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Showing 3 results for Saberi
Estimation of Global Solar Radiation Using NOAA Satellite Images and Statistical Equations in Southeast of Tehran
A. Rahimikhoob, P. Saberi, S. M. Behbahani, M. H. Nazarifar,
Volume 15, Issue 56 (sumer 2011)
Abstract
In this study, the remote sensing statistical approach was used to determine the global solar radiation from NOAA-AVHRR satellite data in southeast of Tehran. This approach is based on the linear correlation between a satellite derived cloud index and the atmospheric transmission measured by the clearness index on the ground. A multiple linear regression model was also used to convert the five AVHRR data channels and extraterrestrial radiation to global solar radiation. The results of this study showed that multiple linear regression model estimated the solar radiation with an R2 of 0.93 and a root mean square error (RMSE) of 5.8 percent, which was better than the statistical approach.
Evaluation of Pb, Zn, Cu and Ni Concentration in Arak Mighan Wetland Based on Sediment Pollution Indices
F. Saberinasab, S. Mortazavi,
Volume 22, Issue 1 (Spring 2018)
Abstract

Today, the growth of population, the excessive growth of cities and subsequently, the industries development in all parts of the planet are evident. Such development leads to the entrance of chemical martials with various compounds, particularly heavy metals, in aquatic ecosystems. In this study, the contamination of the sediments around the Mighan wetland basin with heavy metals such as lead, zinc, copper and nickel was investigated using CF indexes (contamination factor), IGeo geo-accumulation indexes, and IPOLL sediment contamination severity. The results indicated a high concentration of copper and nickel metals, in comparison to zinc and lead metals, in the studied area. Also, regarding the location of sampling stations and their proximity of the industries around the wetland, the comparison of the results related to the measured metal concentration and the sediment quality indices, indicated a high concentration of the pollutants under examination in the southern part of the wetland, thereby emphasizing the need to control the pollutants in the wetland.


Uncertainty Analysis of Climate Change Impacts on Streamflow Extremes in Zayandeh-Rud River by Bayesian Model Averaging
A. Alinezhad, A. Gohari, S. Eslamian, Z. Saberi,
Volume 23, Issue 4 (Special Issue of Flood and Soil Erosion, Winter 2019)
Abstract
The evaluation of climate change impact on hydrological cycle includes uncertainty. This study aimed to evaluate the uncertainty of climate change impact on the Zayandeh-Rud Reservoir inflow during the future period of 2020-2049. The outputs of 22 GCM models were used under the three emission scenarios including RCP2.6, RCP4.5 and RCP8.5. The Bayesian Model Averaging (BMA) was used as the uncertainty analysis for weighting the 22 GCM models based on their ability to simulate the baseline 1990-2005 period. Results showed that different GCM models had different abilities in estimating climatic and hydrological variables and the application of uncertainty analysis in climate change studies could be necessary. The monthly temperature in the upstream of Zayandeh-Rud reservoir could be raised by 0.85 to 1 ◦C; also, the precipitation might be increased by 2 to 3 percent. The high flow during winter season will increase under climate change, while the spring and autumn seasons’ low flows are expected to reduce. Additionally, the annual reservoir inflow may decrease by 1 to 8 percent, showing the necessity for change in Zayandeh-Rud reservoir’s rule curve and allocation of water resources.

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