Showing 2 results for S. Morid
A. R. Massah Bavani, S. Morid,
Volume 9, Issue 4 (winter 2006)
Abstract
In this study the impact of climate change on temperature, rainfall and river flows of the Zayandeh Rud basin under two climate change scenarios for two periods (2010-2039 and 2070-2099) are investigated. For the evaluation of future climate change impact on stream flow to Chadegan reservoir, the global circulation model (GCM) outputs of the HadCM3 model (monthly temperature and precipitation) with two scenarios, A2 and B2, are obtained and downscaled to the local level for the selected time periods. The results indicate that the annual average of precipitation decreases and temperature increases for both periods that are more pronounced for the period 2079-2099. Such that 10% to 16% decrease in precipitation and 3.2 to 4.6ºC increases in temperature can be anticipated for scenarios A2 and B2, respectively. To predict future stream flow changes due to climate change, artificial neural networks (ANNs) have been applied and trained by the several input models and architectures for rainfall-runoff simulation. The results indicate that the maximum of 5.8% decrease in the annual flows. Comparison of the two scenarios indicates the more critical situation in scenario A2 for the basin.
M. Gholamzadeh, S. Morid, M. Delavar,
Volume 15, Issue 56 (sumer 2011)
Abstract
Application of drought early warning system is an important strategy for drought management. It is more pronounced in the arid regions where dams have vital role to overcome water shortages. This papers aims to develop and apply such a system that includes three main components, which are 1) drought monitoring, 2) forecasting inflows and water demands and 3) calculation of a warning index for decision about drought management. The system is presented for the Zayanderud Dam. For this, the future six months river inflows and demands are forecasted at different probabilistic levels using the artificial neural networks and considering respected uncertainties. Also, five drought levels are indicated based on the historical records of dam’s storage and the self organizing feature map technique. Furthermore, a drought alert index (DAI) is defined using current storage of dam and the forecasted flows and demands. Finally, the different alert levels are estimated, which vary from normal to sever water scarcity. The results showed that application of the designed warning system can have effective role in the dam’s operation, rationing policy and reducing drought losses.
