Hydrological trends and the evolution of catchment research in the Alptal valley,central Switzerland

When the observation of small headwater catchments in the pre-Alpine Alptal valley (central Switzerland) started in the late 1960s, the researchers were mainly interested in questions related to floods and forest management. Investigations of geomorphological processes in the steep torrent channels followed in the 1980s, along with detailed observations of biogeochemical and ecohydrological processes in individual forest stands. More recently, research in the Alptal has addressed the impacts of climate change on water supply and runoff generation. In this article, we describe, for the first time, the evolution of catchment research at Alptal, and present new analyses of long-term trends and short-term hydrologic behaviour. Hydrometeorological time series from the past 50 years show substantial interannual variability, but only minimal long-term trends, except for the ~2°C increase in mean annual air temperature over the 50-year period, and a corresponding shift towards earlier snowmelt. Similar to previous studies in larger Alpine catchments, the decadal variations in mean annual runoff in Alptal's small research catchments reflect the long-term variability in annual precipitation. In the Alptal valley, the most evident hydrological trends were observed in late spring and are related to the substantial change in the duration of the snow cover. Streamflow and water quality are highly variable within and between hydrological events, suggesting rapid shifts in flow pathways and mixing, as well as changing connectivity of runoff-generating areas. This overview illustrates how catchment research in the Alptal has evolved in response to changing societal concerns and emerging scientific questions.     

Salt-induced crestal faults control the formation of Quaternary tunnel valleys in the southern North Sea

Tunnel valleys are major features of glaciated margins and they enable meltwater expulsion from underneath a thick ice cover. Their formation is related to the erosion of subglacial sediments by overpressured meltwater and direct glacial erosion. Yet, the impact of pre-existing structures on their formation and morphology remains poorly known. High-quality 3D seismic data allowed the mapping of a large tunnel valley that eroded underlying preglacial delta deposits in the southern North Sea. The valley follows the N–S strike of crestal faults related to a Zechstein salt wall. A change in downstream tunnel valley orientation towards the SE accompanies a change in the strike direction of salt-induced faults. Fault offsets indicate important activity of crestal faults during the deposition of preglacial deltaic sediments. We propose that crestal faults facilitated tunnel valley erosion by acting as high-permeability pathways and allowing subglacial meltwater to reach low-permeability sediments in the underlying Neogene deltaic sequences, ultimately resulting in meltwater overpressure build-up and tunnel valley excavation. Active faults probably also weakened the near-surface sediment to allow a more efficient erosion of the glacial substrate. This control of substrate structures on tunnel valley morphology is considered as a primary factor in subglacial drainage pattern development in the study area.     

Modelling soil moisture in a high‐latitude landscape using LiDAR and soil data

Soil moisture has a pronounced effect on earth surface processes. Global soil moisture is strongly driven by climate, whereas at finer scales, the role of non‐climatic drivers becomes more important. We provide insights into the significance of soil and land surface properties in landscape‐scale soil moisture variation by utilizing high‐resolution light detection and ranging (LiDAR) data and extensive field investigations. The data consist of 1200 study plots located in a high‐latitude landscape of mountain tundra in north‐western Finland. We measured the plots three times during growing season 2016 with a hand‐held time‐domain reflectometry sensor. To model soil moisture and its temporal variation, we used four statistical modelling methods: generalized linear models, generalized additive models, boosted regression trees, and random forests. The model fit of the soil moisture models were R² = 0.60 and root mean square error (RMSE) 8.04 VWC% on average, while the temporal variation models showed a lower fit of R² = 0.25 and RMSE 13.11 CV%. The predictive performances for the former were R² = 0.47 and RMSE 9.34 VWC%, and for the latter R² = 0.01 and RMSE 15.29 CV%. Results were similar across the modelling methods, demonstrating a consistent pattern. Soil moisture and its temporal variation showed strong heterogeneity over short distances; therefore, soil moisture modelling benefits from high‐resolution predictors, such as LiDAR based variables. In the soil moisture models, the strongest predictor was SAGA (System for Automated Geoscientific Analyses) wetness index (SWI), based on a 1 m² digital terrain model derived from LiDAR data, which outperformed soil predictors. Thus, our study supports the use of LiDAR based SWI in explaining fine‐scale soil moisture variation. In the temporal variation models, the strongest predictor was the field‐quantified organic layer depth variable. Our results show that spatial soil moisture predictions can be based on soil and land surface properties, yet the temporal models require further investigation. Copyright © 2017 John Wiley & Sons, Ltd.     

