Pedotransfer functions estimating soil hydraulic properties using different soil parameters
DEFF Research Database (Denmark)
Børgesen, Christen Duus; Iversen, Bo Vangsø; Jacobsen, Ole Hørbye
2008-01-01
Estimates of soil hydraulic properties using pedotransfer functions (PTF) are useful in many studies such as hydrochemical modelling and soil mapping. The objective of this study was to calibrate and test parametric PTFs that predict soil water retention and unsaturated hydraulic conductivity...... parameters. The PTFs are based on neural networks and the Bootstrap method using different sets of predictors and predict the van Genuchten/Mualem parameters. A Danish soil data set (152 horizons) dominated by sandy and sandy loamy soils was used in the development of PTFs to predict the Mualem hydraulic...... conductivity parameters. A larger data set (1618 horizons) with a broader textural range was used in the development of PTFs to predict the van Genuchten parameters. The PTFs using either three or seven textural classes combined with soil organic mater and bulk density gave the most reliable predictions...
Estimating Soil Hydraulic Parameters using Gradient Based Approach
Rai, P. K.; Tripathi, S.
2017-12-01
The conventional way of estimating parameters of a differential equation is to minimize the error between the observations and their estimates. The estimates are produced from forward solution (numerical or analytical) of differential equation assuming a set of parameters. Parameter estimation using the conventional approach requires high computational cost, setting-up of initial and boundary conditions, and formation of difference equations in case the forward solution is obtained numerically. Gaussian process based approaches like Gaussian Process Ordinary Differential Equation (GPODE) and Adaptive Gradient Matching (AGM) have been developed to estimate the parameters of Ordinary Differential Equations without explicitly solving them. Claims have been made that these approaches can straightforwardly be extended to Partial Differential Equations; however, it has been never demonstrated. This study extends AGM approach to PDEs and applies it for estimating parameters of Richards equation. Unlike the conventional approach, the AGM approach does not require setting-up of initial and boundary conditions explicitly, which is often difficult in real world application of Richards equation. The developed methodology was applied to synthetic soil moisture data. It was seen that the proposed methodology can estimate the soil hydraulic parameters correctly and can be a potential alternative to the conventional method.
Estimation of Compaction Parameters Based on Soil Classification
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.
Revised soil parameter estimates for the soil types of the world
Batjes, N.H.
2002-01-01
A revised set of physical and chemical parameter estimates is presented for the soil units of the world, as described by the two FAO soil legends (version 1974 and 1988). The study is based on 9607 soil profiles, which include profiles held in version 1.0 of the WISE database. Upon a screening of
SOTER-based soil parameter estimates for Jordan (ver. 1.0)
Batjes, N.H.
2013-01-01
This harmonized set of soil parameter estimates has been developed using an updated 1:500 000 scale Soil and Terrain (SOTER) Database for Jordan. The associated soil analytical data were derived from soil survey reports. These sources seldom hold all the physical and chemical attributes ideally
Srivastava, Prashant K.; Han, Dawei; Rico-Ramirez, Miguel A.; O'Neill, Peggy; Islam, Tanvir; Gupta, Manika
2014-01-01
Soil Moisture and Ocean Salinity (SMOS) is the latest mission which provides flow of coarse resolution soil moisture data for land applications. However, the efficient retrieval of soil moisture for hydrological applications depends on optimally choosing the soil and vegetation parameters. The first stage of this work involves the evaluation of SMOS Level 2 products and then several approaches for soil moisture retrieval from SMOS brightness temperature are performed to estimate Soil Moisture Deficit (SMD). The most widely applied algorithm i.e. Single channel algorithm (SCA), based on tau-omega is used in this study for the soil moisture retrieval. In tau-omega, the soil moisture is retrieved using the Horizontal (H) polarisation following Hallikainen dielectric model, roughness parameters, Fresnel's equation and estimated Vegetation Optical Depth (tau). The roughness parameters are empirically calibrated using the numerical optimization techniques. Further to explore the improvement in retrieval models, modifications have been incorporated in the algorithms with respect to the sources of the parameters, which include effective temperatures derived from the European Center for Medium-Range Weather Forecasts (ECMWF) downscaled using the Weather Research and Forecasting (WRF)-NOAH Land Surface Model and Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature (LST) while the s is derived from MODIS Leaf Area Index (LAI). All the evaluations are performed against SMD, which is estimated using the Probability Distributed Model following a careful calibration and validation integrated with sensitivity and uncertainty analysis. The performance obtained after all those changes indicate that SCA-H using WRF-NOAH LSM downscaled ECMWF LST produces an improved performance for SMD estimation at a catchment scale.
DEFF Research Database (Denmark)
Mollerup, Mikkel; Hansen, Søren; Petersen, Carsten
2008-01-01
We combined an inverse routine for assessing the hydraulic soil parameters of the Campbell/Mualem model with the power series solution developed by Philip for describing one-dimensional vertical infiltration into a homogenous soil. We based the estimation routine on a proposed measurement procedure....... An independent measurement of the soil water content at saturation may reduce the uncertainty of estimated parameters. Response surfaces of the objective function were analysed. Scenarios for various soils and conditions, using numerically generated synthetic cumulative infiltration data with normally...
Sreelash, K.; Sekhar, M.; Ruiz, L.; Tomer, S. K.; Guérif, M.; Buis, S.; Durand, P.; Gascuel-Odoux, C.
2012-08-01
SummaryEstimation of soil parameters by inverse modeling using observations on either surface soil moisture or crop variables has been successfully attempted in many studies, but difficulties to estimate root zone properties arise when heterogeneous layered soils are considered. The objective of this study was to explore the potential of combining observations on surface soil moisture and crop variables - leaf area index (LAI) and above-ground biomass for estimating soil parameters (water holding capacity and soil depth) in a two-layered soil system using inversion of the crop model STICS. This was performed using GLUE method on a synthetic data set on varying soil types and on a data set from a field experiment carried out in two maize plots in South India. The main results were (i) combination of surface soil moisture and above-ground biomass provided consistently good estimates with small uncertainity of soil properties for the two soil layers, for a wide range of soil paramater values, both in the synthetic and the field experiment, (ii) above-ground biomass was found to give relatively better estimates and lower uncertainty than LAI when combined with surface soil moisture, especially for estimation of soil depth, (iii) surface soil moisture data, either alone or combined with crop variables, provided a very good estimate of the water holding capacity of the upper soil layer with very small uncertainty whereas using the surface soil moisture alone gave very poor estimates of the soil properties of the deeper layer, and (iv) using crop variables alone (else above-ground biomass or LAI) provided reasonable estimates of the deeper layer properties depending on the soil type but provided poor estimates of the first layer properties. The robustness of combining observations of the surface soil moisture and the above-ground biomass for estimating two layer soil properties, which was demonstrated using both synthetic and field experiments in this study, needs now to
estimation of shear strength parameters of lateritic soils using
African Journals Online (AJOL)
user
... a tool to estimate the. Nigerian Journal of Technology (NIJOTECH). Vol. ... modeling tools for the prediction of shear strength parameters for lateritic ... 2.2 Geotechnical Analysis of the Soils ... The back propagation learning algorithm is the most popular and ..... [10] Alsaleh, M. I., Numerical modeling for strain localization in ...
Wang, Ji-Peng; Hu, Nian; François, Bertrand; Lambert, Pierre
2017-07-01
This study proposed two pedotransfer functions (PTFs) to estimate sandy soil water retention curves. It is based on the van Genuchten's water retention model and from a semiphysical and semistatistical approach. Basic gradation parameters of d60 as particle size at 60% passing and the coefficient of uniformity Cu are employed in the PTFs with two idealized conditions, the monosized scenario and the extremely polydisperse condition, satisfied. Water retention tests are carried out on eight granular materials with narrow particle size distributions as supplementary data of the UNSODA database. The air entry value is expressed as inversely proportional to d60 and the parameter n, which is related to slope of water retention curve, is a function of Cu. The proposed PTFs, although have fewer parameters, have better fitness than previous PTFs for sandy soils. Furthermore, by incorporating with the suction stress definition, the proposed pedotransfer functions are imbedded in shear strength equations which provide a way to estimate capillary induced tensile strength or cohesion at a certain suction or degree of saturation from basic soil gradation parameters. The estimation shows quantitative agreement with experimental data in literature, and it also explains that the capillary-induced cohesion is generally higher for materials with finer mean particle size or higher polydispersity.
Estimating Soil and Root Parameters of Biofuel Crops using a Hydrogeophysical Inversion
Kuhl, A.; Kendall, A. D.; Van Dam, R. L.; Hyndman, D. W.
2017-12-01
Transpiration is the dominant pathway for continental water exchange to the atmosphere, and therefore a crucial aspect of modeling water balances at many scales. The root water uptake dynamics that control transpiration are dependent on soil water availability, as well as the root distribution. However, the root distribution is determined by many factors beyond the plant species alone, including climate conditions and soil texture. Despite the significant contribution of transpiration to global water fluxes, modelling the complex critical zone processes that drive root water uptake remains a challenge. Geophysical tools such as electrical resistivity (ER), have been shown to be highly sensitive to water dynamics in the unsaturated zone. ER data can be temporally and spatially robust, covering large areas or long time periods non-invasively, which is an advantage over in-situ methods. Previous studies have shown the value of using hydrogeophysical inversions to estimate soil properties. Others have used hydrological inversions to estimate both soil properties and root distribution parameters. In this study, we combine these two approaches to create a coupled hydrogeophysical inversion that estimates root and retention curve parameters for a HYDRUS model. To test the feasibility of this new approach, we estimated daily water fluxes and root growth for several biofuel crops at a long-term ecological research site in Southwest Michigan, using monthly ER data from 2009 through 2011. Time domain reflectometry data at seven depths was used to validate modeled soil moisture estimates throughout the model period. This hydrogeophysical inversion method shows promise for improving root distribution and transpiration estimates across a wide variety of settings.
Modeling the vertical soil organic matter profile using Bayesian parameter estimation
Directory of Open Access Journals (Sweden)
M. C. Braakhekke
2013-01-01
Full Text Available The vertical distribution of soil organic matter (SOM in the profile may constitute an important factor for soil carbon cycling. However, the formation of the SOM profile is currently poorly understood due to equifinality, caused by the entanglement of several processes: input from roots, mixing due to bioturbation, and organic matter leaching. In this study we quantified the contribution of these three processes using Bayesian parameter estimation for the mechanistic SOM profile model SOMPROF. Based on organic carbon measurements, 13 parameters related to decomposition and transport of organic matter were estimated for two temperate forest soils: an Arenosol with a mor humus form (Loobos, the Netherlands, and a Cambisol with mull-type humus (Hainich, Germany. Furthermore, the use of the radioisotope ^{210}Pb_{ex} as tracer for vertical SOM transport was studied. For Loobos, the calibration results demonstrate the importance of organic matter transport with the liquid phase for shaping the vertical SOM profile, while the effects of bioturbation are generally negligible. These results are in good agreement with expectations given in situ conditions. For Hainich, the calibration offered three distinct explanations for the observations (three modes in the posterior distribution. With the addition of ^{210}Pb_{ex} data and prior knowledge, as well as additional information about in situ conditions, we were able to identify the most likely explanation, which indicated that root litter input is a dominant process for the SOM profile. For both sites the organic matter appears to comprise mainly adsorbed but potentially leachable material, pointing to the importance of organo-mineral interactions. Furthermore, organic matter in the mineral soil appears to be mainly derived from root litter, supporting previous studies that highlighted the importance of root input for soil carbon sequestration. The ^{210 }
Parinussa, R.M.; Meesters, A.G.C.A.; Liu, Y.Y.; Dorigo, W.; Wagner, W.; de Jeu, R.A.M.
2011-01-01
A time-efficient solution to estimate the error of satellite surface soil moisture from the land parameter retrieval model is presented. The errors are estimated using an analytical solution for soil moisture retrievals from this radiative-transfer-based model that derives soil moisture from
Montzka, Carsten; Hendricks Franssen, Harrie-Jan; Moradkhani, Hamid; Pütz, Thomas; Han, Xujun; Vereecken, Harry
2013-04-01
An adequate description of soil hydraulic properties is essential for a good performance of hydrological forecasts. So far, several studies showed that data assimilation could reduce the parameter uncertainty by considering soil moisture observations. However, these observations and also the model forcings were recorded with a specific measurement error. It seems a logical step to base state updating and parameter estimation on observations made at multiple time steps, in order to reduce the influence of outliers at single time steps given measurement errors and unknown model forcings. Such outliers could result in erroneous state estimation as well as inadequate parameters. This has been one of the reasons to use a smoothing technique as implemented for Bayesian data assimilation methods such as the Ensemble Kalman Filter (i.e. Ensemble Kalman Smoother). Recently, an ensemble-based smoother has been developed for state update with a SIR particle filter. However, this method has not been used for dual state-parameter estimation. In this contribution we present a Particle Smoother with sequentially smoothing of particle weights for state and parameter resampling within a time window as opposed to the single time step data assimilation used in filtering techniques. This can be seen as an intermediate variant between a parameter estimation technique using global optimization with estimation of single parameter sets valid for the whole period, and sequential Monte Carlo techniques with estimation of parameter sets evolving from one time step to another. The aims are i) to improve the forecast of evaporation and groundwater recharge by estimating hydraulic parameters, and ii) to reduce the impact of single erroneous model inputs/observations by a smoothing method. In order to validate the performance of the proposed method in a real world application, the experiment is conducted in a lysimeter environment.
Sedaghat, A.; Bayat, H.; Safari Sinegani, A. A.
2016-03-01
The saturated hydraulic conductivity ( K s ) of the soil is one of the main soil physical properties. Indirect estimation of this parameter using pedo-transfer functions (PTFs) has received considerable attention. The Purpose of this study was to improve the estimation of K s using fractal parameters of particle and micro-aggregate size distributions in smectitic soils. In this study 260 disturbed and undisturbed soil samples were collected from Guilan province, the north of Iran. The fractal model of Bird and Perrier was used to compute the fractal parameters of particle and micro-aggregate size distributions. The PTFs were developed by artificial neural networks (ANNs) ensemble to estimate K s by using available soil data and fractal parameters. There were found significant correlations between K s and fractal parameters of particles and microaggregates. Estimation of K s was improved significantly by using fractal parameters of soil micro-aggregates as predictors. But using geometric mean and geometric standard deviation of particles diameter did not improve K s estimations significantly. Using fractal parameters of particles and micro-aggregates simultaneously, had the most effect in the estimation of K s . Generally, fractal parameters can be successfully used as input parameters to improve the estimation of K s in the PTFs in smectitic soils. As a result, ANNs ensemble successfully correlated the fractal parameters of particles and micro-aggregates to K s .
SOTER-based soil parameter estimates for Central Africa - DR of Congo, Burundi and Rwanda (ver. 1.0)
Batjes, N.H.
2014-01-01
This harmonized set of soil parameter estimates for Central Africa, comprising Burundi, the Democratic Republic of the Congo and Rwanda, was derived from the Soil and Terrain Database for Central Africa (SOTERCAF ver. 1.0) and the ISRIC-WISE soil profile database, using standardized taxonomy-based
Application of spreadsheet to estimate infiltration parameters
Directory of Open Access Journals (Sweden)
Mohammad Zakwan
2016-09-01
Full Text Available Infiltration is the process of flow of water into the ground through the soil surface. Soil water although contributes a negligible fraction of total water present on earth surface, but is of utmost importance for plant life. Estimation of infiltration rates is of paramount importance for estimation of effective rainfall, groundwater recharge, and designing of irrigation systems. Numerous infiltration models are in use for estimation of infiltration rates. The conventional graphical approach for estimation of infiltration parameters often fails to estimate the infiltration parameters precisely. The generalised reduced gradient (GRG solver is reported to be a powerful tool for estimating parameters of nonlinear equations and it has, therefore, been implemented to estimate the infiltration parameters in the present paper. Field data of infiltration rate available in literature for sandy loam soils of Umuahia, Nigeria were used to evaluate the performance of GRG solver. A comparative study of graphical method and GRG solver shows that the performance of GRG solver is better than that of conventional graphical method for estimation of infiltration rates. Further, the performance of Kostiakov model has been found to be better than the Horton and Philip's model in most of the cases based on both the approaches of parameter estimation.
Varella, H.-V.
2009-04-01
Dynamic crop models are very useful to predict the behavior of crops in their environment and are widely used in a lot of agro-environmental work. These models have many parameters and their spatial application require a good knowledge of these parameters, especially of the soil parameters. These parameters can be estimated from soil analysis at different points but this is very costly and requires a lot of experimental work. Nevertheless, observations on crops provided by new techniques like remote sensing or yield monitoring, is a possibility for estimating soil parameters through the inversion of crop models. In this work, the STICS crop model is studied for the wheat and the sugar beet and it includes more than 200 parameters. After a previous work based on a large experimental database for calibrate parameters related to the characteristics of the crop, a global sensitivity analysis of the observed variables (leaf area index LAI and absorbed nitrogen QN provided by remote sensing data, and yield at harvest provided by yield monitoring) to the soil parameters is made, in order to determine which of them have to be estimated. This study was made in different climatic and agronomic conditions and it reveals that 7 soil parameters (4 related to the water and 3 related to the nitrogen) have a clearly influence on the variance of the observed variables and have to be therefore estimated. For estimating these 7 soil parameters, a Bayesian data assimilation method is chosen (because of available prior information on these parameters) named Importance Sampling by using observations, on wheat and sugar beet crop, of LAI and QN at various dates and yield at harvest acquired on different climatic and agronomic conditions. The quality of parameter estimation is then determined by comparing the result of parameter estimation with only prior information and the result with the posterior information provided by the Bayesian data assimilation method. The result of the
Impacts of Different Types of Measurements on Estimating Unsaturatedflow Parameters
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.
Pedotransfer functions to estimate water retention parameters of soils in northeastern Brazil
Directory of Open Access Journals (Sweden)
Alexandre Hugo Cezar Barros
2013-04-01
Full Text Available Pedotransfer functions (PTF were developed to estimate the parameters (α, n, θr and θs of the van Genuchten model (1980 to describe soil water retention curves. The data came from various sources, mainly from studies conducted by universities in Northeast Brazil, by the Brazilian Agricultural Research Corporation (Embrapa and by a corporation for the development of the São Francisco and Parnaíba river basins (Codevasf, totaling 786 retention curves, which were divided into two data sets: 85 % for the development of PTFs, and 15 % for testing and validation, considered independent data. Aside from the development of general PTFs for all soils together, specific PTFs were developed for the soil classes Ultisols, Oxisols, Entisols, and Alfisols by multiple regression techniques, using a stepwise procedure (forward and backward to select the best predictors. Two types of PTFs were developed: the first included all predictors (soil density, proportions of sand, silt, clay, and organic matter, and the second only the proportions of sand, silt and clay. The evaluation of adequacy of the PTFs was based on the correlation coefficient (R and Willmott index (d. To evaluate the PTF for the moisture content at specific pressure heads, we used the root mean square error (RMSE. The PTF-predicted retention curve is relatively poor, except for the residual water content. The inclusion of organic matter as a PTF predictor improved the prediction of parameter a of van Genuchten. The performance of soil-class-specific PTFs was not better than of the general PTF. Except for the water content of saturated soil estimated by particle size distribution, the tested models for water content prediction at specific pressure heads proved satisfactory. Predictions of water content at pressure heads more negative than -0.6 m, using a PTF considering particle size distribution, are only slightly lower than those obtained by PTFs including bulk density and organic matter
Hu, Shun; Shi, Liangsheng; Zha, Yuanyuan; Williams, Mathew; Lin, Lin
2017-12-01
Improvements to agricultural water and crop managements require detailed information on crop and soil states, and their evolution. Data assimilation provides an attractive way of obtaining these information by integrating measurements with model in a sequential manner. However, data assimilation for soil-water-atmosphere-plant (SWAP) system is still lack of comprehensive exploration due to a large number of variables and parameters in the system. In this study, simultaneous state-parameter estimation using ensemble Kalman filter (EnKF) was employed to evaluate the data assimilation performance and provide advice on measurement design for SWAP system. The results demonstrated that a proper selection of state vector is critical to effective data assimilation. Especially, updating the development stage was able to avoid the negative effect of ;phenological shift;, which was caused by the contrasted phenological stage in different ensemble members. Simultaneous state-parameter estimation (SSPE) assimilation strategy outperformed updating-state-only (USO) assimilation strategy because of its ability to alleviate the inconsistency between model variables and parameters. However, the performance of SSPE assimilation strategy could deteriorate with an increasing number of uncertain parameters as a result of soil stratification and limited knowledge on crop parameters. In addition to the most easily available surface soil moisture (SSM) and leaf area index (LAI) measurements, deep soil moisture, grain yield or other auxiliary data were required to provide sufficient constraints on parameter estimation and to assure the data assimilation performance. This study provides an insight into the response of soil moisture and grain yield to data assimilation in SWAP system and is helpful for soil moisture movement and crop growth modeling and measurement design in practice.
Application of isotopic information for estimating parameters in Philip infiltration model
Directory of Open Access Journals (Sweden)
Tao Wang
2016-10-01
Full Text Available Minimizing parameter uncertainty is crucial in the application of hydrologic models. Isotopic information in various hydrologic components of the water cycle can expand our knowledge of the dynamics of water flow in the system, provide additional information for parameter estimation, and improve parameter identifiability. This study combined the Philip infiltration model with an isotopic mixing model using an isotopic mass balance approach for estimating parameters in the Philip infiltration model. Two approaches to parameter estimation were compared: (a using isotopic information to determine the soil water transmission and then hydrologic information to estimate the soil sorptivity, and (b using hydrologic information to determine the soil water transmission and the soil sorptivity. Results of parameter estimation were verified through a rainfall infiltration experiment in a laboratory under rainfall with constant isotopic compositions and uniform initial soil water content conditions. Experimental results showed that approach (a, using isotopic and hydrologic information, estimated the soil water transmission in the Philip infiltration model in a manner that matched measured values well. The results of parameter estimation of approach (a were better than those of approach (b. It was also found that the analytical precision of hydrogen and oxygen stable isotopes had a significant effect on parameter estimation using isotopic information.
Lubis, A. S.; Muis, Z. A.; Pasaribu, M. I.
2017-03-01
The strength and durability of pavement construction is highly dependent on the properties and subgrade bearing capacity. This then led to the idea of the selection methods to estimate the density of the soil with the proper implementation of the system, fast and economical. This study aims to estimate the compaction parameter value namely the maximum dry unit weight (γd max) and optimum moisture content (wopt) of the soil properties value that stabilized with Portland Cement. Tests conducted in the laboratory of soil mechanics to determine the index properties (fines and liquid limit) and Standard Compaction Test. Soil samples that have Plasticity Index (PI) between 0-15% then mixed with Portland Cement (PC) with variations of 2%, 4%, 6%, 8% and 10%, each 10 samples. The results showed that the maximum dry unit weight (γd max) and wopt has a significant relationship with percent fines, liquid limit and the percentation of cement. Equation for the estimated maximum dry unit weight (γd max) = 1.782 - 0.011*LL + 0,000*F + 0.006*PS with R2 = 0.915 and the estimated optimum moisture content (wopt) = 3.441 + 0.594*LL + 0,025*F + 0,024*PS with R2 = 0.726.
International Nuclear Information System (INIS)
Killough, G.G.; Rope, S.K.; Shleien, B.; Voilleque, P.G.
1999-01-01
A dynamic mass-balance model has been calibrated by a nonlinear parameter estimation method, using time-series measurements of uranium in surface soil near the former Feed Materials Production Center (FMPC) near Fernald, Ohio, USA. The time-series data, taken at six locations near the site boundary since 1971, show a statistically significant downtrend of above-background uranium concentration in surface soil for all six locations. The dynamic model is based on first-order kinetics in a surface-soil compartment 10 cm in depth. Median estimates of weathering rate coefficients for insoluble uranium in this soil compartment range from about 0.065-0.14 year -1 , corresponding to mean transit times of about 7-15 years, depending on the location sampled. The model, calibrated by methods similar to those discussed in this paper, has been used to simulate surface soil kinetics of uranium for a dose reconstruction study. It was also applied, along with other data, to make confirmatory estimates of airborne releases of uranium from the FMPC between 1951 and 1988. Two soil-column models (one diffusive and one advective, the latter similar to a catenary first-order kinetic box model) were calibrated to profile data taken at one of the six locations in 1976. The temporal predictions of the advective model approximate the trend of the time series data for that location. (Copyright (c) 1999 Elsevier Science B.V., Amsterdam. All rights reserved.)
Year-round estimation of soil moisture content using temporally variable soil hydraulic parameters
Czech Academy of Sciences Publication Activity Database
Šípek, Václav; Tesař, Miroslav
2017-01-01
Roč. 31, č. 6 (2017), s. 1438-1452 ISSN 0885-6087 R&D Projects: GA ČR GA16-05665S Institutional support: RVO:67985874 Keywords : hydrological modelling * pore-size distribution * saturated hydraulic conductivity * seasonal variability * soil hydraulic parameters * soil moisture Subject RIV: DA - Hydrology ; Limnology OBOR OECD: Hydrology Impact factor: 3.014, year: 2016
Moret-Fernández, D.; Latorre, B.
2017-01-01
The water retention curve (θ(h)), which defines the relationship between the volumetric water content (θ) and the matric potential (h), is of paramount importance to characterize the hydraulic behaviour of soils. Because current methods to estimate θ(h) are, in general, tedious and time consuming, alternative procedures to determine θ(h) are needed. Using an upward infiltration curve, the main objective of this work is to present a method to determine the parameters of the van Genuchten (1980) water retention curve (α and n) from the sorptivity (S) and the β parameter defined in the 1D infiltration equation proposed by Haverkamp et al. (1994). The first specific objective is to present an equation, based on the Haverkamp et al. (1994) analysis, which allows describing an upward infiltration process. Secondary, assuming a known saturated hydraulic conductivity, Ks, calculated on a finite soil column by the Darcy's law, a numerical procedure to calculate S and β by the inverse analysis of an exfiltration curve is presented. Finally, the α and n values are numerically calculated from Ks, S and β. To accomplish the first specific objective, cumulative upward infiltration curves simulated with HYDRUS-1D for sand, loam, silt and clay soils were compared to those calculated with the proposed equation, after applying the corresponding β and S calculated from the theoretical Ks, α and n. The same curves were used to: (i) study the influence of the exfiltration time on S and β estimations, (ii) evaluate the limits of the inverse analysis, and (iii) validate the feasibility of the method to estimate α and n. Next, the θ(h) parameters estimated with the numerical method on experimental soils were compared to those obtained with pressure cells. The results showed that the upward infiltration curve could be correctly described by the modified Haverkamp et al. (1994) equation. While S was only affected by early-time exfiltration data, the β parameter had a
A new Bayesian recursive technique for parameter estimation
Kaheil, Yasir H.; Gill, M. Kashif; McKee, Mac; Bastidas, Luis
2006-08-01
The performance of any model depends on how well its associated parameters are estimated. In the current application, a localized Bayesian recursive estimation (LOBARE) approach is devised for parameter estimation. The LOBARE methodology is an extension of the Bayesian recursive estimation (BARE) method. It is applied in this paper on two different types of models: an artificial intelligence (AI) model in the form of a support vector machine (SVM) application for forecasting soil moisture and a conceptual rainfall-runoff (CRR) model represented by the Sacramento soil moisture accounting (SAC-SMA) model. Support vector machines, based on statistical learning theory (SLT), represent the modeling task as a quadratic optimization problem and have already been used in various applications in hydrology. They require estimation of three parameters. SAC-SMA is a very well known model that estimates runoff. It has a 13-dimensional parameter space. In the LOBARE approach presented here, Bayesian inference is used in an iterative fashion to estimate the parameter space that will most likely enclose a best parameter set. This is done by narrowing the sampling space through updating the "parent" bounds based on their fitness. These bounds are actually the parameter sets that were selected by BARE runs on subspaces of the initial parameter space. The new approach results in faster convergence toward the optimal parameter set using minimum training/calibration data and fewer sets of parameter values. The efficacy of the localized methodology is also compared with the previously used BARE algorithm.
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.
EVALUATING SOIL EROSION PARAMETER ESTIMATES FROM DIFFERENT DATA SOURCES
Topographic factors and soil loss estimates that were derived from thee data sources (STATSGO, 30-m DEM, and 3-arc second DEM) were compared. Slope magnitudes derived from the three data sources were consistently different. Slopes from the DEMs tended to provide a flattened sur...
Jadoon, Khan
2012-06-01
We present an integrated hydrogeophysical inversion approach that uses time-lapse off-ground ground-penetrating radar (GPR) data to estimate soil hydraulic parameters, and apply it to a dataset collected in the field. Off-ground GPR data are mainly sensitive to the near-surface water content profile and dynamics, and are thus related to soil hydraulic parameters, such as the parameters of the hydraulic conductivity and water retention functions. The hydrological simulator HYDRUS 1-D was used with a two-layer single- and dual-porosity model. To monitor the soil water content dynamics, time-lapse GPR and time domain reflectometry (TDR) measurements were performed, whereby only GPR data was used in the inversion. The dual porosity model provided better results compared to the single porosity model for describing the soil water dynamics, which is supported by field observations of macropores. Furthermore, the GPR-derived water content profiles reconstructed from the integrated hydrogeophysical inversion were in good agreement with TDR observations. These results suggest that the proposed method is promising for non-invasive characterization of the shallow subsurface hydraulic properties and monitoring water dynamics at the field scale.
Ecological estimation of the irrigated soils in Qarabagh plain
International Nuclear Information System (INIS)
Mammadov, Q.S.; Nuriyeva, K.Q.
2009-01-01
Contemporary agricultural science improved the known adaptive approaches in the past, for it accounting natural peccularities of the concrete region is offered with the assistance of agroecological estimation of soil. Using of collecting materials of the soil esological parameters of soil cover of the studying territory and applying the system of the private scales of the soil estimation on degree of display of their separate signs, the ecological estimation of the irrigated soils of Qarabagh steppe where the highest ecological marks have been got such as grey-brown dark (94 marks) and ordinary soils (93 marks) has been carried out
Schwen, Andreas; Bodner, Gernot; Loiskandl, Willibald
2010-05-01
Hydraulic properties are key factors controlling water and solute movement in soils. While several recent studies have focused on the assessment of the spatial variability of hydraulic properties, the temporal dynamics are commonly not taken into account, primarily because its measurement is costly and time-consuming. However, there is extensive empirical evidence that these properties are subject to temporal changes, particularly in the near-saturated range where soil structure strongly influences water flow. One main source of temporal variability is soil tillage. It can improve macroporosity by loosening the soil and thereby changing the pore-size distribution. Since these modifications are quite unstable over time, the pore space partially collapses after tillage. This effect should be largest for conventional tillage (CT), where the soil is ploughed after harvest every year. Assessing the effect of different tillage treatments on the temporal variability of hydraulic properties requires adequate measurement techniques. Tension infiltrometry has become a popular and convenient method providing not only the hydraulic conductivity function but also the soil rentention properties. The inverse estimation of parameters from infiltration measurements remains challenging, despite some progress since the first approach of ŠimÅ¯nek et al. (1998). Measured data like the cumulative infiltration, the initial and final volumetric water content, as well as independently measured retention data from soil core analysis with laboratory methods, have to be considered to find an optimum solution describing the soil's pore space. In the present study we analysed tension infiltration measurements obtained several times between August 2008 and December 2009 on an arable field in the Moravian Basin, Lower Austria. The tillage treatments were conventional tillage including ploughing (CT), reduced tillage with chisel only (RT), and no-tillage treatment using a direct seeding
The estimation of parameter compaction values for pavement subgrade stabilized with lime
Lubis, A. S.; Muis, Z. A.; Simbolon, C. A.
2018-02-01
The type of soil material, field control, maintenance and availability of funds are several factors that must be considered in compaction of the pavement subgrade. In determining the compaction parameters in laboratory desperately requires considerable materials, time and funds, and reliable laboratory operators. If the result of soil classification values can be used to estimate the compaction parameters of a subgrade material, so it would save time, energy, materials and cost on the execution of this work. This is also a clarification (cross check) of the work that has been done by technicians in the laboratory. The study aims to estimate the compaction parameter values ie. maximum dry unit weight (γdmax) and optimum water content (Wopt) of the soil subgrade that stabilized with lime. The tests that conducted in the laboratory of soil mechanics were to determine the index properties (Fines and Liquid Limit/LL) and Standard Compaction Test. Soil samples that have Plasticity Index (PI) > 10% were made with additional 3% lime for 30 samples. By using the Goswami equation, the compaction parameter values can be estimated by equation γd max # = -0,1686 Log G + 1,8434 and Wopt # = 2,9178 log G + 17,086. From the validation calculation, there was a significant positive correlation between the compaction parameter values laboratory and the compaction parameter values estimated, with a 95% confidence interval as a strong relationship.
Characterization of unsaturated hydraulic parameters for homogeneous and heterogeneous soils
Energy Technology Data Exchange (ETDEWEB)
Wildenschild, Dorthe
1997-09-01
Application of numerical models for predicting future spreading of contaminants into ground water aquifers is dependent on appropriate characterization of the soil hydraulic properties controlling flow and transport in the unsaturated zone. This thesis reviews the current knowledge on two aspects of characterization of unsaturated hydraulic parameters; estimation of the basic hydraulic parameters for homogeneous soils and statistical representation of heterogeneity for spatially variable soils. The retention characteristic is traditionally measured using steady-state procedures, but new ideas based on dynamic techniques have been developed that reduce experimental efforts and that produce retention curves which compare to those measured by traditional techniques. The unsaturated hydraulic conductivity is difficult to establish by steady-state procedures, and extensive research efforts have been focused on alternative methods that are based on inverse estimation. The inverse methods have commonly been associated with problems of numerical instability and ill-posedness of the parameter estimates, but recent investigations have shown that the uniqueness of parameter estimates can be improved by including additional, independent information on, for instance, the retention characteristic. Also, uniqueness may be improved by careful selection of experimental conditions are parametric functions. (au) 234 refs.
Hysteresis and uncertainty in soil water-retention curve parameters
Likos, William J.; Lu, Ning; Godt, Jonathan W.
2014-01-01
Accurate estimates of soil hydraulic parameters representing wetting and drying paths are required for predicting hydraulic and mechanical responses in a large number of applications. A comprehensive suite of laboratory experiments was conducted to measure hysteretic soil-water characteristic curves (SWCCs) representing a wide range of soil types. Results were used to quantitatively assess differences and uncertainty in three simplifications frequently adopted to estimate wetting-path SWCC parameters from more easily measured drying curves. They are the following: (1) αw=2αd, (2) nw=nd, and (3) θws=θds, where α, n, and θs are fitting parameters entering van Genuchten’s commonly adopted SWCC model, and the superscripts w and d indicate wetting and drying paths, respectively. The average ratio αw/αd for the data set was 2.24±1.25. Nominally cohesive soils had a lower αw/αd ratio (1.73±0.94) than nominally cohesionless soils (3.14±1.27). The average nw/nd ratio was 1.01±0.11 with no significant dependency on soil type, thus confirming the nw=nd simplification for a wider range of soil types than previously available. Water content at zero suction during wetting (θws) was consistently less than during drying (θds) owing to air entrapment. The θws/θds ratio averaged 0.85±0.10 and was comparable for nominally cohesive (0.87±0.11) and cohesionless (0.81±0.08) soils. Regression statistics are provided to quantitatively account for uncertainty in estimating hysteretic retention curves. Practical consequences are demonstrated for two case studies.
Soil parameter retrieval under vegetation cover using SAR polarimetery
Energy Technology Data Exchange (ETDEWEB)
Jagdhuber, Thomas
2012-07-01
Soil conditions under vegetation cover and their spatial and temporal variations from point to catchment scale are crucial for understanding hydrological processes within the vadose zone, for managing irrigation and consequently maximizing yield by precision farming. Soil moisture and soil roughness are the key parameters that characterize the soil status. In order to monitor their spatial and temporal variability on large scales, remote sensing techniques are required. Therefore the determination of soil parameters under vegetation cover was approached in this thesis by means of (multi-angular) polarimetric SAR acquisitions at a longer wavelength (L-band, {lambda}{sub c}=23cm). In this thesis, the penetration capabilities of L-band are combined with newly developed (multi-angular) polarimetric decomposition techniques to separate the different scattering contributions, which are occurring in vegetation and on ground. Subsequently the ground components are inverted to estimate the soil characteristics. The novel (multi-angular) polarimetric decomposition techniques for soil parameter retrieval are physically-based, computationally inexpensive and can be solved analytically without any a priori knowledge. Therefore they can be applied without test site calibration directly to agricultural areas. The developed algorithms are validated with fully polarimetric SAR data acquired by the airborne E-SAR sensor of the German Aerospace Center (DLR) for three different study areas in Germany. The achieved results reveal inversion rates up to 99% for the soil moisture and soil roughness retrieval in agricultural areas. However, in forested areas the inversion rate drops significantly for most of the algorithms, because the inversion in forests is invalid for the applied scattering models at L-band. The validation against simultaneously acquired field measurements indicates an estimation accuracy (root mean square error) of 5-10vol.% for the soil moisture (range of in situ
Soil Moisture Estimations Based on Airborne CAROLS L-Band Microwave Data
Directory of Open Access Journals (Sweden)
Arnaud Mialon
2011-12-01
Full Text Available The SMOS satellite mission, launched in 2009, allows global soil moisture estimations to be made using the L-band Microwave Emission of the Biosphere (L-MEB model, which simulates the L-band microwave emissions produced by the soil–vegetation layer. This model was calibrated using various sources of in situ and airborne data. In the present study, we propose to evaluate the L-MEB model on the basis of a large set of airborne data, recorded by the CAROLS radiometer during the course of 20 flights made over South West France (the SMOSMANIA site, and supported by simultaneous soil moisture measurements, made in 2009 and 2010. In terms of volumetric soil moisture, the retrieval accuracy achieved with the L-MEB model, with two default roughness parameters, ranges between 8% and 13%. Local calibrations of the roughness parameter, using data from the 2009 flights for different areas of the site, allowed an accuracy of approximately 5.3% to be achieved with the 2010 CAROLS data. Simultaneously we estimated the vegetation optical thickness (t and we showed that, when roughness is locally adjusted, MODIS NDVI values are correlated (R2 = 0.36 to t. Finally, as a consequence of the significant influence of the roughness parameter on the estimated absolute values of soil moisture, we propose to evaluate the relative variability of the soil moisture, using a default soil roughness parameter. The soil moisture variations are estimated with an uncertainty of approximately 6%.
Estimation of improved resolution soil moisture in vegetated areas using passive AMSR-E data
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.
State-Space Estimation of Soil Organic Carbon Stock
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.
DEFF Research Database (Denmark)
Sales-Cruz, Mauricio; Heitzig, Martina; Cameron, Ian
2011-01-01
of optimisation techniques coupled with dynamic solution of the underlying model. Linear and nonlinear approaches to parameter estimation are investigated. There is also the application of maximum likelihood principles in the estimation of parameters, as well as the use of orthogonal collocation to generate a set......In this chapter the importance of parameter estimation in model development is illustrated through various applications related to reaction systems. In particular, rate constants in a reaction system are obtained through parameter estimation methods. These approaches often require the application...... of algebraic equations as the basis for parameter estimation.These approaches are illustrated using estimations of kinetic constants from reaction system models....
Microwave remote sensing of soil moisture for estimation of profile soil property
International Nuclear Information System (INIS)
Mattikalli, N.M.; Engman, E.T.; Ahuja, L.R.; Jackson, T.J.
1998-01-01
Multi-temporal microwave remotely-sensed soil moisture has been utilized for the estimation of profile soil property, viz. the soil hydraulic conductivity. Passive microwave remote sensing was employed to collect daily soil moisture data across the Little Washita watershed, Oklahoma, during 10-18 June 1992. The ESTAR (Electronically Steered Thin Array Radiometer) instrument operating at L -band was flown on a NASA C-130 aircraft. Brightness temperature (TB) data collected at a ground resolution of 200m were employed to derive spatial distribution of surface soil moisture. Analysis of spatial and temporal soil moisture information in conjunction with soils data revealed a direct relation between changes in soil moisture and soil texture. A geographical information system (GIS) based analysis suggested that 2-days initial drainage of soil, measured from remote sensing, was related to an important soil hydraulic property viz. the saturated hydraulic conductivity (Ksat). A hydrologic modelling methodology was developed for estimation of Ksat of surface and sub-surface soil layers. Specifically, soil hydraulic parameters were optimized to obtain a good match between model estimated and field measured soil moisture profiles. Relations between 2-days soil moisture change and Ksat of 0-5 cm, 0-30 cm and 0-60cm depths yielded correla tions of 0.78, 0.82 and 0.71, respectively. These results are comparable to the findings of previous studies involving laboratory-controlled experiments and numerical simulations, and support their extension to the field conditions of the Little Washita watershed. These findings have potential applications of microwave remote sensing to obtain 2-days of soil moisture and then to quickly estimate the spatial distribution of Ksat over large areas. (author)
Estimation of riverbank soil erodibility parameters using genetic ...
Indian Academy of Sciences (India)
Tapas Karmaker
2017-11-07
Nov 7, 2017 ... process. Therefore, this is a study to verify the applicability of inverse parameter ... successful modelling of the riverbank erosion, precise estimation of ..... For this simulation, about 40 iterations are found to attain the convergence. ..... rithm for function optimization: a Matlab implementation. NCSU-IE TR ...
Directory of Open Access Journals (Sweden)
Amir LAKZIAN
2010-09-01
Full Text Available This paper presents the comparison of three different approaches to estimate soil water content at defined values of soil water potential based on selected parameters of soil solid phase. Forty different sampling locations in northeast of Iran were selected and undisturbed samples were taken to measure the water content at field capacity (FC, -33 kPa, and permanent wilting point (PWP, -1500 kPa. At each location solid particle of each sample including the percentage of sand, silt and clay were measured. Organic carbon percentage and soil texture were also determined for each soil sample at each location. Three different techniques including pattern recognition approach (k nearest neighbour, k-NN, Artificial Neural Network (ANN and pedotransfer functions (PTF were used to predict the soil water at each sampling location. Mean square deviation (MSD and its components, index of agreement (d, root mean square difference (RMSD and normalized RMSD (RMSDr were used to evaluate the performance of all the three approaches. Our results showed that k-NN and PTF performed better than ANN in prediction of water content at both FC and PWP matric potential. Various statistics criteria for simulation performance also indicated that between kNN and PTF, the former, predicted water content at PWP more accurate than PTF, however both approach showed a similar accuracy to predict water content at FC.
Automated Soil Physical Parameter Assessment Using Smartphone and Digital Camera Imagery
Directory of Open Access Journals (Sweden)
Matt Aitkenhead
2016-12-01
Full Text Available Here we present work on using different types of soil profile imagery (topsoil profiles captured with a smartphone camera and full-profile images captured with a conventional digital camera to estimate the structure, texture and drainage of the soil. The method is adapted from earlier work on developing smartphone apps for estimating topsoil organic matter content in Scotland and uses an existing visual soil structure assessment approach. Colour and image texture information was extracted from the imagery. This information was linked, using geolocation information derived from the smartphone GPS system or from field notes, with existing collections of topography, land cover, soil and climate data for Scotland. A neural network model was developed that was capable of estimating soil structure (on a five-point scale, soil texture (sand, silt, clay, bulk density, pH and drainage category using this information. The model is sufficiently accurate to provide estimates of these parameters from soils in the field. We discuss potential improvements to the approach and plans to integrate the model into a set of smartphone apps for estimating health and fertility indicators for Scottish soils.
Ecological estimation of the soils good for grape in Ganja-Gazakh zone
International Nuclear Information System (INIS)
Mammadov, Q.S.; Yusifova, M.M.
2009-01-01
Contemporary agricultural science improved the known adaptive approaches in the past, for it accounting natural peccularities of the concrete region is offered with the assistance of agroecological estimation of soil. Using of collecting materials of the soil ecological parameters of soil cover of the studing territory and applying the system of the private scales of the soil estimation on degree of display of their separate signs, the ecological estimation of the soils good for grape in Ganja-Gazakh zone where the highest ecological markshave been got mountain-grey-brown dark (97 marks) and grey-brown dark (96 marks) soils has been carried out
Radon levels and transport parameters in Atlantic Forest soils
International Nuclear Information System (INIS)
Farias, E.E.G. de; Silva Neto, P.C. da; Souza, E.M. de; De Franca, E.J.; Hazin, C.A.
2016-01-01
In natural forest soils, the radon transport processes can be significantly intensified due to the contribution of living organism activities to soil porosity. In this paper, the first results of the radon concentrations were obtained for soil gas from the Atlantic Forest, particularly in the Refugio Ecologico Charles Darwin, Brazil. The estimation of permeability and radon exhalation rate were carried out in this conservation unit. For forested soils, radon concentrations as high as 40 kBq m -3 were found. Based on the radon concentrations and on the permeability parameter, the results indicated considerable radon hazard for human occupation in the neighborhood. (author)
International Nuclear Information System (INIS)
Vandenhove, H.; Hees, M. van; Wouters, K.; Wannijn, J.
2007-01-01
Present study aims to quantify the influence of soil parameters on soil solution uranium concentration for 238 U spiked soils. Eighteen soils collected under pasture were selected such that they covered a wide range for those parameters hypothesised as being potentially important in determining U sorption. Maximum soil solution uranium concentrations were observed at alkaline pH, high inorganic carbon content and low cation exchange capacity, organic matter content, clay content, amorphous Fe and phosphate levels. Except for the significant correlation between the solid-liquid distribution coefficients (K d , L kg -1 ) and the organic matter content (R 2 = 0.70) and amorphous Fe content (R 2 = 0.63), there was no single soil parameter significantly explaining the soil solution uranium concentration (which varied 100-fold). Above pH = 6, log(K d ) was linearly related with pH [log(K d ) = - 1.18 pH + 10.8, R 2 = 0.65]. Multiple linear regression analysis did result in improved predictions of the soil solution uranium concentration but the model was complex. - Uranium solubility in soil can be predicted from organic matter or amorphous iron content and pH or with complex multilinear models considering several soil parameters
Uncertainty in dual permeability model parameters for structured soils
Arora, B.; Mohanty, B. P.; McGuire, J. T.
2012-01-01
Successful application of dual permeability models (DPM) to predict contaminant transport is contingent upon measured or inversely estimated soil hydraulic and solute transport parameters. The difficulty in unique identification of parameters for the additional macropore- and matrix-macropore interface regions, and knowledge about requisite experimental data for DPM has not been resolved to date. Therefore, this study quantifies uncertainty in dual permeability model parameters of experimental soil columns with different macropore distributions (single macropore, and low- and high-density multiple macropores). Uncertainty evaluation is conducted using adaptive Markov chain Monte Carlo (AMCMC) and conventional Metropolis-Hastings (MH) algorithms while assuming 10 out of 17 parameters to be uncertain or random. Results indicate that AMCMC resolves parameter correlations and exhibits fast convergence for all DPM parameters while MH displays large posterior correlations for various parameters. This study demonstrates that the choice of parameter sampling algorithms is paramount in obtaining unique DPM parameters when information on covariance structure is lacking, or else additional information on parameter correlations must be supplied to resolve the problem of equifinality of DPM parameters. This study also highlights the placement and significance of matrix-macropore interface in flow experiments of soil columns with different macropore densities. Histograms for certain soil hydraulic parameters display tri-modal characteristics implying that macropores are drained first followed by the interface region and then by pores of the matrix domain in drainage experiments. Results indicate that hydraulic properties and behavior of the matrix-macropore interface is not only a function of saturated hydraulic conductivity of the macroporematrix interface (Ksa) and macropore tortuosity (lf) but also of other parameters of the matrix and macropore domains.
Directory of Open Access Journals (Sweden)
hossein sabourifard
2018-02-01
Full Text Available Introduction: One of the most important requirements in planning production and processing of medicinal plants in order to obtain high yield and high-quality is the initial assessment of the physical and chemical properties of soil, which reduces the production cost by avoiding the use of unnecessary soil analysis. Summer savory (Satureja hortensis L. is one the most widely used medicinal plants that quality index of plant is related to the quantity and the constituent of its essential oil content. Understanding the relations between the quantity and quality of medicinal plants with the very physical and chemical properties of soil is very complex and the estimation of parameters changes of medicinal plants affect by soil quality characteristics is more difficult. Today, with the arrival of multivariable regression models and artificial lattice models in the research, many complex relationships found in nature is understandable. Hence the need for estimation the biomass yield of savory using fast, cheap and with acceptable accuracy is feeling. Materials and Methods: The present study was performed at the Agricultural Research Station Neyshabur as pot experiment based on a completely randomized design with three replications. Around 53 soil samples were collected from different parts of Neyshabur city, and soil texture, organic matter, pH, salinity, phosphorus, potassium, nitrogen and carbon content were selected as the easily available parameters. Before planting the parameters were measured in laboratory. Approximately 90 days after planting seeds in pots containing soil samples, the sampling of plants was done based on the treatments. For drying, samples were placed for 24 hours in an oven at 40 °C. Finally, the relationship between the biomass yield and easily available soil parameters was determined using artificial neural network by Matlab7.9 software. Results and Discussion: The results showed that soil variability, is a key element in
Estimating unsaturated hydraulic conductivity from soil moisture-tim function
International Nuclear Information System (INIS)
El Gendy, R.W.
2002-01-01
The unsaturated hydraulic conductivity for soil can be estimated from o(t) function, and the dimensionless soil water content parameter (Se)Se (β - βr)/ (φ - θ)), where θ, is the soil water content at any time (from soil moisture depletion curve l; θ is the residual water content and θ, is the total soil porosity (equals saturation point). Se can be represented as a time function (Se = a t b ), where t, is the measurement time and (a and b) are the regression constants. The recommended equation in this method is given by
Prediction of compressibility parameters of the soils using artificial neural network.
Kurnaz, T Fikret; Dagdeviren, Ugur; Yildiz, Murat; Ozkan, Ozhan
2016-01-01
The compression index and recompression index are one of the important compressibility parameters to determine the settlement calculation for fine-grained soil layers. These parameters can be determined by carrying out laboratory oedometer test on undisturbed samples; however, the test is quite time-consuming and expensive. Therefore, many empirical formulas based on regression analysis have been presented to estimate the compressibility parameters using soil index properties. In this paper, an artificial neural network (ANN) model is suggested for prediction of compressibility parameters from basic soil properties. For this purpose, the input parameters are selected as the natural water content, initial void ratio, liquid limit and plasticity index. In this model, two output parameters, including compression index and recompression index, are predicted in a combined network structure. As the result of the study, proposed ANN model is successful for the prediction of the compression index, however the predicted recompression index values are not satisfying compared to the compression index.
Estimating soil moisture using the Danish polarimetric SAR
DEFF Research Database (Denmark)
Jiankang, Ji; Thomsen, A.; Skriver, Henning
1995-01-01
The results of applying data from the Danish polarimetric SAR (EMISAR) to estimate soil moisture for bare fields are presented. Fully calibrated C-band SAR images for hh, vv and cross polarizations have been used in this study. The measured surface roughness data showed that classical roughness a...... of surface parameters with the bilinear model, the correlation coefficient between the estimated and measured soil moisture, as well as rms height, is about 0.77. To improve the result, the local incidence angles need to be taken into account......The results of applying data from the Danish polarimetric SAR (EMISAR) to estimate soil moisture for bare fields are presented. Fully calibrated C-band SAR images for hh, vv and cross polarizations have been used in this study. The measured surface roughness data showed that classical roughness...... autocorrelation functions (Gaussian and Exponential) were not able to fit natural surfaces well. A Gauss-Exp hybrid model which agreed better with the measured data has been proposed. Theoretical surface scattering models (POM, IEM), as well as an empirical model for retrieval of soil moisture and surface rms...
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
Enhancing PTFs with remotely sensed data for multi-scale soil water retention estimation
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
Jadoon, Khan
2012-01-01
An integrated hydrogeophysical inversion approach was used to remotely infer the unsaturated soil hydraulic parameters from time-lapse ground-penetrating radar (GPR) data collected at a fixed location over a bare agricultural field. The GPR model combines a full-waveform solution of Maxwell\\'s equations for three-dimensional wave propaga- tion in planar layered media together with global reflection and transmission functions to account for the antenna and its interactions with the medium. The hydrological simu- lator HYDRUS-1D was used with a two layer single- and dual-porosity model. The radar model was coupled to the hydrodynamic model, such that the soil electrical properties (permitivity and conductivity) that serve as input to the GPR model become a function of the hydrodynamic model output (water content), thereby permiting estimation of the soil hydraulic parameters from the GPR data in an inversion loop. To monitor the soil water con- tent dynamics, time-lapse GPR and time domain reflectometry (TDR) measurements were performed, whereby only GPR data was used in the inversion. Significant effects of water dynamics were observed in the time-lapse GPR data and in particular precipitation and evaporation events were clearly visible. The dual porosity model provided betier results compared to the single porosity model for describing the soil water dynamics, which is sup- ported by field observations of macropores. Furthermore, the GPR-derived water content profiles reconstructed from the integrated hydrogeophysical inversion were in good agree- ment with TDR observations. These results suggest that the proposed method is promising for non-invasive characterization of the shallow subsurface hydraulic properties and moni- toring water dynamics at the field scale. © Soil Science Society of America.
Reginato, R. J.; Idso, S. B.; Jackson, R. D.; Vedder, J. F.; Blanchard, M. B.; Goettelman, R.
1976-01-01
Soil water contents from both smooth and rough bare soil were estimated from remotely sensed surface soil and air temperatures. An inverse relationship between two thermal parameters and gravimetric soil water content was found for Avondale loam when its water content was between air-dry and field capacity. These parameters, daily maximum minus minimum surface soil temperature and daily maximum soil minus air temperature, appear to describe the relationship reasonably well. These two parameters also describe relative soil water evaporation (actual/potential). Surface soil temperatures showed good agreement among three measurement techniques: in situ thermocouples, a ground-based infrared radiation thermometer, and the thermal infrared band of an airborne multispectral scanner.
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Yi-Bo Li
2018-01-01
Full Text Available The accurate estimation of soil hydraulic parameters (θs, α, n, and Ks of the van Genuchten–Mualem model has attracted considerable attention. In this study, we proposed a new two-step inversion method, which first estimated the hydraulic parameter θs using objective function by the final water content, and subsequently estimated the soil hydraulic parameters α, n, and Ks, using a vector-evaluated genetic algorithm and particle swarm optimization (VEGA-PSO method based on objective functions by cumulative infiltration and infiltration rate. The parameters were inversely estimated for four types of soils (sand, loam, silt, and clay under an in silico experiment simulating the tension disc infiltration at three initial water content levels. The results indicated that the method is excellent and robust. Because the objective function had multilocal minima in a tiny range near the true values, inverse estimation of the hydraulic parameters was difficult; however, the estimated soil water retention curves and hydraulic conductivity curves were nearly identical to the true curves. In addition, the proposed method was able to estimate the hydraulic parameters accurately despite substantial measurement errors in initial water content, final water content, and cumulative infiltration, proving that the method was feasible and practical for field application.
Combined Radar-Radiometer Surface Soil Moisture and Roughness Estimation
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.
