proposed for mineral and organic horizons of soil. A physically substantiated model has been developed for the vertical distribution of BOD in the soil, which combines the diffusion transport of oxygen from the atmosphere and its biogenic uptake in the soil by the first-order reaction. An analytical solution of the model in the stationary state has been obtained; from it, the soil oxygen diffusivity and the kinetic constants of O2 uptake have been estimated, and the profile-integrated total BOD value has been calculated (0.4-1.8 g O2/(m2 h)), which is theoretically identical to the potential oxygen flux from the soil surface due to soil respiration. All model parameters reflect the recreation load on the soil cover by the decrease in their values against the control.

Soil Water and Temperature System (SWATS) Instrument Handbook

DOE Office of Scientific and Technical Information (OSTI.GOV)

Cook, David R.

2016-04-01

The soil water and temperature system (SWATS) provides vertical profiles of soil temperature, soil-water potential, and soil moisture as a function of depth below the ground surface at hourly intervals. The temperature profiles are measured directly by in situ sensors at the Central Facility and many of the extended facilities of the U.S. Department of Energy (DOE)’s Atmospheric Radiation Measurement (ARM) Climate Research Facility Southern Great Plains (SGP) site. The soil-water potential and soil moisture profiles are derived from measurements of soil temperature rise in response to small inputs of heat. Atmospheric scientists use the data in climate models tomore » determine boundary conditions and to estimate the surface energy flux. The data are also useful to hydrologists, soil scientists, and agricultural scientists for determining the state of the soil.« less

Soil process-oriented modelling of within-field variability based on high-resolution 3D soil type distribution maps.

NASA Astrophysics Data System (ADS)

Bönecke, Eric; Lück, Erika; Gründling, Ralf; Rühlmann, Jörg; Franko, Uwe

2016-04-01

Today, the knowledge of within-field variability is essential for numerous purposes, including practical issues, such as precision and sustainable soil management. Therefore, process-oriented soil models have been applied for a considerable time to answer question of spatial soil nutrient and water dynamics, although, they can only be as consistent as their variation and resolution of soil input data. Traditional approaches, describe distribution of soil types, soil texture or other soil properties for greater soil units through generalised point information, e.g. from classical soil survey maps. Those simplifications are known to be afflicted with large uncertainties. Varying soil, crop or yield conditions are detected even within such homogenised soil units. However, recent advances of non-invasive soil survey and on-the-go monitoring techniques, made it possible to obtain vertical and horizontal dense information (3D) about various soil properties, particularly soil texture distribution which serves as an essential soil key variable affecting various other soil properties. Thus, in this study we based our simulations on detailed 3D soil type distribution (STD) maps (4x4 m) to adjacently built-up sufficient informative soil profiles including various soil physical and chemical properties. Our estimates of spatial STD are based on high-resolution lateral and vertical changes of electrical resistivity (ER), detected by a relatively new multi-sensor on-the-go ER monitoring device. We performed an algorithm including fuzzy-c-mean (FCM) logic and traditional soil classification to estimate STD from those inverted and layer-wise available ER data. STD is then used as key input parameter for our carbon, nitrogen and water transport model. We identified Pedological horizon depths and inferred hydrological soil variables (field capacity, permanent wilting point) from pedotransferfunctions (PTF) for each horizon. Furthermore, the spatial distribution of soil organic carbon (SOC), as essential input variable, was predicted by measured soil samples and associated to STD of the upper 30 cm. The comprehensive and high-resolution (4x4 m) soil profile information (up to 2 m soil depth) were then used to initialise a soil process model (Carbon and Nitrogen Dynamics - CANDY) for soil functional modelling (daily steps of matter fluxes, soil temperature and water balances). Our study was conducted on a practical field (~32,000 m²) of an agricultural farm in Central Germany with Chernozem soils under arid conditions (average rainfall < 550 mm). This soil region is known to have differences in soil structure mainly occurring within the subsoil, since topsoil conditions are described as homogenous. The modelled soil functions considered local climate information and practical farming activities. Results show, as expected, distinguished functional variability, both on spatial and temporal resolution for subsoil evident structures, e.g. visible differences for available water capacity within 0-100 cm but homogenous conditions for the topsoil.

An empirical model for the complex dielectric permittivity of soils as a function of water content

NASA Technical Reports Server (NTRS)

Wang, J. R.; Chmugge, T. J.

1978-01-01

The recent measurements on the dielectric properties of soils shows that the variation of dielectric constant with moisture content depends on soil types. The observed dielectric constant increases only slowly with moisture content up to a transition point. Beyond the transition it increases rapidly with moisture content. The moisture value of transition region was found to be higher for high clay content soils than for sandy soils. Many mixing formulas were compared with, and were found incompatible with, the measured dielectric variations of soil-water mixtures. A simple empirical model was proposed to describe the dielectric behavior of ths soil-water mixtures. The relationship between transition moisture and wilting point provides a means of estimating soil dielectric properties on the basis of texture information.

Estimation of Land Surface Fluxes and Their Uncertainty via Variational Data Assimilation Approach

NASA Astrophysics Data System (ADS)

Abdolghafoorian, A.; Farhadi, L.

2016-12-01

Accurate estimation of land surface heat and moisture fluxes as well as root zone soil moisture is crucial in various hydrological, meteorological, and agricultural applications. "In situ" measurements of these fluxes are costly and cannot be readily scaled to large areas relevant to weather and climate studies. Therefore, there is a need for techniques to make quantitative estimates of heat and moisture fluxes using land surface state variables. In this work, we applied a novel approach based on the variational data assimilation (VDA) methodology to estimate land surface fluxes and soil moisture profile from the land surface states. This study accounts for the strong linkage between terrestrial water and energy cycles by coupling the dual source energy balance equation with the water balance equation through the mass flux of evapotranspiration (ET). Heat diffusion and moisture diffusion into the column of soil are adjoined to the cost function as constraints. This coupling results in more accurate prediction of land surface heat and moisture fluxes and consequently soil moisture at multiple depths with high temporal frequency as required in many hydrological, environmental and agricultural applications. One of the key limitations of VDA technique is its tendency to be ill-posed, meaning that a continuum of possibilities exists for different parameters that produce essentially identical measurement-model misfit errors. On the other hand, the value of heat and moisture flux estimation to decision-making processes is limited if reasonable estimates of the corresponding uncertainty are not provided. In order to address these issues, in this research uncertainty analysis will be performed to estimate the uncertainty of retrieved fluxes and root zone soil moisture. The assimilation algorithm is tested with a series of experiments using a synthetic data set generated by the simultaneous heat and water (SHAW) model. We demonstrate the VDA performance by comparing the (synthetic) true measurements (including profile of soil moisture and temperature, land surface water and heat fluxes, and root water uptake) with VDA estimates. In addition, the feasibility of extending the proposed approach to use remote sensing observations is tested by limiting the number of LST observations and soil moisture observations.

[Fungal biomass estimation in soils from southwestern Buenos Aires province (Argentina) using calcofluor white stain].

PubMed

Vázquez, María B; Amodeo, Martín R; Bianchinotti, María V

Soil microorganisms are vital for ecosystem functioning because of the role they play in soil nutrient cycling. Agricultural practices and the intensification of land use have a negative effect on microbial activities and fungal biomass has been widely used as an indicator of soil health. The aim of this study was to analyze fungal biomass in soils from southwestern Buenos Aires province using direct fluorescent staining and to contribute to its use as an indicator of environmental changes in the ecosystem as well as to define its sensitivity to weather conditions. Soil samples were collected during two consecutive years. Soil smears were prepared and stained with two different concentrations of calcofluor, and the fungal biomass was estimated under an epifluorescence microscope. Soil fungal biomass varied between 2.23 and 26.89μg fungal C/g soil, being these values in the range expected for the studied soil type. The fungal biomass was positively related to temperature and precipitations. The methodology used was reliable, standardized and sensitive to weather conditions. The results of this study contribute information to evaluate fungal biomass in different soil types and support its use as an indicator of soil health for analyzing the impact of different agricultural practices. Copyright © 2016 Asociación Argentina de Microbiología. Publicado por Elsevier España, S.L.U. All rights reserved.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Nisbet, A.F.; Woodman, R.F.M.

A database of soil-to-plant transfer factors for radiocesium and radiostrontium has been compiled for arable crops from published and unpublished sources. The database is more extensive than previous compilations of data published by the International Union of Radioecologists, containing new information for Scandinavia and Greece in particular. It also contains ancillary data on important soil characteristics. The database is sub-divided into 28 soil-crop combinations, covering four soil types and seven crop groups. Statistical analyses showed that transfer factors for radiocesium could not generally be predicted as a function of climatic region, type of experiment, age of contamination, or silt characteristics.more » However, significant relationships accounting for more than 30% of the variability in transfer factor were identified between transfer factors for radiostrontium and soil pH/organic matter status for a few soil-crop combinations. Best estimate transfer factors for radiocesium and radiostrontium were calculated for 28 soil-crop combinations, based on their geometric means: only the edible parts were considered. To predict the likely value of future individual transfer factors, 95% confidence intervals were also derived. A comparison of best estimate transfer factors derived in this study with recommended values published by the International Union of Radioecologists in 1989 and 1992 was made for comparable soil-crop groupings. While there were no significant differences between the best estimate values derived in this study and the 1992 data, radiological assessments that still use 1989 data may be unnecessarily cautious.« less

Understanding the Day Cent model: Calibration, sensitivity, and identifiability through inverse modeling

USGS Publications Warehouse

Necpálová, Magdalena; Anex, Robert P.; Fienen, Michael N.; Del Grosso, Stephen J.; Castellano, Michael J.; Sawyer, John E.; Iqbal, Javed; Pantoja, Jose L.; Barker, Daniel W.

2015-01-01

The ability of biogeochemical ecosystem models to represent agro-ecosystems depends on their correct integration with field observations. We report simultaneous calibration of 67 DayCent model parameters using multiple observation types through inverse modeling using the PEST parameter estimation software. Parameter estimation reduced the total sum of weighted squared residuals by 56% and improved model fit to crop productivity, soil carbon, volumetric soil water content, soil temperature, N2O, and soil3NO− compared to the default simulation. Inverse modeling substantially reduced predictive model error relative to the default model for all model predictions, except for soil 3NO− and 4NH+. Post-processing analyses provided insights into parameter–observation relationships based on parameter correlations, sensitivity and identifiability. Inverse modeling tools are shown to be a powerful way to systematize and accelerate the process of biogeochemical model interrogation, improving our understanding of model function and the underlying ecosystem biogeochemical processes that they represent.

Understanding the effect of watershed characteristic on the runoff using SCS curve number

NASA Astrophysics Data System (ADS)

Damayanti, Frieta; Schneider, Karl

2015-04-01

Runoff modeling is a key component in watershed management. The temporal course and amount of runoff is a complex function of a multitude of parameters such as climate, soil, topography, land use, and water management. Against the background of the current rapid environmental change, which is due to both i) man-made changes (e.g. urban development, land use change, water management) as well as ii) changes in the natural systems (e.g. climate change), understanding and predicting the impacts of these changes upon the runoff is very important and affects the wellbeing of many people living in the watershed. A main tool for predictions is hydrologic models. Particularly process based models are the method of choice to assess the impact of land use and climate change. However, many regions which experience large changes in the watersheds can be described as rather data poor, which limits the applicability of such models. This is particularly also true for the Telomoyo Watershed (545 km2) which is located in southern part of Central Java province. The average annual rainfall of the study area reaches 2971 mm. Irrigated paddy field are the dominating land use (35%), followed by built-up area and dry land agriculture. The only available soil map is the FAO soil digital map of the world, which provides rather general soil information. A field survey accompanied by a lab analysis 65 soil samples of was carried out to provide more detailed soil texture information. The soil texture map is a key input in the SCS method to define hydrological soil groups. In the frame of our study on 'Integrated Analysis on Flood Risk of Telomoyo Watershed in Response to the Climate and Land Use Change' funded by the German Academic Exchange service (DAAD) we analyzed the sensitivity of the modeled runoff upon the choice of the method to estimate the CN values using the SCS-CN method. The goal of this study is to analyze the impact of different data sources on the curve numbers and the estimated runoff. CN values were estimated using the field measurements of soil textures for different combinations of land use and topography. To transfer the local soil texture measurements to the watershed domain a statistical analysis using the frequency distribution of the measured soil textures is applied and used to derive the effective CN value for a given land use, topography and soil texture combination. Since the curve numbers change as a function of parameter combinations, the effect of different methods to estimate the curve number upon the runoff is analyzed and compared to the straight forward method of using the data from the FAO soil map.

Comparison of different models for predicting soil bulk density. Case study - Slovakian agricultural soils

NASA Astrophysics Data System (ADS)

Makovníková, Jarmila; Širáň, Miloš; Houšková, Beata; Pálka, Boris; Jones, Arwyn

2017-10-01

Soil bulk density is one of the main direct indicators of soil health, and is an important aspect of models for determining agroecosystem services potential. By way of applying multi-regression methods, we have created a distributed prediction of soil bulk density used subsequently for topsoil carbon stock estimation. The soil data used for this study were from the Slovakian partial monitoring system-soil database. In our work, two models of soil bulk density in an equilibrium state, with different combinations of input parameters (soil particle size distribution and soil organic carbon content in %), have been created, and subsequently validated using a data set from 15 principal sampling sites of Slovakian partial monitoring system-soil, that were different from those used to generate the bulk density equations. We have made a comparison of measured bulk density data and data calculated by the pedotransfer equations against soil bulk density calculated according to equations recommended by Joint Research Centre Sustainable Resources for Europe. The differences between measured soil bulk density and the model values vary from -0.144 to 0.135 g cm-3 in the verification data set. Furthermore, all models based on pedotransfer functions give moderately lower values. The soil bulk density model was then applied to generate a first approximation of soil bulk density map for Slovakia using texture information from 17 523 sampling sites, and was subsequently utilised for topsoil organic carbon estimation.

Alterations of hydraulic soil properties influenced by land-use changes and agricultural management systems

NASA Astrophysics Data System (ADS)

Weninger, Thomas; Kreiselmeier, Janis; Chandrasekhar, Parvathy; Jülich, Stefan; Schwärzel, Kai; Schwen, Andreas

2016-04-01

Estimation and modeling of soil water movement and the hydrologic balance of soils requires sound knowledge about hydraulic soil properties (HSP). The soil water characteristics, the hydraulic conductivity function and the pore size distribution (PSD) are commonly used instruments for the mathematical representation of HSP. Recent research highlighted the temporal variability of these functions caused by meteorological or land-use influences. State of the art modeling software for the continuous simulation of soil water movement uses a stationary approach for the HSP which means that their time dependent alterations and the subsequent effects on soil water balance is not considered. Mathematical approaches to describe the evolution of PSD are nevertheless known, but there is a lack of sound data basis for parameter estimation. Based on extensive field and laboratory measurements at 5 locations along a climatic gradient across Austria and Germany, this study will quantify short-term changes in HSP, detect driving forces and introduce a method to predict the effects of soil and land management actions on the soil water balance. Amongst several soil properties, field-saturated and unsaturated hydraulic conductivities will be determined using a hood infiltration experiments in the field as well as by evaporation and dewpoint potentiometer method in the lab. All measurements will be carried out multiple times over a span of 2 years which will allow a detailed monitoring of changes in HSP. Experimental sites where we expect significant inter-seasonal changes will be equipped with sensors for soil moisture and matric potential. The choice of experimental field sites follows the intention to involve especially the effects of tillage operations, different cultivation strategies, microclimatically effective structures and land-use changes. The international project enables the coverage of a broad range of soil types as well as climate conditions and hence will have broad applicability of the implemented model modifications.

Polymer tensiometer with ceramic cones: a case study for a Brazilian soil.

NASA Astrophysics Data System (ADS)

Durigon, A.; de Jong van Lier, Q.; van der Ploeg, M. J.; Gooren, H. P. A.; Metselaar, K.; de Rooij, G. H.

2009-04-01

Laboratory outflow experiments, in combination with inverse modeling techniques, allow to simultaneously determine retention and hydraulic conductivity functions. A numerical model solves the pressure-head-based form of the Richards' equation for unsaturated flow in a rigid porous medium. Applying adequate boundary conditions, the cumulative outflow is calculated at prescribed times, and as a function of the set of optimized parameters. These parameters are evaluated by nonlinear least-squares fitting of predicted to observed cumulative outflow with time. An objective function quantifies this difference between calculated and observed cumulative outflow and between predicted and measured soil water retention data. Using outflow data only in the objective function, the multistep outflow method results in unique estimates of the retention and hydraulic conductivity functions. To obtain more reliable estimates of the hydraulic conductivity as a function of the water content using the inverse method, the outflow data must be supplemented with soil retention data. To do so tensiometers filled with a polymer solution instead of water were used. The measurement range of these tensiometers is larger than that of the conventional tensiometers, being able to measure the entire pressure head range over which crops take up water, down to values in the order of -1.6 MPa. The objective of this study was to physically characterize a Brazilian red-yellow oxisol using measurements in outflow experiments by polymer tensiometers and processing these data with the inverse modeling technique for use in the analysis of a field experiment and in modeling. The soil was collected at an experimental site located in Piracicaba, Brazil, 22° 42 S, 47° 38 W, 550 m above sea level.

Experimental techniques and computational methods toward the estimation of the effective two-phase flow coefficients and multi-scale heterogeneities of soils

NASA Astrophysics Data System (ADS)

Tsakiroglou, C. D.; Aggelopoulos, C. A.; Sygouni, V.

2009-04-01

A hierarchical, network-type, dynamic simulator of the immiscible displacement of water by oil in heterogeneous porous media is developed to simulate the rate-controlled displacement of two fluids at the soil column scale. A cubic network is constructed, where each node is assigned a permeability which is chosen randomly from a distribution function. The intensity of heterogeneities is quantified by the width of the permeability distribution function. The capillary pressure at each node is calculated by combining a generalized Leverett J-function with a Corey type model. Information about the heterogeneity of soils at the pore network scale is obtained by combining mercury intrusion porosimetry (MIP) data with back-scattered scanning electron microscope (BSEM) images [1]. In order to estimate the two-phase flow properties of nodes (relative permeability and capillary pressure functions, permeability distribution function) immiscible and miscible displacement experiments are performed on undisturbed soil columns. The transient responses of measured variables (pressure drop, fluid saturation averaged over five successive segments, solute concentration averaged over three cross-sections) are fitted with models accounting for the preferential flow paths at the micro- (multi-region model) and macro-scale (multi flowpath model) because of multi-scale heterogeneities [2,3]. Simulating the immiscible displacement of water by oil (drainage) in a large netork, at each time step, the fluid saturation and pressure of each node are calculated formulating mass balances at each node, accounting for capillary, viscous and gravity forces, and solving the system of coupled equations. At each iteration of the algorithm, the pressure drop is so selected that the total flow rate of the injected fluid is kept constant. The dynamic large-scale network simulator is used (1) to examine the sensitivity of the transient responses of the axial distribution of fluid saturation and total pressure drop across the network to the permeability distribution function, spatial correlations of permeability, and capillary number, and (2) to estimate the effective (up-scaled) relative permeability functions at the soil column scale. In an attempt to clarify potential effects of the permeability distribution and spatial permeability correlations on the transient responses of the pressure drop across a soil column, signal analysis with wavelets is performed [4] on experimental and simulated results. The transient variation of signal energy and frequency of pressure drop fluctuations at the wavelet domain are correlated with macroscopic properties such as the effective water and oil relative permeabilities of the porous medium, and microscopic properties such as the variation of the permeability distribution of oil-occupied nodes. Toward the solution of the inverse problem, a general procedure is suggested to identify macro-heterogeneities from the fast analysis of pressure drop signals. References 1. Tsakiroglou, C.D. and M.A. Ioannidis, "Dual porosity modeling of the pore structure and transport properties of a contaminated soil", Eur. J. Soil Sci., 59, 744-761 (2008). 2. Aggelopoulos, C.A., and C.D. Tsakiroglou, "Quantifying the Soil Heterogeneity from Solute Dispersion Experiments", Geoderma, 146, 412-424 (2008). 3. Aggelopoulos, C.A., and C.D. Tsakiroglou, "A multi-flow path approach to model immiscible displacement in undisturbed heterogeneous soil columns", J. Contam. Hydrol., in press (2009). 4. Sygouni, V., C.D. Tsakiroglou, and A.C. Payatakes, "Using wavelets to characterize the wettability of porous materials", Phys. Rev. E, 76, 056304 (2007).

ECOUL: an interactive computer tool to study hydraulic behavior of swelling and rigid soils

NASA Astrophysics Data System (ADS)

Perrier, Edith; Garnier, Patricia; Leclerc, Christian

2002-11-01

ECOUL is an interactive, didactic software package which simulates vertical water flow in unsaturated soils. End-users are given an easily-used tool to predict the evolution of the soil water profile, with a large range of possible boundary conditions, through a classical numerical solution scheme for the Richards equation. Soils must be characterized by water retention curves and hydraulic conductivity curves, the form of which can be chosen among different analytical expressions from the literature. When the parameters are unknown, an inverse method is provided to estimate them from available experimental flow data. A significant original feature of the software is to include recent algorithms extending the water flow model to deal with deforming porous media: widespread swelling soils, the volume of which varies as a function of water content, must be described by a third hydraulic characteristic property, the deformation curve. Again, estimation of the parameters by means of inverse procedures and visualization facilities enable exploration, understanding and then prediction of soil hydraulic behavior under various experimental conditions.

Variability in soil-water retention properties and implications for physics-based simulation of landslide early warning criteria

USGS Publications Warehouse

Thomas, Matthew A.; Mirus, Benjamin B.; Collins, Brian D.; Lu, Ning; Godt, Jonathan W.

2018-01-01

Rainfall-induced shallow landsliding is a persistent hazard to human life and property. Despite the observed connection between infiltration through the unsaturated zone and shallow landslide initiation, there is considerable uncertainty in how estimates of unsaturated soil-water retention properties affect slope stability assessment. This source of uncertainty is critical to evaluating the utility of physics-based hydrologic modeling as a tool for landslide early warning. We employ a numerical model of variably saturated groundwater flow parameterized with an ensemble of texture-, laboratory-, and field-based estimates of soil-water retention properties for an extensively monitored landslide-prone site in the San Francisco Bay Area, CA, USA. Simulations of soil-water content, pore-water pressure, and the resultant factor of safety show considerable variability across and within these different parameter estimation techniques. In particular, we demonstrate that with the same permeability structure imposed across all simulations, the variability in soil-water retention properties strongly influences predictions of positive pore-water pressure coincident with widespread shallow landsliding. We also find that the ensemble of soil-water retention properties imposes an order-of-magnitude and nearly two-fold variability in seasonal and event-scale landslide susceptibility, respectively. Despite the reduced factor of safety uncertainty during wet conditions, parameters that control the dry end of the soil-water retention function markedly impact the ability of a hydrologic model to capture soil-water content dynamics observed in the field. These results suggest that variability in soil-water retention properties should be considered for objective physics-based simulation of landslide early warning criteria.

Biodiversity and Ecosystem Multi-Functionality: Observed Relationships in Smallholder Fallows in Western Kenya

PubMed Central

Sircely, Jason; Naeem, Shahid

2012-01-01

Recent studies indicate that species richness can enhance the ability of plant assemblages to support multiple ecosystem functions. To understand how and when ecosystem services depend on biodiversity, it is valuable to expand beyond experimental grasslands. We examined whether plant diversity improves the capacity of agroecosystems to sustain multiple ecosystem services—production of wood and forage, and two elements of soil formation—in two types of smallholder fallows in western Kenya. In 18 grazed and 21 improved fallows, we estimated biomass and quantified soil organic carbon, soil base cations, sand content, and soil infiltration capacity. For four ecosystem functions (wood biomass, forage biomass, soil base cations, steady infiltration rates) linked to the focal ecosystem services, we quantified ecosystem service multi-functionality as (1) the proportion of functions above half-maximum, and (2) mean percentage excess above mean function values, and assessed whether plant diversity or environmental favorability better predicted multi-functionality. In grazed fallows, positive effects of plant diversity best explained the proportion above half-maximum and mean percentage excess, the former also declining with grazing intensity. In improved fallows, the proportion above half-maximum was not associated with soil carbon or plant diversity, while soil carbon predicted mean percentage excess better than diversity. Grazed fallows yielded stronger evidence for diversity effects on multi-functionality, while environmental conditions appeared more influential in improved fallows. The contrast in diversity-multi-functionality relationships among fallow types appears related to differences in management and associated factors including disturbance and species composition. Complementary effects of species with contrasting functional traits on different functions and multi-functional species may have contributed to diversity effects in grazed fallows. Biodiversity and environmental favorability may enhance the capacity of smallholder fallows to simultaneously provide multiple ecosystem services, yet their effects are likely to vary with fallow management. PMID:23209662

Representing life in the Earth system with soil microbial functional traits in the MIMICS model

NASA Astrophysics Data System (ADS)

Wieder, W. R.; Grandy, A. S.; Kallenbach, C. M.; Taylor, P. G.; Bonan, G. B.

2015-06-01

Projecting biogeochemical responses to global environmental change requires multi-scaled perspectives that consider organismal diversity, ecosystem processes, and global fluxes. However, microbes, the drivers of soil organic matter decomposition and stabilization, remain notably absent from models used to project carbon (C) cycle-climate feedbacks. We used a microbial trait-based soil C model with two physiologically distinct microbial communities, and evaluate how this model represents soil C storage and response to perturbations. Drawing from the application of functional traits used to model other ecosystems, we incorporate copiotrophic and oligotrophic microbial functional groups in the MIcrobial-MIneral Carbon Stabilization (MIMICS) model; these functional groups are akin to "gleaner" vs. "opportunist" plankton in the ocean, or r- vs. K-strategists in plant and animal communities. Here we compare MIMICS to a conventional soil C model, DAYCENT (the daily time-step version of the CENTURY model), in cross-site comparisons of nitrogen (N) enrichment effects on soil C dynamics. MIMICS more accurately simulates C responses to N enrichment; moreover, it raises important hypotheses involving the roles of substrate availability, community-level enzyme induction, and microbial physiological responses in explaining various soil biogeochemical responses to N enrichment. In global-scale analyses, we show that MIMICS projects much slower rates of soil C accumulation than a conventional soil biogeochemistry in response to increasing C inputs with elevated carbon dioxide (CO2) - a finding that would reduce the size of the land C sink estimated by the Earth system. Our findings illustrate that tradeoffs between theory and utility can be overcome to develop soil biogeochemistry models that evaluate and advance our theoretical understanding of microbial dynamics and soil biogeochemical responses to environmental change.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Erel, Y.

The isotopic composition of Pb measured in soil samples was used to determine rates and mechanisms of anthropogenic Pb migration in the soil. Petrol-Pb found in soluble halogenated aerosols migrates into the soil and is retained in the soil by the stationary soil particles. Lead infiltration velocity is approximately 5 {times} 10{sup {minus}1} cm/year, and its retardation factor is estimated to be on the order of 1 {times} 10{sup 3}. The infiltration of Pb into the soil is best described by the advection-dispersion equation under the assumption that the time scale of the longitudinal dispersion is much longer than themore » time scale of advection. Therefore, the contribution of dispersion to the solution of the advection-dispersion equation is negligible. As a result, the soil profile of petrol-Pb resembles the time-dependent input function of petrol-Pb. The estimated petrol-Pb penetration velocity and the isotopic composition profile of Pb in off-road soil are used for the computation of the fraction of anthropogenic Pb in this soil. It is calculated that the fraction of anthropogenic Pb in the acid-leached soil samples and in the soil residue of this soil profile drops from 60 and 22% near the surface to 6 and 0% at a depth of 33 cm, respectively. The downward migration velocity of Pb in soils of the studied area, which are typically 50 to 100 cm deep, implies a residence time of Pb in the soil of 100 to 200 years.« less

Rainfall estimation over-land using SMOS soil moisture observations: SM2RAIN, LMAA and SMART algorithms

NASA Astrophysics Data System (ADS)

Massari, Christian; Brocca, Luca; Pellarin, Thierry; Kerr, Yann; Crow, Wade; Cascon, Carlos; Ciabatta, Luca

2016-04-01

Recent advancements in the measurement of precipitation from space have provided estimates at scales that are commensurate with the needs of the hydrological and land-surface model communities. However, as demonstrated in a number of studies (Ebert et al. 2007, Tian et al. 2007, Stampoulis et al. 2012) satellite rainfall estimates are characterized by low accuracy in certain conditions and still suffer from a number of issues (e.g., bias) that may limit their utility in over-land applications (Serrat-Capdevila et al. 2014). In recent years many studies have demonstrated that soil moisture observations from ground and satellite sensors can be used for correcting satellite precipitation estimates (e.g. Crow et al., 2011; Pellarin et al., 2013), or directly estimating rainfall (SM2RAIN, Brocca et al., 2014). In this study, we carried out a detailed scientific analysis in which these three different methods are used for: i) estimating rainfall through satellite soil moisture observations (SM2RAIN, Brocca et al., 2014); ii) correcting rainfall through a Land surface Model Assimilation Algorithm (LMAA) (an improvement of a previous work of Crow et al. 2011 and Pellarin et al. 2013) and through the Soil Moisture Analysis Rainfall Tool (SMART, Crow et al. 2011). The analysis is carried within the ESA project "SMOS plus Rainfall" and involves 9 sites in Europe, Australia, Africa and USA containing high-quality hydrometeorological and soil moisture observations. Satellite soil moisture data from Soil Moisture and Ocean Salinity (SMOS) mission are employed for testing their potential in deriving a cumulated rainfall product at different temporal resolutions. The applicability and accuracy of the three algorithms is investigated also as a function of climatic and soil/land use conditions. A particular attention is paid to assess the expected limitations soil moisture based rainfall estimates such as soil saturation, freezing/snow conditions, SMOS RFI, irrigated areas, contribution of surface runoff and evapotranspiration, vegetation coverage, temporal sampling, and the assimilation/modelling approach. The 9 selected sites gather such potential problems which are shown and discussed at the conference. REFERENCES Ebert, E. E.; Janowiak, J. E.; Kidd, C. Comparison of Near-Real-Time Precipitation Estimates from Satellite Observations and Numerical Models. Bull. Am. Meteorol. Soc. 2007, 88, 47-64. Tian, Y.; Peters-Lidard, C. D.; Choudhury, B. J.; Garcia, M. Multitemporal Analysis of TRMM-Based Satellite Precipitation Products for Land Data Assimilation Applications. J. Hydrometeorol. 2007, 8, 1165-1183. Stampoulis, D.; Anagnostou, E. N. Evaluation of Global Satellite Rainfall Products over Continental Europe. J. Hydrometeorol. 2012, 13, 588-603. Serrat-Capdevila, A.; Valdes, J. B.; Stakhiv, E. Z. Water Management Applications for Satellite Precipitation Products: Synthesis and Recommendations. JAWRA J. Am. Water Resour. Assoc. 2014, 50, 509-525. Crow, W. T.; van den Berg, M. J.; Huffman, G. J.; Pellarin, T. Correcting rainfall using satellite-based surface soil moisture retrievals: The Soil Moisture Analysis Rainfall Tool (SMART). Water Resour. Res. 2011, 47, W08521. Pellarin, T.; Louvet, S.; Gruhier, C.; Quantin, G.; Legout, C. A simple and effective method for correcting soil moisture and precipitation estimates using AMSR-E measurements. Remote Sens. Environ. 2013, 136, 28-36. Brocca, L.; Ciabatta, L.; Massari, C.; Moramarco, T.; Hahn, S.; Hasenauer, S.; Kidd, R.; Dorigo, W.; Wagner, W.; Levizzani, V. Soil as a natural rain gauge: Estimating global rainfall from satellite soil moisture data. J. Geophys. Res. Atmos. 2014, 119, 5128-5141.

Using lagged dependence to identify (de)coupled surface and subsurface soil moisture values

NASA Astrophysics Data System (ADS)

Carranza, Coleen D. U.; van der Ploeg, Martine J.; Torfs, Paul J. J. F.

2018-04-01

Recent advances in radar remote sensing popularized the mapping of surface soil moisture at different spatial scales. Surface soil moisture measurements are used in combination with hydrological models to determine subsurface soil moisture values. However, variability of soil moisture across the soil column is important for estimating depth-integrated values, as decoupling between surface and subsurface can occur. In this study, we employ new methods to investigate the occurrence of (de)coupling between surface and subsurface soil moisture. Using time series datasets, lagged dependence was incorporated in assessing (de)coupling with the idea that surface soil moisture conditions will be reflected at the subsurface after a certain delay. The main approach involves the application of a distributed-lag nonlinear model (DLNM) to simultaneously represent both the functional relation and the lag structure in the time series. The results of an exploratory analysis using residuals from a fitted loess function serve as a posteriori information to determine (de)coupled values. Both methods allow for a range of (de)coupled soil moisture values to be quantified. Results provide new insights into the decoupled range as its occurrence among the sites investigated is not limited to dry conditions.

On quantifying active soil carbon using mid-infrared ...

EPA Pesticide Factsheets

Soil organic matter (SOM) is derived from plant or animal residues deposited to soil and is in various stages of decomposition and mineralization. Total SOM is a common measure of soil quality, although due to its heterogeneous composition SOM can vary dramatically in terms of its biochemical properties and residence times, which ultimately affect soil heath and function. One operationally defined SOM fraction is “active soil carbon” (ASC) which is thought to consist of readily oxidizable SOM that is responsive to management practices and may provide one measure of “soil health” closely associated with soil biological activity. ASC can be a useful indicator to assist farmers and land managers in their selection of soil management practices to maintain ASC or to build total SOM. ASC has generally been measured using permanganate oxidation, a costly and time-intensive procedure. Chemometric modeling using mid-infrared spectroscopy (MIR) has been successfully used to estimate a range of soil properties, including total organic carbon (TOC) and particulate organic carbon (POC). Consequently, we hypothesized that we could use MIR to estimate ASC. Here we report on a method that uses MIR and chemometric signal processing to quantify TOC and ASC on a variety of soils collected serially and seasonally from a maximum of 76 locations across the United States. TOC was measured using high temperature oxidation and ASC was measured as permanganate-oxidizabl

Improving Soil Moisture and Temperature Profile and Surface Turbulent Fluxes Estimations in Irrigated Field by Assimilating Multi-source Data into Land Surface Model

NASA Astrophysics Data System (ADS)

Chen, Weijing; Huang, Chunlin; Shen, Huanfeng; Wang, Weizhen

2016-04-01

The optimal estimation of hydrothermal conditions in irrigation field is restricted by the deficiency of accurate irrigation information (when and how much to irrigate). However, the accurate estimation of soil moisture and temperature profile and surface turbulent fluxes are crucial to agriculture and water management in irrigated field. In the framework of land surface model, soil temperature is a function of soil moisture - subsurface moisture influences the heat conductivity at the interface of layers and the heat storage in different layers. In addition, soil temperature determines the phase of soil water content with the transformation between frozen and unfrozen. Furthermore, surface temperature affects the partitioning of incoming radiant energy into ground (sensible and latent heat flux), as a consequence changes the delivery of soil moisture and temperature. Given the internal positive interaction lying in these variables, we attempt to retrieve the accurate estimation of soil moisture and temperature profile via assimilating the observations from the surface under unknown irrigation. To resolve the input uncertainty of imprecise irrigation quantity, original EnKS is implemented with inflation and localization (referred to as ESIL) aiming at solving the underestimation of the background error matrix and the extension of observation information from the top soil to the bottom. EnKS applied in this study includes the states in different time points which tightly connect with adjacent ones. However, this kind of relationship gradually vanishes along with the increase of time interval. Thus, the localization is also employed to readjust temporal scale impact between states and filter out redundant or invalid correlation. Considering the parameter uncertainty which easily causes the systematic deviation of model states, two parallel filters are designed to recursively estimate both states and parameters. The study area consists of irrigated farmland and is located in an artificial oasis in the semi-arid region of northwestern China. Land surface temperature (LST) and soil volumetric water content (SVW) at first layer measured at Daman station are taken as observations in the framework of data assimilation. The study demonstrates the feasibility of ESIL in improving the soil moisture and temperature profile under unknown irrigation. ESIL promotes the coefficient correlation with in-situ measurements for soil moisture and temperature at first layer from 0.3421 and 0.7027 (ensemble simulation) to 0.8767 and 0.8304 meanwhile all the RMSE of soil moisture and temperature in deeper layers dramatically decrease more than 40 percent in different degree. To verify the reliability of ESIL in practical application, thereby promoting the utilization of satellite data, we test ESIL with varying observation internal interval and standard deviation. As a consequence, ESIL shows stabilized and promising effectiveness in soil moisture and soil temperature estimation.

Smart Fluids in Hydrology: Use of Non-Newtonian Fluids for Pore Structure Characterization

NASA Astrophysics Data System (ADS)

Abou Najm, M. R.; Atallah, N. M.; Selker, J. S.; Roques, C.; Stewart, R. D.; Rupp, D. E.; Saad, G.; El-Fadel, M.

2015-12-01

Classic porous media characterization relies on typical infiltration experiments with Newtonian fluids (i.e., water) to estimate hydraulic conductivity. However, such experiments are generally not able to discern important characteristics such as pore size distribution or pore structure. We show that introducing non-Newtonian fluids provides additional unique flow signatures that can be used for improved pore structure characterization while still representing the functional hydraulic behavior of real porous media. We present a new method for experimentally estimating the pore structure of porous media using a combination of Newtonian and non-Newtonian fluids. The proposed method transforms results of N infiltration experiments using water and N-1 non-Newtonian solutions into a system of equations that yields N representative radii (Ri) and their corresponding percent contribution to flow (wi). This method allows for estimating the soil retention curve using only saturated experiments. Experimental and numerical validation comparing the functional flow behavior of different soils to their modeled flow with N representative radii revealed the ability of the proposed method to represent the water retention and infiltration behavior of real soils. The experimental results showed the ability of such fluids to outsmart Newtonian fluids and infer pore size distribution and unsaturated behavior using simple saturated experiments. Specifically, we demonstrate using synthetic porous media that the use of different non-Newtonian fluids enables the definition of the radii and corresponding percent contribution to flow of multiple representative pores, thus improving the ability of pore-scale models to mimic the functional behavior of real porous media in terms of flow and porosity. The results advance the knowledge towards conceptualizing the complexity of porous media and can potentially impact applications in fields like irrigation efficiencies, vadose zone hydrology, soil-root-plant continuum, carbon sequestration into geologic formations, soil remediation, petroleum reservoir engineering, oil exploration and groundwater modeling.

Representing life in the Earth system with soil microbial functional traits in the MIMICS model

NASA Astrophysics Data System (ADS)

Wieder, W. R.; Grandy, A. S.; Kallenbach, C. M.; Taylor, P. G.; Bonan, G. B.

2015-02-01

Projecting biogeochemical responses to global environmental change requires multi-scaled perspectives that consider organismal diversity, ecosystem processes and global fluxes. However, microbes, the drivers of soil organic matter decomposition and stabilization, remain notably absent from models used to project carbon cycle-climate feedbacks. We used a microbial trait-based soil carbon (C) model, with two physiologically distinct microbial communities to improve current estimates of soil C storage and their likely response to perturbations. Drawing from the application of functional traits used to model other ecosystems, we incorporate copiotrophic and oligotrophic microbial functional groups in the MIcrobial-MIneral Carbon Stabilization (MIMICS) model, which incorporates oligotrophic and copiotrophic functional groups, akin to "gleaner" vs. "opportunist" plankton in the ocean, or r vs. K strategists in plant and animals communities. Here we compare MIMICS to a conventional soil C model, DAYCENT, in cross-site comparisons of nitrogen (N) enrichment effects on soil C dynamics. MIMICS more accurately simulates C responses to N enrichment; moreover, it raises important hypotheses involving the roles of substrate availability, community-level enzyme induction, and microbial physiological responses in explaining various soil biogeochemical responses to N enrichment. In global-scale analyses, we show that current projections from Earth system models likely overestimate the strength of the land C sink in response to increasing C inputs with elevated carbon dioxide (CO2). Our findings illustrate that tradeoffs between theory and utility can be overcome to develop soil biogeochemistry models that evaluate and advance our theoretical understanding of microbial dynamics and soil biogeochemical responses to environmental change.

Non-destructive measurement of carbonic anhydrase activity and the oxygen isotope composition of soil water

NASA Astrophysics Data System (ADS)

Jones, Sam; Sauze, Joana; Ogée, Jérôme; Wohl, Steven; Bosc, Alexandre; Wingate, Lisa

2016-04-01

Carbonic anhydrases are a group of metalloenzymes that catalyse the hydration of aqueous carbon dioxide (CO2). The expression of carbonic anhydrase by bacteria, archaea and eukarya has been linked to a variety of important biological processes including pH regulation, substrate supply and biomineralisation. As oxygen isotopes are exchanged between CO2 and water during hydration, the presence of carbonic anhydrase in plants and soil organisms also influences the oxygen isotope budget of atmospheric CO2. Leaf and soil water pools have distinct oxygen isotope compositions, owing to differences in pool sizes and evaporation rates, which are imparted on CO2during hydration. These differences in the isotopic signature of CO2 interacting with leaves and soil can be used to partition the contribution of photosynthesis and soil respiration to net terrestrial CO2 exchange. However, this relies on our knowledge of soil carbonic anhydrase activity and currently, the prevalence and function of these enzymes in soils is poorly understood. Isotopic approaches used to estimate soil carbonic anhydrase activity typically involve the inversion of models describing the oxygen isotope composition of CO2 fluxes to solve for the apparent, potentially catalysed, rate of oxygen exchange during hydration. This requires information about the composition of CO2 in isotopic equilibrium with soil water obtained from destructive, depth-resolved soil water sampling. This can represent a significant challenge in data collection given the considerable potential for spatial and temporal variability in the isotopic composition of soil water and limited a priori information with respect to the appropriate sampling resolution and depth. We investigated whether we could circumvent this requirement by constraining carbonic anhydrase activity and the composition of soil water in isotopic equilibrium with CO2 by solving simultaneously the mass balance for two soil CO2 steady states differing only in the oxygen isotope composition of ambient CO2. This non-destructive approach was tested through laboratory incubations of air-dried soils that were re-wetted with water of known isotopic composition. Performance was assessed by comparing estimates of the soil water oxygen isotope composition derived from open chamber flux measurements with those measured in the irrigation water and soil water extracted following incubations. The influence of soil pH and bovine carbonic anhydrase additions on these estimates was also investigated. Coherent values were found between the soil water composition estimates obtained from the dual steady state approach and those measured for irrigation waters. Estimates of carbonic anhydrase activity made using this approach also reflected well artificial increases to the concentration of carbonic anhydrase and indicated that this activity was sensitive to soil pH.

Optimizing the Hydrological and Biogeochemical Simulations on a Hillslope with Stony Soil

NASA Astrophysics Data System (ADS)

Zhu, Q.

2017-12-01

Stony soils are widely distributed in the hilly area. However, traditional pedotransfer functions are not reliable in predicting the soil hydraulic parameters for these soils due to the impacts of rock fragments. Therefore, large uncertainties and errors may exist in the hillslope hydrological and biogeochemical simulations in stony soils due to poor estimations of soil hydraulic parameters. In addition, homogenous soil hydraulic parameters are usually used in traditional hillslope simulations. However, soil hydraulic parameters are spatially heterogeneous on the hillslope. This may also cause the unreliable simulations. In this study, we obtained soil hydraulic parameters using five different approaches on a tea hillslope in Taihu Lake basin, China. These five approaches included (1) Rossetta predicted and spatially homogenous, (2) Rossetta predicted and spatially heterogeneous), (3) Rossetta predicted, rock fragment corrected and spatially homogenous, (4) Rossetta predicted, rock fragment corrected and spatially heterogeneous, and (5) extracted from observed soil-water retention curves fitted by dual-pore function and spatially heterogeneous (observed). These five sets of soil hydraulic properties were then input into Hydrus-3D and DNDC to simulate the soil hydrological and biogeochemical processes. The aim of this study is testing two hypotheses. First, considering the spatial heterogeneity of soil hydraulic parameters will improve the simulations. Second, considering the impact of rock fragment on soil hydraulic parameters will improve the simulations.

Stochastic Modeling of Soil Salinity

NASA Astrophysics Data System (ADS)

Suweis, Samir; Rinaldo, Andrea; van der Zee, Sjoerd E. A. T. M.; Maritan, Amos; Porporato, Amilcare

2010-05-01

Large areas of cultivated land worldwide are affected by soil salinity. Estimates report that 10% of arable land in over 100 countries, and nine million km2 are salt affected, especially in arid and semi-arid regions. High salinity causes both ion specific and osmotic stress effects, with important consequences for plant production and quality. Salt accumulation in the root zone may be due to natural factors (primary salinization) or due to irrigation (secondary salinization). Simple (e.g., vertically averaged over the soil depth) coupled soil moisture and salt balance equations have been used in the past. Despite their approximations, these models have the advantage of parsimony, thus allowing a direct analysis of the interplay of the main processes. They also provide the ideal starting point to include external, random hydro-climatic fluctuations in the analysis of long-term salinization trends. We propose a minimalist stochastic model of primary soil salinity, in which the rate of soil salinization is determined by the balance between dry and wet salt deposition and the intermittent leaching events caused by rainfall events. The long term probability density functions of salt mass and concentration are found by reducing the coupled soil moisture and salt mass balance equation to a stochastic differential equation driven by multiplicative Poisson noise. The novel analytical solutions provide insight on the interplay of the main soil, plant and climate parameters responsible for long-term soil salinization. In fact, soil salinity statistics are obtained as a function of climate, soil and vegetation parameters. These, in turn, can be combined with soil moisture statistics to obtain a full characterization of soil salt concentrations and the ensuing risk of primary salinization. In particular, the solutions show the existence of two quite distinct regimes, the first one where the mean salt mass remains nearly constant with increasing rainfall frequency, and the second one where mean salt content increases markedly with increasing rainfall frequency. As a result, relatively small reductions of rainfall in drier climates may entail dramatic shifts in long-term soil salinization trends, with significant consequences e.g. for climate change impacts on rain-fed agriculture. The analytical nature of the solution allows direct estimation of the impact of changes in the climatic drivers on soil salinity and makes it suitable for computations of salinity risk at the global scale as a function of simple parameters. Moreover it facilitates their coupling with other models of long-term soil-plant biogeochemistry.

Current status, uncertainty and future needs in soil organic carbon monitoring.

PubMed

Jandl, Robert; Rodeghiero, Mirco; Martinez, Cristina; Cotrufo, M Francesca; Bampa, Francesca; van Wesemael, Bas; Harrison, Robert B; Guerrini, Iraê Amaral; Richter, Daniel Deb; Rustad, Lindsey; Lorenz, Klaus; Chabbi, Abad; Miglietta, Franco

2014-01-15

Increasing human demands on soil-derived ecosystem services requires reliable data on global soil resources for sustainable development. The soil organic carbon (SOC) pool is a key indicator of soil quality as it affects essential biological, chemical and physical soil functions such as nutrient cycling, pesticide and water retention, and soil structure maintenance. However, information on the SOC pool, and its temporal and spatial dynamics is unbalanced. Even in well-studied regions with a pronounced interest in environmental issues information on soil carbon (C) is inconsistent. Several activities for the compilation of global soil C data are under way. However, different approaches for soil sampling and chemical analyses make even regional comparisons highly uncertain. Often, the procedures used so far have not allowed the reliable estimation of the total SOC pool, partly because the available knowledge is focused on not clearly defined upper soil horizons and the contribution of subsoil to SOC stocks has been less considered. Even more difficult is quantifying SOC pool changes over time. SOC consists of variable amounts of labile and recalcitrant molecules of plant, and microbial and animal origin that are often operationally defined. A comprehensively active soil expert community needs to agree on protocols of soil surveying and lab procedures towards reliable SOC pool estimates. Already established long-term ecological research sites, where SOC changes are quantified and the underlying mechanisms are investigated, are potentially the backbones for regional, national, and international SOC monitoring programs. © 2013.

Estimation of Distributed Groundwater Pumping Rates in Yolo County,CA—Intercomparison of Two Modeling Frameworks

NASA Astrophysics Data System (ADS)

Maples, S.; Fogg, G. E.; Harter, T.

2015-12-01

Accurate estimation of groundwater (GW) budgets and effective management of agricultural GW pumping remains a challenge in much of California's Central Valley (CV) due to a lack of irrigation well metering. CVHM and C2VSim are two regional-scale integrated hydrologic models that provide estimates of historical and current CV distributed pumping rates. However, both models estimate GW pumping using conceptually different agricultural water models with uncertainties that have not been adequately investigated. Here, we evaluate differences in distributed agricultural GW pumping and recharge estimates related to important differences in the conceptual framework and model assumptions used to simulate surface water (SW) and GW interaction across the root zone. Differences in the magnitude and timing of GW pumping and recharge were evaluated for a subregion (~1000 mi2) coincident with Yolo County, CA, to provide similar initial and boundary conditions for both models. Synthetic, multi-year datasets of land-use, precipitation, evapotranspiration (ET), and SW deliveries were prescribed for each model to provide realistic end-member scenarios for GW-pumping demand and recharge. Results show differences in the magnitude and timing of GW-pumping demand, deep percolation, and recharge. Discrepancies are related, in large part, to model differences in the estimation of ET requirements and representation of soil-moisture conditions. CVHM partitions ET demand, while C2VSim uses a bulk ET rate, resulting in differences in both crop-water and GW-pumping demand. Additionally, CVHM assumes steady-state soil-moisture conditions, and simulates deep percolation as a function of irrigation inefficiencies, while C2VSim simulates deep percolation as a function of transient soil-moisture storage conditions. These findings show that estimates of GW-pumping demand are sensitive to these important conceptual differences, which can impact conjunctive-use water management decisions in the CV.

Autocorrelated residuals in inverse modelling of soil hydrological processes: a reason for concern or something that can safely be ignored?

NASA Astrophysics Data System (ADS)

Scharnagl, Benedikt; Durner, Wolfgang

2013-04-01

Models are inherently imperfect because they simplify processes that are themselves imperfectly known and understood. Moreover, the input variables and parameters needed to run a model are typically subject to various sources of error. As a consequence of these imperfections, model predictions will always deviate from corresponding observations. In most applications in soil hydrology, these deviations are clearly not random but rather show a systematic structure. From a statistical point of view, this systematic mismatch may be a reason for concern because it violates one of the basic assumptions made in inverse parameter estimation: the assumption of independence of the residuals. But what are the consequences of simply ignoring the autocorrelation in the residuals, as it is current practice in soil hydrology? Are the parameter estimates still valid even though the statistical foundation they are based on is partially collapsed? Theory and practical experience from other fields of science have shown that violation of the independence assumption will result in overconfident uncertainty bounds and that in some cases it may lead to significantly different optimal parameter values. In our contribution, we present three soil hydrological case studies, in which the effect of autocorrelated residuals on the estimated parameters was investigated in detail. We explicitly accounted for autocorrelated residuals using a formal likelihood function that incorporates an autoregressive model. The inverse problem was posed in a Bayesian framework, and the posterior probability density function of the parameters was estimated using Markov chain Monte Carlo simulation. In contrast to many other studies in related fields of science, and quite surprisingly, we found that the first-order autoregressive model, often abbreviated as AR(1), did not work well in the soil hydrological setting. We showed that a second-order autoregressive, or AR(2), model performs much better in these applications, leading to parameter and uncertainty estimates that satisfy all the underlying statistical assumptions. For theoretical reasons, these estimates are deemed more reliable than those estimates based on the neglect of autocorrelation in the residuals. In compliance with theory and results reported in the literature, our results showed that parameter uncertainty bounds were substantially wider if autocorrelation in the residuals was explicitly accounted for, and also the optimal parameter vales were slightly different in this case. We argue that the autoregressive model presented here should be used as a matter of routine in inverse modeling of soil hydrological processes.

Including Effects of Water Stress on Dead Organic Matter Decay to a Forest Carbon Model

NASA Astrophysics Data System (ADS)

Kim, H.; Lee, J.; Han, S. H.; Kim, S.; Son, Y.

2017-12-01

Decay of dead organic matter is a key process of carbon (C) cycling in forest ecosystems. The change in decay rate depends on temperature sensitivity and moisture conditions. The Forest Biomass and Dead organic matter Carbon (FBDC) model includes a decay sub-model considering temperature sensitivity, yet does not consider moisture conditions as drivers of the decay rate change. This study aimed to improve the FBDC model by including a water stress function to the decay sub-model. Also, soil C sequestration under climate change with the FBDC model including the water stress function was simulated. The water stress functions were determined with data from decomposition study on Quercus variabilis forests and Pinus densiflora forests of Korea, and adjustment parameters of the functions were determined for both species. The water stress functions were based on the ratio of precipitation to potential evapotranspiration. Including the water stress function increased the explained variances of the decay rate by 19% for the Q. variabilis forests and 7% for the P. densiflora forests, respectively. The increase of the explained variances resulted from large difference in temperature range and precipitation range across the decomposition study plots. During the period of experiment, the mean annual temperature range was less than 3°C, while the annual precipitation ranged from 720mm to 1466mm. Application of the water stress functions to the FBDC model constrained increasing trend of temperature sensitivity under climate change, and thus increased the model-estimated soil C sequestration (Mg C ha-1) by 6.6 for the Q. variabilis forests and by 3.1 for the P. densiflora forests, respectively. The addition of water stress functions increased reliability of the decay rate estimation and could contribute to reducing the bias in estimating soil C sequestration under varying moisture condition. Acknowledgement: This study was supported by Korea Forest Service (2017044B10-1719-BB01)

High resolution modelling of soil moisture patterns with TerrSysMP: A comparison with sensor network data

NASA Astrophysics Data System (ADS)

Gebler, S.; Hendricks Franssen, H.-J.; Kollet, S. J.; Qu, W.; Vereecken, H.

2017-04-01

The prediction of the spatial and temporal variability of land surface states and fluxes with land surface models at high spatial resolution is still a challenge. This study compares simulation results using TerrSysMP including a 3D variably saturated groundwater flow model (ParFlow) coupled to the Community Land Model (CLM) of a 38 ha managed grassland head-water catchment in the Eifel (Germany), with soil water content (SWC) measurements from a wireless sensor network, actual evapotranspiration recorded by lysimeters and eddy covariance stations and discharge observations. TerrSysMP was discretized with a 10 × 10 m lateral resolution, variable vertical resolution (0.025-0.575 m), and the following parameterization strategies of the subsurface soil hydraulic parameters: (i) completely homogeneous, (ii) homogeneous parameters for different soil horizons, (iii) different parameters for each soil unit and soil horizon and (iv) heterogeneous stochastic realizations. Hydraulic conductivity and Mualem-Van Genuchten parameters in these simulations were sampled from probability density functions, constructed from either (i) soil texture measurements and Rosetta pedotransfer functions (ROS), or (ii) estimated soil hydraulic parameters by 1D inverse modelling using shuffle complex evolution (SCE). The results indicate that the spatial variability of SWC at the scale of a small headwater catchment is dominated by topography and spatially heterogeneous soil hydraulic parameters. The spatial variability of the soil water content thereby increases as a function of heterogeneity of soil hydraulic parameters. For lower levels of complexity, spatial variability of the SWC was underrepresented in particular for the ROS-simulations. Whereas all model simulations were able to reproduce the seasonal evapotranspiration variability, the poor discharge simulations with high model bias are likely related to short-term ET dynamics and the lack of information about bedrock characteristics and an on-site drainage system in the uncalibrated model. In general, simulation performance was better for the SCE setups. The SCE-simulations had a higher inverse air entry parameter resulting in SWC dynamics in better correspondence with data than the ROS simulations during dry periods. This illustrates that small scale measurements of soil hydraulic parameters cannot be transferred to the larger scale and that interpolated 1D inverse parameter estimates result in an acceptable performance for the catchment.

Global Soil Respiration: Interaction with Environmental Variables and Response to Climate Change

NASA Astrophysics Data System (ADS)

Jian, J.; Steele, M.

2016-12-01

Background, methods, objectivesTerrestrial ecosystems take up around 1.7 Pg C per year; however, the role of terrestrial ecosystems as a carbon sink may change to carbon source by 2050, as a result of positive feedback of soil respiration response to global warming. Nevertheless, limited evidence shows that soil carbon is decreasing and the role of terrestrial ecosystems is changing under warming. One possibility is the positive feedback may slow due to the acclimation of soil respiration as a result of decreasing temperature sensitivity (Q10) with warming. To verify and quantify the uncertainty in soil carbon cycling and feedbacks to climate change, we assembled soil respiration observations from 1961 to 2014 from 724 publications into a monthly global soil respiration database (MSRDB), which included 13482 soil respiration measurements together with 38 other ancillary measurements from 538 sites. Using this database we examined macroscale variation in the relationship between soil respiration and air temperature, precipitation, leaf area index and soil properties. We also quantified global soil respiration, the sources of uncertainty, and its feedback to warming based on climate region-oriented models with variant Q10function. Results and ConclusionsOur results showed substantial heterogeneity in the relationship between soil respiration and environmental factors across different climate regions. For example, soil respiration was strongly related to vegetation (via leaf area index) in colder regions, but not in tropical region. Only in tropical and arid regions did soil properties explain any variation in soil respiration. Global annual mean soil respiration from 1961 to 2014 was estimated to be 72.41 Pg C yr-1 based on monthly global soil respiration database, 25 Pg lower than estimated based on yearly soil respiration database. By using the variable Q10 models, we estimated that global soil respiration increased at a rate of 0.03 Pg C yr-1 from 1961 to 2014, smaller than previous studies ( 0.1 Pg C yr-1). The substantial variations in these relationships suggest that regional scales is important for understanding and prediction of global carbon cycling and how it response to climate change.

Soil contamination with olive mill wastes negatively affects microbial communities, invertebrates and plants.

PubMed

Hentati, Olfa; Oliveira, Vanessa; Sena, Clara; Bouji, Mohamed Seddik Mahmoud; Wali, Ahmed; Ksibi, Mohamed

2016-10-01

The aim of the present study was to evaluate the ecotoxicological effects of olive mill waste (OMW) on soil habitat function. To this end, soil samples from OMW evaporating ponds (S1-S5) located at Agareb (Sfax, Tunisia) and a reference soil (R) were collected. The effects of OMW on the springtails Folsomia candida (F.c.), the earthworm species Eisenia fetida (E.f.), Enchytraeus crypticus (E.c.) reproduction and on the soil living microbial communities were investigated. E.f. reproduction and tomato growth assays were performed in the reference soil amended with 0.43 to 7.60 % (w OMW /w ref-soil ) mass ratios of dried OMW. Changes in microbial function diversity were explored using sole-carbon-source utilization profiles (BiologEcoPlates ® ). E.f. absolutely avoided (100 %) the most polluted soil (S4) while the F.c. moderately avoided (37.5 ± 7.5 %) the same soil. E.c. reproduction in S4 was significantly lower than in S1, S2, S3 and S5, and was the highest in R soil. Estimated effect concentration EC 50 for juveniles' production by E.f., and for tomato fresh weight and chlorophyll content were 0.138, 0.6 and 1.13 %, respectively. Community level physiological profiles (CLPPs) were remarkably different in R and S4 and a higher similarity was observed between soils S1, S2, S3 and S5. Principal component analysis (PCA) revealed that differences between soil microbial functional diversity were mainly due to high polyphenol concentrations, while the salinity negatively affected E.c. reproduction in OMW contaminated soils. These results clearly reflect the high toxicity of dried OMW when added to agricultural soils, causing severe threats to terrestrial ecosystem functions and services provided by invertebrates and microbial communities.

Enhancing PTFs with remotely sensed data for multi-scale soil water retention estimation

NASA Astrophysics Data System (ADS)

Jana, Raghavendra B.; Mohanty, Binayak P.

2011-03-01

SummaryUse of remotely sensed data products in the earth science and water resources fields is growing due to increasingly easy availability of the data. Traditionally, pedotransfer functions (PTFs) employed for soil hydraulic parameter estimation from other easily available data have used basic soil texture and structure information as inputs. Inclusion of surrogate/supplementary data such as topography and vegetation information has shown some improvement in the PTF's ability to estimate more accurate soil hydraulic parameters. Artificial neural networks (ANNs) are a popular tool for PTF development, and are usually applied across matching spatial scales of inputs and outputs. However, different hydrologic, hydro-climatic, and contaminant transport models require input data at different scales, all of which may not be easily available from existing databases. In such a scenario, it becomes necessary to scale the soil hydraulic parameter values estimated by PTFs to suit the model requirements. Also, uncertainties in the predictions need to be quantified to enable users to gauge the suitability of a particular dataset in their applications. Bayesian Neural Networks (BNNs) inherently provide uncertainty estimates for their outputs due to their utilization of Markov Chain Monte Carlo (MCMC) techniques. In this paper, we present a PTF methodology to estimate soil water retention characteristics built on a Bayesian framework for training of neural networks and utilizing several in situ and remotely sensed datasets jointly. The BNN is also applied across spatial scales to provide fine scale outputs when trained with coarse scale data. Our training data inputs include ground/remotely sensed soil texture, bulk density, elevation, and Leaf Area Index (LAI) at 1 km resolutions, while similar properties measured at a point scale are used as fine scale inputs. The methodology was tested at two different hydro-climatic regions. We also tested the effect of varying the support scale of the training data for the BNNs by sequentially aggregating finer resolution training data to coarser resolutions, and the applicability of the technique to upscaling problems. The BNN outputs are corrected for bias using a non-linear CDF-matching technique. Final results show good promise of the suitability of this Bayesian Neural Network approach for soil hydraulic parameter estimation across spatial scales using ground-, air-, or space-based remotely sensed geophysical parameters. Inclusion of remotely sensed data such as elevation and LAI in addition to in situ soil physical properties improved the estimation capabilities of the BNN-based PTF in certain conditions.

Estimating Soil Moisture Using Polsar Data: a Machine Learning Approach

NASA Astrophysics Data System (ADS)

Khedri, E.; Hasanlou, M.; Tabatabaeenejad, A.

2017-09-01

Soil moisture is an important parameter that affects several environmental processes. This parameter has many important functions in numerous sciences including agriculture, hydrology, aerology, flood prediction, and drought occurrence. However, field procedures for moisture calculations are not feasible in a vast agricultural region territory. This is due to the difficulty in calculating soil moisture in vast territories and high-cost nature as well as spatial and local variability of soil moisture. Polarimetric synthetic aperture radar (PolSAR) imaging is a powerful tool for estimating soil moisture. These images provide a wide field of view and high spatial resolution. For estimating soil moisture, in this study, a model of support vector regression (SVR) is proposed based on obtained data from AIRSAR in 2003 in C, L, and P channels. In this endeavor, sequential forward selection (SFS) and sequential backward selection (SBS) are evaluated to select suitable features of polarized image dataset for high efficient modeling. We compare the obtained data with in-situ data. Output results show that the SBS-SVR method results in higher modeling accuracy compared to SFS-SVR model. Statistical parameters obtained from this method show an R2 of 97% and an RMSE of lower than 0.00041 (m3/m3) for P, L, and C channels, which has provided better accuracy compared to other feature selection algorithms.

Three-dimensional data interpolation for environmental purpose: lead in contaminated soils in southern Brazil.

PubMed

Piedade, Tales Campos; Melo, Vander Freitas; Souza, Luiz Cláudio Paula; Dieckow, Jeferson

2014-09-01

Monitoring of heavy metal contamination plume in soils can be helpful in establishing strategies to minimize its hazardous impacts to the environment. The objective of this study was to apply a new approach of visualization, based on tridimensional (3D) images, of pseudo-total (extracted with concentrated acids) and exchangeable (extracted with 0.5 mol L(-1) Ca(NO3)2) lead (Pb) concentrations in soils of a mining and metallurgy area to determine the spatial distribution of this pollutant and to estimate the most contaminated soil volumes. Tridimensional images were obtained after interpolation of Pb concentrations of 171 soil samples (57 points × 3 depths) with regularized spline with tension in a 3D function version. The tridimensional visualization showed great potential of use in environmental studies and allowed to determine the spatial 3D distribution of Pb contamination plume in the area and to establish relationships with soil characteristics, landscape, and pollution sources. The most contaminated soil volumes (10,001 to 52,000 mg Pb kg(-1)) occurred near the metallurgy factory. The main contamination sources were attributed to atmospheric emissions of particulate Pb through chimneys. The large soil volume estimated to be removed to industrial landfills or co-processing evidenced the difficulties related to this practice as a remediation strategy.

Soil moisture sensitivity of autotrophic and heterotrophic forest floor respiration in boreal xeric pine and mesic spruce forests

NASA Astrophysics Data System (ADS)

Ťupek, Boris; Launiainen, Samuli; Peltoniemi, Mikko; Heikkinen, Jukka; Lehtonen, Aleksi

2016-04-01

Litter decomposition rates of the most process based soil carbon models affected by environmental conditions are linked with soil heterotrophic CO2 emissions and serve for estimating soil carbon sequestration; thus due to the mass balance equation the variation in measured litter inputs and measured heterotrophic soil CO2 effluxes should indicate soil carbon stock changes, needed by soil carbon management for mitigation of anthropogenic CO2 emissions, if sensitivity functions of the applied model suit to the environmental conditions e.g. soil temperature and moisture. We evaluated the response forms of autotrophic and heterotrophic forest floor respiration to soil temperature and moisture in four boreal forest sites of the International Cooperative Programme on Assessment and Monitoring of Air Pollution Effects on Forests (ICP Forests) by a soil trenching experiment during year 2015 in southern Finland. As expected both autotrophic and heterotrophic forest floor respiration components were primarily controlled by soil temperature and exponential regression models generally explained more than 90% of the variance. Soil moisture regression models on average explained less than 10% of the variance and the response forms varied between Gaussian for the autotrophic forest floor respiration component and linear for the heterotrophic forest floor respiration component. Although the percentage of explained variance of soil heterotrophic respiration by the soil moisture was small, the observed reduction of CO2 emissions with higher moisture levels suggested that soil moisture response of soil carbon models not accounting for the reduction due to excessive moisture should be re-evaluated in order to estimate right levels of soil carbon stock changes. Our further study will include evaluation of process based soil carbon models by the annual heterotrophic respiration and soil carbon stocks.

"LOVE TO HATE" pesticides: felicity or curse for the soil microbial community? An FP7 IAPP Marie Curie project aiming to establish tools for the assessment of the mechanisms controlling the interactions of pesticides with soil microorganisms.

PubMed

Karpouzas, D G; Tsiamis, G; Trevisan, M; Ferrari, F; Malandain, C; Sibourg, O; Martin-Laurent, F

2016-09-01

Pesticides end up in soil where they interact with soil microorganisms in various ways. On the Yin Side of the interaction, pesticides could exert toxicity on soil microorganisms, while on the Yang side of interaction, pesticides could be used as energy source by a fraction of the soil microbial community. The LOVE TO HATE project is an IAPP Marie Curie project which aims to study these complex interactions of pesticides with soil microorganisms and provide novel tools which will be useful both for pesticide regulatory purposes and agricultural use. On the Yin side of the interactions, a new regulatory scheme for assessing the soil microbial toxicity of pesticides will be proposed based on the use of advanced standardized tools and a well-defined experimental tiered scheme. On the Yang side of the interactions, advanced molecular tools like amplicon sequencing and functional metagenomics will be applied to define microbes that are involved in the rapid transformation of pesticides in soils and isolate novel pesticide biocatalysts. In addition, a functional microarray has been designed to estimate the biodegradation genetic potential of the microbial community of agricultural soils for a range of pesticide groups.

A Bayesian approach to assessing the uncertainty in estimating bioconcentration factors in earthworms--the example of quinoxyfen.

PubMed

Fragoulis, George; Merli, Annalisa; Reeves, Graham; Meregalli, Giovanna; Stenberg, Kristofer; Tanaka, Taku; Capri, Ettore

2011-06-01

Quinoxyfen is a fungicide of the phenoxyquinoline class used to control powdery mildew, Uncinula necator (Schw.) Burr. Owing to its high persistence and strong sorption in soil, it could represent a risk for soil organisms if they are exposed at ecologically relevant concentrations. The objective of this paper is to predict the bioconcentration factors (BCFs) of quinoxyfen in earthworms, selected as a representative soil organism, and to assess the uncertainty in the estimation of this parameter. Three fields in each of four vineyards in southern and northern Italy were sampled over two successive years. The measured BCFs varied over time, possibly owing to seasonal changes and the consequent changes in behaviour and ecology of earthworms. Quinoxyfen did not accumulate in soil, as the mean soil concentrations at the end of the 2 year monitoring period ranged from 9.16 to 16.0 µg kg⁻¹ dw for the Verona province and from 23.9 to 37.5 µg kg⁻¹ dw for the Taranto province, with up to eight applications per season. To assess the uncertainty of the BCF in earthworms, a probabilistic approach was used, firstly by building with weighted bootstrapping techniques a generic probabilistic density function (PDF) accounting for variability and incompleteness of knowledge. The generic PDF was then used to derive prior distribution functions, which, by application of Bayes' theorem, were updated with the new measurements and a posterior distribution was finally created. The study is a good example of probabilistic risk assessment. The means of mean and SD posterior estimates of log BCFworm (2.06, 0.91) are the 'best estimate values'. Further risk assessment of quinoxyfen and other phenoxyquinoline fungicides and realistic representative scenarios for modelling exercises required for future authorization and post-authorization requirements can now use this value as input. Copyright © 2011 Society of Chemical Industry.

Using rank-order geostatistics for spatial interpolation of highly skewed data in a heavy-metal contaminated site.

PubMed

Juang, K W; Lee, D Y; Ellsworth, T R

2001-01-01

The spatial distribution of a pollutant in contaminated soils is usually highly skewed. As a result, the sample variogram often differs considerably from its regional counterpart and the geostatistical interpolation is hindered. In this study, rank-order geostatistics with standardized rank transformation was used for the spatial interpolation of pollutants with a highly skewed distribution in contaminated soils when commonly used nonlinear methods, such as logarithmic and normal-scored transformations, are not suitable. A real data set of soil Cd concentrations with great variation and high skewness in a contaminated site of Taiwan was used for illustration. The spatial dependence of ranks transformed from Cd concentrations was identified and kriging estimation was readily performed in the standardized-rank space. The estimated standardized rank was back-transformed into the concentration space using the middle point model within a standardized-rank interval of the empirical distribution function (EDF). The spatial distribution of Cd concentrations was then obtained. The probability of Cd concentration being higher than a given cutoff value also can be estimated by using the estimated distribution of standardized ranks. The contour maps of Cd concentrations and the probabilities of Cd concentrations being higher than the cutoff value can be simultaneously used for delineation of hazardous areas of contaminated soils.

Coupling transfer function and GIS for assessing non-point-source groundwater vulnerability at regional scale

NASA Astrophysics Data System (ADS)

Coppola, A.; Comegna, V.; de Simone, L.

2009-04-01

Non-point source (NPS) pollution in the vadose zone is a global environmental problem. The knowledge and information required to address the problem of NPS pollutants in the vadose zone cross several technological and sub disciplinary lines: spatial statistics, geographic information systems (GIS), hydrology, soil science, and remote sensing. The main issues encountered by NPS groundwater vulnerability assessment, as discussed by Stewart [2001], are the large spatial scales, the complex processes that govern fluid flow and solute transport in the unsaturated zone, the absence of unsaturated zone measurements of diffuse pesticide concentrations in 3-D regional-scale space as these are difficult, time consuming, and prohibitively costly, and the computational effort required for solving the nonlinear equations for physically-based modeling of regional scale, heterogeneous applications. As an alternative solution, here is presented an approach that is based on coupling of transfer function and GIS modeling that: a) is capable of solute concentration estimation at a depth of interest within a known error confidence class; b) uses available soil survey, climatic, and irrigation information, and requires minimal computational cost for application; c) can dynamically support decision making through thematic mapping and 3D scenarios This result was pursued through 1) the design and building of a spatial database containing environmental and physical information regarding the study area, 2) the development of the transfer function procedure for layered soils, 3) the final representation of results through digital mapping and 3D visualization. One side GIS modeled environmental data in order to characterize, at regional scale, soil profile texture and depth, land use, climatic data, water table depth, potential evapotranspiration; on the other side such information was implemented in the up-scaling procedure of the Jury's TFM resulting in a set of texture based travel time probability density functions for layered soils each describing a characteristic leaching behavior for soil profiles with similar hydraulic properties. Such behavior, in terms of solute travel time to water table, was then imported back into GIS and finally estimation groundwater vulnerability for each soil unit was represented into a map as well as visualized in 3D.

