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Showing 2 results for Ashrafzadeh
Qualitative Assessment of the Coastal Plain of Talesh using the Modified DRASTIC Vulnerability Method
H. Mahmoudpour, S. Janatrostami, A. Ashrafzadeh,
Volume 24, Issue 3 (Fall 2020)
Abstract
Given the fact that the DRASTIC index is ineffective in addressing the saltwater uprising issue in coastal plains, in the present study, three factors including land use, distance to shoreline, and differences between groundwater and sea level were added to the DRASTIC index. The proposed modification to DRASTIC was validated using the measured electrical conductivity (EC) data gathered from groundwater monitoring wells throughout the Talesh Plain. The results showed that the coefficient of correlation between the map of EC over the region and the modified DRASTIC was 0.52, while for the original DRASTIC, the coefficient was 0.45, thereby implying a stronger relationship between EC and the modified DRASTIC in the Talesh Plain. Sensitivity analysis also showed that DRASTIC and the modified DRASTIC were the most sensitive to, respectively, depth to groundwater (D) and land use (Lu). According to the single-parameter sensitivity analysis results, depth to water table and net recharge were the most effective parameters in DRASTIC,  whereas the modified DRASTIC was the most sensitive to land use and depth to groundwater. It could be concluded that modifying the DRASTIC index would result in decreasing the area of very high and high vulnerable classes, and the area classified as low and moderate vulnerable could be increased.

Assessing Water Resource Planning Policies in the Sefidroud Irrigation and Drainage Network Using the Water-Land-Food Nexus Approach
S. Ashkevari, S. Janatrostami, A. Ashrafzadeh,
Volume 29, Issue 1 (Spring 2025)
Abstract
In this study, a conceptual model based on dynamic systems was developed to optimize the management of water, land, and agricultural production (tea and rice) in the irrigation zones of the Sefidroud irrigation and drainage network. To understand the behavior of the network and create a simulation model of the system, a dynamic systems modeling approach was employed, and the simulation was conducted using MATLAB/Simulink. Subsequently, the optimization model of the studied system was developed as a multi-objective model using a genetic algorithm. Various management scenarios were implemented through the weighting of the objective functions. The results showed that selecting the best response from multi-objective optimization models depends on the weighted values of the objective functions, and by changing these values, decision-makers can provide various responses to complex optimization problems. The optimization model determines the cultivated area and water allocation in such a way as to minimize water scarcity and maximize crop performance through different weighting combinations. Furthermore, the findings indicate that the canals of the irrigation network play a crucial role in meeting water needs, and equitable water allocation is essential to prevent excessive extraction and negative consequences, such as saline intrusion and land subsidence. The study demonstrates that the best solutions are contingent upon local conditions and decision-makers' policies. To achieve maximum economic benefits and address water needs, it is suggested to use a weighting combination close to (w1=1,w2=2). Ultimately, this model assists managers and decision-makers in minimizing water scarcity in the region by adjusting cropping levels and optimizing the use of available water resources.

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