Geostatistical integration using 2-D electrical resistivity and 3-D gravity methods for detecting cavities in a karst area

Two-dimensional (2-D) electrical resistivity and three-dimensional (3-D) gravity explorations were undertaken to estimate the 3-D distribution of karst cavities at the area of Yongweol-ri in Korea where ground subsidence has occurred. Although the gravity method is a low-cost way of analyzing a 3-D continuous structure, its vertical resolving power is poor. In contrast, the electrical resistivity method can provide a 2-D and/or 3-D subsurface structure with a much higher lateral and vertical resolution than the gravity method. Accordingly, geostatistical methods and density information were used to enhance the 2-D resistivity structure revealed by the electrical resistivity method into a 3-D structure. The assumptions are, first, that each data set senses the same underlying geological structure in terms of different material properties and, secondly, that two different material properties are correlated locally or globally throughout the entire target area. As a result, the distribution of limestone cavities can be estimated under the assumption that they are mostly filled with groundwater and clayey soils and have abnormally low levels of resistivity and density. The estimated distribution corresponds with the grouting, borehole imaging and monitoring data. In this example, it can be seen that the integration analysis of 2-D electrical resistivity and 3-D gravity methods is a very powerful tool for 3-D subsurface imaging and that the method can provide enhanced imaging capabilities for 3-D cavities.     

Determination and application of the weights for landslide susceptibility mapping using an artificial neural network

The purpose of this study is the development, application, and assessment of probability and artificial neural network methods for assessing landslide susceptibility in a chosen study area. As the basic analysis tool, a Geographic Information System (GIS) was used for spatial data management and manipulation. Landslide locations and landslide-related factors such as slope, curvature, soil texture, soil drainage, effective thickness, wood type, and wood diameter were used for analyzing landslide susceptibility. A probability method was used for calculating the rating of the relative importance of each factor class to landslide occurrence. For calculating the weight of the relative importance of each factor to landslide occurrence, an artificial neural network method was developed. Using these methods, the landslide susceptibility index (LSI) was calculated using the rating and weight, and a landslide susceptibility map was produced using the index. The results of the landslide susceptibility analysis, with and without weights, were confirmed from comparison with the landslide location data. The comparison result with weighting was better than the results without weighting. The calculated weight and rating can be used to landslide susceptibility mapping.     

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.     

Simultaneous stabilization of arsenic, lead, and copper in contaminated soil using mixed waste resources

In the present study, stabilization treatment using waste resource stabilizers was performed for soil contaminated with As and heavy metals (Pb and Cu). Calcined oyster shell (COS) and coal mine drainage sludge (CMDS) were used as a mixed stabilizing agent for a wet-curing duration of 28 days. After the stabilization treatment, the treatment process efficiency was evaluated by the results of various batch- and column-leaching tests. Neutral and weak acid extraction methods, such as water-soluble extraction and SPLP, did not exhibit satisfactory results for heavy metal stabilization, even if they showed very low leachability. On the other hand, TCLP and 0.1 M HCl extraction showed that the stabilizers significantly reduced the amount of heavy metals leached from the soil, which strongly supports the thesis that the stabilization treatment is efficient in the acidic leaching conditions that were explored. Specifically, in the 0.1 M HCl extraction, the reduction efficiencies of As, Pb, and Cu leachings were more than 90 %, compared with control experiments. This study demonstrates that the application of waste resources for the stabilization of As and heavy metals is feasible. However, some limitations observed in the experiments should be considered in future studies, such as the mobilization of alkali-soluble elements, and in particular, exchangeable fractions of Cu. In addition, the treatment efficiency can be evaluated by different leaching methods, which suggests that multidirectional approaches are required for the proper evaluation of stabilization treatment.     

Impact of vegetation feedback on the temperature and its diurnal range over the Northern Hemisphere during summer in a 2 × CO2 climate

This study examines the potential impact of vegetation feedback on the changes in the diurnal temperature range (DTR) due to the doubling of atmospheric CO₂ concentrations during summer over the Northern Hemisphere using a global climate model equipped with a dynamic vegetation model. Results show that CO₂ doubling induces significant increases in the daily mean temperature and decreases in DTR regardless of the presence of the vegetation feedback effect. In the presence of vegetation feedback, increase in vegetation productivity related to warm and humid climate lead to (1) an increase in vegetation greenness in the mid-latitude and (2) a greening and the expansion of grasslands and boreal forests into the tundra region in the high latitudes. The greening via vegetation feedback induces contrasting effects on the temperature fields between the mid- and high-latitude regions. In the mid-latitudes, the greening further limits the increase in T _max more than T _min, resulting in further decreases in DTR because the greening amplifies evapotranspiration and thus cools daytime temperature. The greening in high-latitudes, however, it reinforces the warming by increasing T _max more than T _min to result in a further increase in DTR from the values obtained without vegetation feedback. This effect on T _max and DTR in the high latitude is mainly attributed to the reduction in surface albedo and the subsequent increase in the absorbed insolation. Present study indicates that vegetation feedback can alter the response of the temperature field to increases in CO₂ mainly by affecting the T _max and that its effect varies with the regional climate characteristics as a function of latitudes.     

A sensitivity analysis of Hortonian flow

We present a sensitivity analysis for infiltration excess (Hortonian) overland flow based on a classic laboratory experiment by Smith and Woolhiser [Smith RE, Woolhiser DA. Overland flow on an infiltrating surface. Water Resour Res 1971;7(4):899–913]. The model components of the compartment approach are comprised of a diffusive wave approximation to the Saint–Venant equations for overland flow, a Richards model for flow in the variably saturated zone, and an interface coupling concept that combines the two components. In the coupling scheme a hydraulic interface is introduced to allow the definition of an exchange flux between the surface and the unsaturated zone. The effects of friction processes, soil capillarity, hydraulic interface, and vertical soil discretization on both infiltration and runoff prediction are investigated in detail. The corresponding sensitivity analysis is conducted using a small-perturbation method. As a result the importance of the hydraulic processes and related parameters are evaluated for the coupled hydrosystem.     

The variability of the East Asian summer monsoon and its relationship to ENSO in a partially coupled climate model

The variability of the East Asian summer monsoon (EASM) is studied using a partially coupled climate model (PCCM) in which the ocean component is driven by observed monthly mean wind stress anomalies added to the monthly mean wind stress climatology from a fully coupled control run. The thermodynamic coupling between the atmospheric and oceanic components is the same as in the fully coupled model and, in particular, sea surface temperature (SST) is a fully prognostic variable. The results show that the PCCM simulates the observed SST variability remarkably well in the tropical and North Pacific and Indian Oceans. Analysis of the rainfall-SST and rainfall-SST tendency correlation shows that the PCCM exhibits local air-sea coupling as in the fully coupled model and closer to what is seen in observations than is found in an atmospheric model driven by observed SST. An ensemble of experiments using the PCCM is analysed using a multivariate EOF analysis to identify the two major modes of variability of the EASM. The PCCM simulates the spatial pattern of the first two modes seen in the ERA40 reanalysis as well as part of the variability of the first principal component (correlation up to 0.5 for the model ensemble mean). Different from previous studies, the link between the first principal component and ENSO in the previous winter is found to be robust for the ensemble mean throughout the whole period of 1958–2001. Individual ensemble members nevertheless show the breakdown in the relationship before the 1980’s as seen in the observations.