Advantages of using the kriging interpolator to estimate the gravity surface, comparison and spatial variability of gravity data in the El Kef-Ouargha region (northern Tunisia)

Arfaoui, Mohamed; Inoubli, Mohamed Hédi

doi:10.1007/s12517-012-0549-y

Cited by 13 publications

(9 citation statements)

References 14 publications

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“…The total data sample (N) was 42120, and the linear relationship between the ERA-I and NCEP/ NCAR data sets is given by Eqs. (12) and 13, respectively. With Eq.…”

Section: Discussionmentioning

confidence: 99%

“…The kriging interpolation technique was adopted in view of its anisotropic and geometric output advantages over other techniques. The kriging interpolation algorithm produces an unbiased prediction, calculates the spatial distribution of uncertainty and allows for the estimation of the errors in an interpolation for any particular point [12,17,42,46]. The spatio-temporal water vapour and climatological maps of the study area (ERA-I and NCEP/NCAR, respectively) were produced for the period of 2007-2017.…”

Section: Data Processing and Time-series Mappingmentioning

confidence: 99%

“…Therefore, Eqs. (12) and (13) are sufficient for the estimation of AWVC over the study area, giving either of the two as input variable.…”

Section: Data Metric Performance Indexmentioning

confidence: 99%

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Monitoring atmospheric water vapour variability over Nigeria from ERA-Interim and NCEP reanalysis data

Ojigi

Opaluwa

2019

SN Appl. Sci.

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The spatial and temporal variability of water vapour in the atmosphere influences the earth weather, climate system, quality of spatial positioning and radio waves propagation of communications signals amongst others. It is therefore imperative to periodically monitor and map the water vapour phenomenon over specific areas of interest across the globe. This study therefore investigates the time-series variability of the atmospheric water vapour contents (AWVC) over Nigeria from the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA-Interim (ERA-I) and National Centre for Environmental Prediction/National Centre for Atmospheric Research (NCEP/NCAR) reanalysis data. The 2007-2017 daily/monthly mean data sets of ERA-I and NCEP/NCAR were visualised and extracted with Panoply version 4.8.6 and MATLAB 2018b for the 37 states capital in Nigeria. The months of minimum and maximum AWVC for all sampled locations were determined and compared, and the best fit trend equations for six cities (one city from each of the six geopolitical zones) were developed. The monthly means of AWVC over the study area showed spatial heterogeneity trend. The latitudinal variations in both ERA-I and NCEP/NCAR data sets showed that AWVC over Nigeria increases as latitude decreases towards the equator, and vice versa, irrespective of the month or time of the year. The study showed that May-September and November-February of 2007-2017 represent the periods with highest and lowest values of AWVC over Nigeria, respectively, which are the expected wet and dry seasons in the study area, and with peak months of August and January, respectively. The linear regression of the ERA-I and NCEP/NCAR data sets gave a coefficient of correlation of about 96.37%, coefficient of determination (R 2) of about 92.9% and a coefficient of efficiency of 87.83%, which indicate that ERA-I and NCEP/NCAR data sets have close values and the relationship between them in estimating AWVC over any selected location is statistically significant and valid. The coefficient of efficiency (E) of about 87.8% shows high level of internal efficiency of the ERA-I and NCEP/NCAR data sets used in this study. The best line of fit from polynomial models showed a range of the R 2 results for the best line-of-fit determination ranging between 78.36% and 95.75% for ERA-I, and 81.24% and 94.13% for NCEP/NCAR. The models and time-series spatial maps of AWVC produced in the study are recommended for use in the empirical estimation of AWVC and validations of other independent water vapour retrieval solutions such as GNSS and aerospace radiometry over the study area.

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“…The total data sample (N) was 42120, and the linear relationship between the ERA-I and NCEP/ NCAR data sets is given by Eqs. (12) and 13, respectively. With Eq.…”

Section: Discussionmentioning

confidence: 99%

Section: Data Processing and Time-series Mappingmentioning

confidence: 99%

See 1 more Smart Citation

Monitoring atmospheric water vapour variability over Nigeria from ERA-Interim and NCEP reanalysis data

Ojigi

Opaluwa

2019

SN Appl. Sci.

