Characteristics of high-mountain karst based on GIS and Remote Sensing

Spatial data were used to develop complex characteristics of a high-mountain karst. The numeric elaboration was conducted using the analytical capabilities of GIS software. Analysis was based on spatial data gained by elaborating aerial photography. The results were presented in map sets, as well as Geographic Information System format. The set of matched numerical methods used and the proposed algorithm for data analysis may be applied at other karst sites.     

Long‐distance fluid migration defines the diagenetic history of unique Ediacaran sediments in the East European Craton

The diagenetic history of the Ediacaran sedimentary rocks in the East European Craton (EEC) over the area extending from Arkhangelsk (Russia) in the north to Podolia (Ukraine) in the south was revealed by means of the XRD characterization and K–Ar dating of clay fractions, mudstone porosity measurements and organic geochemistry investigations. Mudstone porosity measurements produced direct evidence of shallow maximum burial of the Ediacaran sediments on the craton (Russia, Lithuania, Belarus, Volyn), not exceeding 1.5 km, and much deeper burial at the cratonic margin, in Podolia and Poland. In general, illitization of smectite and biomarker indices indicates more advanced diagenesis at the cratonic margin. K–Ar dating of authigenic illite–smectite and aluminoceladonite revealed the Palaeozoic age of mineral diagenesis (ca. 450–300 Ma) both on the craton and its margin, with older ages generally observed in the north. When the maximum palaeotemperatures were evaluated from illite–smectite and biomarkers, based on the calibrations from the conventional burial diagenetic sections, a major mismatch was detected for the cratonic area: 100°C–130°C from illite––smectite and tens of ^oC lower from the lipid biomarkers. This diagenetic pattern was interpreted as the result of short‐lasting (in ky scale) pulses of potassium‐bearing hot fluids migrating from the Caledonian and Variscan orogens deep in the craton interior, effectively promoting illitization in porous rocks without altering the organic matter. Analogous short pulses of fluids were responsible for numerous diagenetic phenomena, including Mississippi Valley‐Type ore deposits, in the American Midwest, in front of the Appalachians. K–Ar dating indicates that the entire Proterozoic sedimentary cover of the Great Unconformity on the EEC remained untouched by measureable post‐sedimentary changes until the early Palaeozoic, thus for over 1000 My, which is an unprecedented finding.     

A review of underlying surface parameterization methods in hydrologic models

Journal of Geographical Sciences - Numerous topography, land-cover, land-use, and soil-type parameterization methods are required to simulate the hydrologic cycle. In this paper, using the...     

Ito equations out of domino cellular automaton with efficiency parameters

Ito equations are derived for simple stochastic cellular automaton with parameters describing efficiencies for avalanche triggering and cell occupation. Analytical results are compared with the numerical one obtained from the histogram method. Good agreement for various parameters supports the wide applicability of the Ito equation as a macroscopic model of some cellular automata and complex natural phenomena which manifest random energy release. Also, the paper is an example of effectiveness of histogram procedure as an adequate method of nonlinear modeling of time series.     

Determination of Surface Precipitation Type Based on the Data Fusion Approach

Hazardous events related to atmospheric precipitation depend not only on the intensity of surface precipitation,but also on its type.Uncertainty related to determination of the precipitation type(PT)leads to financial losses in many areas of human activity,such as the power industry,agriculture,transportation,and many more.In this study,we use machine learning(ML)algorithms with the data fusion approach to more accurately determine surface PT.Based on surface synoptic observations,ERA5 reanalysis,and radar data,we distinguish between liquid,mixed,and solid precipitation types.The study domain considers the entire area of Poland and a period from 2015 to 2017.The purpose of this work is to address the question:“How can ML techniques applied in observational and NWP data help to improve the recognition of the surface PT?”Despite testing 33 parameters,it was found that a combination of the near-surface air temperature and the depth of the warm layer in the 0-1000 m above ground level(AGL)layer contains most of the signal needed to determine surface PT.The accrued probability of detection for liquid,solid,and mixed PTs according to the developed Random Forest model is 98.0%,98.8%,and 67.3%,respectively.The application of the ML technique and data fusion approach allows to significantly improve the robustness of PT prediction compared to commonly used baseline models and provides promising results for operational forecasters.