Temporal and spatial variability of run on a mediterranean agricultural environment

Correspondance: fax 0098 811 422 7012International audienc

A Multi-GCM Assessment of the Climate Change Impact on the Hydrology and Hydropower Potential of a Semi-Arid Basin (A Case Study of the Dez Dam Basin, Iran)

In this paper, the impact of climate change on the climate and discharge of the Dez Dam Basin and the hydropower potential of two hydropower plants (Bakhtiari and Dez) is investigated based on the downscaled outputs of six GCMs (General Circulation Models) and three SRES (Special Report on Emission Scenarios) scenarios for the early, mid and late 21st century. Projections of all the scenarios and GCMs revealed a significant rise in temperature (up to 4.9 °C) and slight to moderate variation in precipitation (up to 18%). Outputs of the HBV hydrologic model, enforced by projected datasets, show a reduction of the annual flow by 33% under the climate change condition. Further, analyzing the induced changes in the inflow and hydropower generation potential of the Bakhtiari and Dez dams showed that both inflow and hydropower generation is significantly affected by climate change. For the Bakhtiari dam, this indicates a consistent reduction of inflow (up to 27%) and electricity generation (up to 32%). While, in the Dez dam case, the inflow is projected to decrease (up to 22%) and the corresponding hydropower is expected to slightly increase (up to 3%). This contrasting result for the Dez dam is assessed based on its reservoir and hydropower plant capacity, as well as other factors such as the timely releases to meet different demands and flow regime changes under climate change. The results show that the Bakhtiari reservoir and power plant will not meet the design-capacity outputs under the climate change condition as its large capacity cannot be fully utilized; while there is room for the further development of the Dez power plant. Comparing the results of the applied GCMs showed high discrepancies among the outputs of different models

Removal of Lead from Polluted Water Using Corn Silk As a Cheap Biosorbent

Introduction and purpose: Entry of heavy metals into water resources has harmful effects on human health and the environment. In recent years, adsorption methods using natural absorbents to remove contaminants from water resources have been used abundantly. Corn silk as a biosorbent can be effective for the removal of lead ions from aquatic solutions. The purpose of this research was to remove lead by corn silk as a cheap biosorbent from polluted water in vitro. Methods: The effects of variables such as pH, contact time, initial concentration, adsorbent amount, and efficiency in removing lead from contaminated water were studied. Isotherms of Langmuir, Freundlich and Temkin were fitted with the data of the experiment. In addition, kinetics of pseudo first order, pseudo second order, intra-particle diffusion, and Elovich were fitted with the experimental data. Graphs and data analyses were performed using Excel program. Results: For corn silk, Langmuir isotherms showed good agreement with the experimental data. Using these models, maximum absorption capacity of 78.84 (mg.g-1) was obtained for corn silk. Absorption kinetics showed quick responses in less than one hour. The results showed that the adsorption kinetics of pseudo second order was more consistent for lead pollutant (r2=0.99). Conclusion: The results demonstrated that corn silk absorbent is effective in removing lead contaminants from aqueous solutions due to high surface area, having SiOH groups, high absorption capacity, and rapid kinetics of reaction. Therefore, the use of this adsorbent is recommended to remove lead from aqueous solutions

Evaluation of Heavy Metal Contamination Ecological Risk in a Food-Producing Ecosystem

Introduction and purpose: The consumption of agricultural products cultivated&nbsp;in soils contaminated with heavy metals is very health-threatening. Therefore, the&nbsp;implementation of an inclusive and multilateral assessment of the heavy metal&nbsp;risk on the verge of their entrance to the food chain is a matter of fundamental&nbsp;importance. Regarding this, the present study was conducted with the aim of&nbsp;monitoring the concentration of heavy metals in the surface soil of grape gardens&nbsp;and zoning the area in terms of geoaccumulation index (Igeo), contamination&nbsp;factor, degree of contamination, modified degree of contamination (MDC),&nbsp;pollution load index (PLI), and ecological risk index (RI).&nbsp;Methods: For the purpose of the study, 31 grape gardens were selected in Gahru&nbsp;region (i.e., the main center of grape production) through simple random sampling&nbsp;technique. The surface soil samples were transferred to the laboratory for the&nbsp;analysis of the concentration of cadmium, lead, chromium, copper, and zinc.&nbsp;Results: According to the results, the concentration of the metals in the region&nbsp;was Zn > Cu > Pb > Cr > Cd with the mean total concentrations of 74.87, 55.31,&nbsp;22.32, 9.81, and 0.91 mg/kg, respectively. Based on the results of the PLI, six&nbsp;grape gardens were classified as insignificantly contaminated (1&le;PLI&le;2), and the&nbsp;remaining gardens were classified as noncontaminated (PLI300),&nbsp;medium (150<riConclusion: In the present study, the Igeo and MDC had higher efficiency and&nbsp;flexibility in the classification of the area in terms of critical metals and critical&nbsp;stations. Meanwhile, cadmium and copper caused the highest concern in some&nbsp;of the grape gardens of the investigated region. Therefore, it is suggested to&nbsp;prevent the entrance of larger amounts of cadmium in the area by training and&nbsp;raising the awareness of the gardeners about the amount of phosphate fertilizers&nbsp;and fungicide and encouraging them to use animal manures and take preventive&nbsp;measures. In addition, the cadmium contamination in the area should be reduced&nbsp;by implementing a soil refinery program and cultivating cadmium adsorbents.</r

