Characterization of flow and transport dynamics in karst aquifers by analyzing tracer test results in conduits and recharge areas (the Egino Massif,Basque Country,Spain): environmental and management implications

Karst aquifers contribute to supplying drinking water to almost a quarter of the world´s population. Their complex dynamics requires specific approaches aimed at recognizing their singularities, analyzing its vulnerability, and ensuring water resources quality. In this paper, the results of processing and modeling five breakthrough tracer curves obtained under different hydrodynamic conditions in the main conduit of Egino karst aquifer (Basque Country, Spain) are analyzed together with those involving pressure injections of the tracer in the saturated zone of the karst massif recharge area. In the conduit, transport is immediate and highly efficient (recovery rates above 84% and dispersion coefficients from 15.04 to 84.35 m²/min); tracer retentions increase as flow rates decrease and no significant contributions to its surroundings are observed. In contrast, tracer transport from the massif recharge area is more complex: after injection at a pressure of 1 MPa, most tracer remains in the surrounding of the injection borehole, retained in a saturated medium of low effective fracture porosity (? _f ?=?1.02?×?10^?4, assuming a radial divergent flow model); subsequently, the main tracer mobilization to the spring was registered with the first rains, with 0.088 m/min mean velocity and high concentrations per unit mass being injected (C _p /M₀?=?0.03 mg/L/kg), which is evidence that the tracer reaches soon the karst conduit network. In any case, a decreasing tracer presence is registered at the injection zone during a hydrological cycle. In both cases, the observed non-linearity of transport processes should be considered in the development of vulnerability approaches, modeling efforts, and mapping. Furthermore, in the case of karst massif recharge areas, as the presence of pollutants may have a significant impact on the springs and persist over time, their management and protection needs must be revised in each specific site. Simultaneously, quality-monitoring programs at the springs must be adapted to the aquifers recognized dynamics.     

Long-term variability of temperature and precipitation in the Czech Lands: an attribution analysis

Among the key problems associated with the study of climate variability and its evolution are identification of the factors responsible for observed changes and quantification of their effects. Here, correlation and regression analysis are employed to detect the imprints of selected natural forcings (solar and volcanic activity) and anthropogenic influences (amounts of greenhouse gases—GHGs—and atmospheric aerosols), as well as prominent climatic oscillations (Southern Oscillation—SO, North Atlantic Oscillation—NAO, Atlantic Multidecadal Oscillation—AMO) in the Czech annual and monthly temperature and precipitation series for the 1866–2010 period. We show that the long-term evolution of Czech temperature change is dominated by the influence of an increasing concentration of anthropogenic GHGs (explaining most of the observed warming), combined with substantially lower, and generally statistically insignificant, contributions from the sulphate aerosols (mild cooling) and variations in solar activity (mild warming), but with no distinct imprint from major volcanic eruptions. A significant portion of the observed short-term temperature variability can be linked to the influence of NAO. The contributions from SO and AMO are substantially weaker in magnitude. Aside from NAO, no major influence from the explanatory variables was found in the precipitation series. Nonlinear forms of regression were used to test for nonlinear interactions between the predictors and temperature/precipitation; the nonlinearities disclosed were, however, very weak, or not detectable at all. In addition to the outcomes of the attribution analysis for the Czech series, results for European and global land temperatures are also shown and discussed.     

Free advanced modeling and remote-sensing techniques for wetland watershed delineation and monitoring

Watershed scale studies focusing on hydrological pressures influencing freshwater ecosystem dynamics are necessary for the establishment of suitable wetland ecological indicators. Enhanced and reproducible methods for watershed modeling and land-cover assessment are thus essential tools for wetland monitoring and management. However, few integrated studies propose advanced open source methodologies for watershed modeling and assessment. In this study, a set of GIS methodological tools was applied and further developed in order to delineate wetland watersheds and map their land-cover changes over time. Watersheds draining to 11 semiarid Mediterranean saline wetlands were delimited and map algebra operations were applied on the digital elevation model in the Campo de Cartagena coastal plain to enhance watershed delimitation. land-use/land-cover maps of wetland watersheds were obtained for years 1987 and 2008 by means of supervised classification of Landsat images. A set of four spectral indices was included in the classification analysis using a combination of bands in order to improve the discrimination of vegetation, water bodies, infrastructure, and bare soil. An iterative classification procedure based on maximum likelihood and random selection of training areas was applied. Contextual information based on automatic image segmentation of Landsat scenes was also included as ancillary layers. Watershed areas obtained ranged from 70 to 17,000 ha and delineation was improved in the Campo de Cartagena coastal plain. The proposed image classification methodology showed high accuracies and improved standard classification techniques. The proposed methodology is based on free and open source tools, which makes it broadly applicable.     

