The Yigong rock slide – debris avalanche (YRA), which occurred on 9 April 2000, received worldwide attention as one of the largest nonseismic landslides in recent years, with a volume of 0.3 × 10⁹ m³. Sixty-two days after this landslide event, a catastrophic flood happened because of landslide dam failure. One of the special features of this debris avalanche is liquefaction, which plays an important role in the entrainment and long run-out distance and high-speed movement of the debris avalanche. Numerous sand boils were found in the deposition zone, providing strong evidence for liquefaction. The YRA provides the first actual evidence for a theoretical model where the mechanisms of excess pore pressure and liquefaction induced by undrained loading, and entrainment and dissipation control the run out and deposition of the debris avalanche. The damage mode to trees and the presence of debris cones or molards with a rounded top is proven to be the result of strong air waves and eddies. These features all imply that the YRA is a solid–liquid–air mixed-debris avalanche.

In areas where weathering has hydrogeological significance, geophysical methods can assist to map the subsurface characteristics for groundwater occurrence. In this study, electrical resistivity tomography (ERT) survey in combination with joint profile method (JPM), magnetic method and borehole data was conducted to investigate the aquifer potential in strongly weathered volcanic rocks. The aim was to assess the geological units related to the water-bearing formation of aquifer systems in South Guangdong, China. The resistivities were measured along four profiles each with a total of 81 electrodes, a spread length of 400 m and an electrode spacing of 5 m insuring continuous coverage. The data from a borehole survey revealed three different layers i.e., highly weathered layer, partly weathered layer and fresh basement rock, whose respective thickness were integrated into ERT images to get more useful results about the real resistivity ranges of the these layers (i.e., 22 Ωm–345 Ωm for highly weathered layer, 324 Ωm–926 Ωm for partly weathered and 913 Ωm–2579 Ωm for fresh bedrock). The electrical resistivity imaging including the surface topography provides spatial variations in electrical properties of the weathered/unweathered layers since resistivity depends on the properties of a material rather than its thickness. ERT sections were integrated with JPM and magnetic method to delineate the main faults (F1, F2 and F3). ERT sections show a geometric relationship between different layered boundaries, particularly those of the aquifers with fresh basement and surface topographies. These layers comprise an overburden of 50 m thickness revealed by ERT sections. The results show that weathered and partly weathered layers between the topographic surface and bed rock yield maximum aquifer potential in the study area. ERT imaging method provides promising input to groundwater evaluation in the areas of weathered environment with complex geology.

Rock mass quality evaluation is a challenging task in geotechnical investigations given the natural heterogeneity and the limited data. These investigations mainly depend on the traditional drilling tests. However, such tests are expensive and time consuming, provide point measurements, and cannot be conducted in steep topographic areas, and thus cause uncertainties in the geological model. Conversely, geophysical methods such as electrical resistivity tomography (ERT) are non-invasive, user-friendly, and fast. In this work, we establish empirical correlation between ERT and limited drilling data to obtain rock mass integrity coefficient (Kv). The estimated Kv provides 2D/3D imaging of the rock mass quality evaluation via weathered/unweathered rock and faults detection in order to cover the entire area even where no drilling test exists. Compared with the past geotechnical investigations, our work reduce the ambiguities caused by the inadequate well tests and provide more accurate geological model for infrastructures design. Our work proposes that, in case of sparse borehole data, the established empirical equations can be used to determine Kv along different geophysical profiles via 2D/3D insight of the subsurface. Our approach is applicable in any hard rock setting, and the established correlations can be used in areas even where no well test exists.

The dynamic nature and unsustainable exploitation of groundwater aquifers pose a range of management challenges. The accurate basin-wide hydrological assessment is very critical for the quantification of abstraction rates, spatial patterns of groundwater usage, recharge and discharge processes, and identification of critical areas having groundwater mining. This study provides the appraisal of remote sensing technology in comparison with traditionally prevailing tools and methodologies and introduces the practical use of remote sensing technology to bridge the data gaps. It demonstrates the example of Gravity Recovery and Climate Experiment (GRACE) satellite inferred Total Water Storage (TWS) information to quantify the behavior of the Upper Indus Plain Aquifer. The spatio-temporal changes in aquifer usage are investigated particularly for irrigation and anthropogenic purposes in general. The GRACE satellite is effective in capturing the water balance components. The basin-wide monthly scale groundwater storage monitoring is a big opportunity for groundwater managers and policymakers. The remote sensing integrated algorithms are useful tools to provide timely and valuable information on aquifer behavior. Such tools are potentially helpful to support the implementation of groundwater management strategies, especially in the developing world where data scarcity is a major challenge. Groundwater resources have not grown to meet the growing demands of the population, consequently, overexploitation of groundwater resources has occurred in these decades, leading to groundwater decline. However, future developments in the field of space technology are envisioned to overcome the currently faced spatio-temporal challenges.