This paper presents two novel spectral soil area indices to identify bare soil area and distinguish it more accurately from the urban impervious surface area (ISA). This study designs these indices based on medium spatial resolution remote sensing data from Landsat 8 OLI dataset. Extracting bare soil or urban ISA is more challenging than extracting water bodies or vegetation in multispectral Remote Sensing (RS). Bare soil and the urban ISA area often were mixed because of their spectral similarity in multispectral sensors. This study proposes Normalized Soil Area Index 1 (NSAI1) and Normalized Soil Area Index 2 (NSAI2) using typical multispectral bands. Experiments show that these two indices have an overall accuracy of around 90%. The spectral similarity index (SDI) shows these two indices have higher separability between soil area and ISA than previous indices. The result shows that percentile thresholds can effectively classify bare soil areas from the background. The combined use of both indices measured the soil area of the study area over 71 km2. Most importantly, proposed soil indices can refine urban ISA measurement accuracy in spatiotemporal studies.

Due to the high vulnerability of the karst aquifer to the surface contaminants, a precisely defined catchment area has the highest priority. In this study, the influence of slope inclination, the doline density analysis, and the water budget analysis in the delineation process of a complex karst catchment area is discussed. To define hydrogeological role of lithological units, cross sections of slope inclination and doline density were combined with hydrogeological cross sections, while the degree of karstification was used to describe the permeability of rock units. The verification of karst catchment delineation area was performed with water budget analysis. The methodology used for the determination of hydrogeological behavior and delineation of a complex karst catchment area (Slunjčica River basin, Croatia) is shown with the flow diagram. It has been found that the highest doline density appears in the range from 0 to 1° of the slope inclinations, and that it decreases with a higher slope degree. Although the results of this study confirm that even with the relatively small number of input data it is possible to define the karst catchment area, it must be emphasized that the doline density analysis presents an indispensable tool in the research related to the definition of karst catchment areas.

Accurate visibility forecasting is essential for safe aircraft operations. This study examines how various configurations of the Random Forest model can enhance visibility predictions. Preprocessing techniques are employed, including correlation analysis to identify fundamental relationships in weather observations. Time-series data is transformed into a regular Data Frame to facilitate analysis. This study proposes a classification framework for organizing visibility data and phenomena, which is then used to develop a visibility forecast using the Random Forest method. The study also presents procedures for hyperparameter tuning, feature selection, data balancing, and accuracy evaluation for this dataset. The main outcomes are the Random Forest model parameters for a three-hour visibility forecast, along with an analysis of errors in low visibility forecasts. Additionally, models for one-hour forecasts and visibility forecasting under precipitation are also examined. The resulting models demonstrate a deterministic forecast accuracy of approximately 78%, with a false alarm rate of around 6%, providing a comprehensive overview of the capabilities of the Random Forest model for visibility forecasting. As anticipated, the model demonstrated limitations in accurately simulating fast radiative cooling or abrupt decreases in visibility caused by precipitation. Specifically, in relation to precipitation, the model achieved an accuracy of 79%, yet exhibited a false alarm rate of 19%. Additionally, this method sets a foundation for enhancing prediction accuracy through the inclusion of supplementary forecast data, while its implementation on real-world datasets expands the reach of machine learning techniques to the members of the meteorological community.

In this study, trend analyses of six climatic variables (mean, minimum, and maximum temperature, relative humidity, wind speed, and precipitation) for 1966-2020 are conducted for the Southeastern Anatolia Region, which is the main focus of the integrated development project in Turkey (Turkish acronym GAP). The trends for seasonal and annual periods are determined using the Mann-Kendall (MK) test, and Sen's slope method and regression analyses are used to find the trends' slopes. Moreover, Innovative Trend Analysis (ITA) is also used to find the time series changes for low, medium, and high values. As a result of the analyses, the mean, minimum, and maximum temperatures in the GAP region show increasing trends according to both methods. Significant trends are obtained at a limited number of stations for the precipitation, relative humidity, and wind speed with the MK test, while consistent decreasing trends are found at most stations with the ITA method.

Universalities and intriguing analogies in the statistics of avalanches are revealed for three physical systems defined on largely different length and energy scales. Earthquakes induced by tectonic scale dynamics, micro-scale level quakes observed from slipping crystallographic planes in metals and a one-dimensional, room-scale spring-block type Burridge-Knopoff model is studied from similar statistical viewpoints. The validity of the Gutenberg-Richter law for the probability density of the energies dissipated in the avalanches is proven for all three systems. By analysing data for three different seismic zones and performing acoustic detection for different Zn samples under deformation, universality for the involved scaling exponent is revealed. With proper parameter choices the 1D Burridge-Knopoff model is able to reproduce the same scaling law. The recurrence times of earthquakes and micro-quakes with magnitudes above a given threshold present again similar distributions and striking quantitative similarities. However, the 1D Burridge-Knopoff model cannot account for the correlations observed in such statistics.

