Derivative‐Assisted Classification of Fractured Zones Crossing a Deep Borehole

In this study, the derivative analysis using the derivative of drawdown with respect to log‐time was utilized to determine candidates for hydraulic conductor domains (HCDs). At a 500‐m deep borehole in the study site, the fractured rocks crossing the borehole were first classified in fractured and nonfractured zones by core logging and geophysical loggings, such as acoustic televiewing, density, and flow loggings. After conducting the hydraulic tests such as constant head withdrawal and recovery tests at the fractured zones and the nonfractured zones, the derivative analyses were carried out, of which the results were evaluated to determine the candidates for HCDs. For the nonfractured zones, the diagnostic plot has only a big hump indicating poor connection of the background fractures to the permeable geologic media, while those of the candidates for HCDs show various flow regimes. On the basis of these results, the candidates for HCDs among the fractured zones were determined. From discussion on the results, the combination of the spacing analysis and derivative analysis following a hydraulic test is recommended for determining the candidates for HCDs rather than other geophysical loggings.     

Analysis of resistivity data obtained from cone penetrometer in contaminated soil layers

In horizontally layered soils of different electrical properties, electrical potential distribution becomes complex and the obtained resistivity data may be limited in reflecting the actual soil profile. Thus the objective of this study was to identify the factors that affect resistivity measurement on the cone penetrometer and further investigate the sensitivity of measured resistivity to different types and concentrations of contaminants at the subsurface level. First, a theoretical resistivity equation was derived to provide a means of computing the geometric factor. The effective volume of measurement on the cone penetrometer was investigated and laboratory soil box tests verified the dominance of partially high resistivity regions within the effective volume of measurement over the apparent resistivity. Such effect was found to lead to possible discrepancies between the recorded resistivity data and the actual depth of measurement. Measurements on contaminated soil layers indicated that resistivity cones are efffective in delineating inorganic contaminants in soils of high water contents, and detecting NAPLs floating above the groundwater table provided that soils adjacent to the plume are not dry of water. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

Contamination,chemical speciation and vertical distribution of heavy metals in soils of an old and large industrial zone in Northeast China

Heavy metal pollution of soils has become a major concern in China as a consequence of rapid urbanization and industrialization in recent years. However, the evaluation on soil heavy metal pollution in Shenyang, the largest heavy industrial base city in China, has not yet been conducted. In this study, accumulation, chemical speciation, and vertical distribution Cu, Zn, Pb and Cd in soils were studied and pollution condition was assessed in Tiexi Industrial District of Shenyang, the largest and oldest industrial zone in Northeastern China. The results showed that in topsoil, the average concentration of total Cu, Zn, Pb, and Cd was 209.06, 599.92, 470.19 and 8.59 mg kg⁻¹, respectively, much higher than the national threshold limit. The values of pollution index and integrated pollution index showed that the pollution level was Cd > Cu > Zn > Pb, and Cd, Cu and Zn belong to heavy pollution level. The residual, Fe and Mn oxide-bound, and organic-bound species accounted for about 90%, while carbonate-bound and exchangeable species accounted for about 10%. This study indicates that the soils in the industrial zone were widely and extremely polluted by multi-heavy metals as a result of long-term industrial activities.     

Evaluation of Liquefaction Potential Index Along Western Coast of South Korea Using SPT and CPT

For South Korea, liquefaction potential along the western coast has not been widely assessed because South Korea is considered to be a low seismic hazard. However, recent earthquake events and historical records indicate that the seismic hazard of South Korea should not be ignored. Moreover, as artificial fills are extensively used along the western coast for development, liquefaction evaluation of the soils in this area is necessary. In this paper, we present: (1) the seismic characteristics of the study area; (2) procedures for evaluating liquefaction potential, focusing on the liquefaction potential index (LPI) approach; and (3) LPI distributions at several representative locations along the western coast of South Korea under various seismic scenarios. The liquefaction potential index represents the liquefaction potential over the upper 20 m of a boring or sounding. Using cone penetration test (CPT) and standard penetration test (SPT) data from two coastal sites, we compare and discuss CPT-based and SPT-based LPI values, particularly values computed in nonplastic, silt-rich soils. In these soils, it appears that the CPT yields lower liquefaction resistance, resulting in large LPI values. Finally, discussion and suggestions are provided for CPT- and SPT-based liquefaction assessment of low plasticity and high fines content soils.     