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Chen Zeng
2012-01-01
Full Text Available Knowledge of soil hydraulic parameters for the van Genuchten function is important to characterize soil water movement for watershed management. Accurate and rapid prediction of soil water flow in heterogeneous gravel soil has become a hot topic in recent years. However, it is difficult to precisely estimate hydraulic parameters in a heterogeneous soil with rock fragments. In this study, the HYDRUS-2D numerical model was used to evaluate hydraulic parameters for heterogeneous gravel soil that was irregularly embedded with rock fragments in a grape production base. The centrifugal method (CM, tensiometer method (TM and inverse solution method (ISM were compared for various parameters in the van Genuchten function. The soil core method (SCM, disc infiltration method (DIM and inverse solution method (ISM were also investigated for measuring saturated hydraulic conductivity. Simulation with the DIM approach revealed a problem of overestimating soil water infiltration whereas simulation with the SCM approach revealed a problem of underestimating water movement as compared to actual field observation. The ISM approach produced the best simulation result even though this approach slightly overestimated soil moisture by ignoring the impact of rock fragments. This study provides useful information on the overall evaluation of soil hydraulic parameters attained with different measurement methods for simulating soil water movement and distribution in heterogeneous gravel soil.
Vandenhove, H; Van Hees, M; Wouters, K; Wannijn, J
2007-01-01
Present study aims to quantify the influence of soil parameters on soil solution uranium concentration for (238)U spiked soils. Eighteen soils collected under pasture were selected such that they covered a wide range for those parameters hypothesised as being potentially important in determining U sorption. Maximum soil solution uranium concentrations were observed at alkaline pH, high inorganic carbon content and low cation exchange capacity, organic matter content, clay content, amorphous Fe and phosphate levels. Except for the significant correlation between the solid-liquid distribution coefficients (K(d), L kg(-1)) and the organic matter content (R(2)=0.70) and amorphous Fe content (R(2)=0.63), there was no single soil parameter significantly explaining the soil solution uranium concentration (which varied 100-fold). Above pH=6, log(K(d)) was linearly related with pH [log(K(d))=-1.18 pH+10.8, R(2)=0.65]. Multiple linear regression analysis did result in improved predictions of the soil solution uranium concentration but the model was complex.
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Mbagwu, J.S.C.
1994-05-01
Among the many models developed for monitoring the infiltration process those of Philip and Kostiakov have been studied in detail because of their simplicity and the ease of estimating their fitting parameters. The important soil physical factors influencing the fitting parameters in these infiltration models are reported in this study. The results of the study show that the single most important soil property affecting the fitting parameters in these models is the effective porosity. 36 refs, 2 figs, 5 tabs
Dalla Valle, Nicolas; Wutzler, Thomas; Meyer, Stefanie; Potthast, Karin; Michalzik, Beate
2017-04-01
Dual-permeability type models are widely used to simulate water fluxes and solute transport in structured soils. These models contain two spatially overlapping flow domains with different parameterizations or even entirely different conceptual descriptions of flow processes. They are usually able to capture preferential flow phenomena, but a large set of parameters is needed, which are very laborious to obtain or cannot be measured at all. Therefore, model inversions are often used to derive the necessary parameters. Although these require sufficient input data themselves, they can use measurements of state variables instead, which are often easier to obtain and can be monitored by automated measurement systems. In this work we show a method to estimate soil hydraulic parameters from high frequency soil moisture time series data gathered at two different measurement depths by inversion of a simple one dimensional dual-permeability model. The model uses an advection equation based on the kinematic wave theory to describe the flow in the fracture domain and a Richards equation for the flow in the matrix domain. The soil moisture time series data were measured in mesocosms during sprinkling experiments. The inversion consists of three consecutive steps: First, the parameters of the water retention function were assessed using vertical soil moisture profiles in hydraulic equilibrium. This was done using two different exponential retention functions and the Campbell function. Second, the soil sorptivity and diffusivity functions were estimated from Boltzmann-transformed soil moisture data, which allowed the calculation of the hydraulic conductivity function. Third, the parameters governing flow in the fracture domain were determined using the whole soil moisture time series. The resulting retention functions were within the range of values predicted by pedotransfer functions apart from very dry conditions, where all retention functions predicted lower matrix potentials
Dimitrov, Marin
2014-01-01
We coupled a radiative transfer model and a soil hydrologic model (HYDRUS 1D) with an optimization routine to derive soil hydraulic parameters, surface roughness, and soil moisture of a tilled bare soil plot using measured brightness temperatures at 1.4 GHz (L-band), rainfall, and potential soil evaporation. The robustness of the approach was evaluated using five 28-d data sets representing different meteorological conditions. We considered two soil hydraulic property models: the unimodal Mualem-van Genuchten and the bimodal model of Durner. Microwave radiative transfer was modeled by three different approaches: the Fresnel equation with depth-averaged dielectric permittivity of either 2-or 5-cm-thick surface layers and a coherent radiative transfer model (CRTM) that accounts for vertical gradients in dielectric permittivity. Brightness temperatures simulated by the CRTM and the 2-cm-layer Fresnel model fitted well to the measured ones. L-band brightness temperatures are therefore related to the dielectric permittivity and soil moisture in a 2-cm-thick surface layer. The surface roughness parameter that was derived from brightness temperatures using inverse modeling was similar to direct estimates from laser profiler measurements. The laboratory-derived water retention curve was bimodal and could be retrieved consistently for the different periods from brightness temperatures using inverse modeling. A unimodal soil hydraulic property function underestimated the hydraulic conductivity near saturation. Surface soil moisture contents simulated using retrieved soil hydraulic parameters were compared with in situ measurements. Depth-specific calibration relations were essential to derive soil moisture from near-surface installed sensors. © Soil Science Society of America 5585 Guilford Rd., Madison, WI 53711 USA.
Multiscale Bayesian neural networks for soil water content estimation
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
Estimation of Key Parameters of the Coupled Energy and Water Model by Assimilating Land Surface Data
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
CONTAMINATED SOIL VOLUME ESTIMATE TRACKING METHODOLOGY
International Nuclear Information System (INIS)
Durham, L.A.; Johnson, R.L.; Rieman, C.; Kenna, T.; Pilon, R.
2003-01-01
The U.S. Army Corps of Engineers (USACE) is conducting a cleanup of radiologically contaminated properties under the Formerly Utilized Sites Remedial Action Program (FUSRAP). The largest cost element for most of the FUSRAP sites is the transportation and disposal of contaminated soil. Project managers and engineers need an estimate of the volume of contaminated soil to determine project costs and schedule. Once excavation activities begin and additional remedial action data are collected, the actual quantity of contaminated soil often deviates from the original estimate, resulting in cost and schedule impacts to the project. The project costs and schedule need to be frequently updated by tracking the actual quantities of excavated soil and contaminated soil remaining during the life of a remedial action project. A soil volume estimate tracking methodology was developed to provide a mechanism for project managers and engineers to create better project controls of costs and schedule. For the FUSRAP Linde site, an estimate of the initial volume of in situ soil above the specified cleanup guidelines was calculated on the basis of discrete soil sample data and other relevant data using indicator geostatistical techniques combined with Bayesian analysis. During the remedial action, updated volume estimates of remaining in situ soils requiring excavation were calculated on a periodic basis. In addition to taking into account the volume of soil that had been excavated, the updated volume estimates incorporated both new gamma walkover surveys and discrete sample data collected as part of the remedial action. A civil survey company provided periodic estimates of actual in situ excavated soil volumes. By using the results from the civil survey of actual in situ volumes excavated and the updated estimate of the remaining volume of contaminated soil requiring excavation, the USACE Buffalo District was able to forecast and update project costs and schedule. The soil volume
Nonlinear soil parameter effects on dynamic embedment of offshore pipeline on soft clay
Directory of Open Access Journals (Sweden)
Su Young Yu
2015-03-01
Full Text Available In this paper, the effects of nonlinear soft clay on dynamic embedment of offshore pipeline were investigated. Seabed embedment by pipe-soil interactions has impacts on the structural boundary conditions for various subsea structures such as pipeline, riser, pile, and many other systems. A number of studies have been performed to estimate real soil behavior, but their estimation of seabed embedment has not been fully identified and there are still many uncertainties. In this regards, comparison of embedment between field survey and existing empirical models has been performed to identify uncertainties and investigate the effect of nonlinear soil parameter on dynamic embedment. From the comparison, it is found that the dynamic embedment with installation effects based on nonlinear soil model have an influence on seabed embedment. Therefore, the pipe embedment under dynamic condition by nonlinear para- meters of soil models was investigated by Dynamic Embedment Factor (DEF concept, which is defined as the ratio of the dynamic and static embedment of pipeline, in order to overcome the gap between field embedment and currently used empirical and numerical formula. Although DEF through various researches is suggested, its range is too wide and it does not consider dynamic laying effect. It is difficult to find critical parameters that are affecting to the embedment result. Therefore, the study on dynamic embedment factor by soft clay parameters of nonlinear soil model was conducted and the sensitivity analyses about parameters of nonlinear soil model were performed as well. The tendency on dynamic embedment factor was found by conducting numerical analyses using OrcaFlex software. It is found that DEF was influenced by shear strength gradient than other factors. The obtained results will be useful to understand the pipe embedment on soft clay seabed for applying offshore pipeline designs such as on-bottom stability and free span analyses.
Two and Three-Phases Fractal Models Application in Soil Saturated Hydraulic Conductivity Estimation
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ELNAZ Rezaei abajelu
2017-03-01
Full Text Available Introduction: Soil Hydraulic conductivity is considered as one of the most important hydraulic properties in water and solutionmovement in porous media. In recent years, variousmodels as pedo-transfer functions, fractal models and scaling technique are used to estimate the soil saturated hydraulic conductivity (Ks. Fractal models with two subset of two (solid and pore and three phases (solid, pore and soil fractal (PSF are used to estimate the fractal dimension of soil particles. The PSF represents a generalization of the solid and pore mass fractal models. The PSF characterizes both the solid and pore phases of the porous material. It also exhibits self-similarity to some degree, in the sense that where local structure seems to be similar to the whole structure.PSF models can estimate interface fractal dimension using soil pore size distribution data (PSD and soil moisture retention curve (SWRC. The main objective of this study was to evaluate different fractal models to estimate the Ksparameter. Materials and Methods: The Schaapetal data was used in this study. The complex consists of sixty soil samples. Soil texture, soil bulk density, soil saturated hydraulic conductivity and soil particle size distribution curve were measured by hydrometer method, undistributed soil sample, constant head method and wet sieve method, respectively for all soil samples.Soil water retention curve were determined by using pressure plates apparatus.The Ks parameter could be estimated by Ralws model as a function of fractal dimension by seven fractal models. Fractal models included Fuentes at al. (1996, Hunt and Gee (2002, Bird et al. (2000, Huang and Zhang (2005, Tyler and Wheatcraft (1990, Kutlu et al. (2008, Sepaskhah and Tafteh (2013.Therefore The Ks parameter can be estimated as a function of the DS (fractal dimension by seven fractal models (Table 2.Sensitivity analysis of Rawls model was assessed by making changes±10%, ±20% and±30%(in input parameters
Estimation of Soil Moisture in an Alpine Catchment with RADARSAT2 Images
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L. Pasolli
2011-01-01
Full Text Available Soil moisture retrieval is one of the most challenging problems in the context of biophysical parameter estimation from remotely sensed data. Typically, microwave signals are used thanks to their sensitivity to variations in the water content of soil. However, especially in the Alps, the presence of vegetation and the heterogeneity of topography may significantly affect the microwave signal, thus increasing the complexity of the retrieval. In this paper, the effectiveness of RADARSAT2 SAR images for the estimation of soil moisture in an alpine catchment is investigated. We first carry out a sensitivity analysis of the SAR signal to the moisture content of soil and other target properties (e.g., topography and vegetation. Then we propose a technique for estimating soil moisture based on the Support Vector Regression algorithm and the integration of ancillary data. Preliminary results are discussed both in terms of accuracy over point measurements and effectiveness in handling spatially distributed data.
Stucchi Boschi, Raquel; Qin, Mingming; Gimenez, Daniel; Cooper, Miguel
2016-04-01
Modeling is an important tool for better understanding and assessing land use impacts on landscape processes. A key point for environmental modeling is the knowledge of soil hydraulic properties. However, direct determination of soil hydraulic properties is difficult and costly, particularly in vast and remote regions such as one constituting the Amazon Biome. One way to overcome this problem is to extrapolate accurately estimated data to pedologically similar sites. The van Genuchten (VG) parametric equation is the most commonly used for modeling SWRC. The use of a Bayesian approach in combination with the Markov chain Monte Carlo to estimate the VG parameters has several advantages compared to the widely used global optimization techniques. The Bayesian approach provides posterior distributions of parameters that are independent from the initial values and allow for uncertainty analyses. The main objectives of this study were: i) to estimate hydraulic parameters from data of pasture and forest sites by the Bayesian inverse modeling approach; and ii) to investigate the extrapolation of the estimated VG parameters to a nearby toposequence with pedologically similar soils to those used for its estimate. The parameters were estimated from volumetric water content and tension observations obtained after rainfall events during a 207-day period from pasture and forest sites located in the southeastern Amazon region. These data were used to run HYDRUS-1D under a Differential Evolution Adaptive Metropolis (DREAM) scheme 10,000 times, and only the last 2,500 times were used to calculate the posterior distributions of each hydraulic parameter along with 95% confidence intervals (CI) of volumetric water content and tension time series. Then, the posterior distributions were used to generate hydraulic parameters for two nearby toposequences composed by six soil profiles, three are under forest and three are under pasture. The parameters of the nearby site were accepted when
PCB in soils and estimated soil-air exchange fluxes of selected PCB congeners in the south of Sweden
International Nuclear Information System (INIS)
Backe, Cecilia; Cousins, Ian T.; Larsson, Per
2004-01-01
PCB concentrations were studied in different soils to determine the spatial variation over a region of approximately 11 000 km 2 . PCB congener pattern was used to illustrate the spatial differences, as shown by principal component analysis (PCA). The relationship to different soil parameters was studied. PCB concentrations in soil showed a large variation between sampling-areas with median concentrations ranging between 2.3 and 332 ng g -1 (dw). Highest concentrations were found at two sites with sandy soils, one with extremely high organic carbon content. Both sites were located on the west coast of southern Sweden. Soils with similar soil textures (i.e. sandy silt moraine) did not show any significant differences in PCB concentrations. PCB congener composition was shown to differ between sites, with congener patterns almost site-specific. PCB in air and precipitation was measured and the transfer of chemicals between the soil and air compartments was estimated. Soil-air fugacity quotient calculations showed that the PCBs in the soil consistently had a higher fugacity than the PCBs in the air, with a median quotient value of 2.7. The gaseous fluxes between soil and air were estimated using standard modelling equations and a net soil-air flux estimated by subtracting bulk deposition from gaseous soil-air fluxes. It was shown that inclusion of vertical sorbed phase transport of PCBs in the soil had a large effect on the direction of the net soil-air exchange fluxes. - Soil-air exchange of PCBs is investigated and modelled across Sweden
PEDO-TRANSFER FUNCTIONS FOR ESTIMATING SOIL BULK DENSITY IN CENTRAL AMAZONIA
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Henrique Seixas Barros
2015-04-01
Full Text Available Under field conditions in the Amazon forest, soil bulk density is difficult to measure. Rigorous methodological criteria must be applied to obtain reliable inventories of C stocks and soil nutrients, making this process expensive and sometimes unfeasible. This study aimed to generate models to estimate soil bulk density based on parameters that can be easily and reliably measured in the field and that are available in many soil-related inventories. Stepwise regression models to predict bulk density were developed using data on soil C content, clay content and pH in water from 140 permanent plots in terra firme (upland forests near Manaus, Amazonas State, Brazil. The model results were interpreted according to the coefficient of determination (R2 and Akaike information criterion (AIC and were validated with a dataset consisting of 125 plots different from those used to generate the models. The model with best performance in estimating soil bulk density under the conditions of this study included clay content and pH in water as independent variables and had R2 = 0.73 and AIC = -250.29. The performance of this model for predicting soil density was compared with that of models from the literature. The results showed that the locally calibrated equation was the most accurate for estimating soil bulk density for upland forests in the Manaus region.
Fenton, O; Vero, S; Ibrahim, T G; Murphy, P N C; Sherriff, S C; Ó hUallacháin, D
2015-11-01
Elucidation of when the loss of pollutants, below the rooting zone in agricultural landscapes, affects water quality is important when assessing the efficacy of mitigation measures. Investigation of this inherent time lag (t(T)) is divided into unsaturated (t(u)) and saturated (t(s)) components. The duration of these components relative to each other differs depending on soil characteristics and the landscape position. The present field study focuses on tu estimation in a scenario where the saturated zone is likely to constitute a higher proportion of t(T). In such instances, or where only initial breakthrough (IBT) or centre of mass (COM) is of interest, utilisation of site and depth specific "simple" textural class or actual sand-silt-clay percentages to generate soil water characteristic curves with associated soil hydraulic parameters is acceptable. With the same data it is also possible to estimate a soil physical quality (S) parameter for each soil layer which can be used to infer many other physical, chemical and biological quality indicators. In this study, hand texturing in the field was used to determine textural classes of a soil profile. Laboratory methods, including hydrometer, pipette and laser diffraction methods were used to determine actual sand-silt-clay percentages of sections of the same soil profile. Results showed that in terms of S, hand texturing resulted in a lower index value (inferring a degraded soil) than that of pipette, hydrometer and laser equivalents. There was no difference between S index values determined using the pipette, hydrometer and laser diffraction methods. The difference between the three laboratory methods on both the IBT and COM stages of t(u) were negligible, and in this instance were unlikely to affect either groundwater monitoring decisions, or to be of consequence from a policy perspective. When t(u) estimates are made over the full depth of the vadose zone, which may extend to several metres, errors resulting from
Fenton, O.; Vero, S.; Ibrahim, T. G.; Murphy, P. N. C.; Sherriff, S. C.; Ó hUallacháin, D.
2015-11-01
Elucidation of when the loss of pollutants, below the rooting zone in agricultural landscapes, affects water quality is important when assessing the efficacy of mitigation measures. Investigation of this inherent time lag (tT) is divided into unsaturated (tu) and saturated (ts) components. The duration of these components relative to each other differs depending on soil characteristics and the landscape position. The present field study focuses on tu estimation in a scenario where the saturated zone is likely to constitute a higher proportion of tT. In such instances, or where only initial breakthrough (IBT) or centre of mass (COM) is of interest, utilisation of site and depth specific "simple" textural class or actual sand-silt-clay percentages to generate soil water characteristic curves with associated soil hydraulic parameters is acceptable. With the same data it is also possible to estimate a soil physical quality (S) parameter for each soil layer which can be used to infer many other physical, chemical and biological quality indicators. In this study, hand texturing in the field was used to determine textural classes of a soil profile. Laboratory methods, including hydrometer, pipette and laser diffraction methods were used to determine actual sand-silt-clay percentages of sections of the same soil profile. Results showed that in terms of S, hand texturing resulted in a lower index value (inferring a degraded soil) than that of pipette, hydrometer and laser equivalents. There was no difference between S index values determined using the pipette, hydrometer and laser diffraction methods. The difference between the three laboratory methods on both the IBT and COM stages of tu were negligible, and in this instance were unlikely to affect either groundwater monitoring decisions, or to be of consequence from a policy perspective. When tu estimates are made over the full depth of the vadose zone, which may extend to several metres, errors resulting from the use of
Estimates of soil ingestion by wildlife
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
Soil Moisture Estimate under Forest using a Semi-empirical Model at P-Band
Truong-Loi, M.; Saatchi, S.; Jaruwatanadilok, S.
2013-12-01
In this paper we show the potential of a semi-empirical algorithm to retrieve soil moisture under forests using P-band polarimetric SAR data. In past decades, several remote sensing techniques have been developed to estimate the surface soil moisture. In most studies associated with radar sensing of soil moisture, the proposed algorithms are focused on bare or sparsely vegetated surfaces where the effect of vegetation can be ignored. At long wavelengths such as L-band, empirical or physical models such as the Small Perturbation Model (SPM) provide reasonable estimates of surface soil moisture at depths of 0-5cm. However for densely covered vegetated surfaces such as forests, the problem becomes more challenging because the vegetation canopy is a complex scattering environment. For this reason there have been only few studies focusing on retrieving soil moisture under vegetation canopy in the literature. Moghaddam et al. developed an algorithm to estimate soil moisture under a boreal forest using L- and P-band SAR data. For their studied area, double-bounce between trunks and ground appear to be the most important scattering mechanism. Thereby, they implemented parametric models of radar backscatter for double-bounce using simulations of a numerical forest scattering model. Hajnsek et al. showed the potential of estimating the soil moisture under agricultural vegetation using L-band polarimetric SAR data and using polarimetric-decomposition techniques to remove the vegetation layer. Here we use an approach based on physical formulation of dominant scattering mechanisms and three parameters that integrates the vegetation and soil effects at long wavelengths. The algorithm is a simplification of a 3-D coherent model of forest canopy based on the Distorted Born Approximation (DBA). The simplified model has three equations and three unknowns, preserving the three dominant scattering mechanisms of volume, double-bounce and surface for three polarized backscattering
Optomechanical parameter estimation
International Nuclear Information System (INIS)
Ang, Shan Zheng; Tsang, Mankei; Harris, Glen I; Bowen, Warwick P
2013-01-01
We propose a statistical framework for the problem of parameter estimation from a noisy optomechanical system. The Cramér–Rao lower bound on the estimation errors in the long-time limit is derived and compared with the errors of radiometer and expectation–maximization (EM) algorithms in the estimation of the force noise power. When applied to experimental data, the EM estimator is found to have the lowest error and follow the Cramér–Rao bound most closely. Our analytic results are envisioned to be valuable to optomechanical experiment design, while the EM algorithm, with its ability to estimate most of the system parameters, is envisioned to be useful for optomechanical sensing, atomic magnetometry and fundamental tests of quantum mechanics. (paper)
Quantifying Uncertainty in Soil Volume Estimates
International Nuclear Information System (INIS)
Roos, A.D.; Hays, D.C.; Johnson, R.L.; Durham, L.A.; Winters, M.
2009-01-01
Proper planning and design for remediating contaminated environmental media require an adequate understanding of the types of contaminants and the lateral and vertical extent of contamination. In the case of contaminated soils, this generally takes the form of volume estimates that are prepared as part of a Feasibility Study for Comprehensive Environmental Response, Compensation, and Liability Act (CERCLA) sites and/or as part of the remedial design. These estimates are typically single values representing what is believed to be the most likely volume of contaminated soil present at the site. These single-value estimates, however, do not convey the level of confidence associated with the estimates. Unfortunately, the experience has been that pre-remediation soil volume estimates often significantly underestimate the actual volume of contaminated soils that are encountered during the course of remediation. This underestimation has significant implications, both technically (e.g., inappropriate remedial designs) and programmatically (e.g., establishing technically defensible budget and schedule baselines). Argonne National Laboratory (Argonne) has developed a joint Bayesian/geostatistical methodology for estimating contaminated soil volumes based on sampling results, that also provides upper and lower probabilistic bounds on those volumes. This paper evaluates the performance of this method in a retrospective study that compares volume estimates derived using this technique with actual excavated soil volumes for select Formerly Utilized Sites Remedial Action Program (FUSRAP) Maywood properties that have completed remedial action by the U.S. Army Corps of Engineers (USACE) New York District. (authors)
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
Estimation of soil-soil solution distribution coefficient of radiostrontium using soil properties.
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.
Erosivity factor in the Universal Soil Loss Equation estimated from Finnish rainfall data
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Maximilian Posch
1993-07-01
Full Text Available Continuous rainfall data recorded for many years at 8 stations in Finland were used to estimate rainfall erosivity, a quantity needed for soil loss predictions with the Universal Soil Loss Equation (USLE. The obtained erosivity values were then used to determine the 2 parameters of a power-law function describing the relationship between daily precipitation and erosivity. This function is of importance in erosion modeling at locations where no breakpoint rainfall data are available. The parameters of the power-law were estimated both by linear regression of the log-transformed data and by non-linear least-square fitting of the original data. Results indicate a considerable seasonal (monthly variation of the erosivity, whereas the spatial variation over Finland is rather small.
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Moreira Paulo H. S.
2016-03-01
Full Text Available In this study the hydraulic and solute transport properties of an unsaturated soil were estimated simultaneously from a relatively simple small-scale laboratory column infiltration/outflow experiment. As governing equations we used the Richards equation for variably saturated flow and a physical non-equilibrium dual-porosity type formulation for solute transport. A Bayesian parameter estimation approach was used in which the unknown parameters were estimated with the Markov Chain Monte Carlo (MCMC method through implementation of the Metropolis-Hastings algorithm. Sensitivity coefficients were examined in order to determine the most meaningful measurements for identifying the unknown hydraulic and transport parameters. Results obtained using the measured pressure head and solute concentration data collected during the unsaturated soil column experiment revealed the robustness of the proposed approach.
Applied parameter estimation for chemical engineers
Englezos, Peter
2000-01-01
Formulation of the parameter estimation problem; computation of parameters in linear models-linear regression; Gauss-Newton method for algebraic models; other nonlinear regression methods for algebraic models; Gauss-Newton method for ordinary differential equation (ODE) models; shortcut estimation methods for ODE models; practical guidelines for algorithm implementation; constrained parameter estimation; Gauss-Newton method for partial differential equation (PDE) models; statistical inferences; design of experiments; recursive parameter estimation; parameter estimation in nonlinear thermodynam
Tran, A. P.; Dafflon, B.; Hubbard, S.
2017-12-01
Soil organic carbon (SOC) is crucial for predicting carbon climate feedbacks in the vulnerable organic-rich Arctic region. However, it is challenging to achieve this property due to the general limitations of conventional core sampling and analysis methods. In this study, we develop an inversion scheme that uses single or multiple datasets, including soil liquid water content, temperature and ERT data, to estimate the vertical profile of SOC content. Our approach relies on the fact that SOC 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. The scheme includes several advantages. First, this is the first time SOC content is estimated by using a coupled hydrogeophysical inversion. Second, by using the Community Land Model, we can account for the land surface dynamics (evapotranspiration, snow accumulation and melting) and ice/liquid phase transition. Third, we combine a deterministic and an adaptive Markov chain Monte Carlo optimization algorithm to better estimate the posterior distributions of desired model parameters. Finally, the simulated subsurface variables are explicitly linked to soil electrical resistivity via petrophysical and geophysical models. We validate the developed scheme using synthetic experiments. The results show that compared to inversion of single dataset, joint inversion of these datasets significantly reduces parameter uncertainty. The joint inversion approach is able to estimate SOC content within the shallow active layer with high reliability. Next, we apply the scheme to estimate OC content along an intensive ERT transect in Barrow, Alaska using multiple datasets acquired in the 2013-2015 period. The preliminary results show a good agreement between modeled and measured soil temperature, thaw layer thickness and electrical resistivity. The accuracy of estimated SOC content
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Ken Okamoto
2015-10-01
Full Text Available We examined the influence of input soil hydraulic parameters on HYDRUS-1D simulations of evapotranspiration and volumetric water contents (VWCs in the unsaturated zone of a sugarcane field on the island of Miyakojima, Japan. We first optimized the parameters for root water uptake and examined the influence of soil hydraulic parameters (water retention curve and hydraulic conductivity on simulations of evapotranspiration. We then compared VWCs simulated using measured soil hydraulic parameters with those using pedotransfer estimates obtained with the ROSETTA software package. Our results confirm that it is important to always use soil hydraulic parameters based on measured data, if available, when simulating evapotranspiration and unsaturated water flow processes, rather than pedotransfer functions.
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.
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K. Schneider-Zapp
2010-05-01
Full Text Available Evaporation is an important process in soil-atmosphere interaction. The determination of hydraulic properties is one of the crucial parts in the simulation of water transport in porous media. Schneider et al. (2006 developed a new evaporation method to improve the estimation of hydraulic properties in the dry range. In this study we used numerical simulations of the experiment to study the physical dynamics in more detail, to optimise the boundary conditions and to choose the optimal combination of measurements. The physical analysis exposed, in accordance to experimental findings in the literature, two different evaporation regimes: (i a soil-atmosphere boundary layer dominated regime (regime I close to saturation and (ii a hydraulically dominated regime (regime II. During this second regime a drying front (interface between unsaturated and dry zone with very steep gradients forms which penetrates deeper into the soil as time passes. The sensitivity analysis showed that the result is especially sensitive at the transition between the two regimes. By changing the boundary conditions it is possible to force the system to switch between the two regimes, e.g. from II back to I. Based on this findings a multistep experiment was developed. The response surfaces for all parameter combinations are flat and have a unique, localised minimum. Best parameter estimates are obtained if the evaporation flux and a potential measurement in 2 cm depth are used as target variables. Parameter estimation from simulated experiments with realistic measurement errors with a two-stage Monte-Carlo Levenberg-Marquardt procedure and manual rejection of obvious misfits lead to acceptable results for three different soil textures.
Improved Estimates of Thermodynamic Parameters
Lawson, D. D.
1982-01-01
Techniques refined for estimating heat of vaporization and other parameters from molecular structure. Using parabolic equation with three adjustable parameters, heat of vaporization can be used to estimate boiling point, and vice versa. Boiling points and vapor pressures for some nonpolar liquids were estimated by improved method and compared with previously reported values. Technique for estimating thermodynamic parameters should make it easier for engineers to choose among candidate heat-exchange fluids for thermochemical cycles.
SOTER-based soil parameter estimates for Kenya (ver. 1.0)
Batjes, N.H.
2013-01-01
This harmonized data set has been derived from the Soil and Terrain Database for Kenya (KENSOTER), at scale 1:1M, compiled by the Kenya Soil Survey. The land surface of the Republic of Kenya - excluding lakes and towns - has been characterized using 397 unique SOTER units corresponding with 623 soil
Application of spreadsheet to estimate infiltration parameters
Zakwan, Mohammad; Muzzammil, Mohammad; Alam, Javed
2016-01-01
Infiltration is the process of flow of water into the ground through the soil surface. Soil water although contributes a negligible fraction of total water present on earth surface, but is of utmost importance for plant life. Estimation of infiltration rates is of paramount importance for estimation of effective rainfall, groundwater recharge, and designing of irrigation systems. Numerous infiltration models are in use for estimation of infiltration rates. The conventional graphical approach ...
Comparison of model microbial allocation parameters in soils of varying texture
Hagerty, S. B.; Slessarev, E.; Schimel, J.
2017-12-01
The soil microbial community decomposes the majority of carbon (C) inputs to the soil. However, not all of this C is respired—rather, a substantial portion of the carbon processed by microbes may remain stored in the soil. The balance between C storage and respiration is controlled by microbial turnover rates and C allocation strategies. These microbial community properties may depend on soil texture, which has the potential to influence both the nature and the fate of microbial necromass and extracellular products. To evaluate the role of texture on microbial turnover and C allocation, we sampled four soils from the University of California's Hastings Reserve that varied in texture (one silt loam, two sandy loam, and on clay soil), but support similar grassland plant communities. We added 14C- glucose to the soil and measured the concentration of the label in the carbon dioxide (CO2), microbial biomass, and extractable C pools over 7 weeks. The labeled biomass turned over the slowest in the clay soil; the concentration of labeled biomass was more than 1.5 times the concentration of the other soils after 8 weeks. The clay soil also had the lowest mineralization rate of the label, and mineralization slowed after two weeks. In contrast, in the sandier soils mineralization rates were higher and did not plateau until 5 weeks into the incubation period. We fit the 14C data to a microbial allocation model and estimated microbial parameters; assimilation efficiency, exudation, and biomass specific respiration and turnover for each soil. We compare these parameters across the soil texture gradient to assess the extent to which models may need to account for variability in microbial C allocation across soils of different texture. Our results suggest that microbial C turns over more slowly in high-clay soils than in sandy soils, and that C lost from microbial biomass is retained at higher rates in high-clay soils. Accounting for these differences in microbial allocation
Jadoon, Khan; Weihermü ller, Lutz; Verrecken, Harry; Lambot, Sé bastien
2012-01-01
sensitive to the near-surface water content profile and dynamics, and are thus related to soil hydraulic parameters, such as the parameters of the hydraulic conductivity and water retention functions. The hydrological simulator HYDRUS 1-D was used with a two
Inference of soil hydrologic parameters from electronic soil moisture records
Soil moisture is an important control on hydrologic function, as it governs vertical fluxes from and to the atmosphere, groundwater recharge, and lateral fluxes through the soil. Historically, the traditional model parameters of saturation, field capacity, and permanent wilting point have been deter...
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Weijing Chen
2017-03-01
Full Text Available Uncertainties in model parameters can easily result in systematic differences between model states and observations, which significantly affect the accuracy of soil moisture estimation in data assimilation systems. In this research, a soil moisture assimilation scheme is developed to jointly assimilate AMSR-E (Advanced Microwave Scanning Radiometer-Earth Observing System brightness temperature (TB and MODIS (Moderate Resolution Imaging Spectroradiometer Land Surface Temperature (LST products, which also corrects model bias by simultaneously updating model states and parameters with a dual ensemble Kalman filter (DEnKS. Common Land Model (CoLM and a Radiative Transfer Model (RTM are adopted as model and observation operator, respectively. The assimilation experiment was conducted in Naqu on the Tibet Plateau from 31 May to 27 September 2011. The updated soil temperature at surface obtained by assimilating MODIS LST serving as inputs of RTM is to reduce the differences between the simulated and observed TB, then AMSR-E TB is assimilated to update soil moisture and model parameters. Compared with in situ measurements, the accuracy of soil moisture estimation derived from the assimilation experiment has been tremendously improved at a variety of scales. The updated parameters effectively reduce the states bias of CoLM. The results demonstrate the potential of assimilating AMSR-E TB and MODIS LST to improve the estimation of soil moisture and related parameters. Furthermore, this study indicates that the developed scheme is an effective way to retrieve downscaled soil moisture when assimilating the coarse-scale microwave TB.
International Nuclear Information System (INIS)
Ishikawa, Nao; Tagami, Keiko; Uchida, Shigeo
2009-01-01
Soil-to-plant transfer factor (TF) is one of the important parameters in radiation dose assessment models for the environmental transfer of radionuclides. Since TFs are affected by several factors, including radionuclides, plant species and soil properties, development of a method for estimation of TF using some soil and plant properties would be useful. In this study, we took a statistical approach to estimating the TF of stable strontium (TF Sr ) from selected soil properties and element concentrations in plants, which was used as an analogue of 90 Sr. We collected the plant and soil samples used for the study from 142 agricultural fields throughout Japan. We applied a multiple linear regression analysis in order to get an empirical equation to estimate TF Sr . TF Sr could be estimated from the Sr concentration in soil (C Sr soil ) and Ca concentration in crop (C Ca crop ) using the following equation: log TF Sr =-0.88·log C Sr soil +0.93·log C Ca crop -2.53. Then, we replaced our data with Ca concentrations in crops from a food composition database compiled by the Japanese government. Finally, we predicted TF Sr using Sr concentration in soil from our data and Ca concentration in crops from the database of food composition. (author)
Soil transport parameters of potassium under a tropical saline soil condition using STANMOD
Suzanye da Silva Santos, Rafaelly; Honorio de Miranda, Jarbas; Previatello da Silva, Livia
2015-04-01
Environmental responsibility and concerning about the final destination of solutes in soil, so more studies allow a better understanding about the solutes behaviour in soil. Potassium is a macronutrient that is required in high concentrations, been an extremely important nutrient for all agricultural crops. It plays essential roles in physiological processes vital for plant growth, from protein synthesis to maintenance of plant water balance, and is available to plants dissolved in soil water while exchangeable K is loosely held on the exchange sites on the surface of clay particles. K will tend to be adsorbed onto the surface of negatively charged soil particles. Potassium uptake is vital for plant growth but in saline soils sodium competes with potassium for uptake across the plasma membrane of plant cells. This can result in high Na+:K+ ratios that reduce plant growth and eventually become toxic. This study aimed to obtain soil transport parameters of potassium in saline soil, such as: pore water velocity in soil (v), retardation factor (R), dispersivity (λ) and dispersion coefficient (D), in a disturbed sandy soil with different concentrations of potassium chlorate solution (KCl), which is one of the most common form of potassium fertilizer. The experiment was carried out using soil samples collected in a depth of 0 to 20 cm, applying potassium chlorate solution containing 28.6, 100, 200 and 500 mg L-1 of K. To obtain transport parameters, the data were adjusted with the software STANMOD. At low concentrations, interaction between potassium and soil occur more efficiently. It was observed that only the breakthrough curve prepared with solution of 500 mg L-1 reached the applied concentration, and the solution of 28.6 mg L-1 overestimated the parameters values. The STANMOD proved to be efficient in obtaining potassium transport parameters; KCl solution to be applied should be greater than 500 mg L-1; solutions with low concentrations tend to overestimate
Contribution to Estimating Bearing Capacity of Pile in Clayey Soils
Drusa, Marián; Gago, Filip; Vlček, Jozef
2016-12-01
The estimation of real geotechnical parameters is key factor for safe and economic design of geotechnical structures. One of these are pile foundations, which require proper design and evaluation due to accessing more deep foundation soil and because remediation work of not bearable piles or broken piles is a crucial operation. For this reason, geotechnical field testing like cone penetration test (CPT), standard penetration (SPT) or dynamic penetration test (DP) are realized in order to receive continuous information about soil strata. Comparing with rotary core drilling type of survey with sampling, these methods are more progressive. From engineering geologist point of view, it is more important to know geological characterization of locality but geotechnical engineers have more interest above the real geotechnical parameters of foundation soils. The role of engineering geologist cannot be underestimated because important geological processes in origin or during history can explain behaviour of a geological environment. In effort to streamline the survey, investigation by penetration tests is done as it is able to provide enough information for designers. This paper deals with actual trends in pile foundation design; because there are no new standards and usable standards are very old. Estimation of the bearing capacity of a single pile can be demonstrated on the example of determination of the cone factor Nk from CPT testing. Then results were compared with other common methods.
Contribution to Estimating Bearing Capacity of Pile in Clayey Soils
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Drusa Marián
2016-12-01
Full Text Available The estimation of real geotechnical parameters is key factor for safe and economic design of geotechnical structures. One of these are pile foundations, which require proper design and evaluation due to accessing more deep foundation soil and because remediation work of not bearable piles or broken piles is a crucial operation. For this reason, geotechnical field testing like cone penetration test (CPT, standard penetration (SPT or dynamic penetration test (DP are realized in order to receive continuous information about soil strata. Comparing with rotary core drilling type of survey with sampling, these methods are more progressive. From engineering geologist point of view, it is more important to know geological characterization of locality but geotechnical engineers have more interest above the real geotechnical parameters of foundation soils. The role of engineering geologist cannot be underestimated because important geological processes in origin or during history can explain behaviour of a geological environment. In effort to streamline the survey, investigation by penetration tests is done as it is able to provide enough information for designers. This paper deals with actual trends in pile foundation design; because there are no new standards and usable standards are very old. Estimation of the bearing capacity of a single pile can be demonstrated on the example of determination of the cone factor Nk from CPT testing. Then results were compared with other common methods.
Development of a matrix approach to estimate soil clean-up levels for BTEX compounds
International Nuclear Information System (INIS)
Erbas-White, I.; San Juan, C.
1993-01-01
A draft state-of-the-art matrix approach has been developed for the State of Washington to estimate clean-up levels for benzene, toluene, ethylbenzene and xylene (BTEX) in deep soils based on an endangerment approach to groundwater. Derived soil clean-up levels are estimated using a combination of two computer models, MULTIMED and VLEACH. The matrix uses a simple scoring system that is used to assign a score at a given site based on the parameters such as depth to groundwater, mean annual precipitation, type of soil, distance to potential groundwater receptor and the volume of contaminated soil. The total score is then used to obtain a soil clean-up level from a table. The general approach used involves the utilization of computer models to back-calculate soil contaminant levels in the vadose zone that would create that particular contaminant concentration in groundwater at a given receptor. This usually takes a few iterations of trial runs to estimate the clean-up levels since the models use the soil clean-up levels as ''input'' and the groundwater levels as ''output.'' The selected contaminant levels in groundwater are Model Toxic control Act (MTCA) values used in the State of Washington
Identification of optimal soil hydraulic functions and parameters for predicting soil moisture
We examined the accuracy of several commonly used soil hydraulic functions and associated parameters for predicting observed soil moisture data. We used six combined methods formed by three commonly used soil hydraulic functions – i.e., Brooks and Corey (1964) (BC), Campbell (19...
Rapid prototyping of soil moisture estimates using the NASA Land Information System
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.
Parameter estimation in plasmonic QED
Jahromi, H. Rangani
2018-03-01
We address the problem of parameter estimation in the presence of plasmonic modes manipulating emitted light via the localized surface plasmons in a plasmonic waveguide at the nanoscale. The emitter that we discuss is the nitrogen vacancy centre (NVC) in diamond modelled as a qubit. Our goal is to estimate the β factor measuring the fraction of emitted energy captured by waveguide surface plasmons. The best strategy to obtain the most accurate estimation of the parameter, in terms of the initial state of the probes and different control parameters, is investigated. In particular, for two-qubit estimation, it is found although we may achieve the best estimation at initial instants by using the maximally entangled initial states, at long times, the optimal estimation occurs when the initial state of the probes is a product one. We also find that decreasing the interqubit distance or increasing the propagation length of the plasmons improve the precision of the estimation. Moreover, decrease of spontaneous emission rate of the NVCs retards the quantum Fisher information (QFI) reduction and therefore the vanishing of the QFI, measuring the precision of the estimation, is delayed. In addition, if the phase parameter of the initial state of the two NVCs is equal to πrad, the best estimation with the two-qubit system is achieved when initially the NVCs are maximally entangled. Besides, the one-qubit estimation has been also analysed in detail. Especially, we show that, using a two-qubit probe, at any arbitrary time, enhances considerably the precision of estimation in comparison with one-qubit estimation.
Parameter Estimation in Continuous Time Domain
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Gabriela M. ATANASIU
2016-12-01
Full Text Available This paper will aim to presents the applications of a continuous-time parameter estimation method for estimating structural parameters of a real bridge structure. For the purpose of illustrating this method two case studies of a bridge pile located in a highly seismic risk area are considered, for which the structural parameters for the mass, damping and stiffness are estimated. The estimation process is followed by the validation of the analytical results and comparison with them to the measurement data. Further benefits and applications for the continuous-time parameter estimation method in civil engineering are presented in the final part of this paper.
Mehdinejadiani, Behrouz
2017-08-01
This study represents the first attempt to estimate the solute transport parameters of the spatial fractional advection-dispersion equation using Bees Algorithm. The numerical studies as well as the experimental studies were performed to certify the integrity of Bees Algorithm. The experimental ones were conducted in a sandbox for homogeneous and heterogeneous soils. A detailed comparative study was carried out between the results obtained from Bees Algorithm and those from Genetic Algorithm and LSQNONLIN routines in FracFit toolbox. The results indicated that, in general, the Bees Algorithm much more accurately appraised the sFADE parameters in comparison with Genetic Algorithm and LSQNONLIN, especially in the heterogeneous soil and for α values near to 1 in the numerical study. Also, the results obtained from Bees Algorithm were more reliable than those from Genetic Algorithm. The Bees Algorithm showed the relative similar performances for all cases, while the Genetic Algorithm and the LSQNONLIN yielded different performances for various cases. The performance of LSQNONLIN strongly depends on the initial guess values so that, compared to the Genetic Algorithm, it can more accurately estimate the sFADE parameters by taking into consideration the suitable initial guess values. To sum up, the Bees Algorithm was found to be very simple, robust and accurate approach to estimate the transport parameters of the spatial fractional advection-dispersion equation. Copyright © 2017 Elsevier B.V. All rights reserved.
Mehdinejadiani, Behrouz
2017-08-01
This study represents the first attempt to estimate the solute transport parameters of the spatial fractional advection-dispersion equation using Bees Algorithm. The numerical studies as well as the experimental studies were performed to certify the integrity of Bees Algorithm. The experimental ones were conducted in a sandbox for homogeneous and heterogeneous soils. A detailed comparative study was carried out between the results obtained from Bees Algorithm and those from Genetic Algorithm and LSQNONLIN routines in FracFit toolbox. The results indicated that, in general, the Bees Algorithm much more accurately appraised the sFADE parameters in comparison with Genetic Algorithm and LSQNONLIN, especially in the heterogeneous soil and for α values near to 1 in the numerical study. Also, the results obtained from Bees Algorithm were more reliable than those from Genetic Algorithm. The Bees Algorithm showed the relative similar performances for all cases, while the Genetic Algorithm and the LSQNONLIN yielded different performances for various cases. The performance of LSQNONLIN strongly depends on the initial guess values so that, compared to the Genetic Algorithm, it can more accurately estimate the sFADE parameters by taking into consideration the suitable initial guess values. To sum up, the Bees Algorithm was found to be very simple, robust and accurate approach to estimate the transport parameters of the spatial fractional advection-dispersion equation.
Error Analysis on the Estimation of Cumulative Infiltration in Soil Using Green and AMPT Model
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Muhamad Askari
2006-08-01
Full Text Available Green and Ampt infiltration model is still useful for the infiltration process because of a clear physical basis of the model and of the existence of the model parameter values for a wide range of soil. The objective of thise study was to analyze error on the esimation of cumulative infiltration in sooil using Green and Ampt model and to design laboratory experiment in measuring cumulative infiltration. Parameter of the model was determined based on soil physical properties from laboratory experiment. Newton –Raphson method was esed to estimate wetting front during calculation using visual Basic for Application (VBA in MS Word. The result showed that contributed the highest error in estimation of cumulative infiltration and was followed by K, H0, H1, and t respectively. It also showed that the calculated cumulative infiltration is always lower than both measured cumulative infiltration and volumetric soil water content.
Low-field NMR logging sensor for measuring hydraulic parameters of model soils
Sucre, Oscar; Pohlmeier, Andreas; Minière, Adrien; Blümich, Bernhard
2011-08-01
SummaryKnowing the exact hydraulic parameters of soils is very important for improving water management in agriculture and for the refinement of climate models. Up to now, however, the investigation of such parameters has required applying two techniques simultaneously which is time-consuming and invasive. Thus, the objective of this current study is to present only one technique, i.e., a new non-invasive method to measure hydraulic parameters of model soils by using low-field nuclear magnetic resonance (NMR). Hereby, two model clay or sandy soils were respectively filled in a 2 m-long acetate column having an integrated PVC tube. After the soils were completely saturated with water, a low-field NMR sensor was moved up and down in the PVC tube to quantitatively measure along the whole column the initial water content of each soil sample. Thereafter, both columns were allowed to drain. Meanwhile, the NMR sensor was set at a certain depth to measure the water content of that soil slice. Once the hydraulic equilibrium was reached in each of the two columns, a final moisture profile was taken along the whole column. Three curves were subsequently generated accordingly: (1) the initial moisture profile, (2) the evolution curve of the moisture depletion at that particular depth, and (3) the final moisture profile. All three curves were then inverse analyzed using a MATLAB code over numerical data produced with the van Genuchten-Mualem model. Hereby, a set of values ( α, n, θr and θs) was found for the hydraulic parameters for the soils under research. Additionally, the complete decaying NMR signal could be analyzed through Inverse Laplace Transformation and averaged on the 1/ T2 space. Through measurement of the decay in pure water, the effect on the relaxation caused by the sample could be estimated from the obtained spectra. The migration of the sample-related average with decreasing saturation speaks for a enhancement of the surface relaxation as the soil dries, in
ESTIMATION ACCURACY OF EXPONENTIAL DISTRIBUTION PARAMETERS
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muhammad zahid rashid
2011-04-01
Full Text Available The exponential distribution is commonly used to model the behavior of units that have a constant failure rate. The two-parameter exponential distribution provides a simple but nevertheless useful model for the analysis of lifetimes, especially when investigating reliability of technical equipment.This paper is concerned with estimation of parameters of the two parameter (location and scale exponential distribution. We used the least squares method (LSM, relative least squares method (RELS, ridge regression method (RR, moment estimators (ME, modified moment estimators (MME, maximum likelihood estimators (MLE and modified maximum likelihood estimators (MMLE. We used the mean square error MSE, and total deviation TD, as measurement for the comparison between these methods. We determined the best method for estimation using different values for the parameters and different sample sizes
Soil-ecological risks for soil degradation estimation
Trifonova, Tatiana; Shirkin, Leonid; Kust, German; Andreeva, Olga
2016-04-01
Soil degradation includes the processes of soil properties and quality worsening, primarily from the point of view of their productivity and decrease of ecosystem services quality. Complete soil cover destruction and/or functioning termination of soil forms of organic life are considered as extreme stages of soil degradation, and for the fragile ecosystems they are normally considered in the network of their desertification, land degradation and droughts /DLDD/ concept. Block-model of ecotoxic effects, generating soil and ecosystem degradation, has been developed as a result of the long-term field and laboratory research of sod-podzol soils, contaminated with waste, containing heavy metals. The model highlights soil degradation mechanisms, caused by direct and indirect impact of ecotoxicants on "phytocenosis- soil" system and their combination, frequently causing synergistic effect. The sequence of occurring changes here can be formalized as a theory of change (succession of interrelated events). Several stages are distinguished here - from heavy metals leaching (releasing) in waste and their migration downward the soil profile to phytoproductivity decrease and certain phytocenosis composition changes. Phytoproductivity decrease leads to the reduction of cellulose content introduced into the soil. The described feedback mechanism acts as a factor of sod-podzolic soil self-purification and stability. It has been shown, that using phytomass productivity index, integrally reflecting the worsening of soil properties complex, it is possible to solve the problems dealing with the dose-reflecting reactions creation and determination of critical levels of load for phytocenosis and corresponding soil-ecological risks. Soil-ecological risk in "phytocenosis- soil" system means probable negative changes and the loss of some ecosystem functions during the transformation process of dead organic substance energy for the new biomass composition. Soil-ecological risks estimation is
Estimating soil zinc concentrations using reflectance spectroscopy
Sun, Weichao; Zhang, Xia
2017-06-01
Soil contamination by heavy metals has been an increasingly severe threat to nature environment and human health. Efficiently investigation of contamination status is essential to soil protection and remediation. Visible and near-infrared reflectance spectroscopy (VNIRS) has been regarded as an alternative for monitoring soil contamination by heavy metals. Generally, the entire VNIR spectral bands are employed to estimate heavy metal concentration, which lacks interpretability and requires much calculation. In this study, 74 soil samples were collected from Hunan Province, China and their reflectance spectra were used to estimate zinc (Zn) concentration in soil. Organic matter and clay minerals have strong adsorption for Zn in soil. Spectral bands associated with organic matter and clay minerals were used for estimation with genetic algorithm based partial least square regression (GA-PLSR). The entire VNIR spectral bands, the bands associated with organic matter and the bands associated with clay minerals were incorporated as comparisons. Root mean square error of prediction, residual prediction deviation, and coefficient of determination (R2) for the model developed using combined bands of organic matter and clay minerals were 329.65 mg kg-1, 1.96 and 0.73, which is better than 341.88 mg kg-1, 1.89 and 0.71 for the entire VNIR spectral bands, 492.65 mg kg-1, 1.31 and 0.40 for the organic matter, and 430.26 mg kg-1, 1.50 and 0.54 for the clay minerals. Additionally, in consideration of atmospheric water vapor absorption in field spectra measurement, combined bands of organic matter and absorption around 2200 nm were used for estimation and achieved high prediction accuracy with R2 reached 0.640. The results indicate huge potential of soil reflectance spectroscopy in estimating Zn concentrations in soil.
Wanders, N.; Bierkens, M. F. P.; de Jong, S. M.; de Roo, A.; Karssenberg, D.
2014-08-01
Large-scale hydrological models are nowadays mostly calibrated using observed discharge. As a result, a large part of the hydrological system, in particular the unsaturated zone, remains uncalibrated. Soil moisture observations from satellites have the potential to fill this gap. Here we evaluate the added value of remotely sensed soil moisture in calibration of large-scale hydrological models by addressing two research questions: (1) Which parameters of hydrological models can be identified by calibration with remotely sensed soil moisture? (2) Does calibration with remotely sensed soil moisture lead to an improved calibration of hydrological models compared to calibration based only on discharge observations, such that this leads to improved simulations of soil moisture content and discharge? A dual state and parameter Ensemble Kalman Filter is used to calibrate the hydrological model LISFLOOD for the Upper Danube. Calibration is done using discharge and remotely sensed soil moisture acquired by AMSR-E, SMOS, and ASCAT. Calibration with discharge data improves the estimation of groundwater and routing parameters. Calibration with only remotely sensed soil moisture results in an accurate identification of parameters related to land-surface processes. For the Upper Danube upstream area up to 40,000 km2, calibration on both discharge and soil moisture results in a reduction by 10-30% in the RMSE for discharge simulations, compared to calibration on discharge alone. The conclusion is that remotely sensed soil moisture holds potential for calibration of hydrological models, leading to a better simulation of soil moisture content throughout the catchment and a better simulation of discharge in upstream areas. This article was corrected on 15 SEP 2014. See the end of the full text for details.
Estimating steady-state evaporation rates from bare soils under conditions of high water table
Ripple, C.D.; Rubin, J.; Van Hylckama, T. E. A.
1970-01-01
A procedure that combines meteorological and soil equations of water transfer makes it possible to estimate approximately the steady-state evaporation from bare soils under conditions of high water table. Field data required include soil-water retention curves, water table depth and a record of air temperature, air humidity and wind velocity at one elevation. The procedure takes into account the relevant atmospheric factors and the soil's capability to conduct 'water in liquid and vapor forms. It neglects the effects of thermal transfer (except in the vapor case) and of salt accumulation. Homogeneous as well as layered soils can be treated. Results obtained with the method demonstrate how the soil evaporation rates·depend on potential evaporation, water table depth, vapor transfer and certain soil parameters.
An assessment of the BEST procedure to estimate the soil water retention curve
Castellini, Mirko; Di Prima, Simone; Iovino, Massimo
2017-04-01
The Beerkan Estimation of Soil Transfer parameters (BEST) procedure represents a very attractive method to accurately and quickly obtain a complete hydraulic characterization of the soil (Lassabatère et al., 2006). However, further investigations are needed to check the prediction reliability of soil water retention curve (Castellini et al., 2016). Four soils with different physical properties (texture, bulk density, porosity and stoniness) were considered in this investigation. Sites of measurement were located at Palermo University (PAL site) and Villabate (VIL site) in Sicily, Arborea (ARB site) in Sardinia and in Foggia (FOG site), Apulia. For a given site, BEST procedure was applied and the water retention curve was estimated using the available BEST-algorithms (i.e., slope, intercept and steady), and the reference values of the infiltration constants (β=0.6 and γ=0.75) were considered. The water retention curves estimated by BEST were then compared with those obtained in laboratory by the evaporation method (Wind, 1968). About ten experiments were carried out with both methods. A sensitivity analysis of the constants β and γ within their feasible range of variability (0.1analysis showed that S tended to increase for increasing β values and decreasing values of γ for all the BEST-algorithms and soils. On the other hand, Ks tended to decrease for increasing β and γ values. Our results also reveal that: i) BEST-intercept and BEST-steady algorithms yield lower S and higher Ks values than BEST-slope; ii) these algorithms yield also more variable values. For the latter, a higher sensitiveness of these two alternative algorithms to β than for γ was established. The decreasing sensitiveness to γ may lead to a possible lack in the correction of the simplified theoretical description of the parabolic two-dimensional and one-dimensional wetting front along the soil profile (Smettem et al., 1994). This likely resulted in lower S and higher Ks values
Multiscale soil moisture estimates using static and roving cosmic-ray soil moisture sensors
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.
