Hydrodynamic study of karstic and intergranular aquifers using isotope geochemistry in Basara basin, Sulaimani, North-Eastern Iraq

The study area is located in the border of the high and low folded zone having a semi-arid climate area. This study initiated in 2009–2010, deals essentially with the investigation of the stable isotope (¹⁸O and deuterium ²H) as well as radioactive isotope represented by tritium ³H in rain, carbonate springs, intergranular aquifer, and surface water in order to investigate the source and relative ages of the groundwater and to show the influence of the altitude variations of the isotope composition. In this study and for the first time in the area, a local meteoric water line have been drawn with an empirical formula δ ²H?=?7.7δ ¹⁸O?+?14.4. The δ ¹⁸O–altitude effect was determined using isotopic data for several samples taken from groundwater and surface water; it was approximated at ?0.79?‰/100 m for the entire area of the study. The output of the tritium concentrations in the springs and water well samples revealed to the conclusion that the values closely resembles to the present time tritium concentration in precipitation.     

Estimating daily pan evaporation using artificial neural network in a semi-arid environment

The objective of this study was to test an artificial neural network (ANN) for estimating the evaporation from pan (E _Pan) as a function of air temperature data in the Safiabad Agricultural Research Center (SARC) located in Khuzestan plain in the southwest of Iran. The ANNs (multilayer perceptron type) were trained to estimate E _Pan as a function of the maximum and minimum air temperature and extraterrestrial radiation. The data used in the network training were obtained from a historical series (1996–2001) of daily climatic data collected in weather station of SARC. The empirical Hargreaves equation (HG) is also considered for the comparison. The HG equation calibrated for converting grass evapotranspiration to open water evaporation by applying the same data used for neural network training. Two historical series (2002–2003) were utilized to test the network and for comparison between the ANN and calibrated Hargreaves method. The results show that both empirical and neural network methods provided closer agreement with the measured values (R ²?>?0.88 and RMSE?<?1.2 mm day^?1), but the ANN method gave better estimates than the calibrated Hargreaves method.     

塔里木盆地大气降尘变化特征及影响因素分析

利用哈密、塔中与和田沙尘暴观测站2005—2008年大气降尘观测资料,结合铁干里克、民丰等18个大气降尘监测点2007年4月以来的大气降尘、沙尘天气等观测资料,分析了塔里木盆地大气降尘变化特征,同时分析了影响大气降尘浓度变化的主要因素。结果表明：①2005—2008年3站中哈密大气降尘量最少,其次为和田,塔中的大气降尘量最多; 2006年是4 a中降尘量最多年份,其次为2005年,2008年相对较少。②春夏季是大气降尘集中季节,秋季略大于冬季。塔里木盆地中部最高,南部及西南部的站点降尘明显高于盆地的东部和西部。③在沙尘暴季节,无论是5月还是6月,2007年大气降尘量基本上都高于2008年。④2007年5月和6月,除盆地东面的哈密、铁干里克以及西缘的阿拉尔3站外,其他18个站沙尘天气较多。沙尘天气是塔里木盆地大气降尘量高的主要因素,沙尘天气越多,大气降尘量越高。     

塔克拉玛干沙漠腹地土壤热通量变化特征

利用2009-2011年塔克拉玛干沙漠大气环境观测试验站测得的土壤热通量数据,分析了塔克拉玛干沙漠腹地土壤热通量在不同天气条件下的变化特征。结果表明：（1）塔克拉玛干沙漠腹地1 cm处土壤热通量年平均值为1.9 W·m^-2,5、20、40 cm处分别为1.0、0.4、0.4 W·m^-2;1 cm处土壤热通量年最大值为334.1 W·m²,年最小值为-184.2 W·m^-2;土壤热通量基本表现为夏季 > 春季 > 秋季 > 冬季。（2）各土层土壤热通量具有明显的日变化特征。随着土壤深度的加大,土壤热通量的日变化幅度明显减小,最大值出现的时间有一定的滞后性。土壤热通量5 cm出现最大值的时间比1 cm处延迟3 h,延迟速率为0.75 h·cm^-1,20 cm比5 cm出现最大值的时间晚2 h,延迟速率约为0.13 h·cm^-1。（3）不同天气情况下的土壤热通量日变化特征有一定的差异,晴天较为规则,阴天、雨天、沙尘天则较不规则,且1 cm处土壤热通量受天气影响最显著。晴天1 cm处土壤热通量平均值为9.0 W·m^-2;阴天、雨天、沙尘天1 cm处土壤热通量值平均值分别为5.1、-6.1、-1.9 W·m^-2。     

Compositions and sources of extractable organic matter in Mesopotamian marshland surface sediments of Iraq: II. Polar compounds

