A methodology to determine thermal conductivity of soils from flux measurement

Determination of thermal properties of soils (viz., thermal resistivity, thermal conductivity, thermal diffusivity and heat capacity), which primarily influence heat migration through the soil mass, is essential in situations where geomaterials are relentlessly subjected to higher temperatures and temperature variations. These properties of the soil mainly depend upon its type, mineralogy, particle size and gradational characteristics, density and water content. In this context, earlier researchers have determined thermal conductivity of soils by employing a thermal probe (a line heat source), which works on the principle of transient method (TM) of heat conduction. However, this methodology cannot be employed for establishing the heat flow (read thermal regime) through the soil. Hence, development of an alternate technique, which facilitates quantification of temporal and spatial variation of the heat flux and temperature in the soil mass, becomes essential. With this in mind, a methodology to determine thermal conductivity of soils by employing the concept of thermal flux measurement (TFM) has been developed and its details are presented in this paper. Results obtained from the TM and TFM have also been critically evaluated for the sake of validation and generating more confidence in the proposed methodology.     

Tree failure prediction model (TFPM): machine learning techniques comparison in failure hazard assessment of Platanus orientalis in urban forestry

Natural Hazards - Trees are generally harmed by multitude factors consisting of ecological condition and anthropogenic pressures in the cities. This study compares the multilayer perceptron (MLP)...     

Nematode community structure in relation to metals in the southern of Caspian Sea

Spatial distribution and structure of nematode assemblages in coastal sediments of the southern part of the Caspian Sea were studied in relation to environmental factors. By considering metals, organic matter, Shannon diversity index(H), maturity index(MI) and trophic diversity(ITD), ecological quality status of sediment was also determined. Fifteen nematode species belonging to eleven genera were identified at the sampling sites. Average density of nematode inhabiting in sediment of the studied area was 139.78±98.91(ind. per 15.20 cm~2). According to redundancy analysis(RDA), there was high correlation between metals and some species. Based on biological indicators, the studied area had different environmental quality. Generally, chemical and biological indices showed different results while biological indices displayed similar results in more sites.     

Facies Analysis, Sequence Stratigraphy and Diagenetic Studies of the Jurassic Carbonates of the Kohat Basin, Northwest Pakistan: Reservoir Implications

The present study deals with the depositional facies, diagenetic processes and sequence stratigraphy of the shallow marine carbonates of the Samana Suk Formation, Kohat Basin, in order to elucidate its reservoir quality. The Samana Suk Formation consists of thin to thick-bedded, oolitic, bioclastic, dolomitic and fractured limestone. Based on the integration of outcrop, petrographic and biofacies analyses, the unit is thought to have been deposited on a gentle homoclinal ramp in peritidal, lagoonal and carbonate shoal settings. Frequent variations in microfacies based sea-level curve have revealed seven Transgressive Systems Tracts (TSTs) and six Regressive Systems Tracts (RSTs). The unit has undergone various stages of diagenetic processes, including mechanical and chemical compaction, cementation, micritization, dissolution and dolomitization. The petrographic analyses show the evolution of porosity in various depositional and diagenetic phases. The fenestral porosity was mainly developed in peritidal carbonates during deposition, while the burial dissolution and diagenetic dolomitization have greatly enhanced the reservoir potential of the rock unit, as is further confirmed by the plug porosity and permeability analyses. The porosities and permeabilities were higher in shoal facies deposited in TSTs, as compared to lagoonal and peritidal facies, except for the dolomite in mudstone, deposited during RSTs. Hence good, moderate and poor reservoir potential is suggested for shoal, lagoonal and peritidal facies, respectively.     

Arsenic in the Muteh gold mining district, Isfahan, Iran

Following the appearance of symptoms of arsenic toxicity in the inhabitants of villages in the Muteh gold mining region, central Iran, the concentration of this element in various parts of biogeochemical cycle is investigated. For this purpose, rock, groundwater, soil, plant, livestock hair and wool, and human hair samples are collected and analysed. Total arsenic content ranges from 23 to 2,500?mg/kg in rock samples, 7?C1,061???g/l in water, 12?C232?mg/kg in soil, 0.5?C16?mg/kg in plant samples, 4.10?C5.69?mg/kg in livestock hair and wool, and 0.64?C5.82?mg/kg in human hair. Arsenic concentration in various parts of biogeochemical cycle near the gold deposit in a metamorphic complex, and also close to the gold-processing plant, is very high and decreases exponentially with increasing distance from them. Arsenic concentration in water from a well close to the Muteh gold mine is above 1?mg/L. Arsenic in hair samples taken from local inhabitants is above the recommended levels, and the control samples in Shahre-Kord city. Arsenic concentration is higher in male population and correlates positively with age. It is suggested that arsenic resulting from the decomposition of ore mineral such as orpiment (As₂S₃), realgar (As₂S₂) and arsenopyrite (FeAsS) is responsible for polluting natural resources and the human intake via drinking water and the food chain. Gold mining and processing has undoubtedly enhanced the release of arsenic and intensified the observed adverse effects in Muteh area.     

