مجله مدیریت بیابان
پیاپی 7 (بهار و تابستان 1395)

Ground water is one of the major resources of fresh water needed for humans, especially in arid areas. It will be seriously threatened in future due to climate change and global warming. The purpose of this study is to examine the effects of climate change on groundwater resources in the Kerman-Baghin plain. Accordingly, climate prediction at 2055 horizon (2065-2046) was used by the atmospheric general circulation model HadCM3 with A2 emissions scenario. Also groundwater table during the years (1986 -2009) and the quality situation (salinity) over the years (2003-2009) were studied. The results of LARS-WG model showed that the average annual rainfall at the 2055 horizon will be reduced about 3.6 mm. Also the annual maximum and minimum temperatures will increase during the 2055 horizon, 2.8°C and 2.8 °C, respectively. Assessing of unit hydrograph and chemograph in Kerman- Baghin plain highlighted a drop in the water table (0.89 meter depletion per year) and increase of aquifer salinity. According to the results of atmospheric general circulation model (HadCM3), this trend in the water table and increase in aquifer salinity expected to be continued.

In recent decades, the increase of temperature has caused the disturbance of climatic balance of the earth and extensive climate changes which is called climate change. The aim of this study is to predict the climate changes using statistical downscaling model (SDSM) based on A2 scenario over future periods. Daily precipitation, minimum and maximum temperature data of Kermanshah synoptic station, for two periods 2015-2040 and 2040-2065, were predicted and compared with the baseline period. The first 27 years of data (1988-1961) were used for calibration and the second 12 years (1989-2001) were used for validation of the model as well. The results showed that based on the A2 scenario, in the periods of 2015- 2040 and 2040- 2065, the average annual precipitation decreases, the average minimum and maximum temperature increases compared to the baseline period in the Kermanshah synoptic station. Since the precipitation reduction and temperature increase are one of the main factors of desertification, so it is necessary for decision makers and planners in Kermanshah province to adopt necessary solutions for mitigation and adaptation with new climatic conditions.

Using of flood to supply plant water requirement for its growing and decreasing of flood damages is possible by many different methods. By using of water spreading systems, seasonal flood in susceptible land will be spread with different effects on groundwater, plant cover and soil on these lands. In order to measure the effect of water spreading system on changes of vegetation, soil salinity and organic carbon in Miankooh floodwater spreading area, 40 random plots of 2 sq. meter were taken in floodwater spreading area and 20 points were selected outside the floodwater spreading(control areas) and vegetation were changes measured. Meanwhile, to investigate soil salinity and organic carbon changes, 8 profiles were taken from four depths ranges including 0-10, 10-20, 20-30, 40-60 cm in the area affected by flood and sediment and some points were selected in outside floodwater spreading(control areas). The statistical results indicated that floodwater spreading have significant effects on vegetation cover and salinity reduction (in depth=0 to 10cm) while no significant effect on soil organic carbon. Generally floodwater spreading from one hand can reduce soil salinity and consequently increase amount of plant cover in arid and semi-arid regions but on the other hand, high volume of flood and deposition of sediments, reduce the efficiency of water spreading over the time.

Human pressures on the world’s landscapes are accelerating at unprecedented rates. In response to these pressures, making the need for sustainable landscape management and planning even more urgent. To have a continuous and sustainable utilization of rangelands these changes must be recognized and managed. Landscape Function Analysis (LFA) is a monitoring procedure that assesses the degradation of landscapes, as brought about by human, animal and natural activities, through rapidly assessing certain soil surface indicators which indicate the biophysical functionality of the system. Indicators of soil surface characteristics and rangeland functional attributes assist researchers in judging the effects of their applied management activities. In this research, three rangelands with similar ecological characteristics were studied in order to compare their health and stability. The aim is to determine qualification of sustainability and rangeland health attribute by the LFA method. It was tried to answer this question that similar ecological and climatic conditions, different management practices how affect on sustainability and health indicators? This purpose was done by three indices of stability, permeability and nutrient cycle for each study region. The differences between these indices was significant. The results showed that Espiro rangeland is stable than two others. Changi rangeland need more reformative activities that rangeland managers have to recognize these relations and proceed.

