• Journal of The Korean Society of Agricultural Engineers
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• v.58 no.5
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• pp.81-90
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• 2016

Due to droughts and water shortages causing severe damage to crops and other vegetations, much attention has been given to efficient irrigation for upland farming. However, little information has been known to measure soil moisture levels in a field scale and apply their spatial variability for proper irrigation scheduling. This study aimed to characterize the spatial variability and temporal stability of soil water contents at depths of 10 cm, 20 cm and 30 cm on flat (loamy soil) and hill-slope fields (silt-loamy soil). Field monitoring of soil moisture contents was used for variogram analysis using GS+ software. Kriging produced from the structural parameters of variogram was applied for the means of spatial prediction. The overall results showed that the surface soil moisture presented a strong spatial dependence at the sampling time and space in the field scale. The coefficient variation (CV) of soil moisture was within 7.0~31.3 % in a flat field and 8.3~39.4 % in a hill-slope field, which was noticeable in the dry season rather than the rainy season. The drought assessment analysis showed that only one day (Dec. 21st) was determined as dry (20.4 % and 24.5 % for flat and hill-slope fields, respectively). In contrary to a hill-slope field where the full irrigation was necessary, the centralized irrigation scheme was appeared to be more effective for a flat field based on the spatial variability of soil moisture contents. The findings of this study clearly showed that the geostatistical analysis of soil moisture contents greatly contributes to proper irrigation scheduling for water-efficient irrigation with maximal crop productivity and environmental benefits.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.50 no.4
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• pp.221-237
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• 2005

For developing the site-specific fertilizer management strategies of crop, it is essential to know the spatial variability of soil factors and to assess their influence on the variability of crop growth and yield. In 2002 and 2003 cropping seasons within-field spatial variability of rice growth and yield was examined in relation to spatial variation of soil properties in the· two paddy fields having each area of ca. $6,600m^2$ in Suwon, Korea. The fields were managed without fertilizer or with uniform application of N, P, and K fertilizer under direct-seeded and transplanted rice. Stable soil properties such as content of clay (Clay), total nitrogen (TN), organic mater (OM), silica (Si), cation exchange capacity (CEC), and rice growth and yield were measured in each grid of $10\times10m$. The two fields showed quite similar spatial variation in soil properties, showing the smallest coefficient of variation (CV) in Clay $(7.6\%)$ and the largest in Si $(21.4\%)$. The CV of plant growth parameters measured at panicle initiation (PIS) and heading stage (HD) ranged from 6 to $38\%$, and that of rice yield ranged from 11 to $21\%$. CEC, OM, TN, and available Si showed significant correlations with rice growth and yield. Multiple linear regression model with stepwise procedure selected independent variables of N fertilizer level, climate condition and soil properties, explaining as much as $76\%$ of yield variability, of which $21.6\%$ is ascribed to soil properties. Among the soil properties, the most important soil factors causing yield spatial variability was OM, followed by Si, TN, and CEC. Boundary line response of rice yield to soil properties was represented well by Mitcherich equation (negative exponential equation) that was used to quantify the influence of soil properties on rice yield, and then the Law of the Minimum was used to identify the soil limiting factor for each grid. This boundary line approach using five stable soil properties as limiting factor explained an average of about $50\%$ of the spatial yield variability. Although the determination coefficient was not very high, an advantage of the method was that it identified clearly which soil parameter was yield limiting factor and where it was distributed in the field.

• Ocean Science Journal
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• v.41 no.1
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• pp.1-10
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• 2006

Total sea surface temperature (SST) in a coupled GCM is diagnosed by separating the variability into signal variance and noise variance. The signal and the noise is calculated from multi-decadal simulations from the COLA anomaly coupled GCM and the interactive ensemble model by assuming both simulations have a similar signal variance. The interactive ensemble model is a new coupling strategy that is designed to increase signal to noise ratio by using an ensemble of atmospheric realizations coupled to a single ocean model. The procedure for separating the signal and the noise variability presented here does not rely on any ad hoc temporal or spatial filter. Based on these simulations, we find that the signal versus the noise of SST variability in the North Pacific is significantly different from that in the equatorial Pacific. The noise SST variability explains the majority of the total variability in the North Pacific, whereas the signal dominates in the deep tropics. It is also found that the spatial characteristics of the signal and the noise are also distinct in the North Pacific and equatorial Pacific.