Comparison of sparse‐grid geometric and random sampling methods in nonlinear inverse solution uncertainty estimation

A new uncertainty estimation method, which we recently introduced in the literature, allows for the comprehensive search of model posterior space while maintaining a high degree of computational efficiency. The method starts with an optimal solution to an inverse problem, performs a parameter reduction step and then searches the resulting feasible model space using prior parameter bounds and sparse‐grid polynomial interpolation methods. After misfit rejection, the resulting model ensemble represents the equivalent model space and can be used to estimate inverse solution uncertainty. While parameter reduction introduces a posterior bias, it also allows for scaling this method to higher dimensional problems. The use of Smolyak sparse‐grid interpolation also dramatically increases sampling efficiency for large stochastic dimensions. Unlike Bayesian inference, which treats the posterior sampling problem as a random process, this geometric sampling method exploits the structure and smoothness in posterior distributions by solving a polynomial interpolation problem and then resampling from the resulting interpolant. The two questions we address in this paper are 1) whether our results are generally compatible with established Bayesian inference methods and 2) how does our method compare in terms of posterior sampling efficiency. We accomplish this by comparing our method for two electromagnetic problems from the literature with two commonly used Bayesian sampling schemes: Gibbs’ and Metropolis‐Hastings. While both the sparse‐grid and Bayesian samplers produce compatible results, in both examples, the sparse‐grid approach has a much higher sampling efficiency, requiring an order of magnitude fewer samples, suggesting that sparse‐grid methods can significantly improve the tractability of inference solutions for problems in high dimensions or with more costly forward physics.     

Verification of the shallow seismic crustal structure of the western Krušné Hory crystalline unit, Czech Republic

We analyze refraction measurements along a short profile in western Kru?né hory crystalline unit. The profile passed close to the seismically active zone of Nový Kostel. The measurements were carried out to distances of about 15 km using quarry blasts near the village of Horní Rozmy?l, located at the eastern margin of the crystalline unit. Smoothed P-wave travel times were interpreted using the Wiechert-Herglotz method, which yielded a 1-D velocity model of the shallow crustal structure of the crystalline unit down to a depth of 1.7 km. The P-wave velocity of the model increases from about 4.0 km/s at the surface to 5.9 km/s at a depth of 1.7 km. The superficial velocities of our model are somewhat higher than the superficial velocities of the model that is routinely used for earthquake location in the region.     

On the limits of using combined U-series/ESR method to date fossil teeth from two Early Pleistocene archaeological sites of the Orce area (Guadix-Baza basin,Spain)

The combined U-series/electron spin resonance (ESR) dating method was applied to nine teeth from two Early Pleistocene archaeological sites located in the Orce area (Guadix-Baza Basin, Southern Spain): Fuente Nueva-3 (FN-3) and Barranco León (BL). The combination of biostratigraphy and magnetostratigraphy places both sites between the Olduvai and Jaramillo subchrons (1.78–1.07 Ma).Our results highlight the difficulty of dating such old sites and point out the limits of the combined U-series/ESR dating method based on the US model. We identified several sources of uncertainties that may lead to inaccurate age estimates. Seven samples could not be dated because the dental tissues had (²³⁰Th/²³⁴U) activity ratios higher than equilibrium, indicating that uranium had probably leached from these tissues. It was however possible to calculate numerical estimates for two of the teeth, both from FN-3. One yielded a Middle Pleistocene age that seems to be strongly underestimated; the other provided an age of 1.19 ± 0.21 Ma, in agreement with data obtained from independent methods. The latter result gives encouragement that there are samples that can be used for routine dating of old sites.     

Database extension for digital soil mapping using artificial neural networks

Cost and time are the two most important factors conditioning soil surveys. Since these surveys provide basic information for modelling and management activities, new methods are needed to speed the soil-mapping process with limited input data. In this study, the polypedon concept was used to extend the spatial representation of sampled pedons (point data) in order to train artificial neural networks (ANNs) for digital soil mapping (DSM). The input database contained 97 soil profiles belonging to 7 different soil series and 15 digital elevation model (DEM) attributes. Pedons were represented in raster format as one-cell areas. The corresponding polypedons were then spatially represented by neighbouring raster cells (e.g. 2 × 2, … up to 6 × 6 cells). The primary database contained 97 pedons (97 cells) that were extended up to 3492 cells (in the case of 6 × 6-cell regions). This approach employed test and validation areas to calculate the respective accuracies of data interpolation and extrapolation. The results showed increased accuracies in training and interpolation (test area) but a poor level of accuracy in the extrapolation process (validation area). However, the overall precision of all predictions increased considerably. Using only topographic attributes for extrapolation was not sufficient to obtain an accurate soil map. To improve prediction, other soil-forming factors, such as landforms and/or geology, should also be considered as input data in the ANN. The proposed method could help to improve existing soil maps by using DSM results in areas with limited soil data and to save time and money in soil survey work.