Landscape ecology is one of theyoungest branches of ecology science, and especially deals on the study of landscape composition, structure and function. Landscape elements are such as patches, corridors, matrixalong the border and ecotons. Landscape metrics are algorithms for quantifying thespatial characteristics of patches, classes or entire landscape mosaics, and thebest way to compare different landscape requirements. Due to this, the aim of this study was to quantifylandscape patterns and the possibility of breakdown of plant communities usinglandscape metrics. Metrics that were used in this study thenumber of patch, class area proportion in the landscape, mean patch size, edge density, mean shape index, mean patch fractal dimension and total edgein the class level and Shannon diversity index in the landscape level. Metrics listed for vegetationmap and morphology units were calculated using the patch analyst extension for Poshtkooh rangelands of Yazd. Then, using the principal component analysis were determined the important metrics for the separation of plant communities. The resultsof the principal component analysis for the separation of class in morphology units map showed that edge density metric have thegreatest impact but in the vegetation map landscape metrics, were equally important and more importance metric could not be determined and compared to separation the units of land shape in the morphology units map were less separation. Finally, with using of discriminate analysis, wasdetermined thedifferentiation function and metrics that create separation between the plant communities and land classes. Discriminate analysis showed that thenumber of patch metric, edge density and mean patch fractal dimension can beseparated plant communities and classes in the map, units are less resolution.

Data recorded in the thermal band satellites, are commonly used in the thermal characteristics of different phenomena, especially in desert areas. Classification is one of the most important steps in the use of satellite imagery. In this study, using ASTER data TERRA satellite on 22 August 2001, East and North East Kashan Salt Lake, were studied to find the role of quantity bands of this sensor to increase the resolution accuracy of phenomena in the maximum likelihood (MLK) and support vector machine (SVM). For this purpose, after the initial radiometric and geometric corrections, eight approaches were chosen with different band combinations step by step in order to investigate the quantitative role of each band to increase the classification accuracy and then the classification accuracy using Kappa index, user and producer accuracy were evaluated. The classification algorithm results showed that the support vector machine algorithm rather than maximum likelihood algorithm has slightly better results. Generally, the use of all spectral and thermal bands (14 bands) had a highest accuracy for both Kappa support vector machine algorithm (83.04) and maximum likelihood (90.82). While the Kappa accuracy by 15% in both algorithm with elimination of all thermal bands (bands 10, 11, 13.12 and 14) was reduced. The maximum likelihood algorithm had the greatest impact on increasing the accuracy of Kappa index bands of thermal 14 (between 8 to 10 percent) and in the support vector machine algorithm bands of 10 and 14 (7%). Finally, it was proven that ASTER sensor due to wide spectrum range in the wavelengths of thermal infrared has very high potential in increasing accuracy coefficient of classification Kappa.

Drought is one of the most important natural disasters that is effective in different parts such as; natural resources, surface and subsurface water resourcesand etc. Notice to the changes trend of drought and its forecasting, can be effective in planning of drought control and reduction of drought impact. In this study climate data of 7 stations of southwest of Iran from 1980 to 2014 was used and the Intensity of seasonal drought was calculated based on RDI index. Then drought index predicted from 2015 to 2019 using time series models and ITSM software, then the changes trend of drought based on parametricand non-parametric tests were evaluated. According to results in Abadeh, Shiarz and Fasa stations MA (5) Innovation, in Bandar Abass station AR (15) Burg, in Bandar Lengeh station AR (15) Yule-Walker, in Boushehr station MA (5) Hannan-Rissanen and in Jask station AR (13) Burg model were the best time series model that fitted in data. Correlation coefficient between observed data and predicted data, P- Value of Ljung – Box test, white noise of models and ACFand PACFResidual of models showed that predicted models were Reliable. Results showed that the changes trend of seasonal droughtin all stations from 1980 to 2014 and 1980 to 2019 periods were non-significant at 0.95 level based on parametric and non parametric statistical methods. However slop of changes trend in (1980-2019) period was more than (1980-2014) period.