• Journal of Biosystems Engineering
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• v.33 no.5
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• pp.362-369
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• 2008

There is developing interest in precision agriculture in Korea, despite the fact that typical Korean fields are less than 1 ha in size. Describing within-field variability in typical Korean production settings is a fundamental first step toward determining the size of management zones and the inter-relationships between limiting factors, for establishment of site-specific management strategies. Measurements of rice (Oriza Sativa L) yield, chlorophyll content, and soil properties were obtained in a small (100-m by 30-m) Korean rice paddy field. Yield data were manually collected on 10-m by 5-m grids (180 samples with 3 samples in each of 60 grid cells) and chlorophyll content was measured using a Minolta SPAD 502 in 2-m by 2-m grids. Soil samples were collected at 275 points to compare results from sampling at different scales. Ten soil properties important for rice production in Korea were determined through laboratory analyses. Variogram analysis and point kriging with and without median polishing were conducted to determine the variability of the measured parameters. Influence of variogram model selection and other parameters on the interpretation of the data was investigated. For many of the data, maximum values were greater than double the minimum values, indicating considerable spatial variability in the small paddy field, and large-scale spatial trends were present. When variograms were fit to the original data, the limits of spatial dependency for rice yield and SP AD reading were 11.5 m and 6.5 m, respectively, and after detrending the limits were reduced to 7.4 m and 3.9 m. The range of spatial dependency for soil properties was variable, with several having ranges as short as 2 m and others having ranges greater than 30 m. Kriged maps of the variables clearly showed the presence of both large-scale (trend) variability and small-scale variability in this small field where it would be reasonable to expect uniformity. These findings indicate the potential for applying the principles and technology of precision agriculture for Korean paddy fields. Additional research is needed to confirm the results with data from other fields and crops.d similar tendency with the result for the frequency less than 20 Hz, but the width of change was reduced highly.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.30 no.3B
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• pp.295-303
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• 2010

The variability of vegetation is strongly related to the variability of hydrometeorological factors such as precipitation, temperature, runoff and so on. Analysis of the variability of vegetation will aid to understand the regional impact of climate change. Thus we analyzed the spatial-temporal variability of NOAA(National Oceanic and Atmospheric Administration)/AVHRR(Advanced Very High Resolution Radiometer) NDVI(Normalized Difference Vegetation Index). In the results from Mann-Kendall test, there is no significant linear trend of annual NDVI from 1982 to 2006 in the most area except the downward trend on the significance level 90% in the Guem-river basin area. In addition, using EOF(Empirical Orthogonal Function) analysis, the variability of NDVI in the region of higher latitude and altitude is higher than that in the other region since the spatial variability of NDVI follows the latitudinal gradient. Also we could get higher NDVI in June, July, August and September. We had the highest NDVI in Han-river basin area and the lowest in Je-Ju island.

• Journal of the Korean Geotechnical Society
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• v.27 no.10
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• pp.93-104
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• 2011

In this paper, a numerical procedure of probabilistic steady seepage analysis that considers the spatial variability of soil permeability is presented. The procedure extends the deterministic analysis based on the finite element method to a probabilistic approach that accounts for the uncertainties and spatial variation of the soil permeability. Two-dimensional random fields are generated based on a Karhunen-Lo$\grave{e}$ve expansion in a fashion consistent with a specified marginal distribution function and an autocorrelation function. A Monte Carlo simulation is then used to determine the statistical response based on the random fields. A series of analyses were performed to verify the application potential of the proposed method and to study the effects of uncertainty due to the spatial heterogeneity on the seepage behavior of soil foundation beneath water retaining structure with a single sheet pile wall. The results showed that the probabilistic framework can be used to efficiently consider the various flow patterns caused by the spatial variability of the soil permeability in seepage assessment for a soil foundation beneath water retaining structures.

