Babak Homayunpur; Massoud Goodarzi; Qolamreza Zehtabian; Baharak Motamedvaziri; Hassan Ahmadi
Abstract
Nowadays, global climate change has been observed which had an impact on both surface and groundwater resources management. The main objective of this research is change detection of groundwater fluctuation caused by climate change in Borkhar Plain, Isfahan Province. In this way, both Mann-Kendal and ...
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Nowadays, global climate change has been observed which had an impact on both surface and groundwater resources management. The main objective of this research is change detection of groundwater fluctuation caused by climate change in Borkhar Plain, Isfahan Province. In this way, both Mann-Kendal and Sen’s slope non-parametric analysis were used using 19 piezometric wells data for the period of 1991-2019. Pearson correlation matrix along with correlation between climatic elements including temperature, precipitation and evapotranspiration (Thornwhite) with water table levels were determined. Based on this, a multivariate regression model was developed to model the annual time series at a confidence level of 0.95. Climatic factors of 2080-2099 period were simulated using the output of HadCM3 model through Lars-WG downscaling model for Maymeh Station under two scenarios A2 and A1B and based on the developed regression model, the water level of Borkhar Plain was simulated. The results showed that in the base period, the water table in the study area has been decreased with an annual slope of 47 cm per year. Correlation analysis showed that the three climatic elements of precipitation, temperature and evapotranspiration in a linear composition modeled 0.75 of the annual groundwater changes in the plain. The results of the exponential microscale model implemented on HadCM3 data showed that in the period 2080-2099 under both scenarios, the groundwater level of the region will decrease between 15 to 17 meters compared to the base period.
Mohammad Hemmati; Davood Nikkami; Hassan Ahmadi; Gholamreza Zehtabian; Mohammad Jafari
Volume 1, Issue 1 , May 2009, , Pages 21-31
Abstract
Soil is one of the four elements of life and one of the most important agricultural and rangeland production factors. Soil erosion has impact on fertility of upstream lands and indirectly causes downstream damages. On a global scale, more than 50% of rangelands and about 80% of farmlands are significantly ...
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Soil is one of the four elements of life and one of the most important agricultural and rangeland production factors. Soil erosion has impact on fertility of upstream lands and indirectly causes downstream damages. On a global scale, more than 50% of rangelands and about 80% of farmlands are significantly suffering from soil erosion and sedimentation phenomenon. Despite of agricultural benefits of rainwater, its erosive energy in the primary stages, detaches soil particles and creates surface runoff which has the most severe impacts on soil erosion. The effective energy in this shape of erosion is called rainfall erosivity, which is rainfall erosion potential and depends on rainfall physical characteristics. Some different indices are developed for quantification of rainfall erosivity. Some indices emphasise on the amount of rainfall or rain intensity. Worldwide investigations show that applying a certain index, like EI60 in USLE equation has not sufficient credit and according to climatic condition the best rainfall erosivity index should be determined. In this research, after establishing 21 erosion plots in Kaboudeh Oliya village, Kermanshah city, on two land use types of dry farming of 6, 12, 16 and 17 percent slopes and rangelands on 25, 35 and 45 percent slopes with three repetitions, 15 rainfall events which produced surface runoff were monitored by a recording rain gauge and the amount of surface runoff and sediment were measured. According to the literature, 45 rainfall erosivity indices were chosen to be compared. For facilitating and increasing accuracy of calculations on rainfall erosivity indices, a computer program was developed to analyze and extract necessary factors from each storm. The result showed that in the study area, EI60 is the best rainfall erosivity index and has maximum correlation coefficient of 0.721 with soil loss.