Bayesian Parameter Estimation for Heavy-Duty Vehicles
Energy Technology Data Exchange (ETDEWEB)
Miller, Eric; Konan, Arnaud; Duran, Adam
2017-03-28
Accurate vehicle parameters are valuable for design, modeling, and reporting. Estimating vehicle parameters can be a very time-consuming process requiring tightly-controlled experimentation. This work describes a method to estimate vehicle parameters such as mass, coefficient of drag/frontal area, and rolling resistance using data logged during standard vehicle operation. The method uses Monte Carlo to generate parameter sets which is fed to a variant of the road load equation. Modeled road load is then compared to measured load to evaluate the probability of the parameter set. Acceptance of a proposed parameter set is determined using the probability ratio to the current state, so that the chain history will give a distribution of parameter sets. Compared to a single value, a distribution of possible values provides information on the quality of estimates and the range of possible parameter values. The method is demonstrated by estimating dynamometer parameters. Results confirm the method's ability to estimate reasonable parameter sets, and indicates an opportunity to increase the certainty of estimates through careful selection or generation of the test drive cycle.
Estimating Soil and Vegetation Parameters using Synergies between Optical and Microwave Observations
Timmermans, J.; Gomez-Dans, J. L.; Lewis, P.; Loew, A.; Schlenz, F.; Mathieu, P. P.; Pounder, N. L.; Styles, J.
2017-12-01
The large amount of remote sensing data available provides a huge potential for various applications, such as crop monitoring. This potential has not been realized yet because inversion-algorithms mostly use a single sensor approach. Consequently, products that combine different low-level observations from different sensors are hard to find. The difficulty in a multi-sensor approach is that 1) different sensor types (microwave/ optical) require different radiative transfer (RT) models and 2) it require consistency between the models. The goal of this research was to investigate the synergistic potential of integrating optical (Opt) and passive microwave (PM) RT models within the Earth Observation Land Data Assimilation System (EOLDAS). EOLDAS uses a Bayesian data assimilation approach together with observation operators such as PROSAIL to estimate state variables. In order to use PM observations, the Community Microwave Emission Model was integrated into the system. Results show a high potential when both Opt and PM observations are used independently. Using only RapidEye only with SAIL RT model, LAI was estimated with R=0.68, with leaf water content and dry matter having lower correlations |R|<0.4. Results for retrieving soil temperature and leaf area index retrievals using only Elbarra observations were good with respectively R=[0.85, 0.79], and for soil moisture also very good with R=0.73 (focusing on dry-spells of at least 9 days only), and with R=0.89 and R=0.77 for respectively the trend and anomalies. Synergistically using Opt and MW observations also shows good potential. Results show that absolute errors decreased (with RMSE=1.22 and S=0.89), but with lower R=0.59; sparse optical observations only improved part of the temporal domain. This shows that PM observations provide good information for the overall trend of the retrieved LAI due to the regular acquisitions, while Opt observations provides better information of the absolute values of the LAI.
Determination of soil degradation from flooding for estimating ecosystem services in Slovakia
Hlavcova, Kamila; Szolgay, Jan; Karabova, Beata; Kohnova, Silvia
2015-04-01
of Myjava), with an emphasis on the determination of soil degradation from flooding for estimating ecosystem services. The parameters of the SCS-CN methodology were regionalised empirically based on actual rainfall and discharge measurements. Since there has been no appropriate methodology provided for the regionalisation of SCS-CN method parameters in Slovakia, such as runoff curve numbers and initial abstraction coefficients (λ), the work presented is important for the correct application of the SCS-CN method in our conditions.
Directory of Open Access Journals (Sweden)
A.K.M. Azad Hossain
2016-01-01
Full Text Available Soil moisture monitoring and characterization of the spatial and temporal variability of this hydrologic parameter at scales from small catchments to large river basins continues to receive much attention, reflecting its critical role in subsurface-land surface-atmospheric interactions and its importance to drought analysis, irrigation planning, crop yield forecasting, flood protection, and forest fire prevention. Synthetic Aperture Radar (SAR data acquired at different spatial resolutions have been successfully used to estimate soil moisture in different semi-arid areas of the world for many years. This research investigated the potential of linear multiple regressions and Artificial Neural Networks (ANN based models that incorporate different geophysical variables with Radarsat 1 SAR fine imagery and concurrently measured soil moisture measurements to estimate surface soil moisture in Nash Draw, NM. An artificial neural network based model with vegetation density, soil type, and elevation data as input in addition to radar backscatter values was found suitable to estimate surface soil moisture in this area with reasonable accuracy. This model was applied to a time series of SAR data acquired in 2006 to produce soil moisture data covering a normal wet season in the study site.
ESTIMATING ANNUAL SOIL LOSS BY WATER EROSION IN THE MIDDLE PRUT PLAIN, REPUBLIC OF MOLDOVA
Directory of Open Access Journals (Sweden)
TUDOR CASTRAVEŢ
2012-11-01
Full Text Available Estimating annual soil loss by water erosion in the middle Prut Plain, Republic of Moldova. Modern technology has provided efficient tools such as advanced models and Geographic Information Systems to facilitate decision making for environmental management. Studies at this subject are available in literature, ranging from those that use a simple model such as USLE to others of a more sophisticated nature. In this study the model selected (modified Universal Soil Loss Equation – USLE and the case itself is kept simple due to significant limitations in data on land processes. An effective investigation of soil loss by using GIS – USLE integration requires spatially distributed data on several parameters describing the terrain surface. Such parameters include topography, rainfall characteristics, soil types, vegetation, land use, and the similar. In Republic of Moldova data on most of these parameters are collected often on a local or individual basis, and therefore, a well-organized regional or basin-wide database is not available. In the Republic of Moldova soil erosion is often as high as 30 tons/ha/year and more than 1.4*106 ha run a potential risk of erosion (Summer & Diernhof, 2003. The model estimated an annual quantity of soil eroded ranging over the Prut River tributaries watersheds between the mean values of 6.2 and 20.4 t/ha/yr. Much of the areas are within the range 10-20 t/ha/yr. The highest values of the quantity of eroded soil is carried out on strong inclined slopes corresponding to areas with agricultural lands and herbaceous vegetation. The results have shown that GIS can be effectively used to investigate critical regions within a basin with respect to erosion.
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...
Directory of Open Access Journals (Sweden)
João Carlos Medeiros
2014-06-01
Full Text Available Knowledge of the soil water retention curve (SWRC is essential for understanding and modeling hydraulic processes in the soil. However, direct determination of the SWRC is time consuming and costly. In addition, it requires a large number of samples, due to the high spatial and temporal variability of soil hydraulic properties. An alternative is the use of models, called pedotransfer functions (PTFs, which estimate the SWRC from easy-to-measure properties. The aim of this paper was to test the accuracy of 16 point or parametric PTFs reported in the literature on different soils from the south and southeast of the State of Pará, Brazil. The PTFs tested were proposed by Pidgeon (1972, Lal (1979, Aina & Periaswamy (1985, Arruda et al. (1987, Dijkerman (1988, Vereecken et al. (1989, Batjes (1996, van den Berg et al. (1997, Tomasella et al. (2000, Hodnett & Tomasella (2002, Oliveira et al. (2002, and Barros (2010. We used a database that includes soil texture (sand, silt, and clay, bulk density, soil organic carbon, soil pH, cation exchange capacity, and the SWRC. Most of the PTFs tested did not show good performance in estimating the SWRC. The parametric PTFs, however, performed better than the point PTFs in assessing the SWRC in the tested region. Among the parametric PTFs, those proposed by Tomasella et al. (2000 achieved the best accuracy in estimating the empirical parameters of the van Genuchten (1980 model, especially when tested in the top soil layer.
Proposal for new best estimates of the soil-to-plant transfer factor of U, Th, Ra, Pb and Po
Energy Technology Data Exchange (ETDEWEB)
Vandenhove, H. [Belgian Nuclear Research Centre, Biosphere Impact Studies, Mol (Belgium)], E-mail: hvandenh@sckcen.be; Olyslaegers, G. [Belgian Nuclear Research Centre, Biosphere Impact Studies, Mol (Belgium); Sanzharova, N.; Shubina, O. [RIAREA, Russian Institute of Agricultural Radiology and Agroecology, Obninsk (Russian Federation); Reed, E. [SENES Oak Ridge Inc., Center for Risk Analysis, Oak Ridge, TN (United States); Shang, Z. [Nuclear Safety Center of SEPA, Beijing (China); Velasco, H. [GEA- IMASL, Universidad Nacional de San Luis, Consejo Nacional de Investigaciones Cientificas y Tecnicas (Argentina)
2009-09-15
There is increasing interest in radiological assessment of discharges of naturally occurring radionuclides into the terrestrial environment. Such assessments require parameter values for the pathways considered in predictive models. An important pathway for human exposure is via ingestion of food crops and animal products. One of the key parameters in environmental assessment is therefore the soil-to-plant transfer factor to food and fodder crops. The objective of this study was to compile data, based on an extensive literature survey, concerning soil-to-plant transfer factors for uranium, thorium, radium, lead, and polonium. Transfer factor estimates were presented for major crop groups (Cereals, Leafy vegetables, Non-leafy vegetables, Root crops, Tubers, Fruits, Herbs, Pastures/grasses, Fodder), and also for some compartments within crop groups. Transfer factors were also calculated per soil group, as defined by their texture and organic matter content (Sand, Loam, Clay and Organic), and evaluation of transfer factors' dependency on specific soil characteristics was performed following regression analysis. The derived estimates were compared with estimates currently in use.
Directory of Open Access Journals (Sweden)
S. G. Wang
2011-05-01
Full Text Available Radar remote sensing has demonstrated its applicability to the retrieval of basin-scale soil moisture. The mechanism of radar backscattering from soils is complicated and strongly influenced by surface roughness. Additionally, retrieval of soil moisture using AIEM (advanced integrated equation model-like models is a classic example of underdetermined problem due to a lack of credible known soil roughness distributions at a regional scale. Characterization of this roughness is therefore crucial for an accurate derivation of soil moisture based on backscattering models. This study aims to simultaneously obtain surface roughness parameters (standard deviation of surface height σ and correlation length cl along with soil moisture from multi-angular ASAR images by using a two-step retrieval scheme based on the AIEM. The method firstly used a semi-empirical relationship that relates the roughness slope, Zs (Zs = σ^{2}/cl and the difference in backscattering coefficient (Δσ from two ASAR images acquired with different incidence angles. Meanwhile, by using an experimental statistical relationship between σ and cl, both these parameters can be estimated. Then, the deduced roughness parameters were used for the retrieval of soil moisture in association with the AIEM. An evaluation of the proposed method was performed in an experimental area in the middle stream of the Heihe River Basin, where the Watershed Allied Telemetry Experimental Research (WATER was taken place. It is demonstrated that the proposed method is feasible to achieve reliable estimation of soil water content. The key challenge is the presence of vegetation cover, which significantly impacts the estimates of surface roughness and soil moisture.
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.
Stand-scale soil respiration estimates based on chamber methods in a Bornean tropical rainforest
Kume, T.; Katayama, A.; Komatsu, H.; Ohashi, M.; Nakagawa, M.; Yamashita, M.; Otsuki, K.; Suzuki, M.; Kumagai, T.
2009-12-01
This study was undertaken to estimate stand-scale soil respiration in an aseasonal tropical rainforest on Borneo Island. To this aim, we identified critical and practical factors explaining spatial variations in soil respiration based on the soil respiration measurements conducted at 25 points in a 40 × 40 m subplot of a 4 ha study plot for five years in relation to soil, root, and forest structural factors. Consequently, we found significant positive correlation between the soil respiration and forest structural parameters. The most important factor was the mean DBH within 6 m of the measurement points, which had a significant linear relationship with soil respiration. Using the derived linear regression and an inventory dataset, we estimated the 4 ha-scale soil respiration. The 4 ha-scale estimation (6.0 μmol m-2 s-1) was nearly identical to the subplot scale measurements (5.7 μmol m-2 s-1), which were roughly comparable to the nocturnal CO2 fluxes calculated using the eddy covariance technique. To confirm the spatial representativeness of soil respiration estimates in the subplot, we performed variogram analysis. Semivariance of DBH(6) in the 4 ha plot showed that there was autocorrelation within the separation distance of about 20 m, and that the spatial dependence was unclear at a separation distance of greater than 20 m. This ascertained that the 40 × 40 m subplot could represent the whole forest structure in the 4 ha plot. In addition, we discuss characteristics of the stand-scale soil respiration at this site by comparing with those of other forests reported in previous literature in terms of the soil C balance. Soil respiration at our site was noticeably greater, relative to the incident litterfall amount, than soil respiration in other tropical and temperate forests probably owing to the larger total belowground C allocation by emergent trees. Overall, this study suggests the arrangement of emergent trees and their bellow ground C allocation could be
de Santiago-Martín, Ana; van Oort, Folkert; González, Concepción; Quintana, José R; Lafuente, Antonio L; Lamy, Isabelle
2015-01-01
The contribution of the nature instead of the total content of soil parameters relevant to metal bioavailability in lettuce was tested using a series of low-polluted Mediterranean agricultural calcareous soils offering natural gradients in the content and composition of carbonate, organic, and oxide fractions. Two datasets were compared by canonical ordination based on redundancy analysis: total concentrations (TC dataset) of main soil parameters (constituents, phases, or elements) involved in metal retention and bioavailability; and chemically defined reactive fractions of these parameters (RF dataset). The metal bioavailability patterns were satisfactorily explained only when the RF dataset was used, and the results showed that the proportion of crystalline Fe oxides, dissolved organic C, diethylene-triamine-pentaacetic acid (DTPA)-extractable Cu and Zn, and a labile organic pool accounted for 76% of the variance. In addition, 2 multipollution scenarios by metal spiking were tested that showed better relationships with the RF dataset than with the TC dataset (up to 17% more) and new reactive fractions involved. For Mediterranean calcareous soils, the use of reactive pools of soil parameters rather than their total contents improved the relationships between soil constituents and metal bioavailability. Such pool determinations should be systematically included in studies dealing with bioavailability or risk assessment. © 2014 SETAC.
Consistent Parameter and Transfer Function Estimation using Context Free Grammars
Klotz, Daniel; Herrnegger, Mathew; Schulz, Karsten
2017-04-01
This contribution presents a method for the inference of transfer functions for rainfall-runoff models. Here, transfer functions are defined as parametrized (functional) relationships between a set of spatial predictors (e.g. elevation, slope or soil texture) and model parameters. They are ultimately used for estimation of consistent, spatially distributed model parameters from a limited amount of lumped global parameters. Additionally, they provide a straightforward method for parameter extrapolation from one set of basins to another and can even be used to derive parameterizations for multi-scale models [see: Samaniego et al., 2010]. Yet, currently an actual knowledge of the transfer functions is often implicitly assumed. As a matter of fact, for most cases these hypothesized transfer functions can rarely be measured and often remain unknown. Therefore, this contribution presents a general method for the concurrent estimation of the structure of transfer functions and their respective (global) parameters. Note, that by consequence an estimation of the distributed parameters of the rainfall-runoff model is also undertaken. The method combines two steps to achieve this. The first generates different possible transfer functions. The second then estimates the respective global transfer function parameters. The structural estimation of the transfer functions is based on the context free grammar concept. Chomsky first introduced context free grammars in linguistics [Chomsky, 1956]. Since then, they have been widely applied in computer science. But, to the knowledge of the authors, they have so far not been used in hydrology. Therefore, the contribution gives an introduction to context free grammars and shows how they can be constructed and used for the structural inference of transfer functions. This is enabled by new methods from evolutionary computation, such as grammatical evolution [O'Neill, 2001], which make it possible to exploit the constructed grammar as a
Directory of Open Access Journals (Sweden)
H. Zhang
2017-09-01
Full Text Available Land surface models (LSMs use a large cohort of parameters and state variables to simulate the water and energy balance at the soil–atmosphere interface. Many of these model parameters cannot be measured directly in the field, and require calibration against measured fluxes of carbon dioxide, sensible and/or latent heat, and/or observations of the thermal and/or moisture state of the soil. Here, we evaluate the usefulness and applicability of four different data assimilation methods for joint parameter and state estimation of the Variable Infiltration Capacity Model (VIC-3L and the Community Land Model (CLM using a 5-month calibration (assimilation period (March–July 2012 of areal-averaged SPADE soil moisture measurements at 5, 20, and 50 cm depths in the Rollesbroich experimental test site in the Eifel mountain range in western Germany. We used the EnKF with state augmentation or dual estimation, respectively, and the residual resampling PF with a simple, statistically deficient, or more sophisticated, MCMC-based parameter resampling method. The performance of the calibrated LSM models was investigated using SPADE water content measurements of a 5-month evaluation period (August–December 2012. As expected, all DA methods enhance the ability of the VIC and CLM models to describe spatiotemporal patterns of moisture storage within the vadose zone of the Rollesbroich site, particularly if the maximum baseflow velocity (VIC or fractions of sand, clay, and organic matter of each layer (CLM are estimated jointly with the model states of each soil layer. The differences between the soil moisture simulations of VIC-3L and CLM are much larger than the discrepancies among the four data assimilation methods. The EnKF with state augmentation or dual estimation yields the best performance of VIC-3L and CLM during the calibration and evaluation period, yet results are in close agreement with the PF using MCMC resampling. Overall, CLM demonstrated the
Instrumentation of Lysimeter Experiments and Monitoring of Soil Parameters
International Nuclear Information System (INIS)
Schmid, T.; Tallos, A.; Millan, R.; Vera, R.; Recreo, F.
2004-01-01
This study forms part of the project Mercurio and Recuperation de Terrenos Afectados por Mercurio Ambiental (RETAMA) , which determines the behaviour of mercury in the soil-plant system within the area of Almaden. The objective of this work is to instrument lysimeters with a set of electronic sensors to monitor physical and chemical soil parameters (moisture content, soil temperature, soil water matrix potential, Eh and pH) over a period of a complete vegetation cycle for selected crops. Physical and chemical soil analyses have been carried out on samples two soil profiles marking the extreme perimeter where the lysimeters were extracted. The monitoring data obtained every half hour show that the physicochemical conditions of the soils in the lysimeter can be correlated with the type of cultivation in the lysimeters. The results for parameters such as soil water matrix potential and the soil temperature reflect the diurnal changes; and fluctuations of the Eh can be related to the biological activities in the soils and are within oxid and sub oxid conditions. Slight fluctuations have been observed for the pH and constant volumetric moisture content is maintained during the period of no hydric stress. (Author) 16 refs
Instrumentation of Lysimeter Experiments and Monitoring of Soil Parameters
Energy Technology Data Exchange (ETDEWEB)
Schmid, T.; Tallos, A.; Millan, R.; Vera, R.; Recreo, F.
2004-07-01
This study forms part of the project Mercurio and Recuperation de Terrenos Afectados por Mercurio Ambiental (RETAMA), which determines the behaviour of mercury in the soil-plant system within the area of Almaden. The objective of this work is to instrument lysimeters with a set of electronic sensors to monitor physical and chemical soil parameters (moisture content, soil temperature, soil water matrix potential. Eh and pH) over a period of a complete vegetation cycle for selected crops. Physical and chemical soil analyses have been carried out on samples two soil profiles marking the extreme perimeter where the lysimeters were extracted. The monitoring data obtained every half hour show that the physicochemical conditions of the soils in the lysimeter can be correlated with the type of cultivation in the lysimeters. The results for parameters such as soil water matrix potential and the soil temperature reflect the diurnal changes; and fluctuations of the Eh can be related to the biological activities in the soils and are within oxid and suboxic conditions. Slight fluctuations have been observed for the pH and constant volumetric moisture content is maintained during the period of no hydric stress. (Author) 16 refs.
Multi-objective optimization in quantum parameter estimation
Gong, BeiLi; Cui, Wei
2018-04-01
We investigate quantum parameter estimation based on linear and Kerr-type nonlinear controls in an open quantum system, and consider the dissipation rate as an unknown parameter. We show that while the precision of parameter estimation is improved, it usually introduces a significant deformation to the system state. Moreover, we propose a multi-objective model to optimize the two conflicting objectives: (1) maximizing the Fisher information, improving the parameter estimation precision, and (2) minimizing the deformation of the system state, which maintains its fidelity. Finally, simulations of a simplified ɛ-constrained model demonstrate the feasibility of the Hamiltonian control in improving the precision of the quantum parameter estimation.
Correlation between soil parameters and natural radioactivity
International Nuclear Information System (INIS)
Jasinska, M.; Niewiadomski, T.; Schwabenthan, J.
1982-01-01
It has been suggested that a linear correlation exists between the concentration of natural elements U-238, Th-232 and K-40 contained in the upper layer of the soil, and the fraction (by weight) of particles of diameter less than 0.02 mm, i.e. the soil's mechanical composition. This hypothesis has been verified on a larger and statistically significant material of soils frequently occurring in Poland: chernozem, podzolic, muds, and anthropogenic, where for a given soil type, samples were chosen to represent various mechanical compositions. And it is concluded that the radioactivity concentrations of the head elements in the soil depend on its mechanical composition rather than on the type of soil. Thus, in principle, one is able to estimate dose rates from terrestrial sources directly from soil maps, without the need for outdoor measurements
Estimating surface soil moisture from SMAP observations using a Neural Network technique.
Kolassa, J; Reichle, R H; Liu, Q; Alemohammad, S H; Gentine, P; Aida, K; Asanuma, J; Bircher, S; Caldwell, T; Colliander, A; Cosh, M; Collins, C Holifield; Jackson, T J; Martínez-Fernández, J; McNairn, H; Pacheco, A; Thibeault, M; Walker, J P
2018-01-01
A Neural Network (NN) algorithm was developed to estimate global surface soil moisture for April 2015 to March 2017 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 Observing System Model version 5 (GEOS-5) land modeling system, and Moderate Resolution Imaging Spectroradiometer-based vegetation water content. The NN was trained on GEOS-5 soil moisture target data, making the NN estimates consistent with the GEOS-5 climatology, such that they may ultimately be assimilated into this model without further bias correction. Evaluated against in situ soil moisture measurements, the average unbiased root mean square error (ubRMSE), correlation and anomaly correlation of the NN retrievals were 0.037 m 3 m -3 , 0.70 and 0.66, respectively, against SMAP core validation site measurements and 0.026 m 3 m -3 , 0.58 and 0.48, respectively, against International Soil Moisture Network (ISMN) measurements. At the core validation sites, the NN retrievals have a significantly higher skill than the GEOS-5 model estimates and a slightly lower correlation skill than the SMAP Level-2 Passive (L2P) product. The feasibility of the NN method was reflected by a lower ubRMSE compared to the L2P retrievals as well as a higher skill when ancillary parameters in physically-based retrievals were uncertain. Against ISMN measurements, the skill of the two retrieval products was more comparable. A triple collocation analysis against Advanced Microwave Scanning Radiometer 2 (AMSR2) and Advanced Scatterometer (ASCAT) soil moisture retrievals showed that the NN and L2P retrieval errors have a similar spatial distribution, but the NN retrieval errors are generally lower in densely vegetated regions and transition zones.
Spatial Data Mining for Estimating Cover Management Factor of Universal Soil Loss Equation
Tsai, F.; Lin, T. C.; Chiang, S. H.; Chen, W. W.
2016-12-01
Universal Soil Loss Equation (USLE) is a widely used mathematical model that describes long-term soil erosion processes. Among the six different soil erosion risk factors of USLE, the cover-management factor (C-factor) is related to land-cover/land-use. The value of C-factor ranges from 0.001 to 1, so it alone might cause a thousandfold difference in a soil erosion analysis using USLE. The traditional methods for the estimation of USLE C-factor include in situ experiments, soil physical parameter models, USLE look-up tables with land use maps, and regression models between vegetation indices and C-factors. However, these methods are either difficult or too expensive to implement in large areas. In addition, the values of C-factor obtained using these methods can not be updated frequently, either. To address this issue, this research developed a spatial data mining approach to estimate the values of C-factor with assorted spatial datasets for a multi-temporal (2004 to 2008) annual soil loss analysis of a reservoir watershed in northern Taiwan. The idea is to establish the relationship between the USLE C-factor and spatial data consisting of vegetation indices and texture features extracted from satellite images, soil and geology attributes, digital elevation model, road and river distribution etc. A decision tree classifier was used to rank influential conditional attributes in the preliminary data mining. Then, factor simplification and separation were considered to optimize the model and the random forest classifier was used to analyze 9 simplified factor groups. Experimental results indicate that the overall accuracy of the data mining model is about 79% with a kappa value of 0.76. The estimated soil erosion amounts in 2004-2008 according to the data mining results are about 50.39 - 74.57 ton/ha-year after applying the sediment delivery ratio and correction coefficient. Comparing with estimations calculated with C-factors from look-up tables, the soil erosion
A simple approach to estimate soil organic carbon and soil co/sub 2/ emission
International Nuclear Information System (INIS)
Abbas, F.
2013-01-01
SOC (Soil Organic Carbon) and soil CO/sub 2/ (Carbon Dioxide) emission are among the indicator of carbon sequestration and hence global climate change. Researchers in developed countries benefit from advance technologies to estimate C (Carbon) sequestration. However, access to the latest technologies has always been challenging in developing countries to conduct such estimates. This paper presents a simple and comprehensive approach for estimating SOC and soil CO/sub 2/ emission from arable- and forest soils. The approach includes various protocols that can be followed in laboratories of the research organizations or academic institutions equipped with basic research instruments and technology. The protocols involve soil sampling, sample analysis for selected properties, and the use of a worldwide tested Rothamsted carbon turnover model. With this approach, it is possible to quantify SOC and soil CO/sub 2/ emission over short- and long-term basis for global climate change assessment studies. (author)
Modeling the vertical soil organic matter profile using Bayesian parameter estimation
Braakhekke, M.C.; Wutzler, T.; Beer, C.; Kattge, J.; Schrumpf, M.; Schöning, I.; Hoosbeek, M.R.; Kruijt, B.; Kabat, P.
2012-01-01
The vertical distribution of soil organic matter (SOM) in the profile may constitute a significant factor for soil carbon cycling. However, the formation of the SOM profile is currently poorly understood due to equifinality, caused by the entanglement of several processes: input from roots, mixing
Modeling the vertical soil organic matter profile using Bayesian parameter estimation
Braakhekke, M.C.; Wutzler, T.; Beer, C.; Kattge, J.; Schrumpf, M.; Ahrens, B.; Schoning, I.; Hoosbeek, M.R.; Kruijt, B.; Kabat, P.; Reichstein, M.
2013-01-01
The vertical distribution of soil organic matter (SOM) in the profile may constitute an important factor for soil carbon cycling. However, the formation of the SOM profile is currently poorly understood due to equifinality, caused by the entanglement of several processes: input from roots, mixing
International Nuclear Information System (INIS)
Medina, Hanoi; Hernández, Yunay; Batista, Giovanni Chirico; Romano, Nunzio
2008-01-01
Effective estimation of soil hydraulic information through the assimilation of surface moisture values, demand the use of approximations necessarily related to highly nonlinear models. The Kalman Filter 'Unscented' ( UKF ) has emerged in the literature as a safe and easy technique to implement than the most rudimentary, but more widely used, Kalman Filter 'Linear' (EKF ), for these purposes. However, the efficiency of these techniques depends not only on the approach itself, but also the numerical scheme that supports it. This work is aimed to demonstrate the advantages and disadvantages encountered during implementation of the UKF and EKF in the scheme of numerical solution of the Richards equation to obtain statements and soil parameters by assimilating surface moisture values. Numerical solutions evaluated were implemented using a finite difference scheme. The results demonstrate that a Crack -Nicolson linearized scheme is much more efficient in terms of security and time that based on an explicit scheme and safer than a UKF based on a traditional implicit numerical scheme for estimating profile soil moisture. The latter approach leads to a systematic bias in the solution 'unscented' when the central state is close to saturation. In the dual estimate (state- parameter), certain physical and mathematical parameter constraints, coupled with the bias in the estimates, resulted in substantial difficulties in the practical implementation of this technique using the UKF, or a solution that combines elements of both techniques Kalman filter
Directory of Open Access Journals (Sweden)
A. R. Adesiji
2012-12-01
Full Text Available The estimation of runoff and soil moisture deficit in Guinea Savannah region using semi arid model based on soil water balance technique (SAMBA was carried out. The input to the SAMBA model are daily rainfall, daily evapotranspiration, type and date of planting of crop, and soil parameters. The estimated runoff was validated with field measurement taken in a 67.23 ha catchment in the study area. The annual rainfall for the year under study (2009 is 1356.2 mm, the estimated annual evapotranspiration. runoff and recharge are 638mm, 132.93mm, and 447.8mm respectively. Recharge was experienced 23 days after a significant depth of rainfall was recorded. For the crop growth in the catchment, the soil was cropped with a pepper and the growth monitored from the planting to the harvesting. The crop enjoyed so much moisture throughout the growing period as Total Available Water in the soil is greater than Soil Moisture Deficit (TAW>SMD. The model results show that the larger percentage of the total annual rainfall was lost to evaporation and recharge during the growing season. The low runoff and high recharge are attributed to soil characteristics of the area and moderate terrain of the study area.
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
International Nuclear Information System (INIS)
Looney, B.B.; Grant, M.W.; King, C.M.
1987-03-01
Geochemical parameter estimates to be used in assessing the subsurface transport of chemicals from Savannah River Plant (SRP) waste sites are presented. Specifically, reference values for soil-solution distribution coefficients, solubility, leach rates, and retardation coefficients are estimated for 31 inorganic chemicals (assuming speciation is governed by reasonable assumptions about controlling variables such as Eh and pH) and 36 organic compounds. Additionally, facilitated transport (the association of chemicals with inorganic and organic ligands or colloids resulting in relatively high mobility) was estimated using field data to derive a fraction of the disposal mass which was assumed to be mobile. Hydrologic parameters such as dispersion coefficient, average moisture content in vadose zone, bulk density, and effective porosity are also presented. The estimates are based on site-specific studies when available, combined with technical literature
7 A GIS Estimation of Soil Loss
African Journals Online (AJOL)
Administrator
of the river channel that is causing flooding in some parts of Accra, Ghana. ... Soil loss factors such as rainfall erosivity, soil erodibilty, .... High rainfall intensity will easily splash or remove top soil, and it can also cause mass movement. In USLE erosivity, (R) is empirically estimated as. (Burrough & McDonnell, 1998):.
On parameter estimation in deformable models
DEFF Research Database (Denmark)
Fisker, Rune; Carstensen, Jens Michael
1998-01-01
Deformable templates have been intensively studied in image analysis through the last decade, but despite its significance the estimation of model parameters has received little attention. We present a method for supervised and unsupervised model parameter estimation using a general Bayesian form...
A simplified dynamic method for field capacity estimation and its parameter analysis
Institute of Scientific and Technical Information of China (English)
Zhen-tao CONG; Hua-fang LÜ; Guang-heng NI
2014-01-01
This paper presents a simplified dynamic method based on the definition of field capacity. Two soil hydraulic characteristics models, the Brooks-Corey (BC) model and the van Genuchten (vG) model, and four soil data groups were used in this study. The relative drainage rate, which is a unique parameter and independent of the soil type in the simplified dynamic method, was analyzed using the pressure-based method with a matric potential of−1/3 bar and the flux-based method with a drainage flux of 0.005 cm/d. As a result, the relative drainage rate of the simplified dynamic method was determined to be 3% per day. This was verified by the similar field capacity results estimated with the three methods for most soils suitable for cultivating plants. In addition, the drainage time calculated with the simplified dynamic method was two to three days, which agrees with the classical definition of field capacity. We recommend the simplified dynamic method with a relative drainage rate of 3% per day due to its simple application and clearly physically-based concept.
Variation of 137Cs migration parameters in soils
International Nuclear Information System (INIS)
Silant'ev, A.N.; Shkuratova, I.G.
1988-01-01
Quasidiffusion model accounting the oriented migration is used to describe the observed profiles of 137 Cs distribution in soil. A way of model use in case of real soil, the quasidiffusion migration factor being varied, is suggested. It is shown that in the upper thin soil layer the quasidiffusion migration factor and oriented migration rate values are constant. With further increase of depth the quasidiffusion coefficient grows, the oriented migration rate is unchanged. On the basis of location data characteristic values of migration parameters for different soils of the USSR European territory depending on the soil texture, vegetative cover and moistening are determined
Parameter Estimation of Partial Differential Equation Models.
Xun, Xiaolei; Cao, Jiguo; Mallick, Bani; Carroll, Raymond J; Maity, Arnab
2013-01-01
Partial differential equation (PDE) models are commonly used to model complex dynamic systems in applied sciences such as biology and finance. The forms of these PDE models are usually proposed by experts based on their prior knowledge and understanding of the dynamic system. Parameters in PDE models often have interesting scientific interpretations, but their values are often unknown, and need to be estimated from the measurements of the dynamic system in the present of measurement errors. Most PDEs used in practice have no analytic solutions, and can only be solved with numerical methods. Currently, methods for estimating PDE parameters require repeatedly solving PDEs numerically under thousands of candidate parameter values, and thus the computational load is high. In this article, we propose two methods to estimate parameters in PDE models: a parameter cascading method and a Bayesian approach. In both methods, the underlying dynamic process modeled with the PDE model is represented via basis function expansion. For the parameter cascading method, we develop two nested levels of optimization to estimate the PDE parameters. For the Bayesian method, we develop a joint model for data and the PDE, and develop a novel hierarchical model allowing us to employ Markov chain Monte Carlo (MCMC) techniques to make posterior inference. Simulation studies show that the Bayesian method and parameter cascading method are comparable, and both outperform other available methods in terms of estimation accuracy. The two methods are demonstrated by estimating parameters in a PDE model from LIDAR data.
Soil-biological parameters as tools in biomonitoring
International Nuclear Information System (INIS)
Kinzel, H.
1992-01-01
Soil-biological parameters (enzyme activities, content of metabolites) are sensitive indicators of environmental changes. On the one hand, we tested the possibilities of this method in the vicinity of the trunks of beeches, where most of the pollutants are washed into the soil with the runoff of precipitation water from the tree trunks. On the other hand, we compared soils used for intensive agriculture with more natural soils in the vicinity. In the first of these cases, especially the activities of dehydrogenase and alkaline phosphatase were influenced by atmospheric pollution. In the latter case, a marked effect of agricultural management on the entire soil-biological state was to be noted. The results are derived from investigations by A. Baumgarten, O. Linher, K. Spadinger and S. Zechmeister-Boltenstern. (orig.) [de
Soil carbon storage estimation in a forested watershed using quantitative soil-landscape modeling
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...
Jadoon, Khan; Weihermü ller, Lutz; Scharnagl, Benedikt; Kowalsky, Michael B.; Bechtold, Michel; Hubbard, Susan S.; Vereecken, Harry; Lambot, Sé bastien
2012-01-01
An integrated hydrogeophysical inversion approach was used to remotely infer the unsaturated soil hydraulic parameters from time-lapse ground-penetrating radar (GPR) data collected at a fixed location over a bare agricultural field. The GPR model
Analysis of Selected Physicochemical Parameters of Soils Used for ...
African Journals Online (AJOL)
Correlation analysis was also employed to examine the relationship between the various parameters in the soil samples. The soil studied can be considered as good sources of essential nutrients and this information will help farmers to solve the problems related to soil nutrients, amount of which fertilizers to be used to ...
Przewłócki, Jarosław; Górski, Jarosław; Świdziński, Waldemar
2016-12-01
The paper deals with the probabilistic analysis of the settlement of a non-cohesive soil layer subjected to cyclic loading. Originally, the settlement assessment is based on a deterministic compaction model, which requires integration of a set of differential equations. However, with the use of the Bessel functions, the settlement of a soil stratum can be calculated by a simplified algorithm. The compaction model parameters were determined for soil samples taken from subsoil near the Izmit Bay, Turkey. The computations were performed for various sets of random variables. The point estimate method was applied, and the results were verified by the Monte Carlo method. The outcome leads to a conclusion that can be useful in the prediction of soil settlement under seismic loading.
Precision Parameter Estimation and Machine Learning
Wandelt, Benjamin D.
2008-12-01
I discuss the strategy of ``Acceleration by Parallel Precomputation and Learning'' (AP-PLe) that can vastly accelerate parameter estimation in high-dimensional parameter spaces and costly likelihood functions, using trivially parallel computing to speed up sequential exploration of parameter space. This strategy combines the power of distributed computing with machine learning and Markov-Chain Monte Carlo techniques efficiently to explore a likelihood function, posterior distribution or χ2-surface. This strategy is particularly successful in cases where computing the likelihood is costly and the number of parameters is moderate or large. We apply this technique to two central problems in cosmology: the solution of the cosmological parameter estimation problem with sufficient accuracy for the Planck data using PICo; and the detailed calculation of cosmological helium and hydrogen recombination with RICO. Since the APPLe approach is designed to be able to use massively parallel resources to speed up problems that are inherently serial, we can bring the power of distributed computing to bear on parameter estimation problems. We have demonstrated this with the CosmologyatHome project.
Zhang, Hongming; Wei, Jicheng; Yang, Qinke; Baartman, Jantiene E.M.; Gai, Lingtong; Yang, Xiaomei; Li, Shu Qin; Yu, Jiantao; Ritsema, Coen J.; Geissen, Violette
2017-01-01
The Universal Soil Loss Equation (USLE) and its revised version (RUSLE) are often used to estimate soil erosion at regional landscape scales. USLE/RUSLE contain parameters for slope length factor (L) and slope steepness factor (S), usually combined as LS. However a major limitation is the difficulty
Plant-available soil water capacity: estimation methods and implications
Directory of Open Access Journals (Sweden)
Bruno Montoani Silva
2014-04-01
Full Text Available The plant-available water capacity of the soil is defined as the water content between field capacity and wilting point, and has wide practical application in planning the land use. In a representative profile of the Cerrado Oxisol, methods for estimating the wilting point were studied and compared, using a WP4-T psychrometer and Richards chamber for undisturbed and disturbed samples. In addition, the field capacity was estimated by the water content at 6, 10, 33 kPa and by the inflection point of the water retention curve, calculated by the van Genuchten and cubic polynomial models. We found that the field capacity moisture determined at the inflection point was higher than by the other methods, and that even at the inflection point the estimates differed, according to the model used. By the WP4-T psychrometer, the water content was significantly lower found the estimate of the permanent wilting point. We concluded that the estimation of the available water holding capacity is markedly influenced by the estimation methods, which has to be taken into consideration because of the practical importance of this parameter.
Estimation of areal soil water content through microwave remote sensing
Oevelen, van P.J.
2000-01-01
In this thesis the use of microwave remote sensing to estimate soil water content is investigated. A general framework is described which is applicable to both passive and active microwave remote sensing of soil water content. The various steps necessary to estimate areal soil water content
Reionization history and CMB parameter estimation
International Nuclear Information System (INIS)
Dizgah, Azadeh Moradinezhad; Kinney, William H.; Gnedin, Nickolay Y.
2013-01-01
We study how uncertainty in the reionization history of the universe affects estimates of other cosmological parameters from the Cosmic Microwave Background. We analyze WMAP7 data and synthetic Planck-quality data generated using a realistic scenario for the reionization history of the universe obtained from high-resolution numerical simulation. We perform parameter estimation using a simple sudden reionization approximation, and using the Principal Component Analysis (PCA) technique proposed by Mortonson and Hu. We reach two main conclusions: (1) Adopting a simple sudden reionization model does not introduce measurable bias into values for other parameters, indicating that detailed modeling of reionization is not necessary for the purpose of parameter estimation from future CMB data sets such as Planck. (2) PCA analysis does not allow accurate reconstruction of the actual reionization history of the universe in a realistic case
Reionization history and CMB parameter estimation
Energy Technology Data Exchange (ETDEWEB)
Dizgah, Azadeh Moradinezhad; Gnedin, Nickolay Y.; Kinney, William H.
2013-05-01
We study how uncertainty in the reionization history of the universe affects estimates of other cosmological parameters from the Cosmic Microwave Background. We analyze WMAP7 data and synthetic Planck-quality data generated using a realistic scenario for the reionization history of the universe obtained from high-resolution numerical simulation. We perform parameter estimation using a simple sudden reionization approximation, and using the Principal Component Analysis (PCA) technique proposed by Mortonson and Hu. We reach two main conclusions: (1) Adopting a simple sudden reionization model does not introduce measurable bias into values for other parameters, indicating that detailed modeling of reionization is not necessary for the purpose of parameter estimation from future CMB data sets such as Planck. (2) PCA analysis does not allow accurate reconstruction of the actual reionization history of the universe in a realistic case.
Parameter Estimation of Partial Differential Equation Models
Xun, Xiaolei
2013-09-01
Partial differential equation (PDE) models are commonly used to model complex dynamic systems in applied sciences such as biology and finance. The forms of these PDE models are usually proposed by experts based on their prior knowledge and understanding of the dynamic system. Parameters in PDE models often have interesting scientific interpretations, but their values are often unknown and need to be estimated from the measurements of the dynamic system in the presence of measurement errors. Most PDEs used in practice have no analytic solutions, and can only be solved with numerical methods. Currently, methods for estimating PDE parameters require repeatedly solving PDEs numerically under thousands of candidate parameter values, and thus the computational load is high. In this article, we propose two methods to estimate parameters in PDE models: a parameter cascading method and a Bayesian approach. In both methods, the underlying dynamic process modeled with the PDE model is represented via basis function expansion. For the parameter cascading method, we develop two nested levels of optimization to estimate the PDE parameters. For the Bayesian method, we develop a joint model for data and the PDE and develop a novel hierarchical model allowing us to employ Markov chain Monte Carlo (MCMC) techniques to make posterior inference. Simulation studies show that the Bayesian method and parameter cascading method are comparable, and both outperform other available methods in terms of estimation accuracy. The two methods are demonstrated by estimating parameters in a PDE model from long-range infrared light detection and ranging data. Supplementary materials for this article are available online. © 2013 American Statistical Association.
Field, laboratory and estimated soil-water content limits
African Journals Online (AJOL)
2005-01-21
Jan 21, 2005 ... silt (0.002 to 0.05 mm) percentage to estimate the soil-water content at a given soil-water .... ar and br are the intercept and slope values of the regres- .... tions use the particle size classification of the South African Soil.
International Nuclear Information System (INIS)
Hattemer-Frey, H.A.; Krieger, G.R.; Lau, V.
1994-01-01
In the absence of site-specific data, the concentration of metals in plants is typically estimated by multiplying the total concentration of metal in soil by a metal-specific soil-to-root bioconcentration factor (BCF). However, this approach does not account for various soil properties, such as pH, organic matter content, and cation exchange capacity, that are known to influence root uptake of some metals. For risk assessment purposes, a simple, predictive method for estimating root uptake of metals that is based on site-specific soil and crop data is needed so that the importance of the produce ingestion pathway and subsequent influence on human exposure can be quantitatively assessed. An easy-to-use method is necessary since collecting site-specific data on the concentration of metals in home-grown produce is often time-consuming and costly. Ideally, it should be possible to develop a statistically-reliable relationship between plant and soil metals levels that includes appropriate weighing factors for various soil properties. Multiple linear regression analyses were used to develop simple, predictive models for estimating the concentration of metals in plants via root uptake using site-specific soil data. This paper presents preliminary predictive equations for estimating root uptake of arsenic, cadmium, copper, and zinc in fruiting, root, and all vegetables combined (i.e., fruiting and root crop data were combined). Results show that by using data on additional soil parameters (other than relying solely on the concentration of metals in soil), the concentration of metals in fruiting and root vegetables can be more confidently predicted
Parameter Estimation for Thurstone Choice Models
Energy Technology Data Exchange (ETDEWEB)
Vojnovic, Milan [London School of Economics (United Kingdom); Yun, Seyoung [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
2017-04-24
We consider the estimation accuracy of individual strength parameters of a Thurstone choice model when each input observation consists of a choice of one item from a set of two or more items (so called top-1 lists). This model accommodates the well-known choice models such as the Luce choice model for comparison sets of two or more items and the Bradley-Terry model for pair comparisons. We provide a tight characterization of the mean squared error of the maximum likelihood parameter estimator. We also provide similar characterizations for parameter estimators defined by a rank-breaking method, which amounts to deducing one or more pair comparisons from a comparison of two or more items, assuming independence of these pair comparisons, and maximizing a likelihood function derived under these assumptions. We also consider a related binary classification problem where each individual parameter takes value from a set of two possible values and the goal is to correctly classify all items within a prescribed classification error. The results of this paper shed light on how the parameter estimation accuracy depends on given Thurstone choice model and the structure of comparison sets. In particular, we found that for unbiased input comparison sets of a given cardinality, when in expectation each comparison set of given cardinality occurs the same number of times, for a broad class of Thurstone choice models, the mean squared error decreases with the cardinality of comparison sets, but only marginally according to a diminishing returns relation. On the other hand, we found that there exist Thurstone choice models for which the mean squared error of the maximum likelihood parameter estimator can decrease much faster with the cardinality of comparison sets. We report empirical evaluation of some claims and key parameters revealed by theory using both synthetic and real-world input data from some popular sport competitions and online labor platforms.
Geotechnical Parameters of Alluvial Soils from in-situ Tests
Młynarek, Zbigniew; Stefaniak, Katarzyna; Wierzbicki, Jedrzej
2012-10-01
The article concentrates on the identification of geotechnical parameters of alluvial soil represented by silts found near Poznan and Elblag. Strength and deformation parameters of the subsoil tested were identified by the CPTU (static penetration) and SDMT (dilatometric) methods, as well as by the vane test (VT). Geotechnical parameters of the subsoil were analysed with a view to using the soil as an earth construction material and as a foundation for buildings constructed on the grounds tested. The article includes an analysis of the overconsolidation process of the soil tested and a formula for the identification of the overconsolidation ratio OCR. Equation 9 reflects the relation between the undrained shear strength and plasticity of the silts analyzed and the OCR value. The analysis resulted in the determination of the Nkt coefficient, which might be used to identify the undrained shear strength of both sediments tested. On the basis of a detailed analysis of changes in terms of the constrained oedometric modulus M0, the relations between the said modulus, the liquidity index and the OCR value were identified. Mayne's formula (1995) was used to determine the M0 modulus from the CPTU test. The usefullness of the sediments found near Poznan as an earth construction material was analysed after their structure had been destroyed and compacted with a Proctor apparatus. In cases of samples characterised by different water content and soil particle density, the analysis of changes in terms of cohesion and the internal friction angle proved that these parameters are influenced by the soil phase composition (Fig. 18 and 19). On the basis of the tests, it was concluded that the most desirable shear strength parameters are achieved when the silt is compacted below the optimum water content.
Use of modeled and satelite soil moisture to estimate soil erosion in central and southern Italy.
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
Establishing principal soil quality parameters influencing earthworms in urban soils using bioassays
International Nuclear Information System (INIS)
Hankard, Peter K.; Bundy, Jacob G.; Spurgeon, David J.; Weeks, Jason M.; Wright, Julian; Weinberg, Claire; Svendsen, Claus
2005-01-01
Potential contamination at ex-industrial sites means that, prior to change of use, it will be necessary to quantify the extent of risks to potential receptors. To assess ecological hazards, it is often suggested to use biological assessment to augment chemical analyses. Here we investigate the potential of a commonly recommended bioassay, the earthworm reproduction test, to assess the status of urban contaminated soils. Sample points at all study sites had contaminant concentrations above the Dutch soil criteria Target Values. In some cases, the relevant Intervention Values were exceeded. Earthworm survival at most points was high, but reproduction differed significantly in soil from separate patches on the same site. When the interrelationships between soil parameters and reproduction were studied, it was not possible to create a good model of site soil toxicity based on single or even multiple chemical measurements of the soils. We thus conclude that chemical analysis alone is not sufficient to characterize soil quality and confirms the value of biological assays for risk assessment of potentially contaminated soils. - Bioassays must be applied for the risk assessment complexly-polluted sites to complement chemical analysis of soils
Ranking as parameter estimation
Czech Academy of Sciences Publication Activity Database
Kárný, Miroslav; Guy, Tatiana Valentine
2009-01-01
Roč. 4, č. 2 (2009), s. 142-158 ISSN 1745-7645 R&D Projects: GA MŠk 2C06001; GA AV ČR 1ET100750401; GA MŠk 1M0572 Institutional research plan: CEZ:AV0Z10750506 Keywords : ranking * Bayesian estimation * negotiation * modelling Subject RIV: BB - Applied Statistics, Operational Research http://library.utia.cas.cz/separaty/2009/AS/karny- ranking as parameter estimation.pdf
Parameter values for the estimation of radionuclide transfer to major food crops in Korea
International Nuclear Information System (INIS)
Choi, Yong-Ho; Lim, Kwang-Muk; Jun, In; Keum, Dong-Kwon; Lee, Chang-Woo
2008-01-01
This paper summarizes the results of the radiotracer experiments and field studies performed in Korea for the past 20 years to obtain parameter values for estimating the environmental transfer of radionuclides to food crops. With regards to direct plant contamination, the interception fractions, weathering half-lives and translocation factors of Cs, Sr, Mn, Co and Ru were measured for depositions at different growth stages of selected food crops. In order to investigate an indirect contamination pathway, the soil-to-plant transfer factors (TF m , dimensionless) of Cs, Sr, Mn, Co and/or Zn were measured for rice, Chinese cabbage, radish, soybean, barley, lettuce and so on in one or more soils. In addition, the transfer factors (TF a , m 2 kg -1 ) based on a deposition density were also measured following depositions at different times during the growth periods of several food crops. Particularly for rice and Chinese cabbage, tritium experiments were also carried out for the TF a . The obtained parameter values varied considerably with the soils, crops, radionuclides and deposition times. These data would be applicable to both normal and acute releases not only in Korea but also in many other countries. (author)
Integrating microbial diversity in soil carbon dynamic models parameters
Louis, Benjamin; Menasseri-Aubry, Safya; Leterme, Philippe; Maron, Pierre-Alain; Viaud, Valérie
2015-04-01
Faced with the numerous concerns about soil carbon dynamic, a large quantity of carbon dynamic models has been developed during the last century. These models are mainly in the form of deterministic compartment models with carbon fluxes between compartments represented by ordinary differential equations. Nowadays, lots of them consider the microbial biomass as a compartment of the soil organic matter (carbon quantity). But the amount of microbial carbon is rarely used in the differential equations of the models as a limiting factor. Additionally, microbial diversity and community composition are mostly missing, although last advances in soil microbial analytical methods during the two past decades have shown that these characteristics play also a significant role in soil carbon dynamic. As soil microorganisms are essential drivers of soil carbon dynamic, the question about explicitly integrating their role have become a key issue in soil carbon dynamic models development. Some interesting attempts can be found and are dominated by the incorporation of several compartments of different groups of microbial biomass in terms of functional traits and/or biogeochemical compositions to integrate microbial diversity. However, these models are basically heuristic models in the sense that they are used to test hypotheses through simulations. They have rarely been confronted to real data and thus cannot be used to predict realistic situations. The objective of this work was to empirically integrate microbial diversity in a simple model of carbon dynamic through statistical modelling of the model parameters. This work is based on available experimental results coming from a French National Research Agency program called DIMIMOS. Briefly, 13C-labelled wheat residue has been incorporated into soils with different pedological characteristics and land use history. Then, the soils have been incubated during 104 days and labelled and non-labelled CO2 fluxes have been measured at ten
Andrew D. Richardson; Mathew Williams; David Y. Hollinger; David J.P. Moore; D. Bryan Dail; Eric A. Davidson; Neal A. Scott; Robert S. Evans; Holly. Hughes
2010-01-01
We conducted an inverse modeling analysis, using a variety of data streams (tower-based eddy covariance measurements of net ecosystem exchange, NEE, of CO2, chamber-based measurements of soil respiration, and ancillary ecological measurements of leaf area index, litterfall, and woody biomass increment) to estimate parameters and initial carbon (C...
Research progress of on-the-go soil parameter sensors based on NIRS
An, Xiaofei; Meng, Zhijun; Wu, Guangwei; Guo, Jianhua
2014-11-01
Both the ever-increasing prices of fertilizer and growing ecological concern over chemical run-off into sources of drinking water have brought the issues of precision agriculture and site-specific management to the forefront of present technological development within agriculture and ecology. Soil is an important and basic element in agriculture production. Acquisition of soil information plays an important role in precision agriculture. The soil parameters include soil total nitrogen, phosporus, potassium, soil organic matter, soil moisture, electrical conductivity and pH value and so on. Field rapid acquisition to all the kinds of soil physical and chemical parameters is one of the most important research directions. And soil parameter real-time monitoring is also the trend of future development in precision agriculture. While developments in precision agriculture and site-specific management procedures have made significant in-roads on these issues and many researchers have developed effective means to determine soil properties, routinely obtaining robust on-the-go measurements of soil properties which are reliable enough to drive effective fertilizer application remains a challenge. NIRS technology provides a new method to obtain soil parameter with low cost and rapid advantage. In this paper, research progresses of soil on-the-go spectral sensors at domestic and abroad was combed and analyzed. There is a need for the sensing system to perform at least six key indexes for any on-the-go soil spectral sensor to be successful. The six indexes are detection limit, specificity, robustness, accuracy, cost and easy-to-use. Both the research status and problems were discussed. Finally, combining the national conditions of china, development tendency of on-the-go soil spectral sensors was proposed. In the future, on-the-go soil spectral sensors with reliable enough, sensitive enough and continuous detection would become popular in precision agriculture.
Cosmological parameter estimation using Particle Swarm Optimization
Prasad, J.; Souradeep, T.
2014-03-01
Constraining parameters of a theoretical model from observational data is an important exercise in cosmology. There are many theoretically motivated models, which demand greater number of cosmological parameters than the standard model of cosmology uses, and make the problem of parameter estimation challenging. It is a common practice to employ Bayesian formalism for parameter estimation for which, in general, likelihood surface is probed. For the standard cosmological model with six parameters, likelihood surface is quite smooth and does not have local maxima, and sampling based methods like Markov Chain Monte Carlo (MCMC) method are quite successful. However, when there are a large number of parameters or the likelihood surface is not smooth, other methods may be more effective. In this paper, we have demonstrated application of another method inspired from artificial intelligence, called Particle Swarm Optimization (PSO) for estimating cosmological parameters from Cosmic Microwave Background (CMB) data taken from the WMAP satellite.
Cosmological parameter estimation using Particle Swarm Optimization
International Nuclear Information System (INIS)
Prasad, J; Souradeep, T
2014-01-01
Constraining parameters of a theoretical model from observational data is an important exercise in cosmology. There are many theoretically motivated models, which demand greater number of cosmological parameters than the standard model of cosmology uses, and make the problem of parameter estimation challenging. It is a common practice to employ Bayesian formalism for parameter estimation for which, in general, likelihood surface is probed. For the standard cosmological model with six parameters, likelihood surface is quite smooth and does not have local maxima, and sampling based methods like Markov Chain Monte Carlo (MCMC) method are quite successful. However, when there are a large number of parameters or the likelihood surface is not smooth, other methods may be more effective. In this paper, we have demonstrated application of another method inspired from artificial intelligence, called Particle Swarm Optimization (PSO) for estimating cosmological parameters from Cosmic Microwave Background (CMB) data taken from the WMAP satellite
Statistics of Parameter Estimates: A Concrete Example
Aguilar, Oscar
2015-01-01
© 2015 Society for Industrial and Applied Mathematics. Most mathematical models include parameters that need to be determined from measurements. The estimated values of these parameters and their uncertainties depend on assumptions made about noise levels, models, or prior knowledge. But what can we say about the validity of such estimates, and the influence of these assumptions? This paper is concerned with methods to address these questions, and for didactic purposes it is written in the context of a concrete nonlinear parameter estimation problem. We will use the results of a physical experiment conducted by Allmaras et al. at Texas A&M University [M. Allmaras et al., SIAM Rev., 55 (2013), pp. 149-167] to illustrate the importance of validation procedures for statistical parameter estimation. We describe statistical methods and data analysis tools to check the choices of likelihood and prior distributions, and provide examples of how to compare Bayesian results with those obtained by non-Bayesian methods based on different types of assumptions. We explain how different statistical methods can be used in complementary ways to improve the understanding of parameter estimates and their uncertainties.