A multi-scale ''soil water structure'' model based on the pedostructure concept

NASA Astrophysics Data System (ADS)

Braudeau, E.; Mohtar, R. H.; El Ghezal, N.; Crayol, M.; Salahat, M.; Martin, P.

2009-02-01

Current soil water models do not take into account the internal organization of the soil medium and, a fortiori, the physical interaction between the water film surrounding the solid particles of the soil structure, and the surface charges of this structure. In that sense they empirically deal with the physical soil properties that are all generated from this soil water-structure interaction. As a result, the thermodynamic state of the soil water medium, which constitutes the local physical conditions, namely the pedo-climate, for biological and geo-chemical processes in soil, is not defined in these models. The omission of soil structure from soil characterization and modeling does not allow for coupling disciplinary models for these processes with soil water models. This article presents a soil water structure model, Kamel®, which was developed based on a new paradigm in soil physics where the hierarchical soil structure is taken into account allowing for defining its thermodynamic properties. After a review of soil physics principles which forms the basis of the paradigm, we describe the basic relationships and functionality of the model. Kamel® runs with a set of 15 soil input parameters, the pedohydral parameters, which are parameters of the physically-based equations of four soil characteristic curves that can be measured in the laboratory. For cases where some of these parameters are not available, we show how to estimate these parameters from commonly available soil information using published pedotransfer functions. A published field experimental study on the dynamics of the soil moisture profile following a pounded infiltration rainfall event was used as an example to demonstrate soil characterization and Kamel® simulations. The simulated soil moisture profile for a period of 60 days showed very good agreement with experimental field data. Simulations using input data calculated from soil texture and pedotransfer functions were also generated and compared to simulations of the more ideal characterization. The later comparison illustrates how Kamel® can be used and adapt to any case of soil data availability. As physically based model on soil structure, it may be used as a standard reference to evaluate other soil-water models and also pedotransfer functions at a given location or agronomical situation.

Glyphosate sorption to soils of Argentina. Estimation of affinity coeficient by pedotransfer function

NASA Astrophysics Data System (ADS)

De Geronimo, Eduardo; Aparicio, Virginia; Costa, José Luis

2017-04-01

Argentine agricultural production is fundamentally based on a technological package that combines direct seeding and glyphosate with transgenic crops (soybean, maize and cotton). Therefore, glyphosate is the most employed herbicide in the country, where 180 to 200 million liters are applied every year. Glyphosate is strongly sorbed to soil by binding to clay minerals, layer silicates, metal oxides, non-crystalline materials or organic matter. Sorption of glyphosate is a reversible process that regulates the half-life and mobility of the herbicide and it is therefore related to the risk of contaminating courses of surface and groundwater. However, this behavior may vary depending on the characteristics of the soil on which it is applied. In addition, pH is a determining factor since it modifies the net charge in the molecule and, with it, the force of the electrostatic interaction between the glyphosate and the components of the soil. For a reliable risk assessment of groundwater contamination from pesticides precise predictions of sorption coefficients are needed. The aim of this work is to study the affinity of glyphosate to different soils of Argentina and create a model to estimate the glyphosate Freundlich sorption coefficient (Kf) from easily measurable soil properties. Adsorption of glyphosate was investigated on 12 different agricultural soils of Argentina using batch equilibration technique and fit to Freundlich sorption model. The correlation coefficients and the effects of soil characteristic factors on glyphosate adsorption parameter were analyzed through principal component and multiple lineal regression analysis. Results indicate that pH and clay contents were found to be the most significant soil factors which affect the glyphosate adsorption process. The Freundlich (Kf) pedotransfer function obtained by stepwise regression analysis was Kf = 735.2*Clay - 104.2*pH + 0.7*Polsen - 3.8*Alin. A 97.9% of the variation of glyphosate sorption coefficient could be attributed to the variation of the soil clay contents, pH, Polsen and Alin.

Distributions of glycerol dialkyl glycerol tetraethers in surface soils of Qinghai-Tibetan Plateau: implications of GDGT-based proxies in cold and dry regions

NASA Astrophysics Data System (ADS)

Ding, S.; Xu, Y.; Wang, Y.; He, Y.; Hou, J.; Chen, L.; He, J.-S.

2015-01-01

The methylation index of branched tetraethers (MBT) and cyclization ratio of branched tetraethers (CBT) based on the distribution of bacteria-derived branched glycerol dialkyl glycerol tetraethers (bGDGTs) are useful proxies for the reconstruction of continental paleotemperature and soil pH. Several calibrations of the MBT-CBT index have been proposed based on global and regional soils and lake sediments. However, little is known about the distribution and applicability of GDGTs proxies in the Qinghai-Tibet Plateau (QTP), a critical region of the global climate system. Here, we investigated 33 surface soils covering a large area of the QTP. Redundancy analysis showed that soil pH was the most important factor affecting GDGT distributions, followed by mean annual precipitation (MAP) and mean annual air temperature (MAT). The branched-isoprenoid tetraether (BIT) index, an indicator for estimation of soil organic matter in aquatic environments, varied from 0.48 to 1 and negatively correlated with soil pH (r2 = 0.38), suggesting that the BIT index should be used with caution in the QTP. A transfer function of the CBT index-soil pH was established to estimate paleo-soil pH in the QTP: pH = 8.33-1.43 × CBT (r2 = 0.80, RMSE = 0.27 pH unit). The local calibration of MBT-CBT index presented a weak, still significant correlation with MAT (r2 = 0.36) mainly owing to the additional influence of MAP (r2 = 0.50). Combining our data with previously reported GDGTs for Chinese soils resulted in a new calibration of MBT/CBT-MAT: MAT = 2.68+26.14 × MBT-3.37 × CBT (r2 = 0.73; RMSE = 4.2 °C, n = 164). The correlation coefficient and residual error of this new transfer function is comparable with global calibrations, suggesting that MBT-CBT paleotemperature proxy is still valid in the QTP.

An Alternative Default Soil Organic Carbon Method for National GHG Inventory Reporting to the UNFCCC

NASA Astrophysics Data System (ADS)

Ogle, S. M.; Gurung, R.; Klepfer, A.; Spencer, S.; Breidt, J.

2016-12-01

Estimating soil organic C stocks is challenging because of the large amount of data needed to evaluate the impact of land use and management on this terrestrial C pool. Moreover, some of the required data are rarely collected by governments through surveys programs, and are not typically available in remote sensing products. Examples include data on organic amendments, cover crops, crop rotation sequences, vegetated fallows, and fertilization practices. Due to these difficulties, only about 20% of the countries report soil organic C stock changes in their national communications to the UNFCCC. Yet, C sequestration in soils represents one of the least expensive options for reducing greenhouse gas emissions, and has the largest potential for mitigation in the agricultural sector. In order to facilitate reporting, we developed an alternative approach to the current default method provided by the Intergovernmental Panel on Climate Change (IPCC) for estimating soil organic C stock changes in mineral soils. The alternative method estimates the steady-state C stocks for a three pool model given annual crop yields or net primary production as the main input, along with monthly average temperature, total precipitation and soil texture data. Yield data are commonly available in a national agricultural census, and global datasets exists with adequate data for weather and soil texture if national datasets are not available. Tillage and irrigation data are also needed to address the impact of these practices on decomposition rates. The change in steady-state stocks is assumed to occur over a few decades. A Bayesian analysis framework has been developed to derive probability distribution functions for the parameters, and the method is being applied in a global analysis of soil organic carbon stock changes.

Prediction of Gross Primary Production during the Drought and Normal Years over the US Using Solar-Induced Chlorophyll Fluorescence

NASA Astrophysics Data System (ADS)

Halubok, M.; Yang, Z. L.

2016-12-01

This study investigates how gross primary production (GPP) estimates can be improved with the use of solar-induced chlorophyll fluorescence (SIF) and presents an effort to produce GPP predictions based on the interdependence between SIF, precipitation, soil moisture and GPP using Global Ozone Monitoring Experiment-2 (GOME-2), Tropical Rainfall Measuring Mission (TRMM), European Space Agency Climate Change Initiative Soil Moisture (ESA CCI SM) datasets and FLUXNET observations. We found that considering the relationships between SIF, precipitation and soil moisture, isolating SIF-GPP relationships for different plant functional types (PFTs), and using precipitation and soil moisture conditions pertinent to the continental US provides the most accurate GPP estimates over the Great Plains and Texas. We found that there exists a lag between a precipitation event and corresponding fluorescence levels, ranging from about 2 weeks for grasses to a month for crops. Using these lead-lag relationships, we estimate GPP using SIF, precipitation and soil moisture data for two different PFTs (C3 non-arctic grass and crop) over the US applying the multiple linear regression technique. GPP values estimated from our lead-lag based SIF show the closest possible match with the observational data from the FLUXNET stations. During the drought 2011 year over Texas, our GPP values show a decrease by 100 gC/m2/month as compared to the reference year of 2007. In 2012 (drought year over the Great Plains), we observe significant decrease in GPP, especially in the area of high production (>500 gC/m2/month) that is reduced in July and August 2012. Hence, estimating GPP using specific SIF-GPP relationships, considering the differences in biomes and their interactions with precipitation and soil moisture pertinent to a certain region can detect the drought trends and produce reasonable GPP estimates. Thus, this simple and computationally efficient method based on derived linear equations can be used to obtain GPP predictions.

Sandy Soil Microaggregates: Rethinking Our Understanding of Hydraulic Function

DOE Office of Scientific and Technical Information (OSTI.GOV)

Paradiś, Ashley; Brueck, Christopher; Meisenheimer, Douglas

2017-01-01

This study investigated the peculiar structure of microaggregates in coarse sandy soils that exhibit only external porosity and investigated their control on soil hydrology. The microstructure underpins a hydrologic existence that differs from finer textured soils where aggregates have internal porosity. Understanding the impact of these microaggregates on soil hydrology will permit improved agricultural irrigation management and estimates associated with ecosystem capacity and resiliency. Microstructure was investigated using a digital microscope, and aspects of the structure were quantified by sedimentation and computed microtomography. Sandy soil microaggregates were observed to be comprised of a solid sand-grain core that is coated withmore » fines, presumably cemented by organic media. This microstructure leads to three distinct water pools during drainage: capillary water, followed by thick films (1–20 μm) enveloping the outer surfaces of the crusted microaggregates, followed by adsorbed thin films (<1 μm). The characteristics of the thick films were investigated using an analytical model. These films may provide as much as 10 to 40% saturation in the range of plant-available water. Using lubrication theory, it was predicted that thick film drainage follows a power law function with an exponent of 2. Thick films may also have a role in the geochemical evolution of soils and in ecosystem function because they provide contiguous water and gas phases at relatively high moisture contents. And, because the rough outer crust of these microaggregates can provide good niches for microbial activity, biofilm physics will dominate thick film processes, and consequently hydrologic, biologic, and geochemical functions for coarse sandy soils.« less

Towards soil property retrieval from space: Proof of concept using in situ observations

NASA Astrophysics Data System (ADS)

Bandara, Ranmalee; Walker, Jeffrey P.; Rüdiger, Christoph

2014-05-01

Soil moisture is a key variable that controls the exchange of water and energy fluxes between the land surface and the atmosphere. However, the temporal evolution of soil moisture is neither easy to measure nor monitor at large scales because of its high spatial variability. This is mainly a result of the local variation in soil properties and vegetation cover. Thus, land surface models are normally used to predict the evolution of soil moisture and yet, despite their importance, these models are based on low-resolution soil property information or typical values. Therefore, the availability of more accurate and detailed soil parameter data than are currently available is vital, if regional or global soil moisture predictions are to be made with the accuracy required for environmental applications. The proposed solution is to estimate the soil hydraulic properties via model calibration to remotely sensed soil moisture observation, with in situ observations used as a proxy in this proof of concept study. Consequently, the feasibility is assessed, and the level of accuracy that can be expected determined, for soil hydraulic property estimation of duplex soil profiles in a semi-arid environment using near-surface soil moisture observations under naturally occurring conditions. The retrieved soil hydraulic parameters were then assessed by their reliability to predict the root zone soil moisture using the Joint UK Land Environment Simulator model. When using parameters that were retrieved using soil moisture observations, the root zone soil moisture was predicted to within an accuracy of 0.04 m3/m3, which is an improvement of ∼0.025 m3/m3 on predictions that used published values or pedo-transfer functions.

The effect of measured and estimated soil hydraulic properties on simulated water regime in the analysis of grapevine adaptability to future climate

NASA Astrophysics Data System (ADS)

Bonfante, Antonello; Alfieri, Silvia Maria; Agrillo, Antonietta; Dragonetti, Giovanna; Mileti, Antonio; Monaco, Eugenia; De Lorenzi, Francesca

2013-04-01

In the last years many research works have been addressed to evaluate the impact of future climate on crop productivity and plant water use at different spatial scales (global, regional, field) by means of simulation models of agricultural crop systems. Most of these approaches use estimated soil hydraulic properties, through pedotransfer functions (PTF). This choice is related to soil data availability: soil data bases lack measured soil hydraulic properties, but generally they contain information that allow the application of PTF . Although the reliability of the predicted future climate scenarios cannot be immediately validated, we address to evaluate the effects of a simplification of the soil system by using PTF. Thus we compare simulations performed with measured soil hydraulic properties versus simulations carried out with estimated properties. The water regimes resulting from the two procedures are evaluated with respect to crop adaptability to future climate. In particular we will examine if the two procedures bring about different seasonal and spatial variations in the soil water regime patterns, and if these patterns influence adaptation options. The present case study uses the agro-hydrological model SWAP (soil-water-atmosphere and plant) and studies future adaptability of grapevine. The study area is a viticultural area of Southern Italy (Valle Telesina, BN) devoted to the production of high quality wines (DOC and DOCG), and characterized by a complex geomorphology and pedology. The future climate scenario (2021-2050) was constructed applying statistical downscaling techniques to GCMs scenarios. The moisture regime for 25 soils of the selected study area was calculated by means of SWAP model, using both measured and estimated soil hydraulic properties. In the simulation, the upper boundary conditions were derived from the regional climate scenarios. Unit gradient in soil water potential was set as lower boundary condition. Crop-specific input data and model parameters were estimated on the basis of scientific literature and assumed to be generically representative of the species. From the output of the simulation runs, the relative evapotranspiration deficit (or Crop Water Stress Index - CWSI) of the soil units was calculated. Since CWSI is considered an important indicator of the qualitative grapevine responses, its pattern in both simulation procedures has been evaluated. The work was carried out within the Italian national project AGROSCENARI funded by the Ministry for Agricultural, Food and Forest Policies (MIPAAF, D.M. 8608/7303/2008)

Coupled land surface-subsurface hydrogeophysical inverse modeling to estimate soil organic carbon content and explore associated hydrological and thermal dynamics in the Arctic tundra

NASA Astrophysics Data System (ADS)

Phuong Tran, Anh; Dafflon, Baptiste; Hubbard, Susan S.

2017-09-01

Quantitative characterization of soil organic carbon (OC) content is essential due to its significant impacts on surface-subsurface hydrological-thermal processes and microbial decomposition of OC, which both in turn are important for predicting carbon-climate feedbacks. While such quantification is particularly important in the vulnerable organic-rich Arctic region, it is challenging to achieve due to the general limitations of conventional core sampling and analysis methods, and to the extremely dynamic nature of hydrological-thermal processes associated with annual freeze-thaw events. In this study, we develop and test an inversion scheme that can flexibly use single or multiple datasets - including soil liquid water content, temperature and electrical resistivity tomography (ERT) data - to estimate the vertical distribution of OC content. Our approach relies on the fact that OC content strongly influences soil hydrological-thermal parameters and, therefore, indirectly controls the spatiotemporal dynamics of soil liquid water content, temperature and their correlated electrical resistivity. We employ the Community Land Model to simulate nonisothermal surface-subsurface hydrological dynamics from the bedrock to the top of canopy, with consideration of land surface processes (e.g., solar radiation balance, evapotranspiration, snow accumulation and melting) and ice-liquid water phase transitions. For inversion, we combine a deterministic and an adaptive Markov chain Monte Carlo (MCMC) optimization algorithm to estimate a posteriori distributions of desired model parameters. For hydrological-thermal-to-geophysical variable transformation, the simulated subsurface temperature, liquid water content and ice content are explicitly linked to soil electrical resistivity via petrophysical and geophysical models. We validate the developed scheme using different numerical experiments and evaluate the influence of measurement errors and benefit of joint inversion on the estimation of OC and other parameters. We also quantify the propagation of uncertainty from the estimated parameters to prediction of hydrological-thermal responses. We find that, compared to inversion of single dataset (temperature, liquid water content or apparent resistivity), joint inversion of these datasets significantly reduces parameter uncertainty. We find that the joint inversion approach is able to estimate OC and sand content within the shallow active layer (top 0.3 m of soil) with high reliability. Due to the small variations of temperature and moisture within the shallow permafrost (here at about 0.6 m depth), the approach is unable to estimate OC with confidence. However, if the soil porosity is functionally related to the OC and mineral content, which is often observed in organic-rich Arctic soil, the uncertainty of OC estimate at this depth remarkably decreases. Our study documents the value of the new surface-subsurface, deterministic-stochastic inversion approach, as well as the benefit of including multiple types of data to estimate OC and associated hydrological-thermal dynamics.

Estimating the spatial distribution of soil organic matter density and geochemical properties in a polygonal shaped Arctic Tundra using core sample analysis and X-ray computed tomography

NASA Astrophysics Data System (ADS)

Soom, F.; Ulrich, C.; Dafflon, B.; Wu, Y.; Kneafsey, T. J.; López, R. D.; Peterson, J.; Hubbard, S. S.

2016-12-01

The Arctic tundra with its permafrost dominated soils is one of the regions most affected by global climate change, and in turn, can also influence the changing climate through biogeochemical processes, including greenhouse gas release or storage. Characterization of shallow permafrost distribution and characteristics are required for predicting ecosystem feedbacks to a changing climate over decadal to century timescales, because they can drive active layer deepening and land surface deformation, which in turn can significantly affect hydrological and biogeochemical responses, including greenhouse gas dynamics. In this study, part of the Next-Generation Ecosystem Experiment (NGEE-Arctic), we use X-ray computed tomography (CT) to estimate wet bulk density of cores extracted from a field site near Barrow AK, which extend 2-3m through the active layer into the permafrost. We use multi-dimensional relationships inferred from destructive core sample analysis to infer organic matter density, dry bulk density and ice content, along with some geochemical properties from nondestructive CT-scans along the entire length of the cores, which was not obtained by the spatially limited destructive laboratory analysis. Multi-parameter cross-correlations showed good agreement between soil properties estimated from CT scans versus properties obtained through destructive sampling. Soil properties estimated from cores located in different types of polygons provide valuable information about the vertical distribution of soil and permafrost properties as a function of geomorphology.

Reflectance spectroscopy for the assessment of soil salt content in soils of the yellow river delta of China

USGS Publications Warehouse

Weng, Yongling; Gong, P.; Zhu, Z.

2008-01-01

There has been growing interest in the use of reflectance spectroscopy as a rapid and inexpensive tool for soil characterization. In this study, we collected 95 soil samples from the Yellow River Delta of China to investigate the level of soil salinity in relation to soil spectra. Sample plots were selected based on a field investigation and the corresponding soil salinity classification map to maximize variations of saline characteristics in the soil. Spectral reflectances of air-dried soil samples were measured using an Analytical Spectral Device (ASD) spectrometer (350-2500 nm) with an artificial light source. In the Yellow River Delta, the dominant chemical in the saline soil was NaCl and MgCl2. Soil spectra were analysed using two-thirds of the available samples, with the remaining one-third withheld for validation purposes. The analysis indicated that with some preprocessing, the reflectance at 1931-2123 nm and 2153-2254 nm was highly correlated with soil salt content (SSC). In the spectral region of 1931-2123 nm, the correlation R ranged from -0.80 to -0.87. In the region of 2153-2254 nm, the SSC was positively correlated with preprocessed reflectance (0.79-0.88). The preprocessing was done by fitting a convex hull to the reflectance curve and dividing the spectral reflectance by the value of the corresponding convex hull band by band. This process is called continuum removal, and the resulting ratio is called continuum removed reflectance (CR reflectance). However, the SSC did not have a high correlation with the unprocessed reflectance, and the correlation was always negative in the entire spectrum (350-2500 nm) with the strongest negative correlation at 1981 nm (R = -0.63). Moreover, we found a strong correlation (R=0.91) between a soil salinity index (SSI: Constructed using CR reflectance at 2052 nm and 2203 nm) and SSC. We estimated SSC as a function of SSI and SSI' (SSI': Constructed using unprocessed reflectance at 2052 nm and 2203 nm) using univariate regression. Validation of the estimation of SSC was conducted by comparing the estimated SSC with the holdout sample points. The comparison produced an estimated root mean squared error (RMSE) of 0.986 (SSC ranging from 0.06 to 12.30 g kg-1) and R2 of 0.873 for SSC with SSI as independent variable and RMSE of 1.248 and R2 of 0.8 for SSC with SSI' as independent variable. This study showed that a soil salinity index developed for CR reflectance at 2052 nm and 2203 nm on the basis of spectral absorption features of saline soil can be used as a quick and inexpensive method for soil salt-content estimation.

Estimation of runoff mitigation by morphologically different cover crop root systems

NASA Astrophysics Data System (ADS)

Yu, Yang; Loiskandl, Willibald; Kaul, Hans-Peter; Himmelbauer, Margarita; Wei, Wei; Chen, Liding; Bodner, Gernot

2016-07-01

Hydrology is a major driver of biogeochemical processes underlying the distinct productivity of different biomes, including agricultural plantations. Understanding factors governing water fluxes in soil is therefore a key target for hydrological management. Our aim was to investigate changes in soil hydraulic conductivity driven by morphologically different root systems of cover crops and their impact on surface runoff. Root systems of twelve cover crop species were characterized and the corresponding hydraulic conductivity was measured by tension infiltrometry. Relations of root traits to Gardner's hydraulic conductivity function were determined and the impact on surface runoff was estimated using HYDRUS 2D. The species differed in both rooting density and root axes thickness, with legumes distinguished by coarser axes. Soil hydraulic conductivity was changed particularly in the plant row where roots are concentrated. Specific root length and median root radius were the best predictors for hydraulic conductivity changes. For an intensive rainfall simulation scenario up to 17% less rainfall was lost by surface runoff in case of the coarsely rooted legumes Melilotus officinalis and Lathyrus sativus, and the densely rooted Linum usitatissimum. Cover crops with coarse root axes and high rooting density enhance soil hydraulic conductivity and effectively reduce surface runoff. An appropriate functional root description can contribute to targeted cover crop selection for efficient runoff mitigation.

Soils as records of past and present environments

NASA Astrophysics Data System (ADS)

Sauer, Daniela

2015-04-01

This contribution reflects selected pedological concepts that are helpful for interpreting soil properties related to past and present environments. These concepts are illustrated by examples from various landscapes, and their combination finally leads to some further conclusions. The concept of Targulian and Gerasimova (2009) distinguishes soil system and soil body. Soil system is defined as "open multiphase system functioning in any solid-phase substrate at its interface with the atmosphere, hydrosphere and biota", and soil body as "solid-phase part of a soil system produced by its long-term functioning and composed of a vertical sequence of genetic horizons". Soil system functioning corresponds to the recent environmental factors and includes heat and moisture dynamics, biomass production, biogeochemical cycles, and other processes. In contrast, a soil body is a record of the long-term functioning of a soil system. It thus provides a record not only of the functioning of the soil system under the present environmental conditions but also under past, possibly different, conditions. Hence, Targulian and Goryachkin (2004) called it the "memory" of the landscape. Richter and Yaalon (2012) argued that most soils comprise both, features that developed under the present environmental conditions and features that reflect different conditions that the soils experienced in the past; they concluded that most soils are polygenetic. Although the current functioning of the soil system in the concept of Targulian and Gerasimova (2009) is mainly controlled by the present-day combination of environmental factors, it should be added that past processes also influence the soil system, because past processes changed the soil properties in a way that also the present-day functioning of the soil system is affected by these changes. Earlier, Yaalon (1971) had categorised soil properties according to the time-span required for their adjustment to the actual environment, distinguishing (i) rapidly adjusting soil properties (adjusting within some hundreds of years), (ii) slowly adjusting soil properties (adjusting within some thousands of years), and (iii) persistent soil properties (showing no changes over ten thousands to millions of years). In a polygenetic soil, rapidly adjusting soil properties may already be in equilibrium with the present conditions, whereas slowly adjusting soil properties may still reflect past conditions. Thus, the lower the rate at which a certain soil property in a polygenetic soil adjusts, the larger is the extent to which this property is still determined by earlier environmental conditions. Knowledge on the rates at which soil properties adjust may hence be used to estimate the time at which a significant environmental change took place, based on the degree of overprinting of the different kinds of soil properties adjusting at different rates in a polygenetic soil. References: Richter, D. de B., Yaalon, D.H., 2012. "The changing model of soil" revisited. Soil Sci. Soc. Am. J. 76, 766-778. Targulian, V. O., Goryachkin, S. V., 2004. Soil memory: Types of records, carriers, hierarchy and diversity. Revista Mexicana Ciencias Geol. 21, 1-8. Targulian, V.O., Gerasimova, M., 2009. Soil geography: geography of soil systems and soil bodies. Soil Geography: New Horizons. International Conference, 16-20 November 2009 in Huatulco, Mexico. Book of abstracts, 39. Yaalon, D.H., 1971. Soil forming processes in time and space. In: Yaalon, D.H. (Ed.), Paleopedology-origin, nature and dating of paleosols. Int. Soc. Soil Sci. and Israel Univ. Press, Jerusalem, pp. 29-39.

Uncertainty in Pedotransfer Functions from Soil Survey Data

NASA Astrophysics Data System (ADS)

Pachepsky, Y. A.; Rawls, W. J.

2002-05-01

Pedotransfer functions (PTFs) are empirical relationships between hard-to-get soil parameters, i.e. hydraulic properties, and more easily obtainable basic soil properties, such as texture. Use of PTFs in large-scale projects and pilot studies relies on data of soil survey that provides soil basic data as a categorical information. Unlike numerical variables, categorical data cannot be directly used in statistical regressions or neural networks to develop PTFs. Objectives of this work were (a) to find and test techniques to develop PTFs for soil water retention and saturated hydraulic conductivity with soil categorical data as inputs, (b) to evaluate sources of uncertainty in results of such PTFs and to research opportunities of mitigating the uncertainty. We used a subset of about 12,000 samples from the US National Soil characterization database to estimate water retention, and the data set for circa 1000 hydraulic conductivity measurements done in the US. Regression trees and polynomial neural networks based on dummy coding were the techniques tried for the PTF development. The jackknife validation was used to prevent the over-parameterization. Both techniques were equally efficient in developing PTFs, but regression trees gave much more transparent results. Textural class was the leading predictor with RMSE values of about 6.5 and 4.1 vol.% for water retention at -33 and -1500 kPa, respectively. The RMSE values decreased 10% when the laboratory textural analysis was used to establish the textural class. Textural class in the field was determined correctly only in 41% of all cases. To mitigate this source of error, we added slopes, position on the slope classes, and land surface shape classes to the list of PTF inputs. Regression trees generated topotextural groups that encompassed several textural classes. Using topographic variables and soil horizon appeared to be the way to make up for errors made in field determination of texture. Adding field descriptors of soil structure to the field-determined textural class gave similar results. No large improvement was achieved probably because textural class, topographic descriptors and structure descriptors were correlated predictors in many cases. Both median values and uncertainty of the saturated hydraulic conductivity had a power-law decrease as clay content increased. Defining two classes of bulk density helped to estimate hydraulic conductivity within textural classes. We conclude that categorical field soil survey data can be used in PTF-based estimating soil water retention and saturated hydraulic conductivity with quantified uncertainty

Evaluating the importance of characterizing soil structure and horizons in parameterizing a hydrologic process model

USGS Publications Warehouse

Mirus, Benjamin B.

2015-01-01

Incorporating the influence of soil structure and horizons into parameterizations of distributed surface water/groundwater models remains a challenge. Often, only a single soil unit is employed, and soil-hydraulic properties are assigned based on textural classification, without evaluating the potential impact of these simplifications. This study uses a distributed physics-based model to assess the influence of soil horizons and structure on effective parameterization. This paper tests the viability of two established and widely used hydrogeologic methods for simulating runoff and variably saturated flow through layered soils: (1) accounting for vertical heterogeneity by combining hydrostratigraphic units with contrasting hydraulic properties into homogeneous, anisotropic units and (2) use of established pedotransfer functions based on soil texture alone to estimate water retention and conductivity, without accounting for the influence of pedon structures and hysteresis. The viability of this latter method for capturing the seasonal transition from runoff-dominated to evapotranspiration-dominated regimes is also tested here. For cases tested here, event-based simulations using simplified vertical heterogeneity did not capture the state-dependent anisotropy and complex combinations of runoff generation mechanisms resulting from permeability contrasts in layered hillslopes with complex topography. Continuous simulations using pedotransfer functions that do not account for the influence of soil structure and hysteresis generally over-predicted runoff, leading to propagation of substantial water balance errors. Analysis suggests that identifying a dominant hydropedological unit provides the most acceptable simplification of subsurface layering and that modified pedotransfer functions with steeper soil-water retention curves might adequately capture the influence of soil structure and hysteresis on hydrologic response in headwater catchments.

Simulating soil-water movement through loess-veneered landscapes using nonconsilient saturated hydraulic conductivity measurements

USGS Publications Warehouse

Williamson, Tanja N.; Lee, Brad D.; Schoeneberger, Philip J.; McCauley, W. M.; Indorante, Samuel J.; Owens, Phillip R.

2014-01-01

Soil Survey Geographic Database (SSURGO) data are available for the entire United States, so are incorporated in many regional and national models of hydrology and environmental management. However, SSURGO does not provide an understanding of spatial variability and only includes saturated hydraulic conductivity (Ksat) values estimated from particle size analysis (PSA). This study showed model sensitivity to the substitution of SSURGO data with locally described soil properties or alternate methods of measuring Ksat. Incorporation of these different soil data sets significantly changed the results of hydrologic modeling as a consequence of the amount of space available to store soil water and how this soil water is moved downslope. Locally described soil profiles indicated a difference in Ksat when measured in the field vs. being estimated from PSA. This, in turn, caused a difference in which soil layers were incorporated in the hydrologic simulations using TOPMODEL, ultimately affecting how soil water storage was simulated. Simulations of free-flowing soil water, the amount of water traveling through pores too large to retain water against gravity, were compared with field observations of water in wells at five slope positions along a catena. Comparison of the simulated data with the observed data showed that the ability to model the range of conditions observed in the field varied as a function of three soil data sets (SSURGO and local field descriptions using PSA-derived Ksat or field-measured Ksat) and that comparison of absolute values of soil water storage are not valid if different characterizations of soil properties are used.

The integral suspension pressure method (ISP) for precise particle-size analysis by gravitational sedimentation

NASA Astrophysics Data System (ADS)

Durner, Wolfgang; Iden, Sascha C.; von Unold, Georg

2017-01-01

The particle-size distribution (PSD) of a soil expresses the mass fractions of various sizes of mineral particles which constitute the soil material. It is a fundamental soil property, closely related to most physical and chemical soil properties and it affects almost any soil function. The experimental determination of soil texture, i.e., the relative amounts of sand, silt, and clay-sized particles, is done in the laboratory by a combination of sieving (sand) and gravitational sedimentation (silt and clay). In the latter, Stokes' law is applied to derive the particle size from the settling velocity in an aqueous suspension. Traditionally, there are two methodologies for particle-size analysis from sedimentation experiments: the pipette method and the hydrometer method. Both techniques rely on measuring the temporal change of the particle concentration or density of the suspension at a certain depth within the suspension. In this paper, we propose a new method which is based on the pressure in the suspension at a selected depth, which is an integral measure of all particles in suspension above the measuring depth. We derive a mathematical model which predicts the pressure decrease due to settling of particles as function of the PSD. The PSD of the analyzed sample is identified by fitting the simulated time series of pressure to the observed one by inverse modeling using global optimization. The new method yields the PSD in very high resolution and its experimental realization completely avoids any disturbance by the measuring process. A sensitivity analysis of different soil textures demonstrates that the method yields unbiased estimates of the PSD with very small estimation variance and an absolute error in the clay and silt fraction of less than 0.5%.

The integral suspension pressure method (ISP) for precise particle-size analysis by gravitational sedimentation

NASA Astrophysics Data System (ADS)

Durner, Wolfgang; Iden, Sascha C.; von Unold, Georg

2017-04-01

The particle-size distribution (PSD) of a soil expresses the mass fractions of various sizes of mineral particles which constitute the soil material. It is a fundamental soil property, closely related to most physical and chemical soil properties and it affects almost any soil function. The experimental determination of soil texture, i.e., the relative amounts of sand, silt, and clay-sized particles, is done in the laboratory by a combination of sieving (sand) and gravitational sedimentation (silt and clay). In the latter, Stokes' law is applied to derive the particle size from the settling velocity in an aqueous suspension. Traditionally, there are two methodologies for particle-size analysis from sedimentation experiments: the pipette method and the hydrometer method. Both techniques rely on measuring the temporal change of the particle concentration or density of the suspension at a certain depth within the suspension. In this paper, we propose a new method which is based on the pressure in the suspension at a selected depth, which is an integral measure of all particles in suspension above the measuring depth. We derive a mathematical model which predicts the pressure decrease due to settling of particles as function of the PSD. The PSD of the analyzed sample is identified by fitting the simulated time series of pressure to the observed one by inverse modeling using global optimization. The new method yields the PSD in very high resolution and its experimental realization completely avoids any disturbance by the measuring process. A sensitivity analysis of different soil textures demonstrates that the method yields unbiased estimates of the PSD with very small estimation variance and an absolute error in the clay and silt fraction of less than 0.5%

Pier and contraction scour prediction in cohesive soils at selected bridges in Illinois

USGS Publications Warehouse

Straub, Timothy D.; Over, Thomas M.

2010-01-01

This report presents the results of testing the Scour Rate In Cohesive Soils-Erosion Function Apparatus (SRICOS-EFA) method for estimating scour depth of cohesive soils at 15 bridges in Illinois. The SRICOS-EFA method for complex pier and contraction scour in cohesive soils has two primary components. The first component includes the calculation of the maximum contraction and pier scour (Zmax). The second component is an integrated approach that considers a time factor, soil properties, and continued interaction between the contraction and pier scour (SRICOS runs). The SRICOS-EFA results were compared to scour prediction results for non-cohesive soils based on Hydraulic Engineering Circular No. 18 (HEC-18). On average, the HEC-18 method predicted higher scour depths than the SRICOS-EFA method. A reduction factor was determined for each HEC-18 result to make it match the maximum of three types of SRICOS run results. The unconfined compressive strength (Qu) for the soil was then matched with the reduction factor and the results were ranked in order of increasing Qu. Reduction factors were then grouped by Qu and applied to each bridge site and soil. These results, and comparison with the SRICOS Zmax calculation, show that less than half of the reduction-factor method values were the lowest estimate of scour; whereas, the Zmax method values were the lowest estimate for over half. A tiered approach to predicting pier and contraction scour was developed. There are four levels to this approach numbered in order of complexity, with the fourth level being a full SRICOS-EFA analysis. Levels 1 and 2 involve the reduction factors and Zmax calculation, and can be completed without EFA data. Level 3 requires some surrogate EFA data. Levels 3 and 4 require streamflow for input into SRICOS. Estimation techniques for both EFA surrogate data and streamflow data were developed.

[Effects of soil data and map scale on assessment of total phosphorus storage in upland soils.

PubMed

Li, Heng Rong; Zhang, Li Ming; Li, Xiao di; Yu, Dong Sheng; Shi, Xue Zheng; Xing, Shi He; Chen, Han Yue

2016-06-01

Accurate assessment of total phosphorus storage in farmland soils is of great significance to sustainable agricultural and non-point source pollution control. However, previous studies haven't considered the estimation errors from mapping scales and various databases with different sources of soil profile data. In this study, a total of 393×10 4 hm 2 of upland in the 29 counties (or cities) of North Jiangsu was cited as a case for study. Analysis was performed of how the four sources of soil profile data, namely, "Soils of County", "Soils of Prefecture", "Soils of Province" and "Soils of China", and the six scales, i.e. 1:50000, 1:250000, 1:500000, 1:1000000, 1:4000000 and1:10000000, used in the 24 soil databases established for the four soil journals, affected assessment of soil total phosphorus. Compared with the most detailed 1:50000 soil database established with 983 upland soil profiles, relative deviation of the estimates of soil total phosphorus density (STPD) and soil total phosphorus storage (STPS) from the other soil databases varied from 4.8% to 48.9% and from 1.6% to 48.4%, respectively. The estimated STPD and STPS based on the 1:50000 database of "Soils of County" and most of the estimates based on the databases of each scale in "Soils of County" and "Soils of Prefecture" were different, with the significance levels of P＜0.001 or P＜0.05. Extremely significant differences (P＜0.001) existed between the estimates based on the 1:50000 database of "Soils of County" and the estimates based on the databases of each scale in "Soils of Province" and "Soils of China". This study demonstrated the significance of appropriate soil data sources and appropriate mapping scales in estimating STPS.

Linearised and non-linearised isotherm models optimization analysis by error functions and statistical means

PubMed Central

2014-01-01

In adsorption study, to describe sorption process and evaluation of best-fitting isotherm model is a key analysis to investigate the theoretical hypothesis. Hence, numerous statistically analysis have been extensively used to estimate validity of the experimental equilibrium adsorption values with the predicted equilibrium values. Several statistical error analysis were carried out. In the present study, the following statistical analysis were carried out to evaluate the adsorption isotherm model fitness, like the Pearson correlation, the coefficient of determination and the Chi-square test, have been used. The ANOVA test was carried out for evaluating significance of various error functions and also coefficient of dispersion were evaluated for linearised and non-linearised models. The adsorption of phenol onto natural soil (Local name Kalathur soil) was carried out, in batch mode at 30 ± 20 C. For estimating the isotherm parameters, to get a holistic view of the analysis the models were compared between linear and non-linear isotherm models. The result reveled that, among above mentioned error functions and statistical functions were designed to determine the best fitting isotherm. PMID:25018878

Do Quercus ilex woodlands undergo abrupt non-linear functional changes in response to human disturbance along a climatic gradient?

NASA Astrophysics Data System (ADS)

Bochet, Esther; García-Fayos, Patricio; José Molina, Maria; Moreno de las Heras, Mariano; Espigares, Tíscar; Nicolau, Jose Manuel; Monleon, Vicente

2017-04-01

Theoretical models predict that drylands are particularly prone to suffer critical transitions with abrupt non-linear changes in their structure and functions as a result of the existing complex interactions between climatic fluctuations and human disturbances. However, so far, few studies provide empirical data to validate these models. We aim at determining how holm oak (Quercus ilex) woodlands undergo changes in their functions in response to human disturbance along an aridity gradient (from semi-arid to sub-humid conditions), in eastern Spain. For that purpose, we used (a) remote-sensing estimations of precipitation-use-efficiency (PUE) from enhanced vegetation index (EVI) observations performed in 231x231 m plots of the Moderate Resolution Imaging Spectroradiometer (MODIS); (b) biological and chemical soil parameter determinations (extracellular soil enzyme activity, soil respiration, nutrient cycling processes) from soil sampled in the same plots; (c) vegetation parameter determinations (ratio of functional groups) from vegetation surveys performed in the same plots. We analyzed and compared the shape of the functional change (in terms of PUE and soil and vegetation parameters) in response to human disturbance intensity for our holm oak sites along the aridity gradient. Overall, our results evidenced important differences in the shape of the functional change in response to human disturbance between climatic conditions. Semi-arid areas experienced a more accelerated non-linear decrease with an increasing disturbance intensity than sub-humid ones. The proportion of functional groups (herbaceous vs. woody cover) played a relevant role in the shape of the functional response of the holm oak sites to human disturbance.

TDR Technique for Estimating the Intensity of Evapotranspiration of Turfgrasses.

PubMed

Janik, Grzegorz; Wolski, Karol; Daniel, Anna; Albert, Małgorzata; Skierucha, Wojciech; Wilczek, Andrzej; Szyszkowski, Paweł; Walczak, Amadeusz

2015-01-01

The paper presents a method for precise estimation of evapotranspiration of selected turfgrass species. The evapotranspiration functions, whose domains are only two relatively easy to measure parameters, were developed separately for each of the grass species. Those parameters are the temperature and the volumetric moisture of soil at the depth of 2.5 cm. Evapotranspiration has the character of a modified logistic function with empirical parameters. It assumes the form ETR(θ (2.5 cm), T (2.5 cm)) = A/(1 + B · e (-C · (θ (2.5 cm) · T (2.5 cm)), where: ETR(θ (2.5 cm), T (2.5 cm)) is evapotranspiration [mm · h(-1)], θ (2.5 cm) is volumetric moisture of soil at the depth of 2.5 cm [m(3) · m(-3)], T (2.5 cm) is soil temperature at the depth of 2.5 cm [°C], and A, B, and C are empirical coefficients calculated individually for each of the grass species [mm · h(1)], and [-], [(m(3) · m(-3) · °C)(-1)]. The values of evapotranspiration calculated on the basis of the presented function can be used as input data for the design of systems for the automatic control of irrigation systems ensuring optimum moisture conditions in the active layer of lawn swards.

The impact of soil degradation on soil functioning in Europe

NASA Astrophysics Data System (ADS)

Montanarella, Luca

2010-05-01

The European Commission has presented in September 2006 its Thematic Strategy for Soil Protection.The Thematic Strategy for Soil Protection consists of a Communication from the Commission to the other European Institutions, a proposal for a framework Directive (a European law), and an Impact Assessment. The Communication (COM(2006) 231) sets the frame. It defines the relevant soil functions for Europe and identifies the major threats. It explains why further action is needed to ensure a high level of soil protection, sets the overall objective of the Strategy and explains what kind of measures must be taken. It establishes a ten-year work program for the European Commission. The proposal for a framework Directive (COM(2006) 232) sets out common principles for protecting soils across the EU. Within this common framework, the EU Member States will be in a position to decide how best to protect soil and how use it in a sustainable way on their own territory. The Impact Assessment (SEC (2006) 1165 and SEC(2006) 620) contains an analysis of the economic, social and environmental impacts of the different options that were considered in the preparatory phase of the strategy and of the measures finally retained by the Commission. Since 2006 a large amount of new evidence has allowed to further document the extensive negative impacts of soil degradation on soil functioning in Europe. Extensive soil erosion, combined with a constant loss of soil organic carbon, have raised attention to the important role soils are playing within the climate change related processes. Other important processes are related to the loss of soil biodiversity, extensive soil sealing by housing and infrastructure, local and diffuse contamination by agricultural and industrial sources, compaction due to unsustainable agricultural practices and salinization by unsustainable irrigation practices. The extended impact assessment by the European Commission has attempted to quantify in monetary terms the actual economic impact of soil degradation in Europe.The total costs of soil degradation that could be assessed for erosion, organic matter decline, salinisation, landslides and contamination on the basis of available data, would be up to €38 billion annually for EU25. These estimates are necessarily wide ranging due to the lack of sufficient quantitative and qualitative data. Future research activities will have to address, in multidisciplinary teams, the social and economic aspects of soil degradation in Europe, in order to come up with more reliable estimates of the economic impact of soil degradation. A more reliable and updated system of indicators needs to be developed in order to cover the full cycle of the Driving forces-Pressures-State-Impact-Response (DPSIR) framework. Recent developments towards a new soil monitoring system for Europe will be presented as well as some of the recent outputs of the European Soil Data Centre (ESDAC).

A practical algorithm to estimate soil thawing onset with the soil moisture active passive (SMAP) data

NASA Astrophysics Data System (ADS)

Chen, X.; Liu, L.

2016-12-01

The Soil Moisture Active Passive (SMAP) satellite simultaneously collected active and passive microwave data at L-band from April to July, 2015. The L-band radiometer brightness temperature (TB) data are strongly sensitive to the change of soil moisture, therefore, can be used to estimate freeze/thaw state of soil. We applied an edge detection method to detect the onset of thawing based on the SMAP level-1C TB data. This method convolves the first derivative of the Gaussian function as a kernel with the TB time series. When thawing occurs, soil moisture increases abruptly and leads to a decrease in TB. Therefore, a primary thaw event can be identified when the convolved signal reaches a local minimum. Considering the noise of the radiometer data, not all local minimums correspond to a thaw event. Therefore, we further applied a filter based on a priori or in situ soil temperature observation to eliminate false events. We compared the TB-based estimates with in situ measurements of soil temperature, moisture, and snow depth from April to June from 5 SNOTEL sites in Alaska. Our results show that at 4 out of the 5 sites the estimated thawing onsets and in-situ data agree within 5 to 10 days. However, we found a distinct inconsistency of 41 days at the fifth site. One possible reason is the mismatch in spatial coverage: one pixel of SMAP radiometer data has a size of 36 km, within which different areas may have different freeze/thaw states. The SMAP radar backscatter coefficient (σ0) data are also very sensitive to soil moisture, and has finer spatial resolution of 1 km, making it more directly comparable with the in situ measurements. We applied a seasonal threshold method to estimate thawing onset based on this data. Firstly, we set a thaw onset based on the in situ soil temperature and moisture measurements at 5 cm depth. Then we averaged σ0 observations from April 14th to 7 days before the thaw onset to represent the frozen soil, and used the mean value from 7 days after the thawing onset to June 1st as thawed reference. Next, the σ0-based freeze/thaw distribution within radiometer pixel can be obtained. Assuming TB and have a linear relationship in 36 km scale during a short time, SMAP provide a down scaling method to obtain 9 km resolution TB data. For further work, we plan to apply the edge detection method on this TB data to estimate the soil state in 9 km.

10Be inventories in Alpine soils and their potentiality for dating land surfaces

NASA Astrophysics Data System (ADS)

Egli, Markus; Brandová, Dagmar; Böhlert, Ralph; Favilli, Filippo; Kubik, Peter W.

2010-05-01

To exploit natural archives and geomorphic objects it is necessary to date them first. Landscape evolution of Alpine areas is often strongly related to the activities of glaciers in the Pleistocene and Holocene. At sites where no organic matter for radiocarbon dating exists and where suitable boulders for surface exposure dating (using in situ produced cosmogenic nuclides) are absent, dating of soils could give information about the timing of landscape evolution. We explored the applicability of soil dating using the inventory of meteoric Be-10 in Alpine soils. For this purpose, a set of 6 soil profiles in the Swiss and Italian Alps was investigated. The surface at these sites had already been dated (using the radiocarbon technique or surface exposure dating using in situ produced Be-10). Consequently, a direct comparison of the ages of the soils using meteoric Be-10 and other dating techniques was made possible. The estimation of Be-10 deposition rates is subject to severe limitations and strongly influences the obtained results. We tested three scenarios using a) the meteoric Be-10 deposition rates as a function of the annual precipitation rate, b) a constant Be-10 input for the Central Alps and c) as b) but assuming a pre-exposure of the parent material. The obtained ages that are based on the Be-10 inventory in soils and on scenario a) for the Be-10 input agreed reasonably well with the expected age (obtained from surface exposure or radiocarbon dating). The ages obtained from soils using scenario b) produced mostly ages that were too old whereas the approach using scenario c) seemed to yield better results than scenario b). Erosion calculations can, in theory, be performed using the Be-10 inventory and Be-10 deposition rates. An erosion estimation was possible using scenario a) and c), but not using b). The estimated erosion rates are in a reasonable range. The dating of soils using Be-10 has several potential error sources. Analytical errors as well as errors from other parameters such as bulk soil density and soil skeleton content have to be taken into account. The error range was from 8 up to 21%. Furthermore, uncertainties in estimating Be-10 deposition rates substantially influence the calculated ages. Relative age estimates and, under optimal conditions, a numerical dating can be carried out. Age determination of Alpine soils using Be-10 gives another possibility to date surfaces when other methods fail or are not possible at all. It is, however, not straightforward, quite laborious and may consequently have some distinct limitations.

Rapid prototyping of soil moisture estimates using the NASA Land Information System

NASA Astrophysics Data System (ADS)

Anantharaj, V.; Mostovoy, G.; Li, B.; Peters-Lidard, C.; Houser, P.; Moorhead, R.; Kumar, S.

2007-12-01

The Land Information System (LIS), developed at the NASA Goddard Space Flight Center, is a functional Land Data Assimilation System (LDAS) that incorporates a suite of land models in an interoperable computational framework. LIS has been integrated into a computational Rapid Prototyping Capabilities (RPC) infrastructure. LIS consists of a core, a number of community land models, data servers, and visualization systems - integrated in a high-performance computing environment. The land surface models (LSM) in LIS incorporate surface and atmospheric parameters of temperature, snow/water, vegetation, albedo, soil conditions, topography, and radiation. Many of these parameters are available from in-situ observations, numerical model analysis, and from NASA, NOAA, and other remote sensing satellite platforms at various spatial and temporal resolutions. The computational resources, available to LIS via the RPC infrastructure, support e- Science experiments involving the global modeling of land-atmosphere studies at 1km spatial resolutions as well as regional studies at finer resolutions. The Noah Land Surface Model, available with-in the LIS is being used to rapidly prototype soil moisture estimates in order to evaluate the viability of other science applications for decision making purposes. For example, LIS has been used to further extend the utility of the USDA Soil Climate Analysis Network of in-situ soil moisture observations. In addition, LIS also supports data assimilation capabilities that are used to assimilate remotely sensed soil moisture retrievals from the AMSR-E instrument onboard the Aqua satellite. The rapid prototyping of soil moisture estimates using LIS and their applications will be illustrated during the presentation.

Feasibility analysis of using inverse modeling for estimating field-scale evapotranspiration in maize and soybean fields from soil water content monitoring networks

NASA Astrophysics Data System (ADS)

Foolad, Foad; Franz, Trenton E.; Wang, Tiejun; Gibson, Justin; Kilic, Ayse; Allen, Richard G.; Suyker, Andrew

2017-03-01

In this study, the feasibility of using inverse vadose zone modeling for estimating field-scale actual evapotranspiration (ETa) was explored at a long-term agricultural monitoring site in eastern Nebraska. Data from both point-scale soil water content (SWC) sensors and the area-average technique of cosmic-ray neutron probes were evaluated against independent ETa estimates from a co-located eddy covariance tower. While this methodology has been successfully used for estimates of groundwater recharge, it was essential to assess the performance of other components of the water balance such as ETa. In light of recent evaluations of land surface models (LSMs), independent estimates of hydrologic state variables and fluxes are critically needed benchmarks. The results here indicate reasonable estimates of daily and annual ETa from the point sensors, but with highly varied soil hydraulic function parameterizations due to local soil texture variability. The results of multiple soil hydraulic parameterizations leading to equally good ETa estimates is consistent with the hydrological principle of equifinality. While this study focused on one particular site, the framework can be easily applied to other SWC monitoring networks across the globe. The value-added products of groundwater recharge and ETa flux from the SWC monitoring networks will provide additional and more robust benchmarks for the validation of LSM that continues to improve their forecast skill. In addition, the value-added products of groundwater recharge and ETa often have more direct impacts on societal decision-making than SWC alone. Water flux impacts human decision-making from policies on the long-term management of groundwater resources (recharge), to yield forecasts (ETa), and to optimal irrigation scheduling (ETa). Illustrating the societal benefits of SWC monitoring is critical to insure the continued operation and expansion of these public datasets.

Optimizing Wastewater Reuse in Agricultural Fields via Merging of Embedded Network Sensor Data and Flow and Transport Models Using Data Assimilation

NASA Astrophysics Data System (ADS)

Wu, C.; Margulis, S. A.

2007-12-01

Wastewater re-use via crop irrigation has the potential to be an effective means of wastewater disposal. However, nitrate in wastewater may contaminate groundwater if it does not decay before reaching the groundwater table. In order to dispose of wastewater while preventing long-term groundwater pollution, irrigation rates need to be optimized based on the current and predicted states of the soil, such as soil moisture content and/or nitrate concentration. A real-time soil states estimation system using the Ensemble Kalman Filter (EnKF) has been developed for application to a test bed for wastewater re-use in Palmdale, CA. This test bed, covered with alfalfa, is a 30-acre irrigation plot with a 200-meter long rotating pivot arm that irrigates the area with reclaimed wastewater. A sensor network is deployed in the soil near the surface. The data assimilation system has shown the ability to characterize soil states and fluxes from sparse measurements. The real-time estimation system will then be used to explore the potential feedback for optimizing the sprinkler operation (i.e. maximizing the magnitude of wastewater release while minimizing the ultimate groundwater pollution). In optimization models, soil states and fluxes can be regarded as functions of irrigation rate. Through optimization, the irrigation rate in a finite horizon can be maximized while still satisfying all criteria in soil states and fluxes to ensure the safety of groundwater. Since the data assimilation system provides reliable estimation of soil states and fluxes, it is expected to define the optimal irrigation rate with higher confidence compared to using models or sensors only.

On the spatial distribution of the transpiration and soil moisture of a Mediterranean heterogeneous ecosystem in water-limited conditions.

NASA Astrophysics Data System (ADS)

Curreli, Matteo; Corona, Roberto; Montaldo, Nicola; Albertson, John D.; Oren, Ram

2014-05-01

Mediterranean ecosystems are characterized by a strong heterogeneity, and often by water-limited conditions. In these conditions contrasting plant functional types (PFT, e.g. grass and woody vegetation) compete for the water use. Both the vegetation cover spatial distribution and the soil properties impact the soil moisture (SM) spatial distribution. Indeed, vegetation cover density and type affects evapotranspiration (ET), which is the main lack of the soil water balance in these ecosystems. With the objective to carefully estimate SM and ET spatial distribution in a Mediterranean water-limited ecosystem and understanding SM and ET relationships, an extended field campaign is carried out. The study was performed in a heterogeneous ecosystem in Orroli, Sardinia (Italy). The experimental site is a typical Mediterranean ecosystem where the vegetation is distributed in patches of woody vegetation (wild olives mainly) and grass. Soil depth is low and spatially varies between 10 cm and 40 cm, without any correlation with the vegetation spatial distribution. ET, land-surface fluxes and CO2 fluxes are estimated by an eddy covariance technique based micrometeorological tower. But in heterogeneous ecosystems a key assumption of the eddy covariance theory, the homogeneity of the surface, is not preserved and the ET estimate may be not correct. Hence, we estimate ET of the woody vegetation using the thermal dissipation method (i.e. sap flow technique) for comparing the two methodologies. Due the high heterogeneity of the vegetation and soil properties of the field a total of 54 sap flux sensors were installed. 14 clumps of wild olives within the eddy covariance footprint were identified as the most representative source of flux and they were instrumented with the thermal dissipation probes. Measurements of diameter at the height of sensor installation (height of 0.4 m above ground) were recorded in all the clumps. Bark thickness and sapwood depth were measured on several trees to obtain a generalized estimates of sapwood depth. The known of allometric relationships between sapwood area, diameter and canopy cover area within the eddy covariance footprint helped for the application of a reliable scaling procedure of the local sap flow estimates which are in a good agreement with the estimates of ET eddy covariance based. Soil moisture were also extensively monitored through 25 probes installed in the eddy covariance footprint. Results show that comparing eddy covariance and sap flow ET estimates eddy covariance technique is still accurate in this heterogeneous field, whereas the key assumption, surface homogeneity, is not preserved. Furthermore, interestingly wild olives still transpire at higher rates for the driest soil moisture conditions, confirming the hydraulic redistribution from soil below the roots, and from roots penetrating deep cracks in the underlying basalt parent rock.

Aerodynamic method for obtaining the soil water retention curve

NASA Astrophysics Data System (ADS)

Alekseev, V. V.; Maksimov, I. I.

2013-07-01

A new method for the rapid plotting of the soil water retention curve (SWRC) has been proposed that considers the soil water as an environment limited by the soil solid phase on one side and by the soil air on the other side. Both contact surfaces have surface energies, which play the main role in water retention. The use of an idealized soil model with consideration for the nonequilibrium thermodynamic laws and the aerodynamic similarity principles allows us to estimate the volumetric specific surface areas of soils and, using the proposed pedotransfer function (PTF), to plot the SWRC. The volumetric specific surface area of the solid phase, the porosity, and the specific free surface energy at the water-air interface are used as the SWRC parameters. Devices for measuring the parameters are briefly described. The differences between the proposed PTF and the experimental data have been analyzed using the statistical processing of the data.

Development of type transfer functions for regional-scale nonpoint source groundwater vulnerability assessments

NASA Astrophysics Data System (ADS)

Stewart, Iris T.; Loague, Keith

2003-12-01

Groundwater vulnerability assessments of nonpoint source agrochemical contamination at regional scales are either qualitative in nature or require prohibitively costly computational efforts. By contrast, the type transfer function (TTF) modeling approach for vadose zone pesticide leaching presented here estimates solute concentrations at a depth of interest, only uses available soil survey, climatic, and irrigation information, and requires minimal computational cost for application. TTFs are soil texture based travel time probability density functions that describe a characteristic leaching behavior for soil profiles with similar soil hydraulic properties. Seven sets of TTFs, representing different levels of upscaling, were developed for six loam soil textural classes with the aid of simulated breakthrough curves from synthetic data sets. For each TTF set, TTFs were determined from a group or subgroup of breakthrough curves for each soil texture by identifying the effective parameters of the function that described the average leaching behavior of the group. The grouping of the breakthrough curves was based on the TTF index, a measure of the magnitude of the peak concentration, the peak arrival time, and the concentration spread. Comparison to process-based simulations show that the TTFs perform well with respect to mass balance, concentration magnitude, and the timing of concentration peaks. Sets of TTFs based on individual soil textures perform better for all the evaluation criteria than sets that span all textures. As prediction accuracy and computational cost increase with the number of TTFs in a set, the selection of a TTF set is determined by a given application.

Inferring Land Surface Model Parameters for the Assimilation of Satellite-Based L-Band Brightness Temperature Observations into a Soil Moisture Analysis System

NASA Technical Reports Server (NTRS)

Reichle, Rolf H.; De Lannoy, Gabrielle J. M.

2012-01-01

The Soil Moisture and Ocean Salinity (SMOS) satellite mission provides global measurements of L-band brightness temperatures at horizontal and vertical polarization and a variety of incidence angles that are sensitive to moisture and temperature conditions in the top few centimeters of the soil. These L-band observations can therefore be assimilated into a land surface model to obtain surface and root zone soil moisture estimates. As part of the observation operator, such an assimilation system requires a radiative transfer model (RTM) that converts geophysical fields (including soil moisture and soil temperature) into modeled L-band brightness temperatures. At the global scale, the RTM parameters and the climatological soil moisture conditions are still poorly known. Using look-up tables from the literature to estimate the RTM parameters usually results in modeled L-band brightness temperatures that are strongly biased against the SMOS observations, with biases varying regionally and seasonally. Such biases must be addressed within the land data assimilation system. In this presentation, the estimation of the RTM parameters is discussed for the NASA GEOS-5 land data assimilation system, which is based on the ensemble Kalman filter (EnKF) and the Catchment land surface model. In the GEOS-5 land data assimilation system, soil moisture and brightness temperature biases are addressed in three stages. First, the global soil properties and soil hydraulic parameters that are used in the Catchment model were revised to minimize the bias in the modeled soil moisture, as verified against available in situ soil moisture measurements. Second, key parameters of the "tau-omega" RTM were calibrated prior to data assimilation using an objective function that minimizes the climatological differences between the modeled L-band brightness temperatures and the corresponding SMOS observations. Calibrated parameters include soil roughness parameters, vegetation structure parameters, and the single scattering albedo. After this climatological calibration, the modeling system can provide L-band brightness temperatures with a global mean absolute bias of less than 10K against SMOS observations, across multiple incidence angles and for horizontal and vertical polarization. Third, seasonal and regional variations in the residual biases are addressed by estimating the vegetation optical depth through state augmentation during the assimilation of the L-band brightness temperatures. This strategy, tested here with SMOS data, is part of the baseline approach for the Level 4 Surface and Root Zone Soil Moisture data product from the planned Soil Moisture Active Passive (SMAP) satellite mission.

Using random forests to explore the effects of site attributes and soil properties on near-saturated and saturated hydraulic conductivity

NASA Astrophysics Data System (ADS)

Jorda, Helena; Koestel, John; Jarvis, Nicholas

2014-05-01

Knowledge of the near-saturated and saturated hydraulic conductivity of soil is fundamental for understanding important processes like groundwater contamination risks or runoff and soil erosion. Hydraulic conductivities are however difficult and time-consuming to determine by direct measurements, especially at the field scale or larger. So far, pedotransfer functions do not offer an especially reliable alternative since published approaches exhibit poor prediction performances. In our study we aimed at building pedotransfer functions by growing random forests (a statistical learning approach) on 486 datasets from the meta-database on tension-disk infiltrometer measurements collected from peer-reviewed literature and recently presented by Jarvis et al. (2013, Influence of soil, land use and climatic factors on the hydraulic conductivity of soil. Hydrol. Earth Syst. Sci. 17(12), 5185-5195). When some data from a specific source publication were allowed to enter the training set whereas others were used for validation, the results of a 10-fold cross-validation showed reasonable coefficients of determination of 0.53 for hydraulic conductivity at 10 cm tension, K10, and 0.41 for saturated conductivity, Ks. The estimated average annual temperature and precipitation at the site were the most important predictors for K10, while bulk density and estimated average annual temperature were most important for Ks prediction. The soil organic carbon content and the diameter of the disk infiltrometer were also important for the prediction of both K10 and Ks. However, coefficients of determination were around zero when all datasets of a specific source publication were excluded from the training set and exclusively used for validation. This may indicate experimenter bias, or that better predictors have to be found or that a larger dataset has to be used to infer meaningful pedotransfer functions for saturated and near-saturated hydraulic conductivities. More research is in progress to further elucidate this question.

Surface Soil Moisture Estimates Across China Based on Multi-satellite Observations and A Soil Moisture Model

NASA Astrophysics Data System (ADS)

Zhang, Ke; Yang, Tao; Ye, Jinyin; Li, Zhijia; Yu, Zhongbo

2017-04-01

Soil moisture is a key variable that regulates exchanges of water and energy between land surface and atmosphere. Soil moisture retrievals based on microwave satellite remote sensing have made it possible to estimate global surface (up to about 10 cm in depth) soil moisture routinely. Although there are many satellites operating, including NASA's Soil Moisture Acitive Passive mission (SMAP), ESA's Soil Moisture and Ocean Salinity mission (SMOS), JAXA's Advanced Microwave Scanning Radiometer 2 mission (AMSR2), and China's Fengyun (FY) missions, key differences exist between different satellite-based soil moisture products. In this study, we applied a single-channel soil moisture retrieval model forced by multiple sources of satellite brightness temperature observations to estimate consistent daily surface soil moisture across China at a spatial resolution of 25 km. By utilizing observations from multiple satellites, we are able to estimate daily soil moisture across the whole domain of China. We further developed a daily soil moisture accounting model and applied it to downscale the 25-km satellite-based soil moisture to 5 km. By comparing our estimated soil moisture with observations from a dense observation network implemented in Anhui Province, China, our estimated soil moisture results show a reasonably good agreement with the observations (RMSE < 0.1 and r > 0.8).

Surface soil moisture retrieval over a Mediterranean semi-arid region using X-band TerraSAR-X SAR data

NASA Astrophysics Data System (ADS)

Azza, Gorrab; Zribi, Mehrez; Baghdadi, Nicolas; Mougenot, Bernard; Boulet, Gilles; Lili-Chabaane, Zohra

2015-04-01

Mapping surface soil moisture with meter-scale spatial resolution is appropriate for multi- domains particularly hydrology and agronomy. It allows water resources and irrigation management decisions, drought monitoring and validation of multi-hydrological water balance models. In the last years, various studies have demonstrated the large potential of radar remote sensing data, mainly from C frequency band, to retrieve soil moisture. However, the accuracy of the soil moisture estimation, by inversing backscattering radar coefficients (σ°), is affected by the influence of surface roughness and vegetation biomass contributions. In recent years, different empirical, semi empirical and physical approaches are developed for bare soil conditions, to estimate accurately spatial soil moisture variability. In this study, we propose an approach based on the change detection method for the retrieval of surface soil moisture at a higher spatial resolution. The proposal algorithm combines multi-temporal X-band SAR images (TerraSAR-X) with different continuous thetaprobe measurements. Seven thetaprobe stations are installed at different depths over the central semi arid region of Tunisia (9°23' - 10°17' E, 35° 1'-35°55' N). They cover approximately the entire of our study site and provide regional scale information. Ground data were collected over agricultural bare soil fields simultaneously to various TerraSAR-X data acquired during 2013-2014 and 2014-2015. More than fourteen test fields were selected for each spatial acquisition campaign, with variations in soil texture and in surface soil roughness. For each date, we considered the volumetric water content with thetaprobe instrument and gravimetric sampling; we measured also the roughness parameters with pin profilor. To retrieve soil moisture from X-band SAR data, we analyzed statistically the sensitivity between radar measurements and ground soil moisture derived from permanent thetaprobe stations. Our analyses are applied over bare soil class identified from an optical image SPOT / HRV acquired in the same period of the measurements. Results have shown linear relationship for the radar signals as a function of volumetric soil moisture with high sensitivity about 0.21 dB/vol%. For estimation of change in soil moisture, we considered two options: On the first one, we applied the change detection approach between successive radar images (∆σ°) assuming unchanged soil roughness effects. Our soil moisture retrieval algorithm was validated on the basis of comparisons between estimated and in situ soil moisture measurements over test fields. Using this option, results have shown an accuracy (RMSE) of about 4.8 %. Secondly, we corrected the sensitivity of the radar backscatter images to the surface roughness variability. Results have shown a reduction of the difference between the retrieved soil moisture and ground measurements with an RMSE about 3.7%.

Genetic Linkage of Soil Carbon Pools and Microbial Functions in Subtropical Freshwater Wetlands in Response to Experimental Warming

PubMed Central

Wang, Hang; He, Zhili; Lu, Zhenmei; Zhou, Jizhong; Van Nostrand, Joy D.; Xu, Xinhua

2012-01-01

Rising climate temperatures in the future are predicted to accelerate the microbial decomposition of soil organic matter. A field microcosm experiment was carried out to examine the impact of soil warming in freshwater wetlands on different organic carbon (C) pools and associated microbial functional responses. GeoChip 4.0, a functional gene microarray, was used to determine microbial gene diversity and functional potential for C degradation. Experimental warming significantly increased soil pore water dissolved organic C and phosphorus (P) concentrations, leading to a higher potential for C emission and P export. Such losses of total organic C stored in soil could be traced back to the decomposition of recalcitrant organic C. Warming preferentially stimulated genes for degrading recalcitrant C over labile C. This was especially true for genes encoding cellobiase and mnp for cellulose and lignin degradation, respectively. We confirmed this with warming-enhanced polyphenol oxidase and peroxidase activities for recalcitrant C acquisition and greater increases in recalcitrant C use efficiency than in labile C use efficiency (average percentage increases of 48% versus 28%, respectively). The relative abundance of lignin-degrading genes increased by 15% under warming; meanwhile, soil fungi, as the primary decomposers of lignin, were greater in abundance by 27%. This work suggests that future warming may enhance the potential for accelerated fungal decomposition of lignin-like compounds, leading to greater microbially mediated C losses than previously estimated in freshwater wetlands. PMID:22923398

Estimated Annual Net Change in Soil Carbon per US County, 1990-2004

DOE Data Explorer

West, Tristram O.; Brandt, Craig C.; Wilson, Bradly S.; Hellwinckel, Chap M.; Tyler, Donald D.; Marland, Gregg; De La Torre Ugarte, Daniel D.; Larson, James A.; Nelson, Richard G.

2008-01-01

These data represent the estimated net change (Megagram per year) in soil carbon due to changes in the crop type and tillage intensity. Estimated accumulation of soil carbon under Conservation Reserve Program (CRP)lands is included in these estimates. Negative values represent a net flux from the atmosphere to the soil; positive values represent a net flux from the soil to the atmosphere. As such, soil carbon sequestration is represented here as a negative value.

Tundra fire disturbance homogonizes belowground food web structure, function and dynamics

NASA Astrophysics Data System (ADS)

Moore, J. C.; Pressler, Y.; Koltz, A.; Asmus, A.; Simpson, R.

2016-12-01

Tundra fires on Alaska's North Slope are on the rise due to increased lightning strikes since 2000. On July 16, 2007 lightning ignited the Anaktuvuk River fire, burning a 40-by-10 mile swath of tundra about 24 miles north of Toolik Field Station. The fire burned 401 square miles, was visible from space, and released more than 2.3 million tons of carbon into the atmosphere. A large amount of the organic layer of the soil was burned, changing the over all composition of the site and exposing deeper soil horizons. Due to fundamental transitions in soil characteristics and vegetation we hypothesized that the belowground food web community would be affected both in terms of biomass and location within the soil profile. Microbial biomass was reduced with burn severity. In the lower organic horizon there was a significant reduction in fungal biomass but we did not observe this effect in the upper organic soil. We did not observe a significant effect of burn severity on individual group biomass within higher trophic levels. Canonical Discriminant Analysis using the biomass estimates of the functional groups in the food webs found that the webs are becoming increasingly homogenized in the severely burned site compared to the moderately burned and unburned sites. The unburned soils differed significantly from soil at both burn sites; the greatest effects on food web structure were at the lower organic depth, whereas. We modeled the effects of the fire on soil organic matter processing rates and energy flow through the three food webs. The model estimated a decrease in C and N mineralization with fire severity, due in large part to the loss of organic material. While the organic horizon at the unburned site had 12 times greater C and N mineralization than the mineral soils, we observed little to no difference in C and N mineralization between the organic and mineral soil horizons in the moderately and severely burned sites. Our results show that the fire significantly altered the trophic structure of the soil food web, with loss of trophic complexity with increasing fire severity, which correlated strongly with C and N processing and food web stability.

Profile soil property estimation using a VIS-NIR-EC-force probe

USDA-ARS?s Scientific Manuscript database

Combining data collected in-field from multiple soil sensors has the potential to improve the efficiency and accuracy of soil property estimates. Optical diffuse reflectance spectroscopy (DRS) has been used to estimate many important soil properties, such as soil carbon, water content, and texture. ...

