Evaporite-karst processes, landforms, and environmental problems

Evaporite karst is widespread, but relatively unknown when compared with carbonate karst; this special issue addresses that lack of familiarity. Evaporite rocks have much higher solubilities and faster dissolution rates than carbonate rocks and they also commonly have lower mechanical strengths and more ductile reheologies. Many of these factors are dependent on the local hydrogeology, and when combined they can result in areas where karst features evolve on a human time scale, rather than a geological timescale. Karst collapse and subsidence are common in such areas, making them problematical for the local population. The evaporite-karst environment is very sensitive to changes in the local hydrology and hydrogeology, so that human factors such as groundwater extraction, drainage, and irrigation can act as triggering events for karst collapses. Some evaporite-karst features such as caves and saline springs have been beneficially exploited, but most of them, including sinkholes, subsidence, and groundwater degradation, pose a threat to the local environment and a hazard to development. The papers in this special issue of Environmental Geology arose from a successful session on Evaporite Karst convened by us at the Sixth International Conference on Geomorphology. This was held in Zaragoza, Spain, in September 2005 and was organised by Zaragoza University and the International Association of Geomorphologists (IAG; http://www.geomorph.org/). Authors of the twenty-eight presented abstracts were invited to submit full papers to this special issue. Nineteen papers were proposed and sixteen papers have been accepted and are published here

Identification, prediction and mitigation of sinkhole hazards in evaporite karst areas

Abstract Sinkholes usually have a higher probability of occurrence and a greater genetic diversity in evaporite terrains than in carbonate karst areas. This is because evaporites have a higher solubility, and commonly a lower mechanical strength. Subsidence damage resulting from evaporite dissolution generates substantial losses throughout the world, but the causes are only well-understood in a few areas. To deal with these hazards, a phased approach is needed for sinkhole identification, investigation, prediction, and mitigation. Identification techniques include field surveys, and geomorphological mapping combined with accounts from local people and historical sources. Detailed sinkhole maps can be constructed from sequential historical maps, recent topographical maps and digital elevation models (DEMs) complemented with building-damage surveying, remote sensing, and high-resolution geodetic surveys. On a more detailed level, information from exposed paleosubsidence features (paleokarst), speleological explorations, geophysical investigations, trenching, dating techniques, and boreholes, may help to recognize dissolution and subsidence features. Information on the hydrogeological pathways including caves, springs and swallow holes, are particularly important especially when corroborated by tracer tests. These diverse data sources make a valuable database - the karst inventory. From this dataset, sinkhole susceptibility zonations (relative probability) may be produced based on the spatial and temporal distribution of the features and good knowledge of the local geology. Sinkhole distribution can be investigated by spatial distribution analysis techniques including studies of preferential elongation, alignment and nearest neighbor analysis. More objective susceptibility models may be obtained by analyzing the statistical relationships between the known sinkholes and the conditioning factors, such as weather conditions. Chronological information on sinkhole formation is required to estimate the probability of occurrence of sinkholes (number of sinkholes/km² year). Such spatial and temporal predictions, derived from limited records and based on the assumption that past sinkhole activity may be extrapolated to the future, are non-corroborated hypotheses. Validation methods allow us to assess the predictive capability of the susceptibility maps and to transform them into probability maps. Avoiding the most hazardous areas by preventive planning is the safest strategy for development in sinkhole-prone areas. Corrective measures could be to reduce the dissolution activity and subsidence processes, but these are difficult. A more practical solution for safe development is to reduce the vulnerability of the structures by using subsidence-proof designs

Two-dimensional Einstein manifolds in geometrothermodynamics

We present a class of thermodynamic systems with constant thermodynamic curvature which, within the context of geometric approaches of thermodynamics, can be interpreted as constant thermodynamic interaction among their components. In particular, for systems constrained by the vanishing of the Hessian curvature we write down the systems of partial differential equations. In such a case it is possible to find a subset of solutions lying on a circumference in an abstract space constructed from the first derivatives of the isothermal coordinates. We conjecture that solutions on the characteristic circumference are of physical relevance, separating them from those of pure mathematical interest. We present the case of a one-parameter family of fundamental relations that -- when lying in the circumference -- describe a polytropic fluid

Forecasting high waters at Venice Lagoon using chaotic time series analisys and nonlinear neural netwoks

Time series analysis using nonlinear dynamics systems theory and multilayer neural networks models have been applied to the time sequence of water level data recorded every hour at 'Punta della Salute' from Venice Lagoon during the years 1980-1994. The first method is based on the reconstruction of the state space attractor using time delay embedding vectors and on the characterisation of invariant properties which define its dynamics. The results suggest the existence of a low dimensional chaotic attractor with a Lyapunov dimension, DL, of around 6.6 and a predictability between 8 and 13 hours ahead. Furthermore, once the attractor has been reconstructed it is possible to make predictions by mapping local-neighbourhood to local-neighbourhood in the reconstructed phase space. To compare the prediction results with another nonlinear method, two nonlinear autoregressive models (NAR) based on multilayer feedforward neural networks have been developed. From the study, it can be observed that nonlinear forecasting produces adequate results for the 'normal' dynamic behaviour of the water level of Venice Lagoon, outperforming linear algorithms, however, both methods fail to forecast the 'high water' phenomenon more than 2-3 hours ahead.Publicad