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|Title||Assessment of Spatial Representation of Groundwater Monitoring and Meteorological Data in Gaza Strip|
The Groundwater aquifer in Gaza Strip (semi-arid region) is considered to be the sole source of water exploitation that is extensively deteriorated and may take long time to restore its fresh water conditions. For protecting the groundwater quality and quantity, understanding the data on spatial and temporal distribution of water quality, groundwater level, and rainfall (the only source of natural recharge) are very important. Geostatistics methods are one of the most advanced techniques for interpolation of groundwater related parameters. Accordingly, perceptive spatial variation in groundwater and climate representation is a key decision to many water resource management specialists. However, the most common sources of groundwater and climatic data in Gaza Strip are the domestic water wells including some of the monitoring wells and the meteorological/rainfall stations respectively. This study examines some of the statistical approaches for interpolating both groundwater parameters represented by chloride concentration "and water level and climatic data represented by rainfall rates over Gaza Strip. It also provides a brief introduction to the applicable interpolation techniques for groundwater & climate variables for use in Water resources studies and in addition, draws recommendations for future research to assess interpolation techniques. Basically, one of the problems which often arise in any hydrogeological studies is to estimate data at a given site because either the data are missing or the site is un-gauged or not accessible as Gaza Strip case. Such estimates can be made by spatial interpolation of data available at other sites. A number of spatial interpolation techniques are available today with varying degrees of complexity. In this study, different interpolation methods (IDW, Kriging, and Spline) were applied for predicting the spatial distribution of water quality and rainfall data generated for more than 170 domestic and monitoring water wells as well as 12 rainfall stations. Statistical investigations through normalization of data, modeling of semivariogram, examining powers, tuning smoothing factors were conducted. RMSE and/or R2 were used to select the best fitted model for each interpolation method, and then cross-validation of the best fitted models was using two independent sets of data (modeling data and calibration data). The best method for interpolation was selected based on the lowest RMSE and the highest R2. Results showed that for interpolation of groundwater quality and water level, Kriging method is superior to IDW & Spline methods. In addition to that, the study recommended using Kriging method for the interpolation of the annual rainfall spatial variability unlike what is practiced locally. Finally, using the best fitted interpolation methods and GIS tools, prediction maps of groundwater parameters and rainfall data were prepared.
|Publisher||the islamic university|
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