Relationship between specific surface area and the dry end of the water retention curve for soils with varying clay and organic carbon contents

NASA Astrophysics Data System (ADS)

Resurreccion, Augustus C.; Moldrup, Per; Tuller, Markus; Ferré, T. P. A.; Kawamoto, Ken; Komatsu, Toshiko; de Jonge, Lis Wollesen

2011-06-01

Accurate description of the soil water retention curve (SWRC) at low water contents is important for simulating water dynamics and biochemical vadose zone processes in arid environments. Soil water retention data corresponding to matric potentials of less than -10 MPa, where adsorptive forces dominate over capillary forces, have also been used to estimate soil specific surface area (SA). In the present study, the dry end of the SWRC was measured with a chilled-mirror dew point psychrometer for 41 Danish soils covering a wide range of clay (CL) and organic carbon (OC) contents. The 41 soils were classified into four groups on the basis of the Dexter number (n = CL/OC), and the Tuller-Or (TO) general scaling model describing water film thickness at a given matric potential (<-10 MPa) was evaluated. The SA estimated from the dry end of the SWRC (SA_SWRC) was in good agreement with the SA measured with ethylene glycol monoethyl ether (SA_EGME) only for organic soils with n > 10. A strong correlation between the ratio of the two surface area estimates and the Dexter number was observed and applied as an additional scaling function in the TO model to rescale the soil water retention curve at low water contents. However, the TO model still overestimated water film thickness at potentials approaching ovendry condition (about -800 MPa). The semi-log linear Campbell-Shiozawa-Rossi-Nimmo (CSRN) model showed better fits for all investigated soils from -10 to -800 MPa and yielded high correlations with CL and SA. It is therefore recommended to apply the empirical CSRN model for predicting the dry part of the water retention curve (-10 to -800 MPa) from measured soil texture or surface area. Further research should aim to modify the more physically based TO model to obtain better descriptions of the SWRC in the very dry range (-300 to -800 MPa).

Comparison of field and laboratory VNIR spectroscopy for profile soil property estimation

USDA-ARS?s Scientific Manuscript database

In-field, in-situ data collection with soil sensors has potential to improve the efficiency and accuracy of soil property estimates. Optical diffuse reflectance spectroscopy (DRS) has been used to estimate important soil properties, such as soil carbon, nitrogen, water content, and texture. Most pre...

Estimation of soil profile physical and chemical properties using a VIS-NIR-EC-force probe

USDA-ARS?s Scientific Manuscript database

Combining data collected in-field from multiple soil sensors has the potential to improve the efficiency and accuracy of soil property estimates. Optical diffuse reflectance spectroscopy (DRS) has been used to estimate many important soil properties, such as soil carbon, water content, and texture. ...

A genetic-algorithm approach for assessing the liquefaction potential of sandy soils

NASA Astrophysics Data System (ADS)

Sen, G.; Akyol, E.

2010-04-01

The determination of liquefaction potential is required to take into account a large number of parameters, which creates a complex nonlinear structure of the liquefaction phenomenon. The conventional methods rely on simple statistical and empirical relations or charts. However, they cannot characterise these complexities. Genetic algorithms are suited to solve these types of problems. A genetic algorithm-based model has been developed to determine the liquefaction potential by confirming Cone Penetration Test datasets derived from case studies of sandy soils. Software has been developed that uses genetic algorithms for the parameter selection and assessment of liquefaction potential. Then several estimation functions for the assessment of a Liquefaction Index have been generated from the dataset. The generated Liquefaction Index estimation functions were evaluated by assessing the training and test data. The suggested formulation estimates the liquefaction occurrence with significant accuracy. Besides, the parametric study on the liquefaction index curves shows a good relation with the physical behaviour. The total number of misestimated cases was only 7.8% for the proposed method, which is quite low when compared to another commonly used method.

Soil microbial community as a proxy for the ecological service condition in karst soils of SW China

NASA Astrophysics Data System (ADS)

Green, Sophie M.; Dungait, Jennifer A. J.; Zhang, Xinyu; Hawkes, Simon; Donovan, Neil; Barrows, Tim; Buss, Heather; Liu, Taoze; Evershed, Richard; Wen, Xuefa; Hartley, Iain; Song, Zhaoliang; Liu, Hongyan; Tu, Chenglong; Johnes, Penny J.; Meersmans, Jeroen; Guo, Dali; Quine, Tim

2017-04-01

Karst is a key landscape covering extensive areas of Southwest China that has undergone rapid intensive land use change and degradation over the last 50 years. Clear evidence of environmental degradation and its damaging consequences for the reduction of intrinsic value of the land for local human populations has led to an increasing focus on landscape rehabilitation. This has included unmanaged abandonment and attempts to re-vegetate denuded surfaces. However, this has achieved limited success and there is a clear need to develop restoration strategies underpinned by robust quantitative and mechanistic understanding of critical zone (CZ) functioning. Thus, a karst Critical Zone Observatory (CZO) was established in June 2016 in Chenqi, Guizhou Province, along a gradient through three levels of human perturbed landscapes: sloping farmland; recovery phase 1 (recently abandoned, within 5 years); and, recovery phase 2 (secondary forest, abandoned > 5 years). We hypothesise that there is a tipping point along the degradation gradient beyond which key biological controls over CZ function are lost, resulting in declining nutrient cycling and rock weathering rates, and increased soil erosion rates. This paper will present preliminary data from the application of the CZ approach using space-for-time substitution. We characterised soil microbial community dynamics along the degradation gradient using geochemical biomarkers and soil properties measured in soil profiles (<1.5 m depth; n = 3) at three slope positions at contrasting topographical aspects around the Chenqi catchment. We integrate measurements of mycorrhizal fungi and free-living soil microbes, and pools of soil carbon (C), nitrogen (N) and phosphorus (P), with estimations of soil erosion rates using radionuclide 137Cs/Pb210, within the karst ecosystem to evaluate the status of key ecosystem functions (e.g. nutrient cycling, carbon sequestration, soil stabilisation).

Surface vapor conductance derived from the ETRHEQ: Dependence on environmental variables and similarity to Oren's stomatal stress model for vapor pressure deficit

NASA Astrophysics Data System (ADS)

Salvucci, G.; Rigden, A. J.

2015-12-01

Daily time series of evapotranspiration and surface conductance to water vapor were estimated using the ETRHEQ method (Evapotranspiration from Relative Humidity at Equilibrium). ETRHEQ has been previously compared with ameriflux site-level measurements of ET at daily and seasonal time scales, with watershed water balance estimates, and with various benchmark ET data sets. The ETRHEQ method uses meteorological data collected at common weather stations and estimates the surface conductance by minimizing the vertical variance of the calculated relative humidity profile averaged over the day. The key advantage of the ETRHEQ method is that it does not require knowledge of the surface state (soil moisture, stomatal conductance, leaf are index, etc.) or site-specific calibration. The daily estimates of conductance from 229 weather stations for 53 years were analyzed for dependence on environmental variables known to impact stomatal conductance and soil diffusivity: surface temperature, surface vapor pressure deficit, solar radiation, antecedent precipitation (as a surrogate for soil moisture), and a seasonal vegetation greenness index. At each site the summertime (JJAS) conductance values estimated from ETRHEQ were fitted to a multiplicate Jarvis-type stress model. Functional dependence was not proscribed, but instead fitted using flexible piecewise-linear splines. The resulting stress functions reproduce the time series of conductance across a wide range of ecosystems and climates. The VPD stress term resembles that proposed by Oren (i.e., 1-m*log(VPD) ), with VPD measured in kilopascals. The equivalent value of m derived from our spline-fits at each station varied over a remarkably small range of 0.58 to 0.62, in agreement with Oren's original analysis based on leaf and tree-level measurements.

Spatial prediction of near surface soil water retention functions using hydrogeophysics and empirical orthogonal functions

NASA Astrophysics Data System (ADS)

Gibson, Justin; Franz, Trenton E.

2018-06-01

The hydrological community often turns to widely available spatial datasets such as the NRCS Soil Survey Geographic database (SSURGO) to characterize the spatial variability of soil properties. When used to spatially characterize and parameterize watershed models, this has served as a reasonable first approximation when lacking localized or incomplete soil data. Within agriculture, soil data has been left relatively coarse when compared to numerous other data sources measured. This is because localized soil sampling is both expensive and time intense, thus a need exists in better connecting spatial datasets with ground observations. Given that hydrogeophysics is data-dense, rapid, non-invasive, and relatively easy to adopt, it is a promising technique to help dovetail localized soil sampling with spatially exhaustive datasets. In this work, we utilize two common near surface geophysical methods, cosmic-ray neutron probe and electromagnetic induction, to identify temporally stable spatial patterns of measured geophysical properties in three 65 ha agricultural fields in western Nebraska. This is achieved by repeat geophysical observations of the same study area across a range of wet to dry field conditions in order to evaluate with an empirical orthogonal function. Shallow cores were then extracted within each identified zone and water retention functions were generated in the laboratory. Using EOF patterns as a covariate, we quantify the predictive skill of estimating soil hydraulic properties in areas without measurement using a bootstrap validation analysis. Results indicate that sampling locations informed via repeat hydrogeophysical surveys, required only five cores to reduce the cross-validation root mean squared error by an average of 64% as compared to soil parameters predicted by a commonly used benchmark, SSURGO and ROSETTA. The reduction to five strategically located samples within the 65 ha fields reduces sampling efforts by up to ∼90% as compared to the common practice of soil grid sampling every 1 ha.

Coupled land surface–subsurface hydrogeophysical inverse modeling to estimate soil organic carbon content and explore associated hydrological and thermal dynamics in the Arctic tundra

DOE PAGES

Tran, Anh Phuong; Dafflon, Baptiste; Hubbard, Susan S.

2017-09-06

Quantitative characterization of soil organic carbon (OC) content is essential due to its significant impacts on surface–subsurface hydrological–thermal processes and microbial decomposition of OC, which both in turn are important for predicting carbon–climate feedbacks. While such quantification is particularly important in the vulnerable organic-rich Arctic region, it is challenging to achieve due to the general limitations of conventional core sampling and analysis methods, and to the extremely dynamic nature of hydrological–thermal processes associated with annual freeze–thaw events. In this study, we develop and test an inversion scheme that can flexibly use single or multiple datasets – including soil liquid watermore » content, temperature and electrical resistivity tomography (ERT) data – to estimate the vertical distribution of OC content. Our approach relies on the fact that OC content strongly influences soil hydrological–thermal parameters and, therefore, indirectly controls the spatiotemporal dynamics of soil liquid water content, temperature and their correlated electrical resistivity. We employ the Community Land Model to simulate nonisothermal surface–subsurface hydrological dynamics from the bedrock to the top of canopy, with consideration of land surface processes (e.g., solar radiation balance, evapotranspiration, snow accumulation and melting) and ice–liquid water phase transitions. For inversion, we combine a deterministic and an adaptive Markov chain Monte Carlo (MCMC) optimization algorithm to estimate a posteriori distributions of desired model parameters. For hydrological–thermal-to-geophysical variable transformation, the simulated subsurface temperature, liquid water content and ice content are explicitly linked to soil electrical resistivity via petrophysical and geophysical models. We validate the developed scheme using different numerical experiments and evaluate the influence of measurement errors and benefit of joint inversion on the estimation of OC and other parameters. We also quantify the propagation of uncertainty from the estimated parameters to prediction of hydrological–thermal responses. We find that, compared to inversion of single dataset (temperature, liquid water content or apparent resistivity), joint inversion of these datasets significantly reduces parameter uncertainty. We find that the joint inversion approach is able to estimate OC and sand content within the shallow active layer (top 0.3 m of soil) with high reliability. Due to the small variations of temperature and moisture within the shallow permafrost (here at about 0.6 m depth), the approach is unable to estimate OC with confidence. However, if the soil porosity is functionally related to the OC and mineral content, which is often observed in organic-rich Arctic soil, the uncertainty of OC estimate at this depth remarkably decreases. Our study documents the value of the new surface–subsurface, deterministic–stochastic inversion approach, as well as the benefit of including multiple types of data to estimate OC and associated hydrological–thermal dynamics.« less

Coupled land surface–subsurface hydrogeophysical inverse modeling to estimate soil organic carbon content and explore associated hydrological and thermal dynamics in the Arctic tundra

DOE Office of Scientific and Technical Information (OSTI.GOV)

Tran, Anh Phuong; Dafflon, Baptiste; Hubbard, Susan S.

Quantitative characterization of soil organic carbon (OC) content is essential due to its significant impacts on surface–subsurface hydrological–thermal processes and microbial decomposition of OC, which both in turn are important for predicting carbon–climate feedbacks. While such quantification is particularly important in the vulnerable organic-rich Arctic region, it is challenging to achieve due to the general limitations of conventional core sampling and analysis methods, and to the extremely dynamic nature of hydrological–thermal processes associated with annual freeze–thaw events. In this study, we develop and test an inversion scheme that can flexibly use single or multiple datasets – including soil liquid watermore » content, temperature and electrical resistivity tomography (ERT) data – to estimate the vertical distribution of OC content. Our approach relies on the fact that OC content strongly influences soil hydrological–thermal parameters and, therefore, indirectly controls the spatiotemporal dynamics of soil liquid water content, temperature and their correlated electrical resistivity. We employ the Community Land Model to simulate nonisothermal surface–subsurface hydrological dynamics from the bedrock to the top of canopy, with consideration of land surface processes (e.g., solar radiation balance, evapotranspiration, snow accumulation and melting) and ice–liquid water phase transitions. For inversion, we combine a deterministic and an adaptive Markov chain Monte Carlo (MCMC) optimization algorithm to estimate a posteriori distributions of desired model parameters. For hydrological–thermal-to-geophysical variable transformation, the simulated subsurface temperature, liquid water content and ice content are explicitly linked to soil electrical resistivity via petrophysical and geophysical models. We validate the developed scheme using different numerical experiments and evaluate the influence of measurement errors and benefit of joint inversion on the estimation of OC and other parameters. We also quantify the propagation of uncertainty from the estimated parameters to prediction of hydrological–thermal responses. We find that, compared to inversion of single dataset (temperature, liquid water content or apparent resistivity), joint inversion of these datasets significantly reduces parameter uncertainty. We find that the joint inversion approach is able to estimate OC and sand content within the shallow active layer (top 0.3 m of soil) with high reliability. Due to the small variations of temperature and moisture within the shallow permafrost (here at about 0.6 m depth), the approach is unable to estimate OC with confidence. However, if the soil porosity is functionally related to the OC and mineral content, which is often observed in organic-rich Arctic soil, the uncertainty of OC estimate at this depth remarkably decreases. Our study documents the value of the new surface–subsurface, deterministic–stochastic inversion approach, as well as the benefit of including multiple types of data to estimate OC and associated hydrological–thermal dynamics.« less

Relationships between stability, maturity, water-extractable organic matter of municipal sewage sludge composts and soil functionality.

PubMed

Sciubba, Luigi; Cavani, Luciano; Grigatti, Marco; Ciavatta, Claudio; Marzadori, Claudio

2015-09-01

Compost capability of restoring or enhancing soil quality depends on several parameters, such as soil characteristics, compost carbon, nitrogen and other nutrient content, heavy metal occurrence, stability and maturity. This study investigated the possibility of relating compost stability and maturity to water-extractable organic matter (WEOM) properties and amendment effect on soil quality. Three composts from municipal sewage sludge and rice husk (AN, from anaerobic wastewater treatment plants; AE, from aerobic ones; MIX, from both anaerobic and aerobic ones) have been analysed and compared to a traditional green waste compost (GM, from green manure, solid waste and urban sewage sludge). To this aim, WEOMs were characterized through chemical analysis; furthermore, compost stability was evaluated through oxygen uptake rate calculation and maturity was estimated through germination index determination, whereas compost impact on soil fertility was studied, in a lab-scale experiment, through indicators as inorganic nitrogen release, soil microbial biomass carbon, basal respiration rate and fluorescein di-acetate hydrolysis. The obtained results indicated that WEOM characterization could be useful to investigate compost stability (which is related to protein and phenol concentrations) and maturity (related to nitrate/ammonium ratio and degree of aromaticity) and then compost impact on soil functionality. Indeed, compost stability resulted inversely related to soil microbial biomass, basal respiration rate and fluorescein di-acetate hydrolysis when the products were applied to the soil.

Assessing the effect of elevated carbon dioxide on soil carbon: a comparison of four meta-analyses.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hungate, B. A.; van Groenigen, K.; Six, J.

2009-08-01

Soil is the largest reservoir of organic carbon (C) in the terrestrial biosphere and soil C has a relatively long mean residence time. Rising atmospheric carbon dioxide (CO{sub 2}) concentrations generally increase plant growth and C input to soil, suggesting that soil might help mitigate atmospheric CO{sub 2} rise and global warming. But to what extent mitigation will occur is unclear. The large size of the soil C pool not only makes it a potential buffer against rising atmospheric CO{sub 2}, but also makes it difficult to measure changes amid the existing background. Meta-analysis is one tool that can overcomemore » the limited power of single studies. Four recent meta-analyses addressed this issue but reached somewhat different conclusions about the effect of elevated CO{sub 2} on soil C accumulation, especially regarding the role of nitrogen (N) inputs. Here, we assess the extent of differences between these conclusions and propose a new analysis of the data. The four meta-analyses included different studies, derived different effect size estimates from common studies, used different weighting functions and metrics of effect size, and used different approaches to address nonindependence of effect sizes. Although all factors influenced the mean effect size estimates and subsequent inferences, the approach to independence had the largest influence. We recommend that meta-analysts critically assess and report choices about effect size metrics and weighting functions, and criteria for study selection and independence. Such decisions need to be justified carefully because they affect the basis for inference. Our new analysis, with a combined data set, confirms that the effect of elevated CO{sub 2} on net soil C accumulation increases with the addition of N fertilizers. Although the effect at low N inputs was not significant, statistical power to detect biogeochemically important effect sizes at low N is limited, even with meta-analysis, suggesting the continued need for long-term experiments.« less

[Soil moisture estimation method based on both ground-based remote sensing data and air temperature in a summer maize ecosystem.

PubMed

Wang, Min Zheng; Zhou, Guang Sheng

2016-06-01

Soil moisture is an important component of the soil-vegetation-atmosphere continuum (SPAC). It is a key factor to determine the water status of terrestrial ecosystems, and is also the main source of water supply for crops. In order to estimate soil moisture at different soil depths at a station scale, based on the energy balance equation and the water deficit index (WDI), a soil moisture estimation model was established in terms of the remote sensing data (the normalized difference vegetation index and surface temperature) and air temperature. The soil moisture estimation model was validated based on the data from the drought process experiment of summer maize (Zea mays) responding to different irrigation treatments carried out during 2014 at Gucheng eco-agrometeorological experimental station of China Meteorological Administration. The results indicated that the soil moisture estimation model developed in this paper was able to evaluate soil relative humidity at different soil depths in the summer maize field, and the hypothesis was reasonable that evapotranspiration deficit ratio (i.e., WDI) linearly depended on soil relative humidity. It showed that the estimation accuracy of 0-10 cm surface soil moisture was the highest (R 2 =0.90). The RMAEs of the estimated and measured soil relative humidity in deeper soil layers (up to 50 cm) were less than 15% and the RMSEs were less than 20%. The research could provide reference for drought monitoring and irrigation management.

Inferences of Strength of Soil Deposits along MER Rover Traverses

NASA Astrophysics Data System (ADS)

Richter, L.; Schmitz, N.; Weiss, S.; Mer/Athena Team

As the two MER Mars Exploration Rovers ,Spirit' and ,Opportunity' traverse terrains within Gusev crater and at Meridiani Planum, respectively, they leave behind wheel tracks that are routinely imaged by the different sets of cameras as part of the Athena instrument suite. Stereo observations of these tracks reveal wheel sinkage depths which are diagnostic of the strength of the soil-like deposits crossed by the vehicles, and observations of track morphology at different imaging scales - including that of the Microscopic Imager - allow estimations of soil grain size distributions. This presentation will discuss results of systematic analyses of MER-A and -B wheel track observations with regard to solutions for soil bearing strength and soil shear strength. Data are analyzed in the context of wheel-soil theory calibrated to the shape of the MER wheel and by consulting comparisons with terrestrial soils. Results are applicable to the top ˜20-30 cm of the soil deposits, the depth primarily affected by the stress distribution under the wheels. The large number of wheel track observations per distance travelled enables investigations of variations of soil physical properties as a function of spatial scale, type of surface feature encountered, and local topography. Exploiting relationships between soil strength and degree of soil consolidation known from lunar regolith and dry terrestrial soils allows one to relate inferred soil strengths to bulk density. This provides a means to ground-truth radar Fresnel reflection coefficients obtained for the landing sites from Earth-based observations. Moreover, bulk density is correlated with soil dielectric constant, a parameter of direct relevance also for Mars-orbiting radars. The obtained estimates for soil bulk density are also used to determine local thermal conductivity of near-surface materials, based on correlations between the two quantities, and to subsequently estimate thermal inertia. This represents an independent method to provide ground truth to thermal inertia determined from orbital thermal measurements of the MER landing sites (MGS TES, MODY THEMIS, MEX PFS & OMEGA), in addition to thermal inertia retrievals from the Athena Mini-TES instrument. Key results suggest different types of soils as judged from their strength, with most materials encountered being similar in consistency to terrestrial sandy loams. Relatively looser soils have been identified on the slopes of crater walls and in local 1 soil patches of smooth appearance, being interpreted as deposits of unconsolidated dust-like soils. Bulk densities for the different soils vary between ˜1100 and ˜1500 kgm-3 . Results of chemical measurements are currently being exploited to relate soil strength to inferred enrichments in salts possibly acting as cementing agents. Thermal inertias of the soil component obtained from the bulk density estimates range between ˜130 and ˜150 Jm-2 s-1/2 K-1 for the MER-A Gusev site and between ˜130 and ˜140 Jm-2 s-1/2 K-1 for the MER-B Meridiani site. 2

Changes in fungal communities along a boreal forest soil fertility gradient.

PubMed

Sterkenburg, Erica; Bahr, Adam; Brandström Durling, Mikael; Clemmensen, Karina E; Lindahl, Björn D

2015-09-01

Boreal forests harbour diverse fungal communities with decisive roles in decomposition and plant nutrition. Although changes in boreal plant communities along gradients in soil acidity and nitrogen (N) availability are well described, less is known about how fungal taxonomic and functional groups respond to soil fertility factors. We analysed fungal communities in humus and litter from 25 Swedish old-growth forests, ranging from N-rich Picea abies stands to acidic and N-poor Pinus sylvestris stands. 454-pyrosequencing of ITS2 amplicons was used to analyse community composition, and biomass was estimated by ergosterol analysis. Fungal community composition was significantly related to soil fertility at the levels of species, genera/orders and functional groups. Ascomycetes dominated in less fertile forests, whereas basidiomycetes increased in abundance in more fertile forests, both in litter and humus. The relative abundance of mycorrhizal fungi in the humus layer remained high even in the most fertile soils. Tolerance to acidity and nitrogen deficiency seems to be of greater importance than plant carbon (C) allocation patterns in determining responses of fungal communities to soil fertility, in old-growth boreal forests. © 2015 The Authors. New Phytologist © 2015 New Phytologist Trust.

Estimation of Annual Average Soil Loss, Based on Rusle Model in Kallar Watershed, Bhavani Basin, Tamil Nadu, India

NASA Astrophysics Data System (ADS)

Rahaman, S. Abdul; Aruchamy, S.; Jegankumar, R.; Ajeez, S. Abdul

2015-10-01

Soil erosion is a widespread environmental challenge faced in Kallar watershed nowadays. Erosion is defined as the movement of soil by water and wind, and it occurs in Kallar watershed under a wide range of land uses. Erosion by water can be dramatic during storm events, resulting in wash-outs and gullies. It can also be insidious, occurring as sheet and rill erosion during heavy rains. Most of the soil lost by water erosion is by the processes of sheet and rill erosion. Land degradation and subsequent soil erosion and sedimentation play a significant role in impairing water resources within sub watersheds, watersheds and basins. Using conventional methods to assess soil erosion risk is expensive and time consuming. A comprehensive methodology that integrates Remote sensing and Geographic Information Systems (GIS), coupled with the use of an empirical model (Revised Universal Soil Loss Equation- RUSLE) to assess risk, can identify and assess soil erosion potential and estimate the value of soil loss. GIS data layers including, rainfall erosivity (R), soil erodability (K), slope length and steepness (LS), cover management (C) and conservation practice (P) factors were computed to determine their effects on average annual soil loss in the study area. The final map of annual soil erosion shows a maximum soil loss of 398.58 t/ h-1/ y-1. Based on the result soil erosion was classified in to soil erosion severity map with five classes, very low, low, moderate, high and critical respectively. Further RUSLE factors has been broken into two categories, soil erosion susceptibility (A=RKLS), and soil erosion hazard (A=RKLSCP) have been computed. It is understood that functions of C and P are factors that can be controlled and thus can greatly reduce soil loss through management and conservational measures.

Mode-based equivalent multi-degree-of-freedom system for one-dimensional viscoelastic response analysis of layered soil deposit

NASA Astrophysics Data System (ADS)

Li, Chong; Yuan, Juyun; Yu, Haitao; Yuan, Yong

2018-01-01

Discrete models such as the lumped parameter model and the finite element model are widely used in the solution of soil amplification of earthquakes. However, neither of the models will accurately estimate the natural frequencies of soil deposit, nor simulate a damping of frequency independence. This research develops a new discrete model for one-dimensional viscoelastic response analysis of layered soil deposit based on the mode equivalence method. The new discrete model is a one-dimensional equivalent multi-degree-of-freedom (MDOF) system characterized by a series of concentrated masses, springs and dashpots with a special configuration. The dynamic response of the equivalent MDOF system is analytically derived and the physical parameters are formulated in terms of modal properties. The equivalent MDOF system is verified through a comparison of amplification functions with the available theoretical solutions. The appropriate number of degrees of freedom (DOFs) in the equivalent MDOF system is estimated. A comparative study of the equivalent MDOF system with the existing discrete models is performed. It is shown that the proposed equivalent MDOF system can exactly present the natural frequencies and the hysteretic damping of soil deposits and provide more accurate results with fewer DOFs.

Descendant root volume varies as a function of root type: estimation of root biomass lost during uprooting in Pinus pinaster.

PubMed

Danjon, Frédéric; Caplan, Joshua S; Fortin, Mathieu; Meredieu, Céline

2013-01-01

Root systems of woody plants generally display a strong relationship between the cross-sectional area or cross-sectional diameter (CSD) of a root and the dry weight of biomass (DWd) or root volume (Vd) that has grown (i.e., is descendent) from a point. Specification of this relationship allows one to quantify root architectural patterns and estimate the amount of material lost when root systems are extracted from the soil. However, specifications of this relationship generally do not account for the fact that root systems are comprised of multiple types of roots. We assessed whether the relationship between CSD and Vd varies as a function of root type. Additionally, we sought to identify a more accurate and time-efficient method for estimating missing root volume than is currently available. We used a database that described the 3D root architecture of Pinus pinaster root systems (5, 12, or 19 years) from a stand in southwest France. We determined the relationship between CSD and Vd for 10,000 root segments from intact root branches. Models were specified that did and did not account for root type. The relationships were then applied to the diameters of 11,000 broken root ends to estimate the volume of missing roots. CSD was nearly linearly related to the square root of Vd, but the slope of the curve varied greatly as a function of root type. Sinkers and deep roots tapered rapidly, as they were limited by available soil depth. Distal shallow roots tapered gradually, as they were less limited spatially. We estimated that younger trees lost an average of 17% of root volume when excavated, while older trees lost 4%. Missing volumes were smallest in the central parts of root systems and largest in distal shallow roots. The slopes of the curves for each root type are synthetic parameters that account for differentiation due to genetics, soil properties, or mechanical stimuli. Accounting for this differentiation is critical to estimating root loss accurately.

Descendant root volume varies as a function of root type: estimation of root biomass lost during uprooting in Pinus pinaster

PubMed Central

Danjon, Frédéric; Caplan, Joshua S.; Fortin, Mathieu; Meredieu, Céline

2013-01-01

Root systems of woody plants generally display a strong relationship between the cross-sectional area or cross-sectional diameter (CSD) of a root and the dry weight of biomass (DWd) or root volume (Vd) that has grown (i.e., is descendent) from a point. Specification of this relationship allows one to quantify root architectural patterns and estimate the amount of material lost when root systems are extracted from the soil. However, specifications of this relationship generally do not account for the fact that root systems are comprised of multiple types of roots. We assessed whether the relationship between CSD and Vd varies as a function of root type. Additionally, we sought to identify a more accurate and time-efficient method for estimating missing root volume than is currently available. We used a database that described the 3D root architecture of Pinus pinaster root systems (5, 12, or 19 years) from a stand in southwest France. We determined the relationship between CSD and Vd for 10,000 root segments from intact root branches. Models were specified that did and did not account for root type. The relationships were then applied to the diameters of 11,000 broken root ends to estimate the volume of missing roots. CSD was nearly linearly related to the square root of Vd, but the slope of the curve varied greatly as a function of root type. Sinkers and deep roots tapered rapidly, as they were limited by available soil depth. Distal shallow roots tapered gradually, as they were less limited spatially. We estimated that younger trees lost an average of 17% of root volume when excavated, while older trees lost 4%. Missing volumes were smallest in the central parts of root systems and largest in distal shallow roots. The slopes of the curves for each root type are synthetic parameters that account for differentiation due to genetics, soil properties, or mechanical stimuli. Accounting for this differentiation is critical to estimating root loss accurately. PMID:24167506

Viticulture microzoning: a functional approach aiming to grape and wine qualities

NASA Astrophysics Data System (ADS)

Bonfante, A.; Agrillo, A.; Albrizio, R.; Basile, A.; Buonomo, R.; De Mascellis, R.; Gambuti, A.; Giorio, P.; Guida, G.; Langella, G.; Manna, P.; Minieri, L.; Moio, L.; Siani, T.; Terribile, F.

2014-12-01

This paper aims to test a new physically oriented approach to viticulture zoning at the farm scale, strongly rooted on hydropedology and aiming to achieve a better use of environmental features with respect to plant requirement and wine production. The physics of our approach is defined by the use of soil-plant-atmosphere simulation models which applies physically-based equations to describe the soil hydrological processes and solves soil-plant water status. This study (ZOVISA project) was conducted in a farm devoted to high quality wines production (Aglianico DOC), located in South Italy (Campania region, Mirabella Eclano-AV). The soil spatial distribution was obtained after standard soil survey informed by geophysical survey. Two Homogenous Zones (HZs) were identified; in each one of those a physically based model was applied to solve the soil water balance and estimate the soil functional behaviour (crop water stress index, CWSI) defining the functional Homogeneous Zones (fHzs). In these last, experimental plots were established and monitored for investigating soil-plant water status, crop development (biometric and physiological parameters) and daily climate variables (temperature, solar radiation, rainfall, wind). The effects of crop water status on crop response over must and wine quality were then evaluated in the fHZs. This was performed by comparing crop water stress with (i) crop physiological measurement (leaf gas exchange, chlorophyll a fluorescence, leaf water potential, chlorophyll content, LAI measurement), (ii) grape bunches measurements (berry weight, sugar content, titratable acidity, etc.) and (iii) wine quality (aromatic response). Eventually this experiment has proved the usefulness of the physical based approach also in the case of mapping viticulture microzoning.

Estimation of Soil Moisture Under Vegetation Cover at Multiple Frequencies

NASA Astrophysics Data System (ADS)

Jadghuber, Thomas; Hajnsek, Irena; Weiß, Thomas; Papathanassiou, Konstantinos P.

2015-04-01

Soil moisture under vegetation cover was estimated by a polarimetric, iterative, generalized, hybrid decomposition and inversion approach at multiple frequencies (X-, C- and L-band). Therefore the algorithm, originally designed for longer wavelength (L-band), was adapted to deal with the short wavelength scattering scenarios of X- and C-band. The Integral Equation Method (IEM) was incorporated together with a pedo-transfer function of Dobson et al. to account for the peculiarities of short wavelength scattering at X- and C-band. DLR's F-SAR system acquired fully polarimetric SAR data in X-, C- and L-band over the Wallerfing test site in Lower Bavaria, Germany in 2014. Simultaneously, soil and vegetation measurements were conducted on different agricultural test fields. The results indicate a spatially continuous inversion of soil moisture in all three frequencies (inversion rates >92%), mainly due to the careful adaption of the vegetation volume removal including a physical constraining of the decomposition algorithm. However, for X- and C-band the inversion results reveal moisture pattern inconsistencies and in some cases an incorrectly high inversion of soil moisture at X-band. The validation with in situ measurements states a stable performance of 2.1- 7.6vol.% at L-band for the entire growing period. At C- and X-band a reliable performance of 3.7-13.4vol.% in RMSE can only be achieved after distinct filtering (X- band) leading to a loss of almost 60% in spatial inversion rate. Hence, a robust inversion for soil moisture estimation under vegetation cover can only be conducted at L-band due to a constant availability of the soil signal in contrast to higher frequencies (X- and C-band).

A model for nematode locomotion in soil

USGS Publications Warehouse

Hunt, H. William; Wall, Diana H.; DeCrappeo, Nicole; Brenner, John S.

2001-01-01

Locomotion of nematodes in soil is important for both practical and theoretical reasons. We constructed a model for rate of locomotion. The first model component is a simple simulation of nematode movement among finite cells by both random and directed behaviours. Optimisation procedures were used to fit the simulation output to data from published experiments on movement along columns of soil or washed sand, and thus to estimate the values of the model's movement coefficients. The coefficients then provided an objective means to compare rates of locomotion among studies done under different experimental conditions. The second component of the model is an equation to predict the movement coefficients as a function of controlling factors that have been addressed experimentally: soil texture, bulk density, water potential, temperature, trophic group of nematode, presence of an attractant or physical gradient and the duration of the experiment. Parameters of the equation were estimated by optimisation to achieve a good fit to the estimated movement coefficients. Bulk density, which has been reported in a minority of published studies, is predicted to have an important effect on rate of locomotion, at least in fine-textured soils. Soil sieving, which appears to be a universal practice in laboratory studies of nematode movement, is predicted to negatively affect locomotion. Slower movement in finer textured soils would be expected to increase isolation among local populations, and thus to promote species richness. Future additions to the model that might improve its utility include representing heterogeneity within populations in rate of movement, development of gradients of chemical attractants, trade-offs between random and directed components of movement, species differences in optimal temperature and water potential, and interactions among factors controlling locomotion.

Biodegradation kinetics for pesticide exposure assessment.

PubMed

Wolt, J D; Nelson, H P; Cleveland, C B; van Wesenbeeck, I J

2001-01-01

Understanding pesticide risks requires characterizing pesticide exposure within the environment in a manner that can be broadly generalized across widely varied conditions of use. The coupled processes of sorption and soil degradation are especially important for understanding the potential environmental exposure of pesticides. The data obtained from degradation studies are inherently variable and, when limited in extent, lend uncertainty to exposure characterization and risk assessment. Pesticide decline in soils reflects dynamically coupled processes of sorption and degradation that add complexity to the treatment of soil biodegradation data from a kinetic perspective. Additional complexity arises from study design limitations that may not fully account for the decline in microbial activity of test systems, or that may be inadequate for considerations of all potential dissipation routes for a given pesticide. Accordingly, kinetic treatment of data must accommodate a variety of differing approaches starting with very simple assumptions as to reaction dynamics and extending to more involved treatments if warranted by the available experimental data. Selection of the appropriate kinetic model to describe pesticide degradation should rely on statistical evaluation of the data fit to ensure that the models used are not overparameterized. Recognizing the effects of experimental conditions and methods for kinetic treatment of degradation data is critical for making appropriate comparisons among pesticide biodegradation data sets. Assessment of variability in soil half-life among soils is uncertain because for many pesticides the data on soil degradation rate are limited to one or two soils. Reasonable upper-bound estimates of soil half-life are necessary in risk assessment so that estimated environmental concentrations can be developed from exposure models. Thus, an understanding of the variable and uncertain distribution of soil half-lives in the environment is necessary to estimate bounding values. Statistical evaluation of measures of central tendency for multisoil kinetic studies shows that geometric means better represent the distribution in soil half-lives than do the arithmetic or harmonic means. Estimates of upper-bound soil half-life values based on the upper 90% confidence bound on the geometric mean tend to accurately represent the upper bound when pesticide degradation rate is biologically driven but appear to overestimate the upper bound when there is extensive coupling of biodegradation with sorptive processes. The limited data available comparing distribution in pesticide soil half-lives between multisoil laboratory studies and multilocation field studies suggest that the probability density functions are similar. Thus, upper-bound estimates of pesticide half-life determined from laboratory studies conservatively represent pesticide biodegradation in the field environment for the purposes of exposure and risk assessment. International guidelines and approaches used for interpretations of soil biodegradation reflect many common elements, but differ in how the source and nature of variability in soil kinetic data are considered. Harmonization of approaches for the use of soil biodegradation data will improve the interpretative power of these data for the purposes of exposure and risk assessment.

How will Shrub Expansion Impact Soil Carbon Sequestration in Arctic Tundra?

NASA Astrophysics Data System (ADS)

Czimczik, C. I.; Holden, S. R.; He, Y.; Randerson, J. T.

2015-12-01

Multiple lines of evidence suggest that plant productivity, and especially shrub abundance, is increasing in the Arctic in response to climate change. This greening is substantiated by increases in the Normalized Difference Vegetation Index, repeat photography and field observations. The implications of a greener Arctic on carbon sequestration by tundra ecosystems remain poorly understood. Here, we explore existing datasets of plant productivity and soil carbon stocks to quantify how greening, and in particular an expansion of woody shrubs, may translate to the sequestration of carbon in arctic soils. As an estimate of carbon storage in arctic tundra soils, we used the Northern Circumpolar Soil Carbon Database v2. As estimates of tundra type and productivity, we used the Circumpolar Arctic Vegetation map as well as the MODIS and Landsat Vegetation Continuous Fields, and MODIS GPP/NPP (MOD17) products. Preliminary findings suggest that in graminoid tundra and erect-shrub tundra higher shrub abundance is associated with greater soil carbon stocks. However, this relationship between shrub abundance and soil carbon is not apparent in prostrate-shrub tundra, or when comparing across graminoid tundra, erect-shrub tundra and prostrate-shrub tundra. Uncertainties originate from the extreme spatial (vertical and horizontal) heterogeneity of organic matter distribution in cryoturbated soils, the fact that (some) permafrost carbon stocks, e.g. yedoma, reflect previous rather than current vegetative cover, and small sample sizes, esp. in the High Arctic. Using Vegetation Continuous Fields and MODIS GPP/NPP (MOD17), we develop quantitative trajectories of soil carbon storage as a function of shrub cover and plant productivity in the Arctic (>60°N). We then compare our greening-derived carbon sequestration estimates to projected losses of carbon from thawing permafrost. Our findings will reduce uncertainties in the magnitude and timing of the carbon-climate feedback from the terrestrial Arctic, and thus provide guidance for future climate mitigation and adaptation strategies.

Global distribution of plant-extractable water capacity of soil

USGS Publications Warehouse

Dunne, K.A.; Willmott, C.J.

1996-01-01

Plant-extractable water capacity of soil is the amount of water that can be extracted from the soil to fulfill evapotranspiration demands. It is often assumed to be spatially invariant in large-scale computations of the soil-water balance. Empirical evidence, however, suggests that this assumption is incorrect. In this paper, we estimate the global distribution of the plant-extractable water capacity of soil. A representative soil profile, characterized by horizon (layer) particle size data and thickness, was created for each soil unit mapped by FAO (Food and Agriculture Organization of the United Nations)/Unesco. Soil organic matter was estimated empirically from climate data. Plant rooting depths and ground coverages were obtained from a vegetation characteristic data set. At each 0.5?? ?? 0.5?? grid cell where vegetation is present, unit available water capacity (cm water per cm soil) was estimated from the sand, clay, and organic content of each profile horizon, and integrated over horizon thickness. Summation of the integrated values over the lesser of profile depth and root depth produced an estimate of the plant-extractable water capacity of soil. The global average of the estimated plant-extractable water capacities of soil is 8??6 cm (Greenland, Antarctica and bare soil areas excluded). Estimates are less than 5, 10 and 15 cm - over approximately 30, 60, and 89 per cent of the area, respectively. Estimates reflect the combined effects of soil texture, soil organic content, and plant root depth or profile depth. The most influential and uncertain parameter is the depth over which the plant-extractable water capacity of soil is computed, which is usually limited by root depth. Soil texture exerts a lesser, but still substantial, influence. Organic content, except where concentrations are very high, has relatively little effect.

TDR Technique for Estimating the Intensity of Evapotranspiration of Turfgrasses

PubMed Central

Janik, Grzegorz; Wolski, Karol; Daniel, Anna; Albert, Małgorzata; Wilczek, Andrzej; Szyszkowski, Paweł; Walczak, Amadeusz

2015-01-01

The paper presents a method for precise estimation of evapotranspiration of selected turfgrass species. The evapotranspiration functions, whose domains are only two relatively easy to measure parameters, were developed separately for each of the grass species. Those parameters are the temperature and the volumetric moisture of soil at the depth of 2.5 cm. Evapotranspiration has the character of a modified logistic function with empirical parameters. It assumes the form ETR(θ 2.5 cm, T 2.5 cm) = A/(1 + B · e −C·(θ2.5 cm · T2.5 cm)), where: ETR(θ 2.5 cm, T 2.5 cm) is evapotranspiration [mm·h−1], θ 2.5 cm is volumetric moisture of soil at the depth of 2.5 cm [m3·m−3], T 2.5 cm is soil temperature at the depth of 2.5 cm [°C], and A, B, and C are empirical coefficients calculated individually for each of the grass species [mm·h1], and [—], [(m3·m−3·°C)−1]. The values of evapotranspiration calculated on the basis of the presented function can be used as input data for the design of systems for the automatic control of irrigation systems ensuring optimum moisture conditions in the active layer of lawn swards. PMID:26448964

Biodiversity effects on the water balance of an experimental grassland

NASA Astrophysics Data System (ADS)

Leimer, Sophia; Kreutziger, Yvonne; Rosenkranz, Stephan; Beßler, Holger; Engels, Christof; Oelmann, Yvonne; Weisser, Wolfgang W.; Wirth, Christian; Wilcke, Wolfgang

2013-04-01

Plant species richness increases aboveground biomass production in biodiversity experiments. Biomass production depends on and feeds back to the water balance, but it remains unclear how plant species richness influences soil water contents and water fluxes (actual evapotranspiration (ETa), downward flux (DF), and upward flux (UF)). Our objective was to determine the effects of plant species and functional richness and functional identity on soil water contents and water fluxes for two soil depths (0-0.3 and 0.3.-0.7 m). To achieve this, we used a water balance model in connection with Bayesian hierarchical modeling. We monitored soil water contents on 86 plots of a grassland plant diversity experiment in Jena, Germany between July 2002 and January 2006. In the field experiment, plant species richness (0, 1, 2, 4, 8, 16, 60) and functional group composition (0-4 functional groups: legumes, grasses, non-leguminous tall herbs, non-leguminous small herbs) were manipulated in a factorial design. Climate data (air temperature, precipitation, wind velocity, relative humidity, global radiation, soil moisture) was measured at a central climate station between July 2002 and December 2007. Root biomass data from July 2006 was available per plot. Missing water contents per plot and depth were estimated in weekly resolution for the years 2003-2007 with a Bayesian hierarchical model using measured water contents per plot and centrally measured soil moisture. To obtain ETa, DF, and UF of the two different soil depths, we modified a soil water balance model which had been developed for our study site. The model is based on changes in soil water content between subsequent observation dates and modeled potential evapotranspiration which was partitioned between soil layers according to percentage of root biomass. The presence of specific functional groups significantly changed water contents and fluxes with partly opposing effects in the two soil depths. Presence of grasses decreased water contents in both depths, DF in topsoil, and ETa in subsoil, but increased ETa in topsoil. As grasses produce less shade than other plant functional groups because of their leaf morphology, higher ETa in topsoil could be explained by higher soil evaporation. Moreover, grasses have an extensive, shallow rooting system which facilitates exhaustive water use from the upper soil layer and therefore probably decreases water contents and DF. Species richness did not significantly affect water contents and fluxes in both soil layers except that the relation between species richness and water contents in subsoil changed over time. This can be explained by two equivalent but opposite effects. Transpiration increases with biomass which is positively correlated with species richness. By contrast, soil evaporation decreases with species richness because the greater vegetation cover in species-rich communities produces more shade. We conclude that the contrasting effects of plant species richness on transpiration and evaporation counterbalance each other and that functional traits of specific plant functional groups mediate the biologically-induced changes in the water balance.

Calculating Soil Wetness, Evapotranspiration and Carbon Cycle Processes Over Large Grid Areas Using a New Scaling Technique

NASA Technical Reports Server (NTRS)

Sellers, Piers

2012-01-01

Soil wetness typically shows great spatial variability over the length scales of general circulation model (GCM) grid areas (approx 100 km ), and the functions relating evapotranspiration and photosynthetic rate to local-scale (approx 1 m) soil wetness are highly non-linear. Soil respiration is also highly dependent on very small-scale variations in soil wetness. We therefore expect significant inaccuracies whenever we insert a single grid area-average soil wetness value into a function to calculate any of these rates for the grid area. For the particular case of evapotranspiration., this method - use of a grid-averaged soil wetness value - can also provoke severe oscillations in the evapotranspiration rate and soil wetness under some conditions. A method is presented whereby the probability distribution timction(pdf) for soil wetness within a grid area is represented by binning. and numerical integration of the binned pdf is performed to provide a spatially-integrated wetness stress term for the whole grid area, which then permits calculation of grid area fluxes in a single operation. The method is very accurate when 10 or more bins are used, can deal realistically with spatially variable precipitation, conserves moisture exactly and allows for precise modification of the soil wetness pdf after every time step. The method could also be applied to other ecological problems where small-scale processes must be area-integrated, or upscaled, to estimate fluxes over large areas, for example in treatments of the terrestrial carbon budget or trace gas generation.

Pollution potential leaching index as a tool to assess water leaching risk of arsenic in excavated urban soils.

PubMed

Li, Jining; Kosugi, Tomoya; Riya, Shohei; Hashimoto, Yohey; Hou, Hong; Terada, Akihiko; Hosomi, Masaaki

2018-01-01

Leaching of hazardous trace elements from excavated urban soils during construction of cities has received considerable attention in recent years in Japan. A new concept, the pollution potential leaching index (PPLI), was applied to assess the risk of arsenic (As) leaching from excavated soils. Sequential leaching tests (SLT) with two liquid-to-solid (L/S) ratios (10 and 20Lkg -1 ) were conducted to determine the PPLI values, which represent the critical cumulative L/S ratios at which the average As concentrations in the cumulative leachates are reduced to critical values (10 or 5µgL -1 ). Two models (a logarithmic function model and an empirical two-site first-order leaching model) were compared to estimate the PPLI values. The fractionations of As before and after SLT were extracted according to a five-step sequential extraction procedure. Ten alkaline excavated soils were obtained from different construction projects in Japan. Although their total As contents were low (from 6.75 to 79.4mgkg -1 ), the As leaching was not negligible. Different L/S ratios at each step of the SLT had little influence on the cumulative As release or PPLI values. Experimentally determined PPLI values were in agreement with those from model estimations. A five-step SLT with an L/S of 10Lkg -1 at each step, combined with a logarithmic function fitting was suggested for the easy estimation of PPLI. Results of the sequential extraction procedure showed that large portions of more labile As fractions (non-specifically and specifically sorbed fractions) were removed during long-term leaching and so were small, but non-negligible, portions of strongly bound As fractions. Copyright © 2017 Elsevier Inc. All rights reserved.

Estimation of Soil Moisture Profile using a Simple Hydrology Model and Passive Microwave Remote Sensing

NASA Technical Reports Server (NTRS)

Soman, Vishwas V.; Crosson, William L.; Laymon, Charles; Tsegaye, Teferi

1998-01-01

Soil moisture is an important component of analysis in many Earth science disciplines. Soil moisture information can be obtained either by using microwave remote sensing or by using a hydrologic model. In this study, we combined these two approaches to increase the accuracy of profile soil moisture estimation. A hydrologic model was used to analyze the errors in the estimation of soil moisture using the data collected during Huntsville '96 microwave remote sensing experiment in Huntsville, Alabama. Root mean square errors (RMSE) in soil moisture estimation increase by 22% with increase in the model input interval from 6 hr to 12 hr for the grass-covered plot. RMSEs were reduced for given model time step by 20-50% when model soil moisture estimates were updated using remotely-sensed data. This methodology has a potential to be employed in soil moisture estimation using rainfall data collected by a space-borne sensor, such as the Tropical Rainfall Measuring Mission (TRMM) satellite, if remotely-sensed data are available to update the model estimates.

State-Space Estimation of Soil Organic Carbon Stock

NASA Astrophysics Data System (ADS)

Ogunwole, Joshua O.; Timm, Luis C.; Obidike-Ugwu, Evelyn O.; Gabriels, Donald M.

2014-04-01

Understanding soil spatial variability and identifying soil parameters most determinant to soil organic carbon stock is pivotal to precision in ecological modelling, prediction, estimation and management of soil within a landscape. This study investigates and describes field soil variability and its structural pattern for agricultural management decisions. The main aim was to relate variation in soil organic carbon stock to soil properties and to estimate soil organic carbon stock from the soil properties. A transect sampling of 100 points at 3 m intervals was carried out. Soils were sampled and analyzed for soil organic carbon and other selected soil properties along with determination of dry aggregate and water-stable aggregate fractions. Principal component analysis, geostatistics, and state-space analysis were conducted on the analyzed soil properties. The first three principal components explained 53.2% of the total variation; Principal Component 1 was dominated by soil exchange complex and dry sieved macroaggregates clusters. Exponential semivariogram model described the structure of soil organic carbon stock with a strong dependence indicating that soil organic carbon values were correlated up to 10.8m.Neighbouring values of soil organic carbon stock, all waterstable aggregate fractions, and dithionite and pyrophosphate iron gave reliable estimate of soil organic carbon stock by state-space.

Landscape scale estimation of soil carbon stock using 3D modelling.

PubMed

Veronesi, F; Corstanje, R; Mayr, T

2014-07-15

Soil C is the largest pool of carbon in the terrestrial biosphere, and yet the processes of C accumulation, transformation and loss are poorly accounted for. This, in part, is due to the fact that soil C is not uniformly distributed through the soil depth profile and most current landscape level predictions of C do not adequately account the vertical distribution of soil C. In this study, we apply a method based on simple soil specific depth functions to map the soil C stock in three-dimensions at landscape scale. We used soil C and bulk density data from the Soil Survey for England and Wales to map an area in the West Midlands region of approximately 13,948 km(2). We applied a method which describes the variation through the soil profile and interpolates this across the landscape using well established soil drivers such as relief, land cover and geology. The results indicate that this mapping method can effectively reproduce the observed variation in the soil profiles samples. The mapping results were validated using cross validation and an independent validation. The cross-validation resulted in an R(2) of 36% for soil C and 44% for BULKD. These results are generally in line with previous validated studies. In addition, an independent validation was undertaken, comparing the predictions against the National Soil Inventory (NSI) dataset. The majority of the residuals of this validation are between ± 5% of soil C. This indicates high level of accuracy in replicating topsoil values. In addition, the results were compared to a previous study estimating the carbon stock of the UK. We discuss the implications of our results within the context of soil C loss factors such as erosion and the impact on regional C process models. Copyright © 2014 Elsevier B.V. All rights reserved.

Influence of Watershed Characteristics on Wetland Hydrology (Tampa, FL)

EPA Science Inventory

The availability of oxygen in wetland soils is a major driver of rate changes for several important ecological functions (e.g. nutrient processing, carbon sequestration) that the Tampa Bay Ecosystem Services Research Program (TB-ESRP) is quantifying to estimate ecosystem services...

PHYSICAL AND ECONOMIC DAMAGE FUNCTIONS FOR AIR POLLUTANTS BY RECEPTOR

EPA Science Inventory

This study is primarily concerned with evaluating regional economic damages to human health, material, and vegetation and of property soiling resulting from air pollution. This study represents a step forward in methodological development of air pollution damage estimation. It at...

Comparison of Two Multifractal Analysis Methods: Generalized Structure Function and Multifractal Spectrum

NASA Astrophysics Data System (ADS)

Morato, M. Carmen; Castellanos, M. Teresa; Bird, Nigel; Tarquis, Ana M.

2016-04-01

Soil variability has often been a constant expected factor to take in account in soil studies. This variability could be considered to be composed of "functional" variations plus random fluctuations or noise. Multifractal formalism, first proposed by Mandelbrot (1982), is suitable for variables with self-similar distribution on a spatial domain. Multifractal analysis can provide insight into spatial variability of crop or soil parameters. In soil science, it has been quite popular to characterize the scaling property of a variable measured along a transect as a mass distribution of a statistical measure on a length domain of the studied transect. To do this, it divides it into a number of self similar segments and estimate the partition function and mass function. Based on this, the multifractal spectra (MFS) is calculated. However, another technique can be applied focus its attention in the variations of a measure analyzing the moments of the absolute differences at different scales, the Generalized Structure Function (GSF), and extracting the Generalized Hurst exponents. The aim of this study is to compare both techniques in a transect data. A common 1024 m transect across arable fields at Silsoe in Bedfordshire, east-central England were analyzed with these two multifractal methods. Properties studied were total porosity (Porosity), gravimetric water content (GWC) and nitrogen oxide flux (NO2 flux). The results showed in both methods that NO2 flux presents a clear multifractal character and a weak one in the GWC and Porosity cases. Several parameters were calculated from both methods and are discussed. On the other hand, using the partition function all the scale ranges were used, meanwhile in the GSF a shorter range of scales showed linear behavior in the bilog plots used to estimate the parameters. GWC exhibits a linear pattern from increments of 4 till 256 meters, Porosity showed this behavior from 4 till 64 meters. In case of NO2 flux only from 32 to 256 meters showed it. However, the relation between the mass exponent function and the GSF, found in the literature, was positively verified in the three variables.

Soil Water Balance and Vegetation Dynamics in two Water-limited Mediterranean Ecosystem on Sardinia under past and future climate change

NASA Astrophysics Data System (ADS)

Corona, R.; Montaldo, N.; Albertson, J. D.

2016-12-01

Water limited conditions strongly impacts soil and vegetation dynamics in Mediterranean regions, which are commonly heterogeneous ecosystems, characterized by inter-annual rainfall variability, topography variability and contrasting plant functional types (PFTs) competing for water use. Historical human influences (e.g., deforestation, urbanization) further altered these ecosystems. Sardinia island is a representative region of Mediterranean ecosystems. It is low urbanized except some plan areas close to the main cities where main agricultural activities are concentrated. Two contrasting case study sites are within the Flumendosa river basin (1700 km2). The first site is a typical grassland on an alluvial plan valley (soil depth > 2m) while the second is a patchy mixture of Mediterranean vegetation species (mainly wild olive trees and C3 herbaceous) that grow in a soil bounded from below by a rocky layer of basalt, partially fractured (soil depth 15 - 40 cm). In both sites land-surface fluxes and CO2 fluxes are estimated by the eddy correlation technique while soil moisture was continuously estimated with water content reflectometers, and periodically leaf area index (LAI) was estimated. The following objectives are addressed:1) pointing out the dynamics of land surface fluxes, soil moisture, CO2 and vegetation cover for two contrasting water-limited ecosystems; 2) assess the impact of the soil depth and type on the CO2 and water balance dynamics; 3) evaluate the impact of past and future climate change scenarios on the two contrasting ecosystems. For reaching the objectives an ecohydrologic model that couples a vegetation dynamic model (VDM), and a 3-component (bare soil, grass and woody vegetation) land surface model (LSM) has been used. Historical meteorological data are available from 1922 and hydro-meteorological scenarios are then generated using a weather generator. The VDM-LSM model predict soil water balance and vegetation dynamics for the generated hydrometeorological scenarios in the two contrasting ecosystems. Results demonstrate that vegetation dynamics are influenced by the inter-annual variability of atmospheric forcing, with vegetation density changing significantly according to seasonal rainfall amount. At the same time the vegetation dynamics affect the soil water balance.

The seed bank longevity index revisited: limited reliability evident from a burial experiment and database analyses.

PubMed

Saatkamp, Arne; Affre, Laurence; Dutoit, Thierry; Poschlod, Peter

2009-09-01

Seed survival in the soil contributes to population persistence and community diversity, creating a need for reliable measures of soil seed bank persistence. Several methods estimate soil seed bank persistence, most of which count seedlings emerging from soil samples. Seasonality, depth distribution and presence (or absence) in vegetation are then used to classify a species' soil seed bank into persistent or transient, often synthesized into a longevity index. This study aims to determine if counts of seedlings from soil samples yield reliable seed bank persistence estimates and if this is correlated to seed production. Seeds of 38 annual weeds taken from arable fields were buried in the field and their viability tested by germination and tetrazolium tests at 6 month intervals for 2.5 years. This direct measure of soil seed survival was compared with indirect estimates from the literature, which use seedling emergence from soil samples to determine seed bank persistence. Published databases were used to explore the generality of the influence of reproductive capacity on seed bank persistence estimates from seedling emergence data. There was no relationship between a species' soil seed survival in the burial experiment and its seed bank persistence estimate from published data using seedling emergence from soil samples. The analysis of complementary data from published databases revealed that while seed bank persistence estimates based on seedling emergence from soil samples are generally correlated with seed production, estimates of seed banks from burial experiments are not. The results can be explained in terms of the seed size-seed number trade-off, which suggests that the higher number of smaller seeds is compensated after germination. Soil seed bank persistence estimates correlated to seed production are therefore not useful for studies on population persistence or community diversity. Confusion of soil seed survival and seed production can be avoided by separate use of soil seed abundance and experimental soil seed survival.

Assessing the Importance of Incorporating Spatial and Temporal Variability of Soil and Plant Parameters into Local Water Balance Models for Precision Agriculture: Investigations within a California Vineyard

NASA Astrophysics Data System (ADS)

Hubbard, S.; Pierce, L.; Grote, K.; Rubin, Y.

2003-12-01

Due Due to the high cash crop nature of premium winegrapes, recent research has focused on developing a better understanding of the factors that influence winegrape spatial and temporal variability. Precision grapevine irrigation schemes require consideration of the factors that regulate vineyard water use such as (1) plant parameters, (2) climatic conditions, and (3) water availability in the soil as a function of soil texture. The inability to sample soil and plant parameters accurately, at a dense enough resolution, and over large enough areas has limited previous investigations focused on understanding the influences of soil water and vegetation on water balance at the local field scale. We have acquired several novel field data sets to describe the small scale (decimeters to a hundred meters) spatial variability of soil and plant parameters within a 4 acre field study site at the Robert Mondavi Winery in Napa County, California. At this site, we investigated the potential of ground penetrating radar data (GPR) for providing estimates of near surface water content. Calibration of grids of 900 MHz GPR groundwave data with conventional soil moisture measurements revealed that the GPR volumetric water content estimation approach was valid to within 1 percent accuracy, and that the data grids provided unparalleled density of soil water content over the field site as a function of season. High-resolution airborne multispectral remote sensing data was also collected at the study site, which was converted to normalized difference vegetation index (NDVI) and correlated to leaf area index (LAI) using plant-based measurements within a parallel study. Meteorological information was available from a weather station of the California Irrigation management Information System, located less than a mile from our study area. The measurements were used within a 2-D Vineyard Soil Irrigation Model (VSIM), which can incorporate the spatially variable, high-resolution soil and plant-based information. VSIM, which is based on the concept that equilibrium exists between climate, soils, and LAI, was used to simulate vine water stress, water use, and irrigation requirements during a single year for the site. Using the simple water-balance model with the dense characterization data, we will discuss: (1) the ability to predict vineyard soil water content at the small scales of soil heterogeneity that are observed in nature at the local-scale, (2) the relative importance of plant, climate, and soil information to predictions of the soil water balance at the site, (3) the influence of crop cover in the water balance predictions.

Measuring and Modeling Root Distribution and Root Reinforcement in Forested Slopes for Slope Stability Calculations

NASA Astrophysics Data System (ADS)

Cohen, D.; Giadrossich, F.; Schwarz, M.; Vergani, C.

2016-12-01

Roots provide mechanical anchorage and reinforcement of soils on slopes. Roots also modify soil hydrological properties (soil moisture content, pore-water pressure, preferential flow paths) via subsurface flow path associated with root architecture, root density, and root-size distribution. Interactions of root-soil mechanical and hydrological processes are an important control of shallow landslide initiation during rainfall events and slope stability. Knowledge of root-distribution and root strength are key components to estimate slope stability in vegetated slopes and for the management of protection forest in steep mountainous area. We present data that show the importance of measuring root strength directly in the field and present methods for these measurements. These data indicate that the tensile force mobilized in roots depends on root elongation (a function of soil displacement), root size, and on whether roots break in tension of slip out of the soil. Measurements indicate that large lateral roots that cross tension cracks at the scarp are important for slope stability calculations owing to their large tensional resistance. These roots are often overlooked and when included, their strength is overestimated because extrapolated from measurements on small roots. We present planned field experiments that will measure directly the force held by roots of different sizes during the triggering of a shallow landslide by rainfall. These field data are then used in a model of root reinforcement based on fiber-bundle concepts that span different spacial scales, from a single root to the stand scale, and different time scales, from timber harvest to root decay. This model computes the strength of root bundles in tension and in compression and their effect on soil strength. Up-scaled to the stand the model yields the distribution of root reinforcement as a function of tree density, distance from tree, tree species and age with the objective of providing quantitative estimates of tree root reinforcement for best management practice of protection forests.

Historical Perspectives and Future Needs in the Development of the Soil Series Concept

NASA Astrophysics Data System (ADS)

Beaudette, Dylan E.; Brevik, Eric C.; Indorante, Samuel J.

2016-04-01

The soil series concept is an ever-evolving understanding of soil profile observations, their connection to the landscape, and functional limits on the range in characteristics that affect management. Historically, the soil series has played a pivotal role in the development of soil-landscape theory, modern soil survey methods, and concise delivery of soils information to the end-user-- in other words, soil series is the palette from which soil survey reports are crafted. Over the last 20 years the soil series has received considerable criticism as a means of soil information organization (soil survey development) and delivery (end-user application of soil survey data), with increasing pressure (internal and external) to retire the soil series. We propose that a modern re-examination of soil series information could help address several of the long-standing critiques of soil survey: consistency across survey vintage and political divisions and more robust estimates of soil properties and associated uncertainty. A new library of soil series data would include classic narratives describing morphology and management, quantitative descriptions of soil properties and their ranges, graphical depiction of the relationships between associated soil series, block diagrams illustrating soil-landscape models, maps of series distribution, and a probabilistic representation of a "typical" soil profile. These data would be derived from re-correlation of existing morphologic and characterization data informed by modern statistical methods and regional expertise.

Estimating phosphorus availability for microbial growth in an emerging landscape

USGS Publications Warehouse

Schmidt, S.K.; Cleveland, C.C.; Nemergut, D.R.; Reed, S.C.; King, A.J.; Sowell, P.

2011-01-01

Estimating phosphorus (P) availability is difficult—particularly in infertile soils such as those exposed after glacial recession—because standard P extraction methods may not mimic biological acquisition pathways. We developed an approach, based on microbial CO2 production kinetics and conserved carbon:phosphorus (C:P) ratios, to estimate the amount of P available for microbial growth in soils and compared this method to traditional, operationally-defined indicators of P availability. Along a primary succession gradient in the High Andes of Perú, P additions stimulated the growth-related (logistic) kinetics of glutamate mineralization in soils that had been deglaciated from 0 to 5 years suggesting that microbial growth was limited by soil P availability. We then used a logistic model to estimate the amount of C incorporated into biomass in P-limited soils, allowing us to estimate total microbial P uptake based on a conservative C:P ratio of 28:1 (mass:mass). Using this approach, we estimated that there was < 1 μg/g of microbial-available P in recently de-glaciated soils in both years of this study. These estimates fell well below estimates of available soil P obtained using traditional extraction procedures. Our results give both theoretical and practical insights into the kinetics of C and P utilization in young soils, as well as show changes in microbial P availability during early stages of soil development.

Estimating Children's Soil/Dust Ingestion Rates through ...

EPA Pesticide Factsheets

Background: Soil/dust ingestion rates are important variables in assessing children’s health risks in contaminated environments. Current estimates are based largely on soil tracer methodology, which is limited by analytical uncertainty, small sample size, and short study duration. Objectives: The objective was to estimate site-specific soil/dust ingestion rates through reevaluation of the lead absorption dose–response relationship using new bioavailability data from the Bunker Hill Mining and Metallurgical Complex Superfund Site (BHSS) in Idaho, USA. Methods: The U.S. Environmental Protection Agency (EPA) in vitro bioavailability methodology was applied to archived BHSS soil and dust samples. Using age-specific biokinetic slope factors, we related bioavailable lead from these sources to children’s blood lead levels (BLLs) monitored during cleanup from 1988 through 2002. Quantitative regression analyses and exposure assessment guidance were used to develop candidate soil/dust source partition scenarios estimating lead intake, allowing estimation of age-specific soil/dust ingestion rates. These ingestion rate and bioavailability estimates were simultaneously applied to the U.S. EPA Integrated Exposure Uptake Biokinetic Model for Lead in Children to determine those combinations best approximating observed BLLs. Results: Absolute soil and house dust bioavailability averaged 33% (SD ± 4%) and 28% (SD ± 6%), respectively. Estimated BHSS age-specific soil/du

Priming effect and microbial diversity in ecosystem functioning and response to global change: a modeling approach using the SYMPHONY model.

PubMed

Perveen, Nazia; Barot, Sébastien; Alvarez, Gaël; Klumpp, Katja; Martin, Raphael; Rapaport, Alain; Herfurth, Damien; Louault, Frédérique; Fontaine, Sébastien

2014-04-01

Integration of the priming effect (PE) in ecosystem models is crucial to better predict the consequences of global change on ecosystem carbon (C) dynamics and its feedbacks on climate. Over the last decade, many attempts have been made to model PE in soil. However, PE has not yet been incorporated into any ecosystem models. Here, we build plant/soil models to explore how PE and microbial diversity influence soil/plant interactions and ecosystem C and nitrogen (N) dynamics in response to global change (elevated CO2 and atmospheric N depositions). Our results show that plant persistence, soil organic matter (SOM) accumulation, and low N leaching in undisturbed ecosystems relies on a fine adjustment of microbial N mineralization to plant N uptake. This adjustment can be modeled in the SYMPHONY model by considering the destruction of SOM through PE, and the interactions between two microbial functional groups: SOM decomposers and SOM builders. After estimation of parameters, SYMPHONY provided realistic predictions on forage production, soil C storage and N leaching for a permanent grassland. Consistent with recent observations, SYMPHONY predicted a CO2 -induced modification of soil microbial communities leading to an intensification of SOM mineralization and a decrease in the soil C stock. SYMPHONY also indicated that atmospheric N deposition may promote SOM accumulation via changes in the structure and metabolic activities of microbial communities. Collectively, these results suggest that the PE and functional role of microbial diversity may be incorporated in ecosystem models with a few additional parameters, improving accuracy of predictions. © 2013 John Wiley & Sons Ltd.

Modeling Net Ecosystem Carbon Exchange of Alpine Grasslands with a Satellite-Driven Model

PubMed Central

Zhao, Yuping; Zhang, Xianzhou; Fan, Yuzhi; Shi, Peili; He, Yongtao; Yu, Guirui; Li, Yingnian

2015-01-01

Estimate of net ecosystem carbon exchange (NEE) between the atmosphere and terrestrial ecosystems, the balance of gross primary productivity (GPP) and ecosystem respiration (Reco) has significant importance for studying the regional and global carbon cycles. Using models driven by satellite data and climatic data is a promising approach to estimate NEE at regional scales. For this purpose, we proposed a semi-empirical model to estimate NEE in this study. In our model, the component GPP was estimated with a light response curve of a rectangular hyperbola. The component Reco was estimated with an exponential function of soil temperature. To test the feasibility of applying our model at regional scales, the temporal variations in the model parameters derived from NEE observations in an alpine grassland ecosystem on Tibetan Plateau were investigated. The results indicated that all the inverted parameters exhibit apparent seasonality, which is in accordance with air temperature and canopy phenology. In addition, all the parameters have significant correlations with the remote sensed vegetation indexes or environment temperature. With parameters estimated with these correlations, the model illustrated fair accuracy both in the validation years and at another alpine grassland ecosystem on Tibetan Plateau. Our results also indicated that the model prediction was less accurate in drought years, implying that soil moisture is an important factor affecting the model performance. Incorporating soil water content into the model would be a critical step for the improvement of the model. PMID:25849325

Modeling net ecosystem carbon exchange of alpine grasslands with a satellite-driven model.

PubMed

Yan, Wei; Hu, Zhongmin; Zhao, Yuping; Zhang, Xianzhou; Fan, Yuzhi; Shi, Peili; He, Yongtao; Yu, Guirui; Li, Yingnian

2015-01-01

Estimate of net ecosystem carbon exchange (NEE) between the atmosphere and terrestrial ecosystems, the balance of gross primary productivity (GPP) and ecosystem respiration (Reco) has significant importance for studying the regional and global carbon cycles. Using models driven by satellite data and climatic data is a promising approach to estimate NEE at regional scales. For this purpose, we proposed a semi-empirical model to estimate NEE in this study. In our model, the component GPP was estimated with a light response curve of a rectangular hyperbola. The component Reco was estimated with an exponential function of soil temperature. To test the feasibility of applying our model at regional scales, the temporal variations in the model parameters derived from NEE observations in an alpine grassland ecosystem on Tibetan Plateau were investigated. The results indicated that all the inverted parameters exhibit apparent seasonality, which is in accordance with air temperature and canopy phenology. In addition, all the parameters have significant correlations with the remote sensed vegetation indexes or environment temperature. With parameters estimated with these correlations, the model illustrated fair accuracy both in the validation years and at another alpine grassland ecosystem on Tibetan Plateau. Our results also indicated that the model prediction was less accurate in drought years, implying that soil moisture is an important factor affecting the model performance. Incorporating soil water content into the model would be a critical step for the improvement of the model.

Assessing quality of citizen scientists’ soil texture estimates to evaluate land potential

USDA-ARS?s Scientific Manuscript database

Texture influences nearly all soil processes and is often the most measured parameter in soil science. Estimating soil texture is a universal and fundamental practice applied by resource scientists to classify and understand the behavior and management of soil systems. While trained soil scientist c...

Global distribution of soil organic carbon, based on the Harmonized World Soil Database - Part 1: Masses and frequency distribution of SOC stocks for the tropics, permafrost regions, wetlands, and the world

NASA Astrophysics Data System (ADS)

Köchy, M.; Hiederer, R.; Freibauer, A.

2014-09-01

The global soil organic carbon (SOC) mass is relevant for the carbon cycle budget. We review current estimates of soil organic carbon stocks (mass/area) and mass (stock × area) in wetlands, permafrost and tropical regions and the world in the upper 1 m of soil. The Harmonized World Soil Database (HWSD) v.1.2 provides one of the most recent and coherent global data sets of SOC, giving a total mass of 2476 Pg. Correcting the HWSD's bulk density of organic soils, especially Histosols, results in a mass of 1062 Pg. The uncertainty of bulk density of Histosols alone introduces a range of -56 to +180 Pg for the estimate of global SOC in the top 1 m, larger than estimates of global soil respiration. We report the spatial distribution of SOC stocks per 0.5 arc minutes, the areal masses of SOC and the quantiles of SOC stocks by continents, wetland types, and permafrost types. Depending on the definition of "wetland", wetland soils contain between 82 and 158 Pg SOC. Incorporating more detailed estimates for permafrost from the Northern Circumpolar Soil Carbon Data Base (496 Pg SOC) and tropical peatland carbon, global soils contain 1324 Pg SOC in the upper 1 m including 421 Pg in tropical soils, whereof 40 Pg occur in tropical wetlands. Global SOC amounts to just under 3000 Pg when estimates for deeper soil layers are included. Variability in estimates is due to variation in definitions of soil units, differences in soil property databases, scarcity of information about soil carbon at depths > 1 m in peatlands, and variation in definitions of "peatland".

What is soil organic matter worth?

