Rainfall and rainy days trend in Iran

In this study, long-term annual and monthly trends in rainfall amount, number of rainy days and maximum precipitation in 24?h are investigated based on the data collected at 33 synoptic stations in Iran. The statistical significance of trend and climate variability is assessed by the Mann-Kendall test. The Linear trend analysis and the Mann-Kendall test indicate that there are no significant linear trends in monthly rainfall at most of the synoptic stations in Iran. However, the maximum number of stations with negative trends have been observed in April (29 station), and then in May (21 stations) and February (21 stations) and with positive trends in December (26 stations) and July (24 stations). The significant linear trends, with a significant level of 0.05, in annual rainfall have been noticed only at five stations. The monthly number of rainy days does not show any significant linear trend for most areas in Iran. The maximum number of stations with monthly negative trends in rainy days has also been observed in April with the minimum in December. In April, out of 24 stations with negative trends, 12 stations have a significant negative trend. Contrary to that, in October there is no significant linear trend. Most stations have positive trends in annual number of rainy days. Also, the monthly maximum precipitation in 24?h does not show any significant linear trend for most areas in Iran. The maximum number of stations with monthly negative trends in maximum precipitation has also been observed in February with the minimum in December. In spite of that, there are almost no significant precipitation variations in Iran during the last 50-odd years, the tendency of decreasing rainfall amount in April and increasing rainfall amount in December and July could indicate an eventual climate change in this area in the future.     

Spatial modelling of hazardous elements at waste dumps using geostatistical approach: a case study Sarcheshmeh copper mine,Iran

Mining activities and resulting wastes can be considered as one of the most important sources of hazardous elements in the environment. Knowledge of the spatial distribution of toxic elements in waste dump systems is necessary to assess environmental hazard and strategy. To achieve this goal, this paper investigates spatial distribution of toxic elements using statistical and geostatistical analysis. A total of 58 soil samples were collected, and the amount of As, Cd, Co, Cr, Cu, Mn, Mo, Ni, Pb and Zn was then determined at “Sarcheshmeh” copper mine waste dumps. In order to evaluate the presence of multivariate outliers, Mahalanobis distance technique (D ²) was applied and the multivariate outlier samples were removed. This resulted in an increase in correlation coefficient. To reduce dimension of data set, principal component analysis was applied and four principal components were determined which indicate 83.463% of the total variance of data set. Estimated PCs together with the toxic elements maps based on the ordinary kriging display aggregation of toxic elements in some parts, and validity of predictions was evaluated using the leave-one-out cross-validation method. The regression coefficients of estimated and observed values presented the reliability of the kriging estimates. Sequential Gaussian simulation method was applied for principal components due to similar results of estimated principal components and toxic elements. The results of simulation maps are almost identical to estimated outcomes.     

Wetland cover change detection using multi-temporal remotely sensed data

Lake Urmia, located in northwest Iran, contains a number of wetlands significantly affecting the environmental, social, and economic conditions of the region. The ecological condition of Lake Urmia has degraded during the past decade, due to climate change, human activities, and unsustainable management. The poor condition of the lake has also affected the surrounding wetlands. This study analyzes the land cover change of one of the wetlands in the southern part of Lake Urmia, known as Ghara-Gheshlagh wetland, in the period 1989–2015 using post-classification change detection and machine learning image classification. For this analysis, three Landsat images, acquired in 1989 (TM), 2001 (TM), and 2015 (Landsat-8), were used for the classification and change detection. Support vector machine learning algorithm, a supervised learning method, is employed, and images are classified into four main land cover classes namely “water,” ”barren,” “salty land,” and “agriculture and grassland.” Change detection was carried out for pairs of years 1989 to 2001 and 2001 until 2015. The results of this classification show that there is a sharp increase in the area of salt-saturated land as well as a decrease in the area of water resources. Overall classification accuracy obtained were high for the individual years: 1989 (91.48%), 2001 (90.63%), and 2015 (88.6%). Also, the Kappa coefficients for individual maps were high: 1989 (0.89), 2001 (0.8742), and 2015 (0.84). After that, the land cover change map of the study area is obtained between 1989 to 2001 and then 2001 to 2015. The results of this analysis suggest that more efforts should be taken to effectively manage water resources in the region and point to potential locations for focused management actions within the wetland area.     

