scholarly journals Salinity and sodicity hazard in water flow processes in the soil

2011 ◽  
Vol 49 (No. 7) ◽  
pp. 314-320 ◽  
Author(s):  
F. Burger ◽  
A. Čelková
Keyword(s):  
Electrical Conductivity ◽  
Interpolation Method ◽  
Regional Scale ◽  
Water Environment ◽  
Sodium Adsorption Ratio ◽  
Kriging Interpolation ◽  
High Sodium ◽  
Kriging Interpolation Method ◽  
Subsurface Soil ◽  
High Hazard

This paper presents the results of the distribution of salinity characteristics (electrical conductivity and sodium adsorption ratio) of groundwater, and based on the results, it reports the evaluation of the salinity and sodicity hazards in the fluctuation processes of shallow mineralised groundwater, or in the processes if such groundwater is used for irrigation. The issue was studied for the soil-water environment in the south-east of the Danube Lowlands for the period 1991 to 1994. The measured data and data taken from archives were processed in the form of graphical attachments (appendixes, supplements, graphical documentation) – maps, by means of the kriging interpolation method. Groundwater in the area in question is classified as highly mineralised with a high hazard of salinisation of the subsurface soil environment. The average annual values of the electrical conductivity of groundwater ranged from 600 to 2100 µS/cm in the examined period. The sodium adsorption ratio values ranged from 1.7 to 22.0 and indicate low, medium to high sodium salinisation of the environment due to groundwater. The distribution of electrical conductivity and sodium adsorption ratio on the regional scale can serve as a reference basis for the evaluation of changes in the groundwater salinity after 1994.

scholarly journals Differences between laboratory and sensor analyses for soil attributes

2021 ◽  
Vol 51 ◽  
Author(s):  
Gabriel Benhossi ◽  
Étore Francisco Reynaldo ◽  
Thiago Martins Machado
Keyword(s):  
Electrical Conductivity ◽  
Organic Matter ◽  
Precision Agriculture ◽  
Interpolation Method ◽  
Kriging Interpolation ◽  
Statistical Correlations ◽  
Kriging Interpolation Method ◽  
Map Generation ◽  
Operational Capacity

ABSTRACT Sensors used in precision agriculture can perform readings of georeferenced points aiming to determine the electrical conductivity, pH and organic matter. These devices have a high operational capacity, but with little information on the quality of their collected data. This study aimed to establish correlations and differences among electrical conductivity, pH and organic matter data collected by sensor and obtained by laboratory analyses. The results were analyzed by statistical correlations, geostatistical analyses and map generation by the kriging interpolation method of electrical conductivity, pH and organic matter, using dedicated softwares. The sensor showed that the data collected for electrical conductivity, pH and organic matter are reliable and robust, due to the high spatial dependence and correct sampling distance confirmed by the range values.

scholarly journals The SAFER geodatabase for the Kathmandu valley: Bayesian kriging for data-scarce regions

2020 ◽  
pp. 875529302097097
Author(s):  
Raffaele De Risi ◽  
Flavia De Luca ◽  
Charlotte EL Gilder ◽  
Rama Mohan Pokhrel ◽  
Paul J Vardanega
Keyword(s):  
Interpolation Method ◽  
Regional Scale ◽  
Geographical Area ◽  
The United States ◽  
Kriging Interpolation ◽  
United States Geological Survey ◽  
Kathmandu Valley ◽  
Precision Data ◽  
Geostatistical Methods ◽  
Kriging Interpolation Method

Geostatistical methods are valuable to better understand the spatial distribution of geotechnical parameters at regional scale and to optimize the locations of future ground investigations. This article investigates the use of the kriging interpolation method to extend the knowledge of a specific geotechnical property from a few sites to a broader geographical area with a focus on the Kathmandu valley (Nepal). A Bayesian form of kriging is proposed in this article. The estimation of the shear wave velocity in the uppermost 30 m of soil ( VS30) in the Kathmandu valley is examined. Slope-based VS30 estimates from the United States Geological Survey are used as prior information, and 15 VS30 measurements are used as more precise data. Considering the limited number of high-quality VS30 measurements available in the valley, it is shown that the Bayesian scheme can lead to a more robust estimation of VS30 than that obtained with the ordinary kriging approach. A methodology for conditioning prior low-precision data to the measurements is also presented.

Numerical Investigation of the Identifiability of Elastomer Mechanical Properties by Nano-Indentation and Shape-Manifold Approach

2021 ◽  
Author(s):  
Oumaima Ezzaamari ◽  
Guénhaël Le Quilliec ◽  
Florian Lacroix ◽  
Stéphane Méo
Keyword(s):  
Numerical Study ◽  
Interpolation Method ◽  
Structural Characteristics ◽  
Numerical Approach ◽  
Kriging Interpolation ◽  
Nano Indentation ◽  
Kriging Interpolation Method ◽  
Elastomeric Materials ◽  
Hyper Elastic ◽  
Characterization Test

