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Abstract
Egypt is one of the countries facing great challenges due to the exceeding demand of water against limited water resources. However, the increasing demands for food production require more attention for reclamation and cultivation of more agricultural areas and consequently increase of the irrigation water consumption. One of the sources to cover this increase is to expand the reuse of drainage water in irrigation. Therefore, the Egyptian government has undertaken large scale projects to reuse drainage water.
The Government can enhance water management and sustainable development to overcome water scarcity by adopting policies that enable water demand, as well as water supply management. However, there is a need for unconventional methods to provide better tools for the assessment and management of water quality problems to adopt management policies and set the limits for sustainable drainage water reuse. Statistical analysis that can be provided within Geographic Information System (GIS) is rapidly becoming an impressive tool for statistical analysis of continuous data. The implementation of GIS in this field offers an ideal tool for measurements with limited number of sampled points. The main objective of this study is to use GIS to in-follow the pollution caused by fertilizers migration to the water and the soil by applying statistical analysis within the GIS using geostatistical analyst. Geostatistical analyst is an extension of Arc MapTM that bridges the gap between geostatistics and GIS and provides a powerful collection of tools for the management and visualization of spatial data by applying Spatial Statistics.
The study was divided into two main parts. The first part is the experimental field work that was formulated to obtain measured data necessary to assess the pollution caused by migration of fertilizers to the water and soil. The second part is the statistical analysis of the data within the GIS using geostatistical analyst.
The field work was carried out in a pilot study area known as Mashtul Pilot Area (MPA). This was controlled under the current farming conditions aiming to apply the data measurement program for two years from June 2005 to May 2007. The measurement program includes collecting water and soil samples. The samples were analyzed in the laboratory to determine the nutrients including the three parameters of nitrate (NO3), ammonia (NH4), and total phosphate (PO4) for soil and water samples to follow the pollution migration. The geostatistical analyst was used to analyze the measured field data that was available in a limited number of sampled points, to plot the maps of pollution distribution and to assess and manage the water quality problems.
The geostatistical analyst was also validated as a new tool for spatial data exploration, evaluation of error in prediction surface models, statistical estimation, and optimal surface The validation process was conducted by removing part of the data and the remaining data was used to predict the removed part of the data. This strategy illustrates the efficiency of carrying out this type of study with limited number of observed samples in addition to help to validate the model.
The validation process was conducted by using the main sets of the observed data. The comparison is mainly dependant on the percentage of error (%Error) and the Mean Absolute Error (MAE) to asses the best and the worst cases. It was found that the mean values for measured and predicted data for all elements are close to each other. The MAE for all cases is small and the %Error is less than 18% for all elements.
So, it is undoubted that the application of geostatistical analyst as an extension of the GIS for spatial data exploration and maps creation is a very important practical value. It has also considerable economic impact as a result of its effectiveness in reducing the number of observed samples and applying geostatistical interpolation techniques. It could also be used for more advanced surface modeling prediction,