Localization analysis in softening RC frame structures

This paper discusses the sensitivity of softening reinforced concrete frame structures to the changes in input ground motion and investigates the possibility of localizations for this type of structure in static and dynamic analysis. A finite element model is used in which the sections resisting force are calculated using a proposed differential hysteretic model. This model is especially developed for modelling softening behaviour under cyclic loading. To obtain parameters of the differential model the moment–curvature of each section is evaluated using a microplane constitutive law for concrete and bi‐linear elasto‐plastic law for reinforcements. The capability of the procedure is verified by comparing results with available experimental data at element level, which shows good accuracy of the procedure. The effect of possible changes in ground motion is assessed using a non‐stationary Kanai–Tajimi process. This process is used to generate ground motions with approximately the same amplitude and frequency content evolution as those of base ground motion. The possibility of localization in static and dynamic loading is investigated using two structures. A measure for the possibility of localization in code‐designed structures is obtained. This study indicates that localization may occur in ordinary moment‐resisting structures located in high seismic zones. Localization may result in substantial drift in global response and instability due to P–δ effect. Also, it is shown that the structure becomes very sensitive to the input ground motion. It is concluded that allowance by some design codes of the use of ordinary moment‐resisting frames in regions with high seismicity should be revised or improvements should be made in the detailing requirements at critical sections of these structures. Copyright © 2002 John Wiley & Sons, Ltd.     

Germination and seedling growth of corn (Zea mays l.) under varying levels of copper and zinc

The heavy metal tolerance in corn (Zea mays L.) var. ‘Neelum’ was assessed at germination and seedling growth after having subjected it to different concentrations of CuSO₄ and ZnSO₄. Germination was not affected by any of the metal tested, whereas initial growth was strongly inhibited by increasing concentrations of ZnSO₄. Seedlings developed toxicity symptoms in the presence of both metals but more chlorotic and necrotic regions were observed at varying levels of ZnSO₄ than CuSO₄. The metal accumulation was concentration dependent. Z. mays seedlings accumulated more copper in roots but greater contents of zinc in their shoots. On the basis of results presented here, it can be concluded that the cultivar of the species tested has shown a marked sensitivity to the presence of small amounts of metals present in the growth medium. The data support the assumption that metal sensitivity is probably due to strong tendency of the species to accumulate them. This justifies that the corn variety ‘Neelum’ is not suitable for the cultivation under situations where water and soil suffer from occasional and/ or transitory metal pollution.     

Bat algorithm for dam–reservoir operation

Optimizing reservoir operation rule is considered as a complex engineering problem which requires an efficient algorithm to solve. During the past decade, several optimization algorithms have been applied to solve complex engineering problems, which water resource decision-makers can employ to optimize reservoir operation. This study investigates one of the new optimization algorithms, namely, Bat Algorithm (BA). The BA is incorporated with different rule curves, including first-, second-, and third-order rule curves. Two case studies, Aydoughmoush dam and Karoun 4 dam in Iran, are considered to evaluate the performance of the algorithm. The main purpose of the Aydoughmoush dam is to supply water for irrigation. Hence, the objective function for the optimization model is to minimize irrigation deficit. On the other hand, Karoun 4 dam is designed for hydropower generation. Three different evaluation indices, namely, reliability, resilience, and vulnerability were considered to examine the performance of the algorithm. Results showed that the bat algorithm with third-order rule curve converged to the minimum objective function for both case studies and achieved the highest values of reliability index and resiliency index and the lowest value of the vulnerability index. Hence, the bat algorithm with third-order rule curve can be considered as an appropriate optimization model for reservoir operation.     