DEFF Research Database (Denmark)
Holm, P.E.; Christensen, T.H.
1998-01-01
Soil water concentrations of cadmium and zinc were measured in plant pots with 15 contaminated soils which differed in origin, texture, pH (5.1-7.8) and concentrations of cadmium (0.2-17 mg Cd kg(-1)) and zinc (36-1300 mg Zn kg(-1)). The soil waters contained total concentrations of 0.5 to 17 mu g...... to 0.1% per year of the total soil content of cadmium and zinc. The measured soil water concentrations of cadmium and zinc did not correlate linearly with the corresponding soil concentrations but correlated fairly well with concentrations measured in Ca(NO(3))(2) extracts of the soils and with soil...... water concentrations estimated from soil concentrations and pH. Such concentration estimates may be useful for estimating amounts of cadmium and zinc being leached from soils....
Badorreck, Annika; Krümmelbein, Julia; Raab, Thomas
2015-04-01
Soil compaction is a major problem of soils on dumped mining substrates in Lusatia, Germany. Deep ripping and cultivation of deep rooting plant species are considered to be effective ways of agricultural recultivation. Six years after experiment start, we studied the effect of initial deep soil loosening (i.e. down to 65 cm) on root systems of rye (Secale cereale) and alfalfa (Medicago sativa) and on soil physical parameters. We conducted a soil monolith sampling for each treatment (deep loosened and unloosened) and for each plant species (in three replicates, respectively) to determine root diameter, length density and dry mass as well as soil bulk density. Further soil physical analysis comprised water retention, hydraulic conductivity and texture in three depths. The results showed different reactions of the root systems of rye and alfalfa six years after deep ripping. In the loosened soil the root biomass of the rye was lower in depths of 20-40 cm and the root biomass of alfalfa was also decreased in depths of 20-50 cm together with a lower root diameter for both plant species. Moreover, total and fine root length density was higher for alfalfa and vice versa for rye. The soil physical parameters such as bulk density showed fewer differences, despite a higher bulk density in 30-40cm for the deep loosened rye plot which indicates a more pronounced plough pan.
Nearing, Grey S.; Crow, Wade T.; Thorp, Kelly R.; Moran, Mary S.; Reichle, Rolf H.; Gupta, Hoshin V.
2012-01-01
Observing system simulation experiments were used to investigate ensemble Bayesian state updating data assimilation of observations of leaf area index (LAI) and soil moisture (theta) for the purpose of improving single-season wheat yield estimates with the Decision Support System for Agrotechnology Transfer (DSSAT) CropSim-Ceres model. Assimilation was conducted in an energy-limited environment and a water-limited environment. Modeling uncertainty was prescribed to weather inputs, soil parameters and initial conditions, and cultivar parameters and through perturbations to model state transition equations. The ensemble Kalman filter and the sequential importance resampling filter were tested for the ability to attenuate effects of these types of uncertainty on yield estimates. LAI and theta observations were synthesized according to characteristics of existing remote sensing data, and effects of observation error were tested. Results indicate that the potential for assimilation to improve end-of-season yield estimates is low. Limitations are due to a lack of root zone soil moisture information, error in LAI observations, and a lack of correlation between leaf and grain growth.
Fancher, J P; Aitkenhead-Peterson, J A; Farris, T; Mix, K; Schwab, A P; Wescott, D J; Hamilton, M D
2017-10-01
Soil samples from the Forensic Anthropology Research Facility (FARF) at Texas State University, San Marcos, TX, were analyzed for multiple soil characteristics from cadaver decomposition islands to a depth of 5centimeters (cm) from 63 human decomposition sites, as well as depths up to 15cm in a subset of 11 of the cadaver decomposition islands plus control soils. Postmortem interval (PMI) of the cadaver decomposition islands ranged from 6 to 1752 days. Some soil chemistry, including nitrate-N (NO 3 -N), ammonium-N (NH 4 -N), and dissolved inorganic carbon (DIC), peaked at early PMI values and their concentrations at 0-5cm returned to near control values over time likely due to translocation down the soil profile. Other soil chemistry, including dissolved organic carbon (DOC), dissolved organic nitrogen (DON), orthophosphate-P (PO 4 -P), sodium (Na + ), and potassium (K + ), remained higher than the control soil up to a PMI of 1752days postmortem. The body mass index (BMI) of the cadaver appeared to have some effect on the cadaver decomposition island chemistry. To estimate PMI using soil chemistry, backward, stepwise multiple regression analysis was used with PMI as the dependent variable and soil chemistry, body mass index (BMI) and physical soil characteristics such as saturated hydraulic conductivity as independent variables. Measures of soil parameters derived from predator and microbial mediated decomposition of human remains shows promise in estimating PMI to within 365days for a period up to nearly five years. This persistent change in soil chemistry extends the ability to estimate PMI beyond the traditionally utilized methods of entomology and taphonomy in support of medical-legal investigations, humanitarian recovery efforts, and criminal and civil cases. Copyright © 2017 Elsevier B.V. All rights reserved.
Sisson, James B.; van Genuchten, Martinus Th.
1991-04-01
The unsaturated hydraulic properties are important parameters in any quantitative description of water and solute transport in partially saturated soils. Currently, most in situ methods for estimating the unsaturated hydraulic conductivity (K) are based on analyses that require estimates of the soil water flux and the pressure head gradient. These analyses typically involve differencing of field-measured pressure head (h) and volumetric water content (θ) data, a process that can significantly amplify instrumental and measurement errors. More reliable methods result when differencing of field data can be avoided. One such method is based on estimates of the gravity drainage curve K'(θ) = dK/dθ which may be computed from observations of θ and/or h during the drainage phase of infiltration drainage experiments assuming unit gradient hydraulic conditions. The purpose of this study was to compare estimates of the unsaturated soil hydraulic functions on the basis of different combinations of field data θ, h, K, and K'. Five different data sets were used for the analysis: (1) θ-h, (2) K-θ, (3) K'-θ (4) K-θ-h, and (5) K'-θ-h. The analysis was applied to previously published data for the Norfolk, Troup, and Bethany soils. The K-θ-h and K'-θ-h data sets consistently produced nearly identical estimates of the hydraulic functions. The K-θ and K'-θ data also resulted in similar curves, although results in this case were less consistent than those produced by the K-θ-h and K'-θ-h data sets. We conclude from this study that differencing of field data can be avoided and hence that there is no need to calculate soil water fluxes and pressure head gradients from inherently noisy field-measured θ and h data. The gravity drainage analysis also provides results over a much broader range of hydraulic conductivity values than is possible with the more standard instantaneous profile analysis, especially when augmented with independently measured soil water retention data.
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
Directory of Open Access Journals (Sweden)
A. P. Tran
2017-09-01
Full Text Available 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
Estimation of radioecological parameters of soil samples from a phosphatic area
Directory of Open Access Journals (Sweden)
Harb Shaaban
2016-01-01
Full Text Available The activity concentrations of natural radionuclides (226Ra, 232Th, and 40K for a set of 31 agricultural soil samples from the Nile River banks in the area of El-Sebaiya city, Aswan Governorate, Egypt were measured by gamma-spectrometry. The study revealed that the average activity concentrations of natural radionuclides 226Ra, 232Th, and 40K were 23.2 ± 2.8Bq/kg, 21.1 ± 2.8 Bq/kg, and 218.6 ± 3.7 Bq/kg, respectively. The obtained results of the activity concentrations are within the range of values reported for neighbouring areas in Egypt. The values obtained for the hazard indices and the representative level index in all sampling sites were lower than unity, showing that there is no significant risk arising from the exposure to the soil in the studied area. The absorbed dose rate and annual effective dose in air outdoors and indoors were calculated from 226Ra, 232Th, and 40K in soil, the average values being 32.64 nGy/h, 40.06 µSv, and 160.25 µSv, respectively. The absorbed dose rate at the eastof El-Sebaiya city is higher than that obtained for the west because of higher concentrations of tri-calcium phosphate in the soil. The studied area is not significantly affected by the industrial activities, except for a few isolated spots.
Directory of Open Access Journals (Sweden)
Kołodziejczyk Katarzyna
2017-01-01
Full Text Available An analysis of the usefulness of two parameter calculation methods (N and k parameters for the Nash Model was performed to transform effective rainfall into discharge based on two rainfall episodes gauged at the Kostrze gauging station as well as urban development data for the city of Cracow for 2014 and data obtained from a soil and agriculture map. The methods were the Rao et al. method and the Bajkiewicz-Grabowska method for regression relationships between instantaneous unit hydrograph model parameters and the physiographic parameters of a catchment. Effective rainfall was calculated for each rainfall episode using the SCS-CN method. A direct discharge hydrograph was calculated based on an effective rainfall hyetograph and using the Nash Model. Research has found that both studied methods yield comparable results, which indicates that both methods of effective rainfall transformation into discharge are useful. In addition, it has been shown that the impact of the Nash Model parameter estimation method on discharge hydrographs is minimal.
Directory of Open Access Journals (Sweden)
Azza Gorrab
2015-01-01
Full Text Available The aim of this paper is to analyze the potential of X-band SAR measurements (COSMO-SkyMed and TerraSAR-X made over bare soils for the estimation of soil moisture and surface geometry parameters at a semi-arid site in Tunisia (North Africa. Radar signals acquired with different configurations (HH and VV polarizations, incidence angles of 26° and 36° are statistically compared with ground measurements (soil moisture and roughness parameters. The radar measurements are found to be highly sensitive to the various soil parameters of interest. A linear relationship is determined for the radar signals as a function of volumetric soil moisture, and a logarithmic correlation is observed between the radar signals and three surface roughness parameters: the root mean square height (Hrms, the parameter Zs = Hrms2/l (where l is the correlation length and the parameter Zg = Hrms × (Hrms/lα (where α is the power of the surface height correlation function. The highest dynamic sensitivity is observed for Zg at high incidence angles. Finally, the performance of different physical and semi-empirical backscattering models (IEM, Baghdadi-calibrated IEM and Dubois models is compared with SAR measurements. The results provide an indication of the limits of validity of the IEM and Dubois models, for various radar configurations and roughness conditions. Considerable improvements in the IEM model performance are observed using the Baghdadi-calibrated version of this model.
Soil Characterization at the Linde FUSRAP Site and the Impact on Soil Volume Estimates
International Nuclear Information System (INIS)
Boyle, J.; Kenna, T.; Pilon, R.
2002-01-01
The former Linde site in Tonawanda, New York is currently undergoing active remediation of Manhattan Engineering District's radiological contamination. This remediation is authorized under the Formerly Utilized Sites Remedial Action Program (FUSRAP). The focus of this paper will be to describe the impact of soil characterization efforts as they relate to soil volume estimates and project cost estimates. An additional objective is to stimulate discussion about other characterization and modeling technologies, and to provide a ''Lessons Learned'' scenario to assist in future volume estimating at other FUSRAP sites. Initial soil characterization efforts at the Linde FUSRAP site in areas known to be contaminated or suspected to be contaminated were presented in the Remedial Investigation Report for the Tonawanda Site, dated February 1993. Results of those initial characterization efforts were the basis for soil volume estimates that were used to estimate and negotiate the current remediation contract. During the course of remediation, previously unidentified areas of contamination were discovered, and additional characterization was initiated. Additional test pit and geoprobe samples were obtained at over 500 locations, bringing the total to over 800 sample locations at the 135-acre site. New data continues to be collected on a routine basis during ongoing remedial actions
Effect of soil parameters on uranium availability to ryegrass
International Nuclear Information System (INIS)
Vandenhove, H.; Van Hees, M.; Wannijn, J.; Wang, L.
2004-01-01
When wishing to assess the impact of radioactive contamination on biota or on an ecosystem, knowledge on the physico-chemical conditions governing the radionuclide availability and speciation in the exposure medium and hence its bioavailability and incorporation is indispensable. The present study explores the dominant soil factors (18 soils collected under pasture) ruling uranium mobility and availability to ryegrass and intents to define and assess the extent of the effect. The soils were selected such that they covered a wide range for those parameters hypothesized as being potentially important in determining U-availability (pH, clay content, Fe and Al oxide and hydroxide content, CaCO 3 , organic carbon). Statistical analysis showed that there were no single soil parameters significantly explaining the uranium concentration in the soil solution, nor the uranium concentration in the plants. Soil pH and iron-oxi-hydroxides explained for 60 % the uranium concentration found in the soil solution (which varied with factor 100). Plant U-concentration was mostly affected by the concentration of U in the soil solution, pH and total inorganic carbon content (R 2 =0.71). Observed U-uptake was highest when pH was below 5.3 or around 7 or higher. The next step was to assess the uranium speciation in the soil solution with a Geochemical Speciation Model. Uranium speciation was found important in explaining the U-uptake observed: apparently, uranyl, UO 2 CO 3 -2 and (UO 2 ) 2 CO 3 (OH) 3 - were the U-species being preferentially transported. (author)
Soil Moisture Estimation Using MODIS Images (Case Study: Mashhad Plain Area
Directory of Open Access Journals (Sweden)
M. Fashaee
2016-09-01
Full Text Available Introduction: Numerous studies have been undertaken based on satellite imagery in order to estimate soil moisture using vegetation indices such as NDVI. Previous studies suffer from a restriction; these indices are not able to estimate where the vegetative coverage is low or where no vegetation exists. Hence, it is essential to develop a model which can overcome this restriction. Focus of this research is on estimation of soil moisture for low or scattered vegetative land covers. Trapezoidal temperature-vegetation (Ts~VI model is able to consider the status of soil moisture and vegetation condition. It can estimate plant water deficit for weak or no vegetation land cover. Materials and Methods: Moran proposed Water Deficit Index (WDI for evaluating field evapotranspiration rates and relative field water deficit for both full-cover and partially vegetated sites. The theoretical basis of this method is based on the energy balance equation. Penman-Monteith equation of energy balance was used to calculate the coordinates of the four vertices of the temperature-vegetation trapezoid also for four different extreme combinations of temperature and vegetation. For the (Ts−Ta~Vc trapezoid, four vertices correspond to 1 well-watered full-cover vegetation, 2 water-stressed full-cover vegetation, 3 saturated bare soil, and 4 dry bare soil. WDI is equal to 0 for well-watered conditions and equals to 1 for maximum stress conditions. As suggested by Moran et al. to draw a trapezoidal shape, some field measurements are required such as wind speed at the height of 2 meters, air pressure, mean daily temperature, vapor pressure-temperature curve slope, Psychrometrics constant, vapor pressure at mean temperature, vapor pressure deficit, external radiation, solar radiation of short wavelength, longwave radiation, net radiation, soil heat flux and air aerodynamic resistance is included. Crop vegetation and canopy resistance should be measured or estimated. The study
Tshikeba Kabantu, Martin; Muamba Tshimanga, Raphael; Onema Kileshye, Jean Marie; Gumindoga, Webster; Tshimpampa Beya, Jules
2018-05-01
Soil erosion has detrimental impacts on socio economic life, thus increasing poverty. This situation is aggravated by poor planning and lack of infrastructure especially in developing countries. In these countries, efforts to planning are challenged by lack of data. Alternative approaches that use remote sensing and geographical information systems are therefore needed to provide decision makers with the so much needed information for planning purposes. This helps to curb the detrimental impacts of soil erosion, mostly emanating from varied land use conditions. This study was carried out in the city of Kinshasa, the Democratic Republic of Congo with the aim of using alternative sources of data, based on earth observation resources, to determine the spatial distribution of soil loss and erosion hazard in the city of Kinshasa. A combined approach based on remote sensing skills and rational equation of soil erosion estimation was used. Soil erosion factors, including rainfall-runoff erosivity R), soil erodibility (K), slope steepness and length (SL), crop/vegetation and management (C) were calculated for the city of Kinshasa. Results show that soil loss in Kinshasa ranges from 0 to 20 t ha-1 yr-1. Most of the south part of the urban area were prone to erosion. From the total area of Kinshasa (996 500 ha), 25 013 ha (2.3 %) is of very high ( > 15 t ha-1 yr-1) risk of soil erosion. Urban areas consist of 4.3 % of the area with very high ( > 15 t ha-1 yr-1) risk of soil erosion compared to a very high risk of 2.3 % ( > 15 t ha-1 yr-1) in the rural area. The study shows that the soil loss in the study area is mostly driven by slope, elevation, and informal settlements.
International Nuclear Information System (INIS)
Owens, P.N.; Walling, D.E.
1988-01-01
A numerical mass-balance model is developed which can be used to estimate rates of soil redistribution on uncultivated land from measurements of bombderived 137 Cs inventories. The model uses a budgeting approach, which takes account of temporal variations in atmospheric fallout of 137 Cs, radioactive decay, and net gains or losses of 137 Cs due to erosion and deposition processes, combined with parameters which describe internal 137 Cs redistribution processes, to estimate the 137 Cs content of topsoil and the 137 Cs inventory at specific points, from the start of 137 Cs fallout in the 1950s to the present day. The model is also able to account for potential differences in particle size composition and organic matter content between mobilised soil particles and the original soil, and the effect that these may have on 137 Cs concentrations and inventories. By running the model for a range of soil erosion and deposition rates, a calibration relationship can be constructed which relates the 137 Cs inventory at a sampling point to the average net soil loss or gain at that location. In addition to the magnitude and temporal distribution of the 137 Cs atmospheric fallout flux, the soil redistribution rates estimated by the model are sensitive to parameters which describe the relative texture and organic matter content of the eroded or deposited material, and the ability of the soil to retain 137 Cs in the upper part of the soil profile. (Copyright (c) 1988 Elsevier Science B.V., Amsterdam. All rights reserved.)
Timmermans, Joris; Gomez-Dans, Jose; Lewis, Philip; Loew, Alexander; Schlenz, Florian
2017-04-01
The large amount of remote sensing data nowadays available provides a huge potential for monitoring crop development, drought conditions and water efficiency. This potential however not been realized yet because algorithms for land surface parameter retrieval mostly use data from only a single sensor. Consequently products that combine different low-level observations from different sensors are hard to find. The lack of synergistic retrieval is caused because it is easier to focus on single sensor types/footprints and temporal observation times, than to find a way to compensate for differences. Different sensor types (microwave/optical) require different radiative transfer (RT) models and also require consistency between the models to have any impact on the retrieval of soil moisture by a microwave instrument. Varying spatial footprints require first proper collocation of the data before one can scale between different resolutions. Considering these problems, merging optical and microwave observations have not been performed yet. The goal of this research was to investigate the potential of integrating optical and microwave RT models within the Earth Observation Land Data Assimilation System (EOLDAS) synergistically to derive biophysical parameters. This system uses a Bayesian data assimilation approach together with observation operators such as the PROSAIL model to estimate land surface parameters. For the purpose of enabling the system to integrate passive microwave radiation (from an ELBARRA II passive microwave radiometer), the Community Microwave Emission Model (CMEM) RT-model, was integrated within the EOLDAS system. In order to quantify the potential, a variety of land surface parameters was chosen to be retrieved from the system, in particular variables that a) impact only optical RT (such as leaf water content and leaf dry matter), b) only impact the microwave RT (such as soil moisture and soil temperature), and c) Leaf Area Index (LAI) that impacts both
Precipitation Estimation Using L-Band and C-Band Soil Moisture Retrievals
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.
DEFF Research Database (Denmark)
Eden, Marie; Møldrup, Per; Schjønning, Per
2012-01-01
Measurements of diffusive and convective gas transport parameters can be used to describe soil functional architecture and reveal key factors for soil structure development. Undisturbed 100-cm(3) soil samples were sampled at the Long-term Research on Agricultural Systems experiment located...... displayed markedly lower D-P/D-0 values at similar air-filled porosity, illustrating soil structure effects on D-P/D-0. The Currie tortuosity-connectivity parameter, X=Log(D-P/D-0)/Log(epsilon), decreased with increasing bulk density in the intact samples at both moisture conditions, suggesting less...
State Estimation-based Transmission line parameter identification
Directory of Open Access Journals (Sweden)
Fredy Andrés Olarte Dussán
2010-01-01
Full Text Available This article presents two state-estimation-based algorithms for identifying transmission line parameters. The identification technique used simultaneous state-parameter estimation on an artificial power system composed of several copies of the same transmission line, using measurements at different points in time. The first algorithm used active and reactive power measurements at both ends of the line. The second method used synchronised phasor voltage and current measurements at both ends. The algorithms were tested in simulated conditions on the 30-node IEEE test system. All line parameters for this system were estimated with errors below 1%.
International Nuclear Information System (INIS)
Yang Hao; Zhao Qiguo; Du Mingyuan; Minami, Katsuyuki; Hatta, Tamao
2000-01-01
The radioisotope 137 Cs has been widely used to determine rates of cultivated soil loss, Many calibration relationships (including both empirical relationships and theoretical models) have been employed to estimate erosion rates from the amount of 137 Cs lost from the cultivated soil profile. However, there are important limitations which restrict the reliability of these models, which consider only the uniform distribution of 137 Cs in the plough layer and the depth. As a result, erosion rates they may be overestimated or underestimated. This article presents a quantitative model for the relation the amount of 137 Cs lost from the cultivate soil profile and the rate of soil erosion. According to a mass balance model, during the construction of this model we considered the following parameters: the remaining fraction of the surface enrichment layer (F R ), the thickness of the surface enrichment layer (H s ), the depth of the plough layer (H p ), input fraction of the total 137 Cs fallout deposition during a given year t (F t ), radioactive decay of 137 Cs (k), and sampling year (t). The simulation results showed that the amounts of erosion rates estimated using this model were very sensitive to changes in the values of the parameters F R , H s , and H p . We also observed that the relationship between the rate of soil loss and 137 Cs depletion is neither linear nor logarithmic, and is very complex. Although the model is an improvement over existing approaches to derive calibration relationships for cultivated soil, it requires empirical information on local soil properties and the behavior of 137 Cs in the soil profile. There is clearly still a need for more precise information on the latter aspect and, in particular, on the retention of 137 Cs fallout in the top few millimeters of the soil profile and on the enrichment and depletion effects associated with soil redistribution (i.e. for determining accurate values of F R and H s ). (author)
Soil TPH Concentration Estimation Using Vegetation Indices in an Oil Polluted Area of Eastern China
Zhu, Linhai; Zhao, Xuechun; Lai, Liming; Wang, Jianjian; Jiang, Lianhe; Ding, Jinzhi; Liu, Nanxi; Yu, Yunjiang; Li, Junsheng; Xiao, Nengwen; Zheng, Yuanrun; Rimmington, Glyn M.
2013-01-01
Assessing oil pollution using traditional field-based methods over large areas is difficult and expensive. Remote sensing technologies with good spatial and temporal coverage might provide an alternative for monitoring oil pollution by recording the spectral signals of plants growing in polluted soils. Total petroleum hydrocarbon concentrations of soils and the hyperspectral canopy reflectance were measured in wetlands dominated by reeds (Phragmites australis) around oil wells that have been producing oil for approximately 10 years in the Yellow River Delta, eastern China to evaluate the potential of vegetation indices and red edge parameters to estimate soil oil pollution. The detrimental effect of oil pollution on reed communities was confirmed by the evidence that the aboveground biomass decreased from 1076.5 g m−2 to 5.3 g m−2 with increasing total petroleum hydrocarbon concentrations ranging from 9.45 mg kg−1 to 652 mg kg−1. The modified chlorophyll absorption ratio index (MCARI) best estimated soil TPH concentration among 20 vegetation indices. The linear model involving MCARI had the highest coefficient of determination (R 2 = 0.73) and accuracy of prediction (RMSE = 104.2 mg kg−1). For other vegetation indices and red edge parameters, the R2 and RMSE values ranged from 0.64 to 0.71 and from 120.2 mg kg−1 to 106.8 mg kg−1 respectively. The traditional broadband normalized difference vegetation index (NDVI), one of the broadband multispectral vegetation indices (BMVIs), produced a prediction (R 2 = 0.70 and RMSE = 110.1 mg kg−1) similar to that of MCARI. These results corroborated the potential of remote sensing for assessing soil oil pollution in large areas. Traditional BMVIs are still of great value in monitoring soil oil pollution when hyperspectral data are unavailable. PMID:23342066
Di Prima, Simone; Bagarello, Vincenzo; Iovino, Massimo
2017-04-01
Simple infiltration experiments carried out in the field allow an easy and inexpensive way of characterizing soil hydraulic behavior, maintaining the functional connection of the sampled soil volume with the surrounding soil. The beerkan method consists of a three-dimensional (3D) infiltration experiment at zero pressure head (Haverkamp et al., 1996). It uses a simple annular ring inserted to a depth of about 0.01 m to avoid lateral loss of the ponded water. Soil disturbance is minimized by the limited ring insertion depth. Infiltration time of small volumes of water repeatedly poured on the confined soil are measured to determine the cumulative infiltration. Different algorithms based on this methodology (the so-called BEST family of algorithms) were developed for the determination of soil hydraulic characteristic parameters (Bagarello et al., 2014a; Lassabatere et al., 2006; Yilmaz et al., 2010). Recently, Bagarello et al. (2014b) developed a Simplified method based on a Beerkan Infiltration run (SBI method) to determine saturated soil hydraulic conductivity, Ks, by only the transient phase of a beerkan infiltration run and an estimate of the α* parameter, expressing the relative importance of gravity and capillary forces during an infiltration process (Reynolds and Elrick, 1990). However, several problems yet arise with the existing BEST-algorithms and the SBI method, including (i) the need of supplementary field and laboratory measurements (Bagarello et al., 2013); (ii) the difficulty to detect a linear relationship between I / √t and √t in the early stage of the infiltration process (Bagarello et al., 2014b); (iii) estimation of negative Ks values for hydrophobic soils (Di Prima et al., 2016). In this investigation, a new Simplified method based on the analysis of the Steady-state Beerkan Infiltration run (SSBI method) was proposed and tested. In particular, analytical data were generated to simulate beerkan infiltration experiments for six contrasting
An Innovative Method for Estimating Soil Retention at a ...
Planning for a sustainable future should include an accounting of services currently provided by ecosystems such as erosion control. Retention of soil improves fertility, increases water retention, and decreases sedimentation in streams and rivers. Landscapes patterns that facilitate these services could help reduce costs for flood control, dredging of reservoirs and waterways, while maintaining habitat for fish and other species important to recreational and tourism industries. Landscape scale geospatial data available for the continental United States was leveraged to estimate sediment erosion (RUSLE-based, Renard, et al. 1997) employing recent geospatial techniques of sediment delivery ratio (SDR) estimation (Cavalli, et al. 2013). The approach was designed to derive a quantitative approximation of the ecological services provided by vegetative cover, management practices, and other surface features with respect to protecting soils from the erosion processes of detachment, transport, and deposition. Quantities of soil retained on the landscape and potential erosion for multiple land cover scenarios relative to current (NLCD 2011) conditions were calculated for each calendar month, and summed to yield annual estimations at a 30-meter grid cell. Continental-scale data used included MODIS NDVI data (2000-2014) to estimate monthly USLE C-factors, gridded soil survey geographic (gSSURGO) soils data (annual USLE K factor), PRISM rainfall data (monthly USLE
Directory of Open Access Journals (Sweden)
Matías Ernesto Barber
2016-06-01
Full Text Available The spatial sampling interval, as related to the ability to digitize a soil profile with a certain number of features per unit length, depends on the profiling technique itself. From a variety of profiling techniques, roughness parameters are estimated at different sampling intervals. Since soil profiles have continuous spectral components, it is clear that roughness parameters are influenced by the sampling interval of the measurement device employed. In this work, we contributed to answer which sampling interval the profiles needed to be measured at to accurately account for the microwave response of agricultural surfaces. For this purpose, a 2-D laser profiler was built and used to measure surface soil roughness at field scale over agricultural sites in Argentina. Sampling intervals ranged from large (50 mm to small ones (1 mm, with several intermediate values. Large- and intermediate-sampling-interval profiles were synthetically derived from nominal, 1 mm ones. With these data, the effect of sampling-interval-dependent roughness parameters on backscatter response was assessed using the theoretical backscatter model IEM2M. Simulations demonstrated that variations of roughness parameters depended on the working wavelength and was less important at L-band than at C- or X-band. In any case, an underestimation of the backscattering coefficient of about 1-4 dB was observed at larger sampling intervals. As a general rule a sampling interval of 15 mm can be recommended for L-band and 5 mm for C-band.
The estimation of soil water fluxes using lysimeter data
Wegehenkel, M.
2009-04-01
The validation of soil water balance models regarding soil water fluxes in the field is still a problem. This requires time series of measured model outputs. In our study, a soil water balance model was validated using lysimeter time series of measured model outputs. The soil water balance model used in our study was the Hydrus-1D-model. This model was tested by a comparison of simulated with measured daily rates of actual evapotranspiration, soil water storage, groundwater recharge and capillary rise. These rates were obtained from twelve weighable lysimeters with three different soils and two different lower boundary conditions for the time period from January 1, 1996 to December 31, 1998. In that period, grass vegetation was grown on all lysimeters. These lysimeters are located in Berlin, Germany. One potential source of error in lysimeter experiments is preferential flow caused by an artificial channeling of water due to the occurrence of air space between the soil monolith and the inside wall of the lysimeters. To analyse such sources of errors, Hydrus-1D was applied with different modelling procedures. The first procedure consists of a general uncalibrated appli-cation of Hydrus-1D. The second one includes a calibration of soil hydraulic parameters via inverse modelling of different percolation events with Hydrus-1D. In the third procedure, the model DUALP_1D was applied with the optimized hydraulic parameter set to test the hy-pothesis of the existence of preferential flow paths in the lysimeters. The results of the different modelling procedures indicated that, in addition to a precise determination of the soil water retention functions, vegetation parameters such as rooting depth should also be taken into account. Without such information, the rooting depth is a calibration parameter. However, in some cases, the uncalibrated application of both models also led to an acceptable fit between measured and simulated model outputs.
Kinetic parameter estimation from attenuated SPECT projection measurements
International Nuclear Information System (INIS)
Reutter, B.W.; Gullberg, G.T.
1998-01-01
Conventional analysis of dynamically acquired nuclear medicine data involves fitting kinetic models to time-activity curves generated from regions of interest defined on a temporal sequence of reconstructed images. However, images reconstructed from the inconsistent projections of a time-varying distribution of radiopharmaceutical acquired by a rotating SPECT system can contain artifacts that lead to biases in the estimated kinetic parameters. To overcome this problem the authors investigated the estimation of kinetic parameters directly from projection data by modeling the data acquisition process. To accomplish this it was necessary to parametrize the spatial and temporal distribution of the radiopharmaceutical within the SPECT field of view. In a simulated transverse slice, kinetic parameters were estimated for simple one compartment models for three myocardial regions of interest, as well as for the liver. Myocardial uptake and washout parameters estimated by conventional analysis of noiseless simulated data had biases ranging between 1--63%. Parameters estimated directly from the noiseless projection data were unbiased as expected, since the model used for fitting was faithful to the simulation. Predicted uncertainties (standard deviations) of the parameters obtained for 500,000 detected events ranged between 2--31% for the myocardial uptake parameters and 2--23% for the myocardial washout parameters
Estimating soil water evaporation using radar measurements
Sadeghi, Ali M.; Scott, H. D.; Waite, W. P.; Asrar, G.
1988-01-01
Field studies were conducted to evaluate the application of radar reflectivity as compared with the shortwave reflectivity (albedo) used in the Idso-Jackson equation for the estimation of daily evaporation under overcast sky and subhumid climatic conditions. Soil water content, water potential, shortwave and radar reflectivity, and soil and air temperatures were monitored during three soil drying cycles. The data from each cycle were used to calculate daily evaporation from the Idso-Jackson equation and from two other standard methods, the modified Penman and plane of zero-flux. All three methods resulted in similar estimates of evaporation under clear sky conditions; however, under overcast sky conditions, evaporation fluxes computed from the Idso-Jackson equation were consistently lower than the other two methods. The shortwave albedo values in the Idso-Jackson equation were then replaced with radar reflectivities and a new set of total daily evaporation fluxes were calculated. This resulted in a significant improvement in computed soil evaporation fluxes from the Idso-Jackson equation, and a better agreement between the three methods under overcast sky conditions.
Soil-related Input Parameters for the Biosphere Model
International Nuclear Information System (INIS)
A. J. Smith
2003-01-01
This analysis is one of the technical reports containing documentation of the Environmental Radiation Model for Yucca Mountain Nevada (ERMYN), a biosphere model supporting the Total System Performance Assessment (TSPA) for the geologic repository at Yucca Mountain. The biosphere model is one of a series of process models supporting the Total System Performance Assessment (TSPA) for the Yucca Mountain repository. A graphical representation of the documentation hierarchy for the ERMYN biosphere model is presented in Figure 1-1. This figure shows the interrelationships among the products (i.e., analysis and model reports) developed for biosphere modeling, and the plan for development of the biosphere abstraction products for TSPA, as identified in the ''Technical Work Plan: for Biosphere Modeling and Expert Support'' (BSC 2003 [163602]). It should be noted that some documents identified in Figure 1-1 may be under development at the time this report is issued and therefore not available. This figure is included to provide an understanding of how this analysis report contributes to biosphere modeling in support of the license application, and is not intended to imply that access to the listed documents is required to understand the contents of this report. This report, ''Soil Related Input Parameters for the Biosphere Model'', is one of the five analysis reports that develop input parameters for use in the ERMYN model. This report is the source documentation for the six biosphere parameters identified in Table 1-1. ''The Biosphere Model Report'' (BSC 2003 [160699]) describes in detail the conceptual model as well as the mathematical model and its input parameters. The purpose of this analysis was to develop the biosphere model parameters needed to evaluate doses from pathways associated with the accumulation and depletion of radionuclides in the soil. These parameters support the calculation of radionuclide concentrations in soil from on-going irrigation and ash
Robust Parameter and Signal Estimation in Induction Motors
DEFF Research Database (Denmark)
Børsting, H.
This thesis deals with theories and methods for robust parameter and signal estimation in induction motors. The project originates in industrial interests concerning sensor-less control of electrical drives. During the work, some general problems concerning estimation of signals and parameters...... in nonlinear systems, have been exposed. The main objectives of this project are: - analysis and application of theories and methods for robust estimation of parameters in a model structure, obtained from knowledge of the physics of the induction motor. - analysis and application of theories and methods...... for robust estimation of the rotor speed and driving torque of the induction motor based only on measurements of stator voltages and currents. Only contimuous-time models have been used, which means that physical related signals and parameters are estimated directly and not indirectly by some discrete...
Soil carbon estimation from eucalyptus grandis using canopy spectra
African Journals Online (AJOL)
Mapping soil fertility parameters, such as soil carbon (C), is fundamentally important for forest management and research related to forest growth and climate change. This study seeks to establish the link between Eucalyptus grandis canopy spectra and soil carbon using raw and continuum-removed spectra. Canopy-level ...
Standard Errors of Estimated Latent Variable Scores with Estimated Structural Parameters
Hoshino, Takahiro; Shigemasu, Kazuo
2008-01-01
The authors propose a concise formula to evaluate the standard error of the estimated latent variable score when the true values of the structural parameters are not known and must be estimated. The formula can be applied to factor scores in factor analysis or ability parameters in item response theory, without bootstrap or Markov chain Monte…
Soil-Related Input Parameters for the Biosphere Model
International Nuclear Information System (INIS)
Smith, A. J.
2004-01-01
This report presents one of the analyses that support the Environmental Radiation Model for Yucca Mountain Nevada (ERMYN). The ''Biosphere Model Report'' (BSC 2004 [DIRS 169460]) describes the details of the conceptual model as well as the mathematical model and the required input parameters. The biosphere model is one of a series of process models supporting the postclosure Total System Performance Assessment (TSPA) for the Yucca Mountain repository. A schematic representation of the documentation flow for the Biosphere input to TSPA is presented in Figure 1-1. This figure shows the evolutionary relationships among the products (i.e., analysis and model reports) developed for biosphere modeling, and the biosphere abstraction products for TSPA, as identified in the ''Technical Work Plan for Biosphere Modeling and Expert Support'' (TWP) (BSC 2004 [DIRS 169573]). This figure is included to provide an understanding of how this analysis report contributes to biosphere modeling in support of the license application, and is not intended to imply that access to the listed documents is required to understand the contents of this report. This report, ''Soil-Related Input Parameters for the Biosphere Model'', is one of the five analysis reports that develop input parameters for use in the ERMYN model. This report is the source documentation for the six biosphere parameters identified in Table 1-1. The purpose of this analysis was to develop the biosphere model parameters associated with the accumulation and depletion of radionuclides in the soil. These parameters support the calculation of radionuclide concentrations in soil from on-going irrigation or ash deposition and, as a direct consequence, radionuclide concentration in other environmental media that are affected by radionuclide concentrations in soil. The analysis was performed in accordance with the TWP (BSC 2004 [DIRS 169573]) where the governing procedure was defined as AP-SIII.9Q, ''Scientific Analyses''. This
DEFF Research Database (Denmark)
Arthur, Emmanuel; Tuller, Markus; Møldrup, Per
2018-01-01
Soil specific surface area (SA) controls fundamental soil processes such as retention of water, ion exchange, and adsorption and release of plant nutrients and contaminants. Conventional methods for determining SA include adsorption of polar or non‐polar fluid molecules with associated advantages...... parameters varied depending on the water activity or relative humidity range of measured data (0.03–0.93 compared with 0.10–0.80), whereas the variation for desorption was minimal. For desorption isotherms, the average water activity value at which the GAB monolayer parameter was obtained was 0......‐based modelling approaches to determine SA. Measured water vapour adsorption and desorption isotherms for 321 soil samples were used to parameterize the GAB model, the Brunauer–Emmet–Teller (BET) equation and a film adsorption Tuller–Or (TO) model to estimate SA. For adsorption isotherms, the values of the GAB...
Huang, Biao; Zhao, Yongcun
2014-01-01
Estimating standard-exceeding probabilities of toxic metals in soil is crucial for environmental evaluation. Because soil pH and land use types have strong effects on the bioavailability of trace metals in soil, they were taken into account by some environmental protection agencies in making composite soil environmental quality standards (SEQSs) that contain multiple metal thresholds under different pH and land use conditions. This study proposed a method for estimating the standard-exceeding probability map of soil cadmium using a composite SEQS. The spatial variability and uncertainty of soil pH and site-specific land use type were incorporated through simulated realizations by sequential Gaussian simulation. A case study was conducted using a sample data set from a 150 km2 area in Wuhan City and the composite SEQS for cadmium, recently set by the State Environmental Protection Administration of China. The method may be useful for evaluating the pollution risks of trace metals in soil with composite SEQSs. PMID:24672364
Directory of Open Access Journals (Sweden)
F. Hossain
2004-01-01
Full Text Available This study presents a simple and efficient scheme for Bayesian estimation of uncertainty in soil moisture simulation by a Land Surface Model (LSM. The scheme is assessed within a Monte Carlo (MC simulation framework based on the Generalized Likelihood Uncertainty Estimation (GLUE methodology. A primary limitation of using the GLUE method is the prohibitive computational burden imposed by uniform random sampling of the model's parameter distributions. Sampling is improved in the proposed scheme by stochastic modeling of the parameters' response surface that recognizes the non-linear deterministic behavior between soil moisture and land surface parameters. Uncertainty in soil moisture simulation (model output is approximated through a Hermite polynomial chaos expansion of normal random variables that represent the model's parameter (model input uncertainty. The unknown coefficients of the polynomial are calculated using limited number of model simulation runs. The calibrated polynomial is then used as a fast-running proxy to the slower-running LSM to predict the degree of representativeness of a randomly sampled model parameter set. An evaluation of the scheme's efficiency in sampling is made through comparison with the fully random MC sampling (the norm for GLUE and the nearest-neighborhood sampling technique. The scheme was able to reduce computational burden of random MC sampling for GLUE in the ranges of 10%-70%. The scheme was also found to be about 10% more efficient than the nearest-neighborhood sampling method in predicting a sampled parameter set's degree of representativeness. The GLUE based on the proposed sampling scheme did not alter the essential features of the uncertainty structure in soil moisture simulation. The scheme can potentially make GLUE uncertainty estimation for any LSM more efficient as it does not impose any additional structural or distributional assumptions.
Energy Technology Data Exchange (ETDEWEB)
Ramirez-Guinart, Oriol; Rigol, Anna; Vidal, Miquel [Analytical Chemistry department, Faculty of Chemistry, University of Barcelona, Mart i Franques 1-11, 08028, Barcelona (Spain)
2014-07-01
In the frame of the revision of the IAEA TRS 364 (Handbook of parameter values for the prediction of radionuclide transfer in temperate environments), a database of radionuclide solid-liquid distribution coefficients (K{sub d}) in soils was compiled with data coming from field and laboratory experiments, from references mostly from 1990 onwards, including data from reports, reviewed papers, and grey literature. The K{sub d} values were grouped for each radionuclide according to two criteria. The first criterion was based on the sand and clay mineral percentages referred to the mineral matter, and the organic matter (OM) content in the soil. This defined the 'texture/OM' criterion. The second criterion was to group soils regarding specific soil factors governing the radionuclide-soil interaction ('cofactor' criterion). The cofactors depended on the radionuclide considered. An advantage of using cofactors was that the variability of K{sub d} ranges for a given soil group decreased considerably compared with that observed when the classification was based solely on sand, clay and organic matter contents. The K{sub d} best estimates were defined as the calculated GM values assuming that K{sub d} values were always log-normally distributed. Risk assessment models may require as input data for a given parameter either a single value (a best estimate) or a continuous function from which not only individual best estimates but also confidence ranges and data variability can be derived. In the case of the K{sub d} parameter, a suitable continuous function which contains the statistical parameters (e.g. arithmetical/geometric mean, arithmetical/geometric standard deviation, mode, etc.) that better explain the distribution among the K{sub d} values of a dataset is the Cumulative Distribution Function (CDF). To our knowledge, appropriate CDFs has not been proposed for radionuclide K{sub d} in soils yet. Therefore, the aim of this works is to create CDFs for
Chaney, Nathaniel W.; Herman, Jonathan D.; Ek, Michael B.; Wood, Eric F.
2016-11-01
With their origins in numerical weather prediction and climate modeling, land surface models aim to accurately partition the surface energy balance. An overlooked challenge in these schemes is the role of model parameter uncertainty, particularly at unmonitored sites. This study provides global parameter estimates for the Noah land surface model using 85 eddy covariance sites in the global FLUXNET network. The at-site parameters are first calibrated using a Latin Hypercube-based ensemble of the most sensitive parameters, determined by the Sobol method, to be the minimum stomatal resistance (rs,min), the Zilitinkevich empirical constant (Czil), and the bare soil evaporation exponent (fxexp). Calibration leads to an increase in the mean Kling-Gupta Efficiency performance metric from 0.54 to 0.71. These calibrated parameter sets are then related to local environmental characteristics using the Extra-Trees machine learning algorithm. The fitted Extra-Trees model is used to map the optimal parameter sets over the globe at a 5 km spatial resolution. The leave-one-out cross validation of the mapped parameters using the Noah land surface model suggests that there is the potential to skillfully relate calibrated model parameter sets to local environmental characteristics. The results demonstrate the potential to use FLUXNET to tune the parameterizations of surface fluxes in land surface models and to provide improved parameter estimates over the globe.
Ecosystem services in grassland associated with biotic and abiotic soil parameters
Eekeren, van N.J.M.; Boer, de Herman; Hanegraaf, M.C.; Bokhorst, J.; Nierop, D.; Bloem, J.; Schouten, T.; Goede, de R.G.M.; Brussaard, L.
2010-01-01
Biotic soil parameters have so far seldom played a role in practical soil assessment and management of grasslands. However, the ongoing reduction of external inputs in agriculture would imply an increasing reliance on ecosystem self-regulating processes. Since soil biota play an important role in
Soil-vegetation-atmosphere transfer modeling
Energy Technology Data Exchange (ETDEWEB)
Ikonen, J P; Sucksdorff, Y [Finnish Environment Agency, Helsinki (Finland)
1997-12-31
In this study the soil/vegetation/atmosphere-model based on the formulation of Deardorff was refined to hour basis and applied to a field in Vihti. The effect of model parameters on model results (energy fluxes, temperatures) was also studied as well as the effect of atmospheric conditions. The estimation of atmospheric conditions on the soil-vegetation system as well as an estimation of the effect of vegetation parameters on the atmospheric climate was estimated. Areal surface fluxes, temperatures and moistures were also modelled for some river basins in southern Finland. Land-use and soil parameterisation was developed to include properties and yearly variation of all vegetation and soil types. One classification was selected to describe the hydrothermal properties of the soils. Evapotranspiration was verified against the water balance method
Soil-vegetation-atmosphere transfer modeling
Energy Technology Data Exchange (ETDEWEB)
Ikonen, J.P.; Sucksdorff, Y. [Finnish Environment Agency, Helsinki (Finland)
1996-12-31
In this study the soil/vegetation/atmosphere-model based on the formulation of Deardorff was refined to hour basis and applied to a field in Vihti. The effect of model parameters on model results (energy fluxes, temperatures) was also studied as well as the effect of atmospheric conditions. The estimation of atmospheric conditions on the soil-vegetation system as well as an estimation of the effect of vegetation parameters on the atmospheric climate was estimated. Areal surface fluxes, temperatures and moistures were also modelled for some river basins in southern Finland. Land-use and soil parameterisation was developed to include properties and yearly variation of all vegetation and soil types. One classification was selected to describe the hydrothermal properties of the soils. Evapotranspiration was verified against the water balance method
Evapotranspiration Estimates for a Stochastic Soil-Moisture Model
Chaleeraktrakoon, Chavalit; Somsakun, Somrit
2009-03-01
Potential evapotranspiration is information that is necessary for applying a widely used stochastic model of soil moisture (I. Rodriguez Iturbe, A. Porporato, L. Ridolfi, V. Isham and D. R. Cox, Probabilistic modelling of water balance at a point: The role of climate, soil and vegetation, Proc. Roy. Soc. London A455 (1999) 3789-3805). An objective of the present paper is thus to find a proper estimate of the evapotranspiration for the stochastic model. This estimate is obtained by comparing the calculated soil-moisture distribution resulting from various techniques, such as Thornthwaite, Makkink, Jensen-Haise, FAO Modified Penman, and Blaney-Criddle, with an observed one. The comparison results using five sequences of daily soil-moisture for a dry season from November 2003 to April 2004 (Udornthani Province, Thailand) have indicated that all methods can be used if the weather information required is available. This is because their soil-moisture distributions are alike. In addition, the model is shown to have its ability in approximately describing the phenomenon at a weekly or biweekly time scale which is desirable for agricultural engineering applications.
Parameter estimation and inverse problems
Aster, Richard C; Thurber, Clifford H
2005-01-01
Parameter Estimation and Inverse Problems primarily serves as a textbook for advanced undergraduate and introductory graduate courses. Class notes have been developed and reside on the World Wide Web for faciliting use and feedback by teaching colleagues. The authors'' treatment promotes an understanding of fundamental and practical issus associated with parameter fitting and inverse problems including basic theory of inverse problems, statistical issues, computational issues, and an understanding of how to analyze the success and limitations of solutions to these probles. The text is also a practical resource for general students and professional researchers, where techniques and concepts can be readily picked up on a chapter-by-chapter basis.Parameter Estimation and Inverse Problems is structured around a course at New Mexico Tech and is designed to be accessible to typical graduate students in the physical sciences who may not have an extensive mathematical background. It is accompanied by a Web site that...
A PEDOTRANSFER FUNCTION FOR ESTIMATING THE SOIL ERODIBILITY FACTOR IN SICILY
Directory of Open Access Journals (Sweden)
Vincenzo Bagarello
2009-09-01
Full Text Available The soil erodibility factor, K, of the Universal Soil Loss Equation (USLE is a simple descriptor of the soil susceptibility to rill and interrill erosion. The original procedure for determining K needs a knowledge of soil particle size distribution (PSD, soil organic matter, OM, content, and soil structure and permeability characteristics. However, OM data are often missing and soil structure and permeability are not easily evaluated in regional analyses. The objective of this investigation was to develop a pedotransfer function (PTF for estimating the K factor of the USLE in Sicily (south Italy using only soil textural data. The nomograph soil erodibility factor and its associated first approximation, K’, were determined at 471 sampling points distributed throughout the island of Sicily. Two existing relationships for estimating K on the basis of the measured geometric mean particle diameter were initially tested. Then, two alternative PTFs for estimating K’ and K, respectively, on the basis of the measured PSD were derived. Testing analysis showed that the K estimate by the proposed PTF (eq.11, which was characterized by a Nash-Suttcliffe efficiency index, NSEI, varying between 0.68 and 0.76, depending on the considered data set, was appreciably more accurate than the one obtained by other existing equations, yielding NSEI values varying between 0.21 and 0.32.
Pollen parameters estimates of genetic variability among newly ...
African Journals Online (AJOL)
Pollen parameters estimates of genetic variability among newly selected Nigerian roselle (Hibiscus sabdariffa L.) genotypes. ... Estimates of some pollen parameters where used to assess the genetic diversity among ... HOW TO USE AJOL.
Energy Technology Data Exchange (ETDEWEB)
Haverland, R. L.; Post, D. F.; Cooper, L. R.; Shirley, E. D.
1985-07-01
Particle -size distribution and plant available water are basic input to studies of range, forest and cultivated land. Since the conventional laboratory procedures for determining these parameters are time consuming, an improved method for making these measurements is desirable. Weiss and Frock (1976) reported results from an instrument employing the principle of laser light scattering to measure particle -size distribution. The instrument was reported to be of high precision, and yielded reproducible results. The laser light- scattering instrument used in this study is the Microtrac Particle -size Analyzer Model 7991- 0, manufactured by Leeds and Northrup. The particle -size analysis range of this model is from 1.9 to 176 μm, which does not correspond to the entire fine earth fraction (< 2 mm) usually characterized by soil scientists. It is, therefore, desirable to develop predictive equations to estimate the soil texture of the fine earth fraction. We believe data from this instrument could be used to predict other soil properties. This paper reports on using Microtrac data to estimate the plant available water holding capacity and soil texture of Arizona soils. Two hundred and forty-seven Arizona soils were used in this study. Most of these soils (approximately 230 soils) are thermic or hyperthermic and arid or semiarid soils of dominantly mixed mineralogy, as described on the Arizona General Soils Map (Jay et al., 1975). An array of soil horizons are included, with approximately one half of the samples coming from the A or Ap surface horizons. The other half of the samples are from the subsurface B or C horizons.
A Comparative Study of Distribution System Parameter Estimation Methods
Energy Technology Data Exchange (ETDEWEB)
Sun, Yannan; Williams, Tess L.; Gourisetti, Sri Nikhil Gup
2016-07-17
In this paper, we compare two parameter estimation methods for distribution systems: residual sensitivity analysis and state-vector augmentation with a Kalman filter. These two methods were originally proposed for transmission systems, and are still the most commonly used methods for parameter estimation. Distribution systems have much lower measurement redundancy than transmission systems. Therefore, estimating parameters is much more difficult. To increase the robustness of parameter estimation, the two methods are applied with combined measurement snapshots (measurement sets taken at different points in time), so that the redundancy for computing the parameter values is increased. The advantages and disadvantages of both methods are discussed. The results of this paper show that state-vector augmentation is a better approach for parameter estimation in distribution systems. Simulation studies are done on a modified version of IEEE 13-Node Test Feeder with varying levels of measurement noise and non-zero error in the other system model parameters.
Multi-Parameter Estimation for Orthorhombic Media
Masmoudi, Nabil
2015-08-19
Building reliable anisotropy models is crucial in seismic modeling, imaging and full waveform inversion. However, estimating anisotropy parameters is often hampered by the trade off between inhomogeneity and anisotropy. For instance, one way to estimate the anisotropy parameters is to relate them analytically to traveltimes, which is challenging in inhomogeneous media. Using perturbation theory, we develop travel-time approximations for orthorhombic media as explicit functions of the anellipticity parameters η1, η2 and a parameter Δγ in inhomogeneous background media. Specifically, our expansion assumes inhomogeneous ellipsoidal anisotropic background model, which can be obtained from well information and stacking velocity analysis. This approach has two main advantages: in one hand, it provides a computationally efficient tool to solve the orthorhombic eikonal equation, on the other hand, it provides a mechanism to scan for the best fitting anisotropy parameters without the need for repetitive modeling of traveltimes, because the coefficients of the traveltime expansion are independent of the perturbed parameters. Furthermore, the coefficients of the traveltime expansion provide insights on the sensitivity of the traveltime with respect to the perturbed parameters. We show the accuracy of the traveltime approximations as well as an approach for multi-parameter scanning in orthorhombic media.
Multi-Parameter Estimation for Orthorhombic Media
Masmoudi, Nabil; Alkhalifah, Tariq Ali
2015-01-01
Building reliable anisotropy models is crucial in seismic modeling, imaging and full waveform inversion. However, estimating anisotropy parameters is often hampered by the trade off between inhomogeneity and anisotropy. For instance, one way to estimate the anisotropy parameters is to relate them analytically to traveltimes, which is challenging in inhomogeneous media. Using perturbation theory, we develop travel-time approximations for orthorhombic media as explicit functions of the anellipticity parameters η1, η2 and a parameter Δγ in inhomogeneous background media. Specifically, our expansion assumes inhomogeneous ellipsoidal anisotropic background model, which can be obtained from well information and stacking velocity analysis. This approach has two main advantages: in one hand, it provides a computationally efficient tool to solve the orthorhombic eikonal equation, on the other hand, it provides a mechanism to scan for the best fitting anisotropy parameters without the need for repetitive modeling of traveltimes, because the coefficients of the traveltime expansion are independent of the perturbed parameters. Furthermore, the coefficients of the traveltime expansion provide insights on the sensitivity of the traveltime with respect to the perturbed parameters. We show the accuracy of the traveltime approximations as well as an approach for multi-parameter scanning in orthorhombic media.
Evaluation of design parameters in soil-structure systems through artificial intelligence
International Nuclear Information System (INIS)
Cremonini, M.G.; Vardanega, C.; Parvis, E.
1989-01-01
This study refers to development of an artificial intelligence tool to evaluate design parameters for a soil-structure system as the foundations of Class 1 buildings of a nuclear power plant (NPP). This is based on an expert analysis of a large amount of information, collected during a comprehensive program of site investigations and laboratory tests and stored on a computer data-bank. The methodology comprises the following steps: organization of the available information on the site characteristics in a data-base; implementation and extensive use of a specific knowledge based expert system (KBES) devoted to both the analysis, interpretation and check of the information in the data-base, and to the evaluation of the design parameters; determination of effective access criteria to the data-base, for purposes of reordering the information and extracting design properties from a large number of experimental data; development of design profiles for both index properties and strength/strain parameters; and final evaluation of the design parameters. Results are obtained in the form of: local and general site stratigraphy; summarized soil index properties, detailing the site setting; static and dynamic stress-strain parameters, G/G max behavior and damping factors; condolidation parameters and OCR ratio; spatial distribution of parameters on site area; identification of specific local conditions; and cross correlation of parameters, thus covering the whole range of design parameters for NPP soil-structure systems
FRANCHINI, J. C.; CRISPINO, C. C.; SOUZA, R. A.; TORRES, E.; HUNGRIA, M.
