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, ...
Read More
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.
Ali Fathzadeh; Somayeh Ebdam
Abstract
[1] Ahmad, S. and Simonovic, S.P. (2005). An artificial neural network model for generating hydrograph from hydrometeorological parameters, J. Hydrol, 315, 236-251.
[2] Agarwal, A., Mishra, S.K., Ram, S. and Singh, J.K. (2006). Simulation of runoff and sediment yield using artificial neural ...
Read More
[1] Ahmad, S. and Simonovic, S.P. (2005). An artificial neural network model for generating hydrograph from hydrometeorological parameters, J. Hydrol, 315, 236-251.
[2] Agarwal, A., Mishra, S.K., Ram, S. and Singh, J.K. (2006). Simulation of runoff and sediment yield using artificial neural networks, Biosys. Eng, 94(4), 597-613.
[3] Amini, M., Abbaspour, K.C., Khademi, H., Fathianpour, N., Afyuni, M. and Schulin, R. (2005). Neural network models to predict cation exchange capacity in arid regions of Iran, European Journal of Soil Science, 53, 748-757.
[4] Balk, B. and Elder, K. (2000). Combining binary decision tree and geostatistical methods to estimate snow distribution in a mountain watershed, Water Resources Research, 36, 13-26.
[5] Bagheri Fahrji, R. (2011). Estimating the satial distribution of snow water equivalent in mountain watersheds using geostatistic methods (Case study: Bidakhovid), M.Sc. thesis, Islamic Azad University Maybod branch.
[6] Carrol, S.S. and Cressie, N. (1996). Acomparison of geostatistical methodologies used to estimate snow water equivalent, Water Resources Bull., 32, 267-278.
[7] Chen, J. and Adams, B.J. (2006). Integration of artificial neural networks with conceptual models in rainfall-runoff modeling, J. Hydrol, 318, 232-249.
[8] Elder, K., Dozier, G. and Michaelsen, J. (1991). Snow Accumulation and Distribution in an Alpine Watershed, Water Resources Research, 27(7), 1541-1552.
[9] Elder, K., Michaelsen, J. and Dozzier, J. (1995). Small basin modeling of snow water equivalence using binary regression tree methods, IAHS Publ., No. 228.
[10] Elder, K., Rosenthal, R. and Davis, R.E. (1998). Estimating the spatial distribution of snow water equivalence in a mountain watershed, Hydrological Processes, 12, 1793-1808.
[11] Erickson, T.A., Williams, M.W. and Winstral, A. (2005). Persistence of topographic controls on the spatial distribution of snow in rugged mountain, Colorado, United States, Water Resources Research, 41, 1-17.
[12] Erxleben, J., Elder, K. and Davis, R. (2002). Comparison of spatial interpolation methods for estimating snow stribution in Colorado Rocky Mountains, Hydrological Processes, 16, 3627-3649.
[13] Fathzadeh, A. (2008). Estimating the spatial distribution of snow water equivalent in Karaj watershed using remote sensing and energy balance model, PhD thesis, Tehran University.
[14] Gayoor, H., Kavyani, M., Mohseni, B. (2004). Estimates of coverage and the amount of snowfall in the mountains north of Tehran Case Study: River Basin Rehabilitation (Darband and Glabdarh), Journal of Geographical Research.
[15] Hassani Pak, A. (1998). Geostatistics, Tehran University Publications.
[16] Hosang, J. and Dettwiler, K. (1991). Evalution of a water equivalent of snow cover map in a small catchment area using a geostatistical approach, Hydrological Processes, 5, 283-290.
[17] Huang, M., Peng, G., Zhang, J. and Zhang, S. (2006). Application of artificial neuralnetworks to the prediction of dust storms in Northwest China, Global and Plantetary Change, 52, 216-224.
[18] Marchand, W.D. and Killingtveit, A. (2001). Analyses of the Relation between Spatial Snow Distribution and Terrain Characteristics, 58th Estern Snow Conference Ottawa, Ontario, Canada.
[19] Marchand, W.D. and Killingtveit, A. (2005). Statistical probability distribution of snow depth at the model sub-grid cell spatial scale, Hydrological Processes, 19, 355-369.
[20] Menhaj, M. (2007). Fundamental of Artificial neural networks, Amirkabir Press.
[21] Mohammadi, J. (2001). Considering geostatistics and its application in soil science, Journal of Soil & Water Science, 15(1), 99-121 (In Farsi).
[22] Molotch, N.P., Colee, M.T., Bales, R.C. and Dozier, J. (2005). Estimating the spatial distribution of snow water equivalent in an alpine basin using binary regrnion tree models: the impact of digital elevation data independent variable selection, Hydrological Processes, 19, 1459-1479.
[23] Najafi, M., Sheykhivand, J. and Porhemat, J. (2006). Runoff from melting snow in snowy areas using SRM (Case Study Mahabad), Journal of Agricultural Sciences and Natural Resources (In Farsi).
[24] Roebber, P.J., Bruening, S.L., Schultz, D.M. and Cortinas JR., J.V. (2002). Improving snowfall forecasting by diagnosing snow density, Weather and Forecast, 18, 264-287.
[25] Sharifi, M.R., Akhund Ali, M. and Porhemat, J. (2007). Assess the linear correlation and ordinary kriging method to estimate the spatial distribution of snow depth in the watershed Samsami, Journal of Watershed Management Science & Engineering, 1(1), 24-38 (In Farsi).
[26] Topsoba, D., Fortin, V., Anctil, F. and Hache, M. (2008). Use of the kriging technique with external drift for a map of the water equivalent of snow: application to the Gatineau River Basin, 32(1), 289-297.
[27] Tedesco, M., Pulliainen, J., Takala, M., Hallikainen, M. and Pampaloni, P. (2004). Artificial neural network-based techniques for the retrieval of SWE and snow depth from SSM/I data, Remote Sens. Environ, 90, 76-85.
[28] Tryhorn, L. and DeGaetano, Art (2012). A methodology for statistically downscaling seasonal snow cover characteristics over the Northeastern United States, 10. 1002/joc. 3626.
[29] Vafakhah, M., Mohseni Saravi, M., Mahdavi, M., Alavi Panah, S.k. (2008). Geostatistics application to estimate snow depth and density in the watershed Ourazan, Journal of Watershed Management Science & Engineering, 4(2), 49-55 (In Farsi).
[30] Vaziri, F. (2003). Applied hydrology in Iran-The second book: Identification of glaciers in Iran, Publication Management and Planning Organization.
[31] Zareabyaneh, H. (2012). Estimating the spatial distribution of snow water equivalent and snow density using ANN method (Case study watershed Azarbayejan), Journal of Water Resources Engineering, 5(15), 1-12 (In Farsi).
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 ...
Read More
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.