Constructing rainfall depth-frequency curves considering a linear trend in rainfall observations

Comprehensive flood prevention plans are established in large basins to cope with recent abnormal floods in South Korea. In order to make economically effective plans, appropriate design rainfalls are critically determined from the rainfall depth-frequency curves which take the occurrence of abnormal floods into consideration. Conventional approaches to construct the rainfall depth-frequency curves are based on the stationarity assumption. However, this assumption has a critical weak aspect in that it cannot reflect non-stationarities in rainfall observations. As an alternative, this study suggests the non-stationary Gumbel model (NSGM) which incorporates a linear trend of rainfall observations into rainfall frequency analysis to construct the rainfall depth-frequency curves. A comparison of various schemes employed in the model found that the proposed NSGM permits the estimation of the distribution parameters even when shifted in the future by using linear relationships between rainfall statistics and distribution parameters, and produces more acceptable estimates of design rainfalls in the future than the conventional model. The NSGM was applied at several stations in South Korea and then expected the design rainfalls to increase by up to 15–30% in 2050.     

First retrieval of data regarding spatial distribution of Asian dust aerosol from the Geostationary Ocean Color Imager

Aerosol optical thickness (AOT) was retrieved from the Geostationary Ocean Color Imager (GOCI) on board the Communication, Ocean, and Meteorological Satellite (COMS) for the first time. AOT values were retrieved over the ocean at a spatial scale of 0.5 × 0.5 km² by using the look-up table (LUT)-based separation technique. The radiative transfer model (RTM) was used for different models of atmosphere-ocean environmental conditions, taking into account the realistic variability of scattering and absorption. Ocean surface properties affected by whitecaps and pigment content were also taken into account. The results show that the radiance observed by the GOCI amounts to only 5% of the radiation that penetrated the ocean and, consequently, 95% of the radiation is scattered in the atmosphere or reflected at the ocean surface in the visible wavelengths longer than 0.6 ìm. Within these wavelengths, radiance variations at the top of atmosphere (TOA) due to pigment variations are within 10%, while the radiance variation due to wind speed is considerably higher. For verification of GOCI-retrieved AOTs, comparison between GOCI and ground-based sunphotometer measurement at Gosan, Korea (126.10°E, 33.23°N)) showed good correlation (r = 0.99). The GOCI observations obtained by using the proposed technique showed promising results for the daily monitoring of atmospheric aerosol loading as well as being useful for environmental supervisory authorities.     

Understanding of the Common Land Model performance for water and energy fluxes in a farmland during the growing season in Korea

The Common Land Model (CLM) is one of the most widely used land surface models (LSMs) due to the practicality of its simple parameterization scheme and its versatility in embracing a variety of field datasets. The improved assessment of land surface water and energy fluxes using CLM can be an alternative approach for understanding the complex land–atmosphere interactions in data‐limited regions. The understanding of water and energy cycles in a farmland is crucial because it is a dominant land feature in Korea and Asia. However, the applications of CLM to farmland in Korea are in paucity. The simulations of water and energy fluxes by CLM were conducted against those from the tower‐based measurements during the growing season of 2006 at the Haenam site (a farmland site) in Korea without optimization. According to the International Geosphere–Biosphere Programme (IGBP) land cover classification, a homogeneous cropland was selected initially for this study. Although the simulated soil moisture had a similar pattern to that of the observed, the former was relatively drier (at 0·1 m³ m^?3) than the latter. The simulated net radiation showed good agreement with the observed, with a root mean squared error (RMSE) of 41 W m^?2, whereas relatively large discrepancies between the simulation and observation were found in sensible (RMSE of 66 W m^?2) and latent (RMSE of 60 W m^?2) heat fluxes. On the basis of the sensitivity analysis, soil moisture was more receptive to land cover and soil texture parameterizations when compared to soil temperature and turbulent fluxes. Despite the uncertainty in the predictive capability of CLM employed without optimization, the initial performance of CLM suggests usefulness in a data‐limited heterogeneous farmland in Korea. Further studies are required to identify the controls on water and energy fluxes with an improved parameterization. Copyright © 2010 John Wiley & Sons, Ltd.     