ABSTRACT Various research is covering instrumented nano-indentation in the literature. However, studies on this characterization test remain limited when it comes to the local mechanical behavior of elastomeric materials. The application of nano-indentation on these materials is a difficult task given their complex mechanical and structural characteristics. We try to overcome these experimental limitations and find an effective numerical approach for local mechanical characterization of hyper-elastic materials. For such needs, we carried out a numerical study based on model reduction and shape manifold approach to investigate the parameters identification of different hyper-elastic constitutive laws by using instrumented indentation. Similarly, we studied the influence of the indenter geometry, the friction coefficient variation, and finally the indented material height effect. To this end, we constructed a reduced order model through a design of experiments by proper orthogonal decomposition combined with the kriging interpolation method.

scholarly journals Estimation of Ice Thickness and the Features of Subglacial Media Detected by Ground Penetrating Radar at the Baishui River Glacier No. 1 in Mt. Yulong, China

2020 ◽  
Vol 12 (24) ◽  
pp. 4105
Author(s):  
Jing Liu ◽  
Shijin Wang ◽  
Yuanqing He ◽  
Yuqiang Li ◽  
Yuzhe Wang ◽  
...  
Keyword(s):  
Ground Penetrating Radar ◽  
Interpolation Method ◽  
Kriging Interpolation ◽  
Ice Thickness ◽  
Grid Data ◽  
Differential Gps ◽  
Landsat Images ◽  
Kriging Interpolation Method ◽  
Bedrock Surface ◽  
Ground Penetrating

Using ground-penetrating radar (GPR), we measured and estimated the ice thickness of the Baishui River Glacier No. 1 of Yulong Snow Mountain. According to the position of the reflected media from the GPR image, combined with the radar waveform amplitude and polarity change information, the ice thickness and the changing medium position at the bottom of this temperate glacier were identified. Water paths were found in the measured ice, including ice caves and crevasses. A debris-rich ice layer was found at the bottom of the glacier, which produces strong abrasion and ploughing action at the bedrock surface. This results in the formation of different detrital layers stagnated at the ice-bedrock interface and numerous crevasses on the bedrock surface. Based on the obtained ice thickness and differential GPS data, combined with Landsat images, the kriging interpolation method was used to obtain grid data. The average ice thickness was 52.48 m and between 4740 and 4890 m above sea level, with a maximum depth of 92.83 m. The bedrock topography map of this area was drawn using digital elevation model from the Shuttle Radar Topography Mission. The central part of the glacier was characterized by small ice basins with distributed ice steps and ice ridges at the upper and lower parts.

Numerical Modeling of Fracture Process Using Element-Free Method

2013 ◽  
Vol 427-429 ◽  
pp. 146-149
Author(s):  
Cheng Fan
Keyword(s):  
Interpolation Method ◽  
Kriging Interpolation ◽  
Computational Accuracy ◽  
Simple Method ◽  
Solution Domain ◽  
Kriging Interpolation Method ◽  
Finite Covers ◽  
Crack Problems ◽  
Cover Systems ◽  
Element Free

A new element-free formulation of Kriging interpolation procedure based on finite covers technique and Kriging interpolation method which integrates the flexibilities of the manifold method in dealing with discontinuity and the element-free features of the moving Kriging interpolation. Two cover systems are employed in this method. Mathematical cover of the solution domain under consideration are used to construct shape function and physical cover is used to reproduce the geometry of the solution domain. The mathematical covers can take any types of shape and is much easily formed compared with those in the conventional MM. The presented method can overcome some difficulties in conventional element-free Galerkin methods in treating discontinuous crack problems. The fundamental theory of this procedure is illustrated and numerical analyses of examples show that the proposed procedure is an effective and simple method with higher computational accuracy.

Integration of statistical and spatial methods for distributing precipitation in tropical areas

2012 ◽  
Vol 44 (6) ◽  
pp. 982-994 ◽  
Author(s):  
Mandana Abedini ◽  
Md Azlin Md Said ◽  
Fauziah Ahmad
Keyword(s):  
Multivariate Regression ◽  
Interpolation Method ◽  
Rainfall Data ◽  
Environmental Research ◽  
Kriging Interpolation ◽  
Slope Aspect ◽  
Annual Average ◽  
Rain Gauges ◽  
Kriging Interpolation Method ◽  
Topographical Factors

The high spatial resolution of precipitation distribution is a major concern for experts in environmental research and planning. This paper establishes a combination of multivariate regression algorithm and spatial analysis to predict distribution of precipitation, considering the four topographical factors of altitude, slope, aspect and location. Annual average and seasonal rainfall data were collected in nine rain gauges in Ulu Kinta Catchment in East Malaysia from 1974 to 2010. To examine records and fill gaps from long-term rain gauges, homogeneity analysis was performed using the double-mass curve method. Estimated missing rainfall data were also tested using index gauges from network rainfall stations. Multivariate regression analysis was conducted to propose an empirical equation for the study area. Topographical factors were considered from a 90 m resolution digital elevation model. The multivariate regression model was found to clarify 74% of spatial variability of precipitation on annual average and 78% during wet season. However, the correlation coefficient for the dry season decreased sharply to 63%. By using the kriging interpolation method, the estimated annual average improved to 78.4%; the average improved to 65.2 and 80.3% in the dry and wet seasons, respectively. This confirms the efficiency and significance of the model and its potential for use in other tropical catchments.

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