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

Manfred Stähli  Jan Seibert  James W. Kirchner  Jana von Freyberg  Ilja van Meerveld 《水文研究》2021,35(4):e14113

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.  相似文献   

Modelling soil moisture in a high‐latitude landscape using LiDAR and soil data          下载免费PDF全文

Julia Kemppinen  Pekka Niittynen  Henri Riihimäki  Miska Luoto 《地球表面变化过程与地形》2018,43(5):1019-1031

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.  相似文献   

Biomarker hydrogen isotope composition (δD) as proxy for Holocene hydroclimatic change and seismic activity in SW Peloponnese,Greece          下载免费PDF全文

Elin Norström  Christos Katrantsiotis  Martin Finné  Jan Risberg  Rienk H. Smittenberg  Stefan Bjursäter 《第四纪科学杂志》2018,33(5):563-574

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Boosting for Mineral Prospectivity Modeling: A New GIS Toolbox     

Brandmeier  Melanie  Cabrera Zamora  Irving Gibran  Nykänen  Vesa  Middleton  Maarit 《Natural Resources Research》2020,29(1):71-88

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.

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Estimation of groundwater storage from seismic data using deep learning     

Timo Lähivaara  Alireza Malehmir  Antti Pasanen  Leo Kärkkäinen  Janne M.J. Huttunen  Jan S. Hesthaven 《Geophysical Prospecting》2019,67(8):2115-2126

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.  相似文献   

Intercomparison of measurements of bulk snow density and water equivalent of snow cover with snow core samplers: Instrumental bias and variability induced by observers     

J. Ignacio López-Moreno  Leena Leppänen  Bartłomiej Luks  Ladislav Holko  Ghislain Picard  Alba Sanmiguel-Vallelado  Esteban Alonso-González  David C. Finger  Ali N. Arslan  Katalin Gillemot  Aynur Sensoy  Arda Sorman  M. Cansaran Ertaş  Steven R. Fassnacht  Charles Fierz  Christoph Marty 《水文研究》2020,34(14):3120-3133

Manually collected snow data are often considered as ground truth for many applications such as climatological or hydrological studies. However, there are many sources of uncertainty that are not quantified in detail. For the determination of water equivalent of snow cover (SWE), different snow core samplers and scales are used, but they are all based on the same measurement principle. We conducted two field campaigns with 9 samplers commonly used in observational measurements and research in Europe and northern America to better quantify uncertainties when measuring depth, density and SWE with core samplers. During the first campaign, as a first approach to distinguish snow variability measured at the plot and at the point scale, repeated measurements were taken along two 20 m long snow pits. The results revealed a much higher variability of SWE at the plot scale (resulting from both natural variability and instrumental bias) compared to repeated measurements at the same spot (resulting mostly from error induced by observers or very small scale variability of snow depth). The exceptionally homogeneous snowpack found in the second campaign permitted to almost neglect the natural variability of the snowpack properties and focus on the separation between instrumental bias and error induced by observers. Reported uncertainties refer to a shallow, homogeneous tundra-taiga snowpack less than 1 m deep (loose, mostly recrystallised snow and no wind impact). Under such measurement conditions, the uncertainty in bulk snow density estimation is about 5% for an individual instrument and is close to 10% among different instruments. Results confirmed that instrumental bias exceeded both the natural variability and the error induced by observers, even in the case when observers were not familiar with a given snow core sampler.  相似文献   

Experimental impact cratering: A summary of the major results of the MEMIN research unit          下载免费PDF全文

Thomas Kenkmann  Alex Deutsch  Klaus Thoma  Matthias Ebert  Michael H. Poelchau  Elmar Buhl  Eva-Regine Carl  Andreas N. Danilewsky  Georg Dresen  Anja Dufresne  Nathanaël Durr  Lars Ehm  Christian Grosse  Max Gulde  Nicole Güldemeister  Christopher Hamann  Lutz Hecht  Stefan Hiermaier  Tobias Hoerth  Astrid Kowitz  Falko Langenhorst  Bernd Lexow  Hanns-Peter Liermann  Robert Luther  Ulrich Mansfeld  Dorothee Moser  Manuel Raith  Wolf Uwe Reimold  Martin Sauer  Frank Schäfer  Ralf Thomas Schmitt  Frank Sommer  Jakob Wilk  Rebecca Winkler  Kai Wünnemann 《Meteoritics & planetary science》2018,53(8):1543-1568

