Water infiltration and recharge processes in karst systems are complex and difficult to measure with conventional hydrological methods. In particular, temporarily saturated groundwater reservoirs hosted in the vadose zone can play a buffering role in water infiltration. This results from the pronounced porosity and permeability contrasts created by local karstification processes of carbonate rocks. Analyses of time-lapse 2-D geoelectrical imaging over a period of 3 years at the Rochefort Cave Laboratory (RCL) site in south Belgium highlight variable hydrodynamics in a karst vadose zone. This represents the first long-term and permanently installed electrical resistivity tomography (ERT) monitoring in a karst landscape. The collected data were compared to conventional hydrological measurements (drip discharge monitoring, soil moisture and water conductivity data sets) and a detailed structural analysis of the local geological structures providing a thorough understanding of the groundwater infiltration. Seasonal changes affect all the imaged areas leading to increases in resistivity in spring and summer attributed to enhanced evapotranspiration, whereas winter is characterised by a general decrease in resistivity associated with a groundwater recharge of the vadose zone. Three types of hydrological dynamics, corresponding to areas with distinct lithological and structural features, could be identified via changes in resistivity: (D1) upper conductive layers, associated with clay-rich soil and epikarst, showing the highest variability related to weather conditions; (D2) deeper and more resistive limestone areas, characterised by variable degrees of porosity and clay contents, hence showing more diffuse seasonal variations; and (D3) a conductive fractured zone associated with damped seasonal dynamics, while showing a great variability similar to that of the upper layers in response to rainfall events. This study provides detailed images of the sources of drip discharge spots traditionally monitored in caves and aims to support modelling approaches of karst hydrological processes.

We investigate the potential of causal inference methods (CIMs) to reveal hydrological connections from time series. Four CIMs are selected from two criteria, linear or nonlinear and bivariate or multivariate. A priori, multivariate, and nonlinear CIMs are best suited for revealing hydrological connections because they fit nonlinear processes and deal with confounding factors such as rainfall, evapotranspiration, or seasonality. The four methods are applied to a synthetic case and a real karstic case study. The synthetic experiment confirms our expectation: unlike the other methods, the multivariate nonlinear framework has a low false-positive rate and allows for ruling out a connection between two disconnected reservoirs forced with similar effective precipitation. However, for the real case study, the multivariate nonlinear method was unstable because of the uneven distribution of missing values affecting the final sample size for the multivariate analyses, forcing us to cope with the results' robustness. Nevertheless, if we recommend a nonlinear multivariate framework to reveal actual hydrological connections, all CIMs bring valuable insights into the system's dynamics, making them a cost-effective and recommendable comparative tool for exploring data. Still, causal inference remains attached to subjective choices, operational constraints, and hypotheses challenging to test. As a result, the robustness of the conclusions that the CIMs can draw always deserves caution, especially with real, imperfect, and limited data. Therefore, alongside research perspectives, we encourage a flexible, informed, and limit-aware use of CIMs without omitting any other approach that aims at the causal understanding of a system.

Moisture-induced landslides are a global geohazard; mitigating the risk posed by landslides requires an understanding of the hydrological and geological conditions present within a given slope. Recently, numerous geophysical studies have been attempted to characterise slow-moving landslides, with an emphasis on developing geoelectrical methods as a hydrological monitoring tool. However, landslides pose specific challenges for processing geoelectrical data in long-term monitoring contexts as the sensor arrays can move with slope movements. Here we present an approach for processing long-term (over 8 years) geoelectrical monitoring data from an active slow-moving landslide, Hollin Hill, situated in Lias rocks in the southern Howardian Hills, UK. These slope movements distorted the initial setup of the monitoring array and need to be incorporated into a time-lapse resistivity processing workflow to avoid imaging artefacts. We retrospectively sourced seven digital terrain models to inform the topography of our imaging volumes, which were acquired by either Unmanned Aerial Vehicle (UAV)-based photogrammetry or terrestrial laser ranging systems. An irregular grid of wooden pegs was periodically surveyed with a global position system, from which distortions to the terrain model and electrode positions can be modelled with thin plate splines. In order to effectively model the time-series electrical resistivity images, a baseline constraint is applied within the inversion scheme; the result of the study is a time-lapse series of resistivity volumes which also incorporate slope movements. The workflow presented here should be adaptable for other studies focussed on geophysical/geotechnical monitoring of unstable slopes.

The number of large landslides in India has risen in the recent years, due to an increased occurrence of extreme monsoon rainfall events. There is an urgent need to improve our understanding of moisture-induced landslide dynamics, which vary both spatially and temporally. Geophysical methods provide integrated tools to monitor subsurface hydrological processes in unstable slopes at high spatial resolution. They are complementary to more conventional approaches using networks of point sensors, which can provide high temporal resolution information but are severely limited in terms of spatial resolution. Here, we present and discuss data from an electrical resistivity tomography monitoring system—called PRIME—deployed at the Amrita Landslide Early Warning System (Amrita-LEWS) site located in Munnar in the Western Ghats (Kerala, India). The system monitors changes in electrical resistivity in the subsurface of a landslide-prone slope that directly threatens a local community. The monitoring system provides a 4D resistivity model informing on the moisture dynamics in the subsurface of the slope. Results from a 10-month period spanning from pre-monsoon to the end of the monsoon season 2019 are presented and discussed with regard to the spatial variation of soil moisture. The temporal changes in resistivity within the slope are further investigated through the use of time-series clustering and compared to weather and subsurface pore water pressure data. This study sheds new light on the hydrological processes occurring in the shallow subsurface during the monsoon and potentially leading to slope failure. This geophysical approach aims at better understanding and forecasting slope failure to reduce the risk for the local community, thereby providing a powerful tool to be included in local landslide early warning systems.