Using Bayesian Networks, Ionic Ratios, and Isotopes for Identification of Salinity Origin and Data Requirements in the Gaza Coastal Aquifer
Location
Space Dynamics Laboratory
Event Website
http://water.usu.edu/
Start Date
3-25-2004 11:10 AM
End Date
3-25-2004 11:15 AM
Description
Groundwater is the only source of fresh water in the Gaza Strip. However, it is severely polluted and requires immediate effort to improve its quality and increase its usable quantity. Intensive exploitation of groundwater in the Gaza Strip over the past 40 years has disturbed the natural equilibrium between fresh and saline water, and has resulted in increased salinity in most areas. Salinization in the coastal aquifer may be caused by a single process or a combination of different processes, including seawater intrusion, upconing of brines from the deeper parts of the aquifer, flow of saline water from the adjacent Eocene aquifer, return flow from irrigation water, and leakage of wastewater. Each of these sources is characterized by a distinguishable chemistry and well known isotopic ratios. In this paper Na/Cl, SO4/Cl, Br/Cl, Ca/(HCO3+SO4), and Mg/Ca ionic ratios were used to distinguish different salinization sources. These ionic ratios, along with water isotopic composition such as δ11B and 87Sr/86Sr, have been successfully used in other parts of the coastal aquifer to understand the different salinization processes. The task of monitoring and the associated decision making process are characterized by a high degree of uncertainty with respect to input data and accuracy of models. For this reason, probabilistic expert systems, and more specifically, Bayesian belief networks (BBNs) is used to identify salinization origins. The BBN model incorporates the theoretical background of salinity sources, area-specific monitoring data that are characteristically incomplete in their coverage, expert judgment, and common sense reasoning to produce a geographic distribution for the most probable sources of salinization. The model is also designed to show areas where additional data on chemical and isotopic parameters are needed to understand the contribution of each of these sources to the problem. The model has successfully identified areas where seawater intrusion, deep brines, wastewater leakage, agricultural return flows, and Eocene waters exist with high probability. It has also identified areas where there is missing information or incomplete data especially in the eastern part of the coastal aquifer outside Gaza Strip.
Using Bayesian Networks, Ionic Ratios, and Isotopes for Identification of Salinity Origin and Data Requirements in the Gaza Coastal Aquifer
Space Dynamics Laboratory
Groundwater is the only source of fresh water in the Gaza Strip. However, it is severely polluted and requires immediate effort to improve its quality and increase its usable quantity. Intensive exploitation of groundwater in the Gaza Strip over the past 40 years has disturbed the natural equilibrium between fresh and saline water, and has resulted in increased salinity in most areas. Salinization in the coastal aquifer may be caused by a single process or a combination of different processes, including seawater intrusion, upconing of brines from the deeper parts of the aquifer, flow of saline water from the adjacent Eocene aquifer, return flow from irrigation water, and leakage of wastewater. Each of these sources is characterized by a distinguishable chemistry and well known isotopic ratios. In this paper Na/Cl, SO4/Cl, Br/Cl, Ca/(HCO3+SO4), and Mg/Ca ionic ratios were used to distinguish different salinization sources. These ionic ratios, along with water isotopic composition such as δ11B and 87Sr/86Sr, have been successfully used in other parts of the coastal aquifer to understand the different salinization processes. The task of monitoring and the associated decision making process are characterized by a high degree of uncertainty with respect to input data and accuracy of models. For this reason, probabilistic expert systems, and more specifically, Bayesian belief networks (BBNs) is used to identify salinization origins. The BBN model incorporates the theoretical background of salinity sources, area-specific monitoring data that are characteristically incomplete in their coverage, expert judgment, and common sense reasoning to produce a geographic distribution for the most probable sources of salinization. The model is also designed to show areas where additional data on chemical and isotopic parameters are needed to understand the contribution of each of these sources to the problem. The model has successfully identified areas where seawater intrusion, deep brines, wastewater leakage, agricultural return flows, and Eocene waters exist with high probability. It has also identified areas where there is missing information or incomplete data especially in the eastern part of the coastal aquifer outside Gaza Strip.
https://digitalcommons.usu.edu/runoff/2004/AllPosters/12