PubMed

Sparling, G P; Wheeler, D; Vesely, E-T; Schipper, L A

2006-01-01

The conservation and restoration of soil organic matter are often advocated because of the generally beneficial effects on soil attributes for plant growth and crop production. More recently, organic matter has become important as a terrestrial sink and store for C and N. We have attempted to derive a monetary value of soil organic matter for crop production and storage functions in three contrasting New Zealand soil orders (Gley, Melanic, and Granular Soils). Soil chemical and physical characteristics of real-life examples of three pairs of matched soils with low organic matter contents (after long-term continuous cropping for vegetables or maize) or high organic matter content (continuous pasture) were used as input data for a pasture (grass-clover) production model. The differences in pasture dry matter yields (non-irrigated) were calculated for three climate scenarios (wet, dry, and average years) and the yields converted to an equivalent weight and financial value of milk solids. We also estimated the hypothetical value of the C and N sequestered during the recovery phase of the low organic matter content soils assuming trading with C and N credits. For all three soil orders, and for the three climate scenarios, pasture dry matter yields were decreased in the soils with lower organic matter contents. The extra organic matter in the high C soils was estimated to be worth NZ$27 to NZ$150 ha(-1) yr(-1) in terms of increased milk solids production. The decreased yields from the previously cropped soils were predicted to persist for 36 to 125 yr, but with declining effect as organic matter gradually recovered, giving an accumulated loss in pastoral production worth around NZ$518 to NZ$1239 ha(-1). This was 42 to 73 times lower than the hypothetical value of the organic matter as a sequestering agent for C and N, which varied between NZ$22,963 to NZ$90,849 depending on the soil, region, discount rates, and values used for carbon and nitrogen credits.

A time-series approach to estimating soil moisture from vegetated surfaces using L-band radar backscatter

USDA-ARS?s Scientific Manuscript database

Many previous studies have shown the sensitivity of radar backscatter to surface soil moisture content, particularly at L-band. Moreover, the estimation of soil moisture from radar for bare soil surfaces is well-documented, but estimation underneath a vegetation canopy remains unsolved. Vegetation s...

Bioavailability Of Arsenic In Arsenical Pesticide-Amended Soils: Preliminary Greenhouse Study

NASA Astrophysics Data System (ADS)

Quazi, S.; Sarkar, D.; Khairom, A.; Datta, R.; Sharma, S.

2005-05-01

Long-term application of arsenical pesticides in agricultural lands has resulted in high levels of arsenic (As). Conversion of former agricultural lands to residential areas has resulted in increased human contact with soil As. Soil ingestion from incidental hand-to-mouth activity by children is now a very important issue in assessing human health risk associated with exposure to arsenical pesticide-applied former agricultural soils. Human health risk from direct exposure to soil As via hand to mouth action is restricted only to those fractions of As in the soil that are available to the human gastrointestinal system. Thus this study aimed at addressing the issue of soil variability on As bioavailability as a function of soil physiochemical properties in a dynamic interaction between soils, water and plants and pesticides. In the current greenhouse study two soils with drastically different chemical characteristics w.r.t As reactivity (Immokalee-low As retention potential and Millhopper-high As retention potential) and one pesticide (sodium arsenate) were used. Soils were amended with sodium arsenate at two rates representing the high and low ends of As contamination, generally representative of Superfunds site conditions: 675 and 1500 mg/kg As. Rice (Oryza sativa) was used as the test crop. Sequential digestion to estimate in-vitro As in the stomach phase and the intestinal phase was employed on soils sampled at 4 times: 0-time, after 3 mo, 6 mo and 9 mo of soil-pesticide equilibration. In-vitro bioavailability experiments were also performed with the same soils in order to obtain an estimate of the amount of As that would be absorbed to the intestinal linings in simulated systems. Following the greenhouse study, selective in-vivo bioavailability studies using As-contaminated soils will be conducted on male and female mice to correlate in-vitro results with the in-vivo data. Treatments will consist of a soil group (As in soil), a positive control group (only As) and a negative control group (no soil, no As). Results from the in-vitro and in-vivo studies will help understand the effects of soil properties on As bioavailability. Keywords: Bioavailability, pesticide, soil, arsenic, greenhouse.

Functional homogeneous zones (fHZs) in viticultural zoning procedure: an Italian case study on Aglianico vine

NASA Astrophysics Data System (ADS)

Bonfante, A.; Agrillo, A.; Albrizio, R.; Basile, A.; Buonomo, R.; De Mascellis, R.; Gambuti, A.; Giorio, P.; Guida, G.; Langella, G.; Manna, P.; Minieri, L.; Moio, L.; Siani, T.; Terribile, F.

2015-06-01

This paper aims to test a new physically oriented approach to viticulture zoning at farm scale that is strongly rooted in hydropedology and aims to achieve a better use of environmental features with respect to plant requirements and wine production. The physics of our approach are defined by the use of soil-plant-atmosphere simulation models, applying physically based equations to describe the soil hydrological processes and solve soil-plant water status. This study (part of the ZOVISA project) was conducted on a farm devoted to production of high-quality wines (Aglianico DOC), located in southern Italy (Campania region, Mirabella Eclano, AV). The soil spatial distribution was obtained after standard soil survey informed by geophysical survey. Two homogeneous zones (HZs) were identified; in each one a physically based model was applied to solve the soil water balance and estimate the soil functional behaviour (crop water stress index, CWSI) defining the functional homogeneous zones (fHZs). For the second process, experimental plots were established and monitored for investigating soil-plant water status, crop development (biometric and physiological parameters) and daily climate variables (temperature, solar radiation, rainfall, wind). The effects of crop water status on crop response over must and wine quality were then evaluated in the fHZs. This was performed by comparing crop water stress with (i) crop physiological measurement (leaf gas exchange, chlorophyll a fluorescence, leaf water potential, chlorophyll content, leaf area index (LAI) measurement), (ii) grape bunches measurements (berry weight, sugar content, titratable acidity, etc.) and (iii) wine quality (aromatic response). This experiment proved the usefulness of the physically based approach, also in the case of mapping viticulture microzoning.

Multi-site assimilation of a terrestrial biosphere model (BETHY) using satellite derived soil moisture data

NASA Astrophysics Data System (ADS)

Wu, Mousong; Sholze, Marko

2017-04-01

We investigated the importance of soil moisture data on assimilation of a terrestrial biosphere model (BETHY) for a long time period from 2010 to 2015. Totally, 101 parameters related to carbon turnover, soil respiration, as well as soil texture were selected for optimization within a carbon cycle data assimilation system (CCDAS). Soil moisture data from Soil Moisture and Ocean Salinity (SMOS) product was derived for 10 sites representing different plant function types (PFTs) as well as different climate zones. Uncertainty of SMOS soil moisture data was also estimated using triple collocation analysis (TCA) method by comparing with ASCAT dataset and BETHY forward simulation results. Assimilation of soil moisture to the system improved soil moisture as well as net primary productivity(NPP) and net ecosystem productivity (NEP) when compared with soil moisture derived from in-situ measurements and fluxnet datasets. Parameter uncertainties were largely reduced relatively to prior values. Using SMOS soil moisture data for assimilation of a terrestrial biosphere model proved to be an efficient approach in reducing uncertainty in ecosystem fluxes simulation. It could be further used in regional an global assimilation work to constrain carbon dioxide concentration simulation by combining with other sources of measurements.

Ultrasound Algorithm Derivation for Soil Moisture Content Estimation

NASA Technical Reports Server (NTRS)

Belisle, W.R.; Metzl, R.; Choi, J.; Aggarwal, M. D.; Coleman, T.

1997-01-01

Soil moisture content can be estimated by evaluating the velocity at which sound waves travel through a known volume of solid material. This research involved the development of three soil algorithms relating the moisture content to the velocity at which sound waves moved through dry and moist media. Pressure and shear wave propagation equations were used in conjunction with soil property descriptions to derive algorithms appropriate for describing the effects of moisture content variation on the velocity of sound waves in soils with and without complete soil pore water volumes, An elementary algorithm was used to estimate soil moisture contents ranging from 0.08 g/g to 0.5 g/g from sound wave velocities ranging from 526 m/s to 664 m/s. Secondary algorithms were also used to estimate soil moisture content from sound wave velocities through soils with pores that were filled predominantly with air or water.

Stochastic modeling of macrodispersion in unsaturated heterogeneous porous media. Final report

DOE Office of Scientific and Technical Information (OSTI.GOV)

Yeh, T.C.J.

1995-02-01

Spatial heterogeneity of geologic media leads to uncertainty in predicting both flow and transport in the vadose zone. In this work an efficient and flexible, combined analytical-numerical Monte Carlo approach is developed for the analysis of steady-state flow and transient transport processes in highly heterogeneous, variably saturated porous media. The approach is also used for the investigation of the validity of linear, first order analytical stochastic models. With the Monte Carlo analysis accurate estimates of the ensemble conductivity, head, velocity, and concentration mean and covariance are obtained; the statistical moments describing displacement of solute plumes, solute breakthrough at a compliancemore » surface, and time of first exceedance of a given solute flux level are analyzed; and the cumulative probability density functions for solute flux across a compliance surface are investigated. The results of the Monte Carlo analysis show that for very heterogeneous flow fields, and particularly in anisotropic soils, the linearized, analytical predictions of soil water tension and soil moisture flux become erroneous. Analytical, linearized Lagrangian transport models also overestimate both the longitudinal and the transverse spreading of the mean solute plume in very heterogeneous soils and in dry soils. A combined analytical-numerical conditional simulation algorithm is also developed to estimate the impact of in-situ soil hydraulic measurements on reducing the uncertainty of concentration and solute flux predictions.« less

Estimating Sources and Sinks of Methane from Soils in the Contiguous United States (CONUS)

NASA Astrophysics Data System (ADS)

Shu, S.; Jain, A. K.; Kheshgi, H. S.

2017-12-01

The global methane (CH4) budget estimated based on state-of-the-art models remains highly uncertain. Sources and sinks of CH4 from soils, including wetlands, are the most important source of uncertainty. Soils are estimated to account for about 45% of global CH4 emissions. At the same time oxidation of CH4 by soils is a significant sink, representing about 10% of the total sink. However, most regional and global scale modeling studies of soil CH4 fluxes have ignored the sink through soil oxidation and the source of CH4 emissions from the wet soils with shallow water tables. In this study, we link a bottom-up soil gas diffusion and CH4 biogeochemistry model to a land surface model, ISAM, to calculate the sources, emissions from both wetlands and non-wetlands, and sinks, soil oxidation, of CH4 from soils for the CONUS over the period 1900-2100. The newly developed soil CH4 model framework consists of a gas diffusion module with the vertically resolved soil hydrology (depth up to 3.5 m soil) and soil organic carbon (SOC) and CH4 biogeochemistry module. SOC profile is estimated by modeling vertical soil mixing and thus can represent the deep SOC content and estimate CH4 production from the deep non-wetland soil. For the diffusion calculations, we separately consider both the dissolved and gaseous O2 and CH4 at each soil layer. For CH4 biogeochemistry, we parameterize the production, soil oxidation, ebullition and aerenchyma transportation of CH4 for both seasonal/permanent wetland and wet soil. The SWAMP inundated fraction dataset with 8-day temporal resolution is incorporated to prescribe the extent of permanent and seasonal wetland extent for the recent decade. The model is first evaluated using a compilation of published CH4 site measurement data for CONUS. We then perform two different model experiments: 1) forced by the CRUNCEP climate data from 1900 to 2010 to estimate the contemporary CH4 emission and 2) forced by a climate projection of IPCC's highest representative concentration pathway (RCP8.5) from 2011 to 2100. Our study shows that soil oxidation has an important role attenuating the estimated natural CH4 source. We also find a wetter and warmer climate affects the dry soil CH4 sink and wet soil CH4 emissions and increases the estimated CH4 source over the CONUS.

Don't soil your chances with solar energy: Experiments of natural dust accumulation on solar modules and the effect on light transmission

NASA Astrophysics Data System (ADS)

Boyle, Liza

Dust accumulation, or soiling, on solar energy harvesting systems can cause significant losses that reduce the power output of the system, increase pay-back time of the system, and reduce confidence in solar energy overall. Developing a method of estimating soiling losses could greatly improve estimates of solar energy system outputs, greatly improve operation and maintenance of solar systems, and improve siting of solar energy systems. This dissertation aims to develop a soiling model by collecting ambient soiling data as well as other environmental data and fitting a model to these data. In general a process-level approach is taken to estimating soiling. First a comparison is made between mass of deposited particulates and transmission loss. Transmission loss is the reduction in light that a solar system would see due to soiling, and mass accumulation represents the level of soiling in the system. This experiment is first conducted at two sites in the Front Range of Colorado and then expanded to three additional sites. Second mass accumulation is examined as a function of airborne particulate matter (PM) concentrations, airborne size distributions, and meteorological data. In depth analysis of this process step is done at the first two sites in Colorado, and a more general analysis is done at the three additional sites. This step is identified as less understood step, but with results still allowing for a general soiling model to be developed. Third these two process steps are combined, and spatial variability of these steps are examined. The three additional sites (an additional site in the Front Range of Colorado, a site in Albuquerque New Mexico, and a site in Cocoa Florida) represent a much more spatially and climatically diverse set of locations than the original two sites and provide a much broader sample space in which to develop the combined soiling model. Finally a few additional parameters, precipitation, micro-meteorology, and some sampling artifacts, are cursorily examined. This is to provide a broader context for these results and to help future researchers in understanding the strengths and weaknesses of this dissertation and the results presented within.

A Comparison of Selected Statistical Techniques to Model Soil Cation Exchange Capacity

NASA Astrophysics Data System (ADS)

Khaledian, Yones; Brevik, Eric C.; Pereira, Paulo; Cerdà, Artemi; Fattah, Mohammed A.; Tazikeh, Hossein

2017-04-01

Cation exchange capacity (CEC) measures the soil's ability to hold positively charged ions and is an important indicator of soil quality (Khaledian et al., 2016). However, other soil properties are more commonly determined and reported, such as texture, pH, organic matter and biology. We attempted to predict CEC using different advanced statistical methods including monotone analysis of variance (MONANOVA), artificial neural networks (ANNs), principal components regressions (PCR), and particle swarm optimization (PSO) in order to compare the utility of these approaches and identify the best predictor. We analyzed 170 soil samples from four different nations (USA, Spain, Iran and Iraq) under three land uses (agriculture, pasture, and forest). Seventy percent of the samples (120 samples) were selected as the calibration set and the remaining 50 samples (30%) were used as the prediction set. The results indicated that the MONANOVA (R2= 0.82 and Root Mean Squared Error (RMSE) =6.32) and ANNs (R2= 0.82 and RMSE=5.53) were the best models to estimate CEC, PSO (R2= 0.80 and RMSE=5.54) and PCR (R2= 0.70 and RMSE=6.48) also worked well and the overall results were very similar to each other. Clay (positively correlated) and sand (negatively correlated) were the most influential variables for predicting CEC for the entire data set, while the most influential variables for the various countries and land uses were different and CEC was affected by different variables in different situations. Although the MANOVA and ANNs provided good predictions of the entire dataset, PSO gives a formula to estimate soil CEC using commonly tested soil properties. Therefore, PSO shows promise as a technique to estimate soil CEC. Establishing effective pedotransfer functions to predict CEC would be productive where there are limitations of time and money, and other commonly analyzed soil properties are available. References Khaledian, Y., Kiani, F., Ebrahimi, S., Brevik, E.C., Aitkenhead-Peterson, J. 2016. Assessment and monitoring of soil degradation during land use change using multivariate analysis. Land Degradation and Development. doi: 10.1002/ldr.2541.

Functional and Structural Succession of Soil Microbial Communities below Decomposing Human Cadavers

PubMed Central

Cobaugh, Kelly L.; Schaeffer, Sean M.; DeBruyn, Jennifer M.

2015-01-01

The ecological succession of microbes during cadaver decomposition has garnered interest in both basic and applied research contexts (e.g. community assembly and dynamics; forensic indicator of time since death). Yet current understanding of microbial ecology during decomposition is almost entirely based on plant litter. We know very little about microbes recycling carcass-derived organic matter despite the unique decomposition processes. Our objective was to quantify the taxonomic and functional succession of microbial populations in soils below decomposing cadavers, testing the hypotheses that a) periods of increased activity during decomposition are associated with particular taxa; and b) human-associated taxa are introduced to soils, but do not persist outside their host. We collected soils from beneath four cadavers throughout decomposition, and analyzed soil chemistry, microbial activity and bacterial community structure. As expected, decomposition resulted in pulses of soil C and nutrients (particularly ammonia) and stimulated microbial activity. There was no change in total bacterial abundances, however we observed distinct changes in both function and community composition. During active decay (7 - 12 days postmortem), respiration and biomass production rates were high: the community was dominated by Proteobacteria (increased from 15.0 to 26.1% relative abundance) and Firmicutes (increased from 1.0 to 29.0%), with reduced Acidobacteria abundances (decreased from 30.4 to 9.8%). Once decay rates slowed (10 - 23 d postmortem), respiration was elevated, but biomass production rates dropped dramatically; this community with low growth efficiency was dominated by Firmicutes (increased to 50.9%) and other anaerobic taxa. Human-associated bacteria, including the obligately anaerobic Bacteroides, were detected at high concentrations in soil throughout decomposition, up to 198 d postmortem. Our results revealed the pattern of functional and compositional succession in soil microbial communities during decomposition of human-derived organic matter, provided insight into decomposition processes, and identified putative predictor populations for time since death estimation. PMID:26067226

Using satellite image data to estimate soil moisture

NASA Astrophysics Data System (ADS)

Chuang, Chi-Hung; Yu, Hwa-Lung

2017-04-01

Soil moisture is considered as an important parameter in various study fields, such as hydrology, phenology, and agriculture. In hydrology, soil moisture is an significant parameter to decide how much rainfall that will infiltrate into permeable layer and become groundwater resource. Although soil moisture is a critical role in many environmental studies, so far the measurement of soil moisture is using ground instrument such as electromagnetic soil moisture sensor. Use of ground instrumentation can directly obtain the information, but the instrument needs maintenance and consume manpower to operation. If we need wide range region information, ground instrumentation probably is not suitable. To measure wide region soil moisture information, we need other method to achieve this purpose. Satellite remote sensing techniques can obtain satellite image on Earth, this can be a way to solve the spatial restriction on instrument measurement. In this study, we used MODIS data to retrieve daily soil moisture pattern estimation, i.e., crop water stress index (cwsi), over the year of 2015. The estimations are compared with the observations at the soil moisture stations from Taiwan Bureau of soil and water conservation. Results show that the satellite remote sensing data can be helpful to the soil moisture estimation. Further analysis can be required to obtain the optimal parameters for soil moisture estimation in Taiwan.

Estimation of Key Parameters of the Coupled Energy and Water Model by Assimilating Land Surface Data

NASA Astrophysics Data System (ADS)

Abdolghafoorian, A.; Farhadi, L.

2017-12-01

Accurate estimation of land surface heat and moisture fluxes, as well as root zone soil moisture, is crucial in various hydrological, meteorological, and agricultural applications. Field measurements of these fluxes are costly and cannot be readily scaled to large areas relevant to weather and climate studies. Therefore, there is a need for techniques to make quantitative estimates of heat and moisture fluxes using land surface state observations that are widely available from remote sensing across a range of scale. In this work, we applies the variational data assimilation approach to estimate land surface fluxes and soil moisture profile from the implicit information contained Land Surface Temperature (LST) and Soil Moisture (SM) (hereafter the VDA model). The VDA model is focused on the estimation of three key parameters: 1- neutral bulk heat transfer coefficient (CHN), 2- evaporative fraction from soil and canopy (EF), and 3- saturated hydraulic conductivity (Ksat). CHN and EF regulate the partitioning of available energy between sensible and latent heat fluxes. Ksat is one of the main parameters used in determining infiltration, runoff, groundwater recharge, and in simulating hydrological processes. In this study, a system of coupled parsimonious energy and water model will constrain the estimation of three unknown parameters in the VDA model. The profile of SM (LST) at multiple depths is estimated using moisture diffusion (heat diffusion) equation. In this study, the uncertainties of retrieved unknown parameters and fluxes are estimated from the inverse of Hesian matrix of cost function which is computed using the Lagrangian methodology. Analysis of uncertainty provides valuable information about the accuracy of estimated parameters and their correlation and guide the formulation of a well-posed estimation problem. The results of proposed algorithm are validated with a series of experiments using a synthetic data set generated by the simultaneous heat and water (SHAW) model. In addition, the feasibility of extending this algorithm to use remote sensing observations that have low temporal resolution is examined by assimilating the limited number of land surface moisture and temperature observations.

Soil Moisture Monitoring using Surface Electrical Resistivity measurements

NASA Astrophysics Data System (ADS)

Calamita, Giuseppe; Perrone, Angela; Brocca, Luca; Straface, Salvatore

2017-04-01

The relevant role played by the soil moisture (SM) for global and local natural processes results in an explicit interest for its spatial and temporal estimation in the vadose zone coming from different scientific areas - i.e. eco-hydrology, hydrogeology, atmospheric research, soil and plant sciences, etc... A deeper understanding of natural processes requires the collection of data on a higher number of points at increasingly higher spatial scales in order to validate hydrological numerical simulations. In order to take the best advantage of the Electrical Resistivity (ER) data with their non-invasive and cost-effective properties, sequential Gaussian geostatistical simulations (sGs) can be applied to monitor the SM distribution into the soil by means of a few SM measurements and a densely regular ER grid of monitoring. With this aim, co-located SM measurements using mobile TDR probes (MiniTrase), and ER measurements, obtained by using a four-electrode device coupled with a geo-resistivimeter (Syscal Junior), were collected during two surveys carried out on a 200 × 60 m2 area. Two time surveys were carried out during which Data were collected at a depth of around 20 cm for more than 800 points adopting a regular grid sampling scheme with steps (5 m) varying according to logistic and soil compaction constrains. The results of this study are robust due to the high number of measurements available for either variables which strengthen the confidence in the covariance function estimated. Moreover, the findings obtained using sGs show that it is possible to estimate soil moisture variations in the pedological zone by means of time-lapse electrical resistivity and a few SM measurements.

Carbon stock and turnover in riparian soils under lowland rainforest transformation systems on Sumatra, Indonesia

NASA Astrophysics Data System (ADS)

Hennings, Nina; Kuzyakov, Yakov

2017-04-01

In many tropical areas, rainforests are being cleared in order to exploit timber and other forest products as well as plant crops for food, feed and fuel use. The determinants of different patterns of deforestation and the roles of resulting transformation systems of tropical riparian rainforests for ecological functions have yet received little attention in scientific research. Especially C stocks in riparian zones are strongly affected by climate and land use changes that lead to changes in water regime and ground water level drops. We investigated the effects of land transformations in riparian ecosystems of Sumatra, on soil C content, stocks and decomposability at the landscape scale. We compare C losses in transformation systems and rainforests and estimate the contribution of soil erosion and organic matter mineralization. Further, these losses are related to changing water level and temperature increase along increasing distance to the stream. This approach is based on changing δ13C values of SOC in the topsoil as compared to those in subsoil. The shift of δ13C of SOC in the topsoil from the linear regression calculated by δ13C value with log(SOC) in the topsoil represents the modification of the C turnover rate in the top soil. Erosion is estimated by the shift of the δ13C value of SOC in the subsoil under plantations. Further, the δ13C and δ15N soil profiles and their comparison with litter of local vegetation, can be used to estimate the contribution of autochthonous and allochthonous organics to soil C stocks. Preliminary results show strong increase of erosive losses, increased decomposition with land-use transformation and decrease of C stocks with decreasing water table.

Use of visible, near-infrared, and thermal infrared remote sensing to study soil moisture

NASA Technical Reports Server (NTRS)

Blanchard, M. B.; Greeley, R.; Goettelman, R.

1974-01-01

Two methods are described which are used to estimate soil moisture remotely using the 0.4- to 14.0 micron wavelength region: (1) measurement of spectral reflectance, and (2) measurement of soil temperature. The reflectance method is based on observations which show that directional reflectance decreases as soil moisture increases for a given material. The soil temperature method is based on observations which show that differences between daytime and nighttime soil temperatures decrease as moisture content increases for a given material. In some circumstances, separate reflectance or temperature measurements yield ambiguous data, in which case these two methods may be combined to obtain a valid soil moisture determination. In this combined approach, reflectance is used to estimate low moisture levels; and thermal inertia (or thermal diffusivity) is used to estimate higher levels. The reflectance method appears promising for surface estimates of soil moisture, whereas the temperature method appears promising for estimates of near-subsurface (0 to 10 cm).

Use of visible, near-infrared, and thermal infrared remote sensing to study soil moisture

NASA Technical Reports Server (NTRS)

Blanchard, M. B.; Greeley, R.; Goettelman, R.

1974-01-01

Two methods are used to estimate soil moisture remotely using the 0.4- to 14.0-micron wavelength region: (1) measurement of spectral reflectance, and (2) measurement of soil temperature. The reflectance method is based on observations which show that directional reflectance decreases as soil moisture increases for a given material. The soil temperature method is based on observations which show that differences between daytime and nighttime soil temperatures decrease as moisture content increases for a given material. In some circumstances, separate reflectance or temperature measurements yield ambiguous data, in which case these two methods may be combined to obtain a valid soil moisture determination. In this combined approach, reflectance is used to estimate low moisture levels; and thermal inertia (or thermal diffusivity) is used to estimate higher levels. The reflectance method appears promising for surface estimates of soil moisture, whereas the temperature method appears promising for estimates of near-subsurface (0 to 10 cm).

Review of mechanisms, methods, and theory for determining recharge to shallow aquifers in North Dakota

USGS Publications Warehouse

Horak, W.F.

1988-01-01

Effective management of ground-water resources requires knowledge of all components of the water budget for the aquifer of interest. Efforts to simulate ground-water flow prior to development and the effects of proposed pumping in several of North Dakota's shallow glacial aquifers have been hindered by the lack of reliable estimates of ground-water recharge. This study was done to (1) review the methods that have been used to measure recharge, (2) review the theory of unsaturated flow and the methods for characterizing the physical properties of unsaturated media, (3) consider the relative merits of a rigorous data-intensive approach versus an estimation approach to the study of recharge, and (4) review past and current agronomic research in North Dakota for applicability of the research and the data generated to the study of recharge.Direct, quantitative techniques for evaluating recharge are rarely applied. The theory for computing fluxes in unsaturated media is well established and numerous physics-based models that effectively implement the theory are available, but the data required for the models generally are lacking. Many parametric approaches have been developed to avoid the large data requirements of the physics-based approaches for analyzing flow in the unsaturated zone. However, the parametric approaches normally include fitting coefficients that must be calibrated for every study site, thereby detracting from the general utility of the parametric approach. The functional relation of matric potential to moisture content is required for physics-based soil-water models, whether analytic or numeric. Laboratory methods to determine these relations are tedious, costly, and may not give results representative of the soils as they occur in the field. Many models have been proposed to estimate the moisture-characteristic curve and hydraulic-conductivity function from basic soil properties, but none yield results that are universally satisfactory. In situ methods, because they require minimal disturbance of the soil profile and may be used repeatedly on the same soil mass, have become the preferred means for acquiring physical data, especially hydraulic conductivity. Hydro logic investigations, except for recent studies of hazardous-waste disposal sites, rarely have included physical characterizations of unsaturated media. Any of four phenomena could hinder attempts to simulate unsaturated flow in settings typical of North Dakota; variability of soil properties, hysteresis, frozen ground, and macropore development. The spatial and temporal variability of soil properties probably is the greatest complicating phenomenon and must be dealt with by detailed characterization of the properties. Hysteresis can detract from the accuracy of flow calculations for some soils under certain conditions but, for the present, our scant knowledge of soil physical properties is a greater hindrance to reliable soi1-water mode 1 ing than is the hysteresis phenomenon. A1 though seasona1ly frozen ground undoubtedly affects hydrologic processes in North Dakota, much more research is needed before meaningful quantitative treatment is possible. Finally, macropores can influence soil-water movement significantly, but macropore development may not be common on the intensively farmed, coarse-textured soils that typically overlie North Dakota's glacial aquifers. Lysimetry currently is the only reliable means of analyzing macropore flow.The soil-related research that has been conducted in North Dakota to date (1983) provides little of the type of information required to estimate ground-water recharge. Useful data could be developed by systematically evaluating the hydraulic characteristics of the prominent soil types overlying North Dakota's shallow glacial aquifers. These data would be required to enable use of a physics-based approach to estimating recharge. The size of the aquifer under study, its economic value, and the resources available for data collection should be considered when choosing between parametric or physics-based methods.

AirMOSS P-Band Radar Retrieval of Subcanopy Soil Moisture Profile

NASA Astrophysics Data System (ADS)

Tabatabaeenejad, A.; Burgin, M. S.; Duan, X.; Moghaddam, M.

2013-12-01

Knowledge of soil moisture, as a key variable of the Earth system, plays an important role in our under-standing of the global water, energy, and carbon cycles. The importance of such knowledge has led NASA to fund missions such as Soil Moisture Active and Passive (SMAP) and Airborne Microwave Observatory of Subcanopy and Subsurface (AirMOSS). The AirMOSS mission seeks to improve the estimates of the North American Net Ecosystem Exchange (NEE) by providing high-resolution observations of the root zone soil moisture (RZSM) over regions representative of the major North American biomes. AirMOSS flies a P-band SAR to penetrate vegetation and into the root zone to provide estimates of RZSM. The flights cover areas containing flux tower sites in regions from the boreal forests in Saskatchewan, Canada, to the tropical forests in La Selva, Costa Rica. The radar snapshots are used to generate estimates of RZSM via inversion of a scattering model of vegetation overlying soils with variable moisture profiles. These retrievals will be used to generate a time record of RZSM, which will be integrated with an ecosystem demography model in order to estimate the respiration and photosynthesis carbon fluxes. The aim of this work is the retrieval of the moisture profile over AirMOSS sites using the collected P-band radar data. We have integrated layered-soil scattering models into a forest scattering model; for the backscattering from ground and for the trunk-ground double-bounce mechanism, we have used a layered small perturbation method and a coherent scattering model of layered soil, respectively. To estimate the soil moisture profile, we represent it as a second-order polynomial in the form of az2 + bz + c, where z is the depth and a, b, and c are the coefficients to be retrieved from radar measurements. When retrieved, these coefficients give us the soil moisture up to a prescribed depth of validity. To estimate the unknown coefficients of the polynomial, we use simulated annealing to minimize a cost function. Considering the required accuracy and reasonableness of the computational cost, and guided by in-situ field observations from several sites and prior field campaigns, the inversion algorithm parameters are chosen judiciously after extensive simulations using synthetic and real radar data. The ancillary data necessary to characterize a pixel are readily available. For example, the slope of each pixel is included in the radar data delivered by JPL. For land cover type within the continental United States, we use the National Land Cover Database (NLCD). Soil texture data are available from the Soil Survey Geographic (SSURGO) database for the United States. The handling and processing of the ancillary data is an involved and detailed process that will be briefly presented at the talk. We apply the retrieval method to the data acquired over several AirMOSS sites, and validate the results using in-situ soil moisture measurements. Retrieved profiles from several specific pixels at each site, the retrieval errors, and the retrieved moisture maps of the 100 km by 25 km imaged domains will be reported at the talk.

Impacts of Long-Term Irrigation of Domestic Treated Wastewater on Soil Biogeochemistry and Bacterial Community Structure

PubMed Central

Wafula, Denis; White, John R.; Canion, Andy; Jagoe, Charles; Pathak, Ashish

2015-01-01

Freshwater scarcity and regulations on wastewater disposal have necessitated the reuse of treated wastewater (TWW) for soil irrigation, which has several environmental and economic benefits. However, TWW irrigation can cause nutrient loading to the receiving environments. We assessed bacterial community structure and associated biogeochemical changes in soil plots irrigated with nitrate-rich TWW (referred to as pivots) for periods ranging from 13 to 30 years. Soil cores (0 to 40 cm) were collected in summer and winter from five irrigated pivots and three adjacently located nonirrigated plots. Total bacterial and denitrifier gene abundances were estimated by quantitative PCR (qPCR), and community structure was assessed by 454 massively parallel tag sequencing (MPTS) of small-subunit (SSU) rRNA genes along with terminal restriction fragment length polymorphism (T-RFLP) analysis of nirK, nirS, and nosZ functional genes responsible for denitrification of the TWW-associated nitrate. Soil physicochemical analyses showed that, regardless of the seasons, pH and moisture contents (MC) were higher in the irrigated (IR) pivots than in the nonirrigated (NIR) plots; organic matter (OM) and microbial biomass carbon (MBC) were higher as a function of season but not of irrigation treatment. MPTS analysis showed that TWW loading resulted in the following: (i) an increase in the relative abundance of Proteobacteria, especially Betaproteobacteria and Gammaproteobacteria; (ii) a decrease in the relative abundance of Actinobacteria; (iii) shifts in the communities of acidobacterial groups, along with a shift in the nirK and nirS denitrifier guilds as shown by T-RFLP analysis. Additionally, bacterial biomass estimated by genus/group-specific real-time qPCR analyses revealed that higher numbers of total bacteria, Acidobacteria, Actinobacteria, Alphaproteobacteria, and the nirS denitrifier guilds were present in the IR pivots than in the NIR plots. Identification of the nirK-containing microbiota as a proxy for the denitrifier community indicated that bacteria belonged to alphaproteobacteria from the Rhizobiaceae family within the agroecosystem studied. Multivariate statistical analyses further confirmed some of the above soil physicochemical and bacterial community structure changes as a function of long-term TWW application within this agroecosystem. PMID:26253672

The Role of Evapotranspiration on Soil Moisture Depletion in a Small Alaskan Subarctic Farm

NASA Astrophysics Data System (ADS)

Ruairuen, W.; Fochesatto, G. J.; Sparrow, E. B.; Schnabel, W.; Zhang, M.

2013-12-01

At high latitudes the period for agriculture production is very short (110 frost-free days) and strongly depends on the availability of soil water content for vegetables to grow. In this context the evapotranspiration (ET) cycle is key variable underpinning mass and energy balance modulating therefore moisture gradients and soil dryness. Evapotranspiration (ET) from field-grown crops water stress is virtually unknown in the subarctic region. Understanding ET cycles in high latitude agricultural ecosystem is essential in terms of water management and sustainability and projection of agricultural activity. To investigate the ET cycle in farming soils a field experiment was conducted in the summer of 2012 and 2013 at the University of Alaska Fairbanks Agricultural and Forestry Experiment Station combining micrometeorological and hydrological measurements. In this case experimental plots of lettuce (Lactuca sativa) plants were grown. The experiment evaluated several components of the ET cycle such as actual evapotranspiration, reference evaporation, pan evaporation as well as soil water content and temperature profiles to link them to the vegetable growing functions. We investigated the relationship of soil moisture content and crop water use across the growing season as a function of the ET cycle. Soil water depletion was compared to daily estimates of water loss by ET during dry and wet periods. We also investigated the dependence of ET on the atmospheric boundary layer flow patterns set by the synoptic large scale weather patterns.

Assessing HYDRUS-2D model to estimate soil water contents and olive tree transpiration fluxes under different water distribution systems

NASA Astrophysics Data System (ADS)

Autovino, Dario; Negm, Amro; Rallo, Giovanni; Provenzano, Giuseppe

2016-04-01

In Mediterranean countries characterized by limited water resources for agricultural and societal sectors, irrigation management plays a major role to improve water use efficiency at farm scale, mainly where irrigation systems are correctly designed to guarantee a suitable application efficiency and the uniform water distribution throughout the field. In the last two decades, physically-based agro-hydrological models have been developed to simulate mass and energy exchange processes in the soil-plant-atmosphere (SPA) system. Mechanistic models like HYDRUS 2D/3D (Šimunek et al., 2011) have been proposed to simulate all the components of water balance, including actual crop transpiration fluxes estimated according to a soil potential-dependent sink term. Even though the suitability of these models to simulate the temporal dynamics of soil and crop water status has been reported in the literature for different horticultural crops, a few researches have been considering arboreal crops where the higher gradients of root water uptake are the combination between the localized irrigation supply and the three dimensional root system distribution. The main objective of the paper was to assess the performance of HYDRUS-2D model to evaluate soil water contents and transpiration fluxes of an olive orchard irrigated with two different water distribution systems. Experiments were carried out in Castelvetrano (Sicily) during irrigation seasons 2011 and 2012, in a commercial farm specialized in the production of table olives (Olea europaea L., var. Nocellara del Belice), representing the typical variety of the surrounding area. During the first season, irrigation water was provided by a single lateral placed along the plant row with four emitters per plant (ordinary irrigation), whereas during the second season a grid of emitters laid on the soil was installed in order to irrigate the whole soil surface around the selected trees. The model performance was assessed based on the comparison between measured and simulated soil water content and actual transpiration fluxes, under the hypothesis to neglect the contribute of the tree capacitance. Moreover, two different crop water stress functions and in particular the linear model proposed by Feddes et al. (1978) and the S-shape model suggested by van Genuchten et al. (1987), were considered. The result of the study evidenced that for the investigated crop and under the examined conditions, HYDRUS-2D model reproduces fairly well the dynamic of soil water contents at different distances from the emitters (RMSE<0.09 cm3 cm-3) and actual crop transpiration fluxes (RMSE<0.11 mm d-1), whose estimations can be slightly improved by assuming a S-shape crop water stress function. Key-words: Olive tree, HYDRUS-2D, Soil water content, Actual transpiration fluxes

Derivation of an Explicit Form of the Percolation-Based Effective-Medium Approximation for Thermal Conductivity of Partially Saturated Soils

NASA Astrophysics Data System (ADS)

Sadeghi, Morteza; Ghanbarian, Behzad; Horton, Robert

2018-02-01

Thermal conductivity is an essential component in multiphysics models and coupled simulation of heat transfer, fluid flow, and solute transport in porous media. In the literature, various empirical, semiempirical, and physical models were developed for thermal conductivity and its estimation in partially saturated soils. Recently, Ghanbarian and Daigle (GD) proposed a theoretical model, using the percolation-based effective-medium approximation, whose parameters are physically meaningful. The original GD model implicitly formulates thermal conductivity λ as a function of volumetric water content θ. For the sake of computational efficiency in numerical calculations, in this study, we derive an explicit λ(θ) form of the GD model. We also demonstrate that some well-known empirical models, e.g., Chung-Horton, widely applied in the HYDRUS model, as well as mixing models are special cases of the GD model under specific circumstances. Comparison with experiments indicates that the GD model can accurately estimate soil thermal conductivity.

Research on dynamic creep strain and settlement prediction under the subway vibration loading.

PubMed

Luo, Junhui; Miao, Linchang

2016-01-01

This research aims to explore the dynamic characteristics and settlement prediction of soft soil. Accordingly, the dynamic shear modulus formula considering the vibration frequency was utilized and the dynamic triaxial test conducted to verify the validity of the formula. Subsequently, the formula was applied to the dynamic creep strain function, with the factors influencing the improved dynamic creep strain curve of soft soil being analyzed. Meanwhile, the variation law of dynamic stress with sampling depth was obtained through the finite element simulation of subway foundation. Furthermore, the improved dynamic creep strain curve of soil layer was determined based on the dynamic stress. Thereafter, it could to estimate the long-term settlement under subway vibration loading by norms. The results revealed that the dynamic shear modulus formula is straightforward and practical in terms of its application to the vibration frequency. The values predicted using the improved dynamic creep strain formula closed to the experimental values, whilst the estimating settlement closed to the measured values obtained in the field test.

Symbolic Regression for the Estimation of Transfer Functions of Hydrological Models

NASA Astrophysics Data System (ADS)

Klotz, D.; Herrnegger, M.; Schulz, K.

2017-11-01

Current concepts for parameter regionalization of spatially distributed rainfall-runoff models rely on the a priori definition of transfer functions that globally map land surface characteristics (such as soil texture, land use, and digital elevation) into the model parameter space. However, these transfer functions are often chosen ad hoc or derived from small-scale experiments. This study proposes and tests an approach for inferring the structure and parametrization of possible transfer functions from runoff data to potentially circumvent these difficulties. The concept uses context-free grammars to generate possible proposition for transfer functions. The resulting structure can then be parametrized with classical optimization techniques. Several virtual experiments are performed to examine the potential for an appropriate estimation of transfer function, all of them using a very simple conceptual rainfall-runoff model with data from the Austrian Mur catchment. The results suggest that a priori defined transfer functions are in general well identifiable by the method. However, the deduction process might be inhibited, e.g., by noise in the runoff observation data, often leading to transfer function estimates of lower structural complexity.

Near-surface turbulence as a missing link in modeling evapotranspiration-soil moisture relationships

NASA Astrophysics Data System (ADS)

Haghighi, Erfan; Kirchner, James W.

2017-07-01

Despite many efforts to develop evapotranspiration (ET) models with improved parametrizations of resistance terms for water vapor transfer into the atmosphere, estimates of ET and its partitioning remain prone to bias. Much of this bias could arise from inadequate representations of physical interactions near nonuniform surfaces from which localized heat and water vapor fluxes emanate. This study aims to provide a mechanistic bridge from land-surface characteristics to vertical transport predictions, and proposes a new physically based ET model that builds on a recently developed bluff-rough bare soil evaporation model incorporating coupled soil moisture-atmospheric controls. The newly developed ET model explicitly accounts for (1) near-surface turbulent interactions affecting soil drying and (2) soil-moisture-dependent stomatal responses to atmospheric evaporative demand that influence leaf (and canopy) transpiration. Model estimates of ET and its partitioning were in good agreement with available field-scale data, and highlight hidden processes not accounted for by commonly used ET schemes. One such process, nonlinear vegetation-induced turbulence (as a function of vegetation stature and cover fraction) significantly influences ET-soil moisture relationships. Our results are particularly important for water resources and land use planning of semiarid sparsely vegetated ecosystems where soil surface interactions are known to play a critical role in land-climate interactions. This study potentially facilitates a mathematically tractable description of the strength (i.e., the slope) of the ET-soil moisture relationship, which is a core component of models that seek to predict land-atmosphere coupling and its feedback to the climate system in a changing climate.

Use of satellite and modeled soil moisture data for predicting event soil loss at plot scale

NASA Astrophysics Data System (ADS)

Todisco, F.; Brocca, L.; Termite, L. F.; Wagner, W.

2015-09-01

The potential of coupling soil moisture and a Universal Soil Loss Equation-based (USLE-based) model for event soil loss estimation at plot scale is carefully investigated at the Masse area, in central Italy. The derived model, named Soil Moisture for Erosion (SM4E), is applied by considering the unavailability of in situ soil moisture measurements, by using the data predicted by a soil water balance model (SWBM) and derived from satellite sensors, i.e., the Advanced SCATterometer (ASCAT). The soil loss estimation accuracy is validated using in situ measurements in which event observations at plot scale are available for the period 2008-2013. The results showed that including soil moisture observations in the event rainfall-runoff erosivity factor of the USLE enhances the capability of the model to account for variations in event soil losses, the soil moisture being an effective alternative to the estimated runoff, in the prediction of the event soil loss at Masse. The agreement between observed and estimated soil losses (through SM4E) is fairly satisfactory with a determination coefficient (log-scale) equal to ~ 0.35 and a root mean square error (RMSE) of ~ 2.8 Mg ha-1. These results are particularly significant for the operational estimation of soil losses. Indeed, currently, soil moisture is a relatively simple measurement at the field scale and remote sensing data are also widely available on a global scale. Through satellite data, there is the potential of applying the SM4E model for large-scale monitoring and quantification of the soil erosion process.

Estimating Soil Cation Exchange Capacity from Soil Physical and Chemical Properties

NASA Astrophysics Data System (ADS)

Bateni, S. M.; Emamgholizadeh, S.; Shahsavani, D.

2014-12-01

The soil Cation Exchange Capacity (CEC) is an important soil characteristic that has many applications in soil science and environmental studies. For example, CEC influences soil fertility by controlling the exchange of ions in the soil. Measurement of CEC is costly and difficult. Consequently, several studies attempted to obtain CEC from readily measurable soil physical and chemical properties such as soil pH, organic matter, soil texture, bulk density, and particle size distribution. These studies have often used multiple regression or artificial neural network models. Regression-based models cannot capture the intricate relationship between CEC and soil physical and chemical attributes and provide inaccurate CEC estimates. Although neural network models perform better than regression methods, they act like a black-box and cannot generate an explicit expression for retrieval of CEC from soil properties. In a departure with regression and neural network models, this study uses Genetic Expression Programming (GEP) and Multivariate Adaptive Regression Splines (MARS) to estimate CEC from easily measurable soil variables such as clay, pH, and OM. CEC estimates from GEP and MARS are compared with measurements at two field sites in Iran. Results show that GEP and MARS can estimate CEC accurately. Also, the MARS model performs slightly better than GEP. Finally, a sensitivity test indicates that organic matter and pH have respectively the least and the most significant impact on CEC.

Cost-effective sampling of ¹³⁷Cs-derived net soil redistribution: part 1--estimating the spatial mean across scales of variation.

PubMed

Li, Y; Chappell, A; Nyamdavaa, B; Yu, H; Davaasuren, D; Zoljargal, K

2015-03-01

The (137)Cs technique for estimating net time-integrated soil redistribution is valuable for understanding the factors controlling soil redistribution by all processes. The literature on this technique is dominated by studies of individual fields and describes its typically time-consuming nature. We contend that the community making these studies has inappropriately assumed that many (137)Cs measurements are required and hence estimates of net soil redistribution can only be made at the field scale. Here, we support future studies of (137)Cs-derived net soil redistribution to apply their often limited resources across scales of variation (field, catchment, region etc.) without compromising the quality of the estimates at any scale. We describe a hybrid, design-based and model-based, stratified random sampling design with composites to estimate the sampling variance and a cost model for fieldwork and laboratory measurements. Geostatistical mapping of net (1954-2012) soil redistribution as a case study on the Chinese Loess Plateau is compared with estimates for several other sampling designs popular in the literature. We demonstrate the cost-effectiveness of the hybrid design for spatial estimation of net soil redistribution. To demonstrate the limitations of current sampling approaches to cut across scales of variation, we extrapolate our estimate of net soil redistribution across the region, show that for the same resources, estimates from many fields could have been provided and would elucidate the cause of differences within and between regional estimates. We recommend that future studies evaluate carefully the sampling design to consider the opportunity to investigate (137)Cs-derived net soil redistribution across scales of variation. Copyright © 2014 Elsevier Ltd. All rights reserved.

Bioavailability of xenobiotics in the soil environment.

PubMed

Katayama, Arata; Bhula, Raj; Burns, G Richard; Carazo, Elizabeth; Felsot, Allan; Hamilton, Denis; Harris, Caroline; Kim, Yong-Hwa; Kleter, Gijs; Koedel, Werner; Linders, Jan; Peijnenburg, J G M Willie; Sabljic, Aleksandar; Stephenson, R Gerald; Racke, D Kenneth; Rubin, Baruch; Tanaka, Keiji; Unsworth, John; Wauchope, R Donald

2010-01-01

It is often presumed that all chemicals in soil are available to microorganisms, plant roots, and soil fauna via dermal exposure. Subsequent bioaccumulation through the food chain may then result in exposure to higher organisms. Using the presumption of total availability, national governments reduce environmental threshold levels of regulated chemicals by increasing guideline safety margins. However, evidence shows that chemical residues in the soil environment are not always bioavailable. Hence, actual chemical exposure levels of biota are much less than concentrations present in soil would suggest. Because "bioavailability" conveys meaning that combines implications of chemical sol persistency, efficacy, and toxicity, insights on the magnitude of a chemicals soil bioavailability is valuable. however, soil bioavailability of chemicals is a complex topic, and is affected by chemical properties, soil properties, species exposed, climate, and interaction processes. In this review, the state-of-art scientific basis for bioavailability is addressed. Key points covered include: definition, factors affecting bioavailability, equations governing key transport and distributive kinetics, and primary methods for estimating bioavailability. Primary transport mechanisms in living organisms, critical to an understanding of bioavailability, also presage the review. Transport of lipophilic chemicals occurs mainly by passive diffusion for all microorganisms, plants, and soil fauna. Therefore, the distribution of a chemical between organisms and soil (bioavailable proportion) follows partition equilibrium theory. However, a chemical's bioavailability does not always follow partition equilibrium theory because of other interactions with soil, such as soil sorption, hysteretic desorption, effects of surfactants in pore water, formation of "bound residue", etc. Bioassays for estimating chemical bioavailability have been introduced with several targeted endpoints: microbial degradation, uptake by higher plants and soil fauna, and toxicity to organisms. However, there bioassays are often time consuming and laborious. Thus, mild extraction methods have been employed to estimate bioavailability of chemicals. Mild methods include sequential extraction using alcohols, hexane/water, supercritical fluids (carbon dioxide), aqueous hydroxypropyl-beta-cyclodextrin extraction, polymeric TENAX beads extraction, and poly(dimethylsiloxane)-coated solid-phase microextraction. It should be noted that mild extraction methods may predict bioavailability at the moment when measurements are carried out, but not the changes in bioavailability that may occur over time. Simulation models are needed to estimate better bioavailability as a function of exposure time. In the past, models have progressed significantly by addressing each group of organisms separately: microbial degradation, plant uptake via evapotranspiration processes, and uptake of soil fauna in their habitat. This approach has been used primarily because of wide differences in the physiology and behaviors of such disparate organisms. However, improvement of models is badly needed, Particularly to describe uptake processes by plant and animals that impinge on bioavailability. Although models are required to describe all important factors that may affect chemical bioavailability to individual organisms over time (e.g., sorption/desorption to soil/sediment, volatilization, dissolution, aging, "bound residue" formation, biodegradation, etc.), these models should be simplified, when possible, to limit the number of parameters to the practical minimum. Although significant scientific progress has been made in understanding the complexities in specific methodologies dedicated to determining bioavailability, no method has yet emerged to characterized bioavailability across a wide range of chemicals, organisms, and soils/sediments. The primary aim in studying bioavailability is to define options for addressing bioremediation or environmental toxicity (risk assessment), and that is unlikely to change. Because of its importance in estimating research is needed to more comprehensively address the key environmental issue of "bioavailability of chemicals in soil/sediment."

Estimation of global soil respiration by accounting for land-use changes derived from remote sensing data.

PubMed

Adachi, Minaco; Ito, Akihiko; Yonemura, Seiichiro; Takeuchi, Wataru

2017-09-15

Soil respiration is one of the largest carbon fluxes from terrestrial ecosystems. Estimating global soil respiration is difficult because of its high spatiotemporal variability and sensitivity to land-use change. Satellite monitoring provides useful data for estimating the global carbon budget, but few studies have estimated global soil respiration using satellite data. We provide preliminary insights into the estimation of global soil respiration in 2001 and 2009 using empirically derived soil temperature equations for 17 ecosystems obtained by field studies, as well as MODIS climate data and land-use maps at a 4-km resolution. The daytime surface temperature from winter to early summer based on the MODIS data tended to be higher than the field-observed soil temperatures in subarctic and temperate ecosystems. The estimated global soil respiration was 94.8 and 93.8 Pg C yr -1 in 2001 and 2009, respectively. However, the MODIS land-use maps had insufficient spatial resolution to evaluate the effect of land-use change on soil respiration. The spatial variation of soil respiration (Q 10 ) values was higher but its spatial variation was lower in high-latitude areas than in other areas. However, Q 10 in tropical areas was more variable and was not accurately estimated (the values were >7.5 or <1.0) because of the low seasonal variation in soil respiration in tropical ecosystems. To solve these problems, it will be necessary to validate our results using a combination of remote sensing data at higher spatial resolution and field observations for many different ecosystems, and it will be necessary to account for the effects of more soil factors in the predictive equations. Copyright © 2017 Elsevier Ltd. All rights reserved.

Estimates of amounts of soil removal for clean-up of transuranics at NAEG offsite safety-shot sites

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kinnison, R.R.; Gilbert, R.O.

Rough estimates are given for the amount of soil removal necessary to decontaminate five representative safety-shot areas. In order to decontaminate to levels of less than 160 pCi /sup 239/Pu per gram of surface soil, it is estimated that over one-half million tons of soil would have to be removed from the five areas. This is a preliminary estimate based on summary data and concentration contour maps readily available in NAEG publications. More accurate estimates could be obtained by applying Kriging techniques to available soil data if the need arises. The inclusion of /sup 241/Am and /sup 238/Pu activities domore » not significantly increase the soil tonnage estimates obtained for /sup 239/ /sup 240/Pu because of their relatively small contributions to total transuranic activity. The magnitude of the errors inherent in our use of summary data to obtain rough estimates also suggests that a revision of the tonnage estimates for /sup 239/ /sup 240/Pu to include /sup 241/Am and /sup 238/Pu is not warranted.« less

Handling the unknown soil hydraulic parameters in data assimilation for unsaturated flow problems

NASA Astrophysics Data System (ADS)

Lange, Natascha; Erdal, Daniel; Neuweiler, Insa

2017-04-01

Model predictions of flow in the unsaturated zone require the soil hydraulic parameters. However, these parameters cannot be determined easily in applications, in particular if observations are indirect and cover only a small range of possible states. Correlation of parameters or their correlation in the range of states that are observed is a problem, as different parameter combinations may reproduce approximately the same measured water content. In field campaigns this problem can be helped by adding more measurement devices. Often, observation networks are designed to feed models for long term prediction purposes (i.e. for weather forecasting). A popular way of making predictions with such kind of observations are data assimilation methods, like the ensemble Kalman filter (Evensen, 1994). These methods can be used for parameter estimation if the unknown parameters are included in the state vector and updated along with the model states. Given the difficulties related to estimation of the soil hydraulic parameters in general, it is questionable, though, whether these methods can really be used for parameter estimation under natural conditions. Therefore, we investigate the ability of the ensemble Kalman filter to estimate the soil hydraulic parameters. We use synthetic identical twin-experiments to guarantee full knowledge of the model and the true parameters. We use the van Genuchten model to describe the soil water retention and relative permeability functions. This model is unfortunately prone to the above mentioned pseudo-correlations of parameters. Therefore, we also test the simpler Russo Gardner model, which is less affected by that problem, in our experiments. The total number of unknown parameters is varied by considering different layers of soil. Besides, we study the influence of the parameter updates on the water content predictions. We test different iterative filter approaches and compare different observation strategies for parameter identification. Considering heterogeneous soils, we discuss the representativeness of different observation types to be used for the assimilation. G. Evensen. Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte Carlo methods to forecast error statistics. Journal of Geophysical Research: Oceans, 99(C5):10143-10162, 1994

Application of IEM model on soil moisture and surface roughness estimation

NASA Technical Reports Server (NTRS)

Shi, Jiancheng; Wang, J. R.; Oneill, P. E.; Hsu, A. Y.; Engman, E. T.

1995-01-01

Monitoring spatial and temporal changes of soil moisture are of importance to hydrology, meteorology, and agriculture. This paper reports a result on study of using L-band SAR imagery to estimate soil moisture and surface roughness for bare fields. Due to limitations of the Small Perturbation Model, it is difficult to apply this model on estimation of soil moisture and surface roughness directly. In this study, we show a simplified model derived from the Integral Equation Model for estimation of soil moisture and surface roughness. We show a test of this model using JPL L-band AIRSAR data.

Estimation of soil organic partition coefficients: from retention factors measured by soil column chromatography with water as eluent.

PubMed

Xu, Feng; Liang, Xinmiao; Lin, Bingcheng; Schramm, Karl-Werner; Kettrup, Antonius

2002-08-30

The retention factors (k) of 104 hydrophobic organic chemicals (HOCs) were measured in soil column chromatography (SCC) over columns filled with three naturally occurring reference soils and eluted with Milli-Q water. A novel method for the estimation of soil organic partition coefficient (Koc) was developed based on correlations with k in soil/water systems. Strong log Koc versus log k correlations (r>0.96) were found. The estimated Koc values were in accordance with the literature values with a maximum deviation of less than 0.4 log units. All estimated Koc values from three soils were consistent with each other. The SCC approach is promising for fast screening of a large number of chemicals in their environmental applications.

Using boosted regression trees to predict the near-saturated hydraulic conductivity of undisturbed soils

NASA Astrophysics Data System (ADS)

Koestel, John; Bechtold, Michel; Jorda, Helena; Jarvis, Nicholas

2015-04-01

The saturated and near-saturated hydraulic conductivity of soil is of key importance for modelling water and solute fluxes in the vadose zone. Hydraulic conductivity measurements are cumbersome at the Darcy scale and practically impossible at larger scales where water and solute transport models are mostly applied. Hydraulic conductivity must therefore be estimated from proxy variables. Such pedotransfer functions are known to work decently well for e.g. water retention curves but rather poorly for near-saturated and saturated hydraulic conductivities. Recently, Weynants et al. (2009, Revisiting Vereecken pedotransfer functions: Introducing a closed-form hydraulic model. Vadose Zone Journal, 8, 86-95) reported a coefficients of determination of 0.25 (validation with an independent data set) for the saturated hydraulic conductivity from lab-measurements of Belgian soil samples. In our study, we trained boosted regression trees on a global meta-database containing tension-disk infiltrometer data (see Jarvis et al. 2013. Influence of soil, land use and climatic factors on the hydraulic conductivity of soil. Hydrology & Earth System Sciences, 17, 5185-5195) to predict the saturated hydraulic conductivity (Ks) and the conductivity at a tension of 10 cm (K10). We found coefficients of determination of 0.39 and 0.62 under a simple 10-fold cross-validation for Ks and K10. When carrying out the validation folded over the data-sources, i.e. the source publications, we found that the corresponding coefficients of determination reduced to 0.15 and 0.36, respectively. We conclude that the stricter source-wise cross-validation should be applied in future pedotransfer studies to prevent overly optimistic validation results. The boosted regression trees also allowed for an investigation of relevant predictors for estimating the near-saturated hydraulic conductivity. We found that land use and bulk density were most important to predict Ks. We also observed that Ks is large in fine and coarse textured soils and smaller in medium textured soils. Completely different predictors were important for appraising K10, where the soil macropore system is air-filled and therefore inactive. Here, the average annual temperature and precipitation where most important. The reasons for this are unclear and require further research. The clay content and the organic matter content were also important predictors of K10. We suggest that a larger and more complete database may help to improve the prediction of K10, whereas it may be more fruitful to estimate Ks statistics of sampling sites instead of individual values since the Ks is highly variable over very short distances.

Assesment of hydraulics properties of technosoil constructed with waste material using Beerkan infiltration

NASA Astrophysics Data System (ADS)

Yilmaz, Deniz; Peyneau, Pierre-Emmanuel; Beaudet, Laure; Cannavo, Patrice; Sere, Geoffroy

2017-04-01

For the characterization of hydraulics soils functions, in situ infiltration experiments are commonly used. The BEST method based on the infiltration through a single ring is well suited for soils containing coarse material. Technosols built from Civil engineering waste material such as brick waste, concrete waste, track ballast and demolition rubble wastes contain large part of coarse material. In this work, different materials made of civil engineering wastes mixed with organic wastes are tested for greening applications in an urban environment using in situ lysimeters. Beerkan infiltrations experiments were performed on these technosols. Experimental data are used to estimate hydraulics properties through the BEST method. The results shows from a hydraulic point of view that studied technosols can achieve the role of urban soil for greening application. Five combinations of artefacts were tested either as "growing material" (one combination) or "structural material" (4 combinations) - as support for traffic. Structural materials consisted in 27 wt.% earth material, 60 wt.% mineral coarse material and 3 wt.% organic material. These constructed technosols were studied in situ using lysimeters under two contrasted climatic conditions in two sites in France (Angers, in northwestern France and Homécourt, in northeastern France). Constructed technosols exhibited high porosities (31-48 vol% for structural materials, 70 vol% for the growing material). The dry bulk density of the growing material is estimated to 0.66 kg/m3 and 1.59 kg/m3 for structural material. The particle size distribution analysis, involving manual sieving (> 2 mm) and complemented by a grain size analysis (< 2 mm) were used as described in the BEST method (2006) for the estimation of the shape parameter n of hydraulics functions (Van-Genuchten -Mualem, 1980). This n parameter was estimated to 2.23 for growing materials and 2.29 for structural materials. Beerkan infiltrations experiments data were inversed using the BEST method, the results exhibited high saturated hydraulic conductivities 10.7 cm/h for structural materials and 14,8 cm/h for the growing material. Beerkan infiltration experiements are well suited for assesment of hydraulic properties of technosol constructed with civil engineering wastes. According to the estimated hydraulics functions, the studied technosols can be classified between a sand and a loam soil. It shows that these materials can achieve the role of alternative to the consumption of natural arable earth for urban greening applications such as gardens, parks and trees lines.

A simplified, data-constrained approach to estimate the permafrost carbon-climate feedback.

PubMed

Koven, C D; Schuur, E A G; Schädel, C; Bohn, T J; Burke, E J; Chen, G; Chen, X; Ciais, P; Grosse, G; Harden, J W; Hayes, D J; Hugelius, G; Jafarov, E E; Krinner, G; Kuhry, P; Lawrence, D M; MacDougall, A H; Marchenko, S S; McGuire, A D; Natali, S M; Nicolsky, D J; Olefeldt, D; Peng, S; Romanovsky, V E; Schaefer, K M; Strauss, J; Treat, C C; Turetsky, M

2015-11-13

We present an approach to estimate the feedback from large-scale thawing of permafrost soils using a simplified, data-constrained model that combines three elements: soil carbon (C) maps and profiles to identify the distribution and type of C in permafrost soils; incubation experiments to quantify the rates of C lost after thaw; and models of soil thermal dynamics in response to climate warming. We call the approach the Permafrost Carbon Network Incubation-Panarctic Thermal scaling approach (PInc-PanTher). The approach assumes that C stocks do not decompose at all when frozen, but once thawed follow set decomposition trajectories as a function of soil temperature. The trajectories are determined according to a three-pool decomposition model fitted to incubation data using parameters specific to soil horizon types. We calculate litterfall C inputs required to maintain steady-state C balance for the current climate, and hold those inputs constant. Soil temperatures are taken from the soil thermal modules of ecosystem model simulations forced by a common set of future climate change anomalies under two warming scenarios over the period 2010 to 2100. Under a medium warming scenario (RCP4.5), the approach projects permafrost soil C losses of 12.2-33.4 Pg C; under a high warming scenario (RCP8.5), the approach projects C losses of 27.9-112.6 Pg C. Projected C losses are roughly linearly proportional to global temperature changes across the two scenarios. These results indicate a global sensitivity of frozen soil C to climate change (γ sensitivity) of -14 to -19 Pg C °C(-1) on a 100 year time scale. For CH4 emissions, our approach assumes a fixed saturated area and that increases in CH4 emissions are related to increased heterotrophic respiration in anoxic soil, yielding CH4 emission increases of 7% and 35% for the RCP4.5 and RCP8.5 scenarios, respectively, which add an additional greenhouse gas forcing of approximately 10-18%. The simplified approach presented here neglects many important processes that may amplify or mitigate C release from permafrost soils, but serves as a data-constrained estimate on the forced, large-scale permafrost C response to warming. © 2015 The Authors.

A simplified, data-constrained approach to estimate the permafrost carbon–climate feedback

PubMed Central

Koven, C. D.; Schuur, E. A. G.; Schädel, C.; Bohn, T. J.; Burke, E. J.; Chen, G.; Chen, X.; Ciais, P.; Grosse, G.; Harden, J. W.; Hayes, D. J.; Hugelius, G.; Jafarov, E. E.; Krinner, G.; Kuhry, P.; Lawrence, D. M.; MacDougall, A. H.; Marchenko, S. S.; McGuire, A. D.; Natali, S. M.; Nicolsky, D. J.; Olefeldt, D.; Peng, S.; Romanovsky, V. E.; Schaefer, K. M.; Strauss, J.; Treat, C. C.; Turetsky, M.

2015-01-01

We present an approach to estimate the feedback from large-scale thawing of permafrost soils using a simplified, data-constrained model that combines three elements: soil carbon (C) maps and profiles to identify the distribution and type of C in permafrost soils; incubation experiments to quantify the rates of C lost after thaw; and models of soil thermal dynamics in response to climate warming. We call the approach the Permafrost Carbon Network Incubation–Panarctic Thermal scaling approach (PInc-PanTher). The approach assumes that C stocks do not decompose at all when frozen, but once thawed follow set decomposition trajectories as a function of soil temperature. The trajectories are determined according to a three-pool decomposition model fitted to incubation data using parameters specific to soil horizon types. We calculate litterfall C inputs required to maintain steady-state C balance for the current climate, and hold those inputs constant. Soil temperatures are taken from the soil thermal modules of ecosystem model simulations forced by a common set of future climate change anomalies under two warming scenarios over the period 2010 to 2100. Under a medium warming scenario (RCP4.5), the approach projects permafrost soil C losses of 12.2–33.4 Pg C; under a high warming scenario (RCP8.5), the approach projects C losses of 27.9–112.6 Pg C. Projected C losses are roughly linearly proportional to global temperature changes across the two scenarios. These results indicate a global sensitivity of frozen soil C to climate change (γ sensitivity) of −14 to −19 Pg C °C−1 on a 100 year time scale. For CH4 emissions, our approach assumes a fixed saturated area and that increases in CH4 emissions are related to increased heterotrophic respiration in anoxic soil, yielding CH4 emission increases of 7% and 35% for the RCP4.5 and RCP8.5 scenarios, respectively, which add an additional greenhouse gas forcing of approximately 10–18%. The simplified approach presented here neglects many important processes that may amplify or mitigate C release from permafrost soils, but serves as a data-constrained estimate on the forced, large-scale permafrost C response to warming. PMID:26438276

A simplified, data-constrained approach to estimate the permafrost carbon–climate feedback

USGS Publications Warehouse

Koven, C.D.; Schuur, E.A.G.; Schädel, C.; Bohn, T. J.; Burke, E. J.; Chen, G.; Chen, X.; Ciais, P.; Grosse, G.; Harden, J.W.; Hayes, D.J.; Hugelius, G.; Jafarov, Elchin E.; Krinner, G.; Kuhry, P.; Lawrence, D.M.; MacDougall, A. H.; Marchenko, Sergey S.; McGuire, A. David; Natali, Susan M.; Nicolsky, D.J.; Olefeldt, David; Peng, S.; Romanovsky, V.E.; Schaefer, Kevin M.; Strauss, J.; Treat, C.C.; Turetsky, M.

2015-01-01

We present an approach to estimate the feedback from large-scale thawing of permafrost soils using a simplified, data-constrained model that combines three elements: soil carbon (C) maps and profiles to identify the distribution and type of C in permafrost soils; incubation experiments to quantify the rates of C lost after thaw; and models of soil thermal dynamics in response to climate warming. We call the approach the Permafrost Carbon Network Incubation–Panarctic Thermal scaling approach (PInc-PanTher). The approach assumes that C stocks do not decompose at all when frozen, but once thawed follow set decomposition trajectories as a function of soil temperature. The trajectories are determined according to a three-pool decomposition model fitted to incubation data using parameters specific to soil horizon types. We calculate litterfall C inputs required to maintain steady-state C balance for the current climate, and hold those inputs constant. Soil temperatures are taken from the soil thermal modules of ecosystem model simulations forced by a common set of future climate change anomalies under two warming scenarios over the period 2010 to 2100. Under a medium warming scenario (RCP4.5), the approach projects permafrost soil C losses of 12.2–33.4 Pg C; under a high warming scenario (RCP8.5), the approach projects C losses of 27.9–112.6 Pg C. Projected C losses are roughly linearly proportional to global temperature changes across the two scenarios. These results indicate a global sensitivity of frozen soil C to climate change (γ sensitivity) of −14 to −19 Pg C °C−1 on a 100 year time scale. For CH4 emissions, our approach assumes a fixed saturated area and that increases in CH4 emissions are related to increased heterotrophic respiration in anoxic soil, yielding CH4 emission increases of 7% and 35% for the RCP4.5 and RCP8.5 scenarios, respectively, which add an additional greenhouse gas forcing of approximately 10–18%. The simplified approach presented here neglects many important processes that may amplify or mitigate C release from permafrost soils, but serves as a data-constrained estimate on the forced, large-scale permafrost C response to warming.

A new Downscaling Approach for SMAP, SMOS and ASCAT by predicting sub-grid Soil Moisture Variability based on Soil Texture

NASA Astrophysics Data System (ADS)

Montzka, C.; Rötzer, K.; Bogena, H. R.; Vereecken, H.

2017-12-01

Improving the coarse spatial resolution of global soil moisture products from SMOS, SMAP and ASCAT is currently an up-to-date topic. Soil texture heterogeneity is known to be one of the main sources of soil moisture spatial variability. A method has been developed that predicts the soil moisture standard deviation as a function of the mean soil moisture based on soil texture information. It is a closed-form expression using stochastic analysis of 1D unsaturated gravitational flow in an infinitely long vertical profile based on the Mualem-van Genuchten model and first-order Taylor expansions. With the recent development of high resolution maps of basic soil properties such as soil texture and bulk density, relevant information to estimate soil moisture variability within a satellite product grid cell is available. Here, we predict for each SMOS, SMAP and ASCAT grid cell the sub-grid soil moisture variability based on the SoilGrids1km data set. We provide a look-up table that indicates the soil moisture standard deviation for any given soil moisture mean. The resulting data set provides important information for downscaling coarse soil moisture observations of the SMOS, SMAP and ASCAT missions. Downscaling SMAP data by a field capacity proxy indicates adequate accuracy of the sub-grid soil moisture patterns.

Estimating Children's Soil/Dust Ingestion Rates through Retrospective Analyses of Blood Lead Biomonitoring from the Bunker Hill Superfund Site in Idaho.

PubMed

von Lindern, Ian; Spalinger, Susan; Stifelman, Marc L; Stanek, Lindsay Wichers; Bartrem, Casey

2016-09-01

Soil/dust ingestion rates are important variables in assessing children's health risks in contaminated environments. Current estimates are based largely on soil tracer methodology, which is limited by analytical uncertainty, small sample size, and short study duration. The objective was to estimate site-specific soil/dust ingestion rates through reevaluation of the lead absorption dose-response relationship using new bioavailability data from the Bunker Hill Mining and Metallurgical Complex Superfund Site (BHSS) in Idaho, USA. The U.S. Environmental Protection Agency (EPA) in vitro bioavailability methodology was applied to archived BHSS soil and dust samples. Using age-specific biokinetic slope factors, we related bioavailable lead from these sources to children's blood lead levels (BLLs) monitored during cleanup from 1988 through 2002. Quantitative regression analyses and exposure assessment guidance were used to develop candidate soil/dust source partition scenarios estimating lead intake, allowing estimation of age-specific soil/dust ingestion rates. These ingestion rate and bioavailability estimates were simultaneously applied to the U.S. EPA Integrated Exposure Uptake Biokinetic Model for Lead in Children to determine those combinations best approximating observed BLLs. Absolute soil and house dust bioavailability averaged 33% (SD ± 4%) and 28% (SD ± 6%), respectively. Estimated BHSS age-specific soil/dust ingestion rates are 86-94 mg/day for 6-month- to 2-year-old children and 51-67 mg/day for 2- to 9-year-old children. Soil/dust ingestion rate estimates for 1- to 9-year-old children at the BHSS are lower than those commonly used in human health risk assessment. A substantial component of children's exposure comes from sources beyond the immediate home environment. von Lindern I, Spalinger S, Stifelman ML, Stanek LW, Bartrem C. 2016. Estimating children's soil/dust ingestion rates through retrospective analyses of blood lead biomonitoring from the Bunker Hill Superfund Site in Idaho. Environ Health Perspect 124:1462-1470; http://dx.doi.org/10.1289/ehp.1510144.

Use of satellite and modelled soil moisture data for predicting event soil loss at plot scale

NASA Astrophysics Data System (ADS)

Todisco, F.; Brocca, L.; Termite, L. F.; Wagner, W.