Mesocosm experiments identifying hotspots of groundwater upwelling in a water column by fibre optic distributed temperature sensing

Lacustrine groundwater discharge (LGD) can substantially impact ecosystem characteristics and functions. Fibre optic distributed temperature sensing (FO‐DTS) has been successfully used to locate groundwater discharge into lakes and rivers at the sediment–water interface, but locating groundwater discharge would be easier if it could be detected from the more accessible water surface. So far, it is not clear if how and under which conditions the LGD signal propagates through the water column to the water surface–atmosphere interface, and what perturbations and signal losses occur along this pathway. In the present study, LGD was simulated in a mesocosm experiment. Under winter conditions, water with temperatures of 14 to 16 °C was discharged at the bottom of a 10 × 2.8‐m mesocosm. Water within this mesocosm ranged from 4.0 to 7.4 °C. Four layers (20, 40, 60, and 80 cm above the sediment) of the 82 cm deep mesocosm were equipped with FO‐DTS for tracing thermal patterns in the mesocosm. Aims are (a) to test whether the positive buoyancy of relatively warm groundwater imported by LGD into shallow water bodies allows detection of LGD at the lake's water surface–atmosphere interface by FO‐DTS, (b) to analyse the propagation of the temperature signal from the sediment‐water interface through the water column, and (c) to learn more about detectability of the signal under different discharge rates and weather conditions. The experiments supported the benchmarking of scale dependencies and robustness of FO‐DTS applications for measuring upwelling into aquatic environments and revealed that weather conditions can have important impacts on the detection of upwelling at water surface–atmosphere interfaces at larger scales.     

Boosting for Mineral Prospectivity Modeling: A New GIS Toolbox

With an increasing demand for raw materials, predictive models that support successful mineral exploration targeting are of great importance. We evaluated different machine learning techniques with an emphasis on boosting algorithms and implemented them in an ArcGIS toolbox. Performance was tested on an exploration dataset from the Iberian Pyrite Belt (IPB) with respect to accuracy, performance, stability, and robustness. Boosting algorithms are ensemble methods used in supervised learning for regression and classification. They combine weak classifiers, i.e., classifiers that perform slightly better than random guessing to obtain robust classifiers. Each time a weak learner is added; the learning set is reweighted to give more importance to misclassified samples. Our test area, the IPB, is one of the oldest mining districts in the world and hosts giant volcanic-hosted massive sulfide (VMS) deposits. The spatial density of ore deposits, as well as the size and tonnage, makes the area unique, and due to the high data availability and number of known deposits, well-suited for testing machine learning algorithms. We combined several geophysical datasets, as well as layers derived from geological maps as predictors of the presence or absence of VMS deposits. Boosting algorithms such as BrownBoost and Adaboost were tested and compared to Logistic Regression (LR), Random Forests (RF) and Support Vector machines (SVM) in several experiments. We found performance results relatively similar, especially to BrownBoost, which slightly outperformed LR and SVM with respective accuracies of 0.96 compared to 0.89 and 0.93. Data augmentation by perturbing deposit location led to a 7% improvement in results. Variations in the split ratio of training and test data led to a reduction in the accuracy of the prediction result with relative stability occurring at a critical point at around 26 training samples out of 130 total samples. When lower numbers of training data were introduced accuracy dropped significantly. In comparison with other machine learning methods, Adaboost is user-friendly due to relatively short training and prediction times, the low likelihood of overfitting and the reduced number of hyperparameters for optimization. Boosting algorithms gave high predictive accuracies, making them a potential data-driven alternative for regional scale and/or brownfields mineral exploration.

