فهرست مطالب h. fathizad

The purpose of this research was to investigate the trend of annual changes in Yazd stationchr('39')s meteorological parameters including minimum and maximum average daily temperature and average daily precipitation (1961-2005), as well as the predicted annual mean of these parameters in the three upcoming thirty years of the 2040s, 2070s and 2100s, by the SDSM model, under RCP2.6, RCP4.5, RCP8.5, A2, and B2 scenarios. Accordingly, by using the coefficient of determination and the MAE, R2, RMSE indicators, we evaluated the data generated by the SDSM model in comparison with the observed data in the base period. The lowest value of R2 based on the calibration and validation of the mean values of observed and simulated SRES was obtained for precipitation (86 and 80%). In terms of the R2 evaluation index, the accuracy of the small-scaled results of the minimum and maximum average temperature values was more than that of the average precipitation; however, in terms of the MAE and RMSE evaluation indicators, the accuracy of the small-scaled results of the average precipitation was higher than that of the minimum and maximum average temperature values. Subsequently, HadCM3 large-scale climatological data was used to predict the future periods (2010-2100). The results indicated that the temperature was raised in all months and seasons and the precipitation was decreasing in most of them, thereby confirming that the climate was changing in the studied region.

To understand and manage the natural and human-made ecosystems and develop long-term planning, it is necessary to model Land Use Change (LUC) and predict future changes. Therefore, we used Landsat satellite imagery, Multilayer Perceptron neural network (MLP) and Markov Chain model (MCA) to monitor the regional changes over 30 years in the central arid regions of Iran. In the present research, the stratified maps derived from the object-oriented algorithm were used to detect and map the changes of land use classes from 1986 to 2016. Furthermore, the land use in 2030 was predicted using Land use Change Modeler (LCM). Slop, contour elevation lines, distance from river, road, afforestation, agricultural lands/gardens, barren lands, poor rangelands, residential lands, rocky land, and sand dunes were considered as factors influencing the changes in the ANN. The Cramer's V coefficient was employed to select appropriate parameters with the highest significant correlation. Our results showed that the sub-models performed well (75-85%). Besides, the highest and lowest accuracy of sub-models were related to the distance from barren lands and distance from residential areas (75.23 and 85.91%, respectively). The results of land use change monitoring from 2016 to 2030 revealed that land use such as forest, residential lands, gardens, and sand dunes would be increased by about 0.11, 1.53, 2.36 and 0.56 %, respectively, by 2030 compared to 2016. On the other, the area of barren land and poor rangeland would be reduced by 2.88 and 1.68 %, respectively. Our results can be used in land change evaluations, environmental studies, and integrated planning and management regarding appropriate and logical use of natural resources and reducing resource degradation.