the eastern Jura Mountains consist of the Jura fold-thrust belt and the autochthonous tabular Jura and Vesoul-Montbéliard Plateau. they are drained by the river rhine, which flows into the North sea, and the river Doubs, which flows into the Mediterranean. the internal drainage systems of the Jura fold-thrust belt consist of rivers flowing in synclinal valleys that are linked by river segments cutting orthogonally through anticlines. the latter appear to employ parts of the antecedent Jura Nagelfluh drainage system that had developed in response to Late burdigalian uplift of the Vosgesback Forest Arch, prior to Late Miocene-Pliocene deformation of the Jura fold-thrust belt.the following stages are recognized in the evolution of the Jura Mountain drainage systems: 1) middle to late tortonian (10-7.2 Ma) folding-related overpowering and partial reversal of the south-directed Jura Nagelfluh drainage system, 2) Messinian to early Pliocene (7.2-4.2 Ma) Aare-Danube and protoDoubs stage, 3) early to middle Pliocene (4.2-2.9 Ma) Aare-Doubs stage, 4) late Pliocene to early Quaternary (2.9-1.7 Ma) Aare-rhine and Doubs stage and 5) Quaternary (1.7-0 Ma) Alpine-rhine and Doubs stage.Development of the thin-skinned Jura fold-thrust belt controlled the first three stages of this drainage system evolution, whilst the last two stages were essentially governed by the subsidence of the Upper rhine Graben, which resumed during the late Pliocene. Late Pliocene and Quaternary deep incision of the Aare-rhine/Alpine-rhine and its tributaries in the Jura Mountains and black Forest is mainly attributed to lowering of the erosional base level in the continuously subsiding Upper rhine Graben. Incision of the Doubs and Dessoubre canyons reflects uplift of the Franches-Montagnes and Franche-comté in response to thick-skinned deformation of the Jura fold-thrust belt, which had commenced around 3 Ma.Geodetic data indicate that uplift of the Jura Mountains, relative to the tabular Jura, presently continues at very low strain rates whilst the Upper rhine Graben subsides very slowly and the black Forest is relatively stable. Introductionthe Jura fold-thrust belt (JFtb), forming the core of the Jura Mountains, is the youngest and most external element of the central Alpine orogenic system. It has accounted for up to 30 km of essentially thin-skinned shortening since late Miocene times (Laubscher 1961(Laubscher , 1992Philippe et al. 1996;Affolter & Gratier 2004) and is still seismotectonically active (becker 2000;Lacombe & Mouthereau 2002;Edel et al. 2006). the JFtb is flanked to the sE by the flexural swiss Molasse foreland basin of the Alps whilst its most external elements encroach on the bresse and Upper rhine grabens ( Fig. 1; chauve et al. 1980;Dèzes et al. 2004).Evolution of the JFtb combined with the development of the Upper rhine Graben (UrG) and the bresse Graben, exerted strong control on the location of the repeatedly shifting watersheds between the rivers Danube, Doubs and rhine, which flow into the black sea, the Mediterran...
Abstract. High-resolution maps of soil properties are a prerequisite for assessing soil threats and soil functions and for fostering the sustainable use of soil resources. For many regions in the world, accurate maps of soil properties are missing, but often sparsely sampled (legacy) soil data are available. Soil property data (response) can then be related by digital soil mapping (DSM) to spatially exhaustive environmental data that describe soilforming factors (covariates) to create spatially continuous maps. With airborne and space-borne remote sensing and multi-scale terrain analysis, large sets of covariates have become common. Building parsimonious models amenable to pedological interpretation is then a challenging task.We propose a new boosted geoadditive modelling framework (geoGAM) for DSM. The geoGAM models smooth non-linear relations between responses and single covariates and combines these model terms additively. Residual spatial autocorrelation is captured by a smooth function of spatial coordinates, and non-stationary effects are included through interactions between covariates and smooth spatial functions. The core of fully automated model building for geoGAM is component-wise gradient boosting.We illustrate the application of the geoGAM framework by using soil data from the Canton of Zurich, Switzerland. We modelled effective cation exchange capacity (ECEC) in forest topsoils as a continuous response. For agricultural land we predicted the presence of waterlogged horizons in given soil depths as binary and drainage classes as ordinal responses. For the latter we used proportional odds geoGAM, taking the ordering of the response properly into account. Fitted geoGAM contained only a few covariates (7 to 17) selected from large sets (333 covariates for forests, 498 for agricultural land). Model sparsity allowed for covariate interpretation through partial effects plots. Prediction intervals were computed by model-based bootstrapping for ECEC. The predictive performance of the fitted geoGAM, tested with independent validation data and specific skill scores for continuous, binary and ordinal responses, compared well with other studies that modelled similar soil properties. Skill score (SS) values of 0.23 to 0.53 (with SS = 1 for perfect predictions and SS = 0 for zero explained variance) were achieved depending on the response and type of score. GeoGAM combines efficient model building from large sets of covariates with effects that are easy to interpret and therefore likely raises the acceptance of DSM products by end-users.