     

Proglacial groundwater storage dynamics under climate change and glacier retreat

Proglacial aquifers are an important water store in glacierised mountain catchments that supplement meltwater-fed river flows and support freshwater ecosystems. Climate change and glacier retreat will perturb water storage in these aquifers, yet the climate-glacier-groundwater response cascade has rarely been studied and remains poorly understood. This study implements an integrated modelling approach that combines distributed glacio-hydrological and groundwater models with climate change projections to evaluate the evolution of groundwater storage dynamics and surface-groundwater exchanges in a temperate, glacierised catchment in Iceland. Focused infiltration along the meltwater-fed Virkisá River channel is found to be an important source of groundwater recharge and is projected to provide 14%–20% of total groundwater recharge by the 2080s. The simulations highlight a mechanism by which glacier retreat could inhibit river recharge in the future due to the loss of diurnal melt cycling in the runoff hydrograph. However, the evolution of proglacial groundwater level dynamics show considerable resilience to changes in river recharge and, instead, are driven by changes in the magnitude and seasonal timing of diffuse recharge from year-round rainfall. The majority of scenarios simulate an overall reduction in groundwater levels with a maximum 30-day average groundwater level reduction of 1 m. The simulations replicate observational studies of baseflow to the river, where up to 15% of the 30-day average river flow comes from groundwater outside of the melt season. This is forecast to reduce to 3%–8% by the 2080s due to increased contributions from rainfall and meltwater runoff. During the melt season, groundwater will continue to contribute 1%–3% of river flow despite significant reductions in meltwater runoff inputs. Therefore it is concluded that, in the proglacial region, groundwater will continue to provide only limited buffering of river flows as the glacier retreats.     

Plant diversity and community structure of Brazilian <Emphasis Type="Italic">Páramos</Emphasis>

In Eastern South America, high altitude grasslands represent a mountain system that has a high number of endemic species. However, studies on the ecology of plant communities in these environments remain scarce. We aimed to evaluate the patterns of biodiversity and structure of plant communities from rocky outcrops in high altitude grasslands of three areas at the Caparaó National Park, southeastern Brazil, by sampling 300 randomly distributed plots. Then, we compared the floristic composition, relative abundance, and biological and vegetation spectra among areas. We classified species as endemic and non-endemic and verified the occurrence of endangered species. Species richness was evaluated by rarefaction analysis on the sampling units. The importance value and species abundance distribution (SAD) models were assessed. We also performed an indicator species analysis. We sampled 58 species belonging to 49 genera and 32 families. The number of species decreased with increasing altitude, with significant differences being observed among areas regarding richness, abundance, and cover. Of the total number of species, 10 are endemic to the Caparaó National Park and 17 are listed on the Brazilian Red List of endangered species. The dominant families on all peaks were Asteraceae and Poaceae. The SAD models showed lognormal and geometric distributions, corroborating the fact that 10 species that were common to all three areas were also the most dominant ones in the communities and showed the highest importance values, which ranged between 35% and 60%. Indicator species analysis revealed that 28 species (48.27%) were indicators. Of these, 42.85% had maximum specificity, meaning that they occurred only in one area. Thus, the number of species per life form ratio was similar among areas, yet vegetation spectra differed, especially for hemicryptophytes. The altimetric difference among the areas showed to be a very important driver in the community assembly, influencing the evaluated variables, however, other drivers as soil depth, slope and water could also influence the community structure on a smaller and local spatial scale.     

Estimation of groundwater storage from seismic data using deep learning

Convolutional neural networks can provide a potential framework to characterize groundwater storage from seismic data. Estimation of key components, such as the amount of groundwater stored in an aquifer and delineate water table level, from active-source seismic data are performed in this study. The data to train, validate and test the neural networks are obtained by solving wave propagation in a coupled poroviscoelastic–elastic media. A discontinuous Galerkin method is applied to model wave propagation, whereas a deep convolutional neural network is used for the parameter estimation problem. In the numerical experiment, the primary unknowns estimated are the amount of stored groundwater and water table level, while the remaining parameters, assumed to be of less of interest, are marginalized in the convolutional neural network-based solution. Results, obtained through synthetic data, illustrate the potential of deep learning methods to extract additional aquifer information from seismic data, which otherwise would be impossible based on a set of reflection seismic sections or velocity tomograms.