This paper reviews major findings of the Multidisciplinary Experimental and Modeling Impact Crater Research Network (MEMIN). MEMIN is a consortium, funded from 2009 till 2017 by the German Research Foundation, and is aimed at investigating impact cratering processes by experimental and modeling approaches. The vision of this network has been to comprehensively quantify impact processes by conducting a strictly controlled experimental campaign at the laboratory scale, together with a multidisciplinary analytical approach. Central to MEMIN has been the use of powerful two-stage light-gas accelerators capable of producing impact craters in the decimeter size range in solid rocks that allowed detailed spatial analyses of petrophysical, structural, and geochemical changes in target rocks and ejecta. In addition, explosive setups, membrane-driven diamond anvil cells, as well as laser irradiation and split Hopkinson pressure bar technologies have been used to study the response of minerals and rocks to shock and dynamic loading as well as high-temperature conditions. We used Seeberger sandstone, Taunus quartzite, Carrara marble, and Weibern tuff as major target rock types. In concert with the experiments we conducted mesoscale numerical simulations of shock wave propagation in heterogeneous rocks resolving the complex response of grains and pores to compressive, shear, and tensile loading and macroscale modeling of crater formation and fracturing. Major results comprise (1) projectile–target interaction, (2) various aspects of shock metamorphism with special focus on low shock pressures and effects of target porosity and water saturation, (3) crater morphologies and cratering efficiencies in various nonporous and porous lithologies, (4) in situ target damage, (5) ejecta dynamics, and (6) geophysical survey of experimental craters.  相似文献   

Geomorphological evidence of paleoseismicity: Surficial and underground structures of Pasmajärvi postglacial fault     

Mira Markovaara-Koivisto  Antti E.K. Ojala  Jussi Mattila  Ilmo Kukkonen  Ilkka Aro  Arto Pullinen  Pekka Hänninen  Maarit Middleton  Aleksi Sutinen  Juha Majaniemi  Timo Ruskeeniemi  Raimo Sutinen 《地球表面变化过程与地形》2020,45(12):3011-3024

The cyclic nature of glaciations and related postglacial faulting represents a risk for the deep geological disposal of spent nuclear fuel in areas likely to be affected by future glaciations. Seismic history was therefore studied by means of detecting geomorphological structures on airborne laser scanning digital elevation models and underground by excavating in an esker and trenching across a postglacial fault located in northern Fennoscandia. OLS dating and assessing the geomorphological structures was used for timing of the seismic history. The results suggest that the faulting of different segments in the Pasmajärvi complex is due to at least two late Weichselian events, which probably occurred both subglacially and postglacially. The most reliable input for the moment magnitude estimates was vertical slip profiles, and therefore these estimates (M_W ≈ 6.4–6.9) are suggested. © 2020 John Wiley & Sons, Ltd.  相似文献   

Configurations of community in flood risk management     

Aleksi Räsänen  Vera Kauppinen  Sirkku Juhola  Gunhild Setten  Haakon Lein 《Norsk geografisk tidsskrift. Norwegian journal of geography》2020,74(3):165-180

ABSTRACT

Despite a notable increase in the literature on community resilience, the notion of ‘community’ remains underproblematised. This is evident within flood risk management (FRM) literature, in which the understanding and roles of communities may be acknowledged but seldom discussed in any detail. The purpose of the article is to demonstrate how community networks are configured by different actors, whose roles and responsibilities span spatial scales within the context of FRM. Accordingly, the authors analyse findings from semi-structured interviews, policy documents, and household surveys from two flood prone areas in Finnish Lapland. The analysis reveals that the ways in which authorities, civil society, and informal actors take on multiple roles are intertwined and form different types of networks. By implication, the configuration of community is fuzzy, elusive and situated, and not confined to a fixed spatiality. The authors discuss the implications of the complex nature of community for FRM specifically, and for community resilience more broadly. They conclude that an analysis of different actors across scales contributes to an understanding of the configuration of community, including community resilience, and how the meaning of community takes shape according to the differing aims of FRM in combination with differing geographical settings.  相似文献   

Analysis of uncertainties in the hydrological response of a model‐based climate change impact assessment in a subcatchment of the Spree River,Germany          下载免费PDF全文

Anne Gädeke  Herwig Hölzel  Hagen Koch  Ina Pohle  Uwe Grünewald 《水文研究》2014,28(12):3978-3998

Climate change impact assessments form the basis for the development of suitable climate change adaptation strategies. For this purpose, ensembles consisting of stepwise coupled models are generally used [emission scenario → global circulation model → downscaling approach (DA) → bias correction → impact model (hydrological model)], in which every item is affected by considerable uncertainty. The aim of the current study is (1) to analyse the uncertainty related to the choice of the DA as well as the hydrological model and its parameterization and (2) to evaluate the vulnerability of the studied catchment, a subcatchment of the highly anthropogenically impacted Spree River catchment, to hydrological change. Four different DAs are used to drive four different model configurations of two conceptually different hydrological models (Water Balance Simulation Model developed at ETH Zürich and HBV‐light). In total, 452 simulations are carried out. The results show that all simulations compute an increase in air temperature and potential evapotranspiration. For precipitation, runoff and actual evapotranspiration, opposing trends are computed depending on the DA used to drive the hydrological models. Overall, the largest source of uncertainty can be attributed to the choice of the DA, especially regarding whether it is statistical or dynamical. The choice of the hydrological model and its parameterization is of less importance when long‐term mean annual changes are compared. The large bandwidth at the end of the modelling chain may exacerbate the formulation of suitable climate change adaption strategies on the regional scale. Copyright © 2013 John Wiley & Sons, Ltd.  相似文献