2006-01-01
Metadata only record This article attempts to recognize soil parameters that can be used to monitor soil quality under different crop and soil management systems. The rates of CO2 emissions (soil respiration) were affected by variations in the sampling period, as well as in soil management and crop rotation. Considering all samples, CO2 emissions were 21% greater in conventional tillage. Soil microbial biomass was also influenced by sampling period and soil management, but not by crop rota...
A METHOD USING GNSS LH-REFLECTED SIGNALS FOR SOIL ROUGHNESS ESTIMATION
Directory of Open Access Journals (Sweden)
Y. Jia
2018-04-01
Full Text Available Global Navigation Satellite System Reflectometry (GNSS-R is based on the concept of receiving GPS signals reflected by the ground using a passive receiver. The receiver can be on the ground or installed on a small aircraft or UAV and collects the electromagnetic field scattered from the surface of the Earth. The received signals are then analyzed to determine the characteristics of the surface. Many research has been reported showing the capability of the GNSS-R technique. However, the roughness of the surface impacts the phase and amplitude of the received signals, which is still a worthwhile study. This paper presented a method can be used by GNSS-R to estimate the surface roughness. First, the data was calculated in the specular reflection with the assumption of a flat surface with different permittivity. Since the power reflectivity can be evaluated as the ratio of left-hand (LH reflected signal to the direct right-hand (RH signal. Then a semi-empirical roughness model was applied to the data for testing. The results showed the method can distinguish the water and the soil surface. The sensitivity of the parameters was also analyzed. It indicates this method for soil roughness estimation can be used by GNSS-R LH reflected signals. In the next step, several experiments need to be done for improving the model and exploring the way of the estimation.
a Method Using Gnss Lh-Reflected Signals for Soil Roughness Estimation
Jia, Y.; Li, W.; Chen, Y.; Lv, H.; Pei, Y.
2018-04-01
Global Navigation Satellite System Reflectometry (GNSS-R) is based on the concept of receiving GPS signals reflected by the ground using a passive receiver. The receiver can be on the ground or installed on a small aircraft or UAV and collects the electromagnetic field scattered from the surface of the Earth. The received signals are then analyzed to determine the characteristics of the surface. Many research has been reported showing the capability of the GNSS-R technique. However, the roughness of the surface impacts the phase and amplitude of the received signals, which is still a worthwhile study. This paper presented a method can be used by GNSS-R to estimate the surface roughness. First, the data was calculated in the specular reflection with the assumption of a flat surface with different permittivity. Since the power reflectivity can be evaluated as the ratio of left-hand (LH) reflected signal to the direct right-hand (RH) signal. Then a semi-empirical roughness model was applied to the data for testing. The results showed the method can distinguish the water and the soil surface. The sensitivity of the parameters was also analyzed. It indicates this method for soil roughness estimation can be used by GNSS-R LH reflected signals. In the next step, several experiments need to be done for improving the model and exploring the way of the estimation.
Parameter and State Estimator for State Space Models
Directory of Open Access Journals (Sweden)
Ruifeng Ding
2014-01-01
Full Text Available This paper proposes a parameter and state estimator for canonical state space systems from measured input-output data. The key is to solve the system state from the state equation and to substitute it into the output equation, eliminating the state variables, and the resulting equation contains only the system inputs and outputs, and to derive a least squares parameter identification algorithm. Furthermore, the system states are computed from the estimated parameters and the input-output data. Convergence analysis using the martingale convergence theorem indicates that the parameter estimates converge to their true values. Finally, an illustrative example is provided to show that the proposed algorithm is effective.
Soil-Related Input Parameters for the Biosphere Model
Energy Technology Data Exchange (ETDEWEB)
A. J. Smith
2004-09-09
This report presents one of the analyses that support the Environmental Radiation Model for Yucca Mountain Nevada (ERMYN). The ''Biosphere Model Report'' (BSC 2004 [DIRS 169460]) describes the details of the conceptual model as well as the mathematical model and the required input parameters. The biosphere model is one of a series of process models supporting the postclosure Total System Performance Assessment (TSPA) for the Yucca Mountain repository. A schematic representation of the documentation flow for the Biosphere input to TSPA is presented in Figure 1-1. This figure shows the evolutionary relationships among the products (i.e., analysis and model reports) developed for biosphere modeling, and the biosphere abstraction products for TSPA, as identified in the ''Technical Work Plan for Biosphere Modeling and Expert Support'' (TWP) (BSC 2004 [DIRS 169573]). This figure is included to provide an understanding of how this analysis report contributes to biosphere modeling in support of the license application, and is not intended to imply that access to the listed documents is required to understand the contents of this report. This report, ''Soil-Related Input Parameters for the Biosphere Model'', is one of the five analysis reports that develop input parameters for use in the ERMYN model. This report is the source documentation for the six biosphere parameters identified in Table 1-1. The purpose of this analysis was to develop the biosphere model parameters associated with the accumulation and depletion of radionuclides in the soil. These parameters support the calculation of radionuclide concentrations in soil from on-going irrigation or ash deposition and, as a direct consequence, radionuclide concentration in other environmental media that are affected by radionuclide concentrations in soil. The analysis was performed in accordance with the TWP (BSC 2004 [DIRS 169573]) where the governing procedure
Estimating Soil Bulk Density and Total Nitrogen from Catchment ...
African Journals Online (AJOL)
Even though data on soil bulk density (BD) and total nitrogen (TN) are essential for planning modern farming techniques, their data availability is limited for many applications in the developing word. This study is designed to estimate BD and TN from soil properties, land-use systems, soil types and landforms in the ...
Directory of Open Access Journals (Sweden)
K. Butterbach-Bahl
2012-10-01
Full Text Available Assessing the uncertainties of simulation results of ecological models is becoming increasingly important, specifically if these models are used to estimate greenhouse gas emissions on site to regional/national levels. Four general sources of uncertainty effect the outcome of process-based models: (i uncertainty of information used to initialise and drive the model, (ii uncertainty of model parameters describing specific ecosystem processes, (iii uncertainty of the model structure, and (iv accurateness of measurements (e.g., soil-atmosphere greenhouse gas exchange which are used for model testing and development. The aim of our study was to assess the simulation uncertainty of the process-based biogeochemical model LandscapeDNDC. For this we set up a Bayesian framework using a Markov Chain Monte Carlo (MCMC method, to estimate the joint model parameter distribution. Data for model testing, parameter estimation and uncertainty assessment were taken from observations of soil fluxes of nitrous oxide (N2O, nitric oxide (NO and carbon dioxide (CO2 as observed over a 10 yr period at the spruce site of the Höglwald Forest, Germany. By running four independent Markov Chains in parallel with identical properties (except for the parameter start values, an objective criteria for chain convergence developed by Gelman et al. (2003 could be used. Our approach shows that by means of the joint parameter distribution, we were able not only to limit the parameter space and specify the probability of parameter values, but also to assess the complex dependencies among model parameters used for simulating soil C and N trace gas emissions. This helped to improve the understanding of the behaviour of the complex LandscapeDNDC model while simulating soil C and N turnover processes and associated C and N soil-atmosphere exchange. In a final step the parameter distribution of the most sensitive parameters determining soil-atmosphere C and N exchange were used to obtain
Parameter Estimation in Stochastic Grey-Box Models
DEFF Research Database (Denmark)
Kristensen, Niels Rode; Madsen, Henrik; Jørgensen, Sten Bay
2004-01-01
An efficient and flexible parameter estimation scheme for grey-box models in the sense of discretely, partially observed Ito stochastic differential equations with measurement noise is presented along with a corresponding software implementation. The estimation scheme is based on the extended...... Kalman filter and features maximum likelihood as well as maximum a posteriori estimation on multiple independent data sets, including irregularly sampled data sets and data sets with occasional outliers and missing observations. The software implementation is compared to an existing software tool...... and proves to have better performance both in terms of quality of estimates for nonlinear systems with significant diffusion and in terms of reproducibility. In particular, the new tool provides more accurate and more consistent estimates of the parameters of the diffusion term....
Kinetic parameter estimation from SPECT cone-beam projection measurements
International Nuclear Information System (INIS)
Huesman, Ronald H.; Reutter, Bryan W.; Zeng, G. Larry; Gullberg, Grant T.
1998-01-01
Kinetic parameters are commonly estimated from dynamically acquired nuclear medicine data by first reconstructing a dynamic sequence of images and subsequently fitting the parameters to time-activity curves generated from regions of interest overlaid upon the image sequence. Biased estimates can result from images reconstructed using inconsistent projections of a time-varying distribution of radiopharmaceutical acquired by a rotating SPECT system. If the SPECT data are acquired using cone-beam collimators wherein the gantry rotates so that the focal point of the collimators always remains in a plane, additional biases can arise from images reconstructed using insufficient, as well as truncated, projection samples. To overcome these problems we have investigated the estimation of kinetic parameters directly from SPECT cone-beam projection data by modelling the data acquisition process. To accomplish this it was necessary to parametrize the spatial and temporal distribution of the radiopharmaceutical within the SPECT field of view. In a simulated chest image volume, kinetic parameters were estimated for simple one-compartment models for four myocardial regions of interest. Myocardial uptake and washout parameters estimated by conventional analysis of noiseless simulated cone-beam data had biases ranging between 3-26% and 0-28%, respectively. Parameters estimated directly from the noiseless projection data were unbiased as expected, since the model used for fitting was faithful to the simulation. Statistical uncertainties of parameter estimates for 10 000 000 events ranged between 0.2-9% for the uptake parameters and between 0.3-6% for the washout parameters. (author)
Soil moisture estimation using multi linear regression with terraSAR-X data
Directory of Open Access Journals (Sweden)
G. García
2016-06-01
Full Text Available The first five centimeters of soil form an interface where the main heat fluxes exchanges between the land surface and the atmosphere occur. Besides ground measurements, remote sensing has proven to be an excellent tool for the monitoring of spatial and temporal distributed data of the most relevant Earth surface parameters including soil’s parameters. Indeed, active microwave sensors (Synthetic Aperture Radar - SAR offer the opportunity to monitor soil moisture (HS at global, regional and local scales by monitoring involved processes. Several inversion algorithms, that derive geophysical information as HS from SAR data, were developed. Many of them use electromagnetic models for simulating the backscattering coefficient and are based on statistical techniques, such as neural networks, inversion methods and regression models. Recent studies have shown that simple multiple regression techniques yield satisfactory results. The involved geophysical variables in these methodologies are descriptive of the soil structure, microwave characteristics and land use. Therefore, in this paper we aim at developing a multiple linear regression model to estimate HS on flat agricultural regions using TerraSAR-X satellite data and data from a ground weather station. The results show that the backscatter, the precipitation and the relative humidity are the explanatory variables of HS. The results obtained presented a RMSE of 5.4 and a R2 of about 0.6
Estimation of improved resolution soil moisture in vegetated areas ...
Indian Academy of Sciences (India)
Mina Moradizadeh
2018-03-06
Mar 6, 2018 ... soil parameters have been obtained using Simultaneous Land Parameters Retrieval Model algorithm, the downscaling method has been applied to ... retrieving multiple land surface parameters using passive microwave remote .... spheric information that is not routinely available. (Zhang and Wegehenkel ...
Traveltime approximations and parameter estimation for orthorhombic media
Masmoudi, Nabil
2016-05-30
Building anisotropy models is necessary for seismic modeling and imaging. However, anisotropy estimation is challenging due to the trade-off between inhomogeneity and anisotropy. Luckily, we can estimate the anisotropy parameters Building anisotropy models is necessary for seismic modeling and imaging. However, anisotropy estimation is challenging due to the trade-off between inhomogeneity and anisotropy. Luckily, we can estimate the anisotropy parameters if we relate them analytically to traveltimes. Using perturbation theory, we have developed traveltime approximations for orthorhombic media as explicit functions of the anellipticity parameters η1, η2, and Δχ in inhomogeneous background media. The parameter Δχ is related to Tsvankin-Thomsen notation and ensures easier computation of traveltimes in the background model. Specifically, our expansion assumes an inhomogeneous ellipsoidal anisotropic background model, which can be obtained from well information and stacking velocity analysis. We have used the Shanks transform to enhance the accuracy of the formulas. A homogeneous medium simplification of the traveltime expansion provided a nonhyperbolic moveout description of the traveltime that was more accurate than other derived approximations. Moreover, the formulation provides a computationally efficient tool to solve the eikonal equation of an orthorhombic medium, without any constraints on the background model complexity. Although, the expansion is based on the factorized representation of the perturbation parameters, smooth variations of these parameters (represented as effective values) provides reasonable results. Thus, this formulation provides a mechanism to estimate the three effective parameters η1, η2, and Δχ. We have derived Dix-type formulas for orthorhombic medium to convert the effective parameters to their interval values.
Evaluation of physico-chemical parameters of agricultural soils ...
African Journals Online (AJOL)
Evaluation of physico-chemical parameters of agricultural soils irrigated by the waters of the hydrolic basin of Sebou River and their influences on the transfer of trace elements into sugar crops (the case of sugar cane)
Parameter Estimation of Nonlinear Models in Forestry.
Fekedulegn, Desta; Mac Siúrtáin, Máirtín Pádraig; Colbert, Jim J.
1999-01-01
Partial derivatives of the negative exponential, monomolecular, Mitcherlich, Gompertz, logistic, Chapman-Richards, von Bertalanffy, Weibull and the Richard’s nonlinear growth models are presented. The application of these partial derivatives in estimating the model parameters is illustrated. The parameters are estimated using the Marquardt iterative method of nonlinear regression relating top height to age of Norway spruce (Picea abies L.) from the Bowmont Norway Spruce Thinnin...
Harshan, S.; Roth, M.; Velasco, E.
2014-12-01
Forecasting of the urban weather and climate is of great importance as our cities become more populated and considering the combined effects of global warming and local land use changes which make urban inhabitants more vulnerable to e.g. heat waves and flash floods. In meso/global scale models, urban parameterization schemes are used to represent the urban effects. However, these schemes require a large set of input parameters related to urban morphological and thermal properties. Obtaining all these parameters through direct measurements are usually not feasible. A number of studies have reported on parameter estimation and sensitivity analysis to adjust and determine the most influential parameters for land surface schemes in non-urban areas. Similar work for urban areas is scarce, in particular studies on urban parameterization schemes in tropical cities have so far not been reported. In order to address above issues, the town energy balance (TEB) urban parameterization scheme (part of the SURFEX land surface modeling system) was subjected to a sensitivity and optimization/parameter estimation experiment at a suburban site in, tropical Singapore. The sensitivity analysis was carried out as a screening test to identify the most sensitive or influential parameters. Thereafter, an optimization/parameter estimation experiment was performed to calibrate the input parameter. The sensitivity experiment was based on the "improved Sobol's global variance decomposition method" . The analysis showed that parameters related to road, roof and soil moisture have significant influence on the performance of the model. The optimization/parameter estimation experiment was performed using the AMALGM (a multi-algorithm genetically adaptive multi-objective method) evolutionary algorithm. The experiment showed a remarkable improvement compared to the simulations using the default parameter set. The calibrated parameters from this optimization experiment can be used for further model
McNally, A.; Funk, C. C.; Yatheendradas, S.; Michaelsen, J.; Cappelarere, B.; Peters-Lidard, C. D.; Verdin, J. P.
2012-12-01
that is appropriate for estimating soil moisture across the West Africa Sahel. Frequency domain analysis allows us to evaluate how phase shifts (lags) and gains (changes in amplitude) vary across sites depending on soil and vegetation characteristics (e.g. from Food and Agriculture Organization (FAO) soil and University of Maryland (UMD) vegetation parameter maps). We compare observed and NDVI estimated soil moisture to outputs from the LIS-Noah LSM to assess the potential for data assimilation and use of the NDVI estimated soil moisture for model validation at the regional scale.
Soil profile property estimation with field and laboratory VNIR spectroscopy
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 ...
DEFF Research Database (Denmark)
Sommer, Helle Mølgaard; Holst, Helle; Spliid, Henrik
1995-01-01
Three identical microbiological experiments were carried out and analysed in order to examine the variability of the parameter estimates. The microbiological system consisted of a substrate (toluene) and a biomass (pure culture) mixed together in an aquifer medium. The degradation of the substrate...... and the growth of the biomass are described by the Monod model consisting of two nonlinear coupled first-order differential equations. The objective of this study was to estimate the kinetic parameters in the Monod model and to test whether the parameters from the three identical experiments have the same values....... Estimation of the parameters was obtained using an iterative maximum likelihood method and the test used was an approximative likelihood ratio test. The test showed that the three sets of parameters were identical only on a 4% alpha level....
Parameter estimation in X-ray astronomy
International Nuclear Information System (INIS)
Lampton, M.; Margon, B.; Bowyer, S.
1976-01-01
The problems of model classification and parameter estimation are examined, with the objective of establishing the statistical reliability of inferences drawn from X-ray observations. For testing the validities of classes of models, the procedure based on minimizing the chi 2 statistic is recommended; it provides a rejection criterion at any desired significance level. Once a class of models has been accepted, a related procedure based on the increase of chi 2 gives a confidence region for the values of the model's adjustable parameters. The procedure allows the confidence level to be chosen exactly, even for highly nonlinear models. Numerical experiments confirm the validity of the prescribed technique.The chi 2 /sub min/+1 error estimation method is evaluated and found unsuitable when several parameter ranges are to be derived, because it substantially underestimates their joint errors. The ratio of variances method, while formally correct, gives parameter confidence regions which are more variable than necessary
Application of minidisk infiltrometer to estimate soil water repellency
Alagna, Vincenzo; Iovino, Massimo; Bagarello, Vincenzo; Mataix-Solera, Jorge; Lichner, Ľubomír
2016-04-01
Soil water repellency (SWR) reduces affinity of soils to water resulting in detrimental implication for plants growth as well as for hydrological processes. During the last decades, it has become clear that SWR is much more widespread than formerly thought, having been reported for a wide variety of soils, land uses and climatic conditions. The repellency index (RI), based on soil-water to soil-ethanol sorptivity ratio, was proposed to characterize subcritical SWR that is the situation where a low degree of repellency impedes infiltration but does not prevent it. The minidisk infiltrometer allows adequate field assessment of RI inherently scaled to account for soil physical properties other than hydrophobicity (e.g., the volume, connectivity and the geometry of pores) that directly influence the hydrological processes. There are however some issues that still need consideration. For example, use of a fixed time for both water and ethanol sorptivity estimation may lead to inaccurate RI values given that water infiltration could be negligible whereas ethanol sorptivity could be overestimated due to influence of gravity and lateral diffusion that rapidly come into play when the infiltration process is very fast. Moreover, water and ethanol sorptivity values need to be determined at different infiltration sites thus implying that a large number of replicated runs should be carried out to obtain a reliable estimate of RI for a given area. Minidisk infiltrometer tests, conducted under different initial soil moisture and management conditions in the experimental sites of Ciavolo, Trapani (Italy) and Javea, Alicante (East Spain), were used to investigate the best applicative procedure to estimate RI. In particular, different techniques to estimate the water, Sw, and ethanol, Se, sorptivities were compared including i) a fixed 1-min time interval, ii) the slope of early-time 1D infiltration equation and iii) the two-term transient 3D infiltration equation that explicitly
Exchangeable phosphorus and others parameters in soil samples from Sapucai
International Nuclear Information System (INIS)
Facetti, J.F.; Zanotti, J.F.
1972-01-01
Soils samples from the alkaline rocks area at Sapucai were studied. The total amount of P in the soils shows to be high, as well as the E value for the 32 P exclangeable phosphorus. Other parameters like V values, TEC, etc., and their relationschip also were analyzed
Exchangeable phosphorus and others parameters in soil samples from Sapucai
Energy Technology Data Exchange (ETDEWEB)
Facetti, J F; Zanotti, J F [Asuncion Nacional Univ. (Paraguay). Inst. de Ciencias
1972-01-01
Soils samples from the alkaline rocks area at Sapucai were studied. The total amount of P in the soils shows to be high, as well as the E value for the 32 P exclangeable phosphorus. Other parameters like V values, TEC, etc., and their relationschip also were analyzed.
A Novel Nonlinear Parameter Estimation Method of Soft Tissues
Directory of Open Access Journals (Sweden)
Qianqian Tong
2017-12-01
Full Text Available The elastic parameters of soft tissues are important for medical diagnosis and virtual surgery simulation. In this study, we propose a novel nonlinear parameter estimation method for soft tissues. Firstly, an in-house data acquisition platform was used to obtain external forces and their corresponding deformation values. To provide highly precise data for estimating nonlinear parameters, the measured forces were corrected using the constructed weighted combination forecasting model based on a support vector machine (WCFM_SVM. Secondly, a tetrahedral finite element parameter estimation model was established to describe the physical characteristics of soft tissues, using the substitution parameters of Young’s modulus and Poisson’s ratio to avoid solving complicated nonlinear problems. To improve the robustness of our model and avoid poor local minima, the initial parameters solved by a linear finite element model were introduced into the parameter estimation model. Finally, a self-adapting Levenberg–Marquardt (LM algorithm was presented, which is capable of adaptively adjusting iterative parameters to solve the established parameter estimation model. The maximum absolute error of our WCFM_SVM model was less than 0.03 Newton, resulting in more accurate forces in comparison with other correction models tested. The maximum absolute error between the calculated and measured nodal displacements was less than 1.5 mm, demonstrating that our nonlinear parameters are precise.
Estimates for the parameters of the heavy quark expansion
Energy Technology Data Exchange (ETDEWEB)
Heinonen, Johannes; Mannel, Thomas [Universitaet Siegen (Germany)
2015-07-01
We give improved estimates for the non-perturbative parameters appearing in the heavy quark expansion for inclusive decays. While the parameters appearing in low orders of this expansion can be extracted from data, the number of parameters in higher orders proliferates strongly, making a determination of these parameters from data impossible. Thus, one has to rely on theoretical estimates which may be obtained from an insertion of intermediate states. We refine this method and attempt to estimate the uncertainties of this approach.
On robust parameter estimation in brain-computer interfacing
Samek, Wojciech; Nakajima, Shinichi; Kawanabe, Motoaki; Müller, Klaus-Robert
2017-12-01
Objective. The reliable estimation of parameters such as mean or covariance matrix from noisy and high-dimensional observations is a prerequisite for successful application of signal processing and machine learning algorithms in brain-computer interfacing (BCI). This challenging task becomes significantly more difficult if the data set contains outliers, e.g. due to subject movements, eye blinks or loose electrodes, as they may heavily bias the estimation and the subsequent statistical analysis. Although various robust estimators have been developed to tackle the outlier problem, they ignore important structural information in the data and thus may not be optimal. Typical structural elements in BCI data are the trials consisting of a few hundred EEG samples and indicating the start and end of a task. Approach. This work discusses the parameter estimation problem in BCI and introduces a novel hierarchical view on robustness which naturally comprises different types of outlierness occurring in structured data. Furthermore, the class of minimum divergence estimators is reviewed and a robust mean and covariance estimator for structured data is derived and evaluated with simulations and on a benchmark data set. Main results. The results show that state-of-the-art BCI algorithms benefit from robustly estimated parameters. Significance. Since parameter estimation is an integral part of various machine learning algorithms, the presented techniques are applicable to many problems beyond BCI.
Wu, Hui; Hu, Liming; Wen, Qingbo
2017-06-01
Electro-osmotic consolidation is an effective method for soft ground improvement. A main limitation of previous numerical models on this technique is the ignorance of the non-linear variation of soil parameters. In the present study, a multi-field numerical model is developed with the consideration of the non-linear variation of soil parameters during electro-osmotic consolidation process. The numerical simulations on an axisymmetric model indicated that the non-linear variation of soil parameters showed remarkable impact on the development of the excess pore water pressure and degree of consolidation. A field experiment with complex geometry, boundary conditions, electrode configuration and voltage application was further simulated with the developed numerical model. The comparison between field and numerical data indicated that the numerical model coupling of the non-linear variation of soil parameters gave more reasonable results. The developed numerical model is capable to analyze engineering cases with complex operating conditions.
Directory of Open Access Journals (Sweden)
Nita Suhartini
2010-06-01
Full Text Available A study of soil erosion rates had been done on a slightly and long slope of cultivated area in Ciawi - Bogor, using 137Cs technique. The objective of the present study was to evaluate the applicability of the 137Cs technique in obtaining spatially distributed information of soil redistribution at small catchment. This paper reports the result of the choice of conversion model for erosion rate estimates and the sensitive of the changes in the model parameter. For this purpose, small site was selected, namely landuse I (LU-I. The top of a slope was chosen as a reference site. The erosion/deposit rate of individual sampling points was estimated using the conversion models, namely Proportional Model (PM, Mass Balance Model 1 (MBM1 and Mass Balance Model 2 (MBM2. A comparison of the conversion models showed that the lowest value is obtained by the PM. The MBM1 gave values closer to MBM2, but MBM2 gave a reliable values. In this study, a sensitivity analysis suggest that the conversion models are sensitive to changes in parameters that depend on the site conditions, but insensitive to changes in parameters that interact to the onset of 137Cs fallout input. Keywords: soil erosion, environmental radioisotope, cesium
Estimation of water retention and availability in soils of Rio Grande do Sul
Reichert,José Miguel; Albuquerque,Jackson Adriano; Kaiser,Douglas Rodrigo; Reinert,Dalvan José; Urach,Felipe Lavarda; Carlesso,Reimar
2009-01-01
Dispersed information on water retention and availability in soils may be compiled in databases to generate pedotransfer functions. The objectives of this study were: to generate pedotransfer functions to estimate soil water retention based on easily measurable soil properties; to evaluate the efficiency of existing pedotransfer functions for different geographical regions for the estimation of water retention in soils of Rio Grande do Sul (RS); and to estimate plant-available water capacity ...
Meeting on the Microbiology of Soils, Autumn 2001: Estimation of protozoan diversity in soil
DEFF Research Database (Denmark)
Ekelund, Flemming
2002-01-01
Different methods of estimating protozoan diversity in soil are discussed in this paper, with the major emphasis on heterotrophic flagellates. Although many species of ciliates and testate amoebae seem to be unique to the soil environment, the communities of heterotrophic flagellates and naked am...
A simulation of water pollution model parameter estimation
Kibler, J. F.
1976-01-01
A parameter estimation procedure for a water pollution transport model is elaborated. A two-dimensional instantaneous-release shear-diffusion model serves as representative of a simple transport process. Pollution concentration levels are arrived at via modeling of a remote-sensing system. The remote-sensed data are simulated by adding Gaussian noise to the concentration level values generated via the transport model. Model parameters are estimated from the simulated data using a least-squares batch processor. Resolution, sensor array size, and number and location of sensor readings can be found from the accuracies of the parameter estimates.
Mishra, V.; Cruise, J.; Mecikalski, J. R.
2017-12-01
Much effort has been expended recently on the assimilation of remotely sensed soil moisture into operational land surface models (LSM). These efforts have normally been focused on the use of data derived from the microwave bands and results have often shown that improvements to model simulations have been limited due to the fact that microwave signals only penetrate the top 2-5 cm of the soil surface. It is possible that model simulations could be further improved through the introduction of geostationary satellite thermal infrared (TIR) based root zone soil moisture in addition to the microwave deduced surface estimates. In this study, root zone soil moisture estimates from the TIR based Atmospheric Land Exchange Inverse (ALEXI) model were merged with NASA Soil Moisture Active Passive (SMAP) based surface estimates through the application of informational entropy. Entropy can be used to characterize the movement of moisture within the vadose zone and accounts for both advection and diffusion processes. The Principle of Maximum Entropy (POME) can be used to derive complete soil moisture profiles and, fortuitously, only requires a surface boundary condition as well as the overall mean moisture content of the soil column. A lower boundary can be considered a soil parameter or obtained from the LSM itself. In this study, SMAP provided the surface boundary while ALEXI supplied the mean and the entropy integral was used to tie the two together and produce the vertical profile. However, prior to the merging, the coarse resolution (9 km) SMAP data were downscaled to the finer resolution (4.7 km) ALEXI grid. The disaggregation scheme followed the Soil Evaporative Efficiency approach and again, all necessary inputs were available from the TIR model. The profiles were then assimilated into a standard agricultural crop model (Decision Support System for Agrotechnology, DSSAT) via the ensemble Kalman Filter. The study was conducted over the Southeastern United States for the
Estimation of Poisson-Dirichlet Parameters with Monotone Missing Data
Directory of Open Access Journals (Sweden)
Xueqin Zhou
2017-01-01
Full Text Available This article considers the estimation of the unknown numerical parameters and the density of the base measure in a Poisson-Dirichlet process prior with grouped monotone missing data. The numerical parameters are estimated by the method of maximum likelihood estimates and the density function is estimated by kernel method. A set of simulations was conducted, which shows that the estimates perform well.
Directory of Open Access Journals (Sweden)
Filipović Vilim
2018-06-01
Full Text Available Global climate change is projected to continue and result in prolonged and more intense droughts, which can increase soil water repellency (SWR. To be able to estimate the consequences of SWR on vadose zone hydrology, it is important to determine soil hydraulic properties (SHP. Sequential modeling using HYDRUS (2D/3D was performed on an experimental field site with artificially imposed drought scenarios (moderately M and severely S stressed and a control plot. First, inverse modeling was performed for SHP estimation based on water and ethanol infiltration experimental data, followed by model validation on one selected irrigation event. Finally, hillslope modeling was performed to assess water balance for 2014. Results suggest that prolonged dry periods can increase soil water repellency. Inverse modeling was successfully performed for infiltrating liquids, water and ethanol, with R2 and model efficiency (E values both > 0.9. SHP derived from the ethanol measurements showed large differences in van Genuchten-Mualem (VGM parameters for the M and S plots compared to water infiltration experiments. SWR resulted in large saturated hydraulic conductivity (Ks decrease on the M and S scenarios. After validation of SHP on water content measurements during a selected irrigation event, one year simulations (2014 showed that water repellency increases surface runoff in non-structured soils at hillslopes.
A neural network model for estimating soil phosphorus using terrain analysis
Directory of Open Access Journals (Sweden)
Ali Keshavarzi
2015-12-01
Full Text Available Artificial neural network (ANN model was developed and tested for estimating soil phosphorus (P in Kouhin watershed area (1000 ha, Qazvin province, Iran using terrain analysis. Based on the soil distribution correlation, vegetation growth pattern across the topographically heterogeneous landscape, the topographic and vegetation attributes were used in addition to pedologic information for the development of ANN model in area for estimating of soil phosphorus. Totally, 85 samples were collected and tested for phosphorus contents and corresponding attributes were estimated by the digital elevation model (DEM. In order to develop the pedo-transfer functions, data linearity was checked, correlated and 80% was used for modeling and ANN was tested using 20% of collected data. Results indicate that 68% of the variation in soil phosphorus could be explained by elevation and Band 1 data and significant correlation was observed between input variables and phosphorus contents. There was a significant correlation between soil P and terrain attributes which can be used to derive the pedo-transfer function for soil P estimation to manage nutrient deficiency. Results showed that P values can be calculated more accurately with the ANN-based pedo-transfer function with the input topographic variables along with the Band 1.
Analysis of soil chemical parameters of an uncleaned crude oil spill ...
African Journals Online (AJOL)
Analysis of soil chemical parameters of an uncleaned crude oil spill site at Biara was carried out. Soil samples were collected at 0 -15 cm and 15 – 30 cm soil depths from both polluted and unpolluted sites for analysis. Significant increase in high total hydrocarbon content (1015±80.5 – 1150±90.1 mg/kg) in polluted site was ...
Parameter estimation in stochastic differential equations
Bishwal, Jaya P N
2008-01-01
Parameter estimation in stochastic differential equations and stochastic partial differential equations is the science, art and technology of modelling complex phenomena and making beautiful decisions. The subject has attracted researchers from several areas of mathematics and other related fields like economics and finance. This volume presents the estimation of the unknown parameters in the corresponding continuous models based on continuous and discrete observations and examines extensively maximum likelihood, minimum contrast and Bayesian methods. Useful because of the current availability of high frequency data is the study of refined asymptotic properties of several estimators when the observation time length is large and the observation time interval is small. Also space time white noise driven models, useful for spatial data, and more sophisticated non-Markovian and non-semimartingale models like fractional diffusions that model the long memory phenomena are examined in this volume.
International Nuclear Information System (INIS)
Gil-Garcia, C.; Tagami, K.; Uchida, S.; Rigol, A.; Vidal, M.
2009-01-01
New best estimates for the solid-liquid distribution coefficient (K d ) for a set of radionuclides are proposed, based on a selective data search and subsequent calculation of geometric means. The K d best estimates are calculated for soils grouped according to the texture and organic matter content. For a limited number of radionuclides this is extended to consider soil cofactors affecting soil-radionuclide interaction, such as pH, organic matter content, and radionuclide chemical speciation. Correlations between main soil properties and radionuclide K d are examined to complete the information derived from the best estimates with a rough prediction of K d based on soil parameters. Although there are still gaps for many radionuclides, new data from recent studies improve the calculation of K d best estimates for a number of radionuclides, such as selenium, antimony, and iodine.
Use of Radar Vegetation Index (RVI) in Passive Microwave Algorithms for Soil Moisture Estimates
Rowlandson, T. L.; Berg, A. A.
2013-12-01
The Soil Moisture Active Passive (SMAP) satellite will provide a unique opportunity for the estimation of soil moisture by having simultaneous radar and radiometer measurements available. As with the Soil Moisture and Ocean Salinity (SMOS) satellite, the soil moisture algorithms will need to account for the contribution of vegetation to the brightness temperature. Global maps of vegetation volumetric water content (VWC) are difficult to obtain, and the SMOS mission has opted to estimate the optical depth of standing vegetation by using a relationship between the VWC and the leaf area index (LAI). LAI is estimated from optical remote sensing or through soil-vegetation-atmosphere transfer modeling. During the growing season, the VWC of agricultural crops can increase rapidly, and if cloud cover exists during an optical acquisition, the estimation of LAI may be delayed, resulting in an underestimation of the VWC and overestimation of the soil moisture. Alternatively, the radar vegetation index (RVI) has shown strong correlation and linear relationship with VWC for rice and soybeans. Using the SMAP radar to produce RVI values that are coincident to brightness temperature measurements may eliminate the need for LAI estimates. The SMAP Validation Experiment 2012 (SMAPVEX12) was a cal/val campaign for the SMAP mission held in Manitoba, Canada, during a 6-week period in June and July, 2012. During this campaign, soil moisture measurements were obtained for 55 fields with varying soil texture and vegetation cover. Vegetation was sampled from each field weekly to determine the VWC. Soil moisture measurements were taken coincident to overpasses by an aircraft carrying the Passive and Active L-band System (PALS) instrumentation. The aircraft flew flight lines at both high and low altitudes. The low altitude flight lines provided a footprint size approximately equivalent to the size of the SMAPVEX12 field sites. Of the 55 field sites, the low altitude flight lines provided
How to fool cosmic microwave background parameter estimation
International Nuclear Information System (INIS)
Kinney, William H.
2001-01-01
With the release of the data from the Boomerang and MAXIMA-1 balloon flights, estimates of cosmological parameters based on the cosmic microwave background (CMB) have reached unprecedented precision. In this paper I show that it is possible for these estimates to be substantially biased by features in the primordial density power spectrum. I construct primordial power spectra which mimic to within cosmic variance errors the effect of changing parameters such as the baryon density and neutrino mass, meaning that even an ideal measurement would be unable to resolve the degeneracy. Complementary measurements are necessary to resolve this ambiguity in parameter estimation efforts based on CMB temperature fluctuations alone
Directory of Open Access Journals (Sweden)
Jonathan R Karr
2015-05-01
Full Text Available Whole-cell models that explicitly represent all cellular components at the molecular level have the potential to predict phenotype from genotype. However, even for simple bacteria, whole-cell models will contain thousands of parameters, many of which are poorly characterized or unknown. New algorithms are needed to estimate these parameters and enable researchers to build increasingly comprehensive models. We organized the Dialogue for Reverse Engineering Assessments and Methods (DREAM 8 Whole-Cell Parameter Estimation Challenge to develop new parameter estimation algorithms for whole-cell models. We asked participants to identify a subset of parameters of a whole-cell model given the model's structure and in silico "experimental" data. Here we describe the challenge, the best performing methods, and new insights into the identifiability of whole-cell models. We also describe several valuable lessons we learned toward improving future challenges. Going forward, we believe that collaborative efforts supported by inexpensive cloud computing have the potential to solve whole-cell model parameter estimation.
A novel method for estimating soil precompression stress from uniaxial confined compression tests
DEFF Research Database (Denmark)
Lamandé, Mathieu; Schjønning, Per; Labouriau, Rodrigo
2017-01-01
. Stress-strain curves were obtained by performing uniaxial, confined compression tests on undisturbed soil cores for three soil types at three soil water potentials. The new method performed better than the Gompertz fitting method in estimating precompression stress. The values of precompression stress...... obtained from the new method were linearly related to the maximum stress experienced by the soil samples prior to the uniaxial, confined compression test at each soil condition with a slope close to 1. Precompression stress determined with the new method was not related to soil type or dry bulk density......The concept of precompression stress is used for estimating soil strength of relevance to fieldtraffic. It represents the maximum stress experienced by the soil. The most recently developed fitting method to estimate precompression stress (Gompertz) is based on the assumption of an S-shape stress...
Estimation of bare soil surface temperature from air temperature and ...
African Journals Online (AJOL)
Soil surface temperature has critical influence on climate, agricultural and hydrological activities since it serves as a good indicator of the energy budget of the earth's surface. Two empirical models for estimating soil surface temperature from air temperature and soil depth temperature were developed. The coefficient of ...
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 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.
Kibblewhite, M G; Bellamy, P H; Brewer, T R; Graves, A R; Dawson, C A; Rickson, R J; Truckell, I; Stuart, J
2014-03-01
Methods for the spatial estimation of risk of harm to soil by erosion by water and wind and by soil organic matter decline are explored. Rates of harm are estimated for combinations of soil type and land cover (as a proxy for hazard frequency) and used to estimate risk of soil erosion and loss of soil organic carbon (SOC) for 1 km(2)pixels. Scenarios are proposed for defining the acceptability of risk of harm to soil: the most precautionary one corresponds to no net harm after natural regeneration of soil (i.e. a 1 in 20 chance of exceeding an erosion rate of soils and a carbon stock decline of 0 tha(-1)y(-1) for organic soils). Areas at higher and lower than possible acceptable risk are mapped. The veracity of boundaries is compromised if areas of unacceptable risk are mapped to administrative boundaries. Errors in monitoring change in risk of harm to soil and inadequate information on risk reduction measures' efficacy, at landscape scales, make it impossible to use or monitor quantitative targets for risk reduction adequately. The consequences for priority area definition of expressing varying acceptable risk of harm to soil as a varying probability of exceeding a fixed level of harm, or, a varying level of harm being exceeded with a fixed probability, are discussed. Soil data and predictive models for rates of harm to soil would need considerable development and validation to implement a priority area approach robustly. Copyright © 2013 Elsevier B.V. All rights reserved.
Parameter Estimation for Improving Association Indicators in Binary Logistic Regression
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Mahdi Bashiri
2012-02-01
Full Text Available The aim of this paper is estimation of Binary logistic regression parameters for maximizing the log-likelihood function with improved association indicators. In this paper the parameter estimation steps have been explained and then measures of association have been introduced and their calculations have been analyzed. Moreover a new related indicators based on membership degree level have been expressed. Indeed association measures demonstrate the number of success responses occurred in front of failure in certain number of Bernoulli independent experiments. In parameter estimation, existing indicators values is not sensitive to the parameter values, whereas the proposed indicators are sensitive to the estimated parameters during the iterative procedure. Therefore, proposing a new association indicator of binary logistic regression with more sensitivity to the estimated parameters in maximizing the log- likelihood in iterative procedure is innovation of this study.
Dobriyal, Pariva; Qureshi, Ashi; Badola, Ruchi; Hussain, Syed Ainul
2012-08-01
SummaryThe maintenance of elevated soil moisture is an important ecosystem service of the natural ecosystems. Understanding the patterns of soil moisture distribution is useful to a wide range of agencies concerned with the weather and climate, soil conservation, agricultural production and landscape management. However, the great heterogeneity in the spatial and temporal distribution of soil moisture and the lack of standard methods to estimate this property limit its quantification and use in research. This literature based review aims to (i) compile the available knowledge on the methods used to estimate soil moisture at the landscape level, (ii) compare and evaluate the available methods on the basis of common parameters such as resource efficiency, accuracy of results and spatial coverage and (iii) identify the method that will be most useful for forested landscapes in developing countries. On the basis of the strengths and weaknesses of each of the methods reviewed we conclude that the direct method (gravimetric method) is accurate and inexpensive but is destructive, slow and time consuming and does not allow replications thereby having limited spatial coverage. The suitability of indirect methods depends on the cost, accuracy, response time, effort involved in installation, management and durability of the equipment. Our review concludes that measurements of soil moisture using the Time Domain Reflectometry (TDR) and Ground Penetrating Radar (GPR) methods are instantaneously obtained and accurate. GPR may be used over larger areas (up to 500 × 500 m a day) but is not cost-effective and difficult to use in forested landscapes in comparison to TDR. This review will be helpful to researchers, foresters, natural resource managers and agricultural scientists in selecting the appropriate method for estimation of soil moisture keeping in view the time and resources available to them and to generate information for efficient allocation of water resources and
Estimation of light transport parameters in biological media using ...
Indian Academy of Sciences (India)
Estimation of light transport parameters in biological media using coherent backscattering ... backscattered light for estimating the light transport parameters of biological media has been investigated. ... Pramana – Journal of Physics | News.
Statistics of Parameter Estimates: A Concrete Example
Aguilar, Oscar; Allmaras, Moritz; Bangerth, Wolfgang; Tenorio, Luis
2015-01-01
© 2015 Society for Industrial and Applied Mathematics. Most mathematical models include parameters that need to be determined from measurements. The estimated values of these parameters and their uncertainties depend on assumptions made about noise
Parameter Estimation of Partial Differential Equation Models
Xun, Xiaolei; Cao, Jiguo; Mallick, Bani; Maity, Arnab; Carroll, Raymond J.
2013-01-01
PDEs used in practice have no analytic solutions, and can only be solved with numerical methods. Currently, methods for estimating PDE parameters require repeatedly solving PDEs numerically under thousands of candidate parameter values, and thus
Linking soil type and rainfall characteristics towards estimation of surface evaporative capacitance
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.
KIZILKAYA, RIDVAN; SAMOFALOVA, IRAIDA; MUDRYKH, NATALYA; MİKAİLSOY, FARİZ; AKÇA, İZZET; SUSHKOVA, SVETLANA; MINKINA, TATIANA
2015-01-01
Abstract: The kinetic parameters of soil urease have attracted considerable attention; however, little information is available on its kinetic parameters and behaviors in response to azadirachtin application to the soil. A short (14-day) field experiment was conducted using Albic Luvisol soil (loam texture; pH 6.70; electrical conductivity 0.81 dS m-1; CaCO3 content 0.04%; total organic carbon 0.99%) as the experimental soil in the Perm region of the Russian Federation to investigate the effe...
Pedotransfer functions to estimate soil water content at field capacity ...
Indian Academy of Sciences (India)
20
available scarce water resources in dry land agriculture, but 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 ...
Directory of Open Access Journals (Sweden)
Alfonso F. Torres-Rua
2016-04-01
Full Text Available Spatial surface soil moisture can be an important indicator of crop conditions on farmland, but its continuous estimation remains challenging due to coarse spatial and temporal resolution of existing remotely-sensed products. Furthermore, while preceding research on soil moisture using remote sensing (surface energy balance, weather parameters, and vegetation indices has demonstrated a relationship between these factors and soil moisture, practical continuous spatial quantification of the latter is still unavailable for use in water and agricultural management. In this study, a methodology is presented to estimate volumetric surface soil moisture by statistical selection from potential predictors that include vegetation indices and energy balance products derived from satellite (Landsat imagery and weather data as identified in scientific literature. This methodology employs a statistical learning machine called a Relevance Vector Machine (RVM to identify and relate the potential predictors to soil moisture by means of stratified cross-validation and forward variable selection. Surface soil moisture measurements from irrigated agricultural fields in Central Utah in the 2012 irrigation season were used, along with weather data, Landsat vegetation indices, and energy balance products. The methodology, data collection, processing, and estimation accuracy are presented and discussed.
Dual-permeability models are increasingly used to quantify the transport of solutes and microorganisms in soils with preferential flow. An ability to accurately determine the model parameters and their variation with preferential pathway characteristics is crucial for predicting the transport of mi...
Directory of Open Access Journals (Sweden)
Miroslav Dumbrovský
2011-01-01
Full Text Available This paper evaluates different technologies of soil cultivation (conventional and minimization in terms of physical properties and water regime of soils, where infiltration of surface water is a major component of subsurface water. Soil physical properties (the current humidity, reduced bulk density, porosity, water retention capacity of soil, pore distribution and soil aeration is determined from soil samples taken from the organic horizon according to standard methodology. To observe the infiltration characteristics of surface layers of topsoil, the drench method (double ring infiltrometers was used. For the evaluation of field measurements of infiltration, empirical and physically derived equations by Kostiakov and Philip and the three-parameter Philip-type equation were used. The Philip three-parameter equation provides physical based parameters near the theoretical values, a good estimation of saturated hydraulic conductivity Ks and sorptivity C1. The parameter S of Philip’s equation describes the real value of the sorptivity of the soil. Experimental research work on the experimental plots H. Meziříčko proceeded in the years 2005–2008.
Sensitivity Analysis of the USLE Soil Erodibility Factor to Its Determining Parameters
Mitova, Milena; Rousseva, Svetla
2014-05-01
Soil erosion is recognized as one of the most serious soil threats worldwide. Soil erosion prediction is the first step in soil conservation planning. The Universal Soil Loss Equation (USLE) is one of the most widely used models for soil erosion predictions. One of the five USLE predictors is the soil erodibility factor (K-factor), which evaluates the impact of soil characteristics on soil erosion rates. Soil erodibility nomograph defines K-factor depending on soil characteristics, such as: particle size distribution (fractions finer that 0.002 mm and from 0.1 to 0.002 mm), organic matter content, soil structure and soil profile water permeability. Identifying the soil characteristics, which mostly influence the K-factor would give an opportunity to control the soil loss through erosion by controlling the parameters, which reduce the K-factor value. The aim of the report is to present the results of analysis of the relative weight of these soil characteristics in the K-factor values. The relative impact of the soil characteristics on K-factor was studied through a series of statistical analyses of data from the geographic database for soil erosion risk assessments in Bulgaria. Degree of correlation between K-factor values and the parameters that determine it was studied by correlation analysis. The sensitivity of the K-factor was determined by studying the variance of each parameter within the range between minimum and maximum possible values considering average value of the other factors. Normalizing transformation of data sets was applied because of the different dimensions and the orders of variation of the values of the various parameters. The results show that the content of particles finer than 0.002 mm has the most significant relative impact on the soil erodibility, followed by the content of particles with size from 0.1 mm to 0.002 mm, the class of the water permeability of the soil profile, the content of organic matter and the aggregation class. The
Using machine learning to predict soil bulk density on the basis of visual parameters
Bondi, Giulia; Creamer, Rachel; Ferrari, Alessio; Fenton, Owen; Wall, David
2018-01-01
Soil structure is a key factor that supports all soil functions. Extracting intact soil cores and horizon specific samples for determination of soil physical parameters (e.g. bulk density (Bd) or particle size distribution) is a common practice for assessing indicators of soil structure. However,
Soil moisture estimation using reflected solar and emitted thermal infrared radiation
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.
Becker, Joscha; Gütlein, Adrian; Sierra Cornejo, Natalia; Kiese, Ralf; Hertel, Dietrich; Kuzyakov, Yakov
2015-04-01
The savannah biome is a hotspot for biodiversity and wildlife conservation in Africa and recently got in the focus of research on carbon sequestration. Savannah ecosystems are under strong pressure from climate and land-use change, especially around populous areas like the Mt. Kilimanjaro region. Savannah vegetation in this area consists of grassland with isolated trees and is therefore characterized by high spatial variation of canopy cover, aboveground biomass and root structure. Canopy structure is known to affect microclimate, throughfall and evapotranspiration and thereby controls soil moisture conditions. Consequently, the canopy structure is a major regulator for soil ecological parameters and soil-atmospheric trace gas exchange (CO2, N2O, CH4) in water limited environments. The spatial distribution of these parameters and the connection between above and belowground processes are important to understand and predict ecosystem changes and estimate its vulnerability. Our objective was to determine trends and changes of soil parameters and relate their spatial variability to the vegetation structure. We chose three trees from each of the two most dominant species (Acacia nilotica and Balanites aegyptiaca) in our research area. For each tree, we selected transects with nine sampling points of the same relative distances to the stem. Distances were calculated in relation to the crown radius. At these each sampling point a soil core was taken and separated in 0-10 cm and 10-30 cm depth. We measured soil carbon (C) and nitrogen (N) storage, microbial biomass carbon C and N, soil respiration as well as root biomass and -density, soil temperature and soil water content. Each tree was characterized by crown spread, leaf area index and basal area. Preliminary results show that C and N stocks decreased about 50% with depth independently of distance to the tree. Soil water content under the tree crown increased with depth while it decreased under grass cover. Microbial
nabili, sara; shahbazi majd, nafiseh
2013-04-01
any specified level were estimated by three several method including the strain energy in which is the areas of hysteresis loops, the arias intensity and the kinetic energy computed from the acceleration time histories at its corresponding level. Finally, the dependency of the demand energy to the soil and seismological parameters was shown by means of several diagrams.
Kalman filter data assimilation: targeting observations and parameter estimation.
Bellsky, Thomas; Kostelich, Eric J; Mahalov, Alex
2014-06-01
This paper studies the effect of targeted observations on state and parameter estimates determined with Kalman filter data assimilation (DA) techniques. We first provide an analytical result demonstrating that targeting observations within the Kalman filter for a linear model can significantly reduce state estimation error as opposed to fixed or randomly located observations. We next conduct observing system simulation experiments for a chaotic model of meteorological interest, where we demonstrate that the local ensemble transform Kalman filter (LETKF) with targeted observations based on largest ensemble variance is skillful in providing more accurate state estimates than the LETKF with randomly located observations. Additionally, we find that a hybrid ensemble Kalman filter parameter estimation method accurately updates model parameters within the targeted observation context to further improve state estimation.
Kalman filter data assimilation: Targeting observations and parameter estimation
International Nuclear Information System (INIS)
Bellsky, Thomas; Kostelich, Eric J.; Mahalov, Alex
2014-01-01
This paper studies the effect of targeted observations on state and parameter estimates determined with Kalman filter data assimilation (DA) techniques. We first provide an analytical result demonstrating that targeting observations within the Kalman filter for a linear model can significantly reduce state estimation error as opposed to fixed or randomly located observations. We next conduct observing system simulation experiments for a chaotic model of meteorological interest, where we demonstrate that the local ensemble transform Kalman filter (LETKF) with targeted observations based on largest ensemble variance is skillful in providing more accurate state estimates than the LETKF with randomly located observations. Additionally, we find that a hybrid ensemble Kalman filter parameter estimation method accurately updates model parameters within the targeted observation context to further improve state estimation
International Nuclear Information System (INIS)
Khakural, B.R.; Robert, P.C.; Hugins, D.R.
1998-01-01
There is a growing interest in real-time estimation of soil moisture for site-specific crop management. Non-contacting electromagnetic inductive (EMI) methods have potentials to provide real-time estimate of soil profile water contents. Soil profile water contents were monitored with a neutron probe at selected sites. A Geonics LTD EM-38 terrain meter was used to record bulk soil electrical conductivity (EC(A)) readings across a soil-landscape in West central Minnesota with variable moisture regimes. The relationships among EC(A), selected soil and landscape properties were examined. Bulk soil electrical conductivity (0-1.0 and 0-0.5 m) was negatively correlated with relative elevation. It was positively correlated with soil profile (1.0 m) clay content and negatively correlated with soil profile coarse fragments (2 mm) and sand content. There was significant linear relationship between ECA (0-1.0 and 0-0.5) and soil profile water storage. Soil water storage estimated from ECA reflected changes in landscape and soil characteristics
Modeling and Parameter Estimation of a Small Wind Generation System
Directory of Open Access Journals (Sweden)
Carlos A. Ramírez Gómez
2013-11-01
Full Text Available The modeling and parameter estimation of a small wind generation system is presented in this paper. The system consists of a wind turbine, a permanent magnet synchronous generator, a three phase rectifier, and a direct current load. In order to estimate the parameters wind speed data are registered in a weather station located in the Fraternidad Campus at ITM. Wind speed data were applied to a reference model programed with PSIM software. From that simulation, variables were registered to estimate the parameters. The wind generation system model together with the estimated parameters is an excellent representation of the detailed model, but the estimated model offers a higher flexibility than the programed model in PSIM software.
Estimation of soil profile properties using field and laboratory VNIR spectroscopy
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 parameter sensitivities for stochastic reaction networks
Gupta, Ankit
2016-01-07
Quantification of the effects of parameter uncertainty is an important and challenging problem in Systems Biology. We consider this problem in the context of stochastic models of biochemical reaction networks where the dynamics is described as a continuous-time Markov chain whose states represent the molecular counts of various species. For such models, effects of parameter uncertainty are often quantified by estimating the infinitesimal sensitivities of some observables with respect to model parameters. The aim of this talk is to present a holistic approach towards this problem of estimating parameter sensitivities for stochastic reaction networks. Our approach is based on a generic formula which allows us to construct efficient estimators for parameter sensitivity using simulations of the underlying model. We will discuss how novel simulation techniques, such as tau-leaping approximations, multi-level methods etc. can be easily integrated with our approach and how one can deal with stiff reaction networks where reactions span multiple time-scales. We will demonstrate the efficiency and applicability of our approach using many examples from the biological literature.
Melching, C.S.; Marquardt, J.S.
1997-01-01
Design hydrographs computed from design storms, simple models of abstractions (interception, depression storage, and infiltration), and synthetic unit hydrographs provide vital information for stormwater, flood-plain, and water-resources management throughout the United States. Rainfall and runoff data for small watersheds in Lake County collected between 1990 and 1995 were studied to develop equations for estimation of synthetic unit-hydrograph parameters on the basis of watershed and storm characteristics. The synthetic unit-hydrograph parameters of interest were the time of concentration (TC) and watershed-storage coefficient (R) for the Clark unit-hydrograph method, the unit-graph lag (UL) for the Soil Conservation Service (now known as the Natural Resources Conservation Service) dimensionless unit hydrograph, and the hydrograph-time lag (TL) for the linear-reservoir method for unit-hydrograph estimation. Data from 66 storms with effective-precipitation depths greater than 0.4 inches on 9 small watersheds (areas between 0.06 and 37 square miles (mi2)) were utilized to develop the estimation equations, and data from 11 storms on 8 of these watersheds were utilized to verify (test) the estimation equations. The synthetic unit-hydrograph parameters were determined by calibration using the U.S. Army Corps of Engineers Flood Hydrograph Package HEC-1 (TC, R, and UL) or by manual analysis of the rainfall and run-off data (TL). The relation between synthetic unit-hydrograph parameters, and watershed and storm characteristics was determined by multiple linear regression of the logarithms of the parameters and characteristics. Separate sets of equations were developed with watershed area and main channel length as the starting parameters. Percentage of impervious cover, main channel slope, and depth of effective precipitation also were identified as important characteristics for estimation of synthetic unit-hydrograph parameters. The estimation equations utilizing area
Prediction of Radionuclide transfer based on soil parameters: application to vulnerability studies
International Nuclear Information System (INIS)
Roig, M.; Vidal, M.; Rauret, G.