Sedimentology,mineralogy, and geochemistry of the Late Quaternary Meyghan Playa sediments,NE Arak,Iran: palaeoclimate implications

Playas are shallow ephemeral lakes that form in arid and semi-arid regions. Iran has a large number of playas such as Meyghan Playa, which is located in the northeast of Arak city that borders the central Iran and Sanandaj-Sirjan zones. This study aims to investigate the mineralogical, sedimentological, and geochemical characteristics of the playa sediments. In order to determine the palaeoenvironment, we carried out X-ray diffraction (XRD), X-ray fluorescence (XRF), and scanning electron microscopy (SEM) studies. Meyghan Playa sediments consist of very fine-grained sediments and contain both evaporite and clastic minerals. The evaporite minerals include calcite, gypsum, halite, glauberite, and thenardite, whereas clastic minerals are quartz and clay. The calcite abundance decreases from the margin to the central portion of the playa but gypsum and halite abundances show an increasing trend from the margin to the center. This observation is consistent with the general zonation of other playas. Variations of calcite and gypsum concentration profiles present increasing and decreasing trends with depth, which could be ascribed to the changes in climatic factors. These factors include brine chemical modifications owing to changes in evaporation and precipitation rates and variations in relative abundance of anions-cations or in the rate of clastic and evaporite minerals due to variations in the freshwater influx (climatic changes) with time. A decrease in calcite and increase in sulfate minerals (especially gypsum) with depth is probably due to the higher water level and rainfall, a more humid climate, and salinity variations.     

Prediction of river flow using hybrid neuro-fuzzy models

The complex nature of hydrological phenomena, like rainfall and river flow, causes some limitations for some admired soft computing models in order to predict the phenomenon. Evolutionary algorithms (EA) are novel methods that used to cover the weaknesses of the classic training algorithms, such as trapping in local optima, poor performance in networks with large parameters, over-fitting, and etc. In this study, some evolutionary algorithms, including genetic algorithm (GA), ant colony optimization for continuous domain (ACO_R), and particle swarm optimization (PSO), have been used to train adaptive neuro-fuzzy inference system (ANFIS) in order to predict river flow. For this purpose, classic and hybrid ANFIS models were trained using river flow data obtained from upstream stations to predict 1-, 3-, 5-, and 7-day ahead river flow of downstream station. The best inputs were selected using correlation coefficient and a sensitivity analysis test (cosine amplitude). The results showed that PSO improved the performance of classic ANFIS in all the periods such that the averages of coefficient of determination, R², root mean square error, RMSE (m³/s), mean absolute relative error, MARE, and Nash-Sutcliffe efficiency coefficient (NSE) were improved up to 0.19, 0.30, 43.8, and 0.13%, respectively. Classic ANFIS was only capable to predict river flow in 1-day ahead while EA improved this ability to 5-day ahead. Cosine amplitude method was recognized as an appropriate sensitivity analysis method in order to select the best inputs.     

Spatial assessment of the potential of groundwater quality using fuzzy AHP in GIS

In this study, fuzzy AHP method is used for extracting the water quality indicators based on the Schuler standard and World Health Organization (WHO) guidelines during a 20-year period. For this purpose, the best fit of the zoning model was performed. Furthermore, by comparing the standard errors, the continuous Raster layer was extracted from the important parameters used in generating the qualitative potential assessment index. The classified layer was generated by integrating continuous layers in the GIS environment and with the use of Python programming. The similarity of the outputs of both methods indicates the presence of large sections of aquifers in the middle and southwestern regions of Iran in the “temporarily drinkable” and “bad” classes. The calculations showed that the majority of aquifers that were located in the “inappropriate” class during the first 10 years fell to less valuable class types. Based on the results of the model, there is a direct correlation between the drop in water resources and the decline in the quality indices. In addition, in the Urmia and Bushehr coastal aquifers, due to excessive water withdrawal and salty water penetration, the quality of the table water is in critical condition. Based on the results of the research, the aquifers in the range of Zagros and Alborz mountains show the least change in water quality. The reason for this is the depth of the aquifer and the ability to recharge it.