Zahra Noori; Arash Malekian
Abstract
Groundwater is important water resource supply, especially in arid and semi- arid regions. Increased utilization of the ground water aquifer leads to significant reduction in the storage of reservoirs. This study evaluates the hydrogeological drought in Garmsar plain using Groundwater Resource Index ...
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Groundwater is important water resource supply, especially in arid and semi- arid regions. Increased utilization of the ground water aquifer leads to significant reduction in the storage of reservoirs. This study evaluates the hydrogeological drought in Garmsar plain using Groundwater Resource Index (GRI). First, we used 17 piezometric wells data over 2001-2011 statistical period to calculate GRI in the beginning, middle and end of the period. So, we used different interpolation method including geostatiscal method ordinary kriging (OK), simple kriging (SK) and deterministic methods including inverse distance weighting (IDW) to prepare the maps over three periods. The mean absolute error (MAE) and root mean square error (RMSE) indices were used to evaluate the accuracy of simple kriging, ordinary kriging and IDW classification based on the drought maps. The results showed that the values of MAE and RMSE criteria for simple Kriging is better than the other methods and indicates the suitability of this method for zoning GRI. According to the results, the most severe hydrogeological drought in Garmsar plain was at the end of 2011, that 91.16 % of the study area was suffered from severe drought. SPI was used for considering the effects of meteorological drought in the time scale of 3, 6, 9, 12, 24 and 48 months on groundwater. The correlation between SPI and GRI showed long-term timescale of 48 monthly has the greatest correlation with groundwater level.
Arash Malekian; Mahsa Mirdashtvan
Abstract
Nowadays, with the increasing exploitation of groundwater resources, optimal use of these resources is more and more necessary. geostatistical methods can be used to assess and monitor the quality of groundwater resources. Hashtgerd Plain is the case study of this investigation. In this study firstly, ...
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Nowadays, with the increasing exploitation of groundwater resources, optimal use of these resources is more and more necessary. geostatistical methods can be used to assess and monitor the quality of groundwater resources. Hashtgerd Plain is the case study of this investigation. In this study firstly, by using data from qualitative data which were harvested from 41 Piezometric wells, different qualitative parameters were evaluated, then by using the geostatistical methods such as: Kriging, Co-kriging and IDW the best model for mapping for aquifer quality classification was selected. Results showed that most of the indicators are better simulated by Co-kriging method, based on mutual evaluation and RMSE. The parameters of SAR and EC were selected in order to determine the irrigation water quality parameters according to Wilcox diagram. Based on these two parameters by using ArcGIS v.10 software zoning maps were prepared. Results showed that 99% of the aquifer is classified in the category of good quality irrigation water (C2S1) and 1% level in the aquifer is classified as middle class (C3S1) based on Wilcox diagrams. The results of the study can be used in aquifer management and irrigation management in the agricultural purposes.
Hossein Eslami; Ali Salajagheh; Shahram Khalighi sigaroudi; hasan Ahmadi; Shamsollah Ayoubi
Abstract
Rainfall erosivity is the ability of rainfall to detach the soil particles. This study was conducted to evaluate spatial variability of rainfall erosivity indices in Khouzestan Province. The point data of indices (EI30, AIm, KE>1 and Onchev indices) in 74 stations were used to generate spatial erosivity ...
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Rainfall erosivity is the ability of rainfall to detach the soil particles. This study was conducted to evaluate spatial variability of rainfall erosivity indices in Khouzestan Province. The point data of indices (EI30, AIm, KE>1 and Onchev indices) in 74 stations were used to generate spatial erosivity maps through deterministic and geostatistical interpolation methods (Radial Basis Functions, Inverse Distance Weighted, Kriging and Cokriging). Results indicate that cokriging have least error and most correlation with determining coefficient of 0.89, 0.89, 0.48 and 0.49 for EI30, AIm, KE>1 and Onchev indices. Based on the correlation relationships between the basins specific sediment yield (in basins dominating the sedimentation stations) and mean indices of EI30, AIm, KE>1 and Onchev, EI30 index with correlation coefficient of 0.98 (P<0.01) is selected as the appropriate rainfall erosivity index. Based on the prepared map on the basis of Cokriging method with secondary variable of maximum mean monthly rainfall, the east and northeastern regions presented the highest values of EI30 index, while the southern and western regions showed the lowest values of EI30 index. The annual rainfall erosivity (EI30) ranged from 404 to 3064 Mj.mm.ha-1.h-1.y-1.
Kh. Osati; A. Salajegheh; S. Arekhi
Abstract
Spatiality assessment of groundwater pollution is very important to determine water quality condition, pollution sources and management decisions. In this case, GIS and geostatistics methods can be useful tools. Spatiality of groundwater quality parameters, in relation with various land uses, can be ...
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Spatiality assessment of groundwater pollution is very important to determine water quality condition, pollution sources and management decisions. In this case, GIS and geostatistics methods can be useful tools. Spatiality of groundwater quality parameters, in relation with various land uses, can be very extremely. Therefore water samples from 52 wells in the Kurdan area were analyzed in this study. The results show that nitrate concentrations are less than maximum acceptable concentration in drinking water (i.e. 50 mg/L as nitrate recommended by ISIRI and WHO guideline values) except to one sample (2 percent of samples) in the study area. Various geostatistics methods, e.g. IDW (power 1-4), ordinary Kriging and RBF (five Kernel functions) were compared after assessing the variograms and the spatiality of nitrate samples. Then the model parameters were calibrated and through the specific methods, predicted and standard errors maps were prepared. Errors criteria show that Kriging is the best fitting model in the study area. Finally, probability map of NO3 concentrations exceeding the threshold value of 50 mg/L, is generated using the Indictor Kriging method. Spatiality of NO3 show that Nitrate concentration is increased where the rock type is permeable, land use is agriculture and slope is enough low to infiltrate polluted water into the wells. This research also tries to describe how to assess the spatiality of groundwater parameters by GIS.