1998-01-01
The multi factorial character of the radiocaesium and radiostrontium soil-to-plan transfer, which depends on the radionuclide level in the soil solution amplified by a plant factor, prevents from establishing univariate relationships between transfer factors and soil and/or plant parameters. The plant factor is inversely proportional to the level of competitive species in the soil solution (Ca and Mg, for radiostrontium, and K and NH 4 for radiocaesium). Radionuclide level in soil solution depends on the radionuclide available fraction and its distribution coefficient. For radiostrontium, this may be obtained from the Cationic Exchange Capacity (CEC), whereas for radiocaesium the Specific Interception Potential should be calculate, both corrected by the concentrations of the competitive species and selectivity coefficients. Therefore, the transfer factor eventually depends on soil solution composition, the available fraction and the number of sorption sites, as well as on the plant factor. For a given plant, a relative sequence of transfer can be set up based solely on soil parameters, since the plant factor is cancelled. This prediction model has been compared with transfer data from experiments with Mediterranean, mineral soils, contaminated with a thermo generated aerosol, and with podzolic and organic soils, contaminated by the Chernobyl fallout. These studies revealed that it was possible to predict a relative scale of transfer for any type of soil, also allowing a scale of soil vulnerability to radiostrontium and radiocaesium contamination to be set up. (Author)
Estimation of soil water retention curve using fractal dimension ...
African Journals Online (AJOL)
The soil water retention curve (SWRC) is a fundamental hydraulic property majorly used to study flow transport in soils and calculate plant-available water. Since, direct measurement of SWRC is time-consuming and expensive, different models have been developed to estimate SWRC. In this study, a fractal-based model ...
Load Estimation from Modal Parameters
DEFF Research Database (Denmark)
Aenlle, Manuel López; Brincker, Rune; Fernández, Pelayo Fernández
2007-01-01
In Natural Input Modal Analysis the modal parameters are estimated just from the responses while the loading is not recorded. However, engineers are sometimes interested in knowing some features of the loading acting on a structure. In this paper, a procedure to determine the loading from a FRF m...
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
A variational approach to parameter estimation in ordinary differential equations
Directory of Open Access Journals (Sweden)
Kaschek Daniel
2012-08-01
Full Text Available Abstract Background Ordinary differential equations are widely-used in the field of systems biology and chemical engineering to model chemical reaction networks. Numerous techniques have been developed to estimate parameters like rate constants, initial conditions or steady state concentrations from time-resolved data. In contrast to this countable set of parameters, the estimation of entire courses of network components corresponds to an innumerable set of parameters. Results The approach presented in this work is able to deal with course estimation for extrinsic system inputs or intrinsic reactants, both not being constrained by the reaction network itself. Our method is based on variational calculus which is carried out analytically to derive an augmented system of differential equations including the unconstrained components as ordinary state variables. Finally, conventional parameter estimation is applied to the augmented system resulting in a combined estimation of courses and parameters. Conclusions The combined estimation approach takes the uncertainty in input courses correctly into account. This leads to precise parameter estimates and correct confidence intervals. In particular this implies that small motifs of large reaction networks can be analysed independently of the rest. By the use of variational methods, elements from control theory and statistics are combined allowing for future transfer of methods between the two fields.
A variational approach to parameter estimation in ordinary differential equations.
Kaschek, Daniel; Timmer, Jens
2012-08-14
Ordinary differential equations are widely-used in the field of systems biology and chemical engineering to model chemical reaction networks. Numerous techniques have been developed to estimate parameters like rate constants, initial conditions or steady state concentrations from time-resolved data. In contrast to this countable set of parameters, the estimation of entire courses of network components corresponds to an innumerable set of parameters. The approach presented in this work is able to deal with course estimation for extrinsic system inputs or intrinsic reactants, both not being constrained by the reaction network itself. Our method is based on variational calculus which is carried out analytically to derive an augmented system of differential equations including the unconstrained components as ordinary state variables. Finally, conventional parameter estimation is applied to the augmented system resulting in a combined estimation of courses and parameters. The combined estimation approach takes the uncertainty in input courses correctly into account. This leads to precise parameter estimates and correct confidence intervals. In particular this implies that small motifs of large reaction networks can be analysed independently of the rest. By the use of variational methods, elements from control theory and statistics are combined allowing for future transfer of methods between the two fields.
Parameter estimation in stochastic rainfall-runoff models
DEFF Research Database (Denmark)
Jonsdottir, Harpa; Madsen, Henrik; Palsson, Olafur Petur
2006-01-01
A parameter estimation method for stochastic rainfall-runoff models is presented. The model considered in the paper is a conceptual stochastic model, formulated in continuous-discrete state space form. The model is small and a fully automatic optimization is, therefore, possible for estimating all...... the parameter values are optimal for simulation or prediction. The data originates from Iceland and the model is designed for Icelandic conditions, including a snow routine for mountainous areas. The model demands only two input data series, precipitation and temperature and one output data series...
Estimation of soil properties and free product volume from baildown tests
International Nuclear Information System (INIS)
Zhu, J.L.; Parker, J.C.; Lundy, D.A.; Zimmerman, L.M.
1993-01-01
Baildown tests, involving measurement of water and free product levels in a monitoring well after bailing, are often performed at spill sites to estimate the oil volume per unit area -- which the authors refer to as ''oil specific volume.'' Spill volume is estimated by integrating oil specific volume over the areal domain of the spill. Existing methods for interpreting baildown tests are based on grossly simplistic approximations of soil capillary properties that cannot accurately describe the transient well response. A model for vertical equilibrium oil distributions based on the van Genuchten capillary model has been documented and verified in the laboratory and in the field by various authors. The model enables oil specific volume and oil transmissivity to be determined as functions of well product thickness. This paper describes a method for estimating van Genuchten capillary parameters, as well as aquifer hydraulic conductivity, from baildown tests. The results yield the relationships of oil specific volume and oil transmissivity to apparent product thickness, which may be used, in turn, to compute spill volume and to model free product plume movement and free product recovery. The method couples a finite element model for radial flow of oil and water to a well with a nonlinear parameter estimation algorithm. Effects of the filter pack around the well in the fluid level response are considered explicitly by the model. The method, which is implemented in the program BAILTEST, is applied to field data from baildown tests. The results indicate that hydrographs of water and oil levels are accurately described by the model
Michalik, Thomas; Multsch, Sebastian; Frede, Hans-Georg; Breuer, Lutz
2016-04-01
Water for agriculture is strongly limited in arid and semi-arid regions and often of low quality in terms of salinity. The application of saline waters for irrigation increases the salt load in the rooting zone and has to be managed by leaching to maintain a healthy soil, i.e. to wash out salts by additional irrigation. Dynamic simulation models are helpful tools to calculate the root zone water fluxes and soil salinity content in order to investigate best management practices. However, there is little information on structural and parameter uncertainty for simulations regarding the water and salt balance of saline irrigation. Hence, we established a multi-model system with four different models (AquaCrop, RZWQM, SWAP, Hydrus1D/UNSATCHEM) to analyze the structural and parameter uncertainty by using the Global Likelihood and Uncertainty Estimation (GLUE) method. Hydrus1D/UNSATCHEM and SWAP were set up with multiple sets of different implemented functions (e.g. matric and osmotic stress for root water uptake) which results in a broad range of different model structures. The simulations were evaluated against soil water and salinity content observations. The posterior distribution of the GLUE analysis gives behavioral parameters sets and reveals uncertainty intervals for parameter uncertainty. Throughout all of the model sets, most parameters accounting for the soil water balance show a low uncertainty, only one or two out of five to six parameters in each model set displays a high uncertainty (e.g. pore-size distribution index in SWAP and Hydrus1D/UNSATCHEM). The differences between the models and model setups reveal the structural uncertainty. The highest structural uncertainty is observed for deep percolation fluxes between the model sets of Hydrus1D/UNSATCHEM (~200 mm) and RZWQM (~500 mm) that are more than twice as high for the latter. The model sets show a high variation in uncertainty intervals for deep percolation as well, with an interquartile range (IQR) of
Hill, Bryon K.; Walker, Bruce K.
1991-01-01
When using parameter estimation methods based on extended Kalman filter (EKF) theory, it is common practice to assume that the unknown parameter values behave like a random process, such as a random walk, in order to guarantee their identifiability by the filter. The present work is the result of an ongoing effort to quantitatively describe the effect that the assumption of a fictitious noise (called pseudonoise) driving the unknown parameter values has on the parameter estimate convergence rate in filter-based parameter estimators. The initial approach is to examine a first-order system described by one state variable with one parameter to be estimated. The intent is to derive analytical results for this simple system that might offer insight into the effect of the pseudonoise assumption for more complex systems. Such results would make it possible to predict the estimator error convergence behavior as a function of the assumed pseudonoise intensity, and this leads to the natural application of the results to the design of filter-based parameter estimators. The results obtained show that the analytical description of the convergence behavior is very difficult.
Neglect Of Parameter Estimation Uncertainty Can Significantly Overestimate Structural Reliability
Directory of Open Access Journals (Sweden)
Rózsás Árpád
2015-12-01
Full Text Available Parameter estimation uncertainty is often neglected in reliability studies, i.e. point estimates of distribution parameters are used for representative fractiles, and in probabilistic models. A numerical example examines the effect of this uncertainty on structural reliability using Bayesian statistics. The study reveals that the neglect of parameter estimation uncertainty might lead to an order of magnitude underestimation of failure probability.
A "total parameter estimation" method in the varification of distributed hydrological models
Wang, M.; Qin, D.; Wang, H.
2011-12-01
Conventionally hydrological models are used for runoff or flood forecasting, hence the determination of model parameters are common estimated based on discharge measurements at the catchment outlets. With the advancement in hydrological sciences and computer technology, distributed hydrological models based on the physical mechanism such as SWAT, MIKESHE, and WEP, have gradually become the mainstream models in hydrology sciences. However, the assessments of distributed hydrological models and model parameter determination still rely on runoff and occasionally, groundwater level measurements. It is essential in many countries, including China, to understand the local and regional water cycle: not only do we need to simulate the runoff generation process and for flood forecasting in wet areas, we also need to grasp the water cycle pathways and consumption process of transformation in arid and semi-arid regions for the conservation and integrated water resources management. As distributed hydrological model can simulate physical processes within a catchment, we can get a more realistic representation of the actual water cycle within the simulation model. Runoff is the combined result of various hydrological processes, using runoff for parameter estimation alone is inherits problematic and difficult to assess the accuracy. In particular, in the arid areas, such as the Haihe River Basin in China, runoff accounted for only 17% of the rainfall, and very concentrated during the rainy season from June to August each year. During other months, many of the perennial rivers within the river basin dry up. Thus using single runoff simulation does not fully utilize the distributed hydrological model in arid and semi-arid regions. This paper proposed a "total parameter estimation" method to verify the distributed hydrological models within various water cycle processes, including runoff, evapotranspiration, groundwater, and soil water; and apply it to the Haihe river basin in
Parameter estimation of variable-parameter nonlinear Muskingum model using excel solver
Kang, Ling; Zhou, Liwei
2018-02-01
Abstract . The Muskingum model is an effective flood routing technology in hydrology and water resources Engineering. With the development of optimization technology, more and more variable-parameter Muskingum models were presented to improve effectiveness of the Muskingum model in recent decades. A variable-parameter nonlinear Muskingum model (NVPNLMM) was proposed in this paper. According to the results of two real and frequently-used case studies by various models, the NVPNLMM could obtain better values of evaluation criteria, which are used to describe the superiority of the estimated outflows and compare the accuracies of flood routing using various models, and the optimal estimated outflows by the NVPNLMM were closer to the observed outflows than the ones by other models.
Inflation and cosmological parameter estimation
Energy Technology Data Exchange (ETDEWEB)
Hamann, J.
2007-05-15
In this work, we focus on two aspects of cosmological data analysis: inference of parameter values and the search for new effects in the inflationary sector. Constraints on cosmological parameters are commonly derived under the assumption of a minimal model. We point out that this procedure systematically underestimates errors and possibly biases estimates, due to overly restrictive assumptions. In a more conservative approach, we analyse cosmological data using a more general eleven-parameter model. We find that regions of the parameter space that were previously thought ruled out are still compatible with the data; the bounds on individual parameters are relaxed by up to a factor of two, compared to the results for the minimal six-parameter model. Moreover, we analyse a class of inflation models, in which the slow roll conditions are briefly violated, due to a step in the potential. We show that the presence of a step generically leads to an oscillating spectrum and perform a fit to CMB and galaxy clustering data. We do not find conclusive evidence for a step in the potential and derive strong bounds on quantities that parameterise the step. (orig.)
Exploratory Study for Continuous-time Parameter Estimation of Ankle Dynamics
Kukreja, Sunil L.; Boyle, Richard D.
2014-01-01
Recently, a parallel pathway model to describe ankle dynamics was proposed. This model provides a relationship between ankle angle and net ankle torque as the sum of a linear and nonlinear contribution. A technique to identify parameters of this model in discrete-time has been developed. However, these parameters are a nonlinear combination of the continuous-time physiology, making insight into the underlying physiology impossible. The stable and accurate estimation of continuous-time parameters is critical for accurate disease modeling, clinical diagnosis, robotic control strategies, development of optimal exercise protocols for longterm space exploration, sports medicine, etc. This paper explores the development of a system identification technique to estimate the continuous-time parameters of ankle dynamics. The effectiveness of this approach is assessed via simulation of a continuous-time model of ankle dynamics with typical parameters found in clinical studies. The results show that although this technique improves estimates, it does not provide robust estimates of continuous-time parameters of ankle dynamics. Due to this we conclude that alternative modeling strategies and more advanced estimation techniques be considered for future work.
A parameter tree approach to estimating system sensitivities to parameter sets
International Nuclear Information System (INIS)
Jarzemba, M.S.; Sagar, B.
2000-01-01
A post-processing technique for determining relative system sensitivity to groups of parameters and system components is presented. It is assumed that an appropriate parametric model is used to simulate system behavior using Monte Carlo techniques and that a set of realizations of system output(s) is available. The objective of our technique is to analyze the input vectors and the corresponding output vectors (that is, post-process the results) to estimate the relative sensitivity of the output to input parameters (taken singly and as a group) and thereby rank them. This technique is different from the design of experimental techniques in that a partitioning of the parameter space is not required before the simulation. A tree structure (which looks similar to an event tree) is developed to better explain the technique. Each limb of the tree represents a particular combination of parameters or a combination of system components. For convenience and to distinguish it from the event tree, we call it the parameter tree. To construct the parameter tree, the samples of input parameter values are treated as either a '+' or a '-' based on whether or not the sampled parameter value is greater than or less than a specified branching criterion (e.g., mean, median, percentile of the population). The corresponding system outputs are also segregated into similar bins. Partitioning the first parameter into a '+' or a '-' bin creates the first level of the tree containing two branches. At the next level, realizations associated with each first-level branch are further partitioned into two bins using the branching criteria on the second parameter and so on until the tree is fully populated. Relative sensitivities are then inferred from the number of samples associated with each branch of the tree. The parameter tree approach is illustrated by applying it to a number of preliminary simulations of the proposed high-level radioactive waste repository at Yucca Mountain, NV. Using a
Assimilation of microwave brightness temperatures for soil moisture estimation using particle filter
International Nuclear Information System (INIS)
Bi, H Y; Ma, J W; Qin, S X; Zeng, J Y
2014-01-01
Soil moisture plays a significant role in global water cycles. Both model simulations and remote sensing observations have their limitations when estimating soil moisture on a large spatial scale. Data assimilation (DA) is a promising tool which can combine model dynamics and remote sensing observations to obtain more precise ground soil moisture distribution. Among various DA methods, the particle filter (PF) can be applied to non-linear and non-Gaussian systems, thus holding great potential for DA. In this study, a data assimilation scheme based on the residual resampling particle filter (RR-PF) was developed to assimilate microwave brightness temperatures into the macro-scale semi-distributed Variance Infiltration Capacity (VIC) Model to estimate surface soil moisture. A radiative transfer model (RTM) was used to link brightness temperatures with surface soil moisture. Finally, the data assimilation scheme was validated by experimental data obtained at Arizona during the Soil Moisture Experiment 2004 (SMEX04). The results show that the estimation accuracy of soil moisture can be improved significantly by RR-PF through assimilating microwave brightness temperatures into VIC model. Both the overall trends and specific values of the assimilation results are more consistent with ground observations compared with model simulation results
Online State Space Model Parameter Estimation in Synchronous Machines
Directory of Open Access Journals (Sweden)
Z. Gallehdari
2014-06-01
The suggested approach is evaluated for a sample synchronous machine model. Estimated parameters are tested for different inputs at different operating conditions. The effect of noise is also considered in this study. Simulation results show that the proposed approach provides good accuracy for parameter estimation.
Unifying parameter estimation and the Deutsch-Jozsa algorithm for continuous variables
International Nuclear Information System (INIS)
Zwierz, Marcin; Perez-Delgado, Carlos A.; Kok, Pieter
2010-01-01
We reveal a close relationship between quantum metrology and the Deutsch-Jozsa algorithm on continuous-variable quantum systems. We develop a general procedure, characterized by two parameters, that unifies parameter estimation and the Deutsch-Jozsa algorithm. Depending on which parameter we keep constant, the procedure implements either the parameter-estimation protocol or the Deutsch-Jozsa algorithm. The parameter-estimation part of the procedure attains the Heisenberg limit and is therefore optimal. Due to the use of approximate normalizable continuous-variable eigenstates, the Deutsch-Jozsa algorithm is probabilistic. The procedure estimates a value of an unknown parameter and solves the Deutsch-Jozsa problem without the use of any entanglement.
Pedotransfer functions to estimate soil water content at field capacity ...
Indian Academy of Sciences (India)
Priyabrata Santra
2018-03-27
Mar 27, 2018 ... of the global population (Millennium Ecosystem. Assessment 2005). Likewise, there is a .... Therefore, the main objective of this study was to develop PTFs for arid soils of India to estimate soil water content at FC and PWP.
Accelerated maximum likelihood parameter estimation for stochastic biochemical systems
Directory of Open Access Journals (Sweden)
Daigle Bernie J
2012-05-01
Full Text Available Abstract Background A prerequisite for the mechanistic simulation of a biochemical system is detailed knowledge of its kinetic parameters. Despite recent experimental advances, the estimation of unknown parameter values from observed data is still a bottleneck for obtaining accurate simulation results. Many methods exist for parameter estimation in deterministic biochemical systems; methods for discrete stochastic systems are less well developed. Given the probabilistic nature of stochastic biochemical models, a natural approach is to choose parameter values that maximize the probability of the observed data with respect to the unknown parameters, a.k.a. the maximum likelihood parameter estimates (MLEs. MLE computation for all but the simplest models requires the simulation of many system trajectories that are consistent with experimental data. For models with unknown parameters, this presents a computational challenge, as the generation of consistent trajectories can be an extremely rare occurrence. Results We have developed Monte Carlo Expectation-Maximization with Modified Cross-Entropy Method (MCEM2: an accelerated method for calculating MLEs that combines advances in rare event simulation with a computationally efficient version of the Monte Carlo expectation-maximization (MCEM algorithm. Our method requires no prior knowledge regarding parameter values, and it automatically provides a multivariate parameter uncertainty estimate. We applied the method to five stochastic systems of increasing complexity, progressing from an analytically tractable pure-birth model to a computationally demanding model of yeast-polarization. Our results demonstrate that MCEM2 substantially accelerates MLE computation on all tested models when compared to a stand-alone version of MCEM. Additionally, we show how our method identifies parameter values for certain classes of models more accurately than two recently proposed computationally efficient methods
Postprocessing MPEG based on estimated quantization parameters
DEFF Research Database (Denmark)
Forchhammer, Søren
2009-01-01
the case where the coded stream is not accessible, or from an architectural point of view not desirable to use, and instead estimate some of the MPEG stream parameters based on the decoded sequence. The I-frames are detected and the quantization parameters are estimated from the coded stream and used...... in the postprocessing. We focus on deringing and present a scheme which aims at suppressing ringing artifacts, while maintaining the sharpness of the texture. The goal is to improve the visual quality, so perceptual blur and ringing metrics are used in addition to PSNR evaluation. The performance of the new `pure......' postprocessing compares favorable to a reference postprocessing filter which has access to the quantization parameters not only for I-frames but also on P and B-frames....
Role and development of soil parameters for seismic responses of buried lifelines
Energy Technology Data Exchange (ETDEWEB)
Wang, L.R.L.
1983-01-01
Buried lifelines, e.g. oil, gas, water and sewer pipelines have been damaged heavily in recent earthquakes such as 1971 San Fernando Earthquake, in U.S.A., 1976 Tangshan Earthquake, in China, and 1978 MiyagiKen-Oki Earthquake, in Japan, among others. Researchers on the seismic performance of these buried lifelines have been initiated in the United States and many other countries. Various analytical models have been proposed. However, only limited experimental investigations are available. The sources of earthquake damage to buried lifelines include landslide, tectonic uplift-subsidence, soil liquefaction, fault displacement and ground shaking (effects of wave propagation). This paper is concerned with the behavior of buried lifeline systems subjected to surface faulting and ground shaking. The role and development of soil parameters that significantly influence the seismic responses are discussed. The scope of this paper is to examine analytically the influence of various soil and soilstructure interaction parameters to the seismic responses of buried pipelines, to report the currently available physical data of these and related parameters for immediate applications, and to describe the experiments to obtain additional information on soil resistant characteristics to longitudinal pipe motions.
Applicability of Different Hydraulic Parameters to Describe Soil Detachment in Eroding Rills
Wirtz, Stefan; Seeger, Manuel; Zell, Andreas; Wagner, Christian; Wagner, Jean-Frank; Ries, Johannes B.
2013-01-01
This study presents the comparison of experimental results with assumptions used in numerical models. The aim of the field experiments is to test the linear relationship between different hydraulic parameters and soil detachment. For example correlations between shear stress, unit length shear force, stream power, unit stream power and effective stream power and the detachment rate does not reveal a single parameter which consistently displays the best correlation. More importantly, the best fit does not only vary from one experiment to another, but even between distinct measurement points. Different processes in rill erosion are responsible for the changing correlations. However, not all these procedures are considered in soil erosion models. Hence, hydraulic parameters alone are not sufficient to predict detachment rates. They predict the fluvial incising in the rill's bottom, but the main sediment sources are not considered sufficiently in its equations. The results of this study show that there is still a lack of understanding of the physical processes underlying soil erosion. Exerted forces, soil stability and its expression, the abstraction of the detachment and transport processes in shallow flowing water remain still subject of unclear description and dependence. PMID:23717669
International Nuclear Information System (INIS)
Nath, Samuel; Dere, Ashlee
2016-01-01
Bacillus anthracis is the pathogenic bacterium that causes anthrax, which dwells in soils as highly resilient endospores. B. anthracis spore viability in soil is dependent upon environmental conditions, but the soil properties necessary for spore survival are unclear. In this study we used a range of soil geochemical and physical parameters to predict the spatial distribution of B. anthracis in northwest Minnesota, where 64 cases of anthrax in livestock were reported from 2000 to 2013. Two modeling approaches at different spatial scales were used to identify the soil conditions most correlated to known anthrax cases using both statewide and locally collected soil data. Ecological niche models were constructed using the Maximum Entropy (Maxent) approach and included 11 soil parameters as environmental inputs and recorded anthrax cases as known presences. One ecological niche model used soil data and anthrax presences for the entire state while a second model used locally sampled soil data (n = 125) and a subset of anthrax presences, providing a test of spatial scale. In addition, simple logistic regression models using the localized soil data served as an independent measure of variable importance. Maxent model results indicate that at a statewide level, soil calcium and magnesium concentrations, soil pH, and sand content are the most important properties for predicting soil suitability for B. anthracis while at the local level, clay and sand content along with phosphorous and strontium concentrations are most important. These results also show that the spatial scale of analysis is important when considering soil parameters most important for B. anthracis spores. For example, at a broad scale, B. anthracis spores may require Ca-rich soils and an alkaline pH, but may also concentrate in microenvironments with high Sr concentrations. The study is also one of the first ecological niche models that demonstrates the major importance of soil texture for defining
Jeanbille, M; Buée, M; Bach, C; Cébron, A; Frey-Klett, P; Turpault, M P; Uroz, S
2016-02-01
Soil and climatic conditions as well as land cover and land management have been shown to strongly impact the structure and diversity of the soil bacterial communities. Here, we addressed under a same land cover the potential effect of the edaphic parameters on the soil bacterial communities, excluding potential confounding factors as climate. To do this, we characterized two natural soil sequences occurring in the Montiers experimental site. Spatially distant soil samples were collected below Fagus sylvatica tree stands to assess the effect of soil sequences on the edaphic parameters, as well as the structure and diversity of the bacterial communities. Soil analyses revealed that the two soil sequences were characterized by higher pH and calcium and magnesium contents in the lower plots. Metabolic assays based on Biolog Ecoplates highlighted higher intensity and richness in usable carbon substrates in the lower plots than in the middle and upper plots, although no significant differences occurred in the abundance of bacterial and fungal communities along the soil sequences as assessed using quantitative PCR. Pyrosequencing analysis of 16S ribosomal RNA (rRNA) gene amplicons revealed that Proteobacteria, Acidobacteria and Bacteroidetes were the most abundantly represented phyla. Acidobacteria, Proteobacteria and Chlamydiae were significantly enriched in the most acidic and nutrient-poor soils compared to the Bacteroidetes, which were significantly enriched in the soils presenting the higher pH and nutrient contents. Interestingly, aluminium, nitrogen, calcium, nutrient availability and pH appeared to be the best predictors of the bacterial community structures along the soil sequences.
da Silveira, Christian L; Mazutti, Marcio A; Salau, Nina P G
2016-07-08
Process modeling can lead to of advantages such as helping in process control, reducing process costs and product quality improvement. This work proposes a solid-state fermentation distributed parameter model composed by seven differential equations with seventeen parameters to represent the process. Also, parameters estimation with a parameters identifyability analysis (PIA) is performed to build an accurate model with optimum parameters. Statistical tests were made to verify the model accuracy with the estimated parameters considering different assumptions. The results have shown that the model assuming substrate inhibition better represents the process. It was also shown that eight from the seventeen original model parameters were nonidentifiable and better results were obtained with the removal of these parameters from the estimation procedure. Therefore, PIA can be useful to estimation procedure, since it may reduce the number of parameters that can be evaluated. Further, PIA improved the model results, showing to be an important procedure to be taken. © 2016 American Institute of Chemical Engineers Biotechnol. Prog., 32:905-917, 2016. © 2016 American Institute of Chemical Engineers.
Composite likelihood estimation of demographic parameters
Directory of Open Access Journals (Sweden)
Garrigan Daniel
2009-11-01
Full Text Available Abstract Background Most existing likelihood-based methods for fitting historical demographic models to DNA sequence polymorphism data to do not scale feasibly up to the level of whole-genome data sets. Computational economies can be achieved by incorporating two forms of pseudo-likelihood: composite and approximate likelihood methods. Composite likelihood enables scaling up to large data sets because it takes the product of marginal likelihoods as an estimator of the likelihood of the complete data set. This approach is especially useful when a large number of genomic regions constitutes the data set. Additionally, approximate likelihood methods can reduce the dimensionality of the data by summarizing the information in the original data by either a sufficient statistic, or a set of statistics. Both composite and approximate likelihood methods hold promise for analyzing large data sets or for use in situations where the underlying demographic model is complex and has many parameters. This paper considers a simple demographic model of allopatric divergence between two populations, in which one of the population is hypothesized to have experienced a founder event, or population bottleneck. A large resequencing data set from human populations is summarized by the joint frequency spectrum, which is a matrix of the genomic frequency spectrum of derived base frequencies in two populations. A Bayesian Metropolis-coupled Markov chain Monte Carlo (MCMCMC method for parameter estimation is developed that uses both composite and likelihood methods and is applied to the three different pairwise combinations of the human population resequence data. The accuracy of the method is also tested on data sets sampled from a simulated population model with known parameters. Results The Bayesian MCMCMC method also estimates the ratio of effective population size for the X chromosome versus that of the autosomes. The method is shown to estimate, with reasonable
International Nuclear Information System (INIS)
Strebl, F.; Gerzabek, M.
1997-05-01
In a spruce forest stand 9 pooled soil profiles (ten auger cores each, 4 layers) were collected within a homogeneous area of 200 ha. This sampling technique provides sufficient accuracy for the determination of most physico-chemical soil characteristics as well as for the assessment of vertical gradients and horizontal variability within the investigation area. The results reveal the soils' tendency for podsolization and acidification processes. In spite of the small sample sizes cation wash-out (Ca, Mg) due to differences in the orographic situation was determined with high significance. 86 % of 137 Cs-contamination derived from the Chernobyl-fallout in 1986 are still found in the top-soil (10 cm). Nutrient-cycling and the high binding capacity of soil organic matter retard vertical migration of 137 Cs in forest soils effectively. From the present data sets for different soil parameters the minimum number of soil samples ensuring maximum admissible errors of 10 and 20 % were calculated. (author)
Water storage change estimation from in situ shrinkage measurements of clay soils
Brake, te B.; Ploeg, van der M.J.; Rooij, de G.H.
2012-01-01
Water storage in the unsaturated zone is a major determinant of the hydrological behaviour of the soil, but methods to quantify soil water storage are limited. The objective of this study is to assess the applicability of clay soil surface elevation change measurements to estimate soil water storage
Parameter estimation for an expanding universe
Directory of Open Access Journals (Sweden)
Jieci Wang
2015-03-01
Full Text Available We study the parameter estimation for excitations of Dirac fields in the expanding Robertson–Walker universe. We employ quantum metrology techniques to demonstrate the possibility for high precision estimation for the volume rate of the expanding universe. We show that the optimal precision of the estimation depends sensitively on the dimensionless mass m˜ and dimensionless momentum k˜ of the Dirac particles. The optimal precision for the ratio estimation peaks at some finite dimensionless mass m˜ and momentum k˜. We find that the precision of the estimation can be improved by choosing the probe state as an eigenvector of the hamiltonian. This occurs because the largest quantum Fisher information is obtained by performing projective measurements implemented by the projectors onto the eigenvectors of specific probe states.
Comparison of Optimization and Two-point Methods in Estimation of Soil Water Retention Curve
Ghanbarian-Alavijeh, B.; Liaghat, A. M.; Huang, G.
2009-04-01
Soil water retention curve (SWRC) is one of the soil hydraulic properties in which its direct measurement is time consuming and expensive. Since, its measurement is unavoidable in study of environmental sciences i.e. investigation of unsaturated hydraulic conductivity and solute transport, in this study the attempt is to predict soil water retention curve from two measured points. By using Cresswell and Paydar (1996) method (two-point method) and an optimization method developed in this study on the basis of two points of SWRC, parameters of Tyler and Wheatcraft (1990) model (fractal dimension and air entry value) were estimated and then water content at different matric potentials were estimated and compared with their measured values (n=180). For each method, we used both 3 and 1500 kPa (case 1) and 33 and 1500 kPa (case 2) as two points of SWRC. The calculated RMSE values showed that in the Creswell and Paydar (1996) method, there exists no significant difference between case 1 and case 2. However, the calculated RMSE value in case 2 (2.35) was slightly less than case 1 (2.37). The results also showed that the developed optimization method in this study had significantly less RMSE values for cases 1 (1.63) and 2 (1.33) rather than Cresswell and Paydar (1996) method.
Method for Estimating the Parameters of LFM Radar Signal
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Tan Chuan-Zhang
2017-01-01
Full Text Available In order to obtain reliable estimate of parameters, it is very important to protect the integrality of linear frequency modulation (LFM signal. Therefore, in the practical LFM radar signal processing, the length of data frame is often greater than the pulse width (PW of signal. In this condition, estimating the parameters by fractional Fourier transform (FrFT will cause the signal to noise ratio (SNR decrease. Aiming at this problem, we multiply the data frame by a Gaussian window to improve the SNR. Besides, for a further improvement of parameters estimation precision, a novel algorithm is derived via Lagrange interpolation polynomial, and we enhance the algorithm by a logarithmic transformation. Simulation results demonstrate that the derived algorithm significantly reduces the estimation errors of chirp-rate and initial frequency.
Biological parameters in technogenic soils of a former sulphur mine
Siwik-Ziomek, Anetta; Brzezińska, Małgorzata; Lemanowicz, Joanna; Koper, Jan; Szarlip, Paweł
2018-04-01
This study was conducted on the soils originating from a reclamation area of the former sulphur mine in Tarnobrzeg, Poland. Soil was sampled 16 years after the completion of mining works with the open-pit method at Machów, as well as 7 years after sulphur mining via the `smelting' method in the Jeziórko mine was abandoned. Several biological parameters were examined: soil respiration, soil microbial biomass and the activity of rhodanese and arylsulphatase enzymes taking part in sulphur transformation within the site's soils. The soils showed a high total sulphur and sulphates content. The SO42- constituted a large fraction of total sulphur, in some cases, exceeding 80% or even 95% of total sulphur. The soil pH decreased due to the degrading effects of sulphur mining. In the soils studied from the locations with the lowest soil pH value, no activity of arylsulphatase was reported and the activity of rhodanese was lowest. The highest soil respiration values were recorded from the 0-5 cm layer in the areas covered with forest vegetation. A high soil respiration value at the waste heap at Machów wherein a very high concentration of Stot and SO42- was observed can be due to the ability of fungi to produce hyphal strands and to survive unfavourable conditions.
Yi, K.; Park, C.; Ryu, S.; Lee, K.; Yi, M.; Kim, C.; Park, G.; Kim, R.; Son, Y.
2011-12-01
Soil carbon (C) stocks of Pinus densiflora forests in Korea were estimated using a generic forest soil C dynamics model based on the process of dead organic matter input and decomposition. Annual input of dead organic matter to the soil was determined by stand biomass and turnover rates of tree components (stem, branch, twig, foliage, coarse root, and fine root). The model was designed to have a simplified structure consisting of three dead organic matter C (DOC) pools (aboveground woody debris (AWD), belowground woody debris (BWD), and litter (LTR) pool) and one soil organic C (SOC) pool. C flows in the model were regulated by six turnover rates of stem, branch, twig, foliage, coarse root, and fine root, and four decay rates of AWD, BWD, LTR, and SOC. To simulate the soil C stocks of P. densiflora forests, statistical data of forest land area (1,339,791 ha) and growing stock (191,896,089 m3) sorted by region (nine provinces and seven metropolitan cities) and stand age class (11 to 20- (II), 21 to 30- (III), 31 to 40- (IV), 41 to 50- (V), and 51 to 60-year-old (VI)) were used. The growing stock of each stand age class was calculated for every region and representable site index was also determined by consulting the yield table. Other model parameters related to the stand biomass, annual input of dead organic matter and decomposition were estimated from previous studies conducted on P. densiflora forests in Korea, which were also applied for model validation. As a result of simulation, total soil C stock of P. densiflora forests were estimated as 53.9 MtC and soil C stocks per unit area ranged from 28.71 to 47.81 tC ha-1 within the soil depth of 30 cm. Also, soil C stocks in the P. densiflora forests of age class II, III, IV, V, and VI were 16,780,818, 21,450,812, 12,677,872, 2,366,939, and 578,623 tC, respectively, and highly related to the distribution of age classes. Soil C stocks per unit area initially decreased with stand age class and started to increase
International Nuclear Information System (INIS)
Santos, Anna Karla Gomes dos
2016-01-01
pedological parameters pH, CEC and exchangeable K, was capable of estimating TF soil-plant values in cereals for "1"3"7Cs with deviations inferior to 6% in almost 86% of the cases, showing the availability of the ANN use as a tool to predict the soil-plant transfer factor values for "13"7Cs. The results of this emphasize the influence of these pedological parameters in the control of the "1"3"7Cs soil-plant transfer process. The previous knowledge of transfer factors, enable to infer the degree of radio vulnerability of a soil that's potentially subject to radioactive contamination, which will help in the planning of emergency treatment in short, medium and long term in rural areas close to nuclear plants, as well as the selection of adequate places for the waste storage and nuclear installations. (author)
Estimates of Annual Soil Loss Rates in the State of São Paulo, Brazil
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Grasiela de Oliveira Rodrigues Medeiros
Full Text Available ABSTRACT: Soil is a natural resource that has been affected by human pressures beyond its renewal capacity. For this reason, large agricultural areas that were productive have been abandoned due to soil degradation, mainly caused by the erosion process. The objective of this study was to apply the Universal Soil Loss Equation to generate more recent estimates of soil loss rates for the state of São Paulo using a database with information from medium resolution (30 m. The results showed that many areas of the state have high (critical levels of soil degradation due to the predominance of consolidated human activities, especially in growing sugarcane and pasture use. The average estimated rate of soil loss is 30 Mg ha-1 yr-1 and 59 % of the area of the state (except for water bodies and urban areas had estimated rates above 12 Mg ha-1 yr-1, considered as the average tolerance limit in the literature. The average rates of soil loss in areas with annual agricultural crops, semi-perennial agricultural crops (sugarcane, and permanent agricultural crops were 118, 78, and 38 Mg ha-1 yr-1 respectively. The state of São Paulo requires attention to conservation of soil resources, since most soils led to estimates beyond the tolerance limit.
Improved exposure estimation in soil screening and cleanup criteria for volatile organic chemicals.
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).
A simplified 137Cs transport model for estimating erosion rates in undisturbed soil
International Nuclear Information System (INIS)
Zhang Xinbao; Long Yi; He Xiubin; Fu Jiexiong; Zhang Yunqi
2008-01-01
137 Cs is an artificial radionuclide with a half-life of 30.12 years which released into the environment as a result of atmospheric testing of thermo-nuclear weapons primarily during the period of 1950s-1970s with the maximum rate of 137 Cs fallout from atmosphere in 1963. 137 Cs fallout is strongly and rapidly adsorbed by fine particles in the surface horizons of the soil, when it falls down on the ground mostly with precipitation. Its subsequent redistribution is associated with movements of the soil or sediment particles. The 137 Cs nuclide tracing technique has been used for assessment of soil losses for both undisturbed and cultivated soils. For undisturbed soils, a simple profile-shape model was developed in 1990 to describe the 137 Cs depth distribution in profile, where the maximum 137 Cs occurs in the surface horizon and it exponentially decreases with depth. The model implied that the total 137 Cs fallout amount deposited on the earth surface in 1963 and the 137 Cs profile shape has not changed with time. The model has been widely used for assessment of soil losses on undisturbed land. However, temporal variations of 137 Cs depth distribution in undisturbed soils after its deposition on the ground due to downward transport processes are not considered in the previous simple profile-shape model. Thus, the soil losses are overestimated by the model. On the base of the erosion assessment model developed by Walling, D.E., He, Q. [1999. Improved models for estimating soil erosion rates from cesium-137 measurements. Journal of Environmental Quality 28, 611-622], we discuss the 137 Cs transport process in the eroded soil profile and make some simplification to the model, develop a method to estimate the soil erosion rate more expediently. To compare the soil erosion rates calculated by the simple profile-shape model and the simple transport model, the soil losses related to different 137 Cs loss proportions of the reference inventory at the Kaixian site of the
Assumptions of the primordial spectrum and cosmological parameter estimation
International Nuclear Information System (INIS)
Shafieloo, Arman; Souradeep, Tarun
2011-01-01
The observables of the perturbed universe, cosmic microwave background (CMB) anisotropy and large structures depend on a set of cosmological parameters, as well as the assumed nature of primordial perturbations. In particular, the shape of the primordial power spectrum (PPS) is, at best, a well-motivated assumption. It is known that the assumed functional form of the PPS in cosmological parameter estimation can affect the best-fit-parameters and their relative confidence limits. In this paper, we demonstrate that a specific assumed form actually drives the best-fit parameters into distinct basins of likelihood in the space of cosmological parameters where the likelihood resists improvement via modifications to the PPS. The regions where considerably better likelihoods are obtained allowing free-form PPS lie outside these basins. In the absence of a preferred model of inflation, this raises a concern that current cosmological parameter estimates are strongly prejudiced by the assumed form of PPS. Our results strongly motivate approaches toward simultaneous estimation of the cosmological parameters and the shape of the primordial spectrum from upcoming cosmological data. It is equally important for theorists to keep an open mind towards early universe scenarios that produce features in the PPS. (paper)
Bayesian estimation of Weibull distribution parameters
International Nuclear Information System (INIS)
Bacha, M.; Celeux, G.; Idee, E.; Lannoy, A.; Vasseur, D.
1994-11-01
In this paper, we expose SEM (Stochastic Expectation Maximization) and WLB-SIR (Weighted Likelihood Bootstrap - Sampling Importance Re-sampling) methods which are used to estimate Weibull distribution parameters when data are very censored. The second method is based on Bayesian inference and allow to take into account available prior informations on parameters. An application of this method, with real data provided by nuclear power plants operation feedback analysis has been realized. (authors). 8 refs., 2 figs., 2 tabs
Bhattacharjya, Rajib Kumar
2018-05-01
The unit hydrograph and the infiltration parameters of a watershed can be obtained from observed rainfall-runoff data by using inverse optimization technique. This is a two-stage optimization problem. In the first stage, the infiltration parameters are obtained and the unit hydrograph ordinates are estimated in the second stage. In order to combine this two-stage method into a single stage one, a modified penalty parameter approach is proposed for converting the constrained optimization problem to an unconstrained one. The proposed approach is designed in such a way that the model initially obtains the infiltration parameters and then searches the optimal unit hydrograph ordinates. The optimization model is solved using Genetic Algorithms. A reduction factor is used in the penalty parameter approach so that the obtained optimal infiltration parameters are not destroyed during subsequent generation of genetic algorithms, required for searching optimal unit hydrograph ordinates. The performance of the proposed methodology is evaluated by using two example problems. The evaluation shows that the model is superior, simple in concept and also has the potential for field application.
Karyati, K.; Ipor, I. B.; Jusoh, I.; Wasli, M. E.
2018-04-01
The tree growth is influenced by soil morphological and physicochemical properties in the site. The purpose of this study was to describe correlation between soil properties under various stage secondary forests and vegetation parameters, such as floristic structure parameters and floristic diversity indices. The vegetation surveys were conducted in 5, 10, and 20 years old at secondary tropical forests in Sarawak, Malaysia. Nine sub plots sized 20 m × 20 m were established within each study site. The Pearson analysis showed that soil physicochemical properties were significantly correlated to floristic structure parameters and floristic diversity indices. The result of PCA clarified the correlation among most important soil properties, floristic structure parameters, and floristic diversity indices. The PC1 represented cation retention capacity and soil texture which were little affected by the fallow age and its also were correlated by floristic structure and diversity. The PC2 was linked to the levels of soil acidity. This property reflected the remnant effects of ash addition and fallow duration, and the significant correlation were showed among pH (H2O), floristic structure and diversity. The PC3 represented the soil compactness. The soil hardness could be influenced by fallow period and it was also correlated by floristic structure.
International Nuclear Information System (INIS)
Lecoanet, H.; Leveque, F.; Ambrosi, J.-P.
2003-01-01
Biplots combining magnetic parameters allow identification of different pollutant emission sources. - Biplots combining magnetic parameters allow to identification and differentiation different pollutant emission sources. A major problem in soil pollution is the characterization of the relative contributions of different anthropogenic particles sources. This paper demonstrates the efficiency of magnetic techniques to provide identification and differentiation of contaminating emission sources. About 100 soil samples were collected across a mixed agricultural and industrial area (Crau plain/Berre-Fos basin) in southern France. Nine soil profiles were realized. They are aligned along a transect, from the Mediterranean cost to the north. Measurements of initial magnetic susceptibility (χ) and remanent magnetization (ARM, IRM) have been carried out at room temperature. Several ratios of magnetic parameters were calculated and tested. Bivariate analyses allow to characterize different pollution sources and graphic results suggest three dominant contributions originated from road traffic, airport and steel industry. Moreover, magnetic grain-size discrimination between surface-soil samples and bottom-soil samples is obtained. An increase of hard magnetic components from topsoil towards the bottom of the profiles is evidenced
SCoPE: an efficient method of Cosmological Parameter Estimation
International Nuclear Information System (INIS)
Das, Santanu; Souradeep, Tarun
2014-01-01
Markov Chain Monte Carlo (MCMC) sampler is widely used for cosmological parameter estimation from CMB and other data. However, due to the intrinsic serial nature of the MCMC sampler, convergence is often very slow. Here we present a fast and independently written Monte Carlo method for cosmological parameter estimation named as Slick Cosmological Parameter Estimator (SCoPE), that employs delayed rejection to increase the acceptance rate of a chain, and pre-fetching that helps an individual chain to run on parallel CPUs. An inter-chain covariance update is also incorporated to prevent clustering of the chains allowing faster and better mixing of the chains. We use an adaptive method for covariance calculation to calculate and update the covariance automatically as the chains progress. Our analysis shows that the acceptance probability of each step in SCoPE is more than 95% and the convergence of the chains are faster. Using SCoPE, we carry out some cosmological parameter estimations with different cosmological models using WMAP-9 and Planck results. One of the current research interests in cosmology is quantifying the nature of dark energy. We analyze the cosmological parameters from two illustrative commonly used parameterisations of dark energy models. We also asses primordial helium fraction in the universe can be constrained by the present CMB data from WMAP-9 and Planck. The results from our MCMC analysis on the one hand helps us to understand the workability of the SCoPE better, on the other hand it provides a completely independent estimation of cosmological parameters from WMAP-9 and Planck data
Bayesian parameter estimation in probabilistic risk assessment
International Nuclear Information System (INIS)
Siu, Nathan O.; Kelly, Dana L.
1998-01-01
Bayesian statistical methods are widely used in probabilistic risk assessment (PRA) because of their ability to provide useful estimates of model parameters when data are sparse and because the subjective probability framework, from which these methods are derived, is a natural framework to address the decision problems motivating PRA. This paper presents a tutorial on Bayesian parameter estimation especially relevant to PRA. It summarizes the philosophy behind these methods, approaches for constructing likelihood functions and prior distributions, some simple but realistic examples, and a variety of cautions and lessons regarding practical applications. References are also provided for more in-depth coverage of various topics
Iterative methods for distributed parameter estimation in parabolic PDE
Energy Technology Data Exchange (ETDEWEB)
Vogel, C.R. [Montana State Univ., Bozeman, MT (United States); Wade, J.G. [Bowling Green State Univ., OH (United States)
1994-12-31
The goal of the work presented is the development of effective iterative techniques for large-scale inverse or parameter estimation problems. In this extended abstract, a detailed description of the mathematical framework in which the authors view these problem is presented, followed by an outline of the ideas and algorithms developed. Distributed parameter estimation problems often arise in mathematical modeling with partial differential equations. They can be viewed as inverse problems; the `forward problem` is that of using the fully specified model to predict the behavior of the system. The inverse or parameter estimation problem is: given the form of the model and some observed data from the system being modeled, determine the unknown parameters of the model. These problems are of great practical and mathematical interest, and the development of efficient computational algorithms is an active area of study.
Epelde, Lur; Becerril, José M; Alkorta, Itziar; Garbisu, Carlos
2014-01-01
Phytostabilization is a promising option for the remediation of metal contaminated soils which requires the implementation of long-term monitoring programs. We here propose to incorporate the paradigm of "adaptive monitoring", which enables monitoring programs to evolve iteratively as new information emerges and research questions change, to metal phytostabilization. Posing good questions that cover the chemical, toxicological and ecological concerns associated to metal contaminated soils is critical for an efficient long-term phytostabilization monitoring program. Regarding the ecological concerns, soil microbial parameters are most valuable indicators of the effectiveness of metal phytostabilization processes in terms of recovery of soil health. We suggest to group soil microbial parameters in higher-level categories such as "ecological attributes" (vigor, organization, stability) or "ecosystem services" in order to facilitate interpretation and, most importantly, to provide long-term phytostabilization monitoring programs with the required stability through time against changes in techniques, methods, interests, etc. that will inevitably occur during the monitoring program. Finally, a Phytostabilization Monitoring Card, based on both ecological attributes and ecosystem services, for soil microbial properties is provided.
A distributed approach for parameters estimation in System Biology models
International Nuclear Information System (INIS)
Mosca, E.; Merelli, I.; Alfieri, R.; Milanesi, L.
2009-01-01
Due to the lack of experimental measurements, biological variability and experimental errors, the value of many parameters of the systems biology mathematical models is yet unknown or uncertain. A possible computational solution is the parameter estimation, that is the identification of the parameter values that determine the best model fitting respect to experimental data. We have developed an environment to distribute each run of the parameter estimation algorithm on a different computational resource. The key feature of the implementation is a relational database that allows the user to swap the candidate solutions among the working nodes during the computations. The comparison of the distributed implementation with the parallel one showed that the presented approach enables a faster and better parameter estimation of systems biology models.
Estimation of pharmacokinetic parameters from non-compartmental variables using Microsoft Excel.
Dansirikul, Chantaratsamon; Choi, Malcolm; Duffull, Stephen B
2005-06-01
This study was conducted to develop a method, termed 'back analysis (BA)', for converting non-compartmental variables to compartment model dependent pharmacokinetic parameters for both one- and two-compartment models. A Microsoft Excel spreadsheet was implemented with the use of Solver and visual basic functions. The performance of the BA method in estimating pharmacokinetic parameter values was evaluated by comparing the parameter values obtained to a standard modelling software program, NONMEM, using simulated data. The results show that the BA method was reasonably precise and provided low bias in estimating fixed and random effect parameters for both one- and two-compartment models. The pharmacokinetic parameters estimated from the BA method were similar to those of NONMEM estimation.
Directory of Open Access Journals (Sweden)
Gizachew Tiruneh
2015-12-01
Full Text Available Accelerated soil erosion is a worldwide problem because of its economic and environmental impacts. Enfraz watershed is one of the most erosion-prone watersheds in the highlands of Ethiopia, which received little attention. This study was, therefore, carried out to spatially predict the soil loss rate of the watershed with a Geographic Information System (GIS and Remote Sensing (RS. Revised Universal Soil Loss Equation (RUSLE adapted to Ethiopian conditions was used to estimate potential soil losses by utilizing information on rainfall erosivity (R using interpolation of rainfall data, soil erodibility (K using soil map, vegetation cover (C using satellite images, topography (LS using Digital Elevation Model (DEM and conservation practices (P using satellite images. Based on the analysis, about 92.31% (5914.34 ha of the watershed was categorized none to slight class which under soil loss tolerance (SLT values ranging from 5 to 11 tons ha-1 year-1. The remaining 7.68% (492.21 ha of land was classified under moderate to high class about several times the maximum tolerable soil loss. The total and an average amount of soil loss estimated by RUSLE from the watershed was 30,836.41 ton year-1 and 4.81 tons ha-1year-1, respectively.
Parameter Estimates in Differential Equation Models for Chemical Kinetics
Winkel, Brian
2011-01-01
We discuss the need for devoting time in differential equations courses to modelling and the completion of the modelling process with efforts to estimate the parameters in the models using data. We estimate the parameters present in several differential equation models of chemical reactions of order n, where n = 0, 1, 2, and apply more general…
Estimating physiological skin parameters from hyperspectral signatures
Vyas, Saurabh; Banerjee, Amit; Burlina, Philippe
2013-05-01
We describe an approach for estimating human skin parameters, such as melanosome concentration, collagen concentration, oxygen saturation, and blood volume, using hyperspectral radiometric measurements (signatures) obtained from in vivo skin. We use a computational model based on Kubelka-Munk theory and the Fresnel equations. This model forward maps the skin parameters to a corresponding multiband reflectance spectra. Machine-learning-based regression is used to generate the inverse map, and hence estimate skin parameters from hyperspectral signatures. We test our methods using synthetic and in vivo skin signatures obtained in the visible through the short wave infrared domains from 24 patients of both genders and Caucasian, Asian, and African American ethnicities. Performance validation shows promising results: good agreement with the ground truth and well-established physiological precepts. These methods have potential use in the characterization of skin abnormalities and in minimally-invasive prescreening of malignant skin cancers.
Sugarcane maturity estimation through edaphic-climatic parameters
Directory of Open Access Journals (Sweden)
Scarpari Maximiliano Salles
2004-01-01
Full Text Available Sugarcane (Saccharum officinarum L. grows under different weather conditions directly affecting crop maturation. Raw material quality predicting models are important tools in sugarcane crop management; the goal of these models is to provide productivity estimates during harvesting, increasing the efficiency of strategical and administrative decisions. The objective of this work was developing a model to predict Total Recoverable Sugars (TRS during harvesting, using data related to production factors such as soil water storage and negative degree-days. The database of a sugar mill for the crop seasons 1999/2000, 2000/2001 and 2001/2002 was analyzed, and statistical models were tested to estimate raw material. The maturity model for a one-year old sugarcane proved to be significant, with a coefficient of determination (R² of 0.7049*. No differences were detected between measured and estimated data in the simulation (P < 0.05.
A software for parameter estimation in dynamic models
Directory of Open Access Journals (Sweden)
M. Yuceer
2008-12-01
Full Text Available A common problem in dynamic systems is to determine parameters in an equation used to represent experimental data. The goal is to determine the values of model parameters that provide the best fit to measured data, generally based on some type of least squares or maximum likelihood criterion. In the most general case, this requires the solution of a nonlinear and frequently non-convex optimization problem. Some of the available software lack in generality, while others do not provide ease of use. A user-interactive parameter estimation software was needed for identifying kinetic parameters. In this work we developed an integration based optimization approach to provide a solution to such problems. For easy implementation of the technique, a parameter estimation software (PARES has been developed in MATLAB environment. When tested with extensive example problems from literature, the suggested approach is proven to provide good agreement between predicted and observed data within relatively less computing time and iterations.
Liu, Dongdong; She, Dongli; Yu, Shuang'en; Shao, Guangcheng; Chen, Dan
2014-01-01
This study was conducted to assess the influences of soil surface conditions and initial soil water content on water movement in unsaturated sodic soils of reclaimed coastal areas. Data was collected from column experiments in which two soils from a Chinese coastal area reclaimed in 2007 (Soil A, saline) and 1960 (Soil B, nonsaline) were used, with bulk densities of 1.4 or 1.5 g/cm(3). A 1D-infiltration model was created using a finite difference method and its sensitivity to hydraulic related parameters was tested. The model well simulated the measured data. The results revealed that soil compaction notably affected the water retention of both soils. Model simulations showed that increasing the ponded water depth had little effect on the infiltration process, since the increases in cumulative infiltration and wetting front advancement rate were small. However, the wetting front advancement rate increased and the cumulative infiltration decreased to a greater extent when θ₀ was increased. Soil physical quality was described better by the S parameter than by the saturated hydraulic conductivity since the latter was also affected by the physical chemical effects on clay swelling occurring in the presence of different levels of electrolytes in the soil solutions of the two soils.
Directory of Open Access Journals (Sweden)
Dongdong Liu
2014-01-01
Full Text Available This study was conducted to assess the influences of soil surface conditions and initial soil water content on water movement in unsaturated sodic soils of reclaimed coastal areas. Data was collected from column experiments in which two soils from a Chinese coastal area reclaimed in 2007 (Soil A, saline and 1960 (Soil B, nonsaline were used, with bulk densities of 1.4 or 1.5 g/cm3. A 1D-infiltration model was created using a finite difference method and its sensitivity to hydraulic related parameters was tested. The model well simulated the measured data. The results revealed that soil compaction notably affected the water retention of both soils. Model simulations showed that increasing the ponded water depth had little effect on the infiltration process, since the increases in cumulative infiltration and wetting front advancement rate were small. However, the wetting front advancement rate increased and the cumulative infiltration decreased to a greater extent when θ0 was increased. Soil physical quality was described better by the S parameter than by the saturated hydraulic conductivity since the latter was also affected by the physical chemical effects on clay swelling occurring in the presence of different levels of electrolytes in the soil solutions of the two soils.
