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Mountain regions of the humid tropics are characterized by steep slopes and heavy rains. These regions are thus prone to both high surface runoff and soil erosion. In Southeast Asia, uplands are also subject to rapid land-use change, predominantly as a result of increased population pressure and market forces. Since 1998, the Houay Pano site, located in northern Lao PDR (19.85°N 102.17°E) within the Mekong basin, aims at assessing the long-term impact of the conversion of traditional slash-and-burn cultivation systems to commercial perennial monocultures such as teak tree plantations, on the catchment hydrological response and sediment yield. The instrumented site monitors hydro-meteorological and soil loss parameters at both microplot (1 m2) and small catchment (0.6 km2) scales. The monitored catchment is part of the network of critical zone observatories named Multiscale TROPIcal CatchmentS (M-TROPICS). The data shared by M-TROPICS in Houay Pano are (1) rainfall, (2) air temperature, air relative humidity, wind speed, and global radiation, (3) catchment land use, (4) stream water level, suspended particulate matter, bed particulate matter and stones, (5) soil surface features, and (6) soil surface runoff and soil detachment. The dataset has already been used to interpret suspended particulate matter and bed particulate matter sources and dynamics, to assess the impact of land-use change on catchment hydrology, soil erosion, and sediment yields, to understand bacteria fate and weed seed transport across the catchment, and to build catchment-scale models focused on hydrology and water quality issues. The dataset may be further used to e.g. assess the role of headwater catchments in large tropical river basin hydrology, support the interpretation of new variables measured in the catchment (e.g. contaminants other than fecal bacteria), and assess the relative impacts of both climate and land-use change on the catchment.

In the protection of karstic aquifers, the role played by dolines is of interest as these shallow structures can be preferential infiltration pathways for groundwater recharge. In this study, three depressions in South France (Orniac, Lot) were identified and characterized using combined geophysical methods. Apparent conductivity mapping allows precise definition of dolines corresponding to topographic depressions. It also reveals other dolines that are hidden by soil cover. Electrical resistivity and seismic refraction tomography provide detailed cross‐sections. Even though the dolines studied are close one to another, they exhibit great variability in shape and content. Moreover, time‐lapse data show that the most saturated regions are on the edges of the first doline. Such information helps to evaluate human impacts and forecast potential groundwater contamination.

Near-surface inhomogeneities (NSIs) can lead to severe problems in the interpretation of apparent resistivity pseudosections because their effects significantly complicate the image aspect. In order to carry out a more efficient and reliable interpretation process, these problematic features should be removed from field data. We describe a filtering scheme using two-sided half-Schlumberger array data. The scheme was tested on synthetic data, generated from a simple 2D resistivity model contaminated by NSIs, and is shown to be suitable for eliminating such contaminations from apparent resistivity data. Furthermore, the original model without NSIs can be recovered satisfactorily from the inversion of filtered apparent resistivity data. The algorithm is also applied efficiently to a real data set collected at Nsimi, in southern Cameroon, along a 200-m shallow depth profile crossing a complex transitional zone. For this case, the filtering scheme provides accurate structural and behavioural interpretations of both the geometry of the major soil constituents and the groundwater partitioning.