Effect of the inter-annual variability of rainfall statistics on stochastically generated rainfall time series: part 2. Impact on watershed response variables

This study analyzes how the stochastically generated rainfall time series accounting for the inter-annual variability of rainfall statistics can improve the prediction of watershed response variables such as peak flow and runoff depth. The modified Bartlett–Lewis rectangular pulse (MBLRP) rainfall generation model was improved such that it can account for the inter-annual variability of the observed rainfall statistics. Then, the synthetic rainfall time series was generated using the MBLRP model, which was used as input rainfall data for SCS hydrologic models to produce runoff depth and peak flow in a virtual watershed. These values were compared to the ones derived from the synthetic rainfall time series that is generated from the traditional MBLRP rainfall modeling. The result of the comparison indicates that the rainfall time series reflecting the inter-annual variability of rainfall statistics reduces the biasness residing in the predicted peak flow values derived from the synthetic rainfall time series generated using the traditional MBLRP approach by 26–47 %. In addition, it was observed that the overall variability of the peak flow and run off depth distribution was better represented when the inter-annual variability of rainfall statistics are considered.     

Ensemble-based landslide susceptibility maps in Jinbu area, Korea

Ensemble techniques were developed, applied and validated for the analysis of landslide susceptibility in Jinbu area, Korea using the geographic information system (GIS). Landslide-occurrence areas were detected in the study by interpreting aerial photographs and field survey data. Landslide locations were randomly selected in a 70/30 ratio for training and validation of the models, respectively. Topography, geology, soil and forest databases were also constructed. Maps relevant to landslide occurrence were assembled in a spatial database. Using the constructed spatial database, 17 landslide-related factors were extracted. The relationships between the detected landslide locations and the factors were identified and quantified by frequency ratio, weight of evidence, logistic regression and artificial neural network models and their ensemble models. The relationships were used as factor ratings in the overlay analysis to create landslide susceptibility indexes and maps. Then, the four landslide susceptibility maps were used as new input factors and integrated using the frequency ratio, weight of evidence, logistic regression and artificial neural network models as ensemble methods to make better susceptibility maps. All of the susceptibility maps were validated by comparison with known landslide locations that were not used directly in the analysis. As the result, the ensemble-based landslide susceptibility map that used the new landslide-related input factor maps showed better accuracy (87.11% in frequency ratio, 83.14% in weight of evidence, 87.79% in logistic regression and 84.54% in artificial neural network) than the individual landslide susceptibility maps (84.94% in frequency ratio, 82.82% in weight of evidence, 87.72% in logistic regression and 81.44% in artificial neural network). All accuracy assessments showed overall satisfactory agreement of more than 80%. The ensemble model was found to be more effective in terms of prediction accuracy than the individual model.     

The twentieth century contiguous US temperature changes indicated by daily data and higher statistical moments

The daily surface air temperature data are used to assess the climate changes of the contiguous United States during the period of 1901 to 2000. The assessment is made through the first four statistical moments of the daily maximum, minimum, and mean temperature anomalies, the linear trends of the moments, and the changes of the anomalies?? probability density functions. The results on the first moment, i.e., the mean, are compared with the existing ones in terms of intra-annual means and their linear trends. Our first moment results agree with known ones and demonstrate a decrease from the 1930s to the 1960s and an increase from the 1970s to 2000. The temperature fluctuation is the smallest in the 1960s among the decades from 1931 to 2000. The trends of the higher (second-, third- and fourth-order) moments of the mean, maximum, and minimum surface air temperatures are calculated for the periods 1901?C2000, 1910?C1945, 1946?C1975, and 1976?C2000. The results show a decreasing trend of the second- and third-order moments of all the temperatures. The fourth-order moments of the mean and maximum surface air temperatures have increasing trends, but that of the minimum surface air temperature has a decreasing trend. The seasonal histograms of the mean, maximum, and minimum surface air temperatures are calculated for the three periods 1910?C1945, 1946?C1975, and 1976?C2000 for the stations which have the largest trend of maximum daily surface air temperature. An obvious change has been identified in the probability density functions. Among the changes of statistical parameters, the ones for the minimum temperature are larger than those for the maximum and mean temperatures.