Spatial information on soils and their abilities to fulfil their functions is key to sustainable soil resource use. Maps indicating how soils fulfil their static functions, e.g., regulating nutrient and water flows, providing appropriate habitats, and allowing biomass production, have allowed soil information to be embedded in spatial planning programmes. We adapted 10 static soil function assessment (SFA) methods and applied them to agricultural soils in a study area on the Swiss Plateau. Soil function maps were created by applying the SFA methods to maps of eight basic soil properties generated previously using digital soil mapping techniques. The soil function maps were compared with results obtained by applying the SFA methods to data for more than 7000 soil profiles to determine how credible the maps were. Soil in the study area had distinctive spatial patterns for most of the regulation, habitat, and production functions, clearly indicating the multiple roles played by soil in supporting ecosystem services. The fulfilment of individual soil functions is linked to the inherent soil properties, the terrain, and climatic conditions. The soil function maps agreed well with the SFA results for the profiles in terms of land use, soil type, and drainage class. Four aggregation approaches were tested to give total assessment values (soil indices). Aggregating the 10 soil functions into an overall soil functionality value gave quite diverse spatial patterns, indicating that merging might average out the spatial characteristics of certain soil functions. We conclude that a quite comprehensive set of soil functions can be assessed using spatial information for eight basic soil properties to a soil depth of at least 1 m and approved pedotransfer functions for secondary soil properties. SFA methods for the production function are well established, but methods for assessing habitat and regulation functions need to be developed further. This is also true for forest soils, for which SFA methods are yet to be established. Aggregating soil function maps to give a single indicator map requires the importance of each function to be assessed. We found no evidence, from the soil protection perspective, favouring specific ways of combining soil function maps.
Abstract. High-resolution maps of soil properties are a prerequisite for assessing soil threats and soil functions and to foster sustainable use of soil resources. For many regions in the world precise maps of soil properties are missing, but often sparsely sampled and discontinuous (legacy) soil data are available. Soil property data (response) can then be related by digital soil mapping (DSM) to spatially exhaustive environmental data that describe soil forming factors (covariates) to create spatially continuous maps. With air- and spaceborne remote sensing data and multi-scale terrain analysis large sets of covariates have become common. Building parsimonious models, amenable to pedological interpretation, is then a challenging task. We propose a new boosted geoadditive modelling framework (geoGAM) for DSM. A geoGAM models smooth nonlinear relations between responses and single covariates and combines these model terms additively. Residual spatial autocorrelation is captured by a smooth function of spatial coordinates and nonstationary effects are included by interactions between covariates and smooth spatial functions. The core of fully automated model building for geoGAM is componentwise gradient boosting. We illustrate the application of the geoGAM framework by using soil data from the Canton of Zurich, Switzerland. We modelled effective cation exchange capacity (ECEC) in forest topsoils as continuous response. For agricultural land we predicted the presence of waterlogged horizons in given soil depth layers as binary and drainage classes as ordinal responses. For the latter we used proportional odds geoGAM taking the ordering of the response properly into account. Fitted geoGAM contained only few covariates (7 to 17) selected from large sets (333 covariates for forests, 498 for agricultural land). Model sparsity allowed covariate interpretation by partial effects plots. Prediction intervals were computed by model-based bootstrapping for ECEC. Predictive performance of the fitted geoGAM, tested with independent validation data and specific skill scores (SS) for continuous, binary and ordinal responses, compared well with other studies that modelled similar soil properties. SS of 0.23 up to 0.53 (with SS = 1 for perfect predictions and SS = 0 for zero explained variance) were achieved depending on response and type of score. geoGAM combines efficient model building from large sets of covariates with ease of effect interpretation and therefore likely raises the acceptance of DSM products by end-users.
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