The concentrations of polar organic compounds including n-alkanoic acids, n-alkanols, steroids and triterpenoids were determined in extracts of shallow sediments from the Mesopotamian marshlands of Iraq. The sediments were collected by a stainless steel sediment corer, extracted with a dichloromethane and methanol mixture (3:1 v:v) by ultrasonic agitation and then analyzed by gas chromatography–mass spectrometric (GC–MS). The analysis results showed that the n-alkanoic acids ranged from C₈ to C₂₀ with concentrations of 7.8 ± 1.2 μg/g sample, whereas the concentrations of n-alkanols, which ranged from C₁₂ to C₃₉ were from 28.6 ± 4.3 to 121.7 ± 18.3 μg/g sample. The steroids and triterpenoids included stenols, stanols, stenones, stanones, tetrahymanol, tetrahymanone and extended ββ-hopanes. The total concentrations of steroids and triterpenoids ranged from 26.8 ± 4.1 to 174.6 ± 26.2 μg/g and from 0.74 ± 0.11 to 11.2 ± 1.7 μg/g sample, respectively. The major sources of these lipids were from natural vegetation, microbial (plankton) residues and bacteria in the sediments, with some contribution from anthropogenic sources (livestock, sewage and petroleum). Further studies of these wetlands are needed to characterize the input rate, transformation and diagenesis of the organic matter and to assess its various sources.     

Evaluating scale effects and bearing portions in centrifuge modeling of helical anchors: sand

Acta Geotechnica - Helical anchors are bearing elements that can resist uplift loads by a combination of shaft and helical plate bearing. The application of helical piles as offshore wind turbine...     

Comparison of Volterra Model and Artificial Neural Networks for Rainfall–Runoff Simulation

This study evaluates the performances of two distinct linear and non-linear models for simulating non-linear rainfall–runoff processes and their applications to flood forecasting in the Navrood River basin, Iran. Due to the excellent capacity of the artificial neural networks [multilayer perceptron (MLP)] and Volterra model, these models were used to approximate arbitrary non-linear rainfall–runoff processes. The MLP model was trained using two different training algorithms. The Volterra model was applied as a linear model [the first-order Volterra (FOV) model] and solved using the traditional ordinary least-square (OLS) method. Storm events within the Navrood River basin were used to verify the suitability of the two models. The models’ performances were evaluated and compared using five performance criteria namely coefficient of efficiency, root mean square error, error of total volume, relative error of peak discharge, and error of time for peak to arrive. Results indicated that the non-linear MLP models outperform the linear FOV model. The latter was ineffective because of the non-linearity of the rainfall–runoff process. Moreover, the OLS method is inefficient when the FOV model has many parameters that must be estimated.     

Flooding and its relationship with land cover change,population growth,and road density

Bangladesh experiences frequent hydro-climatic disasters such as flooding.These disasters are believed to be associated with land use changes and climate variability.However,identifying the factors that lead to flooding is challenging.This study mapped flood susceptibility in the northeast region of Bangladesh using Bayesian regularization back propagation(BRBP)neural network,classification and regression trees(CART),a statistical model(STM)using the evidence belief function(EBF),and their ensemble models(EMs)for three time periods(2000,2014,and 2017).The accuracy of machine learning algorithms(MLAs),STM,and EMs were assessed by considering the area under the curve-receiver operating char-acteristic(AUC-ROC).Evaluation of the accuracy levels of the aforementioned algorithms revealed that EM4(BRBP-CART-EBF)outperformed(AUC＞90％)standalone and other ensemble models for the three time periods analyzed.Furthermore,this study investigated the relationships among land cover change(LCC),population growth(PG),road density(RD),and relative change of flooding(RCF)areas for the per-iod between 2000 and 2017.The results showed that areas with very high susceptibility to flooding increased by 19.72％between 2000 and 2017,while the PG rate increased by 51.68％over the same period.The Pearson correlation coefficient for RCF and RD was calculated to be 0.496.These findings highlight the significant association between floods and causative factors.The study findings could be valuable to policymakers and resource managers as they can lead to improvements in flood management and reduction in flood damage and risks.     

A stable isotope and fluid inclusion study of the Qaleh-ZariCu–Au–Ag deposit, Khorasan Province, Iran

The Qaleh-Zari copper deposit, located in South Khorasan in the Central Lut region of Iran, is a polymetallic vein deposit with major amounts of Cu, Au, Ag and minor amounts of Pb, Zn and Bi. Mineralization occurs in a series of NW–SE trending fault planes and breccia zones in Paleogene andesitic to basaltic volcanic rocks. Argillization, sericitization and propylitization characterize alteration halos bordering mineral veins. The main ore minerals are chalcopyrite, pyrite, galena and sphalerite, with quartz, calcite and minor chlorite as the main gangue phases. Microthermometric measurements of fluid inclusions in cogenetic quartz indicate homogenization temperatures between 160 and 300 °C and salinities from 1 to 4 wt% NaCl equiv. Boiling occurred in the mineralising fluids at 160–1000 m below the paleo-water table at pressures of approximately 15−80 bar at various stages in the formation of the ore body. The wide range of pressures and temperatures reflects the multi-stage nature of the mineralization at Qaleh-Zari. The δ¹⁸O values in quartz (relative to SMOW) and δ³⁴S values in chalcopyrite and galena (relative to CDT) range from 6.5 to 7.5‰ and 0.0–1.5‰ (mean: 7.0‰), respectively. At 300 °C, calculated fluid δ¹⁸O values are close to 0‰. These data suggest a magmatic origin for sulfur and a surficial origin for the mineralizing fluid. Mineralization at Qaleh-Zari is interpreted as epithermal and low-sulfidation in style and was probably related to a deep-seated magmatic system. Ore deposition was the result of boiling, cooling and pressure reduction.