2015-03-01

The potential of coupling soil moisture and a~USLE-based model for event soil loss estimation at plot scale is carefully investigated at the Masse area, in Central Italy. The derived model, named Soil Moisture for Erosion (SM4E), is applied by considering the unavailability of in situ soil moisture measurements, by using the data predicted by a soil water balance model (SWBM) and derived from satellite sensors, i.e. the Advanced SCATterometer (ASCAT). The soil loss estimation accuracy is validated using in situ measurements in which event observations at plot scale are available for the period 2008-2013. The results showed that including soil moisture observations in the event rainfall-runoff erosivity factor of the RUSLE/USLE, enhances the capability of the model to account for variations in event soil losses, being the soil moisture an effective alternative to the estimated runoff, in the prediction of the event soil loss at Masse. The agreement between observed and estimated soil losses (through SM4E) is fairly satisfactory with a determination coefficient (log-scale) equal to of ~ 0.35 and a root-mean-square error (RMSE) of ~ 2.8 Mg ha-1. These results are particularly significant for the operational estimation of soil losses. Indeed, currently, soil moisture is a relatively simple measurement at the field scale and remote sensing data are also widely available on a global scale. Through satellite data, there is the potential of applying the SM4E model for large-scale monitoring and quantification of the soil erosion process.

Soil carbon storage estimation in a forested watershed using quantitative soil-landscape modeling

Treesearch

James A. Thompson; Randall K. Kolka

2005-01-01

Carbon storage in soils is important to forest ecosystems. Moreover, forest soils may serve as important C sinks for ameliorating excess atmospheric CO2. Spatial estimates of soil organic C (SOC) storage have traditionally relied upon soil survey maps and laboratory characterization data. This approach does not account for inherent variability...

A novel approach to validate satellite soil moisture retrievals using precipitation data

NASA Astrophysics Data System (ADS)

Karthikeyan, L.; Kumar, D. Nagesh

2016-10-01

A novel approach is proposed that attempts to validate passive microwave soil moisture retrievals using precipitation data (applied over India). It is based on the concept that the expectation of precipitation conditioned on soil moisture follows a sigmoidal convex-concave-shaped curve, the characteristic of which was recently shown to be represented by mutual information estimated between soil moisture and precipitation. On this basis, with an emphasis over distribution-free nonparametric computations, a new measure called Copula-Kernel Density Estimator based Mutual Information (CKDEMI) is introduced. The validation approach is generic in nature and utilizes CKDEMI in tandem with a couple of proposed bootstrap strategies, to check accuracy of any two soil moisture products (here Advanced Microwave Scanning Radiometer-EOS sensor's Vrije Universiteit Amsterdam-NASA (VUAN) and University of Montana (MONT) products) using precipitation (India Meteorological Department) data. The proposed technique yields a "best choice soil moisture product" map which contains locations where any one of the two/none of the two/both the products have produced accurate retrievals. The results indicated that in general, VUA-NASA product has performed well over University of Montana's product for India. The best choice soil moisture map is then integrated with land use land cover and elevation information using a novel probability density function-based procedure to gain insight on conditions under which each of the products has performed well. Finally, the impact of using a different precipitation (Asian Precipitation-Highly-Resolved Observational Data Integration Towards Evaluation of Water Resources) data set over the best choice soil moisture product map is also analyzed. The proposed methodology assists researchers and practitioners in selecting the appropriate soil moisture product for various assimilation strategies at both basin and continental scales.

Concentration of (137)Cs in soil across Nebraska.

PubMed

Weesner, Alexandra Palensky; Fairchild, Robert W

2008-06-01

Atmospheric nuclear weapons testing from 1945 through 1980 produced radioactive fallout that was transported by stratospheric winds and deposited unevenly around the world. The accident at Chernobyl in 1986 also contributed to the fallout in some locations. The (137)Cs activity concentration from fallout has been measured as a function of depth in soil samples from five different locations across Nebraska. Soil samples 2-cm thick down to a depth of 30 cm were collected in Brown, Dawes, Lancaster, Red Willow, and Thurston Counties. Samples taken from each of the sites were dried, sieved, and counted using an HPGe gamma spectroscopy system to measure the activity concentration of (137)Cs at each depth in the soil. Activity concentrations as high as 216 Bq kg(-1) were measured in the samples. Dry soil bulk densities were calculated for each site based on soil type and used to calculate the area density of deposition. Area deposition densities up to 13,100 Bq m(-2) were measured, consistent with published estimates.

Deep Soil Carbon in the Critical Zone: Amount and Nature of Carbon in Weathered Bedrock, and its Implication for Soil Carbon Inventory

NASA Astrophysics Data System (ADS)

Moreland, K. C.; Tian, Z.; Berhe, A. A.; O'Geen, A. T.

2017-12-01

Globally, soils store more carbon (C) than the vegetation and the atmosphere combined. Up to 60-80% of the C stored in soils is found in below 30cm soil depth, but there is little data on C storage in weathered bedrock or saprolite. Deep soil organic matter (SOM) can be a mixture of new and old SOM; that is rendered relatively stable due to burial, aggregation, its disconnection from decomposers, and chemical association that organic matter forms with soil minerals. The limited data available on deep SOM dynamics suggests that stock, distribution, and composition of deep SOM are strongly correlated to climate. The overall objective of this research is to investigate how climate regulates OM storage, composition, stability, and stabilization mechanisms. Expecting that the amount of OM stored in deep soil and the stability are a function of soil thickness and availability of weathering products (i.e. reactive minerals), the stock and stability of deep SOM is expected to follow a similar relationship with climate, as does the intensity of weathering. This research is conducted in the NSF funded Southern Sierra Critical Zone Observatories that is located along a climosequence, the western slopes of the Sierra Naevada Mountains of California. Here we will present results derived from characterization of soils and weathered bedrock using elemental and stable isotope elemental analysis, and Fourier Transformed Infrared Spectroscopy to determine OM concentration and functional group level composition of bulk SOM. Our findings show that adding in subsoil and weathered bedrock C stocks increases estimates of soil C stock by 1/3rd to 2/3rd.

Continuously Monocropped Jerusalem Artichoke Changed Soil Bacterial Community Composition and Ammonia-Oxidizing and Denitrifying Bacteria Abundances.

PubMed

Zhou, Xingang; Wang, Zhilin; Jia, Huiting; Li, Li; Wu, Fengzhi

2018-01-01

Soil microbial communities have profound effects on the growth, nutrition and health of plants in agroecosystems. Understanding soil microbial dynamics in cropping systems can assist in determining how agricultural practices influence soil processes mediated by microorganisms. In this study, soil bacterial communities were monitored in a continuously monocropped Jerusalem artichoke (JA) system, in which JA was successively monocropped for 3 years in a wheat field. Soil bacterial community compositions were estimated by amplicon sequencing of the 16S rRNA gene. Abundances of ammonia-oxidizing and denitrifying bacteria were estimated by quantitative PCR analysis of the amoA , nirS , and nirK genes. Results showed that 1-2 years of monocropping of JA did not significantly impact the microbial alpha diversity, and the third cropping of JA decreased the microbial alpha diversity ( P < 0.05). Principal coordinates analysis and permutational multivariate analysis of variance analyses revealed that continuous monocropping of JA changed soil bacterial community structure and function profile ( P < 0.001). At the phylum level, the wheat field was characterized with higher relative abundances of Latescibacteria , Planctomycetes , and Cyanobacteria , the first cropping of JA with Actinobacteria , the second cropping of JA with Acidobacteria , Armatimonadetes , Gemmatimonadetes , and Proteobacteria . At the genus level, the first cropping of JA was enriched with bacterial species with pathogen-antagonistic and/or plant growth promoting potentials, while members of genera that included potential denitrifiers increased in the second and third cropping of JA. The first cropping of JA had higher relative abundances of KO terms related to lignocellulose degradation and phosphorus cycling, the second cropping of JA had higher relative abundances of KO terms nitrous-oxide reductase and nitric-oxide reductase, and the third cropping of JA had higher relative abundances of KO terms nitrate reductase and nitrite reductase. The abundances of amoA genes decreased while nirK increased in the third cropping of JA, nirS continuously increased in the second and third cropping of JA ( P < 0.05). Redundancy analysis and Mantel test found that soil organic carbon and Olsen phosphorus contents played important roles in shaping soil bacterial communities. Overall, our results revealed that continuous monocropping of JA changed soil bacterial community composition and its functional potentials.

Implementation of the New Approach for the Dose-Response Functions Development for the Case of Athens and Greece

NASA Astrophysics Data System (ADS)

Christodoulakis, J.; Tzanis, C. G.; Varotsos, C. A.; Kouremadas, G.

2016-08-01

Dose-response functions (DRFs) are functions used for estimating corrosion and/or soiling levels of materials used in constructions and cultural monuments. In order to achieve this, DRFs lean on ground-based measurements of specific air pollution and climatic parameters like nitrogen oxides, ozone, temperature and others. In DRAGON 3 2015 Symposium we presented a new approach which proposed a technique for using satellite-based data for the necessary parameters instead of ground-based expanding in this way: a) the usage of DRFs in cases/areas where there is no availability of in situ measurements, b) the applicability of satellite-based data. In this work we present mapping results of deterioration levels (corrosion and soiling) for the case of Athens, Greece but also for the whole Greece country.

Precipitation Estimation Using L-Band and C-Band Soil Moisture Retrievals

NASA Technical Reports Server (NTRS)

Koster, Randal D.; Brocca, Luca; Crow, Wade T.; Burgin, Mariko S.; De Lannoy, Gabrielle J. M.

2016-01-01

An established methodology for estimating precipitation amounts from satellite-based soil moisture retrievals is applied to L-band products from the Soil Moisture Active Passive (SMAP) and Soil Moisture and Ocean Salinity (SMOS) satellite missions and to a C-band product from the Advanced Scatterometer (ASCAT) mission. The precipitation estimates so obtained are evaluated against in situ (gauge-based) precipitation observations from across the globe. The precipitation estimation skill achieved using the L-band SMAP and SMOS data sets is higher than that obtained with the C-band product, as might be expected given that L-band is sensitive to a thicker layer of soil and thereby provides more information on the response of soil moisture to precipitation. The square of the correlation coefficient between the SMAP-based precipitation estimates and the observations (for aggregations to approximately100 km and 5 days) is on average about 0.6 in areas of high rain gauge density. Satellite missions specifically designed to monitor soil moisture thus do provide significant information on precipitation variability, information that could contribute to efforts in global precipitation estimation.

Using Remotely Sensed Soil Moisture to Estimate Fire Risk in Tropical Peatlands

NASA Astrophysics Data System (ADS)

Dadap, N.; Cobb, A.; Hoyt, A.; Harvey, C. F.; Konings, A. G.

2017-12-01

Tropical peatlands in Equatorial Asia have become more vulnerable to fire due to deforestation and peatland drainage over the last 30 years. In these regions, water table depth has been shown to play an important role in mediating fire risk as it serves as a proxy for peat moisture content. However, water table depth observations are sparse and expensive. Soil moisture could provide a more direct indicator of fire risk than water table depth. In this study, we use new soil moisture retrievals from the Soil Moisture Active Passive (SMAP) satellite to demonstrate that - contrary to popular wisdom - remotely sensed soil moisture observations are possible over most Southeast Asian peatlands. Soil moisture estimation in this region was previously thought to be impossible over tropical peatlands because of dense vegetation cover. We show that vegetation density is sufficiently low across most Equatorial Asian peatlands to allow soil moisture estimation, and hypothesize that deforestation and other anthropogenic changes in land cover have combined to reduce overall vegetation density sufficient to allow soil moisture estimation. We further combine burned area estimates from the Global Fire Emissions Database and SMAP soil moisture retrievals to show that soil moisture provides a strong signal for fire risk in peatlands, with fires occurring at a much greater rate over drier soils. We will also develop an explicit fire risk model incorporating soil moisture with additional climatic, land cover, and anthropogenic predictor variables.

Climate and the equilibrium state of land surface hydrology parameterizations

NASA Technical Reports Server (NTRS)

Entekhabi, Dara; Eagleson, Peter S.

1991-01-01

For given climatic rates of precipitation and potential evaporation, the land surface hydrology parameterizations of atmospheric general circulation models will maintain soil-water storage conditions that balance the moisture input and output. The surface relative soil saturation for such climatic conditions serves as a measure of the land surface parameterization state under a given forcing. The equilibrium value of this variable for alternate parameterizations of land surface hydrology are determined as a function of climate and the sensitivity of the surface to shifts and changes in climatic forcing are estimated.

Merging a mechanistic enzymatic model of soil heterotrophic respiration into an ecosystem model in two AmeriFlux sites of northeastern USA

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sihi, Debjani; Davidson, Eric A.; Chen, Min

Heterotrophic respiration (Rh), microbial processing of soil organic matter to carbon dioxide (CO 2), is a major, yet highly uncertain, carbon (C) flux from terrestrial systems to the atmosphere. Temperature sensitivity of Rh is often represented with a simple Q 10 function in ecosystem models and earth system models (ESMs), sometimes accompanied by an empirical soil moisture modifier. More explicit representation of the effects of soil moisture, substrate supply, and their interactions with temperature has been proposed as a way to disentangle the confounding factors of apparent temperature sensitivity of Rh and improve the performance of ecosystem models and ESMs.more » The objective of this work was to insert into an ecosystem model a more mechanistic, but still parsimonious, model of environmental factors controlling Rh and evaluate the model performance in terms of soil and ecosystem respiration. The Dual Arrhenius and Michaelis-Menten (DAMM) model simulates Rh using Michaelis-Menten, Arrhenius, and diffusion functions. Soil moisture affects Rh and its apparent temperature sensitivity in DAMM by regulating the diffusion of oxygen, soluble C substrates, and extracellular enzymes to the enzymatic reaction site. Here, we merged the DAMM soil flux model with a parsimonious ecosystem flux model, FöBAAR (Forest Biomass, Assimilation, Allocation and Respiration). We used high-frequency soil flux data from automated soil chambers and landscape-scale ecosystem fluxes from eddy covariance towers at two AmeriFlux sites (Harvard Forest, MA and Howland Forest, ME) in the northeastern USA to estimate parameters, validate the merged model, and to quantify the uncertainties in a multiple constraints approach. The optimized DAMM-FöBAAR model better captured the seasonal and inter-annual dynamics of soil respiration (Soil R) compared to the FöBAAR-only model for the Harvard Forest, where higher frequency and duration of drying events significantly regulate substrate supply to heterotrophs. However, DAMM-FöBAAR showed improvement over FöBAAR-only at the boreal transition Howland Forest only in unusually dry years. The frequency of synoptic-scale dry periods is lower at Howland, resulting in only brief water limitation of Rh in some years. At both sites, the declining trend of soil R during drying events was captured by the DAMM-FöBAAR model; however, model performance was also contingent on site conditions, climate, and the temporal scale of interest. While the DAMM functions require a few more parameters than a simple Q10 function, we have demonstrated that they can be included in an ecosystem model and reduce the model-data mismatch. Moreover, the mechanistic structure of the soil moisture effects using DAMM functions should be more generalizable than the wide variety of empirical functions that are commonly used, and these DAMM functions could be readily incorporated into other ecosystem models and ESMs.« less

Impact of surface processes and climate variability on clumped isotope thermometry of soil carbonates, southern Central Andes, Argentina (Invited)

NASA Astrophysics Data System (ADS)

Huntington, K. W.; Peters, N.; Roe, G.; Hoke, G. D.; Eiler, J.

2010-12-01

Soil carbonates archive a potentially rich record of past climate, but rates of pedogenic carbonate formation, erosion, and deposition impact how the isotopic composition and formation temperature of carbonate-bearing paleosols reflect the local environmental conditions under which they form. We investigate these processes using conventional stable isotope (δ18O and δ13C) and clumped isotope thermometry data for Quaternary pedogenic carbonates from the southern Central Andes at ~33°S, Argentina. The study area spans over 2 km of relief in the Río Mendoza and Río de las Cuevas valleys, accessing a range of mean annual temperature conditions and vegetative cover and exhibiting large seasonal variations in temperature, precipitation, and soil moisture. Variations in soil conditions influence carbonate precipitation and dissolution reactions and the rate and depth of pedogenic carbonate formation. Because soil temperature varies predictably as a function of depth in the soil and seasonal and secular variations in air temperature, clumped isotope thermometry of samples collected in soil pits offers a direct way to estimate the seasonality of pedogenic carbonate formation and potential biases in the long-term climate record. We explore potential complications due to the effects of radiative solar heating on the relationship between air and soil temperatures by examining clumped isotope thermometry results in the context of site-to-site variations in vegetative cover. Temperature estimates from clumped isotope thermometry of pedogenic carbonate collected 5-110 cm below geomorphically stable soil surfaces from 1200-3400 m a.s.l. are compared to temperature profiles predicted by simple rule-based models of soil carbonate formation. The models use climate reanalysis daily diagnostic data (soil temperature, soil moisture, and latent heat flux as a proxy for evaporation) and weather station data as input to assess how varying rates of pedogenic carbonate formation integrated over millennial timescales might impact the geologic record of temperature and isotopic composition.

Environmental hazard assessment of contaminated soils in Antarctica: Using a structured tier 1 approach to inform decision-making.

PubMed

Pereira, Joana Luísa; Pereira, Patrícia; Padeiro, Ana; Gonçalves, Fernando; Amaro, Eduardo; Leppe, Marcelo; Verkulich, Sergey; Hughes, Kevin A; Peter, Hans-Ulrich; Canário, João

2017-01-01

Generally, Antarctica is considered to be an untouched area of the planet; however, the region's ecosystems have been subject to increased human pressure for at least the past half-century. This study assessed soils of Fildes Peninsula, where trace element pollution is thought to prevail. Four soil samples were collected from different locations and assessed following tier 1 methodologies for chemical and ecotoxicological lines of evidence (LoE) used in typical soil Environmental Risk Assessment (ERA). Trace element quantification was run on soil samples and sequential extracts, and elutriates were used to address their ecotoxicity using a standard ecotoxicological battery. The highest levels of trace elements were found for Cr, Cu, Ni and Zn, which were well above baseline levels in two sites located near previously identified contamination sources. Trace element concentrations in soils were compared with soil quality guidelines to estimate the contribution of the chemical LoE for integrated risk calculations; risk was found high, above 0.5 for all samples. Total concentrations in soil were consistent with corresponding sequentially extracted percentages, with Cu and Zn being the most bioavailable elements. Bacteria did not respond consistently to the elutriate samples and cladocerans did not respond at all. In contrast, the growth of microalgae and macrophytes was significantly impaired by elutriates of all soil samples, consistently to estimated trace element concentrations in the elutriate matrix. These results translated into lower risk values for the ecotoxicological compared to the chemical LoE. Nevertheless, integrated risk calculations generated either an immediate recommendation for further analysis to better understand the hazardous potential of the tested soils or showed that the soils could not adequately sustain natural ecosystem functions. This study suggests that the soil ecosystem in Fildes has been inadequately protected and supports previous claims on the need to reinforce protection measures and remediation activities. Copyright © 2016 Elsevier B.V. All rights reserved.

Estimation of soil clay and organic matter using two quantitative methods (PLSR and MARS) based on reflectance spectroscopy

NASA Astrophysics Data System (ADS)

Nawar, Said; Buddenbaum, Henning; Hill, Joachim

2014-05-01

A rapid and inexpensive soil analytical technique is needed for soil quality assessment and accurate mapping. This study investigated a method for improved estimation of soil clay (SC) and organic matter (OM) using reflectance spectroscopy. Seventy soil samples were collected from Sinai peninsula in Egypt to estimate the soil clay and organic matter relative to the soil spectra. Soil samples were scanned with an Analytical Spectral Devices (ASD) spectrometer (350-2500 nm). Three spectral formats were used in the calibration models derived from the spectra and the soil properties: (1) original reflectance spectra (OR), (2) first-derivative spectra smoothened using the Savitzky-Golay technique (FD-SG) and (3) continuum-removed reflectance (CR). Partial least-squares regression (PLSR) models using the CR of the 400-2500 nm spectral region resulted in R2 = 0.76 and 0.57, and RPD = 2.1 and 1.5 for estimating SC and OM, respectively, indicating better performance than that obtained using OR and SG. The multivariate adaptive regression splines (MARS) calibration model with the CR spectra resulted in an improved performance (R2 = 0.89 and 0.83, RPD = 3.1 and 2.4) for estimating SC and OM, respectively. The results show that the MARS models have a great potential for estimating SC and OM compared with PLSR models. The results obtained in this study have potential value in the field of soil spectroscopy because they can be applied directly to the mapping of soil properties using remote sensing imagery in arid environment conditions. Key Words: soil clay, organic matter, PLSR, MARS, reflectance spectroscopy.

Estimates of soil ingestion by wildlife

USGS Publications Warehouse

Beyer, W.N.; Connor, E.E.; Gerould, S.

1994-01-01

Many wildlife species ingest soil while feeding, but ingestion rates are known for only a few species. Knowing ingestion rates may be important for studies of environmental contaminants. Wildlife may ingest soil deliberately, or incidentally, when they ingest soil-laden forage or animals that contain soil. We fed white-footed mice (Peromyscus leucopus) diets containing 0-15% soil to relate the dietary soil content to the acid-insoluble ash content of scat collected from the mice. The relation was described by an equation that required estimates of the percent acid-insoluble ash content of the diet, digestibility of the diet, and mineral content of soil. We collected scat from 28 wildlife species by capturing animals, searching appropriate habitats for scat, or removing material from the intestines of animals collected for other purposes. We measured the acid-insoluble ash content of the scat and estimated the soil content of the diets by using the soil-ingestion equation. Soil ingestion estimates should be considered only approximate because they depend on estimated rather than measured digestibility values and because animals collected from local populations at one time of the year may not represent the species as a whole. Sandpipers (Calidris spp.), which probe or peck for invertebrates in mud or shallow water, consumed sediments at a rate of 7-30% of their diets. Nine-banded armadillo (Dasypus novemcinctus, soil = 17% of diet), American woodcock (Scolopax minor, 10%), and raccoon (Procyon lotor, 9%) had high rates of soil ingestion, presumably because they ate soil organisms. Bison (Bison bison, 7%), black-tailed prairie dog (Cynomys ludovicianus, 8%), and Canada geese (Branta canadensis, 8%) consumed soil at the highest rates among the herbivores studied, and various browsers studied consumed little soil. Box turtle (Terrapene carolina, 4%), opossum (Didelphis virginiana, 5%), red fox (Vulpes vulpes, 3%), and wild turkey (Meleagris gallopavo, 9%) consumed soil at intermediate rates. Ingested soil may be the principal means of exposure to some environmental contaminants or the principal source of certain minerals. Soil-ingestion estimates may be required for risk assessments of wildlife inhabiting contaminated sites and for computing budgets of those nutrients associated mainly with soil.

Estimation of effective soil hydraulic properties at field scale via ground albedo neutron sensing

NASA Astrophysics Data System (ADS)

Rivera Villarreyes, C. A.; Baroni, G.; Oswald, S. E.

2012-04-01

Upscaling of soil hydraulic parameters is a big challenge in hydrological research, especially in model applications of water and solute transport processes. In this contest, numerous attempts have been made to optimize soil hydraulic properties using observations of state variables such as soil moisture. However, in most of the cases the observations are limited at the point-scale and then transferred to the model scale. In this way inherent small-scale soil heterogeneities and non-linearity of dominate processes introduce sources of error that can produce significant misinterpretation of hydrological scenarios and unrealistic predictions. On the other hand, remote-sensed soil moisture over large areas is also a new promising approach to derive effective soil hydraulic properties over its observation footprint, but it is still limited to the soil surface. In this study we present a new methodology to derive soil moisture at the intermediate scale between point-scale observations and estimations at the remote-sensed scale. The data are then used for the estimation of effective soil hydraulic parameters. In particular, ground albedo neutron sensing (GANS) was used to derive non-invasive soil water content in a footprint of ca. 600 m diameter and a depth of few decimeters. This approach is based on the crucial role of hydrogen compared to other landscape materials as neutron moderator. As natural neutron measured aboveground depends on soil water content, the vertical footprint of the GANS method, i.e. its penetration depth, does also. Firstly, this study was designed to evaluate the dynamics of GANS vertical footprint and derive a mathematical model for its prediction. To test GANS-soil moisture and its penetration depth, it was accompanied by other soil moisture measurements (FDR) located at 5, 20 and 40 cm depths over the GANS horizontal footprint in a sunflower field (Brandenburg, Germany). Secondly, a HYDRUS-1D model was set up with monitored values of crop height and meteorological variables as input during a four-month period. Parameter estimation (PEST) software was coupled to HYDRUS-1D in order to calibrate soil hydraulic properties based on soil water content data. Thirdly, effective soil hydraulic properties were derived from GANS-soil moisture. Our observations show the potential of GANS to compensate the lack of information at the intermediate scale, soil water content estimation and effective soil properties. Despite measurement volumes, GANS-derived soil water content compared quantitatively to FDRs at several depths. For one-hour estimations, root mean square error was estimated as 0.019, 0.029 and 0.036 m3/m3 for 5 cm, 20 cm and 40 cm depths, respectively. In the context of soil hydraulic properties, this first application of GANS method succeed and its estimations were comparable to those derived by other approaches.

Water use implications of biofuel scenarios

NASA Astrophysics Data System (ADS)

Teter, J.; Mishra, G. S.; Yeh, S.

2012-12-01

Existing studies rely upon attributional lifecycle analysis (LCA) approaches to estimate water intensity of biofuels in liters of irrigated/evapotranspiration water consumed for biofuel production. Such approaches can be misleading. From a policy perspective, a better approach is to compare differential water impacts among scenarios on a landscape scale. We address the shortcomings of existing studies by using consequential LCA, and incorporate direct and indirect land use (changes) of biofuel scenarios, marginal vs. average biofuel water use estimates, future climate, and geographic heterogeneity. We use the outputs of a partial equilibrium economic model, climate and soil data, and a process-based crop-soil-climate-water model to estimate differences in green water (GW - directly from precipitation to soil) and blue water (BW - supplied by irrigation) use among three scenarios: (1) business-as-usual (BAU), (2) Renewable Fuels Standard (RFS) mandates, and (3) a national Low Carbon Fuel Standard (LCFS) plus the RFS scenario. We use spatial statistical methods to interpolate key climatic variables using daily climate observations for the contiguous USA. Finally, we use FAO's crop model AquaCrop to estimate the domestic GW and BW impacts of biofuel policies from 2007-2035. We assess the differences among scenarios along the following metrics: (1) crop area expansion at the county level, including prime and marginal lands, (2) crop-specific and overall annual/seasonal water balances including (a) water inflows (irrigation & precipitation), (b) crop-atmosphere interactions: (evaporation & transpiration) and (d) soil-water flows (runoff & soil infiltration), in mm 3 /acre over the relevant time period. The functional unit of analysis is the BW and GW requirements of biofuels (mm3 per Btu biofuel) at the county level. Differential water use impacts among scenarios are a primarily a function of (1) land use conversion, in particular that of formerly uncropped land classes (2) irrigation practices, (3) feedstock water use efficiency, and (4) the longer growing season and a predominance of rainfed cultivation of dedicated biofuel feedstocks. National-level total water use is lowest in the BAU scenario and highest in the RFS2 + LCFS scenario. Figure: Million acres converted to growing miscanthus (top) & switchgrass (bottom) under the RFS + LCFS scenario in 2035. Land use classes are crop pasture (blue), idle cropland (red-purple) & prime cropland (brown).

Heat Fluxes and Evaporation Measurements by Multi-Function Heat Pulse Probe: a Laboratory Experiment

NASA Astrophysics Data System (ADS)

Sharma, V.; Ciocca, F.; Hopmans, J. W.; Kamai, T.; Lunati, I.; Parlange, M. B.

2012-04-01

Multi Functional Heat Pulse Probes (MFHPP) are multi-needles probes developed in the last years able to measure temperature, thermal properties such as thermal diffusivity and volumetric heat capacity, from which soil moisture is directly retrieved, and electric conductivity (through a Wenner array). They allow the simultaneous measurement of coupled heat, water and solute transport in porous media, then. The use of only one instrument to estimate different quantities in the same volume and almost at the same time significantly reduces the need to interpolate different measurement types in space and time, increasing the ability to study the interdependencies characterizing the coupled transports, especially of water and heat, and water and solute. A three steps laboratory experiment is realized at EPFL to investigate the effectiveness and reliability of the MFHPP responses in a loamy soil from Conthey, Switzerland. In the first step specific calibration curves of volumetric heat capacity and thermal conductivity as function of known volumetric water content are obtained placing the MFHPP in small samplers filled with the soil homogeneously packed at different saturation degrees. The results are compared with literature values. In the second stage the ability of the MFHPP to measure heat fluxes is tested within a homemade thermally insulated calibration box and results are matched with those by two self-calibrating Heatflux plates (from Huxseflux), placed in the same box. In the last step the MFHPP are used to estimate the cumulative subsurface evaporation inside a small column (30 centimeters height per 8 centimeters inner diameter), placed on a scale, filled with the same loamy soil (homogeneously packed and then saturated) and equipped with a vertical array of four MFHPP inserted close to the surface. The subsurface evaporation is calculated from the difference between the net sensible heat and the net heat storage in the volume scanned by the probes, and the values obtained are matched with the overall evaporation, estimated through the scale in terms of weight loss. A numerical model able to solve the coupled heat-moisture diffusive equations is used to interpolate the obtained measures in the second and third step.

Estimation of improved resolution soil moisture in vegetated areas using passive AMSR-E data

NASA Astrophysics Data System (ADS)

Moradizadeh, Mina; Saradjian, Mohammad R.

2018-03-01

Microwave remote sensing provides a unique capability for soil parameter retrievals. Therefore, various soil parameters estimation models have been developed using brightness temperature (BT) measured by passive microwave sensors. Due to the low resolution of satellite microwave radiometer data, the main goal of this study is to develop a downscaling approach to improve the spatial resolution of soil moisture estimates with the use of higher resolution visible/infrared sensor data. Accordingly, after the soil parameters have been obtained using Simultaneous Land Parameters Retrieval Model algorithm, the downscaling method has been applied to the soil moisture estimations that have been validated against in situ soil moisture data. Advance Microwave Scanning Radiometer-EOS BT data in Soil Moisture Experiment 2003 region in the south and north of Oklahoma have been used to this end. Results illustrated that the soil moisture variability is effectively captured at 5 km spatial scales without a significant degradation of the accuracy.

Multiscale soil moisture estimates using static and roving cosmic-ray soil moisture sensors

NASA Astrophysics Data System (ADS)

McJannet, David; Hawdon, Aaron; Baker, Brett; Renzullo, Luigi; Searle, Ross

2017-12-01

Soil moisture plays a critical role in land surface processes and as such there has been a recent increase in the number and resolution of satellite soil moisture observations and the development of land surface process models with ever increasing resolution. Despite these developments, validation and calibration of these products has been limited because of a lack of observations on corresponding scales. A recently developed mobile soil moisture monitoring platform, known as the rover, offers opportunities to overcome this scale issue. This paper describes methods, results and testing of soil moisture estimates produced using rover surveys on a range of scales that are commensurate with model and satellite retrievals. Our investigation involved static cosmic-ray neutron sensors and rover surveys across both broad (36 × 36 km at 9 km resolution) and intensive (10 × 10 km at 1 km resolution) scales in a cropping district in the Mallee region of Victoria, Australia. We describe approaches for converting rover survey neutron counts to soil moisture and discuss the factors controlling soil moisture variability. We use independent gravimetric and modelled soil moisture estimates collected across both space and time to validate rover soil moisture products. Measurements revealed that temporal patterns in soil moisture were preserved through time and regression modelling approaches were utilised to produce time series of property-scale soil moisture which may also have applications in calibration and validation studies or local farm management. Intensive-scale rover surveys produced reliable soil moisture estimates at 1 km resolution while broad-scale surveys produced soil moisture estimates at 9 km resolution. We conclude that the multiscale soil moisture products produced in this study are well suited to future analysis of satellite soil moisture retrievals and finer-scale soil moisture models.

Estimation of the ICBM/2 Organic Matter Simulation Model parameters for biogas digestate mineralisaion in soil using Near Infrared Data.

NASA Astrophysics Data System (ADS)

Cabassi, Giovanni; Cavalli, Daniele; Borrelli, Lamberto; Degano, Luigi; Marino Gallina, Pietro

2014-05-01

The use of simulation models to study the turnover of soil organic matter (SOM) can support experimental data interpretation and the optimization of manure management. Icbm/2 (Katter, 2001) is a SOM simulation model that describes the turnover of SOM with three pools : one for old humified SOM (CO) and two for added manure, CL ( labile "young" C) and CS (stable "young" C). C outflows from CL and CR to be humified (h) and lost as CO2-C (1-h). All pools decay with firs-order kinetics with parameter kYL, kYR and kO (fig. 1).With this model of SOM turnover, during manure decomposition into the soil, only the evolved CO2 can be easily measured. Near infrared spectroscopy has been proved to be a useful technique for soil C evaluation. Since different soil C pools are expected to have different chemical composition, it was proven that NIR can be used as a cheap technique to develop calibration models to estimate the amount of C belonging to different pools). The aim of this work was compare the calibration of ICBM/2 using C respiration data or optimal NIR prediction of CO and CL pools. A total of six laboratory treatments were established using the same soil corresponding to the application of five fertilisers and a control treatment: 1) control without N fertilisation; 2) ammonium sulphate; 3) anaerobically digested dairy cow slurry (Digested slurry); 4-5) the liquid (Liquid fraction) and solid (Solid fraction) fractions after mechanical separation of Digested slurry; and 6) anaerobically stored dairy cow slurry (Stored slurry). The "nursery" method was used with 12 sampling dates. NIR analysis were performed on the air dried grounded soils. Spectra were collected using an FT-NIR Spectrometer. Parameters calibration was done separately for each soil using the downhill simplex method. For each manure, a C partitioning factor (Fi) was optimised. In each optimization step respiration measured data or NIR estimates CL and CO were used as imput for minimisation objective function. At the end the algorithm found those parameters that gave the lowest averaged RMSE between errors in the estimation of respired C. The model parameter extimations obtained using C respiration data and NIR predictions were comparable indicating a general ability of the NIR method to estimate model parameters together with a good prediction of C mineralisation.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Haase, Dagmar, E-mail: dagmar.haase@ufz.d

The amount of land consumption required for housing and transport severely conflicts with both the necessity and the legal obligation to maintain the ecological potential afforded by open spaces to meet the needs of current and future generations with regards to the protection of resources and climate change. Owing to an increasing intensity of soil use, soil conditions appear to have deteriorated in most city regions around the world, namely their filter and runoff regulating functions are impaired by land surfacing. As such soil functions depend on the soil's biophysical properties and the degree of imperviousness, the impact on themore » water balance caused by urban growth varies considerably. In response to the demand for sustainably secure urban water resources, it needs to be assessed exactly how land surfacing affects the functions concerned. Analysing and evaluating urban land use change on the long-term water balance should improve our understanding of the impact of urbanisation on the water household. Therefore, this paper analyses the impact of urban land use change and land surfacing on the long-term urban water balance over a 130-year trajectory by using simple model approaches that are based on data available to the public. The test site is the city of Leipzig. In particular, attention is to be paid to estimating changes of evapotranspiration, direct runoff and groundwater recharge.« less

On soil textural classifications and soil-texture-based estimations

NASA Astrophysics Data System (ADS)

Ángel Martín, Miguel; Pachepsky, Yakov A.; García-Gutiérrez, Carlos; Reyes, Miguel

2018-02-01

The soil texture representation with the standard textural fraction triplet sand-silt-clay is commonly used to estimate soil properties. The objective of this work was to test the hypothesis that other fraction sizes in the triplets may provide a better representation of soil texture for estimating some soil parameters. We estimated the cumulative particle size distribution and bulk density from an entropy-based representation of the textural triplet with experimental data for 6240 soil samples. The results supported the hypothesis. For example, simulated distributions were not significantly different from the original ones in 25 and 85 % of cases when the sand-silt-clay and very coarse+coarse + medium sand - fine + very fine sand - silt+clay were used, respectively. When the same standard and modified triplets were used to estimate the average bulk density, the coefficients of determination were 0.001 and 0.967, respectively. Overall, the textural triplet selection appears to be application and data specific.

Evaluation and inversion of a net ecosystem carbon exchange model for grasslands and croplands

NASA Astrophysics Data System (ADS)

Herbst, M.; Klosterhalfen, A.; Weihermueller, L.; Graf, A.; Schmidt, M.; Huisman, J. A.; Vereecken, H.

2017-12-01

A one-dimensional soil water, heat, and CO2 flux model (SOILCO2), a pool concept of soil carbon turnover (RothC), and a crop growth module (SUCROS) was coupled to predict the net ecosystem exchange (NEE) of carbon. This model, further referred to as AgroC, was extended with routines for managed grassland as well as for root exudation and root decay. In a first step, the coupled model was applied to two winter wheat sites and one upland grassland site in Germany. The model was calibrated based on soil water content, soil temperature, biometric, and soil respiration measurements for each site, and validated in terms of hourly NEE measured with the eddy covariance technique. The overall model performance of AgroC was acceptable with a model efficiency >0.78 for NEE. In a second step, AgroC was optimized with the eddy covariance NEE measurements to examine the effect of various objective functions, constraints, and data-transformations on estimated NEE, which showed a distinct sensitivity to the choice of objective function and the inclusion of soil respiration data in the optimization process. Both, day and nighttime fluxes, were found to be sensitive to the selected optimization strategy. Additional consideration of soil respiration measurements improved the simulation of small positive fluxes remarkably. Even though the model performance of the selected optimization strategies did not diverge substantially, the resulting annual NEE differed substantially. We conclude that data-transformation, definition of objective functions, and data sources have to be considered cautiously when using a terrestrial ecosystem model to determine carbon balances by means of eddy covariance measurements.

Generating large-scale estimates from sparse, in-situ networks: multi-scale soil moisture modeling at ARS watersheds for NASA’s soil moisture active passive (SMAP) calibration/validation mission

USDA-ARS?s Scientific Manuscript database

NASA’s SMAP satellite, launched in November of 2014, produces estimates of average volumetric soil moisture at 3, 9, and 36-kilometer scales. The calibration and validation process of these estimates requires the generation of an identically-scaled soil moisture product from existing in-situ networ...

10Be inventories in Alpine soils and their potential for dating land surfaces

NASA Astrophysics Data System (ADS)

Egli, Markus; Brandová, Dagmar; Böhlert, Ralph; Favilli, Filippo; Kubik, Peter W.

2010-07-01

To exploit natural sedimentary archives and geomorphic landforms it is necessary to date them first. Landscape evolution of Alpine areas is often strongly related to the activities of glaciers in the Pleistocene and Holocene. At sites where no organic matter for radiocarbon dating exists and where suitable boulders for surface exposure dating (using in situ produced cosmogenic nuclides) are absent, dating of soils could give information about the timing of landscape evolution. This paper explores the applicability of soil dating using the inventory of meteoric 10Be in Alpine soils. For this purpose, a set of 6 soil profiles in the Swiss and Italian Alps was investigated. The surface at these sites had already been dated (using the radiocarbon technique or the surface exposure determination using in situ produced 10Be). Consequently, a direct comparison of the ages of the soils using meteoric 10Be and other dating techniques was made possible. The estimation of 10Be deposition rates is subject to severe limitations and strongly influences the obtained results. We tested three scenarios using a) the meteoric 10Be deposition rates as a function of the annual precipitation rate, b) a constant 10Be input for the Central Alps, and c) as b) but assuming a pre-exposure of the parent material. The obtained ages that are based on the 10Be inventory in soils and on scenario a) for the 10Be input agreed reasonably well with the age using surface exposure or radiocarbon dating. The ages obtained from soils using scenario b) produced ages that were mostly too old whereas the approach using scenario c) seemed to yield better results than scenario b). Erosion calculations can, in theory, be performed using the 10Be inventory and 10Be deposition rates. An erosion estimation was possible using scenario a) and c), but not using b). The calculated erosion rates using these scenarios seemed to be plausible with values in the range of 0-57 mm/ky. The dating of soils using 10Be has several potential error sources. Analytical errors as well as errors from other parameters such as bulk soil density and soil skeleton content have to be taken into account. The error range was from 8 up to 21%. Furthermore, uncertainties in estimating 10Be deposition rates substantially influence the calculated ages. Relative age estimates and, under optimal conditions, absolute dating can be carried out. Age determination of Alpine soils using 10Be gives another possibility to date surfaces when other methods fail or are not possible at all. It is, however, not straightforward, quite laborious and may consequently have some distinct limitations.

Soil Bulk Density by Soil Type, Land Use and Data Source: Putting the Error in SOC Estimates

NASA Astrophysics Data System (ADS)

Wills, S. A.; Rossi, A.; Loecke, T.; Ramcharan, A. M.; Roecker, S.; Mishra, U.; Waltman, S.; Nave, L. E.; Williams, C. O.; Beaudette, D.; Libohova, Z.; Vasilas, L.

2017-12-01

An important part of SOC stock and pool assessment is the assessment, estimation, and application of bulk density estimates. The concept of bulk density is relatively simple (the mass of soil in a given volume), the specifics Bulk density can be difficult to measure in soils due to logistical and methodological constraints. While many estimates of SOC pools use legacy data in their estimates, few concerted efforts have been made to assess the process used to convert laboratory carbon concentration measurements and bulk density collection into volumetrically based SOC estimates. The methodologies used are particularly sensitive in wetlands and organic soils with high amounts of carbon and very low bulk densities. We will present an analysis across four database measurements: NCSS - the National Cooperative Soil Survey Characterization dataset, RaCA - the Rapid Carbon Assessment sample dataset, NWCA - the National Wetland Condition Assessment, and ISCN - the International soil Carbon Network. The relationship between bulk density and soil organic carbon will be evaluated by dataset and land use/land cover information. Prediction methods (both regression and machine learning) will be compared and contrasted across datasets and available input information. The assessment and application of bulk density, including modeling, aggregation and error propagation will be evaluated. Finally, recommendations will be made about both the use of new data in soil survey products (such as SSURGO) and the use of that information as legacy data in SOC pool estimates.

Validation of Soil Water Content Estimation Method on Agricultural Regions in South Korea

NASA Astrophysics Data System (ADS)

Shin, Y.; Kim, M.

2016-12-01

The continuous water stress caused by decrease of soil water has a direct influence to the crop growth in a upland crop area. The agricultural drought is occured if water requirement is not supplied timely in crop growh process. It is more important to understand the soil characteristics for high accuracy soil moisture estimation because of the soil water contents largely depends on soil properties. The RDA(Rural Development Administration) has provided real-time soil moisture observations corrected for 71 points in the South Korea. In this study, we developed a soil water content estimation method that considered soil hydraulic parameters for the observation points of soil water content in agricultural regions operated by the RDA. SWAP(Soil-Water-Atmosphere-Plant) model was used in the estimation of soil water contents. The soil hydraulic parameters that is the input data of the SWAP model were estimated using the ROSETTA model developed by the U.S. Department of Agriculture(USDA). Meteorological data observed from AWS(Automatic Weather Station) were used including daily maximum temperature(°), daily minimum temperature(°), relative humidity(%), solar radiation, wind speed and precipitation data. We choosed 56 stations there are no missing of meteorological data and have soil physical properties. For the verification of soil water content estimation method, we used Haenam KoFlux observation data that are observed long-term soil water contents over 2009-2015(2014 missing) years. In the case of 2015, there are good reproducibility between observation of soil water contents and results of SWAP model simulation with R2=0.72, RMSE=0.026 and TCC=0.849. In the case of precipitation event, the simulation results were slightly overestimated more than observation. However there are good reproducibility in the case of soil water reduction due to continuous non-precipitation periods. We have simulated the soil water contents of the 56 stations that being operated in the RDA from 4 January 2015 to 31 October 2015 using the SWAP model. The environmental setting of SWAP modle according to the station applied it equally. The results showed a significant difference to the reproducibility according to the observation station.

Temperature sensitivity of gaseous elemental mercury in the active layer of the Qinghai-Tibet Plateau permafrost.

PubMed

Ci, Zhijia; Peng, Fei; Xue, Xian; Zhang, Xiaoshan

2018-07-01

Soils represent the single largest mercury (Hg) reservoir in the global environment, indicating that a tiny change of Hg behavior in soil ecosystem could greatly affect the global Hg cycle. Climate warming is strongly altering the structure and functions of permafrost and then would influence the Hg cycle in permafrost soils. However, Hg biogeochemistry in climate-sensitive permafrost is poorly investigated. Here we report a data set of soil Hg (0) concentrations in four different depths of the active layer in the Qinghai-Tibet Plateau permafrost. We find that soil Hg (0) concentrations exhibited a strongly positive and exponential relationship with temperature and showed different temperature sensitivity under the frozen and unfrozen condition. We conservatively estimate that temperature increases following latest temperature scenarios of the IPCC could result in up to a 54.9% increase in Hg (0) concentrations in surface permafrost soils by 2100. Combining the simultaneous measurement of air-soil Hg (0) exchange, we find that enhanced Hg (0) concentrations in upper soils could favor Hg (0) emissions from surface soil. Our findings indicate that Hg (0) emission could be stimulated by permafrost thawing in a warmer world. Copyright © 2018 Elsevier Ltd. All rights reserved.

Exploring the Impact of Different Input Data Types on Soil Variable Estimation Using the ICRAF-ISRIC Global Soil Spectral Database.

PubMed

Aitkenhead, Matt J; Black, Helaina I J

2018-02-01

Using the International Centre for Research in Agroforestry-International Soil Reference and Information Centre (ICRAF-ISRIC) global soil spectroscopy database, models were developed to estimate a number of soil variables using different input data types. These input types included: (1) site data only; (2) visible-near-infrared (Vis-NIR) diffuse reflectance spectroscopy only; (3) combined site and Vis-NIR data; (4) red-green-blue (RGB) color data only; and (5) combined site and RGB color data. The models produced variable estimation accuracy, with RGB only being generally worst and spectroscopy plus site being best. However, we showed that for certain variables, estimation accuracy levels achieved with the "site plus RGB input data" were sufficiently good to provide useful estimates (r 2  > 0.7). These included major elements (Ca, Si, Al, Fe), organic carbon, and cation exchange capacity. Estimates for bulk density, contrast-to-noise (C/N), and P were moderately good, but K was not well estimated using this model type. For the "spectra plus site" model, many more variables were well estimated, including many that are important indicators for agricultural productivity and soil health. Sum of cation, electrical conductivity, Si, Ca, and Al oxides, and C/N ratio were estimated using this approach with r 2 values > 0.9. This work provides a mechanism for identifying the cost-effectiveness of using different model input data, with associated costs, for estimating soil variables to required levels of accuracy.

Global spatiotemporal distribution of soil respiration modeled using a global database

NASA Astrophysics Data System (ADS)

Hashimoto, S.; Carvalhais, N.; Ito, A.; Migliavacca, M.; Nishina, K.; Reichstein, M.

2015-07-01

The flux of carbon dioxide from the soil to the atmosphere (soil respiration) is one of the major fluxes in the global carbon cycle. At present, the accumulated field observation data cover a wide range of geographical locations and climate conditions. However, there are still large uncertainties in the magnitude and spatiotemporal variation of global soil respiration. Using a global soil respiration data set, we developed a climate-driven model of soil respiration by modifying and updating Raich's model, and the global spatiotemporal distribution of soil respiration was examined using this model. The model was applied at a spatial resolution of 0.5°and a monthly time step. Soil respiration was divided into the heterotrophic and autotrophic components of respiration using an empirical model. The estimated mean annual global soil respiration was 91 Pg C yr-1 (between 1965 and 2012; Monte Carlo 95 % confidence interval: 87-95 Pg C yr-1) and increased at the rate of 0.09 Pg C yr-2. The contribution of soil respiration from boreal regions to the total increase in global soil respiration was on the same order of magnitude as that of tropical and temperate regions, despite a lower absolute magnitude of soil respiration in boreal regions. The estimated annual global heterotrophic respiration and global autotrophic respiration were 51 and 40 Pg C yr-1, respectively. The global soil respiration responded to the increase in air temperature at the rate of 3.3 Pg C yr-1 °C-1, and Q10 = 1.4. Our study scaled up observed soil respiration values from field measurements to estimate global soil respiration and provide a data-oriented estimate of global soil respiration. The estimates are based on a semi-empirical model parameterized with over one thousand data points. Our analysis indicates that the climate controls on soil respiration may translate into an increasing trend in global soil respiration and our analysis emphasizes the relevance of the soil carbon flux from soil to the atmosphere in response to climate change. Further approaches should additionally focus on climate controls in soil respiration in combination with changes in vegetation dynamics and soil carbon stocks, along with their effects on the long temporal dynamics of soil respiration. We expect that these spatiotemporal estimates will provide a benchmark for future studies and also help to constrain process-oriented models.

Application of Multitemporal Remotely Sensed Soil Moisture for the Estimation of Soil Physical Properties

NASA Technical Reports Server (NTRS)

Mattikalli, N. M.; Engman, E. T.; Jackson, T. J.; Ahuja, L. R.

1997-01-01

This paper demonstrates the use of multitemporal soil moisture derived from microwave remote sensing to estimate soil physical properties. The passive microwave ESTAR instrument was employed during June 10-18, 1992, to obtain brightness temperature (TB) and surface soil moisture data in the Little Washita watershed, Oklahoma. Analyses of spatial and temporal variations of TB and soil moisture during the dry-down period revealed a direct relationship between changes in T and soil moisture and soil physical (viz. texture) and hydraulic (viz. saturated hydraulic conductivity, K(sat)) properties. Statistically significant regression relationships were developed for the ratio of percent sand to percent clay (RSC) and K(sat), in terms of change components of TB and surface soil moisture. Validation of results using field measured values and soil texture map indicated that both RSC and K(sat) can be estimated with reasonable accuracy. These findings have potential applications of microwave remote sensing to obtain quick estimates of the spatial distributions of K(sat), over large areas for input parameterization of hydrologic models.

Estimated stocks of circumpolar permafrost carbon with quantified uncertainty ranges and identified data gaps

DOE PAGES

Hugelius, Gustaf; Strauss, J.; Zubrzycki, S.; ...

2014-12-01

Soils and other unconsolidated deposits in the northern circumpolar permafrost region store large amounts of soil organic carbon (SOC). This SOC is potentially vulnerable to remobilization following soil warming and permafrost thaw, but SOC stock estimates were poorly constrained and quantitative error estimates were lacking. This study presents revised estimates of permafrost SOC stocks, including quantitative uncertainty estimates, in the 0–3 m depth range in soils as well as for sediments deeper than 3 m in deltaic deposits of major rivers and in the Yedoma region of Siberia and Alaska. Revised estimates are based on significantly larger databases compared tomore » previous studies. Despite this there is evidence of significant remaining regional data gaps. Estimates remain particularly poorly constrained for soils in the High Arctic region and physiographic regions with thin sedimentary overburden (mountains, highlands and plateaus) as well as for deposits below 3 m depth in deltas and the Yedoma region. While some components of the revised SOC stocks are similar in magnitude to those previously reported for this region, there are substantial differences in other components, including the fraction of perennially frozen SOC. Upscaled based on regional soil maps, estimated permafrost region SOC stocks are 217 ± 12 and 472 ± 27 Pg for the 0–0.3 and 0–1 m soil depths, respectively (±95% confidence intervals). Storage of SOC in 0–3 m of soils is estimated to 1035 ± 150 Pg. Of this, 34 ± 16 Pg C is stored in poorly developed soils of the High Arctic. Based on generalized calculations, storage of SOC below 3 m of surface soils in deltaic alluvium of major Arctic rivers is estimated as 91 ± 52 Pg. In the Yedoma region, estimated SOC stocks below 3 m depth are 181 ± 54 Pg, of which 74 ± 20 Pg is stored in intact Yedoma (late Pleistocene ice- and organic-rich silty sediments) with the remainder in refrozen thermokarst deposits. Total estimated SOC storage for the permafrost region is ∼1300 Pg with an uncertainty range of ∼1100 to 1500 Pg. Of this, ∼500 Pg is in non-permafrost soils, seasonally thawed in the active layer or in deeper taliks, while ∼800 Pg is perennially frozen. In conclusion, this represents a substantial ∼300 Pg lowering of the estimated perennially frozen SOC stock compared to previous estimates.« less

Comparison of Predicted and Measured Soil Retention Curve in Lombardy Region Northern of Italy

NASA Astrophysics Data System (ADS)

Wassar, Fatma; Rienzner, Michele; Chiaradia, Enrico Antonio; Gandolfi, Claudio

2013-04-01

Water retention characteristics are crucial input parameters in any modeling study on water flow and solute transport. These properties are difficult to measure and therefore the use of both direct and indirect methods is required in order to adequately describe them with sufficient accuracy. Several field methods, laboratory methods and theoretical models for such determinations exist, each having their own limitations and advantages (Stephens, 1994). Therefore, extensive comparisons between estimated, field and laboratory results to determine it still requires their validity for a range of different soils and specific cases. This study attempts to make a contribution specifically in this connection. The soil water retention characteristics were determined in two representative sites (PMI-1 and PMI-5) located in Landriano field, in Lombardy region, northern Italy. In the laboratory, values of both volumetric water content (θ) and soil water matric potential (h) are measured in the same sample using the tensiometric box and pressure plate apparatus. Field determination of soil water retention involved measurements of soil water content with SENTEK probes, and matric potential with tensiometers. The retention curve characteristics were also determined using some of the most commonly cited and some recently developed PTFs that use soil properties such as particle-size distribution (sand, silt, and clay content), organic matter or organic Carbon content, and dry bulk density. Field methods are considered to be more representative than laboratory and estimation methods for determining water retention characteristics (Marion et al., 1996). Therefore, field retention curves were compared against retention curves obtained from laboratory measurements and PTFs estimations. The performances of laboratory and PTFs in predicting field measured data were evaluated using root mean square error (RMSE) and bias. The comparison showed that laboratory measurements were the most accurate. They had the highest ranking for the validation indices (RMSE ranging between 2.4 and 7.7% and bias between 0.1 and 6.4%). The second best technique was the PTF Rosetta (Schaap et al. 2001). They perform only slightly poorer than the laboratory measurements (RMSE ranging between 2.7 and 10% and bias between 0.3 and 7.7%). The lowest prediction accuracy is observed for the Rawls and Brakensiek (1985) PTF (RMSE ranging between 6.3 and 17% and bias between 5 and 10%) which is in contradiction with previous finding (Calzolari et al., 2001), showing that this function is well representing the retention characteristics of the area. We conclude that the Rosetta PTF developed by Schaap et al (2001) appears to be well suited to predict the soil moisture retention curve from easily available soil properties in the Lombardy area and further field investigations would be useful to reinforce this finding. Keywords: water retention curve; laboratory measurements; field measurements; pedotransfert functions; comparison.

A simplified, data-constrained approach to estimate the permafrost carbon–climate feedback

DOE PAGES

Koven, C. D.; Schuur, E. A. G.; Schadel, C.; ...

2015-10-05

We present an approach to estimate the feedback from large-scale thawing of permafrost soils using a simplified, data-constrained model that combines three elements: soil carbon (C) maps and profiles to identify the distribution and type of C in permafrost soils; incubation experiments to quantify the rates of C lost after thaw; and models of soil thermal dynamics in response to climate warming. We call the approach the Permafrost Carbon Network Incubation–Panarctic Thermal scaling approach (PInc-PanTher). The approach assumes that C stocks do not decompose at all when frozen, but once thawed follow set decomposition trajectories as a function of soilmore » temperature. The trajectories are determined according to a three-pool decomposition model fitted to incubation data using parameters specific to soil horizon types. We calculate litterfall C inputs required to maintain steady-state C balance for the current climate, and hold those inputs constant. Soil temperatures are taken from the soil thermal modules of ecosystem model simulations forced by a common set of future climate change anomalies under two warming scenarios over the period 2010 to 2100. Under a medium warming scenario (RCP4.5), the approach projects permafrost soil C losses of 12.2–33.4 Pg C; under a high warming scenario (RCP8.5), the approach projects C losses of 27.9–112.6 Pg C. Projected C losses are roughly linearly proportional to global temperature changes across the two scenarios. These results indicate a global sensitivity of frozen soil C to climate change (γ sensitivity) of –14 to –19 Pg C °C–1 on a 100 year time scale. For CH 4 emissions, our approach assumes a fixed saturated area and that increases in CH 4 emissions are related to increased heterotrophic respiration in anoxic soil, yielding CH 4 emission increases of 7% and 35% for the RCP4.5 and RCP8.5 scenarios, respectively, which add an additional greenhouse gas forcing of approximately 10–18%. In conclusion, the simplified approach presented here neglects many important processes that may amplify or mitigate C release from permafrost soils, but serves as a data-constrained estimate on the forced, large-scale permafrost C response to warming.« less

Regional Evapotranspiration Estimation by Using Wireless Sap Flow and Soil Moisture Measurement Systems

NASA Astrophysics Data System (ADS)

Kuo, C.; Yu, P.; Yang, T.; Davis, T. W.; Liang, X.; Tseng, C.; Cheng, C.

2011-12-01

The objective of this study proposed herein is to estimate regional evapotranspiration via sap flow and soil moisture measurements associated with wireless sensor network in the field. Evapotranspiration is one of the important factors in water balance computation. Pan evaporation collected from the meteorological station can only be accounted as a single-point scale measurement rather than the water loss of the entire region. Thus, we need a multiple-site measurement for understanding the regional evapotranspiration. Applying sap flow method with self-made probes, we could calculate transpiration. Soil moisture measurement was used to monitor the daily soil moisture variety for evaporation. Sap flow and soil moisture measurements in multiple sites are integrated by using wireless sensor network (WSN). Then, the measurement results of each site were scaled up and combined into the regional evapotranspiration. This study used thermal dissipation method to measure sap flow in trees to represent the plant transpiration. Sap flow was measured by using the self-made sap probes which needed to be calibrated before setting up at the observation field. Regional transpiration was scaled up through the Leaf Area Index (LAI). The LAI of regional scale was from the MODIS image calculated at 1km X 1km grid size. The soil moistures collected from areas outside the distributing area of tree roots and tree canopy were used to represent the evaporation. The observation was undertaken to collect soil moisture variety from five different soil depths of 10, 20, 30, 40 and 50 cm respectively. The regional evaporation can be estimated by averaging the variation of soil moisture from each site within the region. The result data measured by both sap flow and soil moisture measurements of each site were collected through the wireless sensor network. The WSN performs the functions of P2P and mesh networking. That can collect data in multiple locations simultaneously and has less power consumption. WSN is the best way for collecting sap flow and soil moisture data in this study. Since the data were collected through the radio in the field, there may have some noise randomly. The weighted least-squares method was used to filter the raw data. Through collecting the observation data by WSN and transferring them into regional scale, we could get regional evapotranspiration.

Impacts of different types of measurements on estimating unsaturated flow parameters

NASA Astrophysics Data System (ADS)

Shi, Liangsheng; Song, Xuehang; Tong, Juxiu; Zhu, Yan; Zhang, Qiuru

2015-05-01

This paper assesses the value of different types of measurements for estimating soil hydraulic parameters. A numerical method based on ensemble Kalman filter (EnKF) is presented to solely or jointly assimilate point-scale soil water head data, point-scale soil water content data, surface soil water content data and groundwater level data. This study investigates the performance of EnKF under different types of data, the potential worth contained in these data, and the factors that may affect estimation accuracy. Results show that for all types of data, smaller measurements errors lead to faster convergence to the true values. Higher accuracy measurements are required to improve the parameter estimation if a large number of unknown parameters need to be identified simultaneously. The data worth implied by the surface soil water content data and groundwater level data is prone to corruption by a deviated initial guess. Surface soil moisture data are capable of identifying soil hydraulic parameters for the top layers, but exert less or no influence on deeper layers especially when estimating multiple parameters simultaneously. Groundwater level is one type of valuable information to infer the soil hydraulic parameters. However, based on the approach used in this study, the estimates from groundwater level data may suffer severe degradation if a large number of parameters must be identified. Combined use of two or more types of data is helpful to improve the parameter estimation.

Impacts of Different Types of Measurements on Estimating Unsaturatedflow Parameters

NASA Astrophysics Data System (ADS)

Shi, L.

2015-12-01

This study evaluates the value of different types of measurements for estimating soil hydraulic parameters. A numerical method based on ensemble Kalman filter (EnKF) is presented to solely or jointly assimilate point-scale soil water head data, point-scale soil water content data, surface soil water content data and groundwater level data. This study investigates the performance of EnKF under different types of data, the potential worth contained in these data, and the factors that may affect estimation accuracy. Results show that for all types of data, smaller measurements errors lead to faster convergence to the true values. Higher accuracy measurements are required to improve the parameter estimation if a large number of unknown parameters need to be identified simultaneously. The data worth implied by the surface soil water content data and groundwater level data is prone to corruption by a deviated initial guess. Surface soil moisture data are capable of identifying soil hydraulic parameters for the top layers, but exert less or no influence on deeper layers especially when estimating multiple parameters simultaneously. Groundwater level is one type of valuable information to infer the soil hydraulic parameters. However, based on the approach used in this study, the estimates from groundwater level data may suffer severe degradation if a large number of parameters must be identified. Combined use of two or more types of data is helpful to improve the parameter estimation.

Pedodiversity and Its Significance in the Context of Modern Soil Geography

NASA Astrophysics Data System (ADS)

Krasilnikov, P. V.; Gerasimova, M. I.; Golovanov, D. L.; Konyushkova, M. V.; Sidorova, V. A.; Sorokin, A. S.

2018-01-01

Methodological basics of the study and quantitative assessment of pedodiversity are discussed. It is shown that the application of various indices and models of pedodiversity can be feasible for solving three major issues in pedology: a comparative geographical analysis of different territories, a comparative historical analysis of soil development in the course of landscape evolution, and the analysis of relationships between biodiversity and pedodiversity. Analogous geographic concepts of geodiversity and landscape diversity are also discussed. Certain limitations in the use of quantitative estimates of pedodiversity related to their linkage to the particular soil classification systems and with the initial soil maps are considered. Problems of the interpretation of the results of pedodiversity assessments are emphasized. It is shown that scientific explanations of biodiversity cannot be adequately applied in soil studies. Promising directions of further studies of pedodiversity are outlined. They include the assessment of the functional diversity of soils on the basis of data on their properties, integration with geostatistical methods of evaluation of soil variability, and assessment of pedodiversity on different scales.

Linking annual N2O emission in organic soils to mineral nitrogen input as estimated by heterotrophic respiration and soil C/N ratio.

PubMed

Mu, Zhijian; Huang, Aiying; Ni, Jiupai; Xie, Deti

2014-01-01

Organic soils are an important source of N2O, but global estimates of these fluxes remain uncertain because measurements are sparse. We tested the hypothesis that N2O fluxes can be predicted from estimates of mineral nitrogen input, calculated from readily-available measurements of CO2 flux and soil C/N ratio. From studies of organic soils throughout the world, we compiled a data set of annual CO2 and N2O fluxes which were measured concurrently. The input of soil mineral nitrogen in these studies was estimated from applied fertilizer nitrogen and organic nitrogen mineralization. The latter was calculated by dividing the rate of soil heterotrophic respiration by soil C/N ratio. This index of mineral nitrogen input explained up to 69% of the overall variability of N2O fluxes, whereas CO2 flux or soil C/N ratio alone explained only 49% and 36% of the variability, respectively. Including water table level in the model, along with mineral nitrogen input, further improved the model with the explanatory proportion of variability in N2O flux increasing to 75%. Unlike grassland or cropland soils, forest soils were evidently nitrogen-limited, so water table level had no significant effect on N2O flux. Our proposed approach, which uses the product of soil-derived CO2 flux and the inverse of soil C/N ratio as a proxy for nitrogen mineralization, shows promise for estimating regional or global N2O fluxes from organic soils, although some further enhancements may be warranted.

Global distribution of soil organic carbon - Part 1: Masses and frequency distributions of SOC stocks for the tropics, permafrost regions, wetlands, and the world

NASA Astrophysics Data System (ADS)

Köchy, M.; Hiederer, R.; Freibauer, A.

2015-04-01

The global soil organic carbon (SOC) mass is relevant for the carbon cycle budget and thus atmospheric carbon concentrations. We review current estimates of SOC stocks and mass (stock × area) in wetlands, permafrost and tropical regions and the world in the upper 1 m of soil. The Harmonized World Soil Database (HWSD) v.1.2 provides one of the most recent and coherent global data sets of SOC, giving a total mass of 2476 Pg when using the original values for bulk density. Adjusting the HWSD's bulk density (BD) of soil high in organic carbon results in a mass of 1230 Pg, and additionally setting the BD of Histosols to 0.1 g cm-3 (typical of peat soils), results in a mass of 1062 Pg. The uncertainty in BD of Histosols alone introduces a range of -56 to +180 Pg C into the estimate of global SOC mass in the top 1 m, larger than estimates of global soil respiration. We report the spatial distribution of SOC stocks per 0.5 arcminutes; the areal masses of SOC; and the quantiles of SOC stocks by continents, wetland types, and permafrost types. Depending on the definition of "wetland", wetland soils contain between 82 and 158 Pg SOC. With more detailed estimates for permafrost from the Northern Circumpolar Soil Carbon Database (496 Pg SOC) and tropical peatland carbon incorporated, global soils contain 1325 Pg SOC in the upper 1 m, including 421 Pg in tropical soils, whereof 40 Pg occurs in tropical wetlands. Global SOC amounts to just under 3000 Pg when estimates for deeper soil layers are included. Variability in estimates is due to variation in definitions of soil units, differences in soil property databases, scarcity of information about soil carbon at depths > 1 m in peatlands, and variation in definitions of "peatland".

Use of modeled and satelite soil moisture to estimate soil erosion in central and southern Italy.

NASA Astrophysics Data System (ADS)

Termite, Loris Francesco; Massari, Christian; Todisco, Francesca; Brocca, Luca; Ferro, Vito; Bagarello, Vincenzo; Pampalone, Vincenzo; Wagner, Wolfgang

2016-04-01

This study presents an accurate comparison between two different approaches aimed to enhance accuracy of the Universal Soil Loss Equation (USLE) in estimating the soil loss at the single event time scale. Indeed it is well known that including the observed event runoff in the USLE improves its soil loss estimation ability at the event scale. In particular, the USLE-M and USLE-MM models use the observed runoff coefficient to correct the rainfall erosivity factor. In the first case, the soil loss is linearly dependent on rainfall erosivity, in the second case soil loss and erosivity are related by a power law. However, the measurement of the event runoff is not straightforward or, in some cases, possible. For this reason, the first approach used in this study is the use of Soil Moisture For Erosion (SM4E), a recent USLE-derived model in which the event runoff is replaced by the antecedent soil moisture. Three kinds of soil moisture datasets have been separately used: the ERA-Interim/Land reanalysis data of the European Centre for Medium-range Weather Forecasts (ECMWF); satellite retrievals from the European Space Agency - Climate Change Initiative (ESA-CCI); modeled data using a Soil Water Balance Model (SWBM). The second approach is the use of an estimated runoff rather than the observed. Specifically, the Simplified Continuous Rainfall-Runoff Model (SCRRM) is used to derive the runoff estimates. SCRMM requires soil moisture data as input and at this aim the same three soil moisture datasets used for the SM4E have been separately used. All the examined models have been calibrated and tested at the plot scale, using data from the experimental stations for the monitoring of the erosive processes "Masse" (Central Italy) and "Sparacia" (Southern Italy). Climatic data and runoff and soil loss measures at the event time scale are available for the period 2008-2013 at Masse and for the period 2002-2013 at Sparacia. The results show that both the approaches can provide better results than the USLE. Specifically, the SM4E model has proven to be particularly effective at Masse, providing the best soil loss estimations, especially when the modeled soil moisture is used. In this case, the RSR index (ratio between the Root Mean Square Error and the Observed Standard deviation) is equal to 0.94. Instead, the SCRRM is able to better estimate the event runoff at Sparacia than at Masse, thus resulting in good performances of the USLE-derived models using the estimated runoff; however, even at Sparacia the SM4E with modeled soil moisture gives the better soil loss estimates, with RSR = 0.54. These results open an interesting scenario in the use of empirical models to determine soil loss at a large scale, since soil moisture is a not only a simple in situ measurement, but only a widely available information on a global scale from remote sensing.

Estimation of the water retention curve from the soil hydraulic conductivity and sorptivity in an upward infiltration process

NASA Astrophysics Data System (ADS)

Moret-Fernández, David; Angulo, Marta; Latorre, Borja; González-Cebollada, César; López, María Victoria

2017-04-01

Determination of the saturated hydraulic conductivity, Ks, and the α and n parameters of the van Genuchten (1980) water retention curve, θ(h), are fundamental to fully understand and predict soil water distribution. This work presents a new procedure to estimate the soil hydraulic properties from the inverse analysis of a single cumulative upward infiltration curve followed by an overpressure step at the end of the wetting process. Firstly, Ks is calculated by the Darcy's law from the overpressure step. The soil sorptivity (S) is then estimated using the Haverkamp et al., (1994) equation. Next, a relationship between α and n, f(α,n), is calculated from the estimated Sand Ks. The α and n values are finally obtained by the inverse analysis of the experimental data after applying the f(α,n) relationship to the HYDRUS-1D model. The method was validated on theoretical synthetic curves for three different soils (sand, loam and clay), and subsequently tested on experimental sieved soils (sand, loam, clay loam and clay) of known hydraulic properties. A robust relationship was observed between the theoretical α and nvalues (R2 > 0.99) of the different synthetic soils and those estimated from inverse analysis of the upward infiltration curve. Consistent results were also obtained for the experimental soils (R2 > 0.85). These results demonstrated that this technique allowed accurate estimates of the soil hydraulic properties for a wide range of textures, including clay soils.

Relating environmental availability to bioavailability: soil-type-dependent metal accumulation in the oligochaete Eisenia andrei.

PubMed

Peijnenburg, W J; Baerselman, R; de Groot, A C; Jager, T; Posthuma, L; Van Veen, R P

1999-11-01

Body residues are often better estimates of the amount of a chemical at the sites of toxic action in an organism than ambient soil concentrations, because bioavailability differences among soils are explicitly taken into account in considerations of body residues. Often, however, insufficient attention is paid to the rate and extent at which tissue concentrations respond to soil concentrations and soil characteristics. In this contribution the impact of soil characteristics on the environmental bioavailability of heavy metals for the oligochaete worm Eisenia andrei is reported. Uptake of As, Cd, Cr, Cu, Ni, Pb, and Zn in 20 Dutch field soils and in OECD artificial soil was quantified as a function of time. Internal metal concentrations varied less than the corresponding external levels. Metal uptake and elimination were both metal- and species-dependent. Worms typically attained steady-state concentrations rapidly for Cr, Cu, Ni, and Zn. Internal concentrations similar to those in the cultivation medium, linearly increasing body concentrations, or steady-state internal concentrations well above those in the cultivation medium were found for As, Cd, and Pb. Multivariate expressions were derived to describe uptake rate constants, steady-state concentrations, and bioaccumulation factors as a function of soil characteristics. Soil acidity is the most important solid-phase characteristic modulating the availability of As, Cd, and Pb. Although additional semimechanistic calculations yielded evidence of pore-water-related uptake of Cd and Pb modulated by competition between H(+) and metal ions at the active sites of the membranes, the findings for Cr, Cu, Ni, and Zn point to additional influences, among which is probably regulation. Copyright 1999 Academic Press.

Soil profile property estimation with field and laboratory VNIR spectroscopy

USDA-ARS?s Scientific Manuscript database

Diffuse reflectance spectroscopy (DRS) soil sensors have the potential to provide rapid, high-resolution estimation of multiple soil properties. Although many studies have focused on laboratory-based visible and near-infrared (VNIR) spectroscopy of dried soil samples, previous work has demonstrated ...

Impact of repeated single-metal and multi-metal pollution events on soil quality.

