Groundwater Monitoring Network Design under uncertainty in Climate and Aquifer properties Case Study: Eocene Aquifer, Palestine
Location
Eccles Conference Center
Event Website
http://water.usu.edu/
Start Date
4-20-2010 1:40 PM
End Date
4-20-2010 2:00 PM
Description
Groundwater flow is influenced by both the distribution of recharge in time and space and the hydraulic properties of the aquifer materials. While the variability in recharge comes from the climatic variability, variations in geologic materials and processes result in highly spatially variable hydraulic properties. This variability adds to the uncertainty in determining the variables needed to solve groundwater flow equations. Climate change is another factor that adds to the uncertainty of the future behavior of aquifer systems, and as a result of climate change, Palestine is among the regions in which drier climates have been observed and are expected to increase. This study investigates the impacts of climate change on the Eocene Aquifer, Palestine by utilizing different tools including global climate modeling, groundwater flow modeling, fate and transport modeling, and statistical learning machines. The first step is to predict the future temperature and precipitation based on the different climate change scenarios. Then, these temperature and precipitation values will be used as inputs to the groundwater flow model along with other inputs including soil type, topography, hydrogeology, and land use. After that, the fate and transport of pollutants will be simulated using groundwater flow models and pollutant loading data. Finally, all these models will provide the necessary information for monitoring network design using state of the art, statistical learning machines. The expected outcomes from the study are: a spatial distribution of groundwater flow and nitrate concentration in the study area, an optimal monitoring network design that maximizes probability of detection while minimizing cost (i.e., number of monitoring wells and sampling frequency), and a methodology that incorporates different aspects of monitoring, including environmental health risk and the value-of-information (expanding an existing network, value of adding new points and redundancy reduction in an existing monitoring network).
Groundwater Monitoring Network Design under uncertainty in Climate and Aquifer properties Case Study: Eocene Aquifer, Palestine
Eccles Conference Center
Groundwater flow is influenced by both the distribution of recharge in time and space and the hydraulic properties of the aquifer materials. While the variability in recharge comes from the climatic variability, variations in geologic materials and processes result in highly spatially variable hydraulic properties. This variability adds to the uncertainty in determining the variables needed to solve groundwater flow equations. Climate change is another factor that adds to the uncertainty of the future behavior of aquifer systems, and as a result of climate change, Palestine is among the regions in which drier climates have been observed and are expected to increase. This study investigates the impacts of climate change on the Eocene Aquifer, Palestine by utilizing different tools including global climate modeling, groundwater flow modeling, fate and transport modeling, and statistical learning machines. The first step is to predict the future temperature and precipitation based on the different climate change scenarios. Then, these temperature and precipitation values will be used as inputs to the groundwater flow model along with other inputs including soil type, topography, hydrogeology, and land use. After that, the fate and transport of pollutants will be simulated using groundwater flow models and pollutant loading data. Finally, all these models will provide the necessary information for monitoring network design using state of the art, statistical learning machines. The expected outcomes from the study are: a spatial distribution of groundwater flow and nitrate concentration in the study area, an optimal monitoring network design that maximizes probability of detection while minimizing cost (i.e., number of monitoring wells and sampling frequency), and a methodology that incorporates different aspects of monitoring, including environmental health risk and the value-of-information (expanding an existing network, value of adding new points and redundancy reduction in an existing monitoring network).
https://digitalcommons.usu.edu/runoff/2010/AllAbstracts/15