Due to climate change, floods have been more frequent in recent years. Estimating the flood discharge as a result of flood frequency analysis is very substantial to make necessary preparations for possible floods. Data covering 36 years were collected from different stream gauging stations (SGS No: D17A016 and EIEI 1731) in Eastern Mediterranean Basin. With these data, flood discharge values were computed for return periods of 2, 5, 10, 25, 50, 100, 200, 500 and 1000 years. Normal, Log-Normal, Gumbel, Pearson Type III and Log-Pearson Type III statistical distribution methods were used. Kolmogorov-Smirnov (K-S) and Chi-square goodness-of-fit tests were performed to determine which distribution fitted the flood discharge the best. The study showed that the highest flood discharge among the probability distributions for both SGSs came from the Log-Normal distribution, and the lowest discharge was calculated with the Normal distribution. The K-S tests showed that all probability distributions conformed to the 20% significance level. For SGS D17A016, the flood values calculated with Log-Normal distribution were compatible with a 90% confidence interval according to the Chi-square test. Flood values obtained with the other distributions were found within the 10% significance level. In the Chi-square test for SGS EIEI-1731, all probability distributions fell within a 10% significance.

In this study, temperature characteristics and heat load in the city of Dubrovnik are investigated by using temperature data observed at the local meteorological station in Dubrovnik for the period 1961-2019, satellite data collected by LANDSAT5 satellite for the period 2001-2010, and climate indices data obtained from simulations of an urban climate model (MUKLIMO_3) for the period 2001-2010. Trends in daily mean, maximum, minimum, and seasonal temperatures were analysed by using Sen's slope and the Mann-Kendall test. Results reveal rising trends for all of the studied temperature-related elements. However, it is demonstrated that temperature increase is greatest for the summer season with the highest rise for daily maximum temperatures. The same approach was applied to examine trends of climate indices (summer days and tropical nights), which indicates an increase in the number of both summer days and tropical nights. Results of satellite data of average summer land surface temperatures for the period 2001-2010 indicate that urbanised surfaces and bare rock areas heat up more than natural surfaces with vegetation. Climate indices (summer and hot days, warm evenings, and tropical nights) simulated by the urban climate model MUKLIMO_3 also reveal that, on average, in the city of Dubrovnik urbanised surfaces heat up more than natural surfaces with vegetation and that nocturnal heat load is reduced in lower-density built-up areas.

This study used the efficiency of electrical resistivity tomography (ERT) and borehole logs to map the soil-rock interface beneath four traverses (RS1, RS2, RS3, and RS4) in the granitic terrain of Perak, Peninsular Malaysia. The study aimed to evaluate the impacts of the soil-rock characteristic features and interfaces on groundwater and infrastructure development to meet the needs of the increasing inhabitants yearly. The borehole- and ERT-derived lithologic units are strongly correlated. The delineated lithologic units include the topsoil, weathered granitic units (medium stiff to hard silty clay or clayey silt with <800 Ωm), thin to wide-sized weathered/fractured units, and fresh granitic bedrock. These soil-rock profiles and weathered/fractured apertures support sustainable groundwater developments with drill depths above 45 m. In contrast, the delineated clay/silt alternating with stiffer soils, low load-bearing deep-weathered/fractured zones, and bedrock boulders in most places, except beneath traverse RS3, have high affinities for water retention and differential stresses. These features can adversely impact poorly reinforced foundations. Hence, structural elements of the foundations, such as footings or piles, should be placed on stable bedrock, particularly in the central to western parts of the study area. This study has reduced the paucity of information on using ERT and borehole logs for soil-rock interface studies in the study area.

Soil erosion is one of the most prominent geomorphological hazards threatening environmental sustainability in the coastal region of western Syria. The current war conditions in Syria has led to a lack of field data and measurements related to assessing soil erosion. Mapping the spatial distribution of potential soil erosion is a basic step in implementing soil preservation procedures mainly in the river catchments. The present paper aims to conduct a comprehensive assessment of soil erosion severity using revised universal soil loss equation (RUSLE) and remote sensing (RS) data in geographic information system (GIS) environment across the whole Al-Qshish river basin. Quantitatively, the annual rate of soil erosion in the study basin was 81.1 t ha−1 year−1 with a spatial average reaching 55.2 t ha−1 year−1. Spatially, the soil erosion risk map was produced with classification into five susceptible-zones: very low (41 %), low (40.5%), moderate (8.9%), high (5.4%) and very high (4.2%). The current study presented a reliable assessment of soil loss rates and classification of erosion-susceptible areas within the study basin. These outputs can be relied upon to create measures for maintaining areas with high and very high soil erosion susceptibility under the current war conditions.

Knowing the soil moisture distribution in the unsaturated zone can improve understanding the water flow through the unsaturated zone and thereby enable the calculation of aquifer recharge, which occurs through precipitation. One part of the Zagreb aquifer recharge occurs through infiltration from precipitation. In order to observe and model infiltration from precipitation through the unsaturated zone, the research polygon was constructed at the Velika Gorica well field, located in the southern part of the Zagreb aquifer, Croatia, where hourly measurements of electric conductivity (EC) and soil moisture content were carried out. EC and soil moisture data are measured by Time Domain Reflectometry (TDR) probes which are placed at different depths in the unsaturated zone. Furthermore, electrical resistivity tomography (ERT) measurements were conducted. Geophysical data, along with moisture and EC data from TDR probes, were used as input data for MoisturEC software, in order to obtain soil moisture distribution along a 2D profile. MoisturEC program offers three options for translating EC data to moisture content data which are all tested in this research. We obtained eight moisture content distributions along the observed profile and concluded that MoisturEC provides reasonable results with input data from geophysical measurements and TDR probe measurements. Soil moisture distribution in the unsaturated zone represents the initial conditions for further unsaturated flow modeling. Understanding the flow in the unsaturated zone enables the quantification of effective infiltration and can improve groundwater management.