PubMed

Burges, Aritz; Epelde, Lur; Garbisu, Carlos

2015-02-01

Most frequently, soil metal pollution results from the occurrence of repeated single-metal and, above all, multi-metal pollution events, with concomitant adverse consequences for soil quality. Therefore, in this study, we evaluated the impact of repeated single-metal and multi-metal (Cd, Pb, Cu, Zn) pollution events on soil quality, as reflected by the values of a variety of soil microbial parameters with potential as bioindicators of soil functioning. Specifically, parameters of microbial activity (potentially mineralizable nitrogen, β-glucosidase and acid phosphatase activity) and biomass (fungal and bacterial gene abundance by RT-qPCR) were determined, in the artificially metal-polluted soil samples, at regular intervals over a period of 26 weeks. Similarly, we studied the evolution over time of CaCl2-extractable metal fractions, in order to estimate metal bioavailability in soil. Different metals showed different values of bioavailability and relative bioavailability ([metal]bio/[metal]tot) in soil throughout the experiment, under both repeated single-metal and multi-metal pollution events. Both repeated Zn-pollution and multi-metal pollution events led to a significant reduction in the values of acid phosphatase activity, and bacterial and fungal gene abundance, reflecting the negative impact of these repeated events on soil microbial activity and biomass, and, hence, soil quality. Copyright © 2014 Elsevier Ltd. All rights reserved.

Extrapolating existing soil organic carbon data to estimate soil organic carbon stocks below 20 cm

Treesearch

An-Min Wu; Cinzia Fissore; Charles H. Perry; An-Min Wu; Brent Dalzell; Barry T. Wilson

2015-01-01

Estimates of forest soil organic carbon stocks across the US are currently developed from expert opinion in STATSGO/SSURGO and linked to forest type. The results are reported to the US EPA as the official United States submission to the UN Framework Convention on Climate Change. Beginning in 2015, however, estimates of soil organic carbon (SOC) stocks will be based on...

Phase Sensitiveness to Soil Moisture in Controlled Anechoic Chamber: Measurements and First Results

NASA Astrophysics Data System (ADS)

Ben Khadhra, K.; Nolan, M.; Hounam, D.; Boerner, T.

2005-12-01

To date many radar methods and models have been reported for the estimation of soil moisture, such as the Oh-model or the Dubois model. Those models, which use only the magnitude of the backscattered signal, show results with 5 to 10 % accuracy. In the last two decades SAR Interferometry (InSAR) and differential InSAR (DInSAR), which uses the phase of the backscattered signal, has been shown to be a useful tool for the creation of Digital Elevation Models (DEMs), and temporal changes due to earthquakes, subsidence, and other ground motions. Nolan (2003) also suggested the possibility to use DINSAR penetration depth as a proxy to estimate the soil moisture. The principal is based on the relationship between the penetration depth and the permittivity, which varies as a function of soil moisture. In this paper we will present new interferometric X-band laboratory measurements, which have been carried out in the Bistatic Measurement Facility at the DLR Oberpfaffenhofen, Microwaves and Radar Institute in Germany. The bistatic geometry enables us to have interferometric pairs with different baseline and different soil moistures controlled by a TDR (Time Domain Reflectivity) system. After calibration of the measuring system using a large metal plate, the sensitivity of phase and reflectivity with regard to moisture variation and therefore the penetration depth was evaluated. The effect of the surface roughness has been also reported. Current results demonstrate a non-linear relationship between the signal phase and the soil moisture, as expected, confirming the possibility of using DInSAR to measure variations in soil moisture.

An empirical method to estimate shear wave velocity of soils in the New Madrid seismic zone

USGS Publications Warehouse

Wei, B.-Z.; Pezeshk, S.; Chang, T.-S.; Hall, K.H.; Liu, Huaibao P.

1996-01-01

In this study, a set of charts are developed to estimate shear wave velocity of soils in the New Madrid seismic zone (NMSZ), using the standard penetration test (SPT) N values and soil depths. Laboratory dynamic test results of soil samples collected from the NMSZ showed that the shear wave velocity of soils is related to the void ratio and the effective confining pressure applied to the soils. The void ratio of soils can be estimated from the SPT N values and the effective confining pressure depends on the depth of soils. Therefore, the shear wave velocity of soils can be estimated from the SPT N value and the soil depth. To make the methodology practical, two corrections should be made. One is that field SPT N values of soils must be adjusted to an unified SPT N??? value to account the effects of overburden pressure and equipment. The second is that the effect of water table to effective overburden pressure of soils must be considered. To verify the methodology, shear wave velocities of five sites in the NMSZ are estimated and compared with those obtained from field measurements. The comparison shows that our approach and the field tests are consistent with an error of less than of 15%. Thus, the method developed in this study is useful for dynamic study and practical designs in the NMSZ region. Copyright ?? 1996 Elsevier Science Limited.

Spatial and Temporal Influences on Carbon Storage in Hydric Soils of the Conterminous United States

NASA Astrophysics Data System (ADS)

Sundquist, E. T.; Ackerman, K.; Bliss, N.; Griffin, R.; Waltman, S.; Windham-Myers, L.

2016-12-01

Defined features of hydric soils persist over extensive areas of the conterminous United States (CUS) long after their hydric formation conditions have been altered by historical changes in land and water management. These legacy hydric features may represent previous wetland environments in which soil carbon storage was significantly higher before the influence of human activities. We hypothesize that historical alterations of hydric soil carbon storage can be approximated using carefully selected estimates of carbon storage in currently identified hydric soils. Using the Soil Survey Geographic (SSURGO) database, we evaluate carbon storage in identified hydric soil components that are subject to discrete ranges of current or recent conditions of flooding, ponding, and other indicators of hydric and non-hydric soil associations. We check our evaluations and, where necessary, adjust them using independently published soil data. We compare estimates of soil carbon storage under various hydric and non-hydric conditions within proximal landscapes and similar biophysical settings and ecosystems. By combining these setting- and ecosystem-constrained comparisons with the spatial distribution and attributes of wetlands in the National Wetlands Inventory, we impute carbon storage estimates for soils that occur in current wetlands and for hydric soils that are not associated with current wetlands. Using historical data on land use and water control structures, we map the spatial and temporal distribution of past changes in land and water management that have affected hydric soils. We combine these maps with our imputed carbon storage estimates to calculate ranges of values for historical and present-day carbon storage in hydric soils throughout the CUS. These estimates may provide useful constraints for projections of potential carbon storage in hydric soils under future conditions.

Potential for use of environmental factors in urban planning

NASA Astrophysics Data System (ADS)

Teixeira da Silva, Ricardo; van der Ploeg, Martine; van Delden, Hedwig; Fleskens, Luuk

2016-04-01

Projections for population growth estimate, on top of the current 7.4 billion world population, an increase of 2 billion people for the next 40 years. It is also projected that 66 per cent of the world population in 2050 will live in urban areas. To accommodate the urban population growth cities are changing continuously land cover to urban areas. Such changes are a threat for natural resources and food production systems stability and capability to provide food and other functions. However, little has been done concerning a rational soil management for food production in urban and peri-urban areas. This study focuses on the assessment of soil lost due to urban expansion and discusses the potential loss regarding the quality of the soil for food production and environmental functions. It is relevant to increase the knowledge on the role of soils in peri-urban areas and in the interaction of physical, environmental and social factors. The methodology consists of assessing the soil quality in and around urban and peri-urban areas. It focuses particularly on the physical properties and the environmental factors, for two periods of time and account the potential losses due to urban expansion. This project is on-going, therefore current advances will be presented and will look for a discussion on the contribution of soil quality for decision-making and land management in urban and peri-urban areas.

Importance of Vertical Coupling in Agricultural Models on Assimilation of Satellite-derived Soil Moisture

NASA Astrophysics Data System (ADS)

Mladenova, I. E.; Crow, W. T.; Teng, W. L.; Doraiswamy, P.

2010-12-01

Crop yield in crop production models is simulated as a function of weather, ground conditions and management practices and it is driven by the amount of nutrients, heat and water availability in the root-zone. It has been demonstrated that assimilation of satellite-derived soil moisture data has the potential to improve the model root-zone soil water (RZSW) information. However, the satellite estimates represent the moisture conditions of the top 3 cm to 5 cm of the soil profile depending on system configuration and surface conditions (i.e. soil wetness, density of the canopy cover, etc). The propagation of this superficial information throughout the profile will depend on the model physics. In an Ensemble Kalman Filter (EnKF) data assimilation system, as the one examined here, the update of each soil layer is done through the Kalman Gain, K. K is a weighing factor that determines how much correction will be performed on the forecasts. Furthermore, K depends on the strength of the correlation between the surface and the root-zone soil moisture; the stronger this correlation is, the more observations will impact the analysis. This means that even if the satellite-derived product has higher sensitivity and accuracy as compared to the model estimates, the improvement of the RZSW will be negligible if the surface-root zone coupling is weak, where the later is determined by the model subsurface physics. This research examines: (1) the strength of the vertical coupling in the Environmental Policy Integrated Climate (EPIC) model over corn and soybeans covered fields in Iowa, US, (2) the potential to improve EPIC RZSW information through assimilation of satellite soil moisture data derived from the Advanced Microwave Scanning Radiometer (AMSR-E) and (3) the impact of the vertical coupling on the EnKF performance.

Percolation properties of 3-D multiscale pore networks: how connectivity controls soil filtration processes

NASA Astrophysics Data System (ADS)

Perrier, E. M. A.; Bird, N. R. A.; Rieutord, T. B.

2010-04-01

Quantifying the connectivity of pore networks is a key issue not only for modelling fluid flow and solute transport in porous media but also for assessing the ability of soil ecosystems to filter bacteria, viruses and any type of living microorganisms as well inert particles which pose a contamination risk. Straining is the main mechanical component of filtration processes: it is due to size effects, when a given soil retains a conveyed entity larger than the pores through which it is attempting to pass. We postulate that the range of sizes of entities which can be trapped inside soils has to be associated with the large range of scales involved in natural soil structures and that information on the pore size distribution has to be complemented by information on a Critical Filtration Size (CFS) delimiting the transition between percolating and non percolating regimes in multiscale pore networks. We show that the mass fractal dimensions which are classically used in soil science to quantify scaling laws in observed pore size distributions can also be used to build 3-D multiscale models of pore networks exhibiting such a critical transition. We extend to the 3-D case a new theoretical approach recently developed to address the connectivity of 2-D fractal networks (Bird and Perrier, 2009). Theoretical arguments based on renormalisation functions provide insight into multi-scale connectivity and a first estimation of CFS. Numerical experiments on 3-D prefractal media confirm the qualitative theory. These results open the way towards a new methodology to estimate soil filtration efficiency from the construction of soil structural models to be calibrated on available multiscale data.

Percolation properties of 3-D multiscale pore networks: how connectivity controls soil filtration processes

NASA Astrophysics Data System (ADS)

Perrier, E. M. A.; Bird, N. R. A.; Rieutord, T. B.

2010-10-01

Quantifying the connectivity of pore networks is a key issue not only for modelling fluid flow and solute transport in porous media but also for assessing the ability of soil ecosystems to filter bacteria, viruses and any type of living microorganisms as well inert particles which pose a contamination risk. Straining is the main mechanical component of filtration processes: it is due to size effects, when a given soil retains a conveyed entity larger than the pores through which it is attempting to pass. We postulate that the range of sizes of entities which can be trapped inside soils has to be associated with the large range of scales involved in natural soil structures and that information on the pore size distribution has to be complemented by information on a critical filtration size (CFS) delimiting the transition between percolating and non percolating regimes in multiscale pore networks. We show that the mass fractal dimensions which are classically used in soil science to quantify scaling laws in observed pore size distributions can also be used to build 3-D multiscale models of pore networks exhibiting such a critical transition. We extend to the 3-D case a new theoretical approach recently developed to address the connectivity of 2-D fractal networks (Bird and Perrier, 2009). Theoretical arguments based on renormalisation functions provide insight into multi-scale connectivity and a first estimation of CFS. Numerical experiments on 3-D prefractal media confirm the qualitative theory. These results open the way towards a new methodology to estimate soil filtration efficiency from the construction of soil structural models to be calibrated on available multiscale data.

Sensible heat balance estimates of transient soil ice contents for freezing and thawing conditions

USDA-ARS?s Scientific Manuscript database

Soil ice content is an important component for winter soil hydrology. The sensible heat balance (SHB) method using measurements from heat pulse probes (HPP) is a possible way to determine transient soil ice content. In a previous study, in situ soil ice contents estimates with the SHB method were in...

Measuring the electrical properties of soil using a calibrated ground-coupled GPR system

USGS Publications Warehouse

Oden, C.P.; Olhoeft, G.R.; Wright, D.L.; Powers, M.H.

2008-01-01

Traditional methods for estimating vadose zone soil properties using ground penetrating radar (GPR) include measuring travel time, fitting diffraction hyperbolae, and other methods exploiting geometry. Additional processing techniques for estimating soil properties are possible with properly calibrated GPR systems. Such calibration using ground-coupled antennas must account for the effects of the shallow soil on the antenna's response, because changing soil properties result in a changing antenna response. A prototype GPR system using ground-coupled antennas was calibrated using laboratory measurements and numerical simulations of the GPR components. Two methods for estimating subsurface properties that utilize the calibrated response were developed. First, a new nonlinear inversion algorithm to estimate shallow soil properties under ground-coupled antennas was evaluated. Tests with synthetic data showed that the inversion algorithm is well behaved across the allowed range of soil properties. A preliminary field test gave encouraging results, with estimated soil property uncertainties (????) of ??1.9 and ??4.4 mS/m for the relative dielectric permittivity and the electrical conductivity, respectively. Next, a deconvolution method for estimating the properties of subsurface reflectors with known shapes (e.g., pipes or planar interfaces) was developed. This method uses scattering matrices to account for the response of subsurface reflectors. The deconvolution method was evaluated for use with noisy data using synthetic data. Results indicate that the deconvolution method requires reflected waves with a signal/noise ratio of about 10:1 or greater. When applied to field data with a signal/noise ratio of 2:1, the method was able to estimate the reflection coefficient and relative permittivity, but the large uncertainty in this estimate precluded inversion for conductivity. ?? Soil Science Society of America.

Estimation of soil profile properties using field and laboratory VNIR spectroscopy

USDA-ARS?s Scientific Manuscript database

Diffuse reflectance spectroscopy (DRS) soil sensors have the potential to provide rapid, high-resolution estimation of multiple soil properties. Although many studies have focused on laboratory-based visible and near-infrared (VNIR) spectroscopy of dried soil samples, previous work has demonstrated ...

Estimation of soil-soil solution distribution coefficient of radiostrontium using soil properties.

PubMed

Ishikawa, Nao K; Uchida, Shigeo; Tagami, Keiko

2009-02-01

We propose a new approach for estimation of soil-soil solution distribution coefficient (K(d)) of radiostrontium using some selected soil properties. We used 142 Japanese agricultural soil samples (35 Andosol, 25 Cambisol, 77 Fluvisol, and 5 others) for which Sr-K(d) values had been determined by a batch sorption test and listed in our database. Spearman's rank correlation test was carried out to investigate correlations between Sr-K(d) values and soil properties. Electrical conductivity and water soluble Ca had good correlations with Sr-K(d) values for all soil groups. Then, we found a high correlation between the ratio of exchangeable Ca to Ca concentration in water soluble fraction and Sr-K(d) values with correlation coefficient R=0.72. This pointed us toward a relatively easy way to estimate Sr-K(d) values.

Advances in Land Data Assimilation at the NASA Goddard Space Flight Center

NASA Technical Reports Server (NTRS)

Reichle, Rolf

2009-01-01

Research in land surface data assimilation has grown rapidly over the last decade. In this presentation we provide a brief overview of key research contributions by the NASA Goddard Space Flight Center (GSFC). The GSFC contributions to land assimilation primarily include the continued development and application of the Land Information System (US) and the ensemble Kalman filter (EnKF). In particular, we have developed a method to generate perturbation fields that are correlated in space, time, and across variables and that permit the flexible modeling of errors in land surface models and observations, along with an adaptive filtering approach that estimates observation and model error input parameters. A percentile-based scaling method that addresses soil moisture biases in model and observational estimates opened the path to the successful application of land data assimilation to satellite retrievals of surface soil moisture. Assimilation of AMSR-E surface soil moisture retrievals into the NASA Catchment model provided superior surface and root zone assimilation products (when validated against in situ measurements and compared to the model estimates or satellite observations alone). The multi-model capabilities of US were used to investigate the role of subsurface physics in the assimilation of surface soil moisture observations. Results indicate that the potential of surface soil moisture assimilation to improve root zone information is higher when the surface to root zone coupling is stronger. Building on this experience, GSFC leads the development of the Level 4 Surface and Root-Zone Soil Moisture (L4_SM) product for the planned NASA Soil-Moisture-Active-Passive (SMAP) mission. A key milestone was the design and execution of an Observing System Simulation Experiment that quantified the contribution of soil moisture retrievals to land data assimilation products as a function of retrieval and land model skill and yielded an estimate of the error budget for the SMAP L4_SM product. Terrestrial water storage observations from GRACE satellite system were also successfully assimilated into the NASA Catchment model and provided improved estimates of groundwater variability when compared to the model estimates alone. Moreover, satellite-based land surface temperature (LST) observations from the ISCCP archive were assimilated using a bias estimation module that was specifically designed for LST assimilation. As with soil moisture, LST assimilation provides modest yet statistically significant improvements when compared to the model or satellite observations alone. To achieve the improvement, however, the LST assimilation algorithm must be adapted to the specific formulation of LST in the land model. An improved method for the assimilation of snow cover observations was also developed. Finally, the coupling of LIS to the mesoscale Weather Research and Forecasting (WRF) model enabled investigations into how the sensitivity of land-atmosphere interactions to the specific choice of planetary boundary layer scheme and land surface model varies across surface moisture regimes, and how it can be quantified and evaluated against observations. The on-going development and integration of land assimilation modules into the Land Information System will enable the use of GSFC software with a variety of land models and make it accessible to the research community.

Changes in Landscape-level Carbon Balance of an Arctic Coastal Plain Tundra Ecosystem Between 1970-2100, in Response to Projected Climate Change

NASA Astrophysics Data System (ADS)

Lara, M. J.; McGuire, A. D.; Euskirchen, E. S.; Genet, H.; Sloan, V. L.; Iversen, C. M.; Norby, R. J.; Zhang, Y.; Yuan, F.

2014-12-01

Northern permafrost regions are estimated to cover 16% of the global soil area and account for approximately 50% of the global belowground organic carbon pool. However, there are considerable uncertainties regarding the fate of this soil carbon pool with projected climate warming over the next century. In northern Alaska, nearly 65% of the terrestrial surface is composed of polygonal tundra, where geomorphic land cover types such as high-, flat-, and low-center polygons influence local surface hydrology, plant community composition, nutrient and biogeochemical cycling, over small spatial scales. Due to the lack of representation of these fine-scale geomorphic types and ecosystem processes, in large-scale terrestrial ecosystem models, future uncertainties are large for this tundra region. In this study, we use a new version of the terrestrial ecosystem model (TEM), that couples a dynamic vegetation model (in which plant functional types compete for water, nitrogen, and light) with a dynamic soil organic model (in which temperature, moisture, and associated organic/inorganic carbon and nitrogen pools/fluxes vary together in vertically resolved layers) to simulate ecosystem carbon balance. We parameterized and calibrated this model using data specific to the local climate, vegetation, and soil associated with tundra geomorphic types. We extrapolate model results at a 1km2 resolution across the ~1800 km2 Barrow Peninsula using a tundra geomorphology map, describing ten dominant geomorphic tundra types (Lara et al. submitted), to estimate the likely change in landscape-level carbon balance between 1970 and 2100 in response to projected climate change. Preliminary model runs for this region indicated temporal variability in carbon and active layer dynamics, specific to tundra geomorphic type over time. Overall, results suggest that it is important to consider small-scale discrete polygonal tundra geomorphic types that control local structure and function in regional estimates of carbon balance in northern Alaska.

Non-destructive estimates of soil carbonic anhydrase activity and associated soil water oxygen isotope composition

NASA Astrophysics Data System (ADS)

Jones, Sam P.; Ogée, Jérôme; Sauze, Joana; Wohl, Steven; Saavedra, Noelia; Fernández-Prado, Noelia; Maire, Juliette; Launois, Thomas; Bosc, Alexandre; Wingate, Lisa

2017-12-01

The contribution of photosynthesis and soil respiration to net land-atmosphere carbon dioxide (CO2) exchange can be estimated based on the differential influence of leaves and soils on budgets of the oxygen isotope composition (δ18O) of atmospheric CO2. To do so, the activity of carbonic anhydrases (CAs), a group of enzymes that catalyse the hydration of CO2 in soils and plants, needs to be understood. Measurements of soil CA activity typically involve the inversion of models describing the δ18O of CO2 fluxes to solve for the apparent, potentially catalysed, rate of CO2 hydration. This requires information about the δ18O of CO2 in isotopic equilibrium with soil water, typically obtained from destructive, depth-resolved sampling and extraction of soil water. In doing so, an assumption is made about the soil water pool that CO2 interacts with, which may bias estimates of CA activity if incorrect. Furthermore, this can represent a significant challenge in data collection given the potential for spatial and temporal variability in the δ18O of soil water and limited a priori information with respect to the appropriate sampling resolution and depth. We investigated whether we could circumvent this requirement by inferring the rate of CO2 hydration and the δ18O of soil water from the relationship between the δ18O of CO2 fluxes and the δ18O of CO2 at the soil surface measured at different ambient CO2 conditions. This approach was tested through laboratory incubations of air-dried soils that were re-wetted with three waters of different δ18O. Gas exchange measurements were made on these soils to estimate the rate of hydration and the δ18O of soil water, followed by soil water extraction to allow for comparison. Estimated rates of CO2 hydration were 6.8-14.6 times greater than the theoretical uncatalysed rate of hydration, indicating that CA were active in these soils. Importantly, these estimates were not significantly different among water treatments, suggesting that this represents a robust approach to assay the activity of CA in soil. As expected, estimates of the δ18O of the soil water that equilibrates with CO2 varied in response to alteration to the δ18O of soil water. However, these estimates were consistently more negative than the composition of the soil water extracted by cryogenic vacuum distillation at the end of the gas measurements with differences of up to -3.94 ‰ VSMOW-SLAP. These offsets suggest that, at least at lower water contents, CO2-H2O isotope equilibration primarily occurs with water pools that are bound to particle surfaces and are depleted in 18O compared to bulk soil water.

Coupled Soil-Plant Water Dynamics During Drought-Rewetting Transitions

NASA Astrophysics Data System (ADS)

Volkmann, T. H.; Haberer, K.; Gessler, A.; Weiler, M.

2013-12-01

The predicted climate and land-use changes could have dramatic effects on the water balance of the soil-vegetation system, particularly under frequent drought and subsequent rewetting conditions. Yet, estimation of these effects and associated consequences for the structure and functioning of ecosystems, groundwater recharge, drinking water availability, and the water cycle is currently impeded by gaps in our understanding of the spatiotemporal dynamics of soil water in the rooted soil horizons, the dynamics and driving physiological processes of plant water acquisition, and the transpiration from plant leaves under changing environmental conditions. Combining approaches from the disciplines of plant ecophysiology and soil and isotope hydrology, this work aims to fill this gap by quantitatively characterizing the interaction between plant water use - as affected by rooting patterns and ecophysiology of different plant functional groups - and the water balance of variably complex ecosystems with emphasis on drought and rewetting phases. Results from artificial drought and subsequent rewetting in field experiments using isotopically and dye (Brilliant Blue FCF) labeled water conducted on plots of various surface cover (bare soil, grass, beech, oak, vine) established on luvisol on loess in southwestern Germany are presented. Detailed spatiotemporal insights into the coupled short-term (hours to days) dynamics of soil and plant water during the experiments is facilitated by the application of newly developed techniques for high-frequency in-situ monitoring of stable isotope signatures in both pore water and transpired water using commercial laser-based spectrometers in conjunction with plant ecophysiological, soil physical state, and dye staining observations. On the one hand, the spatiotemporal patterns of plant water uptake are assessed and related to morphological and physiological traits driving plant water uptake, functional adaptations of plants to changes of soil water availability, and intra- and interspecies competition for water resources access. On the other hand, the effects of vegetation cover on infiltration, preferential flow paths characteristics, and soil water storage in the rooted soil horizons are investigated. The results of the experiments and the developed methodology will contribute to an improved understanding of ecosystem response and adaptation to drought and short-term changes in environmental conditions.

Estimating Children’s Soil/Dust Ingestion Rates through Retrospective Analyses of Blood Lead Biomonitoring from the Bunker Hill Superfund Site in Idaho

PubMed Central

von Lindern, Ian; Spalinger, Susan; Stifelman, Marc L.; Stanek, Lindsay Wichers; Bartrem, Casey

2016-01-01

Background: Soil/dust ingestion rates are important variables in assessing children’s health risks in contaminated environments. Current estimates are based largely on soil tracer methodology, which is limited by analytical uncertainty, small sample size, and short study duration. Objectives: The objective was to estimate site-specific soil/dust ingestion rates through reevaluation of the lead absorption dose–response relationship using new bioavailability data from the Bunker Hill Mining and Metallurgical Complex Superfund Site (BHSS) in Idaho, USA. Methods: The U.S. Environmental Protection Agency (EPA) in vitro bioavailability methodology was applied to archived BHSS soil and dust samples. Using age-specific biokinetic slope factors, we related bioavailable lead from these sources to children’s blood lead levels (BLLs) monitored during cleanup from 1988 through 2002. Quantitative regression analyses and exposure assessment guidance were used to develop candidate soil/dust source partition scenarios estimating lead intake, allowing estimation of age-specific soil/dust ingestion rates. These ingestion rate and bioavailability estimates were simultaneously applied to the U.S. EPA Integrated Exposure Uptake Biokinetic Model for Lead in Children to determine those combinations best approximating observed BLLs. Results: Absolute soil and house dust bioavailability averaged 33% (SD ± 4%) and 28% (SD ± 6%), respectively. Estimated BHSS age-specific soil/dust ingestion rates are 86–94 mg/day for 6-month- to 2-year-old children and 51–67 mg/day for 2- to 9-year-old children. Conclusions: Soil/dust ingestion rate estimates for 1- to 9-year-old children at the BHSS are lower than those commonly used in human health risk assessment. A substantial component of children’s exposure comes from sources beyond the immediate home environment. Citation: von Lindern I, Spalinger S, Stifelman ML, Stanek LW, Bartrem C. 2016. Estimating children’s soil/dust ingestion rates through retrospective analyses of blood lead biomonitoring from the Bunker Hill Superfund Site in Idaho. Environ Health Perspect 124:1462–1470; http://dx.doi.org/10.1289/ehp.1510144 PMID:26745545

Methodology for estimating soil carbon for the forest carbon budget model of the United States, 2001

Treesearch

L. S. Heath; R. A. Birdsey; D. W. Williams

2002-01-01

The largest carbon (C) pool in United States forests is the soil C pool. We present methodology and soil C pool estimates used in the FORCARB model, which estimates and projects forest carbon budgets for the United States. The methodology balances knowledge, uncertainties, and ease of use. The estimates are calculated using the USDA Natural Resources Conservation...

Links between plant and fungal communities across a deforestation chronosequence in the Amazon rainforest.

PubMed

Mueller, Rebecca C; Paula, Fabiana S; Mirza, Babur S; Rodrigues, Jorge L M; Nüsslein, Klaus; Bohannan, Brendan J M

2014-07-01

Understanding the interactions among microbial communities, plant communities and soil properties following deforestation could provide insights into the long-term effects of land-use change on ecosystem functions, and may help identify approaches that promote the recovery of degraded sites. We combined high-throughput sequencing of fungal rDNA and molecular barcoding of plant roots to estimate fungal and plant community composition in soil sampled across a chronosequence of deforestation. We found significant effects of land-use change on fungal community composition, which was more closely correlated to plant community composition than to changes in soil properties or geographic distance, providing evidence for strong links between above- and below-ground communities in tropical forests.

Soil moisture assimilation using a modified ensemble transform Kalman filter with water balance constraint

NASA Astrophysics Data System (ADS)

Wu, Guocan; Zheng, Xiaogu; Dan, Bo

2016-04-01

The shallow soil moisture observations are assimilated into Common Land Model (CoLM) to estimate the soil moisture in different layers. The forecast error is inflated to improve the analysis state accuracy and the water balance constraint is adopted to reduce the water budget residual in the assimilation procedure. The experiment results illustrate that the adaptive forecast error inflation can reduce the analysis error, while the proper inflation layer can be selected based on the -2log-likelihood function of the innovation statistic. The water balance constraint can result in reducing water budget residual substantially, at a low cost of assimilation accuracy loss. The assimilation scheme can be potentially applied to assimilate the remote sensing data.

The biogeochemical heterogeneity of tropical forests.

PubMed

Townsend, Alan R; Asner, Gregory P; Cleveland, Cory C

2008-08-01

Tropical forests are renowned for their biological diversity, but also harbor variable combinations of soil age, chemistry and susceptibility to erosion or tectonic uplift. Here we contend that the combined effects of this biotic and abiotic diversity promote exceptional biogeochemical heterogeneity at multiple scales. At local levels, high plant diversity creates variation in chemical and structural traits that affect plant production, decomposition and nutrient cycling. At regional levels, myriad combinations of soil age, soil chemistry and landscape dynamics create variation and uncertainty in limiting nutrients that do not exist at higher latitudes. The effects of such heterogeneity are not well captured in large-scale estimates of tropical ecosystem function, but we suggest new developments in remote sensing can help bridge the gap.

Soil Quality Indicator: a new concept

NASA Astrophysics Data System (ADS)

Barão, Lúcia; Basch, Gottlieb

2017-04-01

During the last century, cultivated soils have been intensively exploited for food and feed production. This exploitation has compromised the soils' natural functions and many of the soil-mediated ecosystems services, including its production potential for agriculture. Also, soils became increasingly vulnerable and less resilient to a wide range of threats. To overcome this situation, new and better management practices are needed to prevent soil from degradation. However, to adopt the best management practices in a specific location, it is necessary to evaluate the soil quality status first. Different soil quality indicators have been suggested over the last decades in order to evaluate the soil status, and those are often based on the performance of soil chemical, physical and biological properties. However, the direct link between these properties and the associated soil functions or soil vulnerability to threats appears more difficult to be established. This present work is part of the iSQAPER project- Interactive Soil Quality Assessment in Europe and China for Agricultural Productivity and Environmental Resilience, where new soil quality concepts are explored to provide better information regarding the effects of the most promising agricultural management practices on soil quality. We have developed a new conceptual soil quality indicator which determines the soil quality status, regarding its vulnerability towards different threats. First, different indicators were specifically developed for each of the eight threats considered - Erosion, SOM decline, Poor Structure, Poor water holding capacity, Compaction, N-Leaching, Soil-borne pests and diseases and Salinization. As an example for the case of Erosion, the RUSLE equation for the estimate of the soil annual loss was used. Secondly, a reference classification was established for each indicator to integrate all possible results into a Good, Intermediate or Bad classification. Finally, all indicators were combined to return a single evaluation of the soil status, using different techniques that are dependent on the final use of the soil quality indicator. Some of the advantages of this new concept include the evaluation of soil quality based on its vulnerability to threats, together with the evaluation of soil properties in a given context while also suggesting soil management practices that are directly capable to mitigate soil vulnerability towards specific threats. Keywords: Soil Quality, Agriculture, Sustainability, Soil threats

Investigating spatial variability in gas-flux dynamics within Big Cypress National Preserve, Florida using hydrogeophysical methods

NASA Astrophysics Data System (ADS)

Sirianni, M.; Comas, X.; Shoemaker, B.; Job, M. J.; Cooper, H.

2016-12-01

Globally, wetland soils play an important role in regulating climate change by functioning as a source or sink for atmospheric carbon, particularly in terms of methane and carbon dioxide. While many historic studies defined the function of wetland soils in the global carbon budget, the gas-flux dynamics of subtropical wetlands is largely unknown. Big Cypress National Preserve is a collection of subtropical wetlands in southwestern Florida, including extensive forested (cypress, pine, hardwood) and sawgrass ecosystems that dry and flood annually in response to rainfall. The U.S. Geological Survey employs eddy covariance methods at several locations within the Preserve to quantify carbon and methane exchanges at ecosystem scales. While eddy covariance towers are a convenient tool for measuring gas fluxes, their footprint is spatially extensive (hundreds of meters); and thus spatial variability at smaller scales is masked by averaging or even overlooked. We intend to estimate small-scale contributions of organic and calcitic soils to gas exchanges measured by the eddy covariance towers using a combination of geophysical, hydrologic and ecologic techniques. Preliminary results suggest that gas releases from flooded calcitic soils are much greater than organic soils. These results - and others - will help build a better understanding of the role of subtropical wetlands in the global carbon budget.

Aggregating available soil water holding capacity data for crop yield models

NASA Technical Reports Server (NTRS)

Seubert, C. E.; Daughtry, C. S. T.; Holt, D. A.; Baumgardner, M. F.

1984-01-01

The total amount of water available to plants that is held against gravity in a soil is usually estimated as the amount present at -0.03 MPa average water potential minus the amount present at -1.5 MPa water potential. This value, designated available water-holding capacity (AWHC), is a very important soil characteristic that is strongly and positively correlated to the inherent productivity of soils. In various applications, including assessing soil moisture status over large areas, it is necessary to group soil types or series as to their productivity. Current methods to classify AWHC of soils consider only total capacity of soil profiles and thus may group together soils which differ greatly in AWHC as a function of depth in the profile. A general approach for evaluating quantitatively the multidimensional nature of AWHC in soils is described. Data for 902 soil profiles, representing 184 soil series, in Indiana were obtained from the Soil Characterization Laboratory at Purdue University. The AWHC for each of ten 150-mm layers in each soil was established, based on soil texture and parent material. A multivariate clustering procedure was used to classify each soil profile into one of 4, 8, or 12 classes based upon ten-dimensional AWHC values. The optimum number of classes depends on the range of AWHC in the population of oil profiles analyzed and on the sensitivity of a crop to differences in distribution of water within the soil profile.

[Estimation of organic matter content of north fluvo-aquic soil based on the coupling model of wavelet transform and partial least squares].

PubMed

Wang, Yan-Cang; Yang, Gui-Jun; Zhu, Jin-Shan; Gu, Xiao-He; Xu, Peng; Liao, Qin-Hong

2014-07-01

For improving the estimation accuracy of soil organic matter content of the north fluvo-aquic soil, wavelet transform technology is introduced. The soil samples were collected from Tongzhou district and Shunyi district in Beijing city. And the data source is from soil hyperspectral data obtained under laboratory condition. First, discrete wavelet transform efficiently decomposes hyperspectral into approximate coefficients and detail coefficients. Then, the correlation between approximate coefficients, detail coefficients and organic matter content was analyzed, and the sensitive bands of the organic matter were screened. Finally, models were established to estimate the soil organic content by using the partial least squares regression (PLSR). Results show that the NIR bands made more contributions than the visible band in estimating organic matter content models; the ability of approximate coefficients to estimate organic matter content is better than that of detail coefficients; The estimation precision of the detail coefficients fir soil organic matter content decreases with the spectral resolution being lower; Compared with the commonly used three types of soil spectral reflectance transforms, the wavelet transform can improve the estimation ability of soil spectral fir organic content; The accuracy of the best model established by the approximate coefficients or detail coefficients is higher, and the coefficient of determination (R2) and the root mean square error (RMSE) of the best model for approximate coefficients are 0.722 and 0.221, respectively. The R2 and RMSE of the best model for detail coefficients are 0.670 and 0.255, respectively.

Neural Network-Based Retrieval of Surface and Root Zone Soil Moisture using Multi-Frequency Remotely-Sensed Observations

NASA Astrophysics Data System (ADS)

Hamed Alemohammad, Seyed; Kolassa, Jana; Prigent, Catherine; Aires, Filipe; Gentine, Pierre

2017-04-01

Knowledge of root zone soil moisture is essential in studying plant's response to different stress conditions since plant photosynthetic activity and transpiration rate are constrained by the water available through their roots. Current global root zone soil moisture estimates are based on either outputs from physical models constrained by observations, or assimilation of remotely-sensed microwave-based surface soil moisture estimates with physical model outputs. However, quality of these estimates are limited by the accuracy of the model representations of physical processes (such as radiative transfer, infiltration, percolation, and evapotranspiration) as well as errors in the estimates of the surface parameters. Additionally, statistical approaches provide an alternative efficient platform to develop root zone soil moisture retrieval algorithms from remotely-sensed observations. In this study, we present a new neural network based retrieval algorithm to estimate surface and root zone soil moisture from passive microwave observations of SMAP satellite (L-band) and AMSR2 instrument (X-band). SMAP early morning observations are ideal for surface soil moisture retrieval. AMSR2 mid-night observations are used here as an indicator of plant hydraulic properties that are related to root zone soil moisture. The combined observations from SMAP and AMSR2 together with other ancillary observations including the Solar-Induced Fluorescence (SIF) estimates from GOME-2 instrument provide necessary information to estimate surface and root zone soil moisture. The algorithm is applied to observations from the first 18 months of SMAP mission and retrievals are validated against in-situ observations and other global datasets.

Roles of functional groups of naproxen in its sorption to kaolinite.

PubMed

Yu, Chenglong; Bi, Erping

2015-11-01

The sorption of acidic anti-inflammatory drugs to soils is important for evaluating their fate and transformations in the water-soil environment. However, roles of functional groups of ionisable drugs onto mineral surfaces have not been sufficiently studied. In this study, batch experiments of naproxen (NPX, anti-inflammatory drug) and two kinds of competitors to kaolinite were studied. The Kd of naproxen to kaolinite is 1.30-1.62 L kg(-1). The n-π electron donor-acceptor (n-π EDA) interaction between diaromatic ring of naproxen (π-electron acceptors) and the siloxane oxygens (n-donors) of kaolinite is the dominant sorption mechanism. The carboxyl group of naproxen can contribute to the overall sorption. A conception model was put forward to elucidate to sorption mechanisms, in which the contribution of n-π EDA and hydrogen bond to overall sorption was quantified. These sorption mechanisms can be helpful for estimating the fate and mobility of acid pharmaceuticals in soil-water environment. Copyright © 2015 Elsevier Ltd. All rights reserved.

A model of the 0.4-GHz scatterometer. [used for agriculture soil moisture program

NASA Technical Reports Server (NTRS)

Wu, S. T.

1978-01-01

The 0.4 GHz aircraft scatterometer system used for the agricultural soil moisture estimation program is analyzed for the antenna pattern, the signal flow in the receiver data channels, and the errors in the signal outputs. The operational principal, system sensitivity, data handling, and resolution cell length requirements are also described. The backscattering characteristics of the agriculture scenes are contained in the form of the functional dependence of the backscattering coefficient on the incidence angle. The substantial gains of the cross-polarization term of the horizontal and vertical antennas have profound effects on the cross-polarized backscattered signals. If these signals are not corrected properly, large errors could result in the estimate of the cross-polarized backscattering coefficient. It is also necessary to correct the variations of the aircraft parameters during data processing to minimize the error in the 0 degree estimation. Recommendations are made to improve the overall performance of the scatterometer system.

Establishment and application of the estimation model for pollutant concentrfation in agriculture drain

NASA Astrophysics Data System (ADS)

Li, Qiangkun; Hu, Yawei; Jia, Qian; Song, Changji

2018-02-01

It is the key point of quantitative research on agricultural non-point source pollution load, the estimation of pollutant concentration in agricultural drain. In the guidance of uncertainty theory, the synthesis of fertilization and irrigation is used as an impulse input to the farmland, meanwhile, the pollutant concentration in agricultural drain is looked as the response process corresponding to the impulse input. The migration and transformation of pollutant in soil is expressed by Inverse Gaussian Probability Density Function. The law of pollutants migration and transformation in soil at crop different growth periods is reflected by adjusting parameters of Inverse Gaussian Distribution. Based on above, the estimation model for pollutant concentration in agricultural drain at field scale was constructed. Taking the of Qing Tong Xia Irrigation District in Ningxia as an example, the concentration of nitrate nitrogen and total phosphorus in agricultural drain was simulated by this model. The results show that the simulated results accorded with measured data approximately and Nash-Sutcliffe coefficients were 0.972 and 0.964, respectively.

BOREAS TGB-12 Soil Carbon and Flux Data of NSA-MSA in Raster Format

NASA Technical Reports Server (NTRS)

Hall, Forrest G. (Editor); Knapp, David E. (Editor); Rapalee, Gloria; Davidson, Eric; Harden, Jennifer W.; Trumbore, Susan E.; Veldhuis, Hugo

2000-01-01

The BOREAS TGB-12 team made measurements of soil carbon inventories, carbon concentration in soil gases, and rates of soil respiration at several sites. This data set provides: (1) estimates of soil carbon stocks by horizon based on soil survey data and analyses of data from individual soil profiles; (2) estimates of soil carbon fluxes based on stocks, fire history, drain-age, and soil carbon inputs and decomposition constants based on field work using radiocarbon analyses; (3) fire history data estimating age ranges of time since last fire; and (4) a raster image and an associated soils table file from which area-weighted maps of soil carbon and fluxes and fire history may be generated. This data set was created from raster files, soil polygon data files, and detailed lab analysis of soils data that were received from Dr. Hugo Veldhuis, who did the original mapping in the field during 1994. Also used were soils data from Susan Trumbore and Jennifer Harden (BOREAS TGB-12). The binary raster file covers a 733-km 2 area within the NSA-MSA.

A new estimation of global soil greenhouse gas fluxes using a simple data-oriented model.

PubMed

Hashimoto, Shoji

2012-01-01

Soil greenhouse gas fluxes (particularly CO(2), CH(4), and N(2)O) play important roles in climate change. However, despite the importance of these soil greenhouse gases, the number of reports on global soil greenhouse gas fluxes is limited. Here, new estimates are presented for global soil CO(2) emission (total soil respiration), CH(4) uptake, and N(2)O emission fluxes, using a simple data-oriented model. The estimated global fluxes for CO(2) emission, CH(4) uptake, and N(2)O emission were 78 Pg C yr(-1) (Monte Carlo 95% confidence interval, 64-95 Pg C yr(-1)), 18 Tg C yr(-1) (11-23 Tg C yr(-1)), and 4.4 Tg N yr(-1) (1.4-11.1 Tg N yr(-1)), respectively. Tropical regions were the largest contributor of all of the gases, particularly the CO(2) and N(2)O fluxes. The soil CO(2) and N(2)O fluxes had more pronounced seasonal patterns than the soil CH(4) flux. The collected estimates, including both the previous and the present estimates, demonstrate that the means of the best estimates from each study were 79 Pg C yr(-1) (291 Pg CO(2) yr(-1); coefficient of variation, CV = 13%, N = 6) for CO(2), 21 Tg C yr(-1) (29 Tg CH(4) yr(-1); CV = 24%, N = 24) for CH(4), and 7.8 Tg N yr(-1) (12.2 Tg N(2)O yr(-1); CV = 38%, N = 11) for N(2)O. For N(2)O, the mean of the estimates that was calculated by excluding the earliest two estimates was 6.6 Tg N yr(-1) (10.4 Tg N(2)O yr(-1); CV = 22%, N = 9). The reported estimates vary and have large degrees of uncertainty but their overall magnitudes are in general agreement. To further minimize the uncertainty of soil greenhouse gas flux estimates, it is necessary to build global databases and identify key processes in describing global soil greenhouse gas fluxes.

A New Estimation of Global Soil Greenhouse Gas Fluxes Using a Simple Data-Oriented Model

PubMed Central

Hashimoto, Shoji

2012-01-01

Soil greenhouse gas fluxes (particularly CO2, CH4, and N2O) play important roles in climate change. However, despite the importance of these soil greenhouse gases, the number of reports on global soil greenhouse gas fluxes is limited. Here, new estimates are presented for global soil CO2 emission (total soil respiration), CH4 uptake, and N2O emission fluxes, using a simple data-oriented model. The estimated global fluxes for CO2 emission, CH4 uptake, and N2O emission were 78 Pg C yr−1 (Monte Carlo 95% confidence interval, 64–95 Pg C yr−1), 18 Tg C yr−1 (11–23 Tg C yr−1), and 4.4 Tg N yr−1 (1.4–11.1 Tg N yr−1), respectively. Tropical regions were the largest contributor of all of the gases, particularly the CO2 and N2O fluxes. The soil CO2 and N2O fluxes had more pronounced seasonal patterns than the soil CH4 flux. The collected estimates, including both the previous and the present estimates, demonstrate that the means of the best estimates from each study were 79 Pg C yr−1 (291 Pg CO2 yr−1; coefficient of variation, CV = 13%, N = 6) for CO2, 21 Tg C yr−1 (29 Tg CH4 yr−1; CV = 24%, N = 24) for CH4, and 7.8 Tg N yr−1 (12.2 Tg N2O yr−1; CV = 38%, N = 11) for N2O. For N2O, the mean of the estimates that was calculated by excluding the earliest two estimates was 6.6 Tg N yr−1 (10.4 Tg N2O yr−1; CV = 22%, N = 9). The reported estimates vary and have large degrees of uncertainty but their overall magnitudes are in general agreement. To further minimize the uncertainty of soil greenhouse gas flux estimates, it is necessary to build global databases and identify key processes in describing global soil greenhouse gas fluxes. PMID:22876295

Accessing the Soil Metagenome for Studies of Microbial Diversity▿ †

PubMed Central

Delmont, Tom O.; Robe, Patrick; Cecillon, Sébastien; Clark, Ian M.; Constancias, Florentin; Simonet, Pascal; Hirsch, Penny R.; Vogel, Timothy M.

2011-01-01

Soil microbial communities contain the highest level of prokaryotic diversity of any environment, and metagenomic approaches involving the extraction of DNA from soil can improve our access to these communities. Most analyses of soil biodiversity and function assume that the DNA extracted represents the microbial community in the soil, but subsequent interpretations are limited by the DNA recovered from the soil. Unfortunately, extraction methods do not provide a uniform and unbiased subsample of metagenomic DNA, and as a consequence, accurate species distributions cannot be determined. Moreover, any bias will propagate errors in estimations of overall microbial diversity and may exclude some microbial classes from study and exploitation. To improve metagenomic approaches, investigate DNA extraction biases, and provide tools for assessing the relative abundances of different groups, we explored the biodiversity of the accessible community DNA by fractioning the metagenomic DNA as a function of (i) vertical soil sampling, (ii) density gradients (cell separation), (iii) cell lysis stringency, and (iv) DNA fragment size distribution. Each fraction had a unique genetic diversity, with different predominant and rare species (based on ribosomal intergenic spacer analysis [RISA] fingerprinting and phylochips). All fractions contributed to the number of bacterial groups uncovered in the metagenome, thus increasing the DNA pool for further applications. Indeed, we were able to access a more genetically diverse proportion of the metagenome (a gain of more than 80% compared to the best single extraction method), limit the predominance of a few genomes, and increase the species richness per sequencing effort. This work stresses the difference between extracted DNA pools and the currently inaccessible complete soil metagenome. PMID:21183646

A statistical method for estimating rates of soil development and ages of geologic deposits: A design for soil-chronosequence studies

USGS Publications Warehouse

Switzer, P.; Harden, J.W.; Mark, R.K.

1988-01-01

A statistical method for estimating rates of soil development in a given region based on calibration from a series of dated soils is used to estimate ages of soils in the same region that are not dated directly. The method is designed specifically to account for sampling procedures and uncertainties that are inherent in soil studies. Soil variation and measurement error, uncertainties in calibration dates and their relation to the age of the soil, and the limited number of dated soils are all considered. Maximum likelihood (ML) is employed to estimate a parametric linear calibration curve, relating soil development to time or age on suitably transformed scales. Soil variation on a geomorphic surface of a certain age is characterized by replicate sampling of soils on each surface; such variation is assumed to have a Gaussian distribution. The age of a geomorphic surface is described by older and younger bounds. This technique allows age uncertainty to be characterized by either a Gaussian distribution or by a triangular distribution using minimum, best-estimate, and maximum ages. The calibration curve is taken to be linear after suitable (in certain cases logarithmic) transformations, if required, of the soil parameter and age variables. Soil variability, measurement error, and departures from linearity are described in a combined fashion using Gaussian distributions with variances particular to each sampled geomorphic surface and the number of sample replicates. Uncertainty in age of a geomorphic surface used for calibration is described using three parameters by one of two methods. In the first method, upper and lower ages are specified together with a coverage probability; this specification is converted to a Gaussian distribution with the appropriate mean and variance. In the second method, "absolute" older and younger ages are specified together with a most probable age; this specification is converted to an asymmetric triangular distribution with mode at the most probable age. The statistical variability of the ML-estimated calibration curve is assessed by a Monte Carlo method in which simulated data sets repeatedly are drawn from the distributional specification; calibration parameters are reestimated for each such simulation in order to assess their statistical variability. Several examples are used for illustration. The age of undated soils in a related setting may be estimated from the soil data using the fitted calibration curve. A second simulation to assess age estimate variability is described and applied to the examples. ?? 1988 International Association for Mathematical Geology.

Analyzing the soil sorption and transfer environmental functions in the South-East part of Western Siberia using Pt and Ni nanoparticles

NASA Astrophysics Data System (ADS)

Kulizhskiy, Sergey; Loyko, Sergey; Morgalev, Yuriy; Istigechev, Georgiy; Novokreshchennykh, Tatiana; Rodikova, Anna

2015-04-01

The soil with flushing water regime has a very important environmental function, the regulative one in the migration of the dispersed substances caused by natural and anthropogenic activity. The study of these processes is necessary to solve questions of the origins and functioning of soils and also to estimate the parameters of finely dispersed xenobiotics (man-made nanoparticles) accumulation and transfer in the landscapes. The model substance to explore the ways and potential function of migration in texture-differentiated soils of the southern forest zone of Western Siberia are the suspensions of nanosized platinum (diameter from 5 to 15 nm). The research is based on the properties and behavior of nanoparticles in porous media and their ability to keep highly dispersed state for a long time in the aqueous suspensions due to the small size (up to 100 nm) and low surface charge. Particle identification tags will be conducted using mass spectrometry with inductively coupled plasma. That is possible due to the small percentage abundance of platinum. Two groups of experiments were conducted with support of RFBR grant №14-04-00967. First one has been done for evaluation the platinum nanoparticles transmission and interception in soil horizons inside undisturbed monoliths. Second group has dealt with the mechanical barriers investigation for nanoparticles behavior in the native Haplic Albeluvisols profiles by standard method application to determine the filtration properties. The significant variability of detention and transmission values of nanoparticles columns through soil horizons has been detected. There are no simple correlations between the evaluated with the nanoparticles pass-through function through the soil column and soil properties. The main factor that determines the conditions of nanoparticles transfer through the horizon is the geometry of the pore space, and the type of filtering suspensions: linear or front one. Thus, the presences of dead-end pores, a fibrous structure of their sides are strongly inhibiting the nanoparticles movement. It was also revealed that the carbonates presence in the soil horizon helps to latch the vast majority of migratory nanoparticles (up to 95 %). The field experience has shown that about 65% of platinum nanoparticles migrate beyond topsoil 50-cm layer, forming the largest accumulative maximum in the upper part of the argic horizon (50-60 cm), where about 35% of platinum has been accumulated. The other part of the argic horizon accumulates about 30% of the nanoparticles. The less inert and larger (50 nm) Nickel nanoparticles have been completely latched within 60-cm topsoil. The soil transfer environmental function depends as on the parameters of the moving matter, as on the soil properties. The presence of preferential pathways for migration with flushing waters increases the depth of finely dispersed substances transfer in the soil. At the same time the front migration and the presence of carbonates in soils limits the potential migration of finely dispersed material.

Precipitation pulse dynamics of carbon sequestration and efflux in highly weatherable soils

NASA Astrophysics Data System (ADS)

Barron-Gafford, G.; Minor, R.; Van Haren, J. L.; Dontsova, K.; Troch, P. A.

2013-12-01

Soils are the primary pool for terrestrial carbon on Earth, and loss of that carbon to the atmosphere or hydrosphere represents a significant efflux that can impact a host of other downstream processes. Soil respiration (Rsoil), the efflux of CO2 to the atmosphere, represents the major pathway by which carbon is lost from the soil system in more weathered soils. However, in newly formed soils, chemical weathering can significantly deplete soil CO2 concentrations. As vegetation colonizes these soils, multiple interacting and contradictory pathways evolve such that soil CO2 concentrations increase in response to plant inputs but are decreased through chemical reactions. Furthermore, abiotic drivers of soil temperature and moisture likely differentially affect these processes. Understanding the bio-geo-chemical drivers and feedbacks associated with soil CO2 production and efflux in the critical zone necessitates an integrated science approach, drawing on input from plant physiologists, bio- and geochemists, and hydrologists. Here, we created a series of 1-meter deep mesocosms filled with granular basalt that supported either a woody mesquite shrub, a bunchgrass, or was left as bare soil. Use of multiple plant functional types allowed us to explore the impacts of plant structure (primarily rooting profiles) on critical zone function in terms of water and carbon exchange surrounding precipitation pulse dynamics. Each mesocosm was outfitted with an array of soil moisture, temperature, water potential, and CO2 concentration sensors at the near-surface, 30, 55, and 80cm depths to quantify patterns of soil moisture and respiratory CO2 efflux in response to rainfall events of varying magnitude and intervening periods of drought. Five replicates of each were maintained under current ambient or projected (+4oC) air temperatures. In addition, we used minirhizotrons to quantify the response of roots to episodic rainfall and confirm differences among plant types and collected soils solution samples to quantify dissolved inorganic carbon (DIC), pH, and other solute concentrations. Importantly, we found Rsoil dynamics to be nearly in direct contrast to our classic understanding of patterns of soil CO2 efflux after rain events. Rsoil rates declined immediately upon wetting and gradually increased to pre-rain rates as the soils dried. Investigation into soil CO2 profile data showed that CO2 concentrations just below the surface declined significantly from near-ambient levels to near ~50ppm, which would directly impact rates of Rsoil. We detected differences among plant functional types in terms of rooting depth, water use, photosynthetic uptake, base rates of Rsoil, the time required to return to pre-rain rates of Rsoil, and the rates of soil weathering. Combining aboveground measurements of carbon uptake with these belowground estimates of carbon pools and efflux will allow us to make much more informed projections of carbon dynamics within highly weatherable soils across a range of global climate change projections and plant functional types.

Microbial structural diversity estimated by dilution-extinction of phenotypic traits and T-RFLP analysis along a land-use intensification gradient

NASA Technical Reports Server (NTRS)

Gomez, Elena del V.; Garland, Jay L.; Roberts, Michael S.

2004-01-01

The present work tested whether the relationship between functional traits and inoculum density reflected structural diversity in bacterial communities from a land-use intensification gradient applying a mathematical model. Terminal restriction fragment length polymorphism (T-RFLP) analysis was also performed to provide an independent assessment of species richness. Successive 10-fold dilutions of a soil suspension were inoculated onto Biolog GN(R) microplates. Soil bacterial density was determined by total cell and plate counts. The relationship between phenotypic traits and inoculum density fit the model, allowing the estimation of maximal phenotypic potential (Rmax) and inoculum density (KI) at which Rmax will be half-reduced. Though Rmax decreased with time elapsed since clearing of native vegetation, KI remained high in two of the disturbed sites. The genetic pool of bacterial community did not experience a significant reduction, but the active fraction responding in the Biolog assay was adversely affected, suggesting a reduction in the functional potential. c2004 Federation of European Microbiological Societies. Published by Elsevier B.V. All rights reserved.

A Method for a Multi-Platform Approach to Generate Gridded Surface Evaporation

NASA Astrophysics Data System (ADS)

Badger, A.; Livneh, B.; Small, E. E.; Abolafia-Rosenzweig, R.

2017-12-01

Evapotranspiration is an integral component of the surface water balance. While there are many estimates of evapotranspiration, there are fewer estimates that partition evapotranspiration into evaporation and transpiration components. This study aims to generate a CONUS-scale, observationally-based soil evaporation dataset by using the time difference of surface soil moisture by Soil Moisture Active Passive (SMAP) satellite with adjustments for transpiration and a bottom flux out of the surface layer. In concert with SMAP, the Moderate-Resolution Imaging Spectroradiometer (MODIS) satellite, North American Land Data Assimilation Systems (NLDAS) and the Hydrus-1D model are used to fully analyze the surface water balance. A biome specific estimate of the total terrestrial ET is calculated through a variation of the Penman-Monteith equation with NLDAS forcing and NLDAS Noah Model output for meteorological variables. A root density restriction and SMAP-based soil moisture restriction are applied to obtain terrestrial transpiration estimates. By forcing Hydrus-1D with NLDAS meteorology and our terrestrial transpiration estimates, an estimate of the flux between the soil surface and root zone layers (qbot) will dictate the proportion of water that is available for soil evaporation. After constraining transpiration and the bottom flux from the surface layer, we estimate soil evaporation as the residual of the surface water balance. Application of this method at Fluxnet sites shows soil evaporation estimates of approximately 0­3 mm/day and less than ET estimates. Expanding this methodology to produce a gridded product for CONUS, and eventually a global-scale product, will enable a better understanding of water balance processes and contribute a dataset to validate land-surface model's surface flux processes.

The assessment of spatial distribution of soil salinity risk using neural network.

PubMed

Akramkhanov, Akmal; Vlek, Paul L G

2012-04-01

Soil salinity in the Aral Sea Basin is one of the major limiting factors of sustainable crop production. Leaching of the salts before planting season is usually a prerequisite for crop establishment and predetermined water amounts are applied uniformly to fields often without discerning salinity levels. The use of predetermined water amounts for leaching perhaps partly emanate from the inability of conventional soil salinity surveys (based on collection of soil samples, laboratory analyses) to generate timely and high-resolution salinity maps. This paper has an objective to estimate the spatial distribution of soil salinity based on readily or cheaply obtainable environmental parameters (terrain indices, remote sensing data, distance to drains, and long-term groundwater observation data) using a neural network model. The farm-scale (∼15 km(2)) results were used to upscale soil salinity to a district area (∼300 km(2)). The use of environmental attributes and soil salinity relationships to upscale the spatial distribution of soil salinity from farm to district scale resulted in the estimation of essentially similar average soil salinity values (estimated 0.94 vs. 1.04 dS m(-1)). Visual comparison of the maps suggests that the estimated map had soil salinity that was uniform in distribution. The upscaling proved to be satisfactory; depending on critical salinity threshold values, around 70-90% of locations were correctly estimated.

Evaluation of a simple, point-scale hydrologic model in simulating soil moisture using the Delaware environmental observing system

NASA Astrophysics Data System (ADS)

Legates, David R.; Junghenn, Katherine T.

2018-04-01

Many local weather station networks that measure a number of meteorological variables (i.e. , mesonetworks) have recently been established, with soil moisture occasionally being part of the suite of measured variables. These mesonetworks provide data from which detailed estimates of various hydrological parameters, such as precipitation and reference evapotranspiration, can be made which, when coupled with simple surface characteristics available from soil surveys, can be used to obtain estimates of soil moisture. The question is Can meteorological data be used with a simple hydrologic model to estimate accurately daily soil moisture at a mesonetwork site? Using a state-of-the-art mesonetwork that also includes soil moisture measurements across the US State of Delaware, the efficacy of a simple, modified Thornthwaite/Mather-based daily water balance model based on these mesonetwork observations to estimate site-specific soil moisture is determined. Results suggest that the model works reasonably well for most well-drained sites and provides good qualitative estimates of measured soil moisture, often near the accuracy of the soil moisture instrumentation. The model exhibits particular trouble in that it cannot properly simulate the slow drainage that occurs in poorly drained soils after heavy rains and interception loss, resulting from grass not being short cropped as expected also adversely affects the simulation. However, the model could be tuned to accommodate some non-standard siting characteristics.

Linking Annual N2O Emission in Organic Soils to Mineral Nitrogen Input as Estimated by Heterotrophic Respiration and Soil C/N Ratio

PubMed Central

Mu, Zhijian; Huang, Aiying; Ni, Jiupai; Xie, Deti

2014-01-01

Organic soils are an important source of N2O, but global estimates of these fluxes remain uncertain because measurements are sparse. We tested the hypothesis that N2O fluxes can be predicted from estimates of mineral nitrogen input, calculated from readily-available measurements of CO2 flux and soil C/N ratio. From studies of organic soils throughout the world, we compiled a data set of annual CO2 and N2O fluxes which were measured concurrently. The input of soil mineral nitrogen in these studies was estimated from applied fertilizer nitrogen and organic nitrogen mineralization. The latter was calculated by dividing the rate of soil heterotrophic respiration by soil C/N ratio. This index of mineral nitrogen input explained up to 69% of the overall variability of N2O fluxes, whereas CO2 flux or soil C/N ratio alone explained only 49% and 36% of the variability, respectively. Including water table level in the model, along with mineral nitrogen input, further improved the model with the explanatory proportion of variability in N2O flux increasing to 75%. Unlike grassland or cropland soils, forest soils were evidently nitrogen-limited, so water table level had no significant effect on N2O flux. Our proposed approach, which uses the product of soil-derived CO2 flux and the inverse of soil C/N ratio as a proxy for nitrogen mineralization, shows promise for estimating regional or global N2O fluxes from organic soils, although some further enhancements may be warranted. PMID:24798347

Modern soil system constraints on reconstructing deep-time atmospheric CO2

NASA Astrophysics Data System (ADS)

Montañez, Isabel P.

2013-01-01

Paleosol carbonate-based estimates of paleo-atmospheric CO2 play a prominent role in constraining radiative-forcing and climate sensitivity in the deep-time. Large uncertainty in paleo-CO2 estimates made using the paleosol-carbonate CO2-barometer, however, arises primarily from their sensitivity to soil-respired CO2 (S(z)). This parameter is poorly constrained due to a paucity of soil CO2 measurements during carbonate formation in modern soils and a lack of widely applicable proxies of paleo-soil CO2. Here the δ13C values of carbonate and soil organic matter (SOM) pairs from 130 Holocene soils are applied to a two-component CO2-mixing equation to define soil order-specific ranges of soil CO2 applicable for constraining S(z) in their corresponding paleosol analogs. Equilibrium carbonate-SOM pairs, characterized by Δ13Ccarb-SOM values of 12.2-15.8‰, define a mean effective fractionation of 14.1‰ and overall inferred total soil CO2 contents during calcite formation of <1000-10,000 ppmv. For those Aridisols and Alfisols, characterized by a net soil-moisture deficit, and their paleosol analogs (Calcisols and Argillisols), a best estimate of S(z) during calcite formation is 1500-2000 ppmv (range of 500-2500 ppmv). Overall higher values (2000-5000 ppmv) are indicated by the subset of these soils characterized by higher moisture content and productivity. Near atmospheric levels (400 ± 200 ppmv) of estimated S(z) are indicated by immature soils, recording their low soil productivity. Vertisols define the largest range in total soil CO2 (<1000 to >25,000 ppmv) reflecting their seasonally driven dynamic hydrochemistry. A S(z) range of 1000-10,000 ppmv is suggested for paleo-Vertisols for which calcite precipitation can be constrained to have occurred in an open system with two-component CO2 mixing, with a best estimate of 2000 ppmv ± 1000 ppmv appropriate for paleo-Vertisols for which evidence of protracted water saturation is lacking. Mollisol pairs define a best estimate of S(z) of 2500 ppmv (range of 600-4000 ppmv) for late Cretaceous and Cenozoic analogs. Non-equilibrium pairs with Δ13C values >16‰ make up 51% of the dataset, lending support to the hypothesis that pedogenic carbonate precipitation occurs during periods of low productivity in a soil atmosphere with a large component of atmospheric CO2. Predictable scaling between estimated soil CO2 and the difference in δ13C between measured pedogenic carbonate and that predicted to have formed from soil-respired CO2 (inferred from measured SOM) can be used to further constrain appropriate ranges of S(z) for reconstruction of paleo-atmospheric pCO2. Soil CO2 estimates are poorly correlated to mean annual precipitation likely reflecting that for carbonate-bearing soils, where moisture limits CO2 production, total soil CO2 is most strongly influenced by actual evapotranspiration.

On the challenges of using field spectroscopy to measure the impact of soil type on leaf traits

NASA Astrophysics Data System (ADS)

Nunes, Matheus H.; Davey, Matthew P.; Coomes, David A.

2017-07-01

Understanding the causes of variation in functional plant traits is a central issue in ecology, particularly in the context of global change. Spectroscopy is increasingly used for rapid and non-destructive estimation of foliar traits, but few studies have evaluated its accuracy when assessing phenotypic variation in multiple traits. Working with 24 chemical and physical leaf traits of six European tree species growing on strongly contrasting soil types (i.e. deep alluvium versus nearby shallow chalk), we asked (i) whether variability in leaf traits is greater between tree species or soil type, and (ii) whether field spectroscopy is effective at predicting intraspecific variation in leaf traits as well as interspecific differences. Analysis of variance showed that interspecific differences in traits were generally much stronger than intraspecific differences related to soil type, accounting for 25 % versus 5 % of total trait variation, respectively. Structural traits, phenolic defences and pigments were barely affected by soil type. In contrast, foliar concentrations of rock-derived nutrients did vary: P and K concentrations were lower on chalk than alluvial soils, while Ca, Mg, B, Mn and Zn concentrations were all higher, consistent with the findings of previous ecological studies. Foliar traits were predicted from 400 to 2500 nm reflectance spectra collected by field spectroscopy using partial least square regression, a method that is commonly employed in chemometrics. Pigments were best modelled using reflectance data from the visible region (400-700 nm), while all other traits were best modelled using reflectance data from the shortwave infrared region (1100-2500 nm). Spectroscopy delivered accurate predictions of species-level variation in traits. However, it was ineffective at detecting intraspecific variation in rock-derived nutrients (with the notable exception of P). The explanation for this failure is that rock-derived elements do not have absorption features in the 400-2500 nm region, and their estimation is indirect, relying on elemental concentrations covarying with structural traits that do have absorption features in that spectral region (constellation effects). Since the structural traits did not vary with soil type, it was impossible for our regression models to predict intraspecific variation in rock-derived nutrients via constellation effects. This study demonstrates the value of spectroscopy for rapid, non-destructive estimation of foliar traits across species, but highlights problems with predicting intraspecific variation indirectly. We discuss the implications of these findings for mapping functional traits by airborne imaging spectroscopy.

Modeling stomatal conductance in the Earth system: linking leaf water-use efficiency and water transport along the soil-plant-atmosphere continuum

NASA Astrophysics Data System (ADS)

Bonan, G. B.; Williams, M.; Fisher, R. A.; Oleson, K. W.

2014-05-01

The empirical Ball-Berry stomatal conductance model is commonly used in Earth system models to simulate biotic regulation of evapotranspiration. However, the dependence of stomatal conductance (gs) on vapor pressure deficit (Ds) and soil moisture must both be empirically parameterized. We evaluated the Ball-Berry model used in the Community Land Model version 4.5 (CLM4.5) and an alternative stomatal conductance model that links leaf gas exchange, plant hydraulic constraints, and the soil-plant-atmosphere continuum (SPA) to numerically optimize photosynthetic carbon gain per unit water loss while preventing leaf water potential dropping below a critical minimum level. We evaluated two alternative optimization algorithms: intrinsic water-use efficiency (Δ An/Δ gs, the marginal carbon gain of stomatal opening) and water-use efficiency (Δ An/Δ El, the marginal carbon gain of water loss). We implemented the stomatal models in a multi-layer plant canopy model, to resolve profiles of gas exchange, leaf water potential, and plant hydraulics within the canopy, and evaluated the simulations using: (1) leaf analyses; (2) canopy net radiation, sensible heat flux, latent heat flux, and gross primary production at six AmeriFlux sites spanning 51 site-years; and (3) parameter sensitivity analyses. Without soil moisture stress, the performance of the SPA stomatal conductance model was generally comparable to or somewhat better than the Ball-Berry model in flux tower simulations, but was significantly better than the Ball-Berry model when there was soil moisture stress. Functional dependence of gs on soil moisture emerged from the physiological theory linking leaf water-use efficiency and water flow to and from the leaf along the soil-to-leaf pathway rather than being imposed a priori, as in the Ball-Berry model. Similar functional dependence of gs on Ds emerged from the water-use efficiency optimization. Sensitivity analyses showed that two parameters (stomatal efficiency and root hydraulic conductivity) minimized errors with the SPA stomatal conductance model. The critical stomatal efficiency for optimization (ι) was estimated from leaf trait datasets and is related to the slope parameter (g1) of the Ball-Berry model. The optimized parameter value was consistent with this estimate. Optimized root hydraulic conductivity was consistent with estimates from literature surveys. The two central concepts embodied in the stomatal model, that plants account for both water-use efficiency and for hydraulic safety in regulating stomatal conductance, imply a notion of optimal plant strategies and provide testable model hypotheses, rather than empirical descriptions of plant behavior.

[Analysis of soil humus and components after 26 years' fertilization by infrared spectroscopy method].

PubMed

Zhang, Yu-Lan; Sun, Cai-Xia; Chen, Zhen-Hua; Li, Dong-Po; Liu, Xing-Bin; Chen, Li-Jun; Wu, Zhi-Jie; Du, Jian-Xiong

2010-05-01

The infrared spectrum was used to discuss structure change of soil humus and components of chemical groups in soil humic acids (HA) and fulvic acids (FA) isolated from soils in different fertilization treatment after 26 year's fertilization. The result indicated that using the infrared spectroscopy method for the determination of humus, humus fractions (HA and FA) and their structure is feasible. Fertilization affected the structure and content of soil humus and aromatization degree. After 26 years' fertilization, the infrared spectrum shapes with different treatments are similar, but the characteristic peak intensity is obviously different, which reflects the effects of different fertilization treatments on the structure and amounts of soil humus or functional groups. Compared with no fertilization, little molecule saccharides decreased and aryl-groups increased under application of inorganic fertilizer or combined application of organic and chemical fertilizer. The effect was greater in Treatment NPK and M+NPK than in Treatment M1 N and M2 N. Organic and NPK fertilizer increased the development of soil and increased soil quality to a certain extent. Results showed that organic fertilization increased aromatization degree of soil humus and humus fractions distinctly. The authors could estimate soil humus evolvement of different fertilization with infrared spectroscopy.

Soil organic carbon stocks in Alaska estimated with spatial and pedon data

USGS Publications Warehouse

Bliss, Norman B.; Maursetter, J.

2010-01-01

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.

Estimating effective roughness parameters of the L-MEB model for soil moisture retrieval using passive microwave observations from SMAPVEX12

USDA-ARS?s Scientific Manuscript database

Although there have been efforts to improve existing soil moisture retrieval algorithms, the ability to estimate soil moisture from passive microwave observations is still hampered by problems in accurately modeling the observed microwave signal. This paper focuses on the estimation of effective sur...

Stoichiometry of microbial carbon use efficiency in soils

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sinsabaugh, Robert L.; Turner, Benjamin L.; Talbot, Jennifer M.

The carbon use efficiency (CUE) of microbial communities partitions the flow of C from primary producers to the atmosphere, decomposer food webs, and soil C stores. CUE, usually defined as the ratio of growth to assimilation, is a critical parameter in ecosystem models, but is seldom measured directly in soils because of the methodological difficulty of measuring in situ rates of microbial growth and respiration. Alternatively, CUE can be estimated indirectly from the elemental stoichiometry of organic matter and microbial biomass, and the ratios of C to nutrient-acquiring ecoenzymatic activities. In this paper, we used this approach to estimate andmore » compare microbial CUE in >2000 soils from a broad range of ecosystems. Mean CUE based on C:N stoichiometry was 0.269 ± 0.110 (mean ± SD). A parallel calculation based on C:P stoichiometry yielded a mean CUE estimate of 0.252 ± 0.125. The mean values and frequency distributions were similar to those from aquatic ecosystems, also calculated from stoichiometric models, and to those calculated from direct measurements of bacterial and fungal growth and respiration. CUE was directly related to microbial biomass C with a scaling exponent of 0.304 (95% CI 0.237–0.371) and inversely related to microbial biomass P with a scaling exponent of -0.234 (95% CI -0.289 to -0.179). Relative to CUE, biomass specific turnover time increased with a scaling exponent of 0.509 (95% CI 0.467–0.551). CUE increased weakly with mean annual temperature. CUE declined with increasing soil pH reaching a minimum at pH 7.0, then increased again as soil pH approached 9.0, a pattern consistent with pH trends in the ratio of fungal : bacteria abundance and growth. Structural equation models that related geographic variables to CUE component variables showed the strongest connections for paths linking latitude and pH to β-glucosidase activity and soil C:N:P ratios. Finally, the integration of stoichiometric and metabolic models provides a quantitative description of the functional organization of soil microbial communities that can improve the representation of CUE in microbial process and ecosystem simulation models.« less

Stoichiometry of microbial carbon use efficiency in soils

DOE PAGES

Sinsabaugh, Robert L.; Turner, Benjamin L.; Talbot, Jennifer M.; ...