Eco-Friendly Estimation of Heavy Metal Contents in Grapevine Foliage Using In-Field Hyperspectral Data and Multivariate Analysis

Heavy metal monitoring in food-producing ecosystems can play an important role in human health safety. Since they are able to interfere with plants&rsquo; physiochemical characteristics, which influence the optical properties of leaves, they can be measured by in-field spectroscopy. In this study, the predictive power of spectroscopic data is examined. Five treatments of heavy metal stress (Cu, Zn, Pb, Cr, and Cd) were applied to grapevine seedlings and hyperspectral data (350&ndash;2500 nm), and heavy metal contents were collected based on in-field and laboratory experiments. The partial least squares (PLS) method was used as a feature selection technique, and multiple linear regressions (MLR) and support vector machine (SVM) regression methods were applied for modelling purposes. Based on the PLS results, the wavelengths in the vicinity of 2431, 809, 489, and 616 nm; 2032, 883, 665, 564, 688, and 437 nm; 1865, 728, 692, 683, and 356 nm; 863, 2044, 415, 652, 713, and 1036 nm; and 1373, 631, 744, and 438 nm were found most sensitive for the estimation of Cu, Zn, Pb, Cr, and Cd contents in the grapevine leaves, respectively. Therefore, visible and red-edge regions were found most suitable for estimating heavy metal contents in the present study. Heavy metals played a significant role in reforming the spectral pattern of stressed grapevine compared to healthy samples, meaning that in the best structures of the SVM regression models, the concentrations of Cu, Zn, Pb, Cr, and Cd were estimated with R2 rates of 0.56, 0.85, 0.71, 0.80, and 0.86 in the testing set, respectively. The results confirm the efficiency of in-field spectroscopy in estimating heavy metals content in grapevine foliage

Optimal Spectral Wavelengths for Discriminating Orchard Species Using Multivariate Statistical Techniques

Sustainable management of orchard fields requires detailed information about the tree types, which is a main component of precision agriculture programs. To this end, hyperspectral imagery can play a major role in orchard tree species mapping. Efficient use of hyperspectral data in combination with field measurements requires the development of optimized band selection strategies to separate tree species. In this study, field spectroscopy (350 to 2500 nm) was performed through scanning 165 spectral leaf samples of dominant orchard tree species (almond, walnut, and grape) in Chaharmahal va Bakhtiyari province, Iran. Two multivariable methods were employed to identify the optimum wavelengths: the first includes three-step approach ANOVA, random forest classifier (RFC) and principal component analysis (PCA), and the second employs partial least squares (PLS). For both methods we determined whether tree species can be spectrally separated using discriminant analysis (DA) and then the optimal wavelengths were identified for this purpose. Results indicate that all species express distinct spectral behaviors at the beginning of the visible range (from 350 to 439 nm), the red edge and the near infrared wavelengths (from 701 to 1405 nm). The ANOVA test was able to reduce primary wavelengths (2151) to 792, which had a significant difference (99% confidence level), then the RFC further reduced the wavelengths to 118. By removing the overlapping wavelengths, the PCA represented five components (99.87% of variance) which extracted optimal wavelengths were: 363, 423, 721, 1064, and 1388 nm. The optimal wavelengths for the species discrimination using the best PLS-DA model (100% accuracy) were at 397, 515, 647, 1386, and 1919 nm

Transboundary Basins Need More Attention: Anthropogenic Impacts on Land Cover Changes in Aras River Basin, Monitoring and Prediction

Changes in land cover (LC) can alter the basin hydrology by affecting the evaporation, infiltration, and surface and subsurface flow processes, and ultimately affect river water quantity and quality. This study aimed to monitor and predict the LC composition of a major, transboundary basin contributing to the Caspian Sea, the Aras River Basin (ARB). To this end, four LC maps of ARB corresponding to the years 1984, 2000, 2010, and 2017 were generated using Landsat satellite imagery from Armenia and the Nakhchivan Autonomous Republic. The LC gains and losses, net changes, exchanges, and the spatial trend of changes over 33 years (1984–2017) were investigated. The most important drivers of these changes and the most accurate LC transformation scenarios were identified, and a land change modeler (LCM) was applied to predict the LC change for the years 2027 and 2037. Validation results showed that LCM, with a Kappa index higher than 81%, is appropriate for predicting LC changes in the study area. The LC changes observed in the past indicate significant anthropogenic impacts on the basin, mainly by constructing new reservoir dams and expanding agriculture and urban areas, which are the major water-consuming sectors. Results show that over the past 33 years, agricultural areas have grown by more than 57% from 1984 to 2017 in the study area. Results also indicate that the given similar anthropogenic activities will keep on continuing in the ARB, and agricultural areas will increase by 2% from 2017 to 2027, and by another 1% from 2027 to 2037. Results of this study can support transboundary decision-making processes to analyze potential adverse impacts following past policies with neighboring countries that share the same water resources