Earthquakes of 1838 and 1839 in the Slovene Hills (Slovenia)-Međimurje (Croatia) area

Analyses of available data (newspaper reports, historical and church chronicles, chronical earthquake overviews, travel books, monographies, research papers, etc.) on effects of the earthquakes that shook the greater Ormož area at the Slovenian-Croatian border in the 1838 and 1839 revealed that one of them, recorded in a number of regional and global catalogues, is in fact a fake - the earthquake of 26 August 1838 never happened. This error creeped into various reports and studies, and then into many relevant catalogues, so this event should by systematically erased from the catalogues used to estimate seismicity rates in the neighbourhoods of north-western Croatia, north-eastern Slovenia, and south-western Hungary.Regarding the earthquake of 31 July 1838, we used important new sources of information that have not been consulted in any previous study. This made inversion of macroseismic parameters more robust. Our estimates of the macroseismic moment magnitude (Mwm = 4.8) is mostly higher than the values reported in the available catalogues. Reliable information on the effects of the smaller event of 22 March 1839 were found for two localities only, so its epicentre was placed into the town of Ormož where the maximum intensity was observed. Its estimated moment magnitude (Mwm) is close to the median of values found in the six consulted catalogues that listed this event.The macroseismic epicentre of the 1838 earthquake lies close to the junction of surface traces of the Donat strike-slip fault and the reverse Čakovec fault. Based on their assumed geometry and the location of the macroseismic hypocentre, we give slight preference to the Donat fault as the seismogenic source.

Forecasting the hydroelectric power generation of GCMs using machine learning techniques and deep learning (Almus Dam, Turkey)

Renewable energy is one of the most important factors for developed and sustainable societies. However, its utilization in electrical power grid systems can be very challenging regarding rates predictably. Renewable energy depends mainly on environmental conditions such as rainfall-runoff ratios and temperature. Because of that, the expected power production heavily fluctuates, which makes the prediction and calculation of feed-in into the power grid very challenging. The accurate forecasting of energy production is a very crucial issue for power management process. This paper presents the results of deploying Machine Learning Techniques in short-term forecasting of the amount of energy produced of General Circulation Models (GCMs) Data by Almus Dam and Hydroelectric Power Plant in Tokat, Turkey. The study demonstrates the use of modeling techniques in hydropower forecasting process using the predicted monthly hydroelectric power generation data of GCMs from 2018 to 2080. Decision Tree, Deep Learning, Generalized Linear, Gradient Boosted Trees and Random Forest models are utilized to forecast the hydropower production. The results show that the correlation value of the gradient boosted trees model equals 0.717, which means that the gradient boosted trees model is the most successful model for the present data. The gradient boosted trees model used in the prediction process for each GCM in each scenario is 4.5 and 8.5. The results show that there are small differences between the models, which means that the predictions are going in similar directions for all these models.

Analysis of the hydraulic connection of the Plitvica stream and the groundwater of the Varaždin alluvial aquifer

A combination of different statistical methods and flow duration curves was used to examine hydraulic connection between the Plitvica stream and the surrounding piezometers that capture the groundwater of the Varaždin alluvial aquifer. Also, rainfall quantities over a wider study area were considered to examine the effect of precipitation on Plitvica water levels and groundwater levels. The following statistical methods were used in this paper: the correlation method, the auto- correlation method, and the cross-correlation method. Correlation analysis show that there is generally a significant correlation between the Plitvica water levels and groundwater levels, with positive direction of the correlation. The analysis of auto-correlograms for groundwater and surface water shows that the correlation coefficient value drops below 0.2 after a longer period, which indicates a long-term memory of the system that can be explained by the slow flow and thus slow pressure transfer. Cross-correlation analyses of the time series of the Plitvica water levels and groundwater levels showed a time lag of 1-2 days with a fairly significant cross-correlation coefficient. For precipitation and groundwater levels, the relationship is much weaker, with a lag time of 4-5 days with a weak cross- correlation coefficient. The least time lag, within a day, was established between precipitation and Plitvica water levels. Analyses of the flow duration curves revealed that Plitvica almost completely drains groundwater, except in the vicinity of the piezometer 2178 where Plitvica recharges the aquifer about a quarter of the time.

