Islamic Azad University

About: Islamic Azad University is a education organization based out in Tehran, Iran. It is known for research contribution in the topics: Population & Catalysis. The organization has 83635 authors who have published 113437 publications receiving 1275049 citations. The organization is also known as: Azad University.

Temporal trends and spatial characteristics of drought and rainfall in arid and semiarid regions of Iran

Abstract: Drought, a normal recurrent event in arid and semiarid lands such as Iran, is typically of a temporary nature usually leaving little permanent aftermath. In the current study, the rainfall and drought severity time series were analyzed at 10 stations in the eastern half of Iran for the period 1966–2005. The drought severity was computed using the Standardized Precipitation Index (SPI) for a 12-month timescale. The trend analyses of the data were also performed using the Kendall and Spearman tests. The results of this study showed that the rainfall and drought severity data had high variations to average values in the study period, and these variations increased with increasing aridity towards the south of the study area. The negative serial correlations found in the seasonal and annual rainfall time series were mostly insignificant. The trend tests detected a significant decreasing trend in the spring rainfall series of Birjand station at the rate of 8.56 mm per season per decade and a significant increasing trend in the summer rainfall series of Torbateheydarieh station at the rate of 0.14 mm per season per decade, whereas the rest of the trends were insignificant. Furthermore, the 12-month values of the standardized precipitation index decreased at all the stations except Zabol during the past four decades. During the study period, all of the stations experienced at least one extreme drought which mainly occurred in the winter season. Copyright © 2011 John Wiley & Sons, Ltd.

Assessment of groundwater quality and its suitability for drinking and agricultural uses in the Oshnavieh area, northwest of Iran.

Abstract: The Oshnavieh plain is part of the West Azarbaijan province, which is located; 100 km south of Urmia City, northwestern of Iran, and its groundwater resources are developed for water supply and irrigation purposes. In order to evaluate the quality of groundwater in study area, 31 groundwater samples were collected and analyzed for various parameters. Physical and chemical parameters of groundwater such as electrical conductivity, pH, total dissolved solids, Na, K, Ca, Mg, Cl, HCO3, CO3, SO4, NO3, NH3, PO4, Fe, F were determined. Chemical index like percentage of sodium, sodium ad-sorption ratio, and residual sodium carbonated, permeability index (PI) and chloroalkaline indices were calculated. Based on the analytical results, groundwater in the area is generally fresh and hard to very hard. The abundance of the major ions is as follows: HCO3 > SO4 > Cl and Ca > Mg > Na > K. The dominant hydrochemical facieses of groundwater is Ca-HCO3 and Ca-Mg-HCO3 type. According to Gibbs diagrams samples fall in the rock dominance field and the chemical quality of groundwater is related to the lithology of the area. The results of calculation saturation index by computer pro-gram PHREEQC shows that the nearly all of the water samples were saturated to undersaturated with respect to carbon-ate minerals and undersaturated with respect to sulfate minerals. Assessment of water samples from various methods in-dicated that groundwater in study area is chemically suitable for drinking and agricultural uses. Fluoride and nitrate are within the permissible limits for human consumption and crops as per the international standards.

Ensemble models with uncertainty analysis for multi-day ahead forecasting of chlorophyll a concentration in coastal waters

Abstract: In this study, ensemble models using the Bates–Granger approach and least square method are developed to combine forecasts of multi-wavelet artificial neural network (ANN) models. Originally, this study is aimed to investigate the proposed models for forecasting of chlorophyll a concentration. However, the modeling procedure was repeated for water salinity forecasting to evaluate the generality of the approach. The ensemble models are employed for forecasting purposes in Hilo Bay, Hawaii. Moreover, the efficacy of the forecasting models for up to three days in advance is investigated. To predict chlorophyll a and salinity with different lead, the previous daily time series up to three lags are decomposed via different wavelet functions to be applied as input parameters of the models. Further, outputs of the different wavelet-ANN models are combined using the least square boosting ensemble and Bates–Granger techniques to achieve more accurate and more reliable forecasts. To examine the efficiency and reliability of the proposed models for different lead times, uncertainty analysis is conducted for the best single wavelet-ANN and ensemble models as well. The results indicate that accurate forecasts of water temperature and salinity up to three days ahead can be achieved using the ensemble models. Increasing the time horizon, the reliability and accuracy of the models decrease. Ensemble models are found to be superior to the best single models for both forecasting variables and for all the three lead times. The results of this study are promising with respect to multi-step forecasting of water quality parameters such as chlorophyll a and salinity, important indicators of ecosystem status in coastal and ocean regions.