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Showing 3 results for Alamdari

O. Ahmadi, P. Alamdari, M. Servati, T. Khoshzaman, A. Shahbaee Kootenaee,
Volume 23, Issue 1 (Spring 2019)
Abstract

Changes in Climate parameters have been accelerated in the coming age, which can affect agricultural activities directly and indirectly. Temperature and precipitation are the most complex climatic factors. Spectral analysis is a scientific and efficient technique used to recognize and detect the hidden behaviors of these variables. In this research, in order to study and analyze the temperature and precipitation return periods using spectral analysis, the statistics of climate parameters (precipitation, mean, maximum and minimum temperature) for a period of 27 years (1989-2015) were used for the sustainable land management. For this purpose, the climatic data of temperature and precipitation entered the MATLAB software environment and Periodogram of each of the climatic parameters was drawn in a separate way. The results of each Periodogram study showed that the absolute minimum of temperature had significant cycles with the return periods of 3.8 and 2.4 years; the absolute maximum of temperature had a significant cycle with a return period of 2.1 years and the mean temperature was significant with a return period of 2.7 years. Also, the review of the Periodogram related to precipitation showed a significant cycle with a return period of 3.4 years. The Results from studying cycles indicated the existence of short-term return periods for climate variables in the region. Given this issue and the need to protect agricultural products, especially garden products, it should be done by applying water and soil resources management methods, including creating terraces and increasing soil roughness; Also, cultivation of appropriate plant species for the suitable regional climatic conditions, Drought resistant and low water requirement, the most optimal conditions could be created for the cultivation of horticultural and agricultural products.

S. Ghobadi Alamdari, A. Asghari Moghaddam, A. Shahsavari,
Volume 23, Issue 4 (Special Issue of Flood and Soil Erosion, Winter 2019)
Abstract

Lack of the proper conjunctive use of surface and groundwater resources causes large water stresses in one of these resources. Conjunctive use of surface and groundwater, especially in arid and semi-arid regions, is a scientific and practical solution for sustainable water resources management. The aim of this research was to prepare some mathematical modeling to apply the conjunctive use of surface and groundwater in the Dehloran plain aquifer. In this study, the mathematical model of the Dehloran plain aquifer was developed using GMS 9.1 and the river data were entered. For the steady state condition, the time series data in the average year 2010-2011 were utilized. In the next step, the time series data from October, 2010, to September, 2011, were used for the unsteady state analysis. In the unsteady state, four stress periods were taken; then the model calibration was carried out in three steps for each stress period; after the optimization of the hydrogeological parameters of the model, its verification was done for the period of 2011-2012 period. After the calibration of the model in the unsteady state, the values of the mean error (ME), the mean absolute error (MAE) and the root mean squared (RMS) errors measured in piezometers were obtained to be -0.24, 0.46 and 0.65, respectively. The results of verification confirmed the ability of the model in simulating the natural conditions of the aquifer. Finally, applying different scenarios to the model showed that the proper conjunctive use of surface and groundwater could increase the volume of water at a rate of 2.23 million cubic meters per year.

A. Barikloo, S. Rezapour, P. Alamdari, R. Taghizadeh Mehrjardi,
Volume 27, Issue 4 (Winter 2023)
Abstract

Soil quality is one of the most crucial factors determining crop productivity and production stability. The soil's physical, chemical, biological, and ecological characteristics affect its quality. Numerous researchers have concentrated the evaluation on a small number of soil quality indicators because measuring all soil quality indicators would be time-consuming and expensive. This study looked at the spatial autocorrelation of soil quality in the southwest areas of the Urmia Plain to establish the minimal data set for quantitative assessment. To accomplish this, 120 composite soil samples were collected from a depth of 0 to 60 cm, and the soil quality index was then calculated using the IQI method in 4 modes: Total-Linear (IQIwL-TDS), Total-Nonlinear (IQIwNL-TDS), Minimum-Linear (IQIwL-MDS), and Minimum nonlinearity (IQIwNL-MDS). 22 physical and chemical characteristics were used to choose the data set. The characteristics of sand percentage, sodium absorption ratio, cation exchange capacity, Available phosphorus, active calcium carbonate, and nickel concentration were chosen as the minimum data set (MDS) using the decomposition method into principal components. The linear IQIMDS mode produced the greatest soil quality index result, whereas the non-linear IQIMDS mode produced the lowest. The non-linear mode of the IQI index has a greater correlation coefficient (R2=0.85) than the linear mode of the IQI index (R2=0.73), according to an analysis of the linear and non-linear correlation coefficient between the soil quality index with the total category and minimum data. The findings of computing the global Moran's index for study sets of IQI soil quality index data revealed that the soil quality data are not independent of each other and are spatially autocorrelated, distributed in clusters, and have spatial autocorrelation. Getis-ord GI statistics indicated that the eastern and southeastern parts of the research region comprise clusters with poor soil quality, salt marshes produced by Lake Urmia's drying up, and surrounding arid plains.


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