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“…Several geostatistical methods have been used by the researchers for developing the spatial variability maps of soil properties, depending upon the requirements and situations of field experiments. Kriging is a useful tool to predict and interpolate data between measured locations (Burgess and Webster 1980;Reza et al 2010Reza et al , 2012aArfaoui and HédiInoubli 2013;Marko et al 2014;Shahbeik et al 2014).…”

Section: Introductionmentioning

confidence: 99%

Spatial variability of soil properties using geostatistical method: a case study of lower Brahmaputra plains, India

et al. 2016

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Soil properties like pH, organic carbon (OC), available nitrogen (AN), available phosphorus (AP), and available potassium (AK) vary spatially from a field to a larger region scale and determine the soil fertility. This study addressed the spatial variability of soil properties in Brahmaputra plains, northeastern India using geostatistical method. For this, a total of 767 soil samples from a depth of 0-25 cm at an approximate interval of 1 km were collected over the entire Bongaigaon district of Assam. Data were analyzed both statistically and geostatistically on the basis of semivariogram. Soil properties showed large variability with greatest variation was observed in AP (86 %) where as the smallest variation was in pH (19 %). The semivariogram for all soil properties were best fitted by exponential models and showed a highest (2.7 km) range for OC and lowest (1.2 km) for AP. The nugget/sill ratio indicates a strong dependence for pH (12 %), moderate spatial dependence for available nutrients (53-72 %) and a weak spatial dependence for OC (77 %). Evaluation of spatial maps indicated that except for AN due to high root mean square error (61.8), kriging could successfully interpolate other soil properties. Soil pH highly negatively correlated with OC (−0.330**) and AN (−0.228**) and highly positive correlated with AP (0.334**) and AK (0.164**). A highly significant correlation was also found between OC and AN (0.490 ** ).

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“…The Bouguer anomaly map is established from a grid calculated by the kriging interpolator (Arfaoui and Inoubli 2013). Bouguer anomaly values vary between −3 and 17.6 mGal in the SE and NW, respectively (Fig.…”

Section: Contribution Of Gravity Analysis To the Structural Interpretmentioning

confidence: 99%

Major faults, salt structures and paleo-ridge at tectonic nodes in Northern Tunisia: contribution of tectonics and gravity analysis

et al. 2014

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Structural analysis, fault kinematics, detailed geological mapping and gravity analysis provide a new approach regarding to the tectonic-model of the Bazina-Aouana structure (North-Eastern Tunisia). They show that the Bazina Triassic outcrops, such as several other structures, are related to the NE and E trending major faults junction, controlling its halokinesis, and pouring out to the NW of tectonic nodes in the collapsed compartment. During the Eocene and Tortonian compressive phases, the Triassic materials previously forming a intumescence in the NE and E major faults intersection have acted as a barrier on which collided the Aouana structure. Therefore, this structure is blocked between overthrust nappes and the Bazina paleo-ridge forming an imbricated zone overlapping the Triassic materials. This paper aims to present a new evolutionary geodynamic model for the Bazina-Aouana structure related to the tectonic node easing the Triassic salt mobilization, the paleo-ridge formation and the development of a collapsed zone. By comparing with previous works, this model is likely applicable to the important Triassic outcrops of Northern Tunisia.

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Advantages of using the kriging interpolator to estimate the gravity surface, comparison and spatial variability of gravity data in the El Kef-Ouargha region (northern Tunisia)

Cited by 13 publications

References 14 publications

Monitoring atmospheric water vapour variability over Nigeria from ERA-Interim and NCEP reanalysis data

Monitoring atmospheric water vapour variability over Nigeria from ERA-Interim and NCEP reanalysis data

Spatial variability of soil properties using geostatistical method: a case study of lower Brahmaputra plains, India

Major faults, salt structures and paleo-ridge at tectonic nodes in Northern Tunisia: contribution of tectonics and gravity analysis

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