The assessment of local site effects and dynamic behaviour in Nicosia, Cyprus

Single-station microtremor measurements were conducted to investigate earthquake and soil behaviour for the first time in Nicosia, Cyprus. Cyprus is located in a tectonically complex area in the Eastern Mediterranean where three plates meet. The study area was chosen to cover the areas to be opened for new development. Nicosia, the capital of Cyprus, is also the island's most important cultural, industrial, commercial, and transportation centre. The study creates base maps for the soil to assess earthquake resistance crucial for construction. Microtremor Method was applied at 100 stations and the Multi-Channel Analysis of Surface Waves (MASW) method was used at 52 stations. Also, RefractionMicrotremor (Re-Mi) and L-Shaped Spatial Autocorrelation (L-SPAC) methods were carried out at 17 stations to substantiate the research. The results of the microtremor method indicate that the predominant soil period values have an average of 1 second and pre-dominant peak period values are generally found between 0.1 to 5 s at the study area. Peak amplitude values are observed between 1 and 2.4. The Vulnerability Index Parameter (Kg) exceeded 20 at the central and the southern stations, and Kg values change between 7 and 54 units. The Kg values were found to be higher than 20 in soils where shear wave velocity is lower than 760 m/s. At the same time, the values of the predominant peak period were greater than 1 second. Cyprus is located in the Alpine Himalayan earthquake zone. The Cyprus Arc is known as the main seismic source of the island, It constitutes the tectonic border among African and Eurasian lithospheric plates in the region. During an earthquake in Nicosia, seismic waves will be amplified by an average of 1.5 times and soil deformation will occur due to the exceeding elastic limits. The results provided important insight into soil behaviour and indicated its reactions in a potential earthquake.

Obtaining the Manning roughness with terrestrialremote sensing technique and flood modeling using FLO-2D

Determining the Manning roughness coefficients is one of the most important steps in flood modeling. The roughness coefficients cause differences in flood areas, water levels, and velocities in the process of modeling. This study aims to determine both the Manning roughness coefficient in the river sections and outside of the river regions by using the Cowan method and remote sensing technique in the flood modeling. In the flood modeling, FLO-2D Pro program which can simulate flood propagation in two dimensions was utilized. Mert River in Samsun province located in the northern part of Turkey was chosen as the study area. Samples taken from the river were subjected to sieve analysis, the types of constituent material were determined according to the median diameters and the roughness coefficients were obtained using the Cowan method. For regions outside of the river were applied the maximum likelihood method being one of the controlled classification methods. Manning roughness values were assigned the classified image sections. Remote sensing techniques were meticulously employed to achieve time management in areas outside the river and a new approach was proposed in the Manning assessment of flood areas to ensure uniformity in the study area. In the classification made using the maximum likelihood method, the overall classification accuracy was 92.9% and the kappa ratio “κ” was 90.64%. The results were calibrated with the last hazardous flood images in 2012 and HEC-RAS 2D program, another flood modeling program.

Conversion between the local magnitude (ML) and the moment magnitude (Mw) for earthquakes in the Croatian Earthquake Catalogue

Based on 153 earthquakes (1959–2020) listed in the Croatian Earthquake Catalogue, a conversion relation was obtained between the local magnitude ML,CR and the corresponding moment magnitude Mw as reported by the global and regional agencies. As errors were present in both variables the York regression was used. The best fit line is given by: MwL = (–0.106 ± 0.122) + (1.002 ± 0.027) ML,CR (coefficient of determination R2 = 0.90). The earthquakes considered occurred in Croatia and the neighbouring regions, and their local magnitudes ML,CR ranged between 3.5 and 6.5. Residual analysis suggests that an artificial positive magnitude shift of up to 0.3 magnitude units may have occurred in the early 1980s, when Wiechert mechanical seismographs were replaced by the instruments with velocity proportional recordings without proper recalibration of the magnitude formula. The slope of the regression close to 1.0 indicates that on the average the faults’ aspect ratio (width/length) is about 1/2.

Usporedba različitih pristupa Bouguer-ove redukcije na temelju satelitskih gravimetrijskih podataka

Satellite gravity data are widely used in the field of geophysics to study deep structures at the regional and global scales. These data comprise free-air gravity anomaly data, which usually need to be corrected to a Bouguer gravity anomaly for practical application. Bouguer reduction approaches can be divided into two methods based on the coordinate system: the spherical coordinates method (SBG) and the Cartesian coordinates method; the latter is further divided into the CEBG and CBG methods, which do and do not include the Earth’s curvature correction. In this paper, free-air gravity anomaly data from the eastern Tibetan Plateau and its adjacent areas were used as the basic data to compare the CBG, CEBG, and SBG Bouguer gravity correction methods. The comparison of these three Bouguer gravity correction methods shows that the effect of the Earth’s curvature on the gravitational effect increases with increasing elevation in the study area. We want to understand the inversion accuracy for the data obtained by different Bouguer gravity reduction approaches. The depth distributions of the Moho were obtained by the interface inversion of the Bouguer gravity anomalies obtained by the CBG, CEBG, and SBG, and active seismic profiles were used as references for comparison and evaluation. The results show that the depths of the Moho obtained by the SBG inversion are more consistent with the measured seismic profile depths. Therefore, the SBG method is recommended as the most realistic approach in the process of global or regional research employing gravity data.