Nonlinear adaptive control system design with asymptotically stable parameter estimation error
Mishkov, Rumen; Darmonski, Stanislav
2018-01-01
The paper presents a new general method for nonlinear adaptive system design with asymptotic stability of the parameter estimation error. The advantages of the approach include asymptotic unknown parameter estimation without persistent excitation and capability to directly control the estimates transient response time. The method proposed modifies the basic parameter estimation dynamics designed via a known nonlinear adaptive control approach. The modification is based on the generalised prediction error, a priori constraints with a hierarchical parameter projection algorithm, and the stable data accumulation concepts. The data accumulation principle is the main tool for achieving asymptotic unknown parameter estimation. It relies on the parametric identifiability system property introduced. Necessary and sufficient conditions for exponential stability of the data accumulation dynamics are derived. The approach is applied in a nonlinear adaptive speed tracking vector control of a three-phase induction motor.
CTER—Rapid estimation of CTF parameters with error assessment
Energy Technology Data Exchange (ETDEWEB)
Penczek, Pawel A., E-mail: Pawel.A.Penczek@uth.tmc.edu [Department of Biochemistry and Molecular Biology, The University of Texas Medical School, 6431 Fannin MSB 6.220, Houston, TX 77054 (United States); Fang, Jia [Department of Biochemistry and Molecular Biology, The University of Texas Medical School, 6431 Fannin MSB 6.220, Houston, TX 77054 (United States); Li, Xueming; Cheng, Yifan [The Keck Advanced Microscopy Laboratory, Department of Biochemistry and Biophysics, University of California, San Francisco, CA 94158 (United States); Loerke, Justus; Spahn, Christian M.T. [Institut für Medizinische Physik und Biophysik, Charité – Universitätsmedizin Berlin, Charitéplatz 1, 10117 Berlin (Germany)
2014-05-01
In structural electron microscopy, the accurate estimation of the Contrast Transfer Function (CTF) parameters, particularly defocus and astigmatism, is of utmost importance for both initial evaluation of micrograph quality and for subsequent structure determination. Due to increases in the rate of data collection on modern microscopes equipped with new generation cameras, it is also important that the CTF estimation can be done rapidly and with minimal user intervention. Finally, in order to minimize the necessity for manual screening of the micrographs by a user it is necessary to provide an assessment of the errors of fitted parameters values. In this work we introduce CTER, a CTF parameters estimation method distinguished by its computational efficiency. The efficiency of the method makes it suitable for high-throughput EM data collection, and enables the use of a statistical resampling technique, bootstrap, that yields standard deviations of estimated defocus and astigmatism amplitude and angle, thus facilitating the automation of the process of screening out inferior micrograph data. Furthermore, CTER also outputs the spatial frequency limit imposed by reciprocal space aliasing of the discrete form of the CTF and the finite window size. We demonstrate the efficiency and accuracy of CTER using a data set collected on a 300 kV Tecnai Polara (FEI) using the K2 Summit DED camera in super-resolution counting mode. Using CTER we obtained a structure of the 80S ribosome whose large subunit had a resolution of 4.03 Å without, and 3.85 Å with, inclusion of astigmatism parameters. - Highlights: • We describe methodology for estimation of CTF parameters with error assessment. • Error estimates provide means for automated elimination of inferior micrographs. • High computational efficiency allows real-time monitoring of EM data quality. • Accurate CTF estimation yields structure of the 80S human ribosome at 3.85 Å.
Directory of Open Access Journals (Sweden)
Mehmet Siraç Özerdem
2017-04-01
Full Text Available Determining the soil moisture in agricultural fields is a significant parameter to use irrigation systems efficiently. In contrast to standard soil moisture measurements, good results might be acquired in a shorter time over large areas by remote sensing tools. In order to estimate the soil moisture over vegetated agricultural areas, a relationship between Radarsat-2 data and measured ground soil moistures was established by polarimetric decomposition models and a generalized regression neural network (GRNN. The experiments were executed over two agricultural sites on the Tigris Basin, Turkey. The study consists of four phases. In the first stage, Radarsat-2 data were acquired on different dates and in situ measurements were implemented simultaneously. In the second phase, the Radarsat-2 data were pre-processed and the GPS coordinates of the soil sample points were imported to this data. Then the standard sigma backscattering coefficients with the Freeman–Durden and H/A/α polarimetric decomposition models were employed for feature extraction and a feature vector with four sigma backscattering coefficients (σhh, σhv, σvh, and σvv and six polarimetric decomposition parameters (entropy, anisotropy, alpha angle, volume scattering, odd bounce, and double bounce were generated for each pattern. In the last stage, GRNN was used to estimate the regional soil moisture with the aid of feature vectors. The results indicated that radar is a strong remote sensing tool for soil moisture estimation, with mean absolute errors around 2.31 vol %, 2.11 vol %, and 2.10 vol % for Datasets 1–3, respectively; and 2.46 vol %, 2.70 vol %, 7.09 vol %, and 5.70 vol % on Datasets 1 & 2, 2 & 3, 1 & 3, and 1 & 2 & 3, respectively.
Brocca, Luca; Pellarin, Thierry; Crow, Wade T.; Ciabatta, Luca; Massari, Christian; Ryu, Dongryeol; Su, Chun-Hsu; Rüdiger, Christoph; Kerr, Yann
2016-10-01
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 Soil Moisture and Ocean Salinity (SMOS) satellite is used for improving satellite rainfall estimates obtained from the Tropical Rainfall Measuring Mission multisatellite precipitation analysis product (TMPA) using three different "bottom up" techniques: SM2RAIN, Soil Moisture Analysis Rainfall Tool, and Antecedent Precipitation Index Modification. The implementation of these techniques aims at improving the well-known "top down" rainfall estimate derived from TMPA products (version 7) available in near real time. Ground observations provided by the Australian Water Availability Project are considered as a separate validation data set. The three algorithms are calibrated against the gauge-corrected TMPA reanalysis product, 3B42, and used for adjusting the TMPA real-time product, 3B42RT, using SMOS soil moisture data. The study area covers the entire Australian continent, and the analysis period ranges from January 2010 to November 2013. Results show that all the SMOS-based rainfall products improve the performance of 3B42RT, even at daily time scale (differently from previous investigations). The major improvements are obtained in terms of estimation of accumulated rainfall with a reduction of the root-mean-square error of more than 25%. Also, in terms of temporal dynamic (correlation) and rainfall detection (categorical scores) the SMOS-based products provide slightly better results with respect to 3B42RT, even though the relative performance between the methods is not always the same. The strengths and weaknesses of each algorithm and the spatial variability of their performances are identified in order to indicate the ways forward for this promising research activity. Results show that the integration of bottom up and top down approaches
Application of a mer-lux biosensor for estimating bioavailable mercury in soil
DEFF Research Database (Denmark)
Rasmussen, Lasse Dam; Sørensen, S. J.; Turner, R. R.
2000-01-01
A previously described bioassay using a mer-lux gene fusion for detection of bioavailable mercury was applied for the estimation of the bioavailable fraction of mercury in soil. The bioavailable fraction is defined here as being part of the water leachable fraction. Due to masking of light emission...... responses. The utility of the mer-lux biosensor assay was tested by relating measurements of bioavailable and total mercury to the response of the soil microbial community to mercury exposure. Two different soil types (an agricultural and a beech forest soil) were spiked with 2.5 µg Hg(II) g-1 in microcosms...... in resistance or diversity. This study showed that the bioassay using the mer-lux biosensor is a useful and sensitive tool for estimation of bioavailable mercury in soil....
Novel Method for 5G Systems NLOS Channels Parameter Estimation
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Vladeta Milenkovic
2017-01-01
Full Text Available For the development of new 5G systems to operate in mm bands, there is a need for accurate radio propagation modelling at these bands. In this paper novel approach for NLOS channels parameter estimation will be presented. Estimation will be performed based on LCR performance measure, which will enable us to estimate propagation parameters in real time and to avoid weaknesses of ML and moment method estimation approaches.
Aguilar Lopez, Juan Pablo; Warmink, Jord Jurriaan; Schielen, Ralph Mathias Johannes; Hulscher, Suzanne J.M.H.
2016-01-01
Piping erosion has been proved to be one of the failure mechanisms that contributes the most to the total probability of failure on the Dutch flood defence systems. The present study aimed to find the impact of correlation and tail dependence between soil parameters present in the Sellmeijer revised
A Physically—Based Geometry Model for Transport Distance Estimation of Rainfall-Eroded Soil Sediment
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Qian-Gui Zhang
2016-01-01
Full Text Available Estimations of rainfall-induced soil erosion are mostly derived from the weight of sediment measured in natural runoff. The transport distance of eroded soil is important for evaluating landscape evolution but is difficult to estimate, mainly because it cannot be linked directly to the eroded sediment weight. The volume of eroded soil is easier to calculate visually using popular imaging tools, which can aid in estimating the transport distance of eroded soil through geometry relationships. In this study, we present a straightforward geometry model to predict the maximum sediment transport distance incurred by rainfall events of various intensity and duration. In order to verify our geometry prediction model, a series of experiments are reported in the form of a sediment volume. The results show that cumulative rainfall has a linear relationship with the total volume of eroded soil. The geometry model can accurately estimate the maximum transport distance of eroded soil by cumulative rainfall, with a low root-mean-square error (4.7–4.8 and a strong linear correlation (0.74–0.86.
Leveraging Machine Learning to Estimate Soil Salinity through Satellite-Based Remote Sensing
Welle, P.; Ravanbakhsh, S.; Póczos, B.; Mauter, M.
2016-12-01
Human-induced salinization of agricultural soils is a growing problem which now affects an estimated 76 million hectares and causes billions of dollars of lost agricultural revenues annually. While there are indications that soil salinization is increasing in extent, current assessments of global salinity levels are outdated and rely heavily on expert opinion due to the prohibitive cost of a worldwide sampling campaign. A more practical alternative to field sampling may be earth observation through remote sensing, which takes advantage of the distinct spectral signature of salts in order to estimate soil conductivity. Recent efforts to map salinity using remote sensing have been met with limited success due to tractability issues of managing the computational load associated with large amounts of satellite data. In this study, we use Google Earth Engine to create composite satellite soil datasets, which combine data from multiple sources and sensors. These composite datasets contain pixel-level surface reflectance values for dates in which the algorithm is most confident that the surface contains bare soil. We leverage the detailed soil maps created and updated by the United States Geological Survey as label data and apply machine learning regression techniques such as Gaussian processes to learn a smooth mapping from surface reflection to noisy estimates of salinity. We also explore a semi-supervised approach using deep generative convolutional networks to leverage the abundance of unlabeled satellite images in producing better estimates for salinity values where we have relatively fewer measurements across the globe. The general method results in two significant contributions: (1) an algorithm that can be used to predict levels of soil salinity in regions without detailed soil maps and (2) a general framework that serves as an example for how remote sensing can be paired with extensive label data to generate methods for prediction of physical phenomenon.
Relating Bioavailability Parameters to the Sorbent Characteristics of PAH Polluted Soils
DEFF Research Database (Denmark)
Bartolome, N.; Hilber, I.; Schulin, R.
2015-01-01
Regulation of Hydrophobic Organic Contaminants (HOC) such as polycyclic aromatic hydrocarbons (PAHs) in soil is still based on total concentrations. However, many studies have demonstrated that not all of a pollutant’s content in soil is equally available to organisms (Reichenberg & Mayer 2006...... to several sorbent characteristics including organic and black carbon content. The results will provide a better understanding of bioavailability of PAHs in soils. Moreover, the outcomes will be discussed regarding to the potential application of chemical proxies in soil pollution risk assessment......). Over the last decade, intensive effort has been made to incorporate bioavailability into risk assessment (Cachada et al. 2014). Here, we compare total concentrations of PAH with two bioavailability parameters in 30 different soil samples from the archive of the standardized National and Zurich Cantonal...
Directory of Open Access Journals (Sweden)
Ibrahim M. Safwat
2017-11-01
Full Text Available State-of-charge (SOC estimations of Li-ion batteries have been the focus of many research studies in previous years. Many articles discussed the dynamic model’s parameters estimation of the Li-ion battery, where the fixed forgetting factor recursive least square estimation methodology is employed. However, the change rate of each parameter to reach the true value is not taken into consideration, which may tend to poor estimation. This article discusses this issue, and proposes two solutions to solve it. The first solution is the usage of a variable forgetting factor instead of a fixed one, while the second solution is defining a vector of forgetting factors, which means one factor for each parameter. After parameters estimation, a new idea is proposed to estimate state-of-charge (SOC of the Li-ion battery based on Newton’s method. Also, the error percentage and computational cost are discussed and compared with that of nonlinear Kalman filters. This methodology is applied on a 36 V 30 A Li-ion pack to validate this idea.
Xing, Wanqiu; Wang, Weiguang; Shao, Quanxi; Yong, Bin
2018-01-01
Quantifying precipitation (P) partition into evapotranspiration (E) and runoff (Q) is of great importance for global and regional water availability assessment. Budyko framework serves as a powerful tool to make simple and transparent estimation for the partition, using a single parameter, to characterize the shape of the Budyko curve for a "specific basin", where the single parameter reflects the overall effect by not only climatic seasonality, catchment characteristics (e.g., soil, topography and vegetation) but also agricultural activities (e.g., cultivation and irrigation). At the regional scale, these influencing factors are interconnected, and the interactions between them can also affect the single parameter of Budyko-type equations' estimating. Here we employ the multivariate adaptive regression splines (MARS) model to estimate the Budyko curve shape parameter (n in the Choudhury's equation, one form of the Budyko framework) of the selected 96 catchments across China using a data set of long-term averages for climatic seasonality, catchment characteristics and agricultural activities. Results show average storm depth (ASD), vegetation coverage (M), and seasonality index of precipitation (SI) are three statistically significant factors affecting the Budyko parameter. More importantly, four pairs of interactions are recognized by the MARS model as: The interaction between CA (percentage of cultivated land area to total catchment area) and ASD shows that the cultivation can weaken the reducing effect of high ASD (>46.78 mm) on the Budyko parameter estimating. Drought (represented by the value of Palmer drought severity index 0.23) tend to enhance the Budyko parameter reduction by large SI (>0.797). Low vegetation coverage (34.56%) is likely to intensify the rising effect on evapotranspiration ratio by IA (percentage of irrigation area to total catchment area). The Budyko n values estimated by the MARS model reproduce the calculated ones by the observation well
An estimate of energy dissipation due to soil-moisture hysteresis
McNamara, H.
2014-01-01
Processes of infiltration, transport, and outflow in unsaturated soil necessarily involve the dissipation of energy through various processes. Accounting for these energetic processes can contribute to modeling hydrological and ecological systems. The well-documented hysteretic relationship between matric potential and moisture content in soil suggests that one such mechanism of energy dissipation is associated with the cycling between wetting and drying processes, but it is challenging to estimate the magnitude of the effect in situ. The Preisach model, a generalization of the Independent Domain model, allows hysteresis effects to be incorporated into dynamical systems of differential equations. Building on earlier work using such systems with field data from the south-west of Ireland, this work estimates the average rate of hysteretic energy dissipation. Through some straightforward assumptions, the magnitude of this rate is found to be of O(10-5) W m-3. Key Points Hysteresis in soil-water dissipates energy The rate of dissipation can be estimated directly from saturation data The rate of heating caused is significant ©2013. American Geophysical Union. All Rights Reserved.
Estimation of the acid sensitivity of a soil
International Nuclear Information System (INIS)
Thimm, H.F.
1991-01-01
Current regulations for environmental monitoring in the sour gas industry require annual reporting of soil pH. It is well known that this procedure may produce results with wide scatter, and without a clear trend over time. An alternative method which overcomes this problem is proposed. Rather than relying on soil monitoring to indicate the beginning of an irreversible pH drop, the new method allows the time of this occurrence to be calculated if the mean sulfur dioxide or sulfur deposition rate is known or can be estimated. It is also possible, in at least some cases, to identify the minerals that govern initial pH control of the soil. The method rests on kinetic measurement of soil pH change with time after acidification in the laboratory. It is recommended for monitoring, and especially for environmental impact assessment submissions to regulatory authorities. 8 refs., 3 figs
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.
On the estimation of water pure compound parameters in association theories
DEFF Research Database (Denmark)
Grenner, Andreas; Kontogeorgis, Georgios; Michelsen, Michael Locht
2007-01-01
Determination of the appropriate number of association sites and estimation of parameters for association (SAFT-type) theories is not a trivial matter. Building further on a recently published manuscript by Clark et al., this work investigates aspects of the parameter estimation for water using t...... different association theories. Their performance for various properties as well as against the results presented earlier is demonstrated.......Determination of the appropriate number of association sites and estimation of parameters for association (SAFT-type) theories is not a trivial matter. Building further on a recently published manuscript by Clark et al., this work investigates aspects of the parameter estimation for water using two...
Parameter Estimation for a Computable General Equilibrium Model
DEFF Research Database (Denmark)
Arndt, Channing; Robinson, Sherman; Tarp, Finn
We introduce a maximum entropy approach to parameter estimation for computable general equilibrium (CGE) models. The approach applies information theory to estimating a system of nonlinear simultaneous equations. It has a number of advantages. First, it imposes all general equilibrium constraints...
Water storage change estimation from in situ shrinkage measurements of clay soils
Brake, te B.; Ploeg, van der M.J.; Rooij, de G.H.
2013-01-01
The objective of this study is to assess the applicability of clay soil elevation change measurements to estimate soil water storage changes, using a simplified approach. We measured moisture contents in aggregates by EC-5 sensors, and in multiple aggregate and inter-aggregate spaces (bulk soil) by
Modular Estimation Strategy of Vehicle Dynamic Parameters for Motion Control Applications
Directory of Open Access Journals (Sweden)
Rawash Mustafa
2018-01-01
Full Text Available The presence of motion control or active safety systems in vehicles have become increasingly important for improving vehicle performance and handling and negotiating dangerous driving situations. The performance of such systems would be improved if combined with knowledge of vehicle dynamic parameters. Since some of these parameters are difficult to measure, due to technical or economic reasons, estimation of those parameters might be the only practical alternative. In this paper, an estimation strategy of important vehicle dynamic parameters, pertaining to motion control applications, is presented. The estimation strategy is of a modular structure such that each module is concerned with estimating a single vehicle parameter. Parameters estimated include: longitudinal, lateral, and vertical tire forces – longitudinal velocity – vehicle mass. The advantage of this strategy is its independence of tire parameters or wear, road surface condition, and vehicle mass variation. Also, because of its modular structure, each module could be later updated or exchanged for a more effective one. Results from simulations on a 14-DOF vehicle model are provided here to validate the strategy and show its robustness and accuracy.
Estimation of object motion parameters from noisy images.
Broida, T J; Chellappa, R
1986-01-01
An approach is presented for the estimation of object motion parameters based on a sequence of noisy images. The problem considered is that of a rigid body undergoing unknown rotational and translational motion. The measurement data consists of a sequence of noisy image coordinates of two or more object correspondence points. By modeling the object dynamics as a function of time, estimates of the model parameters (including motion parameters) can be extracted from the data using recursive and/or batch techniques. This permits a desired degree of smoothing to be achieved through the use of an arbitrarily large number of images. Some assumptions regarding object structure are presently made. Results are presented for a recursive estimation procedure: the case considered here is that of a sequence of one dimensional images of a two dimensional object. Thus, the object moves in one transverse dimension, and in depth, preserving the fundamental ambiguity of the central projection image model (loss of depth information). An iterated extended Kalman filter is used for the recursive solution. Noise levels of 5-10 percent of the object image size are used. Approximate Cramer-Rao lower bounds are derived for the model parameter estimates as a function of object trajectory and noise level. This approach may be of use in situations where it is difficult to resolve large numbers of object match points, but relatively long sequences of images (10 to 20 or more) are available.
State and parameter estimation in biotechnical batch reactors
Keesman, K.J.
2000-01-01
In this paper the problem of state and parameter estimation in biotechnical batch reactors is considered. Models describing the biotechnical process behaviour are usually nonlinear with time-varying parameters. Hence, the resulting large dimensions of the augmented state vector, roughly > 7, in
Parameter Estimation of Damped Compound Pendulum Using Bat Algorithm
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Saad Mohd Sazli
2016-01-01
Full Text Available In this study, the parameter identification of the damped compound pendulum system is proposed using one of the most promising nature inspired algorithms which is Bat Algorithm (BA. The procedure used to achieve the parameter identification of the experimental system consists of input-output data collection, ARX model order selection and parameter estimation using bat algorithm (BA method. PRBS signal is used as an input signal to regulate the motor speed. Whereas, the output signal is taken from position sensor. Both, input and output data is used to estimate the parameter of the autoregressive with exogenous input (ARX model. The performance of the model is validated using mean squares error (MSE between the actual and predicted output responses of the models. Finally, comparative study is conducted between BA and the conventional estimation method (i.e. Least Square. Based on the results obtained, MSE produce from Bat Algorithm (BA is outperformed the Least Square (LS method.
Global parameter estimation for thermodynamic models of transcriptional regulation.
Suleimenov, Yerzhan; Ay, Ahmet; Samee, Md Abul Hassan; Dresch, Jacqueline M; Sinha, Saurabh; Arnosti, David N
2013-07-15
Deciphering the mechanisms involved in gene regulation holds the key to understanding the control of central biological processes, including human disease, population variation, and the evolution of morphological innovations. New experimental techniques including whole genome sequencing and transcriptome analysis have enabled comprehensive modeling approaches to study gene regulation. In many cases, it is useful to be able to assign biological significance to the inferred model parameters, but such interpretation should take into account features that affect these parameters, including model construction and sensitivity, the type of fitness calculation, and the effectiveness of parameter estimation. This last point is often neglected, as estimation methods are often selected for historical reasons or for computational ease. Here, we compare the performance of two parameter estimation techniques broadly representative of local and global approaches, namely, a quasi-Newton/Nelder-Mead simplex (QN/NMS) method and a covariance matrix adaptation-evolutionary strategy (CMA-ES) method. The estimation methods were applied to a set of thermodynamic models of gene transcription applied to regulatory elements active in the Drosophila embryo. Measuring overall fit, the global CMA-ES method performed significantly better than the local QN/NMS method on high quality data sets, but this difference was negligible on lower quality data sets with increased noise or on data sets simplified by stringent thresholding. Our results suggest that the choice of parameter estimation technique for evaluation of gene expression models depends both on quality of data, the nature of the models [again, remains to be established] and the aims of the modeling effort. Copyright © 2013 Elsevier Inc. All rights reserved.
Uncertainty estimation of core safety parameters using cross-correlations of covariance matrix
International Nuclear Information System (INIS)
Yamamoto, A.; Yasue, Y.; Endo, T.; Kodama, Y.; Ohoka, Y.; Tatsumi, M.
2012-01-01
An uncertainty estimation method for core safety parameters, for which measurement values are not obtained, is proposed. We empirically recognize the correlations among the prediction errors among core safety parameters, e.g., a correlation between the control rod worth and assembly relative power of corresponding position. Correlations of uncertainties among core safety parameters are theoretically estimated using the covariance of cross sections and sensitivity coefficients for core parameters. The estimated correlations among core safety parameters are verified through the direct Monte-Carlo sampling method. Once the correlation of uncertainties among core safety parameters is known, we can estimate the uncertainty of a safety parameter for which measurement value is not obtained. Furthermore, the correlations can be also used for the reduction of uncertainties of core safety parameters. (authors)
Parameter Estimation for a Computable General Equilibrium Model
DEFF Research Database (Denmark)
Arndt, Channing; Robinson, Sherman; Tarp, Finn
2002-01-01
We introduce a maximum entropy approach to parameter estimation for computable general equilibrium (CGE) models. The approach applies information theory to estimating a system of non-linear simultaneous equations. It has a number of advantages. First, it imposes all general equilibrium constraints...
Bias-Corrected Estimation of Noncentrality Parameters of Covariance Structure Models
Raykov, Tenko
2005-01-01
A bias-corrected estimator of noncentrality parameters of covariance structure models is discussed. The approach represents an application of the bootstrap methodology for purposes of bias correction, and utilizes the relation between average of resample conventional noncentrality parameter estimates and their sample counterpart. The…
Reynolds, C. A.; Jackson, T. J.; Rawls, W. J.
2000-12-01
Spatial soil water-holding capacities were estimated for the Food and Agriculture Organization (FAO) digital Soil Map of the World (SMW) by employing continuous pedotransfer functions (PTF) within global pedon databases and linking these results to the SMW. The procedure first estimated representative soil properties for the FAO soil units by statistical analyses and taxotransfer depth algorithms [Food and Agriculture Organization (FAO), 1996]. The representative soil properties estimated for two layers of depths (0-30 and 30-100 cm) included particle-size distribution, dominant soil texture, organic carbon content, coarse fragments, bulk density, and porosity. After representative soil properties for the FAO soil units were estimated, these values were substituted into three different pedotransfer functions (PTF) models by Rawls et al. [1982], Saxton et al. [1986], and Batjes [1996a]. The Saxton PTF model was finally selected to calculate available water content because it only required particle-size distribution data and results closely agreed with the Rawls and Batjes PTF models that used both particle-size distribution and organic matter data. Soil water-holding capacities were then estimated by multiplying the available water content by the soil layer thickness and integrating over an effective crop root depth of 1 m or less (i.e., encountered shallow impermeable layers) and another soil depth data layer of 2.5 m or less.
Vero, S E; Ibrahim, T G; Creamer, R E; Grant, J; Healy, M G; Henry, T; Kramers, G; Richards, K G; Fenton, O
2014-12-01
The true efficacy of a programme of agricultural mitigation measures within a catchment to improve water quality can be determined only after a certain hydrologic time lag period (subsequent to implementation) has elapsed. As the biophysical response to policy is not synchronous, accurate estimates of total time lag (unsaturated and saturated) become critical to manage the expectations of policy makers. The estimation of the vertical unsaturated zone component of time lag is vital as it indicates early trends (initial breakthrough), bulk (centre of mass) and total (Exit) travel times. Typically, estimation of time lag through the unsaturated zone is poor, due to the lack of site specific soil physical data, or by assuming saturated conditions. Numerical models (e.g. Hydrus 1D) enable estimates of time lag with varied levels of input data. The current study examines the consequences of varied soil hydraulic and meteorological complexity on unsaturated zone time lag estimates using simulated and actual soil profiles. Results indicated that: greater temporal resolution (from daily to hourly) of meteorological data was more critical as the saturated hydraulic conductivity of the soil decreased; high clay content soils failed to converge reflecting prevalence of lateral component as a contaminant pathway; elucidation of soil hydraulic properties was influenced by the complexity of soil physical data employed (textural menu, ROSETTA, full and partial soil water characteristic curves), which consequently affected time lag ranges; as the importance of the unsaturated zone increases with respect to total travel times the requirements for high complexity/resolution input data become greater. The methodology presented herein demonstrates that decisions made regarding input data and landscape position will have consequences for the estimated range of vertical travel times. Insufficiencies or inaccuracies regarding such input data can therefore mislead policy makers regarding
Optimal design criteria - prediction vs. parameter estimation
Waldl, Helmut
2014-05-01
G-optimality is a popular design criterion for optimal prediction, it tries to minimize the kriging variance over the whole design region. A G-optimal design minimizes the maximum variance of all predicted values. If we use kriging methods for prediction it is self-evident to use the kriging variance as a measure of uncertainty for the estimates. Though the computation of the kriging variance and even more the computation of the empirical kriging variance is computationally very costly and finding the maximum kriging variance in high-dimensional regions can be time demanding such that we cannot really find the G-optimal design with nowadays available computer equipment in practice. We cannot always avoid this problem by using space-filling designs because small designs that minimize the empirical kriging variance are often non-space-filling. D-optimality is the design criterion related to parameter estimation. A D-optimal design maximizes the determinant of the information matrix of the estimates. D-optimality in terms of trend parameter estimation and D-optimality in terms of covariance parameter estimation yield basically different designs. The Pareto frontier of these two competing determinant criteria corresponds with designs that perform well under both criteria. Under certain conditions searching the G-optimal design on the above Pareto frontier yields almost as good results as searching the G-optimal design in the whole design region. In doing so the maximum of the empirical kriging variance has to be computed only a few times though. The method is demonstrated by means of a computer simulation experiment based on data provided by the Belgian institute Management Unit of the North Sea Mathematical Models (MUMM) that describe the evolution of inorganic and organic carbon and nutrients, phytoplankton, bacteria and zooplankton in the Southern Bight of the North Sea.
Fusion of spectral and electrochemical sensor data for estimating soil macronutrients
Rapid and efficient quantification of plant-available soil phosphorus (P) and potassium (K) is needed to support variable-rate fertilization strategies. Two methods that have been used for estimating these soil macronutrients are diffuse reflectance spectroscopy in visible and near-infrared (VNIR) w...
Campbell, D A; Chkrebtii, O
2013-12-01
Statistical inference for biochemical models often faces a variety of characteristic challenges. In this paper we examine state and parameter estimation for the JAK-STAT intracellular signalling mechanism, which exemplifies the implementation intricacies common in many biochemical inference problems. We introduce an extension to the Generalized Smoothing approach for estimating delay differential equation models, addressing selection of complexity parameters, choice of the basis system, and appropriate optimization strategies. Motivated by the JAK-STAT system, we further extend the generalized smoothing approach to consider a nonlinear observation process with additional unknown parameters, and highlight how the approach handles unobserved states and unevenly spaced observations. The methodology developed is generally applicable to problems of estimation for differential equation models with delays, unobserved states, nonlinear observation processes, and partially observed histories. Crown Copyright © 2013. Published by Elsevier Inc. All rights reserved.
Parameter estimation of Lorenz chaotic system using a hybrid swarm intelligence algorithm
International Nuclear Information System (INIS)
Lazzús, Juan A.; Rivera, Marco; López-Caraballo, Carlos H.
2016-01-01
A novel hybrid swarm intelligence algorithm for chaotic system parameter estimation is present. For this purpose, the parameters estimation on Lorenz systems is formulated as a multidimensional problem, and a hybrid approach based on particle swarm optimization with ant colony optimization (PSO–ACO) is implemented to solve this problem. Firstly, the performance of the proposed PSO–ACO algorithm is tested on a set of three representative benchmark functions, and the impact of the parameter settings on PSO–ACO efficiency is studied. Secondly, the parameter estimation is converted into an optimization problem on a three-dimensional Lorenz system. Numerical simulations on Lorenz model and comparisons with results obtained by other algorithms showed that PSO–ACO is a very powerful tool for parameter estimation with high accuracy and low deviations. - Highlights: • PSO–ACO combined particle swarm optimization with ant colony optimization. • This study is the first research of PSO–ACO to estimate parameters of chaotic systems. • PSO–ACO algorithm can identify the parameters of the three-dimensional Lorenz system with low deviations. • PSO–ACO is a very powerful tool for the parameter estimation on other chaotic system.
Parameter estimation of Lorenz chaotic system using a hybrid swarm intelligence algorithm
Energy Technology Data Exchange (ETDEWEB)
Lazzús, Juan A., E-mail: jlazzus@dfuls.cl; Rivera, Marco; López-Caraballo, Carlos H.
2016-03-11
A novel hybrid swarm intelligence algorithm for chaotic system parameter estimation is present. For this purpose, the parameters estimation on Lorenz systems is formulated as a multidimensional problem, and a hybrid approach based on particle swarm optimization with ant colony optimization (PSO–ACO) is implemented to solve this problem. Firstly, the performance of the proposed PSO–ACO algorithm is tested on a set of three representative benchmark functions, and the impact of the parameter settings on PSO–ACO efficiency is studied. Secondly, the parameter estimation is converted into an optimization problem on a three-dimensional Lorenz system. Numerical simulations on Lorenz model and comparisons with results obtained by other algorithms showed that PSO–ACO is a very powerful tool for parameter estimation with high accuracy and low deviations. - Highlights: • PSO–ACO combined particle swarm optimization with ant colony optimization. • This study is the first research of PSO–ACO to estimate parameters of chaotic systems. • PSO–ACO algorithm can identify the parameters of the three-dimensional Lorenz system with low deviations. • PSO–ACO is a very powerful tool for the parameter estimation on other chaotic system.
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H. Khalilzade
2016-02-01
Full Text Available Introduction Around the world maize is the second crop with the most cultivated areas and amount of production, so as the most important strategic crop, have a special situation in policies, decision making, resources and inputs allocation. On the other side, negative environmental consequences of intensive consumption of agrochemicals resulted to change view concerning food production. One of the most important visions is sustainable production of enough food plus attention to social, economic and environmental aspects. Many researchers stated that the first step to achieve this goal is optimization and improvement of resources use efficiencies. According to little knowledge on relation between soil electrical conductivity and yield of maize, beside the environmental concerns about nitrogen consumption and need to replace chemical nitrogen by ecological inputs, this study designed and aimed to evaluate agroecological characteristics of corn and some soil characteristics as affected by application of organic and biological fertilizers under field conditions. Materials and Methods In order to probing the possibility of grain yield and soil nitrogen estimation via measurement of soil properties, a field experiment was conducted during growing season 2010 at Research Station, Ferdowsi University of Mashhad, Iran. A randomized complete block design (RCBD with three replications was used. Treatments included: 1- manure (30 ton ha-1, 2-vermicompost (10 ton ha-1, 3- nitroxin (containing Azotobacter sp. and Azospirillum sp., inoculation was done according to Kennedy et al., 4- nitrogen as urea (400 kg ha-1 and 5- control (without fertilizer. Studied traits were soil pH, soil EC, soil respiration rate, N content of soil and maize yield. Soil respiration rate was measured using equation 1: CO2= (V0- V× N×22 Equation 1 In which V0 is the volume of consumed acid for control treatment titration, V is of the volume of consumed acid for sample treatment
Maximum-likelihood estimation of the hyperbolic parameters from grouped observations
DEFF Research Database (Denmark)
Jensen, Jens Ledet
1988-01-01
a least-squares problem. The second procedure Hypesti first approaches the maximum-likelihood estimate by iterating in the profile-log likelihood function for the scale parameter. Close to the maximum of the likelihood function, the estimation is brought to an end by iteration, using all four parameters...
Burgin, M. S.; van Zyl, J. J.
2017-12-01
Traditionally, substantial ancillary data is needed to parametrize complex electromagnetic models to estimate soil moisture from polarimetric radar data. The Soil Moisture Active Passive (SMAP) baseline radar soil moisture retrieval algorithm uses a data cube approach, where a cube of radar backscatter values is calculated using sophisticated models. In this work, we utilize the empirical approach by Kim and van Zyl (2009) which is an optional SMAP radar soil moisture retrieval algorithm; it expresses radar backscatter of a vegetated scene as a linear function of soil moisture, hence eliminating the need for ancillary data. We use 2.5 years of L-band Aquarius radar and radiometer derived soil moisture data to determine two coefficients of a linear model function on a global scale. These coefficients are used to estimate soil moisture with 2.5 months of L-band SMAP and L-band PALSAR-2 data. The estimated soil moisture is compared with the SMAP Level 2 radiometer-only soil moisture product; the global unbiased RMSE of the SMAP derived soil moisture corresponds to 0.06-0.07 cm3/cm3. In this study, we leverage the three diverse L-band radar data sets to investigate the impact of pixel size and pixel heterogeneity on soil moisture estimation performance. Pixel sizes range from 100 km for Aquarius, over 3, 9, 36 km for SMAP, to 10m for PALSAR-2. Furthermore, we observe seasonal variation in the radar sensitivity to soil moisture which allows the identification and quantification of seasonally changing vegetation. Utilizing this information, we further improve the estimation performance. The research described in this paper is supported by the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration. Copyright 2017. All rights reserved.
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.
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S. I. Bartsev
2015-06-01
Full Text Available A possible method for experimental determination of parameters of the previously proposed continual mathematical model of soil organic matter transformation is theoretically considered in this paper. The previously proposed by the authors continual model of soil organic matter transformation, based on using the rate of matter transformation as a continual scale of its recalcitrance, describes the transformation process phenomenologically without going into detail of microbiological mechanisms of transformation. Thereby simplicity of the model is achieved. The model is represented in form of one differential equation in firstorder partial derivatives, which has an analytical solution in elementary functions. The model equation contains a small number of empirical parameters which generally characterize environmental conditions where the matter transformation process occurs and initial properties of the plant litter. Given the values of these parameters, it is possible to calculate dynamics of soil organic matter stocks and its distribution over transformation rate. In the present study, possible approaches for determination of the model parameters are considered and a simple method of their experimental measurement is proposed. An experiment of an incubation of chemically homogeneous samples in soil and multiple sequential measurement of the sample mass loss with time is proposed. An equation of time dynamics of mass loss of incubated homogeneous sample is derived from the basic assumption of the presented soil organic matter transformation model. Thus, fitting by the least squares method the parameters of sample mass loss curve calculated according the proposed mass loss dynamics equation allows to determine the parameters of the general equation of soil organic transformation model.
Dual ant colony operational modal analysis parameter estimation method
Sitarz, Piotr; Powałka, Bartosz
2018-01-01
Operational Modal Analysis (OMA) is a common technique used to examine the dynamic properties of a system. Contrary to experimental modal analysis, the input signal is generated in object ambient environment. Operational modal analysis mainly aims at determining the number of pole pairs and at estimating modal parameters. Many methods are used for parameter identification. Some methods operate in time while others in frequency domain. The former use correlation functions, the latter - spectral density functions. However, while some methods require the user to select poles from a stabilisation diagram, others try to automate the selection process. Dual ant colony operational modal analysis parameter estimation method (DAC-OMA) presents a new approach to the problem, avoiding issues involved in the stabilisation diagram. The presented algorithm is fully automated. It uses deterministic methods to define the interval of estimated parameters, thus reducing the problem to optimisation task which is conducted with dedicated software based on ant colony optimisation algorithm. The combination of deterministic methods restricting parameter intervals and artificial intelligence yields very good results, also for closely spaced modes and significantly varied mode shapes within one measurement point.
The soil classification and the subsurface carbon stock estimation with a ground-penetrating radar
International Nuclear Information System (INIS)
Onishi, K.; Rokugawa, S.; Kato, Y.
2002-01-01
One of the serious problems of the Kyoto Protocol is that we have no effective method to estimate the carbon stock of the subsurface. To solve this problem, we propose the application of ground-penetrating radar (GPR) to the subsurface soil survey. As a result, it is shown that GPR can detect the soil horizons, stones and roots. The fluctuations of the soil horizons in the forest are cleanly indicated as the reflection pattern of the microwaves. Considering the fact that the physical, chemical, and biological characteristics of each soil layer is almost unique, GPR results can be used to estimate the carbon stock in soil by combining with the vertical soil sample survey at one site. Then as a trial, we demonstrate to estimate the carbon content fixed in soil layers based on the soil samples and GPR survey data. we also compare this result with the carbon stock for the flat horizon case. The advantages of GPR usage for this object are not only the reduction of uncertainty and the cost, but also the environmental friendliness of survey manner. Finally, we summarize the adaptabilities of various antennas having different predominant frequencies for the shallow subsurface zone. (author)
Expanded uncertainty estimation methodology in determining the sandy soils filtration coefficient
Rusanova, A. D.; Malaja, L. D.; Ivanov, R. N.; Gruzin, A. V.; Shalaj, V. V.
2018-04-01
The combined standard uncertainty estimation methodology in determining the sandy soils filtration coefficient has been developed. The laboratory researches were carried out which resulted in filtration coefficient determination and combined uncertainty estimation obtaining.
Image Analysis to Estimate Mulch Residue in Soil
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Carmen Moreno
2014-01-01
Full Text Available Mulching is used to improve the condition of agricultural soils by covering the soil with different materials, mainly black polyethylene (PE. However, problems derived from its use are how to remove it from the field and, in the case of it remaining in the soil, the possible effects on it. One possible solution is to use biodegradable plastic (BD or paper (PP, as mulch, which could present an alternative, reducing nonrecyclable waste and decreasing the environmental pollution associated with it. Determination of mulch residues in the ground is one of the basic requirements to estimate the potential of each material to degrade. This study has the goal of evaluating the residue of several mulch materials over a crop campaign in Central Spain through image analysis. Color images were acquired under similar lighting conditions at the experimental field. Different thresholding methods were applied to binarize the histogram values of the image saturation plane in order to show the best contrast between soil and mulch. Then the percentage of white pixels (i.e., soil area was used to calculate the mulch deterioration. A comparison of thresholding methods and the different mulch materials based on percentage of bare soil area obtained is shown.
Petrovsky, E.; Grison, H.; Kapicka, A.; Dlouha, S.; Kodesova, R.; Jaksik, O.
2013-05-01
In this study we have applied magnetism of soils for estimation of erosion at an agricultural land. The testing site is situated in loess region in Southern Moravia (in Central Europe). The approach is based on well-established method of differentiation of magnetic parameters of the topsoil and the subsoil horizons as a result of in situ formation of strongly magnetic iron oxides. Our founding is established on a simple tillage homogenization model described by Royall (2001) using magnetic susceptibility and its frequency dependence to estimate soil loss caused by the tillage and subsequent erosion. The original dominant Soil Unit in the investigated area is Haplic Chernozem, which is due to intensive erosion progressively transformed into different Soil Units. The site is characterized by a flat upper part while the middle part, formed by a substantive side valley, is steeper (up to 15°). The side valley represents a major line of concentrated runoff emptying into a colluvial fan. Field measurements of the topsoil volume magnetic susceptibility were carried out by the Bartington MS2D probe. Data are resulting in regular grid of 101 data points, where the bulk soil material was gathered for further laboratory investigations. Moreover, vertical distribution of magnetic susceptibility (deep to 40 cm) was measured on selected transects using the SM400 kappameter. In the laboratory, after drying and sieving of collected soil samples, mass-specific magnetic susceptibility and its frequency-dependent susceptibility was measured. In order to identify magnetic minerals the thermomagnetic analyses were performed using the AGICO KLY-4S Kappabridge with CS-3 furnace. Hysteresis loops were carried out on vibrating magnetometer ADE EV9 to assess the grain-size distribution of ferrimagnetic particles. Hereafter, the isothermal remanent magnetization acqusition followed by D.C. demagnetization were done. All these laboratory magnetic measurements were performed in order to
Ramirez-Lopez, Leonardo; Alexandre Dematte, Jose
2010-05-01
There is consensus in the scientific community about the great need of spatial soil information. Conventional mapping methods are time consuming and involve high costs. Digital soil mapping has emerged as an area in which the soil mapping is optimized by the application of mathematical and statistical approaches, as well as the application of expert knowledge in pedology. In this sense, the objective of the study was to develop a methodology for the spatial prediction of soil classes by using soil spectroscopy methodologies related with fieldwork, spectral data from satellite image and terrain attributes in simultaneous. The studied area is located in São Paulo State, and comprised an area of 473 ha, which was covered by a regular grid (100 x 100 m). In each grid node was collected soil samples at two depths (layers A and B). There were extracted 206 samples from transect sections and submitted to soil analysis (clay, Al2O3, Fe2O3, SiO2 TiO2, and weathering index). The first analog soil class map (ASC-N) contains only soil information regarding from orders to subgroups of the USDA Soil Taxonomy System. The second (ASC-H) map contains some additional information related to some soil attributes like color, ferric levels and base sum. For the elaboration of the digital soil maps the data was divided into three groups: i) Predicted soil attributes of the layer B (related to the soil weathering) which were obtained by using a local soil spectral library; ii) Spectral bands data extracted from a Landsat image; and iii) Terrain parameters. This information was summarized by a principal component analysis (PCA) in each group. Digital soil maps were generated by supervised classification using a maximum likelihood method. The trainee information for this classification was extracted from five toposequences based on the analog soil class maps. The spectral models of weathering soil attributes shown a high predictive performance with low error (R2 0.71 to 0.90). The spatial
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 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.
Cosmological parameter estimation using particle swarm optimization
Prasad, Jayanti; Souradeep, Tarun
2012-06-01
Constraining theoretical models, which are represented by a set of parameters, using observational data is an important exercise in cosmology. In Bayesian framework this is done by finding the probability distribution of parameters which best fits to the observational data using sampling based methods like Markov chain Monte Carlo (MCMC). It has been argued that MCMC may not be the best option in certain problems in which the target function (likelihood) poses local maxima or have very high dimensionality. Apart from this, there may be examples in which we are mainly interested to find the point in the parameter space at which the probability distribution has the largest value. In this situation the problem of parameter estimation becomes an optimization problem. In the present work we show that particle swarm optimization (PSO), which is an artificial intelligence inspired population based search procedure, can also be used for cosmological parameter estimation. Using PSO we were able to recover the best-fit Λ cold dark matter (LCDM) model parameters from the WMAP seven year data without using any prior guess value or any other property of the probability distribution of parameters like standard deviation, as is common in MCMC. We also report the results of an exercise in which we consider a binned primordial power spectrum (to increase the dimensionality of problem) and find that a power spectrum with features gives lower chi square than the standard power law. Since PSO does not sample the likelihood surface in a fair way, we follow a fitting procedure to find the spread of likelihood function around the best-fit point.
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A. Marais
2012-01-01
Full Text Available Different plants are known to have different soil microbial communities associated with them. Agricultural management practices such as fertiliser and pesticide addition, crop rotation, and grazing animals can lead to different microbial communities in the associated agricultural soils. Soil dilution plates, most-probable-number (MPN, community level physiological profiling (CLPP, and buried slide technique as well as some measured soil physicochemical parameters were used to determine changes during the growing season in the ecosystem profile in wheat fields subjected to wheat monoculture or wheat in annual rotation with medic/clover pasture. Statistical analyses showed that soil moisture had an over-riding effect on seasonal fluctuations in soil physicochemical and microbial populations. While within season soil microbial activity could be differentiated between wheat fields under rotational and monoculture management, these differences were not significant.
Estimating Infiltration Rates for a Loessal Silt Loam Using Soil Properties
M. Dean Knighton
1978-01-01
Soil properties were related to infiltration rates as measured by single-ringsteady-head infiltometers. The properties showing strong simple correlations were identified. Regression models were developed to estimate infiltration rate from several soil properties. The best model gave fair agreement to measured rates at another location.
Influence of xenobiotics on the microbiological and agrochemical parameters of soddy-podzolic soil
Vakkerov-Kouzova, N. D.
2010-08-01
We studied the influence of various chemical compounds, i.e., azobenzene (an insecticide and acaricide), nitrification inhibitors (DCD, dicyandiamide and DMPP, and 3,4-dimetylpyrazolphosphate), and inhibitors of urease activity (HQ-hydroquinone), on the agrochemical and microbiological parameters of a soddy-podzolic soil. It is proved that these xenobiotics are able to influence the agrochemical parameters (the pH and the content of NO{3/-} and NH{4/+}, the microbial activity (the basal respiration, the microbial mass carbon, and the microbial quotient), and the number of bacteria of different physiological groups in soddypodzolic soil. The influence of the xenobiotics was preserved for some time, which testified to their persistence in the soil. Upon cultivating the soil microorganisms in different media, the growth of the heterotrophic bacteria was inhibited, the radial growth velocity was slowed down, and the sporogenesis of the micromycetes was retarded. The toxic effect of the xenobiotics was higher with their increasing concentrations.
International Nuclear Information System (INIS)
Ali, S.
2017-01-01
Soil degradation due to unsustainable land use is a global problem and the biggest challenge for sustainability in mountain areas due to their ecological and socio-economic impacts. The study aims to evaluate the variation in the physical, chemical and microbial parameters of soil across various land uses in the Bagrot valley, Central Karakoram National Park (CKNP), Gilgit-Baltistan. Soil samples from 0-20 cm were collected from three land uses such as arable land, pasture, and adjacently located forest. The variables investigated were soil bulk density, total porosity, saturation percentage, sand, silt, clay, pH, electric conductivity, CaCO/sub 3/, organic matter, TN, available P, K, Fe, Mn, Cu and Zn and microbial parameters (16SrRNA and ITS copies number and fungi-to-bacterial ratio). A sigificant varriation in all parameters were found accross the land uses (ANOVA, p < 0.01). Similarly, the highest bulk density, sand, pH, EC, CaCO/sub 3/ were found in arable land, with the lowest values in forest. In contrast, soil under forest showed a higher total porosity, percent saturation, clay, OM, macro and micronutrients, microbial abundance and fungi-to-bacterial ratio than for other land uses. The differences in soil parameters across the land uses indicated detrimental impacts of agricultural activities on soil health. Soil pH and organic matter are the main controlling factors for microbial indicators as well as physical and chemical parameters. The results suggest that restoration of natural vegetation in degraded land and decrease in intensity of land use could improve soil properties in the study area, as well as other similar mountainous regions. (author)
Estimating RASATI scores using acoustical parameters
International Nuclear Information System (INIS)
Agüero, P D; Tulli, J C; Moscardi, G; Gonzalez, E L; Uriz, A J
2011-01-01
Acoustical analysis of speech using computers has reached an important development in the latest years. The subjective evaluation of a clinician is complemented with an objective measure of relevant parameters of voice. Praat, MDVP (Multi Dimensional Voice Program) and SAV (Software for Voice Analysis) are some examples of software for speech analysis. This paper describes an approach to estimate the subjective characteristics of RASATI scale given objective acoustical parameters. Two approaches were used: linear regression with non-negativity constraints, and neural networks. The experiments show that such approach gives correct evaluations with ±1 error in 80% of the cases.
Use of digital images to estimate soil moisture
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João F. C. dos Santos
Full Text Available ABSTRACT The objective of this study was to analyze the relation between the moisture and the spectral response of the soil to generate prediction models. Samples with different moisture contents were prepared and photographed. The photographs were taken under homogeneous light condition and with previous correction for the white balance of the digital photograph camera. The images were processed for extraction of the median values in the Red, Green and Blue bands of the RGB color space; Hue, Saturation and Value of the HSV color space; and values of the digital numbers of a panchromatic image obtained from the RGB bands. The moisture of the samples was determined with the thermogravimetric method. Regression models were evaluated for each image type: RGB, HSV and panchromatic. It was observed the darkening of the soil with the increase of moisture. For each type of soil, a model with best fit was observed and to use these models for prediction purposes, it is necessary to choose the model with best fit in advance, according to the soil characteristics. Soil moisture estimation as a function of its spectral response by digital image processing proves promising.
A soil-based model to predict radionuclide transfer in a soil-plant system
International Nuclear Information System (INIS)
Roig, M.; Vidal, M.; Tent, J.; Rauret, G.; Roca, M.C.; Vallejo, V.R.
1998-01-01
The aim of this work was to check if the main soil parameters predefined as ruling soil-plant transfer were sufficient to predict a relative scale of radionuclide mobility in mineral soils. Two agricultural soils, two radionuclides ( 85 Sr and 134 Cs), and two crops (lettuce and pea) were used in these experiments following radioactive aerosol deposition simulating the conditions of a site some distance far away from the center of a nuclear accident, for which condensed deposition would be the more significant contribution. The available fraction of these radionuclides was estimated in these soils from experiments in which various reagents were tested and several experimental conditions were compared. As a general conclusion, the soil parameters seemed to be sufficient for prediction purposes, although the model should be improved through the consideration of physiological aspects, especially those depending of the plant selectivity according to the composition of the soil solution
Simultaneous Parameters Identifiability and Estimation of an E. coli Metabolic Network Model
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Kese Pontes Freitas Alberton
2015-01-01
Full Text Available This work proposes a procedure for simultaneous parameters identifiability and estimation in metabolic networks in order to overcome difficulties associated with lack of experimental data and large number of parameters, a common scenario in the modeling of such systems. As case study, the complex real problem of parameters identifiability of the Escherichia coli K-12 W3110 dynamic model was investigated, composed by 18 differential ordinary equations and 35 kinetic rates, containing 125 parameters. With the procedure, model fit was improved for most of the measured metabolites, achieving 58 parameters estimated, including 5 unknown initial conditions. The results indicate that simultaneous parameters identifiability and estimation approach in metabolic networks is appealing, since model fit to the most of measured metabolites was possible even when important measures of intracellular metabolites and good initial estimates of parameters are not available.
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.
Kapicka, A.; Grison, H.; Petrovsky, E.; Jaksik, O.; Kodesova, R.
2015-12-01
Field measurements of magnetic susceptibility were carried out on regular grid, resulting in 101 data points at Brumovice and 65 at Vidim locality. Mass specific magnetic susceptibility χ and its frequency dependence χFD was used to estimate the significance of SP ferrimagnetic particles of pedogenic origin in topsoil horizons. The lowest magnetic susceptibility was obtained on the steep valley sides. Here the original topsoil was eroded and mixed by tillage with the soil substrate (loess). Soil profiles unaffected by erosion were investigated in detail. The vertical distribution of magnetic susceptibility along these "virgin" profiles was measured in laboratory on samples collected with 2-cm spacing. The differences between the distribution of susceptibility in the undisturbed soil profiles and the magnetic signal after uniform mixing of the soil material as a result of erosion and tillage are fundamental for the estimation of soil loss in the studied test fields. Maximum cumulative soil erosion depth in Brumovice and Vidim is around 100 cm and 50 cm respectively. The magnetic method is suitable for mapping at the chernozem localities and measurement of soil magnetic susceptibility is in this case useful and fast technique for quantitative estimation of soil loss caused by erosion. However, it is less suitable (due to lower magnetic differentiation with depth) in areas with luvisol as dominant soil unit. Acknowledgement: This study was supported by NAZV Agency of the Ministry of Agriculture of the Czech Republic through grant No QJ1230319.
Cs-137 migration in soil near NPPs
International Nuclear Information System (INIS)
Silant'ev, A.N.; Shkuratova, I.G.; Khatskevich, R.N.
1984-01-01
A convective-diffusion model has been employed for describing Cs-137 migration in soil. The migration parameters were determined by comparing the calculated vertical distribution profiles with the experimental ones. The migration parameters dependence on the soil state has been studied. Cs-137 penetration rate was found to be function of the soil type, surface state, soil wetness and orography. The obtained values are presented. A method is suggested for revealing the soil surface contamination by Cs-137 produced during NPP operation with distinguishing it from the global contamination background. For this purpose Cs-137 content in the upper 5 mm soil layer is estimated [ru
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
Kalman filter estimation of RLC parameters for UMP transmission line
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Mohd Amin Siti Nur Aishah
2018-01-01
Full Text Available This paper present the development of Kalman filter that allows evaluation in the estimation of resistance (R, inductance (L, and capacitance (C values for Universiti Malaysia Pahang (UMP short transmission line. To overcome the weaknesses of existing system such as power losses in the transmission line, Kalman Filter can be a better solution to estimate the parameters. The aim of this paper is to estimate RLC values by using Kalman filter that in the end can increase the system efficiency in UMP. In this research, matlab simulink model is developed to analyse the UMP short transmission line by considering different noise conditions to reprint certain unknown parameters which are difficult to predict. The data is then used for comparison purposes between calculated and estimated values. The results have illustrated that the Kalman Filter estimate accurately the RLC parameters with less error. The comparison of accuracy between Kalman Filter and Least Square method is also presented to evaluate their performances.
Iterative importance sampling algorithms for parameter estimation
Morzfeld, Matthias; Day, Marcus S.; Grout, Ray W.; Pau, George Shu Heng; Finsterle, Stefan A.; Bell, John B.
