Abstract
In recent years, many countries have faced great challenges due to their limited water resources. According to these challenges, they have undertaken large scale projects to reuse agricultural drainage water in irrigation purpose. The Governments in these countries can enhance water management and sustainable development by adopting policies that enable them to meet water demands and supply management. Therefore, 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. The implementation of Geographic Information System (GIS) in this field offers an ideal tool for measurements with limited number of sampled points. Statistical analysis that can be provided within GIS is rapidly becoming an impressive tool for statistical analysis of continuous data. The main objective of this chapter is to discuss using 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 Map™ 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.
TopIntroduction
Water is one of the most important inputs of economic development. Water management has been identified as one of the elements of sustainable development. Size, type, and location of the economic activities depend on the nature, quantity, quality, and location of the available water resources. In arid and semi arid regions limited water supply constraints economic activity. National governments can enhance economic growth and development by adopting policies that enable water demands management in addition to water supply management to overcome water scarcity.
Hydroinformatics is a branch of Informatics which concentrates on the application of information and communications technologies (ICTs) in addressing the increasingly serious problems of the equitable and efficient use of water for many different purposes. Growing out of the earlier discipline of computational hydraulics, the numerical simulation of water flows and related processes remains a mainstay of hydroinformatics, which encourages a focus not only on the technology but on its application in a social context. On the technical side, in addition to computational hydraulics, hydroinformatics has a strong interest in the use of techniques originating in the so-called artificial intelligence community, such as artificial neural networks or recently support vector machines and genetic programming. These might be used with large collections of observed data for the purpose of data mining for knowledge discovery, or with data generated from an existing, physically based model in order to generate a computationally efficient emulator of that model for some purpose.
Hydroinformatics recognises the inherently social nature of the problems of water management and of decision making processes, and strives to understand the social processes by which technologies are brought into use. Since the problems of water management are most severe in the majority world, while the resources to obtain and develop technological solutions are concentrated in the hands of the minority, the need to examine these social processes are particularly acute.
Hydroinformatics draws on and integrates hydraulics, hydrology, environmental engineering and many other disciplines. It sees application at all points in the water cycle from atmosphere to ocean and in artificial interventions in that cycle such as urban drainage and water supply systems. It provides support for decision making at all levels from governance and policy through management to operations.
Arid and semi arid regions are facing great challenges due to their limited water resources compared to the expanded water demands. The demand for water in agriculture, industry, and municipal purposes has been increasing due to population growth and increase of income. The agricultural sector is the largest consumer of water. The increasing demands for food production require more attention to be given for reclamation and cultivation of more agricultural areas. Consequently, the irrigation water consumption increased. This increase was made available by increasing in drainage water reuse and groundwater abstraction. Therefore, governments have undertaken large scale projects to reuse agricultural drainage water in irrigation. The agricultural drainage water is one of the most feasible resources of reuse because it is relatively of good quality and has nearly no environmental impacts in comparison to other resources such as sewage water.
Taking into consideration the environmental aspects of drainage water reuse, diffuse pollution of water resources from agricultural sources (agrochemicals) is a major environmental issue. Agrochemicals are usually introduced to increase the crop productivity or to resist herbs and insects. Highly productive lands receive large applications of fertilizers, pesticides, and organic amendments. Part of these additions could be leached by irrigation water into the drainage water as water flows over land surface and passes through the plant root zone. Therefore, they are regarded as potential source of environmental pollution. In addition to salts, substances such as nitrates, phosphorus, potassium, and pesticides are the most common pollutants leached into the agricultural drainage water (Abdel Dayem and Abdel Ghany, 1992). Application of fertilizers increased with deterioration of the quality of soils regarding nutrients percentage. This became essential for maintaining soil fertility and increasing crop production. High percentage of the applied fertilizers is lost due to poor management which leads to nutrients leaching in the form of ammonia, nitrate or phosphorus through different crop rotations (Korany, 1997).
Key Terms in this Chapter
Probability Map: A surface that gives the probability that the variable of interest is above (or below) some threshold value that the user specifies.
Cross Covariance: The statistical tendency of variables of different types, attributes, names, and so on, to vary in ways that are related to each other. Positive cross covariance occurs when both variables tend to be above their respective means together, and negative cross covariance occurs if one variable tends to be above its mean when the other variable is below its mean. Compare to covariance.
Partial Sill: A parameter of a covariance or semi-variogram model that represents the variance of a spatially auto correlated process without any nugget effect. In the semivariogram model, the partial sill is the difference between the nugget and the sill.
Quantile: The p-th quantile, where p is between 0 and 1, is the value that has a proportion p of the data below the value. For theoretical distributions, the p-th quantile is the value that has p probability below the value.
Nugget: A parameter of a covariance or semi-variogram model that represents independent error, measurement error and/or micro scale variation at spatial scales that is too fine to detect. The nugget effect is seen as a discontinuity at the origin of either the covariance or semi-variogram model.
Kriging: One of the deterministic interpolation methods used in the Geostatistical Analyst. The interpolated surface is not forced to go through the data, and the method does not have standard errors associated with it. It is a statistical interpolation method that uses data from a single data type (single attribute) to predict (interpolate) values of the same data type at un-sampled locations. Kriging also provides standard errors of the predictions.
Variogram: One of the deterministic interpolation methods used in the Geostatistical Analyst; a special case of radial basis functions. The interpolated surface is forced to go through the data, and no standard errors are available. A function of the distance and direction separating two locations, used to quantify autocorrelation. The variogram is defined as the variance of the difference between two variables at two locations. The variogram generally increases with distance, and is described by nugget, sill, and range parameters.
Anisotropy: A property of a spatial process or data where spatial dependence (autocorrelation) changes with both the distance and the direction between two locations.
Normally Distributed: It is a property of a spatial process where all of the spatial random variables have the same mean value.
Covariance: The statistical tendency of two variables of the same type, attribute, name,and so on, to vary in ways that are related to each other. Positive covariance occurs when both variables tend to be above their respective means together, and negative covariance occurs if one variable tends to be above its mean when the other variable is below its mean.
Sill: A parameter of a variogram or semi-variogram model that represents a value that the semi-variogram tends to when distances get very large. At large distances, variables become uncorrelated, so the sill of the semi-variogram is equal to the variance of the random variable. Some theoretical semi-variogram does not have a sill. All semivariogram models used in the Geostatistical Analyst have a sill.