Defining rainfall intensity clusters in Turkey by using the fuzzy c-means algorithm

Turkey has seven traditionally accepted climatic zones that are defined primarily by maritime and topographic influences. Across these zones, the annual amount of rainfall, including its intensity and its seasonal distribution, vary considerably. These variations, which impact on both urban and rural communities, including the occurrence of water shortages and flash flooding events, are increasing in both frequency and magnitude due to global warming and climate change. Several types of climate occur in Turkey where climate zones have been defined with various methodologies. To better understand rainfall intensity patterns across Turkey, this study used the Fuzzy C-Means (FCM) algorithm to define their spatial distribution. In the first stage, the annual maximum rainfall intensity records for periods ranging from 30 to 78 years were obtained from 95 stations operated by the Turkish State Meteorological Service, and the longitude, latitude and altitude data for the stations were compiled for cluster analysis. Secondly, all rainfall intensities and geographical values were normalized, and in the third stage, the FCM algorithm was applied. The comparison of annual maximum rainfall intensities revealed five clusters. Four clusters were identified as discrete zones and one was identified as a transitional zone. Weather stations located in different geographical regions sometimes fell into the same clusters. In other words, rainfall events of similar intensity can occur in different climatic zones. This study, which brought a different perspective to clustering studies, showed that rainfall intensity values can be successfully analyzed at a national scale with the FCM technique.

Assessing vulnerability for inhabitants of Dhaka City considering flood-hazard exposure

Global flood hazard is gradually increasing. Though it is impossible to avoid them, losses and damage of hazards (e.g., floods, cyclones, and earthquakes) could be efficiently reduced by reducing household vulnerability with appropriate measures. This study aims to quantitatively measure the household vulnerability of flood hazards as a mitigation tool. It also proposed a unique approach to quantify flood-hazard household vulnerability, and shows its application in the flood prone city of Dhaka as an example case. Data were collected from both slum and non-slum areas to cover the entire urban habitat, and to compare their level of flood vulnerability. A total of 300 households were surveyed by structured questionnaire on the basis of five factors (economic, social, environmental, structural, and institutional) of flood vulnerability. The analytical hierarchy process (AHP) was applied to measure individual household vulnerability scores by using the relative weightage of variables and indicators with proper standardisation. Analytical results demonstrated that 63.06% slum and 20.02% non-slum households were highly vulnerable to floods. In addition, this paper determined and assessed responsible factors for household flood vulnerability in Dhaka. For structural vulnerability, results exhibited that 82% of slum households were highly vulnerable, and 95.3% of non-slum households were moderately vulnerable. Socially, 67.3% of slum and 78.7% of non-slum households were moderately and low-vulnerable. The majority of slum and non-slum households (84% and 59.3%, respectively) showed high and moderate vulnerability with respect to economic vulnerability. Moreover, 69.3% of slum and 65.3% of nonslum household institutional vulnerability levels were high. Of slum inhabitants, 63.3% were environmentally at high risk, and 78% of non-slum habitats were in the low-vulnerability category. However, as an effective tool to measure location-specific vulnerability, it is applicable for the measuring vulnerability of other cities in the world with proper customisation. On the basis of this study, future research could be conducted with more factors, variables, and indicators of human vulnerability to natural or artificial hazards/disasters. Future work may provide a better reflection of the vulnerability status of single/multiple hazard(s)/disaster(s).

Internal seiches in a karstic mesotrophic lake (Prošće, Plitvice Lakes, Croatia)

A lake temperature experiment was performed at the Prošće, Plitvice Lakes, Croatia during a 4-month observational period (6 July–4 November, 2019) to investigate the occurrence and characteristics of internal seiches in the lake. Two-minute mean lake temperatures were measured at a single lake point at fifteen depths ranging from 0.2 to 27 m. Analysis of these data provided insight into the previously unknown and rather complex Prošće Lake seiching. Power spectral densities (PSDs) and magnitude-squared coherences (γ2), together with corresponding cross-spectrum phases that were obtained from the hourly mean lake temperature, air pressure and wind speed data, suggested the presence of three vertical modes of an internal seiche. The first mode (V1H1, period of 6.09 h) corresponds to free baroclinic oscillations; the second mode (V2H1, period of 11.64 h) and the third mode (V3H1, period of 25.60 h) are associated with forced baroclinic oscillations of the lake interior. Excitation of the higher vertical modes is attributed to the influence of dense tributary water. Due to this water influence, vertical temperature gradients in the lake interior were relatively weak; consequently, a single thick metalimnion and/or two metalimnetic layers were established, which resulted in the presence of the V2H1 and V3H1 modes, respectively. Additionally, due to the influence of tributary water, the lake did not attain the typical stratification that is characterized by hypolimnetic temperatures of ≈ 4°C. Instead, during the entire observational period, the hypolimnetic temperatures were consistently above 7.6 °C.