2016-03-23

The carbon use efficiency (CUE) of microbial communities partitions the flow of C from primary producers to the atmosphere, decomposer food webs, and soil C stores. CUE, usually defined as the ratio of growth to assimilation, is a critical parameter in ecosystem models, but is seldom measured directly in soils because of the methodological difficulty of measuring in situ rates of microbial growth and respiration. Alternatively, CUE can be estimated indirectly from the elemental stoichiometry of organic matter and microbial biomass, and the ratios of C to nutrient-acquiring ecoenzymatic activities. In this paper, we used this approach to estimate andmore » compare microbial CUE in >2000 soils from a broad range of ecosystems. Mean CUE based on C:N stoichiometry was 0.269 ± 0.110 (mean ± SD). A parallel calculation based on C:P stoichiometry yielded a mean CUE estimate of 0.252 ± 0.125. The mean values and frequency distributions were similar to those from aquatic ecosystems, also calculated from stoichiometric models, and to those calculated from direct measurements of bacterial and fungal growth and respiration. CUE was directly related to microbial biomass C with a scaling exponent of 0.304 (95% CI 0.237–0.371) and inversely related to microbial biomass P with a scaling exponent of -0.234 (95% CI -0.289 to -0.179). Relative to CUE, biomass specific turnover time increased with a scaling exponent of 0.509 (95% CI 0.467–0.551). CUE increased weakly with mean annual temperature. CUE declined with increasing soil pH reaching a minimum at pH 7.0, then increased again as soil pH approached 9.0, a pattern consistent with pH trends in the ratio of fungal : bacteria abundance and growth. Structural equation models that related geographic variables to CUE component variables showed the strongest connections for paths linking latitude and pH to β-glucosidase activity and soil C:N:P ratios. Finally, the integration of stoichiometric and metabolic models provides a quantitative description of the functional organization of soil microbial communities that can improve the representation of CUE in microbial process and ecosystem simulation models.« less

[Quantitative estimation of vegetation cover and management factor in USLE and RUSLE models by using remote sensing data: a review].

PubMed

Wu, Chang-Guang; Li, Sheng; Ren, Hua-Dong; Yao, Xiao-Hua; Huang, Zi-Jie

2012-06-01

Soil loss prediction models such as universal soil loss equation (USLE) and its revised universal soil loss equation (RUSLE) are the useful tools for risk assessment of soil erosion and planning of soil conservation at regional scale. To make a rational estimation of vegetation cover and management factor, the most important parameters in USLE or RUSLE, is particularly important for the accurate prediction of soil erosion. The traditional estimation based on field survey and measurement is time-consuming, laborious, and costly, and cannot rapidly extract the vegetation cover and management factor at macro-scale. In recent years, the development of remote sensing technology has provided both data and methods for the estimation of vegetation cover and management factor over broad geographic areas. This paper summarized the research findings on the quantitative estimation of vegetation cover and management factor by using remote sensing data, and analyzed the advantages and the disadvantages of various methods, aimed to provide reference for the further research and quantitative estimation of vegetation cover and management factor at large scale.

Regionalising MUSLE factors for application to a data-scarce catchment

NASA Astrophysics Data System (ADS)

Gwapedza, David; Slaughter, Andrew; Hughes, Denis; Mantel, Sukhmani

2018-04-01

The estimation of soil loss and sediment transport is important for effective management of catchments. A model for semi-arid catchments in southern Africa has been developed; however, simplification of the model parameters and further testing are required. Soil loss is calculated through the Modified Universal Soil Loss Equation (MUSLE). The aims of the current study were to: (1) regionalise the MUSLE erodibility factors and; (2) perform a sensitivity analysis and validate the soil loss outputs against independently-estimated measures. The regionalisation was developed using Geographic Information Systems (GIS) coverages. The model was applied to a high erosion semi-arid region in the Eastern Cape, South Africa. Sensitivity analysis indicated model outputs to be more sensitive to the vegetation cover factor. The simulated soil loss estimates of 40 t ha-1 yr-1 were within the range of estimates by previous studies. The outcome of the present research is a framework for parameter estimation for the MUSLE through regionalisation. This is part of the ongoing development of a model which can estimate soil loss and sediment delivery at broad spatial and temporal scales.

Method comparison for forest soil carbon and nitrogen estimates in the Delaware River basin

Treesearch

B. Xu; Yude Pan; A.H. Johnson; A.F. Plante

2016-01-01

The accuracy of forest soil C and N estimates is hampered by forest soils that are rocky, inaccessible, and spatially heterogeneous. A composite coring technique is the standard method used in Forest Inventory and Analysis, but its accuracy has been questioned. Quantitative soil pits provide direct measurement of rock content and soil mass from a larger, more...

Improvement in estimation of soil water deficit by integrating airborne imagery data into a soil water balance modelents into a soil water

USDA-ARS?s Scientific Manuscript database

In this paper, an approach that integrates airborne imagery data as inputs was used to improve the estimation of soil water deficit (SWD) for maize and sunflower grown under full and deficit irrigation treatments. The proposed model was applied to optimize the maximum total available soil water (TAW...

Carbon to organic matter ratios for soils in Rocky Mountain coniferous forests

Treesearch

Theresa B. Jain; Russell T. Graham; David L. Adams

1997-01-01

Vegetation type, soils, climate, and conversion ratios influence estimates of terrestrial C. Our objectives were to (i) determine carbon to organic matter (C/OM) ratios for brown cubical rotten wood, litter, surface humus, soil wood, and mineral soils; (ii) evaluate the validity of using 0.58 and 0.50 ratios for estimating C in mineral and organic soil components,...

Linking soil type and rainfall characteristics towards estimation of surface evaporative capacitance

NASA Astrophysics Data System (ADS)

Or, D.; Bickel, S.; Lehmann, P.

2017-12-01

Separation of evapotranspiration (ET) to evaporation (E) and transpiration (T) components for attribution of surface fluxes or for assessment of isotope fractionation in groundwater remains a challenge. Regional estimates of soil evaporation often rely on plant-based (Penman-Monteith) ET estimates where is E is obtained as a residual or a fraction of potential evaporation. We propose a novel method for estimating E from soil-specific properties, regional rainfall characteristics and considering concurrent internal drainage that shelters soil water from evaporation. A soil-dependent evaporative characteristic length defines a depth below which soil water cannot be pulled to the surface by capillarity; this depth determines the maximal soil evaporative capacitance (SEC). The SEC is recharged by rainfall and subsequently emptied by competition between drainage and surface evaporation (considering canopy interception evaporation). We show that E is strongly dependent on rainfall characteristics (mean annual, number of storms) and soil textural type, with up to 50% of rainfall lost to evaporation in loamy soil. The SEC concept applied to different soil types and climatic regions offers direct bounds on regional surface evaporation independent of plant-based parameterization or energy balance calculations.

Proxies for soil organic carbon derived from remote sensing

NASA Astrophysics Data System (ADS)

Rasel, S. M. M.; Groen, T. A.; Hussin, Y. A.; Diti, I. J.

2017-07-01

The possibility of carbon storage in soils is of interest because compared to vegetation it contains more carbon. Estimation of soil carbon through remote sensing based techniques can be a cost effective approach, but is limited by available methods. This study aims to develop a model based on remotely sensed variables (elevation, forest type and above ground biomass) to estimate soil carbon stocks. Field observations on soil organic carbon, species composition, and above ground biomass were recorded in the subtropical forest of Chitwan, Nepal. These variables were also estimated using LiDAR data and a WorldView 2 image. Above ground biomass was estimated from the LiDAR image using a novel approach where the image was segmented to identify individual trees, and for these trees estimates of DBH and Height were made. Based on AIC (Akaike Information Criterion) a regression model with above ground biomass derived from LiDAR data, and forest type derived from WorldView 2 imagery was selected to estimate soil organic carbon (SOC) stocks. The selected model had a coefficient of determination (R2) of 0.69. This shows the scope of estimating SOC with remote sensing derived variables in sub-tropical forests.

Lessons from simultaneous measurements of soil respiration and net ecosystem exchange of CO2 in temperate forests

NASA Astrophysics Data System (ADS)

Renchon, A.; Pendall, E.

2017-12-01

Land-surface exchanges of CO2 play a key role in ameliorating or exacerbating climate change. The eddy-covariance method allows direct measurement of net ecosystem-atmosphere exchange of CO2 (NEE), but partitioning daytime NEE into its components - gross primary productivity (GPP) and ecosystem respiration (RE) - remains challenging. Continuous measurements of soil respiration (RS), along with flux towers, have the potential to better constrain data and models of RE and GPP. We use simultaneous half-hourly NEE and RS data to: (1) compare the short-term (fortnightly) apparent temperature sensitivity (Q10) of nighttime RS and RE; (2) assess whether daytime RS can be estimated using nighttime response functions; and (3) compare the long-term (annual) responses of nighttime RS and nighttime RE to interacting soil moisture and soil temperature. We found that nighttime RS has a lower short-term Q10 than nighttime RE. This suggests that the Q10 of nighttime RE is strongly influenced by the Q10 of nighttime above-ground respiration, or possibly by a bias in RE measurements. The short-term Q10 of RS and RE decreased with increasing temperature. In general, daytime RS could be estimated using nighttime RS temperature and soil moisture (r2 = 0.9). However, this results from little to no diurnal variation in RS, and estimating daytime RS as the average of nighttime RS gave similar results (r2 = 0.9). Furthermore, we observed a day-night hysteresis of RS response to temperature, especially when using air temperature and sometimes when using soil temperature at 5cm depth. In fact, during some months, soil respiration observations were lower during daytime compared to nighttime, despite higher temperature in daytime. Therefore, daytime RS modelled from nighttime RS temperature response was overestimated during these periods. RS and RE responses to the combination of soil moisture and soil temperature were similar, and consistent with the DAMM model of soil-C decomposition. These findings underscore the value of continuous measurements of RS in flux tower footprints. Findings are also relevant to recent research on light inhibition of leaf respiration and contribute to improved understanding of ecosystem carbon cycle - climate feedback processes.

Using a trait-based approach to link microbial community composition and functioning to soil salinity

NASA Astrophysics Data System (ADS)

Rath, Kristin; Fierer, Noah; Rousk, Johannes

2017-04-01

Our knowledge of the dynamics structuring microbial communities and the consequences this has for soil functions is rudimentary. In particular, predictions of the response of microbial communities to environmental change and the implications for associated ecosystem processes remain elusive. Understanding how environmental factors structure microbial communities and regulate the functions they perform is key to a mechanistic understanding of how biogeochemical cycles respond to environmental change. Soil salinization is an agricultural problem in many parts of the world. The activity of soil microorganisms is reduced in saline soils compared to non-saline soil. However, soil salinity often co-varies with other factors, making it difficult to assign responses of microbial communities to direct effects of salinity. A trait-based approach allows us to connect the environmental factor salinity with the responses of microbial community composition and functioning. Salinity along a salinity gradient serves as a filter for the community trait distribution of salt tolerance, selecting for higher salt tolerance at more saline sites. This trait-environment relationship can be used to predict responses of microbial communities to environmental change. Our aims were to (i) use salinity along natural salinity gradients as an environmental filter, and (ii) link the resulting filtered trait-distributions of the communities (the trait being salt tolerance) to the community composition. Soil samples were obtained from two replicated salinity gradients along an Australian salt lake, spanning a wide range of soil salinities (0.1 dS m-1 to >50 dS m-1). In one of the two gradients salinity was correlated with pH. Community trait distributions for salt tolerance were assessed by establishing dose-dependences for extracted bacterial communities using growth rate assays. In addition, functional parameters were measured along the salt gradients. Community composition of sites was compared through 16S rRNA gene amplicon sequencing. Microbial community composition changed greatly along the salinity gradients. Using the salt-tolerance assessments to estimate bacterial trait-distributions we could determine substantial differences in tolerance to salt revealing a strong causal connection between environment and trait distributions. By constraining the community composition with salinity tolerance in ordinations, we could assign which community differences were directly due to a shift in community trait distributions. These analyses revealed that a substantial part (up to 30%) of the community composition differences were directly driven by environmental salt concentrations.. Even though communities in saline soils had trait-distributions aligned to their environment, their performance (respiration, growth rates) was lower than those in non-saline soils and remained low even after input of organic material. Using a trait-based approach we could connect filtered trait distributions along environmental gradients, to the composition of the microbial community. We show that soil salinity played an important role in shaping microbial community composition by selecting for communities with higher salt tolerance. The shift toward bacterial communities with trait distributions matched to salt environments probably compensated for much of the potential loss of function induced by salinity, resulting in a degree of apparent functional redundancy for decomposition. However, more tolerant communities still showed reduced functioning, suggesting a trade-off between salt tolerance and performance.

Estimating surface soil moisture from SMAP observations using a neural network technique

USDA-ARS?s Scientific Manuscript database

A Neural Network (NN) algorithm was developed to estimate global surface soil moisture for April 2015 to June 2016 with a 2-3 day repeat frequency using passive microwave observations from the Soil Moisture Active Passive (SMAP) satellite, surface soil temperatures from the NASA Goddard Earth Observ...

Precipitation estimation using L-Band and C-Band soil moisture retrievals

USDA-ARS?s Scientific Manuscript database

An established methodology for estimating precipitation amounts from satellite-based soil moisture retrievals is applied to L-band products from the Soil Moisture Active Passive (SMAP) and Soil Moisture and Ocean Salinity (SMOS) satellite missions and to a C-band product from the Advanced Scatterome...

Hillslope soil erosion estimated from aerosol concentrations, North Halawa Valley, Oahu, Hawaii

USGS Publications Warehouse

Hill, B.R.; Fuller, C.C.; DeCarlo, E.H.

1997-01-01

Concentrations of aerosolic quartz and 137Cs were used to estimate rates of hillslope soil erosion during 1990-91 in the North Halawa Valley on the island of Oahu, Hawaii. Fluvial transport of quartz was estimated to be 6.1 Mg in 1990 and 14.9 Mg in 1991. Fluvial transport of 137Cs from North Halawa Valley was estimated to be 1.29 ?? 109 pCi in 1991. Results were used with quartz contents, 137Cs activities, and bulk densities of hillslope soils to compute rates of basinwide hillslope soil erosion ranging from 0.1 to 0.3 mm yr-1. These rates are within the range of previous estimates of denudation computed for drainage basins on Oahu. The aerosol-concentration approach, therefore, is a useful method for assessing basinwide soil erosion.

Ground motion estimation in Delhi from postulated regional and local earthquakes

NASA Astrophysics Data System (ADS)

Mittal, Himanshu; Kumar, Ashok; Kamal

2013-04-01

Ground motions are estimated at 55 sites in Delhi, the capital of India from four postulated earthquakes (three regional M w = 7.5, 8.0, and 8.5 and one local). The procedure consists of (1) synthesis of ground motion at a hard reference site (NDI) and (2) estimation of ground motion at other sites in the city via known transfer functions and application of the random vibration theory. This work provides a more extensive coverage than earlier studies (e.g., Singh et al., Bull Seism Soc Am 92:555-569, 2002; Bansal et al., J Seismol 13:89-105, 2009). The Indian code response spectra corresponding to Delhi (zone IV) are found to be conservative at hard soil sites for all postulated earthquakes but found to be deficient for M w = 8.0 and 8.5 earthquakes at soft soil sites. Spectral acceleration maps at four different natural periods are strongly influenced by the shallow geological and soil conditions. Three pockets of high acceleration values are seen. These pockets seem to coincide with the contacts of (a) Aravalli quartzite and recent Yamuna alluvium (towards the East), (b) Aravalli quartzite and older quaternary alluvium (towards the South), and (c) older quaternary alluvium and recent Yamuna alluvium (towards the North).

Detecting Soil Moisture Related Impacts on Gross Primary Productivity using the MODIS-based Photochemical Reflectance Index

NASA Astrophysics Data System (ADS)

He, M.; Kimball, J. S.; Running, S. W.; Ballantyne, A.; Guan, K.; Huemmrich, K. F.

2016-12-01

Satellite remote sensing provides continuous observations of vegetation properties that can be used to estimate ecosystem gross primary production (GPP). The Photochemical Reflectance Index (PRI) has been shown to be sensitive to photosynthetic light use efficiency (LUE), GPP and canopy water-stress. The NASA EOS MODIS (Moderate Resolution Imaging Spectroradiometer) sensor provides potential PRI estimation globally at daily time step and 1-km spatial resolution for more than 10 years. Here, we use the MODIS based PRI with eddy covariance CO2 flux measurements and meteorological observations from 20 tower sites representing 5 major plant functional types (PFT) within the continental USA (CONUS) to assess GPP sensitivity to seasonal water supply variability. The sPRI (scaled PRI) derived using MODIS band 13 as a reference band (sPRI13) generally shows higher correspondence with tower GPP observations than other potential MODIS reference bands (MODIS band 1, 4, 10 and 12). The sPRI13 was used to represent soil moisture related water supply constraints to LUE within a terrestrial carbon flux model to estimate GPP (GPPPRI). The GPPPRI calculations show generally strong relationships with tower GPP observations (0.457 ≤ R2 ≤ 0.818), except for lower GPPPRI performance over evergreen needleleaf forest (ENF) sites. A regional model sensitivity analysis using the sPRI13 as a proxy for soil moisture related water supply limits indicated that water restrictions limit GPP over more than 21% of the CONUS domain, particularly in northwest and southwest CONUS subregions, and drier climate areas where atmospheric moisture deficits (VPD) alone are insufficient to represent both atmosphere demand and soil water supply controls affecting productivity. Our results indicate strong potential of the MODIS sPRI13 to represent GPP sensitivity to seasonal soil moisture related water supply variability, with enhanced (1-km resolution) delineation of these processes closer to the scale of in situ tower observations, providing an effective tool to characterize sub-grid spatial heterogeneity in soil moisture related water supply controls that inform coarser scale observations and estimates determined from other satellite observations and global carbon, and climate models.

Soil Carbon Chemistry and Greenhouse Gas Production in Global Peatlands

NASA Astrophysics Data System (ADS)

Normand, A. E.; Turner, B. L.; Lamit, L. J.; Smith, A. N.; Baiser, B.; Clark, M. W.; Hazlett, C.; Lilleskov, E.; Long, J.; Grover, S.; Reddy, K. R.

2017-12-01

Peatlands play a critical role in the global carbon cycle because they contain approximately 30% of the 1500 Pg of carbon stored in soils worldwide. However, the stability of these vast stores of carbon is under threat from climate and land-use change, with important consequences for global climate. Ecosystem models predict the impact of peatland perturbation on carbon fluxes based on total soil carbon pools, but responses could vary markedly depending on the chemical composition of soil organic matter. Here we combine experimental and observational studies to quantify the chemical nature and response to perturbation of soil organic matter in peatlands worldwide. We quantified carbon functional groups in a global sample of 125 freshwater peatlands using solid-state 13C nuclear magnetic resonance (NMR) spectroscopy to determine the drivers of molecular composition of soil organic matter. We then incubated a representative subset of the soils under aerobic and anaerobic conditions to determine how organic matter composition influences carbon dioxide (CO2) and methane (CH4) emissions following drainage or flooding. The functional chemistry of peat varied markedly at large and small spatial scales, due to long-term land use change, mean annual temperature, nutrient status, and vegetation, but not pH. Despite this variation, we found predictable responses of greenhouse gas production following drainage based on soil carbon chemistry, defined by a novel Global Peat Stability Index, with greater CO2 and CH4 fluxes from soils enriched in oxygen-containing organic carbon (O-alkyl C) and depleted in aromatic and hydrophobic compounds. Incorporation of the Global Peat Stability Index of peatland organic matter into earth system models and management strategies, which will improve estimates of GHG fluxes from peatlands and ultimately advance management to reduce carbon loss from these sensitive ecosystems.

Ground-based Remote Sensing for Quantifying Subsurface and Surface Co-variability to Scale Arctic Ecosystem Functioning

NASA Astrophysics Data System (ADS)

Oktem, R.; Wainwright, H. M.; Curtis, J. B.; Dafflon, B.; Peterson, J.; Ulrich, C.; Hubbard, S. S.; Torn, M. S.

2016-12-01

Predicting carbon cycling in Arctic requires quantifying tightly coupled surface and subsurface processes including permafrost, hydrology, vegetation and soil biogeochemistry. The challenge has been a lack of means to remotely sense key ecosystem properties in high resolution and over large areas. A particular challenge has been characterizing soil properties that are known to be highly heterogeneous. In this study, we exploit tightly-coupled above/belowground ecosystem functioning (e.g., the correlations among soil moisture, vegetation and carbon fluxes) to estimate subsurface and other key properties over large areas. To test this concept, we have installed a ground-based remote sensing platform - a track-mounted tram system - along a 70 m transect in the ice-wedge polygonal tundra near Barrow, Alaska. The tram carries a suite of near-surface remote sensing sensors, including sonic depth, thermal IR, NDVI and multispectral sensors. Joint analysis with multiple ground-based measurements (soil temperature, active layer soil moisture, and carbon fluxes) was performed to quantify correlations and the dynamics of above/belowground processes at unprecedented resolution, both temporally and spatially. We analyzed the datasets with particular focus on correlating key subsurface and ecosystem properties with surface properties that can be measured by satellite/airborne remote sensing over a large area. Our results provided several new insights about system behavior and also opens the door for new characterization approaches. We documented that: (1) soil temperature (at >5 cm depth; critical for permafrost thaw) was decoupled from soil surface temperature and was influenced strongly by soil moisture, (2) NDVI and greenness index were highly correlated with both soil moisture and gross primary productivity (based on chamber flux data), and (3) surface deformation (which can be measured by InSAR) was a good proxy for thaw depth dynamics at non-inundated locations.

Estimation of available water capacity components of two-layered soils using crop model inversion: Effect of crop type and water regime

NASA Astrophysics Data System (ADS)

Sreelash, K.; Buis, Samuel; Sekhar, M.; Ruiz, Laurent; Kumar Tomer, Sat; Guérif, Martine

2017-03-01

Characterization of the soil water reservoir is critical for understanding the interactions between crops and their environment and the impacts of land use and environmental changes on the hydrology of agricultural catchments especially in tropical context. Recent studies have shown that inversion of crop models is a powerful tool for retrieving information on root zone properties. Increasing availability of remotely sensed soil and vegetation observations makes it well suited for large scale applications. The potential of this methodology has however never been properly evaluated on extensive experimental datasets and previous studies suggested that the quality of estimation of soil hydraulic properties may vary depending on agro-environmental situations. The objective of this study was to evaluate this approach on an extensive field experiment. The dataset covered four crops (sunflower, sorghum, turmeric, maize) grown on different soils and several years in South India. The components of AWC (available water capacity) namely soil water content at field capacity and wilting point, and soil depth of two-layered soils were estimated by inversion of the crop model STICS with the GLUE (generalized likelihood uncertainty estimation) approach using observations of surface soil moisture (SSM; typically from 0 to 10 cm deep) and leaf area index (LAI), which are attainable from radar remote sensing in tropical regions with frequent cloudy conditions. The results showed that the quality of parameter estimation largely depends on the hydric regime and its interaction with crop type. A mean relative absolute error of 5% for field capacity of surface layer, 10% for field capacity of root zone, 15% for wilting point of surface layer and root zone, and 20% for soil depth can be obtained in favorable conditions. A few observations of SSM (during wet and dry soil moisture periods) and LAI (within water stress periods) were sufficient to significantly improve the estimation of AWC components. These results show the potential of crop model inversion for estimating the AWC components of two-layered soils and may guide the sampling of representative years and fields to use this technique for mapping soil properties that are relevant for distributed hydrological modelling.

LAI inversion from optical reflectance using a neural network trained with a multiple scattering model

NASA Technical Reports Server (NTRS)

Smith, James A.

1992-01-01

The inversion of the leaf area index (LAI) canopy parameter from optical spectral reflectance measurements is obtained using a backpropagation artificial neural network trained using input-output pairs generated by a multiple scattering reflectance model. The problem of LAI estimation over sparse canopies (LAI < 1.0) with varying soil reflectance backgrounds is particularly difficult. Standard multiple regression methods applied to canopies within a single homogeneous soil type yield good results but perform unacceptably when applied across soil boundaries, resulting in absolute percentage errors of >1000 percent for low LAI. Minimization methods applied to merit functions constructed from differences between measured reflectances and predicted reflectances using multiple-scattering models are unacceptably sensitive to a good initial guess for the desired parameter. In contrast, the neural network reported generally yields absolute percentage errors of <30 percent when weighting coefficients trained on one soil type were applied to predicted canopy reflectance at a different soil background.

Soil erosion increases soil microbial activity at the depositional position of eroding slopes

NASA Astrophysics Data System (ADS)

Meng, Xu; Cardenas, Laura M.; Donovan, Neil; Zhang, Junling; Murray, Phil; Zhang, Fusuo; Dungait, Jennifer A. J.

2016-04-01

Soil erosion is the most widespread form of soil degradation. Estimation of the impact of agricultural soil erosion on global carbon cycle is a topic of scientific debate, with opposing yet similar magnitude estimates of erosion as a net source or sink of atmospheric carbon. The transport and deposition of eroded agricultural soils affects not only the carbon cycle but other nutrient cycles as well. It has been estimated that erosion-induced lateral fluxes of nitrogen (N) and phosphorus (P) could be similar in magnitude to those from fertilizer application and crop removal (Quinton et al., 2010). In particular, the dynamics of soil N in eroding slopes need to be considered because the management of soil N has profound influences on the functioning of soil microorganisms, which are generally considered as the main biotic driver of soil C efflux. Carbon dioxide (CO2) emissions tend to increase in deposition positions of eroded slopes, diminishing the sink potential of eroded soils C (. As the global warming potential of nitrous oxide (N2O) is 310 times relative to that of CO2, the sink potential of agricultural erosion could easily be negated with a small increase in N2O emissions. Therefore, an investigation of the potential emissions of greenhouse gases, and especially N2O from soils affected by agricultural erosion, are required. In the present study, a field experiment was established with contrasting cultivation techniques of a C4 crop (Zea mays; δ13C = -12.2‰) to introduce 13C-enriched SOC to a soil previously cropped with C3 plants (δ13C = -29.3‰). Soils sampled from the top, middle, bottom and foot slope positions along a distinct erosion pathway were analyzed using 13C-phospholipid fatty acid (PLFA) analysis and incubated to investigate the responses of microorganisms and associated potential emissions of greenhouse gases (GHG). The total C and N contents were greatest in soils at the top slope position, whereas soil mineral N (NO3--N and NH4+-N) contents were greater at the bottom and foot slope positions. The biomarker PLFAs for Gram positive bacteria and fungi were relatively 13C-enriched, indicating the incorporation of C from Zea mays residues compared with 13C-depletion in biomarker PLFA in Actinobacteria indicating utilization of SOC. An average of 72% C incorporated by the all microbial groups was derived from SOC at the slope foot, suggesting a large amount of SOC was mineralized at the depositional position. We observed the highest emissions of N2O and CO2 from the incubated soils sampled from the bottom slope position. We conclude that the conditions in the depositional positions of eroding slopes can promote GHG emissions reducing the previously reported sink capacity of soil erosion. Quinton et al (2010) The impact of agricultural soil erosion on biogeochemical cycling. Nature Geoscience 3, 311 - 314.

Benefit of Modeling the Observation Error in a Data Assimilation Framework Using Vegetation Information Obtained From Passive Based Microwave Data

NASA Technical Reports Server (NTRS)

Bolten, John D.; Mladenova, Iliana E.; Crow, Wade; De Jeu, Richard

2016-01-01

A primary operational goal of the United States Department of Agriculture (USDA) is to improve foreign market access for U.S. agricultural products. A large fraction of this crop condition assessment is based on satellite imagery and ground data analysis. The baseline soil moisture estimates that are currently used for this analysis are based on output from the modified Palmer two-layer soil moisture model, updated to assimilate near-real time observations derived from the Soil Moisture Ocean Salinity (SMOS) satellite. The current data assimilation system is based on a 1-D Ensemble Kalman Filter approach, where the observation error is modeled as a function of vegetation density. This allows for offsetting errors in the soil moisture retrievals. The observation error is currently adjusted using Normalized Difference Vegetation Index (NDVI) climatology. In this paper we explore the possibility of utilizing microwave-based vegetation optical depth instead.

Determination of the resistance of fabric printed with triclosan microcapsules to the action of soil micro-flora

NASA Astrophysics Data System (ADS)

Golja, B.; Forte Tavčer, P.

2017-10-01

Microcapsules with a pressure-sensitive melamine-formaldehyde wall and triclosan core were printed to 100% cotton fabric with screen printing technique. Previous research showed excellent antibacterial activity (estimated for E. Coli and S. Aureus) of such fabric, so our aim in this research was to determine its resistance to the action of microorganisms present in the soil. The soil burial test was conducted. The breaking strength of the buried samples was measured and also the scanning electron microscope analysis was done. The results showed that none of the samples are resistant to decay. It is evident from SEM micrographs that on all of the buried samples greater morphological changes occur due to the functions of the soil microflora. It can be concluded that the samples printed with triclosan microcapsules are biodegradable which is environmentally preferable.

A Modified Kriging Method to Interpolate the Soil Moisture Measured by Wireless Sensor Network with the Aid of Remote Sensing Images

NASA Astrophysics Data System (ADS)

Zhang, J.; Liu, Q.; Li, X.; Niu, H.; Cai, E.

2015-12-01

In recent years, wireless sensor network (WSN) emerges to collect Earth observation data at relatively low cost and light labor load, while its observations are still point-data. To learn the spatial distribution of a land surface parameter, interpolating the point data is necessary. Taking soil moisture (SM) for example, its spatial distribution is critical information for agriculture management, hydrological and ecological researches. This study developed a method to interpolate the WSN-measured SM to acquire the spatial distribution in a 5km*5km study area, located in the middle reaches of HEIHE River, western China. As SM is related to many factors such as topology, soil type, vegetation and etc., even the WSN observation grid is not dense enough to reflect the SM distribution pattern. Our idea is to revise the traditional Kriging algorithm, introducing spectral variables, i.e., vegetation index (VI) and abledo, from satellite imagery as supplementary information to aid the interpolation. Thus, the new Extended-Kriging algorithm operates on the spatial & spectral combined space. To run the algorithm, first we need to estimate the SM variance function, which is also extended to the combined space. As the number of WSN samples in the study area is not enough to gather robust statistics, we have to assume that the SM variance function is invariant over time. So, the variance function is estimated from a SM map, derived from the airborne CASI/TASI images acquired in July 10, 2012, and then applied to interpolate WSN data in that season. Data analysis indicates that the new algorithm can provide more details to the variation of land SM. Then, the Leave-one-out cross-validation is adopted to estimate the interpolation accuracy. Although a reasonable accuracy can be achieved, the result is not yet satisfactory. Besides improving the algorithm, the uncertainties in WSN measurements may also need to be controlled in our further work.

Major controlling factors and prediction models for arsenic uptake from soil to wheat plants.

PubMed

Dai, Yunchao; Lv, Jialong; Liu, Ke; Zhao, Xiaoyan; Cao, Yingfei

2016-08-01

The application of current Chinese agriculture soil quality standards fails to evaluate the land utilization functions appropriately due to the diversity of soil properties and plant species. Therefore, the standards should be amended. A greenhouse experiment was conducted to investigate arsenic (As) enrichment in various soils from 18 Chinese provinces in parallel with As transfer to 8 wheat varieties. The goal of the study was to build and calibrate soil-wheat threshold models to forecast the As threshold of wheat soils. In Shaanxi soils, Wanmai and Jimai were the most sensitive and insensitive wheat varieties, respectively; and in Jiangxi soils, Zhengmai and Xumai were the most sensitive and insensitive wheat varieties, respectively. Relationships between soil properties and the bioconcentration factor (BCF) were built based on stepwise multiple linear regressions. Soil pH was the best predictor of BCF, and after normalizing the regression equation (Log BCF=0.2054 pH- 3.2055, R(2)=0.8474, n=14, p<0.001), we obtained a calibrated model. Using the calibrated model, a continuous soil-wheat threshold equation (HC5=10((-0.2054 pH+2.9935))+9.2) was obtained for the species-sensitive distribution curve, which was built on Chinese food safety standards. The threshold equation is a helpful tool that can be applied to estimate As uptake from soil to wheat. Copyright © 2016 Elsevier Inc. All rights reserved.

Do Quercus ilex Woodlands Undergo Abrupt Non-linear Changes in their Functional Dynamics in Response to Human Disturbance and Climatic Variation?

NASA Astrophysics Data System (ADS)

Bochet, E.; García-Fayos, P.; Molina, M. J.; Moreno de las Heras, M.; Espigares, T.; Nicolau, J. M.; Monleon, V. J.

2017-12-01

Theoretical models predict that drylands are particularly prone to suffer critical transitions with abrupt non-linear changes in their structure and functions as a result of the existing complex interactions between climatic fluctuations and human disturbances. How drylands undergo functional change has become an important issue in ecology which needs empirical data to validate theoretical models. We aim at determining the response of Mediterranean holm oak woodlands to human disturbance in three different climatic areas from Eastern Spain, under the hypothesis that semiarid and dry-transition landscapes are more prone to suffer abrupt functional changes than sub-humid ones. We used (a) remote-sensing estimations of precipitation-use-efficiency (PUE) from enhanced vegetation index (EVI) observations performed in 231 x 231 m plots of the Moderate Resolution Imaging Spectroradiometer (MODIS); (b) soil parameter (enzyme activity, organic matter) and (c) vegetation parameter (functional groups) determinations from soil sampling and vegetation surveys, respectively, performed in the same plots. We analyzed and compared the shape of the functional change (in terms of PUE, soil and vegetation parameters) in response to human disturbance intensity for our holm oak sites in the three climatic areas. Although no threshold of abrupt change is observed, important differences in the functional response of holm oak woodlands to disturbance exist between climatic conditions. Overall, semiarid and dry-transition woodlands suffer a non-linear functional decrease in terms of PUE, soil organic matter and enzyme activity with disturbance intensity. Differently, sub-humid woodlands experience a linear decrease of PUE with disturbance intensity and an increase of both soil parameters at high disturbance intensities after an important decrease at low disturbance intensities. The structural change from woody- to herbaceous-dominated landscapes in sub-humid areas explains the recovery of the functional state of the system at high disturbance intensities. This structural change in the vegetation provides resilience to sub-humid woodlands at high intensity levels where semiarid and dry-transition woodlands suffer a pronounced degradation.

Estimating demand for perennial pigeon pea in Malawi using choice experiments.

PubMed

Waldman, Kurt B; Ortega, David L; Richardson, Robert B; Snapp, Sieglinde S

2017-01-01

Perennial crops have numerous ecological and agronomic advantages over their annual counterparts. We estimate discrete choice models to evaluate farmers' preferences for perennial attributes of pigeon pea intercropped with maize in central and southern Malawi. Pigeon pea is a nitrogen-fixing leguminous crop, which has the potential to ameliorate soil fertility problems related to continuous maize cultivation, which are common in Southern Africa. Adoption of annual pigeon pea is relatively low but perennial production of pigeon pea may be more appealing to farmers due to some of the ancillary benefits associated with perenniality. We model perennial production of pigeon pea as a function of the attributes that differ between annual and perennial production: lower labor and seed requirements resulting from a single planting with multiple harvests, enhanced soil fertility and higher levels of biomass production. The primary tradeoff associated with perennial pigeon pea intercropped with maize is competition with maize in subsequent years of production. While maize yield is approximately twice as valuable to farmers as pigeon pea yield, we find positive yet heterogeneous demand for perenniality driven by soil fertility improvements and pigeon pea grain yield.

Environmental impacts on the diversity of methane-cycling microbes and their resultant function

PubMed Central

Aronson, Emma L.; Allison, Steven D.; Helliker, Brent R.

2013-01-01

Methane is an important anthropogenic greenhouse gas that is produced and consumed in soils by microorganisms responding to micro-environmental conditions. Current estimates show that soil consumption accounts for 5–15% of methane removed from the atmosphere on an annual basis. Recent variability in atmospheric methane concentrations has called into question the reliability of estimates of methane consumption and calls for novel approaches in order to predict future atmospheric methane trends. This review synthesizes the environmental and climatic factors influencing the consumption of methane from the atmosphere by non-wetland, terrestrial soil microorganisms. In particular, we focus on published efforts to connect community composition and diversity of methane-cycling microbial communities to observed rates of methane flux. We find abundant evidence for direct connections between shifts in the methane-cycling microbial community, due to climate and environmental changes, and observed methane flux levels. These responses vary by ecosystem and associated vegetation type. This information will be useful in process-based models of ecosystem methane flux responses to shifts in environmental and climatic parameters. PMID:23966984

Empirical evidence for acceleration-dependent amplification factors

USGS Publications Warehouse

Borcherdt, R.D.

2002-01-01

Site-specific amplification factors, Fa and Fv, used in current U.S. building codes decrease with increasing base acceleration level as implied by the Loma Prieta earthquake at 0.1g and extrapolated using numerical models and laboratory results. The Northridge earthquake recordings of 17 January 1994 and subsequent geotechnical data permit empirical estimates of amplification at base acceleration levels up to 0.5g. Distance measures and normalization procedures used to infer amplification ratios from soil-rock pairs in predetermined azimuth-distance bins significantly influence the dependence of amplification estimates on base acceleration. Factors inferred using a hypocentral distance norm do not show a statistically significant dependence on base acceleration. Factors inferred using norms implied by the attenuation functions of Abrahamson and Silva show a statistically significant decrease with increasing base acceleration. The decrease is statistically more significant for stiff clay and sandy soil (site class D) sites than for stiffer sites underlain by gravely soils and soft rock (site class C). The decrease in amplification with increasing base acceleration is more pronounced for the short-period amplification factor, Fa, than for the midperiod factor, Fv.

Soil Water Balance and Vegetation Dynamics in two Contrasting Water-limited Mediterranean Ecosystems on Sardinia, Italy

NASA Astrophysics Data System (ADS)

Montaldo, N.; Albertson, J. D.; Corona, R.

2011-12-01

Water limited conditions strongly impacts soil and vegetation dynamics in Mediterranean regions, which are commonly heterogeneous ecosystems, characterized by inter-annual rainfall variability, topography variability and contrasting plant functional types (PFTs) competing for water use. Mediterranean regions are characterized by two main ecosystems, grassland and woodland, which for both natural and anthropogenic causes can grow in soils with different characteristics, highly impacting water resources. Water resources and forestal planning need a deep understanding of the dynamics between PFTs, soil and atmosphere and their impacts on water and CO2 distributions of these two main ecosystems. The first step is the monitoring of land surface fluxes, soil moisture, and vegetation dynamics of the two contrasting ecosystems. Moreover, due to the large percentage of soils with low depth (< 50 cm), and due to the quick hydrologic answer to atmospheric forcing in these soils, there is also the need to understand the impact of the soil depth in the vegetation dynamics, and make measurements in these types of soils. Sardinia island is a very interesting and representative region of Mediterranean ecosystems. It is low urbanized, and is not irrigated, except some plan areas close to the main cities where main agricultural activities are concentrated. The case study sites are within the Flumendosa river basin on Sardinia. Two sites, both in the Flumendosa river and with similar height a.s.l., are investigated. The distance between the sites is around 4 km but the first is a typically grass site located on an alluvial plan valley with a soil depth more than 2m, while the second site is a patchy mixture of Mediterranean vegetation types Oaks, creepers of the wild olive trees and C3 herbaceous species and the soil thickness varies from 15-40 cm, bounded from below by a rocky layer of basalt, partially fractured. In both sites land-surface fluxes and CO2 fluxes are estimated by eddy correlation technique based micrometeorological towers. Soil moisture profiles were also continuously estimated using water content reflectometers and gravimetric method, and periodically leaf area index PFTs are estimated during the Spring-Summer 2005. The following objectives are addressed:1) pointing out the dynamics of land surface fluxes, soil moisture, CO2 and vegetation cover for two contrasting water-limited ecosystems; 2) assess the impact of the soil depth and type on the CO2 and water balance dynamics. For reaching the objectives an ecohydrologic model is also successfully used and applied to the case studies. It couples a vegetation dynamic model, which computes the change in biomass over time for the PFTs, and a 3-component (bare soil, grass and woody vegetation) land surface model.

Estimating soil hydrological response by combining precipitation-runoff modeling and hydro-functional soil homogeneous units

NASA Astrophysics Data System (ADS)

Aroca-Jimenez, Estefania; Bodoque, Jose Maria; Diez-Herrero, Andres

2015-04-01

Flash floods constitute one of the natural hazards better able to generate risk, particularly with regard to Society. The complexity of this process and its dependence on various factors related to the characteristics of the basin and rainfall make flash floods are difficult to characterize in terms of their hydrological response.To do this, it is essential a proper analysis of the so called 'initial abstractions'. Among all of these processes, infiltration plays a crucial role in explaining the occurrence of floods in mountainous basins.For its characterization the Green-Ampt model , which depends on the characteristics of rainfall and physical properties of soil has been used in this work.This is a method enabling to simulate floods in mountainous basins where hydrological response is sub-daily. However, it has the disadvantage that it is based on physical properties of soil which have a high spatial variability. To address this difficulty soil mapping units have been delineated according to the geomorphological landforms and elements. They represent hydro-functional mapping units that are theoretically homogeneous from the perspective of the pedostructure parameters of the pedon. So the soil texture of each homogeneous group of landform units was studied by granulometric analyses using standarized sieves and Sedigraph devices. In addition, uncertainty associated with the parameterization of the Green-Ampt method has been estimated by implementing a Monte Carlo approach, which required assignment of the proper distribution function to each parameter.The suitability of this method was contrasted by calibrating and validating a hydrological model, in which the generation of runoff hydrograph has been simulated using the SCS unit hydrograph (HEC-GeoHMS software), while flood wave routing has been characterized using the Muskingum-Cunge method. Calibration and validation of the model was from the use of an automatic routine based on the employ of the search algorithm known as univariate gradient, while the objective function to be used was the percentage of error in the flow-peak of the hydrograph. The methodology proposed here was implemented in the torrential Venero Claro basin, which is a tributary of the Alberche river on its right bank, located in the Sierra del Valle (eastern foothills of the Sierra de Gredos, Spanish Central System). Currently this basin has an active network of six rainfall gauges, one stream gauging, three complete weather stations and one weather X-band radar. This hydrologic instrumentation makes this basin, with its 15 km², is one of the most densely instrumented basins from a hydrological and meteorological point of view in Spain.

Climate change-driven treeline advances in the Urals alter soil microbial communities

NASA Astrophysics Data System (ADS)

Djukic, Ika; Moiseev, Pavel; Hagedorn, Frank

2016-04-01

Climatic warming may affect microbial communities and their functions either directly through increased temperatures or indirectly by changes in vegetation. Treelines are temperature-limited vegetation boundaries from tundra to forests. In unmanaged regions of the Ural mountains, there is evidence that the forest-tundra ecotone has shifted upward in response to climate warming during the 20th century. Little is known about the effects of the treeline advances on the microbial structure and function and hence they feedbacks on the belowground carbon and nitrogen cycling In our study, we aimed to estimate how ongoing upward shifts of the treeline ecotone might affect soil biodiversity and its function and hence soil carbon (C) and nitrogen (N) dynamics in the Southern and Polar Ural mountains. Along altitudinal gradients reaching from the tundra to forests, we determined the soil microbial community composition (using Phospholipid Fatty Acids method) and quantified the activity of several extracellular enzymes involved in the C and nutrient cycling. In addition, we measured C pools in biomass and soils and quantified C and N mineralization. The results for the top soils, both in South Urals and in the Polar Ural, indicate a close link between climate change driven vegetation changes and soil microbial communities. The observed changes in microbial structure are induced through the resulting more favorable conditions than due to a shift in litter quality. The activities of chitinase were significantly higher under trees than under herbaceous plants, while activities of cellulase and protease declined with altitude from the tundra to the closed forest. In contrast to enzymatic activities, soil carbon stocks did not change significantly with altitude very likely as a result of a balancing out of increased C inputs from vegetation by an enhanced C output through mineralization with forest expansion. The accelerated organic matter turnover in the forest than in the tundra leads to higher contents of mineral N and net nitrification rates. In turn, the increasing N availability may stimulate plant growth and hence, induce a positive feedback between treeline advances and soil nitrogen cycling through soil microbial communities.

Retrieval of aerosol optical depth over bare soil surfaces using time series of MODIS imagery

NASA Astrophysics Data System (ADS)

Yuan, Zhengwu; Yuan, Ranyin; Zhong, Bo

2014-11-01

Aerosol Optical Depth (AOD) is one of the key parameters which can not only reflect the characterization of atmospheric turbidity, but also identify the climate effects of aerosol. The current MODIS aerosol estimation algorithm over land is based on the "dark-target" approach which works only over densely vegetated surfaces. For non-densely vegetated surfaces (such as snow/ice, desert, and bare soil surfaces), this method will be failed. In this study, we develop an algorithm to derive AOD over the bare soil surfaces. Firstly, this method uses the time series of MODIS imagery to detect the " clearest" observations during the non-growing season in multiple years for each pixel. Secondly, the "clearest" observations after suitable atmospheric correction are used to fit the bare soil's bidirectional reflectance distribution function (BRDF) using Kernel model. As long as the bare soil's BRDF is established, the surface reflectance of "hazy" observations can be simulated. Eventually, the AOD over the bare soil surfaces are derived. Preliminary validation results by comparing with the ground measurements from AERONET at Xianghe sites show a good agreement.

Reducing Contingency through Sampling at the Luckey FUSRAP Site - 13186

DOE Office of Scientific and Technical Information (OSTI.GOV)

Frothingham, David; Barker, Michelle; Buechi, Steve

2013-07-01

Typically, the greatest risk in developing accurate cost estimates for the remediation of hazardous, toxic, and radioactive waste sites is the uncertainty in the estimated volume of contaminated media requiring remediation. Efforts to address this risk in the remediation cost estimate can result in large cost contingencies that are often considered unacceptable when budgeting for site cleanups. Such was the case for the Luckey Formerly Utilized Sites Remedial Action Program (FUSRAP) site near Luckey, Ohio, which had significant uncertainty surrounding the estimated volume of site soils contaminated with radium, uranium, thorium, beryllium, and lead. Funding provided by the American Recoverymore » and Reinvestment Act (ARRA) allowed the U.S. Army Corps of Engineers (USACE) to conduct additional environmental sampling and analysis at the Luckey Site between November 2009 and April 2010, with the objective to further delineate the horizontal and vertical extent of contaminated soils in order to reduce the uncertainty in the soil volume estimate. Investigative work included radiological, geophysical, and topographic field surveys, subsurface borings, and soil sampling. Results from the investigative sampling were used in conjunction with Argonne National Laboratory's Bayesian Approaches for Adaptive Spatial Sampling (BAASS) software to update the contaminated soil volume estimate for the site. This updated volume estimate was then used to update the project cost-to-complete estimate using the USACE Cost and Schedule Risk Analysis process, which develops cost contingencies based on project risks. An investment of $1.1 M of ARRA funds for additional investigative work resulted in a reduction of 135,000 in-situ cubic meters (177,000 in-situ cubic yards) in the estimated base volume estimate. This refinement of the estimated soil volume resulted in a $64.3 M reduction in the estimated project cost-to-complete, through a reduction in the uncertainty in the contaminated soil volume estimate and the associated contingency costs. (authors)« less

Remotely monitoring evaporation rate and soil water status using thermal imaging and "three-temperatures model (3T Model)" under field-scale conditions.

PubMed

Qiu, Guo Yu; Zhao, Ming

2010-03-01

Remote monitoring of soil evaporation and soil water status is necessary for water resource and environment management. Ground based remote sensing can be the bridge between satellite remote sensing and ground-based point measurement. The primary object of this study is to provide an algorithm to estimate evaporation and soil water status by remote sensing and to verify its accuracy. Observations were carried out in a flat field with varied soil water content. High-resolution thermal images were taken with a thermal camera; soil evaporation was measured with a weighing lysimeter; weather data were recorded at a nearby meteorological station. Based on the thermal imaging and the three-temperatures model (3T model), we developed an algorithm to estimate soil evaporation and soil water status. The required parameters of the proposed method were soil surface temperature, air temperature, and solar radiation. By using the proposed method, daily variation in soil evaporation was estimated. Meanwhile, soil water status was remotely monitored by using the soil evaporation transfer coefficient. Results showed that the daily variation trends of measured and estimated evaporation agreed with each other, with a regression line of y = 0.92x and coefficient of determination R(2) = 0.69. The simplicity of the proposed method makes the 3T model a potentially valuable tool for remote sensing.

When bulk density methods matter: Implications for estimating soil organic carbon pools in rocky soils

USDA-ARS?s Scientific Manuscript database

Resolving uncertainty in the carbon cycle is paramount to refining climate predictions. Soil organic carbon (SOC) is a major component of terrestrial C pools, and accuracy of SOC estimates are only as good as the measurements and assumptions used to obtain them. Dryland soils account for a substanti...

Rainfall estimation by inverting SMOS soil moisture estimates: a comparison of different methods over Australia

USDA-ARS?s Scientific Manuscript database

Remote sensing of soil moisture has reached a level of maturity and accuracy for which the retrieved products can be used to improve hydrological and meteorological applications. In this study, the soil moisture product from the European Space Agency’s Soil Moisture and Ocean Salinity (SMOS) is used...

Soil Monitor: an open source web application for real-time soil sealing monitoring and assessment

NASA Astrophysics Data System (ADS)

Langella, Giuliano; Basile, Angelo; Giannecchini, Simone; Iamarino, Michela; Munafò, Michele; Terribile, Fabio

2016-04-01

Soil sealing is one of the most important causes of land degradation and desertification. In Europe, soil covered by impermeable materials has increased by about 80% from the Second World War till nowadays, while population has only grown by one third. There is an increasing concern at the high political levels about the need to attenuate imperviousness itself and its effects on soil functions. European Commission promulgated a roadmap (COM(2011) 571) by which the net land take would be zero by 2050. Furthermore, European Commission also published a report in 2011 providing best practices and guidelines for limiting soil sealing and imperviousness. In this scenario, we developed an open source and an open source based Soil Sealing Geospatial Cyber Infrastructure (SS-GCI) named as "Soil Monitor". This tool merges a webGIS with parallel geospatial computation in a fast and dynamic fashion in order to provide real-time assessments of soil sealing at high spatial resolution (20 meters and below) over the whole Italy. Common open source webGIS packages are used to implement both the data management and visualization infrastructures, such as GeoServer and MapStore. The high-speed geospatial computation is ensured by a GPU parallelism using the CUDA (Computing Unified Device Architecture) framework by NVIDIA®. This kind of parallelism required the writing - from scratch - all codes needed to fulfil the geospatial computation built behind the soil sealing toolbox. The combination of GPU computing with webGIS infrastructures is relatively novel and required particular attention at the Java-CUDA programming interface. As a result, Soil Monitor is smart because it can perform very high time-consuming calculations (querying for instance an Italian administrative region as area of interest) in less than one minute. The web application is embedded in a web browser and nothing must be installed before using it. Potentially everybody can use it, but the main targets are the stakeholders dealing with sealing, such as policy makers, land owners and asphalt/cement companies. As a matter of fact, Soil Monitor can be used to improve the spatial planning therefore limiting the progression of disordered soil sealing which causes both the direct loss of soils due to imperviousness but also the indirect loss caused by fragmentation of soils (which has different negative effects on the durability of soil functions, such as habitat corridors). Further, in a future version, Soil Monitor would estimate the best location for a new building or help compensating soil losses by actions in other areas to offset drawbacks at zero. The presented SS-GCI dealing with soil sealing - if opportunely scaled - would aid the implementation of best practices for limiting soil sealing or mitigating its effects on soil functions.

Salinity controls on plant transpiration and soil water balance

NASA Astrophysics Data System (ADS)

Perri, S.; Molini, A.; Suweis, S. S.; Viola, F.; Entekhabi, D.

2017-12-01

Soil salinization and aridification represent a major threat for the food security and sustainable development of drylands. The two problems are deeply connected, and their interplay is expected to be further enhanced by climate change and projected population growth. Salt-affected land is currently estimated to cover around 1.1 Gha, and is particularly widespread in semi-arid to hyper-arid climates. Over 900 Mha of these saline/sodic soils are potentially available for crop or biomass production. Salt-tolerant plants have been recently proposed as valid solution to exploit or even remediate salinized soils. However the effects of salinity on evapotranspiration, soil water balance and the long-term salt mass balance in the soil, are still largely unexplored. In this contribution we analyze the feedback of evapotranspiration on soil salinization, with particular emphasis on the role of vegetation and plant salt-tolerance. The goal is to introduce a simple modeling framework able to shed some light on how (a) soil salinity controls plant transpiration, and (b) salinization itself is favored/impeded by different vegetation feedback. We introduce at this goal a spatially lumped stochastic model of soil moisture and salt mass dynamics averaged over the active soil depth, and accounting for the effect of salinity on evapotranspiration. Here, the limiting effect of salinity on ET is modeled through a simple plant response function depending on both salt concentration in the soil and plant salt-tolerance. The coupled soil moisture and salt mass balance is hence used to obtain the conditional steady-state probability density function (pdf) of soil moisture for given salt tolerance and salinization level, Our results show that salinity imposes a limit in the soil water balance and this limit depends on plant salt-tolerance mainly through the control of the leaching occurrence (tolerant plants exploit water more efficiently than the sensitive ones). We also analyzed the effect of salt-tolerance on salt concentration patterns pointing out how vegetation imposes an upper bound to concentration of soluble salts in the soil. The long-term effects of plant salt tolerance on soil salinization are also discussed by an approximated expression for the salt mass pdf.

GLEAM v3: updated land evaporation and root-zone soil moisture datasets

NASA Astrophysics Data System (ADS)

Martens, Brecht; Miralles, Diego; Lievens, Hans; van der Schalie, Robin; de Jeu, Richard; Fernández-Prieto, Diego; Verhoest, Niko

2016-04-01

Evaporation determines the availability of surface water resources and the requirements for irrigation. In addition, through its impacts on the water, carbon and energy budgets, evaporation influences the occurrence of rainfall and the dynamics of air temperature. Therefore, reliable estimates of this flux at regional to global scales are of major importance for water management and meteorological forecasting of extreme events. However, the global-scale magnitude and variability of the flux, and the sensitivity of the underlying physical process to changes in environmental factors, are still poorly understood due to the limited global coverage of in situ measurements. Remote sensing techniques can help to overcome the lack of ground data. However, evaporation is not directly observable from satellite systems. As a result, recent efforts have focussed on combining the observable drivers of evaporation within process-based models. The Global Land Evaporation Amsterdam Model (GLEAM, www.gleam.eu) estimates terrestrial evaporation based on daily satellite observations of meteorological drivers of terrestrial evaporation, vegetation characteristics and soil moisture. Since the publication of the first version of the model in 2011, GLEAM has been widely applied for the study of trends in the water cycle, interactions between land and atmosphere and hydrometeorological extreme events. A third version of the GLEAM global datasets will be available from the beginning of 2016 and will be distributed using www.gleam.eu as gateway. The updated datasets include separate estimates for the different components of the evaporative flux (i.e. transpiration, bare-soil evaporation, interception loss, open-water evaporation and snow sublimation), as well as variables like the evaporative stress, potential evaporation, root-zone soil moisture and surface soil moisture. A new dataset using SMOS-based input data of surface soil moisture and vegetation optical depth will also be distributed. The most important updates in GLEAM include the revision of the soil moisture data assimilation system, the evaporative stress functions and the infiltration of rainfall. In this presentation, we will highlight the changes of the methodology and present the new datasets, their validation against in situ observations and the comparisons against alternative datasets of terrestrial evaporation, such as GLDAS-Noah, ERA-Interim and previous GLEAM datasets. Preliminary results indicate that the magnitude and the spatio-temporal variability of the evaporation estimates have been slightly improved upon previous versions of the datasets.

Circumpolar assessment of rhizosphere priming shows limited increase in carbon loss estimates for permafrost soils but large regional variability

NASA Astrophysics Data System (ADS)

Wild, B.; Keuper, F.; Kummu, M.; Beer, C.; Blume-Werry, G.; Fontaine, S.; Gavazov, K.; Gentsch, N.; Guggenberger, G.; Hugelius, G.; Jalava, M.; Koven, C.; Krab, E. J.; Kuhry, P.; Monteux, S.; Richter, A.; Shazhad, T.; Dorrepaal, E.

2017-12-01

Predictions of soil organic carbon (SOC) losses in the northern circumpolar permafrost area converge around 15% (± 3% standard error) of the initial C pool by 2100 under the RCP 8.5 warming scenario. Yet, none of these estimates consider plant-soil interactions such as the rhizosphere priming effect (RPE). While laboratory experiments have shown that the input of plant-derived compounds can stimulate SOC losses by up to 1200%, the magnitude of RPE in natural ecosystems is unknown and no methods for upscaling exist so far. We here present the first spatial and depth explicit RPE model that allows estimates of RPE on a large scale (PrimeSCale). We combine available spatial data (SOC, C/N, GPP, ALT and ecosystem type) and new ecological insights to assess the importance of the RPE at the circumpolar scale. We use a positive saturating relationship between the RPE and belowground C allocation and two ALT-dependent rooting-depth distribution functions (for tundra and boreal forest) to proportionally assign belowground C allocation and RPE to individual soil depth increments. The model permits to take into account reasonable limiting factors on additional SOC losses by RPE including interactions between spatial and/or depth variation in GPP, plant root density, SOC stocks and ALT. We estimate potential RPE-induced SOC losses at 9.7 Pg C (5 - 95% CI: 1.5 - 23.2 Pg C) by 2100 (RCP 8.5). This corresponds to an increase of the current permafrost SOC-loss estimate from 15% of the initial C pool to about 16%. If we apply an additional molar C/N threshold of 20 to account for microbial C limitation as a requirement for the RPE, SOC losses by RPE are further reduced to 6.5 Pg C (5 - 95% CI: 1.0 - 16.8 Pg C) by 2100 (RCP 8.5). Although our results show that current estimates of permafrost soil C losses are robust without taking into account the RPE, our model also highlights high-RPE risk in Siberian lowland areas and Alaska north of the Brooks Range. The small overall impact of the RPE is largely explained by the interaction between belowground plant C allocation and SOC depth distribution. Our findings thus highlight the importance of fine scale interactions between plant and soil properties for large scale carbon fluxes and we provide a first model that bridges this gap and permits the quantification of RPE across a large area.

Soil moisture estimation using reflected solar and emitted thermal infrared radiation

NASA Technical Reports Server (NTRS)

Jackson, R. D.; Cihlar, J.; Estes, J. E.; Heilman, J. L.; Kahle, A.; Kanemasu, E. T.; Millard, J.; Price, J. C.; Wiegand, C. L.

1978-01-01

Classical methods of measuring soil moisture such as gravimetric sampling and the use of neutron moisture probes are useful for cases where a point measurement is sufficient to approximate the water content of a small surrounding area. However, there is an increasing need for rapid and repetitive estimations of soil moisture over large areas. Remote sensing techniques potentially have the capability of meeting this need. The use of reflected-solar and emitted thermal-infrared radiation, measured remotely, to estimate soil moisture is examined.

Mouse Assay for Determination of Arsenic Bioavailability in Contaminated Soils

EPA Science Inventory

Background: Accurate assessment of human exposure estimates from arsenic-contaminated soils depends upon estimating arsenic (As) soil bioavailability. Development of bioavailability assays provides data needed for human health risk assessments and supports development and valida...

Estimation of bare soil evaporation using multifrequency airborne SAR

NASA Technical Reports Server (NTRS)

Soares, Joao V.; Shi, Jiancheng; Van Zyl, Jakob; Engman, E. T.

1992-01-01

It is shown that for homogeneous areas soil moisture can be derived from synthetic aperture radar (SAR) measurements, so that the use of microwave remote sensing can given realistic estimates of energy fluxes if coupled to a simple two-layer model repesenting the soil. The model simulates volumetric water content (Wg) using classical meterological data, provided that some of the soil thermal and hydraulic properties are known. Only four parameters are necessary: mean water content, thermal conductivity and diffusitivity, and soil resistance to evaporation. They may be derived if a minimal number of measured values of Wg and surface layer temperature (Tg) are available together with independent measurements of energy flux to compare with the estimated values. The estimated evaporation is shown to be realistic and in good agreement with drying stage theory in which the transfer of water in the soil is in vapor form.

Aspects of spatial and temporal aggregation in estimating regional carbon dioxide fluxes from temperate forest soils

NASA Technical Reports Server (NTRS)

Kicklighter, David W.; Melillo, Jerry M.; Peterjohn, William T.; Rastetter, Edward B.; Mcguire, A. David; Steudler, Paul A.; Aber, John D.

1994-01-01

We examine the influence of aggregation errors on developing estimates of regional soil-CO2 flux from temperate forests. We find daily soil-CO2 fluxes to be more sensitive to changes in soil temperatures (Q(sub 10) = 3.08) than air temperatures (Q(sub 10) = 1.99). The direct use of mean monthly air temperatures with a daily flux model underestimates regional fluxes by approximately 4%. Temporal aggregation error varies with spatial resolution. Overall, our calibrated modeling approach reduces spatial aggregation error by 9.3% and temporal aggregation error by 15.5%. After minimizing spatial and temporal aggregation errors, mature temperate forest soils are estimated to contribute 12.9 Pg C/yr to the atmosphere as carbon dioxide. Georeferenced model estimates agree well with annual soil-CO2 fluxes measured during chamber studies in mature temperate forest stands around the globe.

Estimating soil hydraulic parameters from transient flow experiments in a centrifuge using parameter optimization technique

USGS Publications Warehouse

Šimůnek, Jirka; Nimmo, John R.

2005-01-01

A modified version of the Hydrus software package that can directly or inversely simulate water flow in a transient centrifugal field is presented. The inverse solver for parameter estimation of the soil hydraulic parameters is then applied to multirotation transient flow experiments in a centrifuge. Using time‐variable water contents measured at a sequence of several rotation speeds, soil hydraulic properties were successfully estimated by numerical inversion of transient experiments. The inverse method was then evaluated by comparing estimated soil hydraulic properties with those determined independently using an equilibrium analysis. The optimized soil hydraulic properties compared well with those determined using equilibrium analysis and steady state experiment. Multirotation experiments in a centrifuge not only offer significant time savings by accelerating time but also provide significantly more information for the parameter estimation procedure compared to multistep outflow experiments in a gravitational field.

Soil-borne bacterial structure and diversity does not reflect community activity in Pampa biome.

PubMed

Lupatini, Manoeli; Suleiman, Afnan Khalil Ahmad; Jacques, Rodrigo Josemar Seminoti; Antoniolli, Zaida Inês; Kuramae, Eiko Eurya; de Oliveira Camargo, Flávio Anastácio; Roesch, Luiz Fernando Würdig

2013-01-01

The Pampa biome is considered one of the main hotspots of the world's biodiversity and it is estimated that half of its original vegetation was removed and converted to agricultural land and tree plantations. Although an increasing amount of knowledge is being assembled regarding the response of soil bacterial communities to land use change, to the associated plant community and to soil properties, our understanding about how these interactions affect the microbial community from the Brazilian Pampa is still poor and incomplete. In this study, we hypothesized that the same soil type from the same geographic region but under distinct land use present dissimilar soil bacterial communities. To test this hypothesis, we assessed the soil bacterial communities from four land-uses within the same soil type by 454-pyrosequencing of 16S rRNA gene and by soil microbial activity analyzes. We found that the same soil type under different land uses harbor similar (but not equal) bacterial communities and the differences were controlled by many microbial taxa. No differences regarding diversity and richness between natural areas and areas under anthropogenic disturbance were detected. However, the measures of microbial activity did not converge with the 16S rRNA data supporting the idea that the coupling between functioning and composition of bacterial communities is not necessarily correlated.

The hidden ecological resource of andic soils in mountain ecosystems: evidence from Italy

NASA Astrophysics Data System (ADS)

Terribile, Fabio; Iamarino, Michela; Langella, Giuliano; Manna, Piero; Mileti, Florindo Antonio; Vingiani, Simona; Basile, Angelo

2018-01-01

Andic soils have unique morphological, physical, and chemical properties that induce both considerable soil fertility and great vulnerability to land degradation. Moreover, they are the most striking mineral soils in terms of large organic C storage and long C residence time. This is especially related to the presence of poorly crystalline clay minerals and metal-humus complexes. Recognition of andic soils is then very important.Here we attempt to show, through a combined analysis of 35 sampling points chosen in accordance to specific physical and vegetation rules, that some andic soils have an utmost ecological importance.More specifically, in Italian non-volcanic mountain ecosystems ( > 600 m a.s.l.) combining low slope (< 21 %) and highly active green biomass (high NDVI values) and in agreement to recent findings, we found the widespread occurrence of andic soils having distinctive physical and hydrological properties including low bulk density and remarkably high water retention. Most importantly, we report a demonstration of the ability of these soils to affect ecosystem functions by analysing their influence on the timescale acceleration of photosynthesis estimated by NDVI measurements.Our results are hoped to be a starting point for better understanding of the ecological importance of andic soils and also possibly to better consider pedological information in C balance calculations.

Ecological effects of soil properties and metal concentrations on the composition and diversity of microbial communities associated with land use patterns in an electronic waste recycling region.

PubMed

Wu, Wencheng; Dong, Changxun; Wu, Jiahui; Liu, Xiaowen; Wu, Yingxin; Chen, Xianbin; Yu, Shixiao

2017-12-01

Soil microbes play vital roles in ecosystem functions, and soil microbial communities may be strongly structured by land use patterns associated with electronic waste (e-waste) recycling activities, which can increase the heavy metal concentration in soils. In this study, a suite of soils from five land use types (paddy field, vegetable field, dry field, forest field, and e-waste recycling site) were collected in Longtang Town, Guangdong Province, South China. Soil physicochemical properties and heavy metal concentrations were measured, and the indigenous microbial assemblages were profiled using 16S rRNA high-throughput sequencing and clone library analyses. The results showed that mercury concentration was positively correlated with both Faith's PD and Chao1 estimates, suggesting that the soil microbial alpha diversity was predominantly regulated by mercury. In addition, redundancy analysis indicated that available phosphorus, soil moisture, and mercury were the three major drivers affecting the microbial assemblages. Overall, the microbial composition was determined primarily by land use patterns, and this study provides a novel insight on the composition and diversity of microbial communities in soils associated with e-waste recycling activities. Copyright © 2017 Elsevier B.V. All rights reserved.

Ground penetrating radar for underground sensing in agriculture: a review

NASA Astrophysics Data System (ADS)

Liu, Xiuwei; Dong, Xuejun; Leskovar, Daniel I.

2016-10-01

Belowground properties strongly affect agricultural productivity. Traditional methods for quantifying belowground properties are destructive, labor-intensive and pointbased. Ground penetrating radar can provide non-invasive, areal, and repeatable underground measurements. This article reviews the application of ground penetrating radar for soil and root measurements and discusses potential approaches to overcome challenges facing ground penetrating radar-based sensing in agriculture, especially for soil physical characteristics and crop root measurements. Though advanced data-analysis has been developed for ground penetrating radar-based sensing of soil moisture and soil clay content in civil engineering and geosciences, it has not been used widely in agricultural research. Also, past studies using ground penetrating radar in root research have been focused mainly on coarse root measurement. Currently, it is difficult to measure individual crop roots directly using ground penetrating radar, but it is possible to sense root cohorts within a soil volume grid as a functional constituent modifying bulk soil dielectric permittivity. Alternatively, ground penetrating radarbased sensing of soil water content, soil nutrition and texture can be utilized to inversely estimate root development by coupling soil water flow modeling with the seasonality of plant root growth patterns. Further benefits of ground penetrating radar applications in agriculture rely on the knowledge, discovery, and integration among differing disciplines adapted to research in agricultural management.

Detection of soil erosion within pinyon-juniper woodlands using Thematic Mapper (TM) data

NASA Technical Reports Server (NTRS)

Price, Kevin P.

1993-01-01

Multispectral measurements collected by Landsat Thematic Mapper (TM) were correlated with field measurements, direct soil loss estimates, and Universal Soil Loss Equation (USLE) estimates to determine the sensitivity of TM data to varying degrees of soil erosion in pinyon-juniper woodland in central Utah. TM data were also evaluated as a predictor of the USLE Crop Management C factor for pinyon-juniper woodlands. TM spectral data were consistently better predictors of soil erosion factors than any combination of field factors. TM data were more sensitive to vegetation variations than the USLE C factor. USLE estimates showed low annual rates of erosion which varied little among the study sites. Direct measurements of rate of soil loss using the SEDIMENT (Soil Erosion DIrect measureMENT) technique, indicated high and varying rates of soil loss among the sites since tree establishment. Erosion estimates from the USLE and SEDIMENT methods suggest that erosion rates have been severe in the past, but because significant amounts of soil have already been eroded, and the surface is now armored by rock debris, present erosion rates are lower. Indicators of accelerated erosion were still present on all sites, however, suggesting that the USLE underestimated erosion within the study area.

Soil water content spatial pattern estimated by thermal inertia from air-borne sensors

NASA Astrophysics Data System (ADS)

Coppola, Antonio; Basile, Angelo; Esposito, Marco; Menenti, Massimo; Buonanno, Maurizio

2010-05-01

Remote sensing of soil water content from air- or space-borne platforms offer the possibility to provide large spatial coverage and temporal continuity. The water content can be actually monitored in a thin soil layer, usually up to a depth of 0.05m below the soil surface. To the contrary, difficulties arise in the estimation of the water content storage along the soil profile and its spatial (horizontal) distribution, which are closely connected to soil hydraulic properties and their spatial distribution. A promising approach for estimating soil water contents profiles is the integration of remote sensing of surface water content and hydrological modeling. A major goal of the scientific group is to develop a practical and robust procedure for estimating water contents throughout the soil profile from surface water content. As a first step, in this work, we will show some preliminary results from aircraft images analysis and their validation by field campaigns data. The data extracted from the airborne sensors provided the opportunity of retrieving land surface temperatures with a very high spatial resolution. The surface water content pattern, as deduced by the thermal inertia estimations, was compared to the surface water contents maps measured in situ by time domain reflectometry-based probes.

Secondary plant succession on disturbed sites at Yucca Mountain, Nevada

DOE Office of Scientific and Technical Information (OSTI.GOV)

Angerer, J.P.; Ostler, W.K.; Gabbert, W.D.