2016-01-01
In parameter estimation problems one computes a posterior distribution over uncertain parameters defined jointly by a prior distribution, a model, and noisy data. Markov Chain Monte Carlo (MCMC) is often used for the numerical solution of such problems. An alternative to MCMC is importance sampling, which can exhibit near perfect scaling with the number of cores on high performance computing systems because samples are drawn independently. However, finding a suitable proposal distribution is ...
Uncertainties in the Item Parameter Estimates and Robust Automated Test Assembly
Veldkamp, Bernard P.; Matteucci, Mariagiulia; de Jong, Martijn G.
2013-01-01
Item response theory parameters have to be estimated, and because of the estimation process, they do have uncertainty in them. In most large-scale testing programs, the parameters are stored in item banks, and automated test assembly algorithms are applied to assemble operational test forms. These algorithms treat item parameters as fixed values,…
Parameter estimation in nonlinear models for pesticide degradation
International Nuclear Information System (INIS)
Richter, O.; Pestemer, W.; Bunte, D.; Diekkrueger, B.
1991-01-01
A wide class of environmental transfer models is formulated as ordinary or partial differential equations. With the availability of fast computers, the numerical solution of large systems became feasible. The main difficulty in performing a realistic and convincing simulation of the fate of a substance in the biosphere is not the implementation of numerical techniques but rather the incomplete data basis for parameter estimation. Parameter estimation is a synonym for statistical and numerical procedures to derive reasonable numerical values for model parameters from data. The classical method is the familiar linear regression technique which dates back to the 18th century. Because it is easy to handle, linear regression has long been established as a convenient tool for analysing relationships. However, the wide use of linear regression has led to an overemphasis of linear relationships. In nature, most relationships are nonlinear and linearization often gives a poor approximation of reality. Furthermore, pure regression models are not capable to map the dynamics of a process. Therefore, realistic models involve the evolution in time (and space). This leads in a natural way to the formulation of differential equations. To establish the link between data and dynamical models, numerical advanced parameter identification methods have been developed in recent years. This paper demonstrates the application of these techniques to estimation problems in the field of pesticide dynamics. (7 refs., 5 figs., 2 tabs.)
Simple method for quick estimation of aquifer hydrogeological parameters
Ma, C.; Li, Y. Y.
2017-08-01
Development of simple and accurate methods to determine the aquifer hydrogeological parameters was of importance for groundwater resources assessment and management. Aiming at the present issue of estimating aquifer parameters based on some data of the unsteady pumping test, a fitting function of Theis well function was proposed using fitting optimization method and then a unitary linear regression equation was established. The aquifer parameters could be obtained by solving coefficients of the regression equation. The application of the proposed method was illustrated, using two published data sets. By the error statistics and analysis on the pumping drawdown, it showed that the method proposed in this paper yielded quick and accurate estimates of the aquifer parameters. The proposed method could reliably identify the aquifer parameters from long distance observed drawdowns and early drawdowns. It was hoped that the proposed method in this paper would be helpful for practicing hydrogeologists and hydrologists.
Compressive Parameter Estimation for Sparse Translation-Invariant Signals Using Polar Interpolation
DEFF Research Database (Denmark)
Fyhn, Karsten; Duarte, Marco F.; Jensen, Søren Holdt
2015-01-01
We propose new compressive parameter estimation algorithms that make use of polar interpolation to improve the estimator precision. Our work extends previous approaches involving polar interpolation for compressive parameter estimation in two aspects: (i) we extend the formulation from real non...... to attain good estimation precision and keep the computational complexity low. Our numerical experiments show that the proposed algorithms outperform existing approaches that either leverage polynomial interpolation or are based on a conversion to a frequency-estimation problem followed by a super...... interpolation increases the estimation precision....
Estimation of Parameters in Mean-Reverting Stochastic Systems
Directory of Open Access Journals (Sweden)
Tianhai Tian
2014-01-01
Full Text Available Stochastic differential equation (SDE is a very important mathematical tool to describe complex systems in which noise plays an important role. SDE models have been widely used to study the dynamic properties of various nonlinear systems in biology, engineering, finance, and economics, as well as physical sciences. Since a SDE can generate unlimited numbers of trajectories, it is difficult to estimate model parameters based on experimental observations which may represent only one trajectory of the stochastic model. Although substantial research efforts have been made to develop effective methods, it is still a challenge to infer unknown parameters in SDE models from observations that may have large variations. Using an interest rate model as a test problem, in this work we use the Bayesian inference and Markov Chain Monte Carlo method to estimate unknown parameters in SDE models.
Statistical distributions applications and parameter estimates
Thomopoulos, Nick T
2017-01-01
This book gives a description of the group of statistical distributions that have ample application to studies in statistics and probability. Understanding statistical distributions is fundamental for researchers in almost all disciplines. The informed researcher will select the statistical distribution that best fits the data in the study at hand. Some of the distributions are well known to the general researcher and are in use in a wide variety of ways. Other useful distributions are less understood and are not in common use. The book describes when and how to apply each of the distributions in research studies, with a goal to identify the distribution that best applies to the study. The distributions are for continuous, discrete, and bivariate random variables. In most studies, the parameter values are not known a priori, and sample data is needed to estimate parameter values. In other scenarios, no sample data is available, and the researcher seeks some insight that allows the estimate of ...
Hydropedological parameters limiting soil moisture regime floodplain ecosystems of south Moravia
Directory of Open Access Journals (Sweden)
Ladislav Kubík
2005-01-01
Full Text Available Soil moisture regime of floodplain ecosystems in southern Moravia is considerably influenced and greatly changed by human activities. It can be changed negatively by water management engineering or positively by landscape revitalizations. The paper deals with problems of hydropedological characteristics (hydrolimits limiting soil moisture regime and solves effect of hydrological factors on soil moisture regime in the floodplain ecosystems. Attention is paid especially to water retention curves and to hydrolimits – wilting point and field capacity. They can be acquired either directly by slow laboratory assessment, derivation from the water retention curves or indirectly by calculation using pedotransfer functions (PTF. This indirect assessment uses hydrolimit dependency on better available soil physical parameters namely soil granularity, bulk density and humus content. The aim is to calculate PTF for wilting point and field capacity and to compare them with measured values. The paper documents suitableness utilization of PTF for the region of interest. The results of correlation and regression analysis for soil moisture and groundwater table are furthermore presented.
Regional vegetation water effects on satellite soil moisture estimations for West Africa
Friesen, J.C.
2008-01-01
Soil moisture information is a vital parameter for water resources planning and food production. In particular for West Africa, where income largely depends on rainfed agriculture, reliable information on available soil water is required for modeling and prediction. Over large areas and,
Uncertainty estimation of core safety parameters using cross-correlations of covariance matrix
International Nuclear Information System (INIS)
Yamamoto, Akio; Yasue, Yoshihiro; Endo, Tomohiro; Kodama, Yasuhiro; Ohoka, Yasunori; Tatsumi, Masahiro
2013-01-01
An uncertainty reduction method for core safety parameters, for which measurement values are not obtained, is proposed. We empirically recognize that there exist some correlations among the prediction errors of core safety parameters, e.g., a correlation between the control rod worth and the assembly relative power at corresponding position. Correlations of errors among core safety parameters are theoretically estimated using the covariance of cross sections and sensitivity coefficients of core parameters. The estimated correlations of errors among core safety parameters are verified through the direct Monte Carlo sampling method. Once the correlation of errors among core safety parameters is known, we can estimate the uncertainty of a safety parameter for which measurement value is not obtained. (author)
Adaptive distributed parameter and input estimation in linear parabolic PDEs
Mechhoud, Sarra
2016-01-01
In this paper, we discuss the on-line estimation of distributed source term, diffusion, and reaction coefficients of a linear parabolic partial differential equation using both distributed and interior-point measurements. First, new sufficient identifiability conditions of the input and the parameter simultaneous estimation are stated. Then, by means of Lyapunov-based design, an adaptive estimator is derived in the infinite-dimensional framework. It consists of a state observer and gradient-based parameter and input adaptation laws. The parameter convergence depends on the plant signal richness assumption, whereas the state convergence is established using a Lyapunov approach. The results of the paper are illustrated by simulation on tokamak plasma heat transport model using simulated data.
Robust estimation of hydrological model parameters
Directory of Open Access Journals (Sweden)
A. Bárdossy
2008-11-01
Full Text Available The estimation of hydrological model parameters is a challenging task. With increasing capacity of computational power several complex optimization algorithms have emerged, but none of the algorithms gives a unique and very best parameter vector. The parameters of fitted hydrological models depend upon the input data. The quality of input data cannot be assured as there may be measurement errors for both input and state variables. In this study a methodology has been developed to find a set of robust parameter vectors for a hydrological model. To see the effect of observational error on parameters, stochastically generated synthetic measurement errors were applied to observed discharge and temperature data. With this modified data, the model was calibrated and the effect of measurement errors on parameters was analysed. It was found that the measurement errors have a significant effect on the best performing parameter vector. The erroneous data led to very different optimal parameter vectors. To overcome this problem and to find a set of robust parameter vectors, a geometrical approach based on Tukey's half space depth was used. The depth of the set of N randomly generated parameters was calculated with respect to the set with the best model performance (Nash-Sutclife efficiency was used for this study for each parameter vector. Based on the depth of parameter vectors, one can find a set of robust parameter vectors. The results show that the parameters chosen according to the above criteria have low sensitivity and perform well when transfered to a different time period. The method is demonstrated on the upper Neckar catchment in Germany. The conceptual HBV model was used for this study.
Targeted estimation of nuisance parameters to obtain valid statistical inference.
van der Laan, Mark J
2014-01-01
In order to obtain concrete results, we focus on estimation of the treatment specific mean, controlling for all measured baseline covariates, based on observing independent and identically distributed copies of a random variable consisting of baseline covariates, a subsequently assigned binary treatment, and a final outcome. The statistical model only assumes possible restrictions on the conditional distribution of treatment, given the covariates, the so-called propensity score. Estimators of the treatment specific mean involve estimation of the propensity score and/or estimation of the conditional mean of the outcome, given the treatment and covariates. In order to make these estimators asymptotically unbiased at any data distribution in the statistical model, it is essential to use data-adaptive estimators of these nuisance parameters such as ensemble learning, and specifically super-learning. Because such estimators involve optimal trade-off of bias and variance w.r.t. the infinite dimensional nuisance parameter itself, they result in a sub-optimal bias/variance trade-off for the resulting real-valued estimator of the estimand. We demonstrate that additional targeting of the estimators of these nuisance parameters guarantees that this bias for the estimand is second order and thereby allows us to prove theorems that establish asymptotic linearity of the estimator of the treatment specific mean under regularity conditions. These insights result in novel targeted minimum loss-based estimators (TMLEs) that use ensemble learning with additional targeted bias reduction to construct estimators of the nuisance parameters. In particular, we construct collaborative TMLEs (C-TMLEs) with known influence curve allowing for statistical inference, even though these C-TMLEs involve variable selection for the propensity score based on a criterion that measures how effective the resulting fit of the propensity score is in removing bias for the estimand. As a particular special
Characterization of soil water content variability and soil texture using GPR groundwave techniques
Energy Technology Data Exchange (ETDEWEB)
Grote, K.; Anger, C.; Kelly, B.; Hubbard, S.; Rubin, Y.
2010-08-15
Accurate characterization of near-surface soil water content is vital for guiding agricultural management decisions and for reducing the potential negative environmental impacts of agriculture. Characterizing the near-surface soil water content can be difficult, as this parameter is often both spatially and temporally variable, and obtaining sufficient measurements to describe the heterogeneity can be prohibitively expensive. Understanding the spatial correlation of near-surface soil water content can help optimize data acquisition and improve understanding of the processes controlling soil water content at the field scale. In this study, ground penetrating radar (GPR) methods were used to characterize the spatial correlation of water content in a three acre field as a function of sampling depth, season, vegetation, and soil texture. GPR data were acquired with 450 MHz and 900 MHz antennas, and measurements of the GPR groundwave were used to estimate soil water content at four different times. Additional water content estimates were obtained using time domain reflectometry measurements, and soil texture measurements were also acquired. Variograms were calculated for each set of measurements, and comparison of these variograms showed that the horizontal spatial correlation was greater for deeper water content measurements than for shallower measurements. Precipitation and irrigation were both shown to increase the spatial variability of water content, while shallowly-rooted vegetation decreased the variability. Comparison of the variograms of water content and soil texture showed that soil texture generally had greater small-scale spatial correlation than water content, and that the variability of water content in deeper soil layers was more closely correlated to soil texture than were shallower water content measurements. Lastly, cross-variograms of soil texture and water content were calculated, and co-kriging of water content estimates and soil texture
Evaluation of soil loss estimation using the RUSLE model and SCS-CN method in hillslope mining areas
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Narayan Kayet
2018-03-01
Full Text Available Mining operations result in the generation of barren land and spoil heaps which are subject to high erosion rate during the rainy season. The present study uses the Revised Universal Soil Loss Equation (RUSLE and SCS-CN (Soil Conservation Service - Curve Number process to estimate in Kiruburu and Meghahatuburu mining sites areas. The geospatial model of annual average soil loss rate was determined by integrating environmental variables parameters in a raster pixels-based GIS framework. GIS layers with, rainfall passivity and runoff erosivity (R, soil erodibility (K, slope length and steepness (LS, cover management(C and conservation practice (P factors were calculated to determine their effects on annual soil erosion in the study area. The coefficient of determination (r2 was 0.834, which indicates a strong correlation of soil loss with runoff and rainfall. Sub -watersheds 5,9,10 and 2 experienced high level of highly runoff. Average annual soil loss was calculated (30*30 m raster grid cell to determine the critical soil loss areas (Sub-watershed 9 and 5. Total soil erosion area was classified into five class, slight (10,025 ha, moderate (3125 ha, high (973 ha, very high (260 ha and severe (53 ha. The resulting map shows greatest soil erosion of >40 t h-1 y-1 (severe through connection to grassland, degraded and open forestry on the erect mining side-escutcheon. The Landsat pan sharpening image and DGPS survey field data were used in the verification of soil erosion results.
minimum variance estimation of yield parameters of rubber tree
African Journals Online (AJOL)
2013-03-01
Mar 1, 2013 ... It is our opinion that Kalman filter is a robust estimator of the ... Kalman filter, parameter estimation, rubber clones, Chow failure test, autocorrelation, STAMP, data ...... Mills, T.C. Modelling Current Temperature Trends.
Parameter Estimation of Multiple Frequency-Hopping Signals with Two Sensors
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Le Zuo
2018-04-01
Full Text Available This paper essentially focuses on parameter estimation of multiple wideband emitting sources with time-varying frequencies, such as two-dimensional (2-D direction of arrival (DOA and signal sorting, with a low-cost circular synthetic array (CSA consisting of only two rotating sensors. Our basic idea is to decompose the received data, which is a superimposition of phase measurements from multiple sources into separated groups and separately estimate the DOA associated with each source. Motivated by joint parameter estimation, we propose to adopt the expectation maximization (EM algorithm in this paper; our method involves two steps, namely, the expectation-step (E-step and the maximization (M-step. In the E-step, the correspondence of each signal with its emitting source is found. Then, in the M-step, the maximum-likelihood (ML estimates of the DOA parameters are obtained. These two steps are iteratively and alternatively executed to jointly determine the DOAs and sort multiple signals. Closed-form DOA estimation formulae are developed by ML estimation based on phase data, which also realize an optimal estimation. Directional ambiguity is also addressed by another ML estimation method based on received complex responses. The Cramer-Rao lower bound is derived for understanding the estimation accuracy and performance comparison. The verification of the proposed method is demonstrated with simulations.
Automatic smoothing parameter selection in GAMLSS with an application to centile estimation.
Rigby, Robert A; Stasinopoulos, Dimitrios M
2014-08-01
A method for automatic selection of the smoothing parameters in a generalised additive model for location, scale and shape (GAMLSS) model is introduced. The method uses a P-spline representation of the smoothing terms to express them as random effect terms with an internal (or local) maximum likelihood estimation on the predictor scale of each distribution parameter to estimate its smoothing parameters. This provides a fast method for estimating multiple smoothing parameters. The method is applied to centile estimation where all four parameters of a distribution for the response variable are modelled as smooth functions of a transformed explanatory variable x This allows smooth modelling of the location, scale, skewness and kurtosis parameters of the response variable distribution as functions of x. © The Author(s) 2013 Reprints and permissions: sagepub.co.uk/journalsPermissions.nav.
Estimation of G-renewal process parameters as an ill-posed inverse problem
International Nuclear Information System (INIS)
Krivtsov, V.; Yevkin, O.
2013-01-01
Statistical estimation of G-renewal process parameters is an important estimation problem, which has been considered by many authors. We view this problem from the standpoint of a mathematically ill-posed, inverse problem (the solution is not unique and/or is sensitive to statistical error) and propose a regularization approach specifically suited to the G-renewal process. Regardless of the estimation method, the respective objective function usually involves parameters of the underlying life-time distribution and simultaneously the restoration parameter. In this paper, we propose to regularize the problem by decoupling the estimation of the aforementioned parameters. Using a simulation study, we show that the resulting estimation/extrapolation accuracy of the proposed method is considerably higher than that of the existing methods
Estimation of soil water storage change from clay shrinkage using satellite radar interferometry
Brake, te Bram
2017-01-01
Measurements of soil water storage are hard to obtain on scales relevant for water management and policy making. Therefore, this research develops a new measurement methodology for soil water storage estimation in clay containing soils. The proposed methodology relies on the speciﬁc property of
Joint Multi-Fiber NODDI Parameter Estimation and Tractography using the Unscented Information Filter
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Yogesh eRathi
2016-04-01
Full Text Available Tracing white matter fiber bundles is an integral part of analyzing brain connectivity. An accurate estimate of the underlying tissue parameters is also paramount in several neuroscience applications. In this work, we propose to use a joint fiber model estimation and tractography algorithm that uses the NODDI (neurite orientation dispersion diffusion imaging model to estimate fiber orientation dispersion consistently and smoothly along the fiber tracts along with estimating the intracellular and extracellular volume fractions from the diffusion signal. While the NODDI model has been used in earlier works to estimate the microstructural parameters at each voxel independently, for the first time, we propose to integrate it into a tractography framework. We extend this framework to estimate the NODDI parameters for two crossing fibers, which is imperative to trace fiber bundles through crossings as well as to estimate the microstructural parameters for each fiber bundle separately. We propose to use the unscented information filter (UIF to accurately estimate the model parameters and perform tractography. The proposed approach has significant computational performance improvements as well as numerical robustness over the unscented Kalman filter (UKF. Our method not only estimates the confidence in the estimated parameters via the covariance matrix, but also provides the Fisher-information matrix of the state variables (model parameters, which can be quite useful to measure model complexity. Results from in-vivo human brain data sets demonstrate the ability of our algorithm to trace through crossing fiber regions, while estimating orientation dispersion and other biophysical model parameters in a consistent manner along the tracts.
Estimation of octanol/water partition coefficients using LSER parameters
Luehrs, Dean C.; Hickey, James P.; Godbole, Kalpana A.; Rogers, Tony N.
1998-01-01
The logarithms of octanol/water partition coefficients, logKow, were regressed against the linear solvation energy relationship (LSER) parameters for a training set of 981 diverse organic chemicals. The standard deviation for logKow was 0.49. The regression equation was then used to estimate logKow for a test of 146 chemicals which included pesticides and other diverse polyfunctional compounds. Thus the octanol/water partition coefficient may be estimated by LSER parameters without elaborate software but only moderate accuracy should be expected.
Ren, Yefei; Wen, Ruizhi; Yao, Xinxin; Ji, Kun
2017-08-01
The consideration of soil nonlinearity is important for the accurate estimation of the site response. To evaluate the soil nonlinearity during the 2008 Ms8.0 Wenchuan Earthquake, 33 strong-motion records obtained from the main shock and 890 records from 157 aftershocks were collected for this study. The horizontal-to-vertical spectral ratio (HVSR) method was used to calculate five parameters: the ratio of predominant frequency (RFp), degree of nonlinearity (DNL), absolute degree of nonlinearity (ADNL), frequency of nonlinearity (fNL), and percentage of nonlinearity (PNL). The purpose of this study was to evaluate the soil nonlinearity level of 33 strong-motion stations and to investigate the characteristics, performance, and effective usage of these five parameters. Their correlations with the peak ground acceleration (PGA), peak ground velocity (PGV), average uppermost 30-m shear-wave velocity ( V S30), and maximum amplitude of HVSR ( A max) were investigated. The results showed that all five parameters correlate well with PGA and PGV. The DNL, ADNL, and PNL also show a good correlation with A max, which means that the degree of soil nonlinearity not only depends on the ground-motion amplitude (e.g., PGA and PGV) but also on the site condition. The fNL correlates with PGA and PGV but shows no correlation with either A max or V S30, implying that the frequency width affected by the soil nonlinearity predominantly depends on the ground-motion amplitude rather than the site condition. At 16 of the 33 stations analyzed in this study, the site response showed evident (i.e., strong and medium) nonlinearity during the main shock of the Wenchuan Earthquake, where the ground-motion level was almost beyond the threshold of PGA > 200 cm/s2 or PGV > 15 cm/s. The site response showed weak and no nonlinearity at the other 14 and 3 stations. These results also confirm that RFp, DNL, ADNL, and PNL are effective in identifying the soil nonlinearity behavior. The identification
MCMC for parameters estimation by bayesian approach
International Nuclear Information System (INIS)
Ait Saadi, H.; Ykhlef, F.; Guessoum, A.
2011-01-01
This article discusses the parameter estimation for dynamic system by a Bayesian approach associated with Markov Chain Monte Carlo methods (MCMC). The MCMC methods are powerful for approximating complex integrals, simulating joint distributions, and the estimation of marginal posterior distributions, or posterior means. The MetropolisHastings algorithm has been widely used in Bayesian inference to approximate posterior densities. Calibrating the proposal distribution is one of the main issues of MCMC simulation in order to accelerate the convergence.
Estimating model parameters in nonautonomous chaotic systems using synchronization
International Nuclear Information System (INIS)
Yang, Xiaoli; Xu, Wei; Sun, Zhongkui
2007-01-01
In this Letter, a technique is addressed for estimating unknown model parameters of multivariate, in particular, nonautonomous chaotic systems from time series of state variables. This technique uses an adaptive strategy for tracking unknown parameters in addition to a linear feedback coupling for synchronizing systems, and then some general conditions, by means of the periodic version of the LaSalle invariance principle for differential equations, are analytically derived to ensure precise evaluation of unknown parameters and identical synchronization between the concerned experimental system and its corresponding receiver one. Exemplifies are presented by employing a parametrically excited 4D new oscillator and an additionally excited Ueda oscillator. The results of computer simulations reveal that the technique not only can quickly track the desired parameter values but also can rapidly respond to changes in operating parameters. In addition, the technique can be favorably robust against the effect of noise when the experimental system is corrupted by bounded disturbance and the normalized absolute error of parameter estimation grows almost linearly with the cutoff value of noise strength in simulation
Adaptive distributed parameter and input estimation in linear parabolic PDEs
Mechhoud, Sarra
2016-01-01
First, new sufficient identifiability conditions of the input and the parameter simultaneous estimation are stated. Then, by means of Lyapunov-based design, an adaptive estimator is derived in the infinite-dimensional framework. It consists of a state observer and gradient-based parameter and input adaptation laws. The parameter convergence depends on the plant signal richness assumption, whereas the state convergence is established using a Lyapunov approach. The results of the paper are illustrated by simulation on tokamak plasma heat transport model using simulated data.
Directory of Open Access Journals (Sweden)
Mousong Wu
2016-02-01
Full Text Available Water and energy processes in frozen soils are important for better understanding hydrologic processes and water resources management in cold regions. To investigate the water and energy balance in seasonally frozen soils, CoupModel combined with the generalized likelihood uncertainty estimation (GLUE method was used. Simulation work on water and heat processes in frozen soil in northern China during the 2012/2013 winter was conducted. Ensemble simulations through the Monte Carlo sampling method were generated for uncertainty analysis. Behavioral simulations were selected based on combinations of multiple model performance index criteria with respect to simulated soil water and temperature at four depths (5 cm, 15 cm, 25 cm, and 35 cm. Posterior distributions for parameters related to soil hydraulic, radiation processes, and heat transport indicated that uncertainties in both input and model structures could influence model performance in modeling water and heat processes in seasonally frozen soils. Seasonal courses in water and energy partitioning were obvious during the winter. Within the day-cycle, soil evaporation/condensation and energy distributions were well captured and clarified as an important phenomenon in the dynamics of the energy balance system. The combination of the CoupModel simulations with the uncertainty-based calibration method provides a way of understanding the seasonal courses of hydrology and energy processes in cold regions with limited data. Additional measurements may be used to further reduce the uncertainty of regulating factors during the different stages of freezing–thawing.
Parameter estimation and determinability analysis applied to Drosophila gap gene circuits
Directory of Open Access Journals (Sweden)
Jaeger Johannes
2008-09-01
Full Text Available Abstract Background Mathematical modeling of real-life processes often requires the estimation of unknown parameters. Once the parameters are found by means of optimization, it is important to assess the quality of the parameter estimates, especially if parameter values are used to draw biological conclusions from the model. Results In this paper we describe how the quality of parameter estimates can be analyzed. We apply our methodology to assess parameter determinability for gene circuit models of the gap gene network in early Drosophila embryos. Conclusion Our analysis shows that none of the parameters of the considered model can be determined individually with reasonable accuracy due to correlations between parameters. Therefore, the model cannot be used as a tool to infer quantitative regulatory weights. On the other hand, our results show that it is still possible to draw reliable qualitative conclusions on the regulatory topology of the gene network. Moreover, it improves previous analyses of the same model by allowing us to identify those interactions for which qualitative conclusions are reliable, and those for which they are ambiguous.
Estimation of delays and other parameters in nonlinear functional differential equations
Banks, H. T.; Lamm, P. K. D.
1983-01-01
A spline-based approximation scheme for nonlinear nonautonomous delay differential equations is discussed. Convergence results (using dissipative type estimates on the underlying nonlinear operators) are given in the context of parameter estimation problems which include estimation of multiple delays and initial data as well as the usual coefficient-type parameters. A brief summary of some of the related numerical findings is also given.
Nonparametric estimation of location and scale parameters
Potgieter, C.J.
2012-12-01
Two random variables X and Y belong to the same location-scale family if there are constants μ and σ such that Y and μ+σX have the same distribution. In this paper we consider non-parametric estimation of the parameters μ and σ under minimal assumptions regarding the form of the distribution functions of X and Y. We discuss an approach to the estimation problem that is based on asymptotic likelihood considerations. Our results enable us to provide a methodology that can be implemented easily and which yields estimators that are often near optimal when compared to fully parametric methods. We evaluate the performance of the estimators in a series of Monte Carlo simulations. © 2012 Elsevier B.V. All rights reserved.
Directory of Open Access Journals (Sweden)
Janusz P. KOGUT
Full Text Available Identification of geotechnical soil conditions often requires execution of laboratory tests, especially if you want to measure dynamic parameters of the soil. At present, the triaxial shear apparatus is widely applied in determination of the parameters of the soil. On the basis of the soil samples analysis, the examination results provide a wide range of data from basic performance parameters, e.g. internal friction angle and cohesion, to most complex ones like Young’s modulus permanent side effective stress of water samples. Furthermore, the Soil Structure Interaction Laboratory of Cracow University of Technology, has carried out the measurements of propagation of shear waves velocity with the use of bender elements tests. This work presents geotechnical conditions and the analysis of the results, which might be found useful to determine the transportation load parameters of designed S-7 and S-52 routes, as well as overall impact on soil/structure and surrounding areas located over the former clay open-pit mine. The landslides existing in the vicinity of the mine have prompted the authors to take that action.
Bayesian estimation of parameters in a regional hydrological model
Directory of Open Access Journals (Sweden)
K. Engeland
2002-01-01
Full Text Available This study evaluates the applicability of the distributed, process-oriented Ecomag model for prediction of daily streamflow in ungauged basins. The Ecomag model is applied as a regional model to nine catchments in the NOPEX area, using Bayesian statistics to estimate the posterior distribution of the model parameters conditioned on the observed streamflow. The distribution is calculated by Markov Chain Monte Carlo (MCMC analysis. The Bayesian method requires formulation of a likelihood function for the parameters and three alternative formulations are used. The first is a subjectively chosen objective function that describes the goodness of fit between the simulated and observed streamflow, as defined in the GLUE framework. The second and third formulations are more statistically correct likelihood models that describe the simulation errors. The full statistical likelihood model describes the simulation errors as an AR(1 process, whereas the simple model excludes the auto-regressive part. The statistical parameters depend on the catchments and the hydrological processes and the statistical and the hydrological parameters are estimated simultaneously. The results show that the simple likelihood model gives the most robust parameter estimates. The simulation error may be explained to a large extent by the catchment characteristics and climatic conditions, so it is possible to transfer knowledge about them to ungauged catchments. The statistical models for the simulation errors indicate that structural errors in the model are more important than parameter uncertainties. Keywords: regional hydrological model, model uncertainty, Bayesian analysis, Markov Chain Monte Carlo analysis
Estimation of common cause failure parameters with periodic tests
Energy Technology Data Exchange (ETDEWEB)
Barros, Anne [Institut Charles Delaunay - Universite de technologie de Troyes - FRE CNRS 2848, 12, rue Marie Curie - BP 2060 -10010 Troyes cedex (France)], E-mail: anne.barros@utt.fr; Grall, Antoine [Institut Charles Delaunay - Universite de technologie de Troyes - FRE CNRS 2848, 12, rue Marie Curie - BP 2060 -10010 Troyes cedex (France); Vasseur, Dominique [Electricite de France, EDF R and D - Industrial Risk Management Department 1, av. du General de Gaulle- 92141 Clamart (France)
2009-04-15
In the specific case of safety systems, CCF parameters estimators for standby components depend on the periodic test schemes. Classically, the testing schemes are either staggered (alternation of tests on redundant components) or non-staggered (all components are tested at the same time). In reality, periodic tests schemes performed on safety components are more complex and combine staggered tests, when the plant is in operation, to non-staggered tests during maintenance and refueling outage periods of the installation. Moreover, the CCF parameters estimators described in the US literature are derived in a consistent way with US Technical Specifications constraints that do not apply on the French Nuclear Power Plants for staggered tests on standby components. Given these issues, the evaluation of CCF parameters from the operating feedback data available within EDF implies the development of methodologies that integrate the testing schemes specificities. This paper aims to formally propose a solution for the estimation of CCF parameters given two distinct difficulties respectively related to a mixed testing scheme and to the consistency with EDF's specific practices inducing systematic non-simultaneity of the observed failures in a staggered testing scheme.
Directory of Open Access Journals (Sweden)
Daniel Renato Lammel
2015-10-01
Full Text Available Ecological processes regulating soil carbon (C and nitrogen (N cycles are still poorly understood, especially in the world’s largest agricultural frontier in Southern Amazonia. We analyzed soil parameters in samples from pristine rainforest and after land use change to pasture and crop fields, and correlated them with abundance of functional and phylogenetic marker genes (amoA, nirK, nirS, norB, nosZ, nifH, mcrA, pmoA, and 16S/18S rRNA. Additionally, we integrated these parameters using path analysis and multiple regressions. Following forest removal, concentrations of soil C and N declined, and pH and nutrient levels increased, which influenced microbial abundances and biogeochemical processes. A seasonal trend was observed, suggesting that abundances of microbial groups were restored to near native levels after the dry winter fallow. Integration of the marker gene abundances with soil parameters using path analysis and multiple regressions provided good predictions of biogeochemical processes, such as the fluxes of NO3, N2O, CO2, and CH4. In the wet season, agricultural soil showed the highest abundance of nitrifiers (amoA and Archaea, however forest soils showed the highest abundances of denitrifiers (nirK, nosZ and high N, which correlated with increased N2O emissions. Methanogens (mcrA and methanotrophs (pmoA were more abundant in forest soil, but methane flux was highest in pasture sites, which was related to soil compaction. Rather than analyzing direct correlations, the data integration using multivariate tools provided a better overview of biogeochemical processes. Overall, in the wet season, land use change from forest to agriculture reduced the abundance of different functional microbial groups related to the soil C and N cycles; integrating the gene abundance data and soil parameters provided a comprehensive overview of these interactions. Path analysis and multiple regressions addressed the need for more comprehensive approaches
Parameter and state estimation in nonlinear dynamical systems
Creveling, Daniel R.
This thesis is concerned with the problem of state and parameter estimation in nonlinear systems. The need to evaluate unknown parameters in models of nonlinear physical, biophysical and engineering systems occurs throughout the development of phenomenological or reduced models of dynamics. When verifying and validating these models, it is important to incorporate information from observations in an efficient manner. Using the idea of synchronization of nonlinear dynamical systems, this thesis develops a framework for presenting data to a candidate model of a physical process in a way that makes efficient use of the measured data while allowing for estimation of the unknown parameters in the model. The approach presented here builds on existing work that uses synchronization as a tool for parameter estimation. Some critical issues of stability in that work are addressed and a practical framework is developed for overcoming these difficulties. The central issue is the choice of coupling strength between the model and data. If the coupling is too strong, the model will reproduce the measured data regardless of the adequacy of the model or correctness of the parameters. If the coupling is too weak, nonlinearities in the dynamics could lead to complex dynamics rendering any cost function comparing the model to the data inadequate for the determination of model parameters. Two methods are introduced which seek to balance the need for coupling with the desire to allow the model to evolve in its natural manner without coupling. One method, 'balanced' synchronization, adds to the synchronization cost function a requirement that the conditional Lyapunov exponents of the model system, conditioned on being driven by the data, remain negative but small in magnitude. Another method allows the coupling between the data and the model to vary in time according to a specific form of differential equation. The coupling dynamics is damped to allow for a tendency toward zero coupling
Parameter estimation for lithium ion batteries
Santhanagopalan, Shriram
With an increase in the demand for lithium based batteries at the rate of about 7% per year, the amount of effort put into improving the performance of these batteries from both experimental and theoretical perspectives is increasing. There exist a number of mathematical models ranging from simple empirical models to complicated physics-based models to describe the processes leading to failure of these cells. The literature is also rife with experimental studies that characterize the various properties of the system in an attempt to improve the performance of lithium ion cells. However, very little has been done to quantify the experimental observations and relate these results to the existing mathematical models. In fact, the best of the physics based models in the literature show as much as 20% discrepancy when compared to experimental data. The reasons for such a big difference include, but are not limited to, numerical complexities involved in extracting parameters from experimental data and inconsistencies in interpreting directly measured values for the parameters. In this work, an attempt has been made to implement simplified models to extract parameter values that accurately characterize the performance of lithium ion cells. The validity of these models under a variety of experimental conditions is verified using a model discrimination procedure. Transport and kinetic properties are estimated using a non-linear estimation procedure. The initial state of charge inside each electrode is also maintained as an unknown parameter, since this value plays a significant role in accurately matching experimental charge/discharge curves with model predictions and is not readily known from experimental data. The second part of the dissertation focuses on parameters that change rapidly with time. For example, in the case of lithium ion batteries used in Hybrid Electric Vehicle (HEV) applications, the prediction of the State of Charge (SOC) of the cell under a variety of
Nonlinear systems time-varying parameter estimation: Application to induction motors
Energy Technology Data Exchange (ETDEWEB)
Kenne, Godpromesse [Laboratoire d' Automatique et d' Informatique Appliquee (LAIA), Departement de Genie Electrique, IUT FOTSO Victor, Universite de Dschang, B.P. 134 Bandjoun (Cameroon); Ahmed-Ali, Tarek [Ecole Nationale Superieure des Ingenieurs des Etudes et Techniques d' Armement (ENSIETA), 2 Rue Francois Verny, 29806 Brest Cedex 9 (France); Lamnabhi-Lagarrigue, F. [Laboratoire des Signaux et Systemes (L2S), C.N.R.S-SUPELEC, Universite Paris XI, 3 Rue Joliot Curie, 91192 Gif-sur-Yvette (France); Arzande, Amir [Departement Energie, Ecole Superieure d' Electricite-SUPELEC, 3 Rue Joliot Curie, 91192 Gif-sur-Yvette (France)
2008-11-15
In this paper, an algorithm for time-varying parameter estimation for a large class of nonlinear systems is presented. The proof of the convergence of the estimates to their true values is achieved using Lyapunov theories and does not require that the classical persistent excitation condition be satisfied by the input signal. Since the induction motor (IM) is widely used in several industrial sectors, the algorithm developed is potentially useful for adjusting the controller parameters of variable speed drives. The method proposed is simple and easily implementable in real-time. The application of this approach to on-line estimation of the rotor resistance of IM shows a rapidly converging estimate in spite of measurement noise, discretization effects, parameter uncertainties (e.g. inaccuracies on motor inductance values) and modeling inaccuracies. The robustness analysis for this IM application also revealed that the proposed scheme is insensitive to the stator resistance variations within a wide range. The merits of the proposed algorithm in the case of on-line time-varying rotor resistance estimation are demonstrated via experimental results in various operating conditions of the induction motor. The experimental results obtained demonstrate that the application of the proposed algorithm to update on-line the parameters of an adaptive controller (e.g. IM and synchronous machines adaptive control) can improve the efficiency of the industrial process. The other interesting features of the proposed method include fault detection/estimation and adaptive control of IM and synchronous machines. (author)
Using LUCAS topsoil database to estimate soil organic carbon content in local spectral libraries
Castaldi, Fabio; van Wesemael, Bas; Chabrillat, Sabine; Chartin, Caroline
2017-04-01
The quantification of the soil organic carbon (SOC) content over large areas is mandatory to obtain accurate soil characterization and classification, which can improve site specific management at local or regional scale exploiting the strong relationship between SOC and crop growth. The estimation of the SOC is not only important for agricultural purposes: in recent years, the increasing attention towards global warming highlighted the crucial role of the soil in the global carbon cycle. In this context, soil spectroscopy is a well consolidated and widespread method to estimate soil variables exploiting the interaction between chromophores and electromagnetic radiation. The importance of spectroscopy in soil science is reflected by the increasing number of large soil spectral libraries collected in the world. These large libraries contain soil samples derived from a consistent number of pedological regions and thus from different parent material and soil types; this heterogeneity entails, in turn, a large variability in terms of mineralogical and organic composition. In the light of the huge variability of the spectral responses to SOC content and composition, a rigorous classification process is necessary to subset large spectral libraries and to avoid the calibration of global models failing to predict local variation in SOC content. In this regard, this study proposes a method to subset the European LUCAS topsoil database into soil classes using a clustering analysis based on a large number of soil properties. The LUCAS database was chosen to apply a standardized multivariate calibration approach valid for large areas without the need for extensive field and laboratory work for calibration of local models. Seven soil classes were detected by the clustering analyses and the samples belonging to each class were used to calibrate specific partial least square regression (PLSR) models to estimate SOC content of three local libraries collected in Belgium (Loam belt
International Nuclear Information System (INIS)
Al-Ain, F.; Attar, J.; Hussein, F.
2007-05-01
A field experiment was conducted on sandy clay and clayey soils at Deir Ezzor to compare the performance of Neutron Scattering (NS) relative to a capacitance probe (CP), Diviner2000, in our local conditions under saline soils. The effect of soil electrical conductivity (ECe) and bulk density (?b) on the precession, accuracy and sensitivity of the tested equipment s were evaluated. Also, the ability to improve the calibration equation for these equipment s, by including ECe and ?b as independent variables in the equation formula, was studied. The study showed that, Diviner2000 was very sensitive to soil bulk density and electrical conductivity of the soil (i.e. soil salinity) compared to the NS. Multiple non-linear regressions improved the fitting when both parameters (?b and ECe) were included in the equation, even though the correlation coefficient (R2) remained low in the case of Diviner2000.(author)
Accuracy and sensitivity analysis on seismic anisotropy parameter estimation
Yan, Fuyong; Han, De-Hua
2018-04-01
There is significant uncertainty in measuring the Thomsen’s parameter δ in laboratory even though the dimensions and orientations of the rock samples are known. It is expected that more challenges will be encountered in the estimating of the seismic anisotropy parameters from field seismic data. Based on Monte Carlo simulation of vertical transversely isotropic layer cake model using the database of laboratory anisotropy measurement from the literature, we apply the commonly used quartic non-hyperbolic reflection moveout equation to estimate the seismic anisotropy parameters and test its accuracy and sensitivities to the source-receive offset, vertical interval velocity error and time picking error. The testing results show that the methodology works perfectly for noise-free synthetic data with short spread length. However, this method is extremely sensitive to the time picking error caused by mild random noises, and it requires the spread length to be greater than the depth of the reflection event. The uncertainties increase rapidly for the deeper layers and the estimated anisotropy parameters can be very unreliable for a layer with more than five overlain layers. It is possible that an isotropic formation can be misinterpreted as a strong anisotropic formation. The sensitivity analysis should provide useful guidance on how to group the reflection events and build a suitable geological model for anisotropy parameter inversion.
Jonard, François
2015-06-01
In this paper, we experimentally analyzed the feasibility of estimating soil hydraulic properties from 1.4 GHz radiometer and 0.8-2.6 GHz ground-penetrating radar (GPR) data. Radiometer and GPR measurements were performed above a sand box, which was subjected to a series of vertical water content profiles in hydrostatic equilibrium with a water table located at different depths. A coherent radiative transfer model was used to simulate brightness temperatures measured with the radiometer. GPR data were modeled using full-wave layered medium Green\\'s functions and an intrinsic antenna representation. These forward models were inverted to optimally match the corresponding passive and active microwave data. This allowed us to reconstruct the water content profiles, and thereby estimate the sand water retention curve described using the van Genuchten model. Uncertainty of the estimated hydraulic parameters was quantified using the Bayesian-based DREAM algorithm. For both radiometer and GPR methods, the results were in close agreement with in situ time-domain reflectometry (TDR) estimates. Compared with radiometer and TDR, much smaller confidence intervals were obtained for GPR, which was attributed to its relatively large bandwidth of operation, including frequencies smaller than 1.4 GHz. These results offer valuable insights into future potential and emerging challenges in the development of joint analyses of passive and active remote sensing data to retrieve effective soil hydraulic properties.
Probabilistic estimation of the constitutive parameters of polymers
Directory of Open Access Journals (Sweden)
Siviour C.R.
2012-08-01
Full Text Available The Mulliken-Boyce constitutive model predicts the dynamic response of crystalline polymers as a function of strain rate and temperature. This paper describes the Mulliken-Boyce model-based estimation of the constitutive parameters in a Bayesian probabilistic framework. Experimental data from dynamic mechanical analysis and dynamic compression of PVC samples over a wide range of strain rates are analyzed. Both experimental uncertainty and natural variations in the material properties are simultaneously considered as independent and joint distributions; the posterior probability distributions are shown and compared with prior estimates of the material constitutive parameters. Additionally, particular statistical distributions are shown to be effective at capturing the rate and temperature dependence of internal phase transitions in DMA data.
Estimating 3D Object Parameters from 2D Grey-Level Images
Houkes, Z.
2000-01-01
This thesis describes a general framework for parameter estimation, which is suitable for computer vision applications. The approach described combines 3D modelling, animation and estimation tools to determine parameters of objects in a scene from 2D grey-level images. The animation tool predicts
Bogner, Christina; Kühnel, Anna; Hepp, Johannes; Huwe, Bernd
2016-04-01
The Kilimanjaro region in Tanzania constitutes a particularity compared to other areas in the country. Because enough water is available the population grows rapidly and large areas are converted from natural ecosystems to agricultural areas. Therefore, the southern slopes of Mt. Kilimanjaro encompass a complex mosaic of different land uses like coffee plantations, maize, agroforestry or natural savannah. Coffee is an important cash crop in the region and is owned mostly by large companies. In contrast, the agroforestry is a traditional way of agriculture and has been sustained by the Chagga tribe for centuries. These so called homegardens are organised as multi-level systems and contain a mixture of different crops. Correlations in soil and vegetation data may serve as indicators for crop and management impacts associated to different types of land use. We hypothesize that Chagga homegardens, for example, show a more pronounced spatial autocorrelation compared to coffee plantations due to manifold above and belowground crop structures, whereas the degree of anisotropy is assumed to be higher in the coffee sites due to linear elements in management. Furthermore, we hypothesize that the overall diversity of soil parameters in homegardens on a larger scale is higher, as individual owners manage their field differently, whereas coffee plantation management often follows general rules. From these general hypotheses we derive two specific research questions: a) Are there characteristic differences in the spatial organisation of soil physical parameters of different land uses? b) Is there a recognizable relationship between vegetation structure and soil physical parameters of topsoils? We measured soil physical parameters in the topsoil (bulk density, stone content, texture, soil moisture and penetration resistance). Additionally, we took spectra of soil samples with a portable VIS-NIR spectrometer to determine C and N and measured leaf area index and troughfall as an
The parameters controlling the strength of soil-steel structures
International Nuclear Information System (INIS)
Barkhordari, M. A.; Abdel-Sayed, G.
2001-01-01
The present paper examines the ultimate load carrying capacity of soil-steel structures taking into consideration the sequence of the developments of plastic hinges, their location, and their sustained plastic moment. Non-linear analysis has been conducted using a micro-computer program in which a structural model is applied with the soil replaced by normal and tangential springs acting at the nodal points of a polygon representing the conduit wall. A comparative study has been conducted for the parameters which affect the load carrying capacity of soil-steel structure, leading to the following conclusions: (1) the load carrying capacity of the composite structure is significantly affected by the shear stiffness (or friction) of the surrounding soil; (2) the conduit span may be used when calculating the buckling load rather than the local radius of the conduit wall; (3) circular arches with sector angle of less than 180 d eg have higher load carrying capacity than equivalent re-entrant arches, i.e. arches with sector angle of more than 180 d eg; (4) the buckling load of the conduit is slightly affected by the rigidity of the lower zone of the conduit wall; (5) eccentric application of the load has practically little effect on its load carrying capacity
Estimations of parameters in Pareto reliability model in the presence of masked data
International Nuclear Information System (INIS)
Sarhan, Ammar M.
2003-01-01
Estimations of parameters included in the individual distributions of the life times of system components in a series system are considered in this paper based on masked system life test data. We consider a series system of two independent components each has a Pareto distributed lifetime. The maximum likelihood and Bayes estimators for the parameters and the values of the reliability of the system's components at a specific time are obtained. Symmetrical triangular prior distributions are assumed for the unknown parameters to be estimated in obtaining the Bayes estimators of these parameters. Large simulation studies are done in order: (i) explain how one can utilize the theoretical results obtained; (ii) compare the maximum likelihood and Bayes estimates obtained of the underlying parameters; and (iii) study the influence of the masking level and the sample size on the accuracy of the estimates obtained
Quasi-Newton methods for parameter estimation in functional differential equations
Brewer, Dennis W.
1988-01-01
A state-space approach to parameter estimation in linear functional differential equations is developed using the theory of linear evolution equations. A locally convergent quasi-Newton type algorithm is applied to distributed systems with particular emphasis on parameters that induce unbounded perturbations of the state. The algorithm is computationally implemented on several functional differential equations, including coefficient and delay estimation in linear delay-differential equations.
SOIL LOSS IN SAMARU ZARIA NIGERIA: A COMPARISON OF ...
African Journals Online (AJOL)
user
2013-07-02
Jul 2, 2013 ... Soil erosion data generated while estimating soil loss in Samaru, Zaria using the EUROSEM model were used as input parameters for the prediction of soil loss in the ... In recent times, the evaluation of soil erosion ... A number of empirically and physically based ... measured data from experimental plots.
Estimating Arrhenius parameters using temperature programmed molecular dynamics
International Nuclear Information System (INIS)
Imandi, Venkataramana; Chatterjee, Abhijit
2016-01-01
Kinetic rates at different temperatures and the associated Arrhenius parameters, whenever Arrhenius law is obeyed, are efficiently estimated by applying maximum likelihood analysis to waiting times collected using the temperature programmed molecular dynamics method. When transitions involving many activated pathways are available in the dataset, their rates may be calculated using the same collection of waiting times. Arrhenius behaviour is ascertained by comparing rates at the sampled temperatures with ones from the Arrhenius expression. Three prototype systems with corrugated energy landscapes, namely, solvated alanine dipeptide, diffusion at the metal-solvent interphase, and lithium diffusion in silicon, are studied to highlight various aspects of the method. The method becomes particularly appealing when the Arrhenius parameters can be used to find rates at low temperatures where transitions are rare. Systematic coarse-graining of states can further extend the time scales accessible to the method. Good estimates for the rate parameters are obtained with 500-1000 waiting times.
Estimating Arrhenius parameters using temperature programmed molecular dynamics
Energy Technology Data Exchange (ETDEWEB)
Imandi, Venkataramana; Chatterjee, Abhijit, E-mail: abhijit@che.iitb.ac.in [Department of Chemical Engineering, Indian Institute of Technology Bombay, Mumbai 400076 (India)
2016-07-21
Kinetic rates at different temperatures and the associated Arrhenius parameters, whenever Arrhenius law is obeyed, are efficiently estimated by applying maximum likelihood analysis to waiting times collected using the temperature programmed molecular dynamics method. When transitions involving many activated pathways are available in the dataset, their rates may be calculated using the same collection of waiting times. Arrhenius behaviour is ascertained by comparing rates at the sampled temperatures with ones from the Arrhenius expression. Three prototype systems with corrugated energy landscapes, namely, solvated alanine dipeptide, diffusion at the metal-solvent interphase, and lithium diffusion in silicon, are studied to highlight various aspects of the method. The method becomes particularly appealing when the Arrhenius parameters can be used to find rates at low temperatures where transitions are rare. Systematic coarse-graining of states can further extend the time scales accessible to the method. Good estimates for the rate parameters are obtained with 500-1000 waiting times.
International Nuclear Information System (INIS)
Zeng, G.L.; Gullberg, G.T.
1995-01-01
It is common practice to estimate kinetic parameters from dynamically acquired tomographic data by first reconstructing a dynamic sequence of three-dimensional reconstructions and then fitting the parameters to time activity curves generated from the time-varying reconstructed images. However, in SPECT, the pharmaceutical distribution can change during the acquisition of a complete tomographic data set, which can bias the estimated kinetic parameters. It is hypothesized that more accurate estimates of the kinetic parameters can be obtained by fitting to the projection measurements instead of the reconstructed time sequence. Estimation from projections requires the knowledge of their relationship between the tissue regions of interest or voxels with particular kinetic parameters and the project measurements, which results in a complicated nonlinear estimation problem with a series of exponential factors with multiplicative coefficients. A technique is presented in this paper where the exponential decay parameters are estimated separately using linear time-invariant system theory. Once the exponential factors are known, the coefficients of the exponentials can be estimated using linear estimation techniques. Computer simulations demonstrate that estimation of the kinetic parameters directly from the projections is more accurate than the estimation from the reconstructed images
Parameter Estimation of Damped Compound Pendulum Differential Evolution Algorithm
Directory of Open Access Journals (Sweden)
Saad Mohd Sazli
2016-01-01
Full Text Available This paper present the parameter identification of damped compound pendulum using differential evolution algorithm. The procedure used to achieve the parameter identification of the experimental system consisted of input output data collection, ARX model order selection and parameter estimation using conventional method least square (LS and differential evolution (DE algorithm. PRBS signal is used to be input signal to regulate the motor speed. Whereas, the output signal is taken from position sensor. Both, input and output data is used to estimate the parameter of the ARX model. The residual error between the actual and predicted output responses of the models is validated using mean squares error (MSE. Analysis showed that, MSE value for LS is 0.0026 and MSE value for DE is 3.6601×10-5. Based results obtained, it was found that DE have lower MSE than the LS method.
Improved Battery Parameter Estimation Method Considering Operating Scenarios for HEV/EV Applications
Directory of Open Access Journals (Sweden)
Jufeng Yang
2016-12-01
Full Text Available This paper presents an improved battery parameter estimation method based on typical operating scenarios in hybrid electric vehicles and pure electric vehicles. Compared with the conventional estimation methods, the proposed method takes both the constant-current charging and the dynamic driving scenarios into account, and two separate sets of model parameters are estimated through different parts of the pulse-rest test. The model parameters for the constant-charging scenario are estimated from the data in the pulse-charging periods, while the model parameters for the dynamic driving scenario are estimated from the data in the rest periods, and the length of the fitted dataset is determined by the spectrum analysis of the load current. In addition, the unsaturated phenomenon caused by the long-term resistor-capacitor (RC network is analyzed, and the initial voltage expressions of the RC networks in the fitting functions are improved to ensure a higher model fidelity. Simulation and experiment results validated the feasibility of the developed estimation method.
Directory of Open Access Journals (Sweden)
M. Zribi
2011-01-01
Full Text Available The present paper proposes a method for the evaluation of soil evaporation, using soil moisture estimations based on radar satellite measurements. We present firstly an approach for the estimation and monitoring of soil moisture in a semi-arid region in North Africa, using ENVISAT ASAR images, over two types of vegetation covers. The first mapping process is dedicated solely to the monitoring of moisture variability related to rainfall events, over areas in the "non-irrigated olive tree" class of land use. The developed approach is based on a simple linear relationship between soil moisture and the backscattered radar signal normalised at a reference incidence angle. The second process is proposed over wheat fields, using an analysis of moisture variability due to both rainfall and irrigation. A semi-empirical model, based on the water-cloud model for vegetation correction, is used to retrieve soil moisture from the radar signal. Moisture mapping is carried out over wheat fields, showing high variability between irrigated and non-irrigated wheat covers. This analysis is based on a large database, including both ENVISAT ASAR and simultaneously acquired ground-truth measurements (moisture, vegetation, roughness, during the 2008–2009 vegetation cycle. Finally, a semi-empirical approach is proposed in order to relate surface moisture to the difference between soil evaporation and the climate demand, as defined by the potential evaporation. Mapping of the soil evaporation is proposed.
Linear Regression between CIE-Lab Color Parameters and Organic Matter in Soils of Tea Plantations
Chen, Yonggen; Zhang, Min; Fan, Dongmei; Fan, Kai; Wang, Xiaochang
2018-02-01
To quantify the relationship between the soil organic matter and color parameters using the CIE-Lab system, 62 soil samples (0-10 cm, Ferralic Acrisols) from tea plantations were collected from southern China. After air-drying and sieving, numerical color information and reflectance spectra of soil samples were measured under laboratory conditions using an UltraScan VIS (HunterLab) spectrophotometer equipped with CIE-Lab color models. We found that soil total organic carbon (TOC) and nitrogen (TN) contents were negatively correlated with the L* value (lightness) ( r = -0.84 and -0.80, respectively), a* value (correlation coefficient r = -0.51 and -0.46, respectively) and b* value ( r = -0.76 and -0.70, respectively). There were also linear regressions between TOC and TN contents with the L* value and b* value. Results showed that color parameters from a spectrophotometer equipped with CIE-Lab color models can predict TOC contents well for soils in tea plantations. The linear regression model between color values and soil organic carbon contents showed it can be used as a rapid, cost-effective method to evaluate content of soil organic matter in Chinese tea plantations.
Directory of Open Access Journals (Sweden)
Ugro Hari Murtiono
2008-12-01
Full Text Available Hydrologic modelling has been developing and it is usefull for basic data in managing water resources. The aim of the reseach is to estimate volume runoff, maximum discharge, and soil erosion with SCS, Rational, and MUSLE models on Keduang Watershed. Explain the data analysis, and flow to get the data. SCS parameters model use are: runoff, rainfall, deferent between rainfall runoff. The deferent rainfall between runoff relationship kurva Runoff Coefisient (Curve Nunmber/CN. This Coefisient connected with Soil Hydrology Group (antecedent moisture content/AMC, landuse, and cultivation method. Rational pa