Modeling flood event characteristics using D-vine structures

The authors investigate the use of drawable (D-)vine structures to model the dependences existing among the main characteristics of a flood event, i.e., flood volume, flood peak, duration, and peak time. Firstly, different three- and four-dimensional probability distributions were built considering all the permutations of the conditioning variables. The Frank copula was used to model the dependence of each pair of variables. Then, the appropriate D-vine structures were selected using information criteria and a goodness-of-fit test. The influence of varying the data length on the selected D-vine structure was also investigated. Finally, flood event characteristics were simulated using the four-dimensional D-vine structure.     

Radar interferometry time series analysis of Mashhad subsidence

A large agricultural area located in 20 km north of the city of Mashhad in the north-east of Iran is subject to land subsidence. The subsidence rate was achieved in a couple of sparse points by precise leveling between 1995 and 2005, and continuous GPS measurements obtained from 2005 to 2006. In order to study the temporal behavior of the deformation in high spatial resolution, the small baseline subset (SBAS) algorithm was used to generate the interferometric SAR time series analysis. Time series analysis was performed using 19 interferograms calculated from 12 ENVISAT ASAR data spanning between 2003 and 2006. The time series results exhibited that the area is subsiding continuously without a significant seasonal effect. Mean LOS deformation velocity map obtained from time series analysis demonstrated a considerable subsidence rate up to 24 (cm/yr). In order to evaluate the time series analysis results, continuous GPS measurements as a geodetic approach were applied. The comparisons showed a great agreement between interferometry results and geodetic technique. Moreover, the information of various piezometric wells distributed in the area corresponding to 1995 to 2005 showed a significant decline in water table up to 20 meters. The correlation between the piezometric information and the surface deformation at well’s locations showed that the subsidence occurrence in Mashhad is due to the excess groundwater withdrawal.     

Projections of temperature changes over South America during the twenty-first century using CMIP6 models

A 22-member ensemble from CMIP6 is used to analyze the projected changes and seasonal behavior in surface air temperature over South America during the twenty-first century. In the future projections, CMIP6 models shown a high dependency to the socioeconomic pathway over each country of South America. The multimodel ensemble projects a continuous increase in the annual mean temperature over South America during the twenty-first century under the three future scenarios (SSP1-2.6, SSP2-4.5 and SSP5-8.5). Besides, it was possible to identify consistent positive trends across all the models, with values between 0.45 ± 0.05 and 2.05 ± 0.31 °C cy⁻¹ under the historical experiment, however largest trends occurs for the projection periods (near, mid and far future), with values between − 0.87 ± 0.84 to 2.88 ± 0.60 °C cy⁻¹ (SSP1-2.6), 1.41 ± 0.88 to 5.32 ± 0.81 °C cy⁻¹ (SSP2-4.5) and 4.75 ± 0.58 to 8.76 ± 0.74 °C cy⁻¹ (SSP5-8.5) with maximum values at Bolivia, Brasil, Paraguay and Venezuela whilst minimum values for Argentina and Uruguay, regardless of the SSP scenario used. From the seasonal behavior analysis was possible to identify maximum values between January and March whilst minimum between June and July, except in Brasil, Venezuela and Guyana–Surinam–French Guayana, with annual range decreasing as the latidude decreases. By the end of the twenty-first century the annual mean temperature over South america is projected to increase between 0.92–2.11 °C, 0.97–3.37 °C and 1.27–6.14 °C under SSP1-2.6, SSP2-4.5 and SSP5-8.5 projection scenarios respectively. This projected increase of temperature across the continent will produce negative repercussions in the social, economic and political spheres. The results obtained in this study provide insights about the CMIP6 performance over this region, which can be used to develop adaptation strategies and might be useful for the adaptation to the climate change.

     

Probabilistic Convective Initiation Nowcasting with Reduced Satellite - NWP Predictors over Iran

Geostationary satellites are able to nowcast Convective Initiation (CI) for the next 0–6 h. Compared to using satellite predictors only, the incorporation of satellite and Numerical Weather Prediction (NWP) predictors can provide the possibility to reduce false alarm rates in 0–1:30 Convective Initiation Nowcasting (COIN). However, the correlation among these predictors not only can cause error in COIN, but also increases the runtime. In this study for the first time, all effective predictors in Satellite Convection Analysis and Tracking version 2 (SATCASTv2) and NWP were applied over Iran from 22nd March 2015 to 9th January 2016. In applying SATCASTv2 over Iran, it was necessary to make some modifications to the algorithm, such as removing case specific thresholds of satellite predictors and rearranging COIN predictors. Then, SATCASTv2 was tested and evaluated with both the full and reduced set of predictors. The results suggested that using fixed thresholds for temporal difference predictors could miss COIN in some cases. To investigate the possibility of improving computational efficiency, a dimension reduction was conducted by Factor Analysis (FA) and the number of predictors was reduced from 22 to 11. The NWP-satellite, reduced NWP-satellite, and satellite predictors were used as input in Random Forest (RF), as a parametric machine learning method, for COIN evaluation. The Combination of NWP model and satellite predictors had lower false alarm rates in contrast with satellite predictors. This is in agreement with previous studies. The results from statistical metrics showed that the reduced NWP-satellite predictors had comparable performance to the NWP-satellite predictors over study area, but decreased the run time by almost 50%. The results indicated that Convective Inhibition (CIN) was the most significant predictor when the reduced set of predictors was used.