1994-12-01

This report presents the results of a study of secondary plant succession on disturbed sites created during initial site investigations in the late 1970s and early 1980s at Yucca Mountain, NV. Specific study objectives were to determine the rate and success of secondary plant succession, identify plant species found in disturbances that may be suitable for site-specific reclamation, and to identify environmental variables that influence succession on disturbed sites. During 1991 and 1992, fifty seven disturbed sites were located. Vegetation parameters, disturbance characteristics and environmental variables were measured at each site. Disturbed site vegetation parameters were compared to that ofmore » undisturbed sites to determine the status of disturbed site plant succession. Vegetation on disturbed sites, after an average of ten years, was different from undisturbed areas. Ambrosia dumosa, Chrysothamnus teretifolius, Hymenoclea salsola, Gutierrezia sarothrae, Atriplex confertifolia, Atriplex canescens, and Stephanomeria pauciflora were the most dominant species across all disturbed sites. With the exception of A. dumosa, these species were generally minor components of the undisturbed vegetation. Elevation, soil compaction, soil potassium, and amounts of sand and gravel in the soil were found to be significant environmental variables influencing the species composition and abundance of perennial plants on disturbed sites. The recovery rate for disturbed site secondary succession was estimated. Using a linear function (which would represent optimal conditions), the recovery rate for perennial plant cover, regardless of which species comprised the cover, was estimated to be 20 years. However, when a logarithmic function (which would represent probable conditions) was used, the recovery rate was estimated to be 845 years. Recommendations for future studies and site-specific reclamation of disturbances are presented.« less

A comparison of the abilities of the USLE-M, RUSLE2 and WEPP to model event erosion from bare fallow areas.

PubMed

Kinnell, P I A

2017-10-15

Traditionally, the Universal Soil Loss Equation (USLE) and the revised version of it (RUSLE) have been applied to predicting the long term average soil loss produced by rainfall erosion in many parts of the world. Overtime, it has been recognized that there is a need to predict soil losses over shorter time scales and this has led to the development of WEPP and RUSLE2 which can be used to predict soil losses generated by individual rainfall events. Data currently exists that enables the RUSLE2, WEPP and the USLE-M to estimate historic soil losses from bare fallow runoff and soil loss plots recorded in the USLE database. Comparisons of the abilities of the USLE-M and RUSLE2 to estimate event soil losses from bare fallow were undertaken under circumstances where both models produced the same total soil loss as observed for sets of erosion events on 4 different plots at 4 different locations. Likewise, comparisons of the abilities of the USLE-M and WEPP to model event soil loss from bare fallow were undertaken for sets of erosion events on 4 plots at 4 different locations. Despite being calibrated specifically for each plot, WEPP produced the worst estimates of event soil loss for all the 4 plots. Generally, the USLE-M using measured runoff to calculate the product of the runoff ratio, storm kinetic energy and the maximum 30-minute rainfall intensity produced the best estimates. As to be expected, ability of the USLE-M to estimate event soil loss was reduced when runoff predicted by either RUSLE2 or WEPP was used. Despite this, the USLE-M using runoff predicted by WEPP estimated event soil loss better than WEPP. RUSLE2 also outperformed WEPP. Copyright © 2017 Elsevier B.V. All rights reserved.

Erosivity, surface runoff, and soil erosion estimation using GIS-coupled runoff-erosion model in the Mamuaba catchment, Brazil.

PubMed

Marques da Silva, Richarde; Guimarães Santos, Celso Augusto; Carneiro de Lima Silva, Valeriano; Pereira e Silva, Leonardo

2013-11-01

This study evaluates erosivity, surface runoff generation, and soil erosion rates for Mamuaba catchment, sub-catchment of Gramame River basin (Brazil) by using the ArcView Soil and Water Assessment Tool (AvSWAT) model. Calibration and validation of the model was performed on monthly basis, and it could simulate surface runoff and soil erosion to a good level of accuracy. Daily rainfall data between 1969 and 1989 from six rain gauges were used, and the monthly rainfall erosivity of each station was computed for all the studied years. In order to evaluate the calibration and validation of the model, monthly runoff data between January 1978 and April 1982 from one runoff gauge were used as well. The estimated soil loss rates were also realistic when compared to what can be observed in the field and to results from previous studies around of catchment. The long-term average soil loss was estimated at 9.4 t ha(-1) year(-1); most of the area of the catchment (60%) was predicted to suffer from a low- to moderate-erosion risk (<6 t ha(-1) year(-1)) and, in 20% of the catchment, the soil erosion was estimated to exceed > 12 t ha(-1) year(-1). Expectedly, estimated soil loss was significantly correlated with measured rainfall and simulated surface runoff. Based on the estimated soil loss rates, the catchment was divided into four priority categories (low, moderate, high and very high) for conservation intervention. The study demonstrates that the AvSWAT model provides a useful tool for soil erosion assessment from catchments and facilitates the planning for a sustainable land management in northeastern Brazil.

Estimating salinity stress in sugarcane fields with spaceborne hyperspectral vegetation indices

NASA Astrophysics Data System (ADS)

Hamzeh, S.; Naseri, A. A.; AlaviPanah, S. K.; Mojaradi, B.; Bartholomeus, H. M.; Clevers, J. G. P. W.; Behzad, M.

2013-04-01

The presence of salt in the soil profile negatively affects the growth and development of vegetation. As a result, the spectral reflectance of vegetation canopies varies for different salinity levels. This research was conducted to (1) investigate the capability of satellite-based hyperspectral vegetation indices (VIs) for estimating soil salinity in agricultural fields, (2) evaluate the performance of 21 existing VIs and (3) develop new VIs based on a combination of wavelengths sensitive for multiple stresses and find the best one for estimating soil salinity. For this purpose a Hyperion image of September 2, 2010, and data on soil salinity at 108 locations in sugarcane (Saccharum officina L.) fields were used. Results show that soil salinity could well be estimated by some of these VIs. Indices related to chlorophyll absorption bands or based on a combination of chlorophyll and water absorption bands had the highest correlation with soil salinity. In contrast, indices that are only based on water absorption bands had low to medium correlations, while indices that use only visible bands did not perform well. From the investigated indices the optimized soil-adjusted vegetation index (OSAVI) had the strongest relationship (R2 = 0.69) with soil salinity for the training data, but it did not perform well in the validation phase. The validation procedure showed that the new salinity and water stress indices (SWSI) implemented in this study (SWSI-1, SWSI-2, SWSI-3) and the Vogelmann red edge index yielded the best results for estimating soil salinity for independent fields with root mean square errors of 1.14, 1.15, 1.17 and 1.15 dS/m, respectively. Our results show that soil salinity could be estimated by satellite-based hyperspectral VIs, but validation of obtained models for independent data is essential for selecting the best model.

Impact of Land Use Change to the Soil Erosion Estimation for Cultural Landscapes: Case Study of Paphos Disrict in Cyprus

NASA Astrophysics Data System (ADS)

Cuca, B.; Agapiou, A.

2017-05-01

In 2006 UNESCO report has identified soil loss as one of the main threats of climate change with possible impact to natural and cultural heritage. The study illustrated in this paper shows the results from geomatic perspective, applying an interdisciplinary approach undertaken in order to identify major natural hazards affecting cultural landscapes and archaeological heritage in rural areas in Cyprus. In particular, Earth Observation (EO) and ground-based methods were identified and applied for mapping, monitoring and estimation of the possible soil loss caused by soil erosion. Special attention was given to the land use/land cover factor (C) and its impact on the overall estimation of the soil-loss. Cover factor represents the effect of soil-disturbing activities, plants, crop sequence and productivity level, soil cover and subsurface bio-mass on soil erosion. Urban areas have a definite role in retarding the recharge process, leading to increased runoff and soil loss in the broader area. On the other hand, natural vegetation plays a predominant role in reducing water erosion. The land use change was estimated based on the difference of the NDVI value between Landsat 5 TM and Sentinel-2 data for the period between 1980s' until today. Cover factor was then estimated for both periods and significant land use changes were further examined in areas of significant cultural and natural landscape value. The results were then compared in order to study the impact of land use change on the soil erosion and hence on the soil loss rate in the selected areas.

Remote Sensing Soil Moisture Analysis by Unmanned Aerial Vehicles Digital Imaging

NASA Astrophysics Data System (ADS)

Yeh, C. Y.; Lin, H. R.; Chen, Y. L.; Huang, S. Y.; Wen, J. C.

2017-12-01

In recent years, remote sensing analysis has been able to apply to the research of climate change, environment monitoring, geology, hydro-meteorological, and so on. However, the traditional methods for analyzing wide ranges of surface soil moisture of spatial distribution surveys may require plenty resources besides the high cost. In the past, remote sensing analysis performed soil moisture estimates through shortwave, thermal infrared ray, or infrared satellite, which requires lots of resources, labor, and money. Therefore, the digital image color was used to establish the multiple linear regression model. Finally, we can find out the relationship between surface soil color and soil moisture. In this study, we use the Unmanned Aerial Vehicle (UAV) to take an aerial photo of the fallow farmland. Simultaneously, we take the surface soil sample from 0-5 cm of the surface. The soil will be baking by 110° C and 24 hr. And the software ImageJ 1.48 is applied for the analysis of the digital images and the hue analysis into Red, Green, and Blue (R, G, B) hue values. The correlation analysis is the result from the data obtained from the image hue and the surface soil moisture at each sampling point. After image and soil moisture analysis, we use the R, G, B and soil moisture to establish the multiple regression to estimate the spatial distributions of surface soil moisture. In the result, we compare the real soil moisture and the estimated soil moisture. The coefficient of determination (R2) can achieve 0.5-0.7. The uncertainties in the field test, such as the sun illumination, the sun exposure angle, even the shadow, will affect the result; therefore, R2 can achieve 0.5-0.7 reflects good effect for the in-suit test by using the digital image to estimate the soil moisture. Based on the outcomes of the research, using digital images from UAV to estimate the surface soil moisture is acceptable. However, further investigations need to be collected more than ten days (four times a day) data to verify the relation between the image hue and the soil moisture for reliable moisture estimated model. And it is better to use the digital single lens reflex camera to prevent the deformation of the image and to have a better auto exposure. Keywords: soil, moisture, remote sensing

Sediment composition for the assessment of water erosion and nonpoint source pollution in natural and fire-affected landscapes.

PubMed

Carkovic, Athena B; Pastén, Pablo A; Bonilla, Carlos A

2015-04-15

Water erosion is a leading cause of soil degradation and a major nonpoint source pollution problem. Many efforts have been undertaken to estimate the amount and size distribution of the sediment leaving the field. Multi-size class water erosion models subdivide eroded soil into different sizes and estimate the aggregate's composition based on empirical equations derived from agricultural soils. The objective of this study was to evaluate these equations on soil samples collected from natural landscapes (uncultivated) and fire-affected soils. Chemical, physical, and soil fractions and aggregate composition analyses were performed on samples collected in the Chilean Patagonia and later compared with the equations' estimates. The results showed that the empirical equations were not suitable for predicting the sediment fractions. Fine particles, including primary clay, primary silt, and small aggregates (<53 μm) were over-estimated, and large aggregates (>53 μm) and primary sand were under-estimated. The uncultivated and fire-affected soils showed a reduced fraction of fine particles in the sediment, as clay and silt were mostly in the form of large aggregates. Thus, a new set of equations was developed for these soils, where small aggregates were defined as particles with sizes between 53 μm and 250 μm and large aggregates as particles>250 μm. With r(2) values between 0.47 and 0.98, the new equations provided better estimates for primary sand and large aggregates. The aggregate's composition was also well predicted, especially the silt and clay fractions in the large aggregates from uncultivated soils (r(2)=0.63 and 0.83, respectively) and the fractions of silt in the small aggregates (r(2)=0.84) and clay in the large aggregates (r(2)=0.78) from fire-affected soils. Overall, these new equations proved to be better predictors for the sediment and aggregate's composition in uncultivated and fire-affected soils, and they reduce the error when estimating soil loss in natural landscapes. Copyright © 2015 Elsevier B.V. All rights reserved.

Worldwide organic soil carbon and nitrogen data

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zinke, P.J.; Stangenberger, A.G.; Post, W.M.

The objective of the research presented in this package was to identify data that could be used to estimate the size of the soil organic carbon pool under relatively undisturbed soil conditions. A subset of the data can be used to estimate amounts of soil carbon storage at equilibrium with natural soil-forming factors. The magnitude of soil properties so defined is a resulting nonequilibrium values for carbon storage. Variation in these values is due to differences in local and geographic soil-forming factors. Therefore, information is included on location, soil nitrogen content, climate, and vegetation along with carbon density and variation.

Carbon sequestration potential of soils in southeast Germany derived from stable soil organic carbon saturation.

PubMed

Wiesmeier, Martin; Hübner, Rico; Spörlein, Peter; Geuß, Uwe; Hangen, Edzard; Reischl, Arthur; Schilling, Bernd; von Lützow, Margit; Kögel-Knabner, Ingrid

2014-02-01

Sequestration of atmospheric carbon (C) in soils through improved management of forest and agricultural land is considered to have high potential for global CO2 mitigation. However, the potential of soils to sequester soil organic carbon (SOC) in a stable form, which is limited by the stabilization of SOC against microbial mineralization, is largely unknown. In this study, we estimated the C sequestration potential of soils in southeast Germany by calculating the potential SOC saturation of silt and clay particles according to Hassink [Plant and Soil 191 (1997) 77] on the basis of 516 soil profiles. The determination of the current SOC content of silt and clay fractions for major soil units and land uses allowed an estimation of the C saturation deficit corresponding to the long-term C sequestration potential. The results showed that cropland soils have a low level of C saturation of around 50% and could store considerable amounts of additional SOC. A relatively high C sequestration potential was also determined for grassland soils. In contrast, forest soils had a low C sequestration potential as they were almost C saturated. A high proportion of sites with a high degree of apparent oversaturation revealed that in acidic, coarse-textured soils the relation to silt and clay is not suitable to estimate the stable C saturation. A strong correlation of the C saturation deficit with temperature and precipitation allowed a spatial estimation of the C sequestration potential for Bavaria. In total, about 395 Mt CO2 -equivalents could theoretically be stored in A horizons of cultivated soils - four times the annual emission of greenhouse gases in Bavaria. Although achieving the entire estimated C storage capacity is unrealistic, improved management of cultivated land could contribute significantly to CO2 mitigation. Moreover, increasing SOC stocks have additional benefits with respect to enhanced soil fertility and agricultural productivity. © 2013 John Wiley & Sons Ltd.

Searching for biogeochemical hot spots in three dimensions: Soil C and N cycling in hydropedologic settings in a northern hardwood forest

NASA Astrophysics Data System (ADS)

Morse, J. L.; Werner, S. F.; Gillin, C. P.; Goodale, C. L.; Bailey, S. W.; McGuire, K. J.; Groffman, P. M.

2014-08-01

Understanding and predicting the extent, location, and function of biogeochemical hot spots at the watershed scale is a frontier in environmental science. We applied a hydropedologic approach to identify (1) biogeochemical differences among morphologically distinct hydropedologic settings and (2) hot spots of microbial carbon (C) and nitrogen (N) cycling activity in a northern hardwood forest in Hubbard Brook Experimental Forest, New Hampshire, USA. We assessed variables related to C and N cycling in spodic hydropedologic settings (typical podzols, bimodal podzols, and Bh podzols) and groundwater seeps during August 2010. We found that soil horizons (Oi/Oe, Oa/A, and B) differed significantly for most variables. B horizons (>10 cm) accounted for 71% (±11%) of C pools and 62% (±10%) of microbial biomass C in the sampled soil profile, whereas the surface horizons (Oi/Oe and Oa/A; 0-10 cm) were dominant zones for N-cycle-related variables. Watershed-wide estimates of C and N cycling were higher by 34 to 43% (±17-19%) when rates, horizon thickness, and areal extent of each hydropedologic setting were incorporated, versus conventionally calculated estimates for typical podzols that included only the top 10 cm of mineral soil. Despite the variation in profile development in typical, bimodal, and Bh podzols, we did not detect significant differences in C and N cycling among them. Across all soil horizons and hydropedologic settings, we found strong links between biogeochemical cycling and soil C, suggesting that the accumulation of C in soils may be a robust indicator of microbial C and N cycling capacity in the landscape.

Do current European policies support soil multifunctionality?

NASA Astrophysics Data System (ADS)

Helming, Katharina; Glaesner, Nadia; de Vries, Wim

2017-04-01

Soils are multifunctional. Maximising one function, e.g. production of biomass, is often at the costs of the other functions, e.g. water purification, carbon sequestration, nutrient recycling, habitat provision. Sustainable soil management actually means the minimization of trade-offs between multiple soil functions. While Europe does not have a policy that explicitly focuses on soil functions, a number of policies exist in the agricultural, environmental and climate domains that may affect soil functions, in particular food production, water purification, climate change mitigation, biodiversity conservation. The objective of this study was to identify gaps and overlaps in existing EU legislation that is related to soil functions. We conducted a cross-policy analysis of 19 legislative policies at European level. Results revealed two key findings: (i) soil functions are addressed in existing legislation but with the approach to their conservation rather than their improvement. (ii) Different legislations addressed isolated soil functions but there is no policy in place that actually addressed the soil multifunctionality, which is the integrated balancing of the multitude of functions. Because soil degradation is ongoing in Europe, it raises the question whether existing legislation is sufficient for maintaining soil resources and achieving sustainable soil management. Addressing soil functions individually in various directives fails to account for the multifunctionality of soil. Here, research has a role to play to better reveal the interacting processes between soil functions and their sensitivity to soil management decisions and to translate such understanding into policy recommendation. We conclude the presentation with some insights into a research approach that integrates the soil systems into the socio-economic systems to improve the understanding of soil management pressures, soil functional reactions and their impacts on societal value systems, including resource efficiency, ecosystem services and intergenerational equity.

Soil functional types: surveying the biophysical dimensions of soil security

NASA Astrophysics Data System (ADS)

Cécillon, Lauric; Barré, Pierre

2015-04-01

Soil is a natural capital that can deliver key ecosystem services (ES) to humans through the realization of a series of soil processes controlling ecosystem functioning. Soil is also a diverse and endangered natural resource. A huge pedodiversity has been described at all scales, which is strongly altered by global change. The multidimensional concept soil security, encompassing biophysical, economic, social, policy and legal frameworks of soils has recently been proposed, recognizing the role of soils in global environmental sustainability challenges. The biophysical dimensions of soil security focus on the functionality of a given soil that can be viewed as the combination of its capability and its condition [1]. Indeed, all soils are not equal in term of functionality. They show different processes, provide different ES to humans and respond specifically to global change. Knowledge of soil functionality in space and time is thus a crucial step towards the achievement soil security. All soil classification systems incorporate some functional information, but soil taxonomy alone cannot fully describe the functioning, limitations, resistance and resilience of soils. Droogers and Bouma [2] introduced functional variants (phenoforms) for each soil type (genoform) so as to fit more closely to soil functionality. However, different genoforms can have the same functionality. As stated by McBratney and colleagues [1], there is a great need of an agreed methodology for defining the reference state of soil functionality. Here, we propose soil functional types (SFT) as a relevant classification system for the biophysical dimensions of soil security. Following the definition of plant functional types widely used in ecology, we define a soil functional type as "a set of soil taxons or phenoforms sharing similar processes (e.g. soil respiration), similar effects on ecosystem functioning (e.g. primary productivity) and similar responses to global change (land-use, management or climate) for a particular soil-provided ecosystem service (e.g. climate regulation)". One SFT can thus include several soil types having the same functionality for a particular soil-provided ES. Another consequence is that SFT maps for two different ES may not superimpose over the same area, since some soils may fall in the same SFT for a service and in different SFT for another one. Soil functional types could be assessed and monitored in space and time by a combination of soil functional traits that correspond to inherent and manageable properties of soils. Their metrology would involve either classic (pedological observations) or advanced (molecular ecology, spectrometry, geophysics) tools. SFT could be studied and mapped at all scales, depending on the purpose of the soil security assessment (e.g. global climate modeling, land planning and management, biodiversity conservation). Overall, research is needed to find a pathway from soil pedological maps to SFT maps which would yield important benefits towards the assessment and monitoring of soil security. Indeed, this methodology would allow (i) reducing the spatial uncertainty on the assessment of ES; (ii) identifying and mapping multifunctional soils, which may be the most important soil resource to preserve. References [1] McBratney et al., 2014. Geoderma 213:203-213. [2] Droogers P, Bouma J, 1997. SSSAJ 61:1704-1710.

Sugars in soil: Review of sources, contents, fate and functions

NASA Astrophysics Data System (ADS)

Gunina, Anna; Kuzyakov, Yakov

2015-04-01

Sugars are the most abundant organic compounds in the biosphere because they are monomers of all polysaccharides. We summarized the results of the last 40 years on sources, content and fate of sugars in soil and discussed their main functions in soil. We especially focused on uptake and utilization of sugars by microorganisms as this is by far the dominating process of sugars transformation in soil. Two databases have been created and analyzed. The 1st database was focused on the contents of cellulose, non-cellulose, hot water and cold water extractable sugars in soils (348 data from 32 studies). This database was also used to determine the primary (plant derived) and secondary (microbially and soil organic matter (SOM) derived) sources of carbohydrates in soil. The galactose+mannose/arabinose+xylose (GM/AX) ratio was calculated to analyze the origin of sugars in soil. The 2nd database was focused on the fate of sugar C in soil (734 data pairs from 32 studies), and only the papers used 13C or 14C labelled sugars were included. All data to the fate were analyzed and presented in dynamics. This allowed to calculate: 1) maximal rate of glucose-C decomposition, 2) mean residence time (MRT) of C of the initially applied sugars, 3) MRT of glucose-C incorporated into microbial biomass (MB) and SOM pools. Content of hexoses was 3-4 times higher than that of pentoses for both cellulose and non-cellulose sugars, because hexoses have two sources in soil: plants and microorganisms. The GM/AX ratio revealed higher contribution of hexoses in forest (ratio was 1.5) than in cropland and grassland soils (ratio was 0.7-1), reflecting high input of hexoses with forest litter. The MRT of sugars in soil solution was much less than 30 minutes. Based on the experiments with 13C or 14C labelled glucose, the maximal rate of glucose C decomposition in microbial biomass was ˜ 1min-1. Considering this rate, the glucose input from plants and content of sugar C in soil, we estimated that only about 20soil originate from the primary source - decomposition of plant biomass and root exudation. The remaining 80from microbial recycling. Estimated MRT of sugar C in MB was about 230 days, showing intense and efficient recycling of sugars in microorganisms. In contrast, MRT of sugar C in SOM was about 360 days, reflecting essential accumulation of sugar C in dead MB. Thus, very fast uptake of sugars by microorganisms as well as intensive microbial recycling clearly shows the importance of sugars for microbes in soil. Based on the assessed MRT we conclude that real contribution of sugar C (not only whole sugar molecules, which are usually determined) in SOM is much higher than commonly measured 10-15

Estimating Soil Organic Carbon Stocks and Spatial Patterns with Statistical and GIS-Based Methods

PubMed Central

Zhi, Junjun; Jing, Changwei; Lin, Shengpan; Zhang, Cao; Liu, Qiankun; DeGloria, Stephen D.; Wu, Jiaping

2014-01-01

Accurately quantifying soil organic carbon (SOC) is considered fundamental to studying soil quality, modeling the global carbon cycle, and assessing global climate change. This study evaluated the uncertainties caused by up-scaling of soil properties from the county scale to the provincial scale and from lower-level classification of Soil Species to Soil Group, using four methods: the mean, median, Soil Profile Statistics (SPS), and pedological professional knowledge based (PKB) methods. For the SPS method, SOC stock is calculated at the county scale by multiplying the mean SOC density value of each soil type in a county by its corresponding area. For the mean or median method, SOC density value of each soil type is calculated using provincial arithmetic mean or median. For the PKB method, SOC density value of each soil type is calculated at the county scale considering soil parent materials and spatial locations of all soil profiles. A newly constructed 1∶50,000 soil survey geographic database of Zhejiang Province, China, was used for evaluation. Results indicated that with soil classification levels up-scaling from Soil Species to Soil Group, the variation of estimated SOC stocks among different soil classification levels was obviously lower than that among different methods. The difference in the estimated SOC stocks among the four methods was lowest at the Soil Species level. The differences in SOC stocks among the mean, median, and PKB methods for different Soil Groups resulted from the differences in the procedure of aggregating soil profile properties to represent the attributes of one soil type. Compared with the other three estimation methods (i.e., the SPS, mean and median methods), the PKB method holds significant promise for characterizing spatial differences in SOC distribution because spatial locations of all soil profiles are considered during the aggregation procedure. PMID:24840890

Improved exposure estimation in soil screening and cleanup criteria for volatile organic chemicals.

PubMed

DeVaull, George E

2017-09-01

Soil cleanup criteria define acceptable concentrations of organic chemical constituents for exposed humans. These criteria sum the estimated soil exposure over multiple pathways. Assumptions for ingestion, dermal contact, and dust exposure generally presume a chemical persists in surface soils at a constant concentration level for the entire exposure duration. For volatile chemicals, this is an unrealistic assumption. A calculation method is presented for surficial soil criteria that include volatile depletion of chemical for these uptake pathways. The depletion estimates compare favorably with measured concentration profiles and with field measurements of soil concentration. Corresponding volatilization estimates compare favorably with measured data for a wide range of volatile and semivolatile chemicals, including instances with and without the presence of a mixed-chemical residual phase. Selected examples show application of the revised factors in estimating screening levels for benzene in surficial soils. Integr Environ Assess Manag 2017;13:861-869. © 2017 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals, Inc. on behalf of Society of Environmental Toxicology & Chemistry (SETAC). © 2017 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals, Inc. on behalf of Society of Environmental Toxicology & Chemistry (SETAC).

Dependence of shear wave seismoelectrics on soil textures: a numerical study in the vadose zone

NASA Astrophysics Data System (ADS)

Zyserman, F. I.; Monachesi, L. B.; Jouniaux, L.

2017-02-01

In this work, we study seismoelectric conversions generated in the vadose zone, when this region is traversed by a pure SH wave. We assume that the soil is a 1-D partially saturated lossy porous medium and we use the van Genuchten's constitutive model to describe the water saturation profile. Correspondingly, we extend Pride's formulation to deal with partially saturated media. In order to evaluate the influence of different soil textures we perform a numerical analysis considering, among other relevant properties, the electrokinetic coupling, coseismic responses and interface responses (IRs). We propose new analytical transfer functions for the electric and magnetic field as a function of the water saturation, modifying those of Bordes et al. and Garambois & Dietrich, respectively. Further, we introduce two substantially different saturation-dependent functions into the electrokinetic (EK) coupling linking the poroelastic and the electromagnetic wave equations. The numerical results show that the electric field IRs markedly depend on the soil texture and the chosen EK coupling model, and are several orders of magnitude stronger than the electric field coseismic ones. We also found that the IRs of the water table for the silty and clayey soils are stronger than those for the sandy soils, assuming a non-monotonous saturation dependence of the EK coupling, which takes into account the charged air-water interface. These IRs have been interpreted as the result of the jump in the viscous electric current density at the water table. The amplitude of the IR is obtained using a plane SH wave, neglecting both the spherical spreading and the restriction of its origin to the first Fresnel zone, effects that could lower the predicted values. However, we made an estimation of the expected electric field IR amplitudes detectable in the field by means of the analytical transfer functions, accounting for spherical spreading of the SH seismic waves. This prediction yields a value of 15 μV m-1, which is compatible with reported values.

Relative Bioavailability and Bioaccessability and Speciation of Arsenic in Contaminated Soils

EPA Science Inventory

Background: Assessment of soil arsenic (As) bioavailability may profoundly affect the extent of remediation required at contaminated sites by improving human exposure estimates. Because small adjustments in soil As bioavailability estimates can significantly alter risk assessment...

Nutrient Estimation Using Subsurface Sensing Methods

USDA-ARS?s Scientific Manuscript database

This report investigates the use of precision management techniques for measuring soil conductivity on feedlot surfaces to estimate nutrient value for crop production. An electromagnetic induction soil conductivity meter was used to collect apparent soil electrical conductivity (ECa) from feedlot p...

Modeling Soil Carbon Dynamics in Northern Forests: Effects of Spatial and Temporal Aggregation of Climatic Input Data.

PubMed

Dalsgaard, Lise; Astrup, Rasmus; Antón-Fernández, Clara; Borgen, Signe Kynding; Breidenbach, Johannes; Lange, Holger; Lehtonen, Aleksi; Liski, Jari

2016-01-01

Boreal forests contain 30% of the global forest carbon with the majority residing in soils. While challenging to quantify, soil carbon changes comprise a significant, and potentially increasing, part of the terrestrial carbon cycle. Thus, their estimation is important when designing forest-based climate change mitigation strategies and soil carbon change estimates are required for the reporting of greenhouse gas emissions. Organic matter decomposition varies with climate in complex nonlinear ways, rendering data aggregation nontrivial. Here, we explored the effects of temporal and spatial aggregation of climatic and litter input data on regional estimates of soil organic carbon stocks and changes for upland forests. We used the soil carbon and decomposition model Yasso07 with input from the Norwegian National Forest Inventory (11275 plots, 1960-2012). Estimates were produced at three spatial and three temporal scales. Results showed that a national level average soil carbon stock estimate varied by 10% depending on the applied spatial and temporal scale of aggregation. Higher stocks were found when applying plot-level input compared to country-level input and when long-term climate was used as compared to annual or 5-year mean values. A national level estimate for soil carbon change was similar across spatial scales, but was considerably (60-70%) lower when applying annual or 5-year mean climate compared to long-term mean climate reflecting the recent climatic changes in Norway. This was particularly evident for the forest-dominated districts in the southeastern and central parts of Norway and in the far north. We concluded that the sensitivity of model estimates to spatial aggregation will depend on the region of interest. Further, that using long-term climate averages during periods with strong climatic trends results in large differences in soil carbon estimates. The largest differences in this study were observed in central and northern regions with strongly increasing temperatures.

Modeling Soil Carbon Dynamics in Northern Forests: Effects of Spatial and Temporal Aggregation of Climatic Input Data

PubMed Central

Dalsgaard, Lise; Astrup, Rasmus; Antón-Fernández, Clara; Borgen, Signe Kynding; Breidenbach, Johannes; Lange, Holger; Lehtonen, Aleksi; Liski, Jari

2016-01-01

Boreal forests contain 30% of the global forest carbon with the majority residing in soils. While challenging to quantify, soil carbon changes comprise a significant, and potentially increasing, part of the terrestrial carbon cycle. Thus, their estimation is important when designing forest-based climate change mitigation strategies and soil carbon change estimates are required for the reporting of greenhouse gas emissions. Organic matter decomposition varies with climate in complex nonlinear ways, rendering data aggregation nontrivial. Here, we explored the effects of temporal and spatial aggregation of climatic and litter input data on regional estimates of soil organic carbon stocks and changes for upland forests. We used the soil carbon and decomposition model Yasso07 with input from the Norwegian National Forest Inventory (11275 plots, 1960–2012). Estimates were produced at three spatial and three temporal scales. Results showed that a national level average soil carbon stock estimate varied by 10% depending on the applied spatial and temporal scale of aggregation. Higher stocks were found when applying plot-level input compared to country-level input and when long-term climate was used as compared to annual or 5-year mean values. A national level estimate for soil carbon change was similar across spatial scales, but was considerably (60–70%) lower when applying annual or 5-year mean climate compared to long-term mean climate reflecting the recent climatic changes in Norway. This was particularly evident for the forest-dominated districts in the southeastern and central parts of Norway and in the far north. We concluded that the sensitivity of model estimates to spatial aggregation will depend on the region of interest. Further, that using long-term climate averages during periods with strong climatic trends results in large differences in soil carbon estimates. The largest differences in this study were observed in central and northern regions with strongly increasing temperatures. PMID:26901763

An objective analysis of the dynamic nature of field capacity

NASA Astrophysics Data System (ADS)

Twarakavi, Navin K. C.; Sakai, Masaru; Å Imå¯Nek, Jirka

2009-10-01

Field capacity is one of the most commonly used, and yet poorly defined, soil hydraulic properties. Traditionally, field capacity has been defined as the amount of soil moisture after excess water has drained away and the rate of downward movement has materially decreased. Unfortunately, this qualitative definition does not lend itself to an unambiguous quantitative approach for estimation. Because of the vagueness in defining what constitutes "drainage of excess water" from a soil, the estimation of field capacity has often been based upon empirical guidelines. These empirical guidelines are either time, pressure, or flux based. In this paper, we developed a numerical approach to estimate field capacity using a flux-based definition. The resulting approach was implemented on the soil parameter data set used by Schaap et al. (2001), and the estimated field capacity was compared to traditional definitions of field capacity. The developed modeling approach was implemented using the HYDRUS-1D software with the capability of simultaneously estimating field capacity for multiple soils with soil hydraulic parameter data. The Richards equation was used in conjunction with the van Genuchten-Mualem model to simulate variably saturated flow in a soil. Using the modeling approach to estimate field capacity also resulted in additional information such as (1) the pressure head, at which field capacity is attained, and (2) the drainage time needed to reach field capacity from saturated conditions under nonevaporative conditions. We analyzed the applicability of the modeling-based approach to estimate field capacity on real-world soils data. We also used the developed method to create contour diagrams showing the variation of field capacity with texture. It was found that using benchmark pressure heads to estimate field capacity from the retention curve leads to inaccurate results. Finally, a simple analytical equation was developed to predict field capacity from soil hydraulic parameter information. The analytical equation was found to be effective in its ability to predict field capacities.

High resolution change estimation of soil moisture and its assimilation into a land surface model

NASA Astrophysics Data System (ADS)

Narayan, Ujjwal

Near surface soil moisture plays an important role in hydrological processes including infiltration, evapotranspiration and runoff. These processes depend non-linearly on soil moisture and hence sub-pixel scale soil moisture variability characterization is important for accurate modeling of water and energy fluxes at the pixel scale. Microwave remote sensing has evolved as an attractive technique for global monitoring of near surface soil moisture. A radiative transfer model has been tested and validated for soil moisture retrieval from passive microwave remote sensing data under a full range of vegetation water content conditions. It was demonstrated that soil moisture retrieval errors of approximately 0.04 g/g gravimetric soil moisture are attainable with vegetation water content as high as 5 kg/m2. Recognizing the limitation of low spatial resolution associated with passive sensors, an algorithm that uses low resolution passive microwave (radiometer) and high resolution active microwave (radar) data to estimate soil moisture change at the spatial resolution of radar operation has been developed and applied to coincident Passive and Active L and S band (PALS) and Airborne Synthetic Aperture Radar (AIRSAR) datasets acquired during the Soil Moisture Experiments in 2002 (SMEX02) campaign with root mean square error of 10% and a 4 times enhancement in spatial resolution. The change estimation algorithm has also been used to estimate soil moisture change at 5 km resolution using AMSR-E soil moisture product (50 km) in conjunction with the TRMM-PR data (5 km) for a 3 month period demonstrating the possibility of high resolution soil moisture change estimation using satellite based data. Soil moisture change is closely related to precipitation and soil hydraulic properties. A simple assimilation framework has been implemented to investigate whether assimilation of surface layer soil moisture change observations into a hydrologic model will potentially improve it performance. Results indicate an improvement in model prediction of near surface and deep layer soil moisture content when the update is performed to the model state as compared to free model runs. It is also seen that soil moisture change assimilation is able to mitigate the effect of erroneous precipitation input data.

The distribution of soil phosphorus for global biogeochemical modeling

DOE PAGES

Yang, Xiaojuan; Post, Wilfred M.; Thornton, Peter E.; ...

2013-04-16

We discuss that phosphorus (P) is a major element required for biological activity in terrestrial ecosystems. Although the total P content in most soils can be large, only a small fraction is available or in an organic form for biological utilization because it is bound either in incompletely weathered mineral particles, adsorbed on mineral surfaces, or, over the time of soil formation, made unavailable by secondary mineral formation (occluded). In order to adequately represent phosphorus availability in global biogeochemistry–climate models, a representation of the amount and form of P in soils globally is required. We develop an approach that buildsmore » on existing knowledge of soil P processes and databases of parent material and soil P measurements to provide spatially explicit estimates of different forms of naturally occurring soil P on the global scale. We assembled data on the various forms of phosphorus in soils globally, chronosequence information, and several global spatial databases to develop a map of total soil P and the distribution among mineral bound, labile, organic, occluded, and secondary P forms in soils globally. The amount of P, to 50cm soil depth, in soil labile, organic, occluded, and secondary pools is 3.6 ± 3, 8.6 ± 6, 12.2 ± 8, and 3.2 ± 2 Pg P (Petagrams of P, 1 Pg = 1 × 10 15g) respectively. The amount in soil mineral particles to the same depth is estimated at 13.0 ± 8 Pg P for a global soil total of 40.6 ± 18 Pg P. The large uncertainty in our estimates reflects our limited understanding of the processes controlling soil P transformations during pedogenesis and a deficiency in the number of soil P measurements. In spite of the large uncertainty, the estimated global spatial variation and distribution of different soil P forms presented in this study will be useful for global biogeochemistry models that include P as a limiting element in biological production by providing initial estimates of the available soil P for plant uptake and microbial utilization.« less

Estimating soil water content from ground penetrating radar coarse root reflections

NASA Astrophysics Data System (ADS)

Liu, X.; Cui, X.; Chen, J.; Li, W.; Cao, X.

2016-12-01

Soil water content (SWC) is an indispensable variable for understanding the organization of natural ecosystems and biodiversity. Especially in semiarid and arid regions, soil moisture is the plants primary source of water and largely determine their strategies for growth and survival, such as root depth, distribution and competition between them. Ground penetrating radar (GPR), a kind of noninvasive geophysical technique, has been regarded as an accurate tool for measuring soil water content at intermediate scale in past decades. For soil water content estimation with surface GPR, fixed antenna offset reflection method has been considered to have potential to obtain average soil water content between land surface and reflectors, and provide high resolution and few measurement time. In this study, 900MHz surface GPR antenna was used to estimate SWC with fixed offset reflection method; plant coarse roots (with diameters greater than 5 mm) were regarded as reflectors; a kind of advanced GPR data interpretation method, HADA (hyperbola automatic detection algorithm), was introduced to automatically obtain average velocity by recognizing coarse root hyperbolic reflection signals on GPR radargrams during estimating SWC. In addition, a formula was deduced to determine interval average SWC between two roots at different depths as well. We examined the performance of proposed method on a dataset simulated under different scenarios. Results showed that HADA could provide a reasonable average velocity to estimate SWC without knowledge of root depth and interval average SWC also be determined. When the proposed method was applied to estimation of SWC on a real-field measurement dataset, a very small soil water content vertical variation gradient about 0.006 with depth was captured as well. Therefore, the proposed method could be used to estimate average soil water content from ground penetrating radar coarse root reflections and obtain interval average SWC between two roots at different depths. It is very promising for measuring root-zone-soil-moisture and mapping soil moisture distribution around a shrub or even in field plot scale.

SMAP Level 4 Surface and Root Zone Soil Moisture

NASA Technical Reports Server (NTRS)

Reichle, R.; De Lannoy, G.; Liu, Q.; Ardizzone, J.; Kimball, J.; Koster, R.

2017-01-01

The SMAP Level 4 soil moisture (L4_SM) product provides global estimates of surface and root zone soil moisture, along with other land surface variables and their error estimates. These estimates are obtained through assimilation of SMAP brightness temperature observations into the Goddard Earth Observing System (GEOS-5) land surface model. The L4_SM product is provided at 9 km spatial and 3-hourly temporal resolution and with about 2.5 day latency. The soil moisture and temperature estimates in the L4_SM product are validated against in situ observations. The L4_SM product meets the required target uncertainty of 0.04 m(exp. 3)m(exp. -3), measured in terms of unbiased root-mean-square-error, for both surface and root zone soil moisture.

Estimation of Compaction Parameters Based on Soil Classification

NASA Astrophysics Data System (ADS)

Lubis, A. S.; Muis, Z. A.; Hastuty, I. P.; Siregar, I. M.

2018-02-01

Factors that must be considered in compaction of the soil works were the type of soil material, field control, maintenance and availability of funds. Those problems then raised the idea of how to estimate the density of the soil with a proper implementation system, fast, and economical. This study aims to estimate the compaction parameter i.e. the maximum dry unit weight (γ dmax) and optimum water content (Wopt) based on soil classification. Each of 30 samples were being tested for its properties index and compaction test. All of the data’s from the laboratory test results, were used to estimate the compaction parameter values by using linear regression and Goswami Model. From the research result, the soil types were A4, A-6, and A-7 according to AASHTO and SC, SC-SM, and CL based on USCS. By linear regression, the equation for estimation of the maximum dry unit weight (γdmax *)=1,862-0,005*FINES- 0,003*LL and estimation of the optimum water content (wopt *)=- 0,607+0,362*FINES+0,161*LL. By Goswami Model (with equation Y=mLogG+k), for estimation of the maximum dry unit weight (γdmax *) with m=-0,376 and k=2,482, for estimation of the optimum water content (wopt *) with m=21,265 and k=-32,421. For both of these equations a 95% confidence interval was obtained.

Challenges and lessons learned in establishing a critical zone observatory in an intensively managed rural landscape of India

NASA Astrophysics Data System (ADS)

Paul, D.; Tripathi, S.; Harsha, K. S.; Adla, S.; Dash, S. K.; Chander, Y.; Mahajan, P.; Tripathi, S. N.; Sen, I. S.; Sinha, R.

2016-12-01

Soil salinization and aridification represent a major threat for the food security and sustainable development of drylands. The two problems are deeply connected, and their interplay is expected to be further enhanced by climate change and projected population growth. Salt-affected land is currently estimated to cover around 1.1 Gha, and is particularly widespread in semi-arid to hyper-arid climates. Over 900 Mha of these saline/sodic soils are potentially available for crop or biomass production. Salt-tolerant plants have been recently proposed as valid solution to exploit or even remediate salinized soils. However the effects of salinity on evapotranspiration, soil water balance and the long-term salt mass balance in the soil, are still largely unexplored. In this contribution we analyze the feedback of evapotranspiration on soil salinization, with particular emphasis on the role of vegetation and plant salt-tolerance. The goal is to introduce a simple modeling framework able to shed some light on how (a) soil salinity controls plant transpiration, and (b) salinization itself is favored/impeded by different vegetation feedback. We introduce at this goal a spatially lumped stochastic model of soil moisture and salt mass dynamics averaged over the active soil depth, and accounting for the effect of salinity on evapotranspiration. Here, the limiting effect of salinity on ET is modeled through a simple plant response function depending on both salt concentration in the soil and plant salt-tolerance. The coupled soil moisture and salt mass balance is hence used to obtain the conditional steady-state probability density function (pdf) of soil moisture for given salt tolerance and salinization level, Our results show that salinity imposes a limit in the soil water balance and this limit depends on plant salt-tolerance mainly through the control of the leaching occurrence (tolerant plants exploit water more efficiently than the sensitive ones). We also analyzed the effect of salt-tolerance on salt concentration patterns pointing out how vegetation imposes an upper bound to concentration of soluble salts in the soil. The long-term effects of plant salt tolerance on soil salinization are also discussed by an approximated expression for the salt mass pdf.

Modelling the water balance of irrigated fields in tropical floodplain soils using Hydrus-1D

NASA Astrophysics Data System (ADS)

Beyene, Abebech; Frankl, Amaury; Verhoest, Niko E. C.; Tilahun, Seifu; Alamirew, Tena; Adgo, Enyew; Nyssen, Jan

2017-04-01

Accurate estimation of evaporation, transpiration and deep percolation is crucial in irrigated agriculture and the sustainable management of water resources. Here, the Hydrus-1D process-based numerical model was used to estimate the actual transpiration, soil evaporation and deep percolation from irrigated fields of floodplain soils. Field experiments were conducted from Dec 2015 to May 2016 in a small irrigation scheme (50 ha) called 'Shina' located in the Lake Tana floodplains of Ethiopia. Six experimental plots (three for onion and three for maize) were selected along a topographic transect to account for soil and groundwater variability. Irrigation amount (400 to 550 mm during the growing period) was measured using V-notches installed at each plot boundary and daily groundwater levels were measured manually from piezometers. There was no surface runoff observed in the growing period and rainfall was measured using a manual rain gauge. All daily weather data required for the evapotranspiration calculation using Pen Man Monteith equation were collected from a nearby metrological station. The soil profiles were described for each field to include the vertical soil heterogeneity in the soil water balance simulations. The soil texture, organic matter, bulk density, field capacity, wilting point and saturated moisture content were measured for all the soil horizons. Soil moisture monitoring at 30 and 60 cm depths was performed. The soil hydraulic parameters for each horizon was estimated using KNN pedotransfer functions for tropical soils and were effectively fitted using the RETC program (R2= 0.98±0.011) for initial prediction. A local sensitivity analysis was performed to select and optimize the most important hydraulic parameters for soil water flow in the unsaturated zone. The most sensitive parameters were saturated hydraulic conductivity (Ks), saturated moisture content (θs) and pore size distribution (n). Inverse modelling using Hydrus-1D further optimized these parameters (R2 =0.74±0.13). Using the optimized hydraulic parameters, the soil water dynamics were simulated using Hydrus-1D. The atmospheric boundary conditions with surface runoff was used as upper boundary condition with measured rainfall and irrigation input data. The variable pressure head was selected as lower boundary conditions with daily records of groundwater level as time-variable input data. The Hydrus-1D model was successfully applied and calibrated in the study area. The average seasonal actual transpiration values are 310±13 mm for onion and 429±24.7 mm for maize fields. The seasonal average soil evaporation ranges from 12±2.05 mm for maize fields to 38±2.85 mm for onion fields. The seasonal deep percolation from irrigation appeared to be 12 to 40% of applied irrigation. The Hydrus-1D model was able to simulate the temporal and the spatial variations of soil water dynamics in the unsaturated zone of tropical floodplain soils. Key words: floodplains, hydraulic parameters, parameter optimization, small-scale irrigation

Survival of Salmonella adelaide and fecal coliforms in coarse sands of the swan costal plain, Western Australia.

PubMed Central

Parker, W F; Mee, B J

1982-01-01

The survival of Salmonella adelaide and fecal coliforms in two coarse sands influenced by two sources of septic tank effluent was studied. The experiments were conducted in conditions that reflected the soil environment beneath functioning septic tank systems. Significant differences in survival were found with different effluent sources. In one experiment the survival of S. adelaide was similar to that of fecal coliforms; in the other it was not. The nonuniform, multiphasic nature of survival curves was variability observed in these experiments suggests that the application of such survival data for establishing management criteria for septic tank systems--by, for example, the use of soil moisture characteristic curves to give estimates of movement in the soil--is inappropriate. PMID:7103482

Restoring Sustainable Forests on Appalachian Mined Lands for Wood Products, Renewable Energy, Carbon Sequestration, and Other Ecosystem Services

DOE Office of Scientific and Technical Information (OSTI.GOV)

James A. Burger; J. Galbraith; T. Fox

2005-12-01

The overall purpose of this project is to evaluate the biological and economic feasibility of restoring high-quality forests on mined land, and to measure carbon sequestration and wood production benefits that would be achieved from forest restoration procedures. We are currently estimating the acreage of lands in Virginia, West Virginia, Kentucky, Ohio, and Pennsylvania mined under SMCRA and reclaimed to non-forested post-mining land uses that are not currently under active management, and therefore can be considered as available for carbon sequestration. To determine actual sequestration under different forest management scenarios, a field study was installed as a 3 x 3more » factorial in a random complete block design with three replications at each of three locations, one each in Ohio, West Virginia, and Virginia. The treatments included three forest types (white pine, hybrid poplar, mixed hardwood) and three silvicultural regimes (competition control, competition control plus tillage, competition control plus tillage plus fertilization). Each individual treatment plot is 0.5 acres. Each block of nine plots is 4.5 acres, and the complete installation at each site is 13.5 acres. Regression models of chemical and physical soil properties were created in order to estimate the SOC content down the soil profile. Soil organic carbon concentration and volumetric percent of the fines decreased exponentially down the soil profile. The results indicated that one-third of the total SOC content on mined lands was found in the surface 0-13 cm soil layer, and more than two-thirds of it was located in the 0-53 cm soil profile. A relative estimate of soil density may be best in broad-scale mine soil mapping since actual D{sub b} values are often inaccurate and difficult to obtain in rocky mine soils. Carbon sequestration potential is also a function of silvicultural practices used for reforestation success. Weed control plus tillage may be the optimum treatment for hardwoods and white pine, as any increased growth resulting from fertilization may not offset the decreased survival that accompanied fertilization. Relative to carbon value, our analysis this quarter shows that although short-rotation hardwood management on reclaimed surface mined lands may have higher LEVs than traditional long-rotation hardwood management, it is only profitable in a limited set of circumstances.« less

Increasing microbial diversity and nitrogen cycling potential of burnt forest soil in Spain through post-fire management

NASA Astrophysics Data System (ADS)

Pereg, Lily; Mataix-Solera, Jorge; McMillan, Mary; García-Orenes, Fuensanta

2016-04-01

Microbial diversity and function in soils are increasingly assessed by the application of molecular methods such as sequencing and PCR technology. We applied these techniques to study microbial recovery in post-fire forest soils. The recovery of forest ecosystems following severe fire is influenced by post-fire management. The removal of burnt tree stumps (salvage logging) is a common practice in Spain following fire. In some cases, the use of heavy machinery in addition to the vulnerability of soils to erosion and degradation make this management potentially damaging to soil, and therefore to the ecosystem. We hypothesized that tree removal slows down the recovery of soil biological communities including microbial and plant communities and contributes to soil degradation in the burnt affected area. The study area is located in "Sierra de Mariola Natural Park" in Alcoi, Alicante (E Spain). A big forest fire (>500 has) occurred in July 2012. The forest is composed mainly of Pinus halepensis trees with an understory of typical Mediterranean shrubs species such as Quercus coccifera, Rosmarinus officinalis, Thymus vulgaris, Brachypodium retusum, etc. Soil is classified as a Typic Xerorthent (Soil Survey Staff, 2014) developed over marls. In February 2013, salvage logging (SL) treatment, with a complete extraction of the burned wood using heavy machinery, was applied to a part of the affected forest. Plots for monitoring the effects of SL were installed in this area and in a similar nearby control (C) area, where no SL treatment was done. The recovery of soil bacterial and fungal communities post-fire with and without tree removal was analysed by using Next-Generation sequencing and the abundance of functional genes, related to nitrogen cycling, in the soil was estimated using quantitative PCR (qPCR). We will present the methods used and the results of our study in this PICO presentation.

Quantification of seasonal biomass effects on cosmic-ray soil water content determination

NASA Astrophysics Data System (ADS)

Baatz, R.; Bogena, H. R.; Hendricks Franssen, H.; Huisman, J. A.; Qu, W.; Montzka, C.; Korres, W.; Vereecken, H.

2013-12-01

The novel cosmic-ray soil moisture probes (CRPs) measure neutron flux density close to the earth surface. High energy cosmic-rays penetrate the Earth's atmosphere from the cosmos and become moderated by terrestrial nuclei. Hydrogen is the most effective neutron moderator out of all chemical elements. Therefore, neutron flux density measured with a CRP at the earth surface correlates inversely with the hydrogen content in the CRP's footprint. A major contributor to the amount of hydrogen in the sensor's footprint is soil water content. The ability to measure changes in soil water content within the CRP footprint at a larger-than-point scale (~30 ha) and at high temporal resolution (hourly) make these sensors an appealing measurement instrument for hydrologic modeling purposes. Recent developments focus on the identification and quantification of major uncertainties inherent in CRP soil moisture measurements. In this study, a cosmic-ray soil moisture network for the Rur catchment in Western Germany is presented. It is proposed to correct the measured neutron flux density for above ground biomass yielding vegetation corrected soil water content from cosmic-ray measurements. The correction for above ground water equivalents aims to remove biases in soil water content measurements on sites with high seasonal vegetation dynamics such as agricultural fields. Above ground biomass is estimated as function of indices like NDVI and NDWI using regression equations. The regression equations were obtained with help of literature information, ground-based control measurements, a crop growth model and globally available data from the Moderate Resolution Imaging Spectrometer (MODIS). The results show that above ground biomass could be well estimated during the first half of the year. Seasonal changes in vegetation water content yielded biases in soil water content of ~0.05 cm3/cm3 that could be corrected for with the vegetation correction. The vegetation correction has particularly high potential when applied at long term cosmic-ray monitoring sites and the cosmic-ray rover.

Assessment of soil moisture dynamics on an irrigated maize field using cosmic ray neutron sensing

NASA Astrophysics Data System (ADS)

Scheiffele, Lena Maria; Baroni, Gabriele; Oswald, Sascha E.

2015-04-01

In recent years cosmic ray neutron sensing (CRS) developed as a valuable, indirect and non-invasive method to estimate soil moisture at a scale of tens of hectares, covering the gap between point scale measurements and large scale remote sensing techniques. The method is particularly promising in cropped and irrigated fields where invasive installation of belowground measurement devices could conflict with the agricultural management. However, CRS is affected by all hydrogen pools in the measurement footprint and a fast growing biomass provides some challenges for the interpretation of the signal and application of the method for detecting soil moisture. For this aim, in this study a cosmic ray probe was installed on a field near Braunschweig (Germany) during one maize growing season (2014). The field was irrigated in stripes of 50 m width using sprinkler devices for a total of seven events. Three soil sampling campaigns were conducted throughout the growing season to assess the effect of different hydrogen pools on calibration results. Additionally, leaf area index and biomass measurements were collected to provide the relative contribution of the biomass on the CRS signal. Calibration results obtained with the different soil sampling campaigns showed some discrepancy well correlated with the biomass growth. However, after the calibration function was adjusted to account also for lattice water and soil organic carbon, thus representing an equivalent water content of the soil, the differences decreased. Soil moisture estimated with CRS responded well to precipitation and irrigation events, confirming also the effective footprint of the method (i.e., radius 300 m) and showing occurring water stress for the crop. Thus, the dynamics are in agreement with the soil moisture determined with point scale measurements but they are less affected by the heterogeneous moisture conditions within the field. For this reason, by applying a detailed calibration, CRS proves to be a valuable method for the application on agricultural sites to assess and improve irrigation management.

Parametric soil water retention models: a critical evaluation of expressions for the full moisture range

NASA Astrophysics Data System (ADS)

Madi, Raneem; Huibert de Rooij, Gerrit; Mielenz, Henrike; Mai, Juliane

2018-02-01

Few parametric expressions for the soil water retention curve are suitable for dry conditions. Furthermore, expressions for the soil hydraulic conductivity curves associated with parametric retention functions can behave unrealistically near saturation. We developed a general criterion for water retention parameterizations that ensures physically plausible conductivity curves. Only 3 of the 18 tested parameterizations met this criterion without restrictions on the parameters of a popular conductivity curve parameterization. A fourth required one parameter to be fixed. We estimated parameters by shuffled complex evolution (SCE) with the objective function tailored to various observation methods used to obtain retention curve data. We fitted the four parameterizations with physically plausible conductivities as well as the most widely used parameterization. The performance of the resulting 12 combinations of retention and conductivity curves was assessed in a numerical study with 751 days of semiarid atmospheric forcing applied to unvegetated, uniform, 1 m freely draining columns for four textures. Choosing different parameterizations had a minor effect on evaporation, but cumulative bottom fluxes varied by up to an order of magnitude between them. This highlights the need for a careful selection of the soil hydraulic parameterization that ideally does not only rely on goodness of fit to static soil water retention data but also on hydraulic conductivity measurements. Parameter fits for 21 soils showed that extrapolations into the dry range of the retention curve often became physically more realistic when the parameterization had a logarithmic dry branch, particularly in fine-textured soils where high residual water contents would otherwise be fitted.

Deterioration pattern of six biodegradable, potentially low-environmental impact mulches in field conditions.

PubMed

Moreno, Marta M; González-Mora, Sara; Villena, Jaime; Campos, Juan A; Moreno, Carmen

2017-09-15

Polyethylene plastic mulches are widely used in agriculture due to the countless advantages they have. However, the environmental problems associated with their use have led us to look for alternative mulch materials which degrade naturally and quickly, impact the environment less and function satisfactorily. To this end, biodegradable plastics and paper mulches are being used, but aspects related to their degradation should be studied more in-depth. This work provides the deterioration pattern of six biodegradable mulch materials (i.e. vegetable starch, polylactic acid plastic films or paper mulches) in horticultural crop in the edaphoclimatic conditions of Central Spain in two situations: over the lifetime of the mulches and after being incorporated into the soil. In the first situation, the deterioration levels were evaluated by recording the puncture resistance, weight and area covered in the above-soil and the in-soil part, and after soil incorporation by the number of fragments, their surfaces and weight. In the above-soil part, biodegradable plastics experienced further deterioration, particularly with no crop, while the paper mulch remained practically intact. However, the in-soil paper experienced complete and rapid degradation. At 200 days after soil incorporation, mulch residues were scarce, with the environmental effects it entails. These findings offer practical implications regarding the type of crop. The measurement of the surface covered, rather than the weight, was shown to be a more reliable indicator of the degradation of mulches. Furthermore, visual estimation was found to underestimate the functionality of mulches in comparison to that of the measurement of the surface covered. Copyright © 2017 Elsevier Ltd. All rights reserved.

Using the Mehlich-3 Soil Test as an Inexpensive Screening Tool to Estimate Total and Bioaccessible Lead in Urban Soils

EPA Science Inventory

In cities nationwide, urban agriculture has been put on hold because of the high costs of soil testing for historical contaminants such as lead (Pb). The Mehlich-3 soil test is commonly used to determine plant available nutrients, is inexpensive, and has the potential to estimate...

Soil moisture and properties estimation by assimilating soil temperatures using particle batch smoother: A new perspective for DTS

NASA Astrophysics Data System (ADS)

Dong, J.; Steele-Dunne, S. C.; Ochsner, T. E.; Van De Giesen, N.

2015-12-01

Soil moisture, hydraulic and thermal properties are critical for understanding the soil surface energy balance and hydrological processes. Here, we will discuss the potential of using soil temperature observations from Distributed Temperature Sensing (DTS) to investigate the spatial variability of soil moisture and soil properties. With DTS soil temperature can be measured with high resolution (spatial <1m, and temporal < 1min) in cables up to kilometers in length. Soil temperature evolution is primarily controlled by the soil thermal properties, and the energy balance at the soil surface. Hence, soil moisture, which affects both soil thermal properties and the energy that participates the evaporation process, is strongly correlated to the soil temperatures. In addition, the dynamics of the soil moisture is determined by the soil hydraulic properties.Here we will demonstrate that soil moisture, hydraulic and thermal properties can be estimated by assimilating observed soil temperature at shallow depths using the Particle Batch Smoother (PBS). The PBS can be considered as an extension of the particle filter, which allows us to infer soil moisture and soil properties using the dynamics of soil temperature within a batch window. Both synthetic and real field data will be used to demonstrate the robustness of this approach. We will show that the proposed method is shown to be able to handle different sources of uncertainties, which may provide a new view of using DTS observations to estimate sub-meter resolution soil moisture and properties for remote sensing product validation.

Intermittent flux from a sand filter for household wastewater and integrated solute transfer to the vadose zone.

PubMed

Nasri, Behzad; Fouché, Olivier

2018-02-24

Depending on the actual number of soil-based on-site wastewater treatment system (OWTS) in an area, on-site sanitation may be a significant source of pollutants and a threat to groundwater. Even in the case of a system functioning correctly, here, a sand filter substituted for the in-situ soil, as the treated effluent may reach to the water table, it is necessary evaluating in situ how much the sand and underneath soil respectively contribute to pollutant removal. On the plot of a household in a small rural community, the functioning of a real scale OWTS was monitored for 1.5 years. This system, composed of a septic tank connected to a 5 × 5 m 2 and 0.7-m thick aerobic sand filter was equipped with soil hydrodynamic probes (water content and matrix potential) during construction. By using the instantaneous profile method of water content, the intermittent infiltrated flux was determined across the sand-pack according to position and time. Treated water infiltrates into underneath soil acting as post-treatment. Quality of interstitial liquid from the sand and the soil was analysed each month on a 12-h pumping sample obtained through porous plates. Results of water fluxes and concentrations provide an estimate of the annual flux to the vadose zone and groundwater of metals, nutrients and some organic micro-pollutants (parabens and triclosan) through the OWTS and subsoil.

Contributions of Precipitation and Soil Moisture Observations to the Skill of Soil Moisture Estimates in a Land Data Assimilation System

NASA Technical Reports Server (NTRS)

Reichle, Rolf H.; Liu, Qing; Bindlish, Rajat; Cosh, Michael H.; Crow, Wade T.; deJeu, Richard; DeLannoy, Gabrielle J. M.; Huffman, George J.; Jackson, Thomas J.

2011-01-01

The contributions of precipitation and soil moisture observations to the skill of soil moisture estimates from a land data assimilation system are assessed. Relative to baseline estimates from the Modern Era Retrospective-analysis for Research and Applications (MERRA), the study investigates soil moisture skill derived from (i) model forcing corrections based on large-scale, gauge- and satellite-based precipitation observations and (ii) assimilation of surface soil moisture retrievals from the Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E). Soil moisture skill is measured against in situ observations in the continental United States at 44 single-profile sites within the Soil Climate Analysis Network (SCAN) for which skillful AMSR-E retrievals are available and at four CalVal watersheds with high-quality distributed sensor networks that measure soil moisture at the scale of land model and satellite estimates. The average skill (in terms of the anomaly time series correlation coefficient R) of AMSR-E retrievals is R=0.39 versus SCAN and R=0.53 versus CalVal measurements. The skill of MERRA surface and root-zone soil moisture is R=0.42 and R=0.46, respectively, versus SCAN measurements, and MERRA surface moisture skill is R=0.56 versus CalVal measurements. Adding information from either precipitation observations or soil moisture retrievals increases surface soil moisture skill levels by IDDeltaR=0.06-0.08, and root zone soil moisture skill levels by DeltaR=0.05-0.07. Adding information from both sources increases surface soil moisture skill levels by DeltaR=0.13, and root zone soil moisture skill by DeltaR=0.11, demonstrating that precipitation corrections and assimilation of satellite soil moisture retrievals contribute similar and largely independent amounts of information.

Impact of interspecific interactions on the soil water uptake depth in a young temperate mixed species plantation

NASA Astrophysics Data System (ADS)

Grossiord, Charlotte; Gessler, Arthur; Granier, André; Berger, Sigrid; Bréchet, Claude; Hentschel, Rainer; Hommel, Robert; Scherer-Lorenzen, Michael; Bonal, Damien

2014-11-01

Interactions between tree species in forests can be beneficial to ecosystem functions and services related to the carbon and water cycles by improving for example transpiration and productivity. However, little is known on below- and above-ground processes leading to these positive effects. We tested whether stratification in soil water uptake depth occurred between four tree species in a 10-year-old temperate mixed species plantation during a dry summer. We selected dominant and co-dominant trees of European beech, Sessile oak, Douglas fir and Norway spruce in areas with varying species diversity, competition intensity, and where different plant functional types (broadleaf vs. conifer) were present. We applied a deuterium labelling approach that consisted of spraying labelled water to the soil surface to create a strong vertical gradient of the deuterium isotope composition in the soil water. The deuterium isotope composition of both the xylem sap and the soil water was measured before labelling, and then again three days after labelling, to estimate the soil water uptake depth using a simple modelling approach. We also sampled leaves and needles from selected trees to measure their carbon isotope composition (a proxy for water use efficiency) and total nitrogen content. At the end of the summer, we found differences in the soil water uptake depth between plant functional types but not within types: on average, coniferous species extracted water from deeper layers than did broadleaved species. Neither species diversity nor competition intensity had a detectable influence on soil water uptake depth, foliar water use efficiency or foliar nitrogen concentration in the species studied. However, when coexisting with an increasing proportion of conifers, beech extracted water from progressively deeper soil layers. We conclude that complementarity for water uptake could occur in this 10-year-old plantation because of inherent differences among functional groups (conifers and broadleaves). Furthermore, water uptake depth of beech was already influenced at this young development stage by interspecific interactions whereas no clear niche differentiation occurred for the other species. This finding does not preclude that plasticity-mediated responses to species interactions could increase as the plantation ages, leading to the coexistence of these species in adult forest stands.

Postwildfire measurement of soil physical and hydraulic properties at selected sampling sites in the 2011 Las Conchas wildfire burn scar, Jemez Mountains, north-central New Mexico

USGS Publications Warehouse

Romero, Orlando C.; Ebel, Brian A.; Martin, Deborah A.; Buchan, Katie W.; Jornigan, Alanna D.

2018-04-10

The generation of runoff and the resultant flash flooding can be substantially larger following wildfire than for similar rainstorms that precede wildfire disturbance. Flash flooding after the 2011 Las Conchas Fire in New Mexico provided the motivation for this investigation to assess postwildfire effects on soil-hydraulic properties (SHPs) and soil-physical properties (SPPs) as a function of remotely sensed burn severity 4 years following the wildfire. A secondary purpose of this report is to illustrate a methodology to determine SHPs that analyzes infiltrometer data by using three different analysis methods. The SPPs and SHPs are measured as a function of remotely sensed burn severity by using the difference in the Normalized Burn Ratio (dNBR) metric for seven sites. The dNBR metric was used to guide field sample collection across a full spectrum of burn severities that covered the range of Monitoring Trends in Burn Severity (MTBS) and Burned Area Reflectance Classification (BARC) thematic classes from low to high severity. The SPPs (initial and saturated soil-water content, bulk density, soil-organic matter, and soil-particle size) and SHPs (field-saturated hydraulic conductivity and sorptivity) were measured under controlled laboratory conditions for soil cores collected in the field. The SHPs were estimated by using tension infiltrometer measurements and three different data analysis methods. These measurements showed large effects of burn severity, focused in the top1 centimeter (cm) of soil, on some SPPs (bulk density, soil organic matter, and particle sizes). The threshold of these bulk density and soil organic matter effects was between 300 and 400 dNBR, which corresponds to a MTBS thematic class between moderate and high burn severity and a BARC4 thematic class of high severity. Gravel content and the content of fines in the top 1 cm of soil had a higher threshold value between 450 and 500 dNBR. Lesser effects on SPPs were observed at depths of 1–3 cm and 3–6 cm. In contrast, SHPs showed little effect from dNBR or from MTBS/BARC4 thematic class. Measurements suggested that 4 years of elapsed time after the wildfire may be sufficient for SHP recovery in this area. These measurements also indicated that SPP differences as a function of burn severity cannot be used as reliable indicators of SHP differences as a function of burn severity.

Is leaf dry matter content a better predictor of soil fertility than specific leaf area?

PubMed Central

Hodgson, J. G.; Montserrat-Martí, G.; Charles, M.; Jones, G.; Wilson, P.; Shipley, B.; Sharafi, M.; Cerabolini, B. E. L.; Cornelissen, J. H. C.; Band, S. R.; Bogard, A.; Castro-Díez, P.; Guerrero-Campo, J.; Palmer, C.; Pérez-Rontomé, M. C.; Carter, G.; Hynd, A.; Romo-Díez, A.; de Torres Espuny, L.; Royo Pla, F.

2011-01-01

Background and Aims Specific leaf area (SLA), a key element of the ‘worldwide leaf economics spectrum’, is the preferred ‘soft’ plant trait for assessing soil fertility. SLA is a function of leaf dry matter content (LDMC) and leaf thickness (LT). The first, LDMC, defines leaf construction costs and can be used instead of SLA. However, LT identifies shade at its lowest extreme and succulence at its highest, and is not related to soil fertility. Why then is SLA more frequently used as a predictor of soil fertility than LDMC? Methods SLA, LDMC and LT were measured and leaf density (LD) estimated for almost 2000 species, and the capacity of LD to predict LDMC was examined, as was the relative contribution of LDMC and LT to the expression of SLA. Subsequently, the relationships between SLA, LDMC and LT with respect to soil fertility and shade were described. Key Results Although LD is strongly related to LDMC, and LDMC and LT each contribute equally to the expression of SLA, the exact relationships differ between ecological groupings. LDMC predicts leaf nitrogen content and soil fertility but, because LT primarily varies with light intensity, SLA increases in response to both increased shade and increased fertility. Conclusions Gradients of soil fertility are frequently also gradients of biomass accumulation with reduced irradiance lower in the canopy. Therefore, SLA, which includes both fertility and shade components, may often discriminate better between communities or treatments than LDMC. However, LDMC should always be the preferred trait for assessing gradients of soil fertility uncoupled from shade. Nevertheless, because leaves multitask, individual leaf traits do not necessarily exhibit exact functional equivalence between species. In consequence, rather than using a single stand-alone predictor, multivariate analyses using several leaf traits is recommended. PMID:21948627

Carbon storage potential increases with increasing ratio of C4 to C3 grass cover and soil productivity in restored tallgrass prairies.

PubMed

Spiesman, Brian J; Kummel, Herika; Jackson, Randall D

2018-02-01

Long-term soil carbon (C) storage is essential for reducing CO 2 in the atmosphere. Converting unproductive and environmentally sensitive agricultural lands to grasslands for bioenergy production may enhance C storage. However, a better understanding of the interacting effects of grass functional composition (i.e., relative abundance of C 4 and C 3 grass cover) and soil productivity on C storage will help guide sustainable grassland management. Our objective was to examine the relationship between grass functional composition and potential C storage and how it varies with potential soil productivity. We estimated C inputs from above- and belowground net primary productivity (ANPP and BNPP), and heterotrophic respiration (R H ) to calculate net ecosystem production (NEP), a measure of potential soil C storage, in grassland plots of relatively high- and low-productivity soils spanning a gradient in the ratio of C 4 to C 3 grass cover (C 4 :C 3 ). NEP increased with increasing C 4 :C 3 , but only in potentially productive soils. The positive relationship likely stemmed from increased ANPP, rather than BNPP, which was possibly related to efficient resource-use and physiological/anatomical advantages of C 4 plants. R H was negatively correlated with C 4 :C 3 , possibly because of changes in microclimate or plant-microbe interactions. It is possible that in potentially productive soils, C storage can be enhanced by favoring C 4 over C 3 grasses through increased ANPP and BNPP and reduced R H . Results also suggest that potential C storage gains from C 4 productivity would not be undermined by a corresponding increase in R H .

Is leaf dry matter content a better predictor of soil fertility than specific leaf area?

PubMed

Hodgson, J G; Montserrat-Martí, G; Charles, M; Jones, G; Wilson, P; Shipley, B; Sharafi, M; Cerabolini, B E L; Cornelissen, J H C; Band, S R; Bogard, A; Castro-Díez, P; Guerrero-Campo, J; Palmer, C; Pérez-Rontomé, M C; Carter, G; Hynd, A; Romo-Díez, A; de Torres Espuny, L; Royo Pla, F

2011-11-01

Specific leaf area (SLA), a key element of the 'worldwide leaf economics spectrum', is the preferred 'soft' plant trait for assessing soil fertility. SLA is a function of leaf dry matter content (LDMC) and leaf thickness (LT). The first, LDMC, defines leaf construction costs and can be used instead of SLA. However, LT identifies shade at its lowest extreme and succulence at its highest, and is not related to soil fertility. Why then is SLA more frequently used as a predictor of soil fertility than LDMC? SLA, LDMC and LT were measured and leaf density (LD) estimated for almost 2000 species, and the capacity of LD to predict LDMC was examined, as was the relative contribution of LDMC and LT to the expression of SLA. Subsequently, the relationships between SLA, LDMC and LT with respect to soil fertility and shade were described. Although LD is strongly related to LDMC, and LDMC and LT each contribute equally to the expression of SLA, the exact relationships differ between ecological groupings. LDMC predicts leaf nitrogen content and soil fertility but, because LT primarily varies with light intensity, SLA increases in response to both increased shade and increased fertility. Gradients of soil fertility are frequently also gradients of biomass accumulation with reduced irradiance lower in the canopy. Therefore, SLA, which includes both fertility and shade components, may often discriminate better between communities or treatments than LDMC. However, LDMC should always be the preferred trait for assessing gradients of soil fertility uncoupled from shade. Nevertheless, because leaves multitask, individual leaf traits do not necessarily exhibit exact functional equivalence between species. In consequence, rather than using a single stand-alone predictor, multivariate analyses using several leaf traits is recommended.