• Journal of the Korean Geotechnical Society
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• v.25 no.12
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• pp.13-25
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• 2009

In this paper, a numerical procedure of probabilistic slope stability analysis that considers the spatial variability of soil properties is presented. The procedure extends the deterministic analysis based on the limit equilibrium method of slices to a probabilistic approach that accounts for the uncertainties and spatial variation of the soil parameters. Making no a priori assumptions about the critical failure surface like the Random Finite Element Method (RFEM), the approach saves the amount of solution time required to perform the analysis. Two-dimensional random fields are generated based on a Karhunen-Lo$\grave{e}$ve expansion in a fashion consistent with a specified marginal distribution function and an autocorrelation function. A Monte Carlo simulation is then used to determine the statistical response based on the random fields. A series of analyses were performed to verify the application potential of the proposed method and to study the effects of uncertainty caused by the spatial heterogeneity on the stability of slope. The results show that the proposed method can efficiently consider the various failure mechanisms caused by the spatial variability of soil property in the probabilistic slope stability assessment.

• Journal of Ship and Ocean Technology
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• v.8 no.2
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• pp.20-28
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• 2004

A dynamic-thermodynamic sea ice model with 50-km spatial and 24-hour temporal resolution is used to investigate the spatial and long-term temporal variability of the sea ice cover the Arctic Basin. The model satisfactorily reproduces the averaged main characteristics of the sea ice and the sea ice extent in the Arctic Basin and its decrease in early 1990th. At times model allows to suppose partial recovery of sea ice cover in the last years of twenty century. The employment of explicit form for description of ridging gives opportunity to assume that the observed thinning is the result of reduction the intensity of ridging processes and to estimate long-term variability of probability the ridge free navigation in the different parts of the Arctic Ocean including the North Sea Route area.

• Current Research on Agriculture and Life Sciences
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• v.32 no.4
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• pp.203-209
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• 2014

This study investigated characteristics of rainfall and water quality in Saemangeum area with attention to temporal and spatial distributions. A high variability in rainfall was noted during July and August. The temporal analysis of water quality data indicated that DO and TN as well as BOD, COD and SS were within national standards except for increased concentrations during spring and summer, unlike TP values that indicated poor water quality. Standard deviation showed a high variability in SS among the seasons most especially during summer. The high dispersion indicated variability in the chemical composition of pollutants where the temporal and spatial variations caused by polluting sources and/or seasonal changes were most evident for BOD and COD during winter and spring. The box plots and bar charts showed steadily low concentrations of BOD, COD, TN and TP except within Iksan and notable significant variations in SS concentrations among the monitoring stations. Thus, high pollution levels requiring intervention were identified in Mangyeong river basin with particular concern for areas represented by Iksan station. It was noted that Iksan received a considerable amount of rainfall which meant high runoff which could explain the significant pollution levels revealed in the water quality spatial distribution. Major pollution contributing pollutants within Saemangeum area were identified as SS, BOD, COD and TN. Therefore the present results could be used as a guideline for the temporal and spatial distributions analysis of both rainfall and water quality in Saemangeum watershed.

• Journal of the Korean Geotechnical Society
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• v.19 no.2
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• pp.123-133
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• 2003

A practical statistical approach that can be used to model various sources of uncertainty systematically is presented in the context of reliability analysis of slope stability. New expressions for probabilistic characterization of soil properties incorporate sampling and measurement errors, as well as spatial variability and its reduced variance due to spatial averaging. The stochastic nature of seismic loading is studied by generating a large series of hazard-compatible artificial motions, and by using them in subsequent response analyses. The analyses indicate that in a seismically less active region such as the Korean Peninsular, a moderate variability in soil properties has an effect as large as the characterization of earthquake hazard on the computed risk of slope failure and excessive slope deformations.