An automated, web-based groundwater mapping and visualization system
Location
Room 303/305
Event Website
http://water.usu.edu/
Start Date
4-10-2013 1:30 PM
End Date
4-10-2013 1:50 PM
Description
In response to the record one-year drought in 2011-2012, a Texas Drought Technology Steering Committee (TDTSC) consisting of academics and water managers was formed to develop new tools and strategies to assist the state in monitoring, predicting, and responding to drought events. Conjoining with this effort, as part of the CI-WATER initiative to advance cyber infrastructure in water resources we developed a cloud-based water level mapping system for generating and visualizing changes in groundwater levels over time. To accurately assess impacts and trends in groundwater levels requires the development of detailed water level maps at various scales. Creating such dynamic maps can be challenging due to the massive amounts of data and processing required. Furthermore, wells are not typically sampled at the same points in time, and so developing a spatial water table map for a particular date requires both temporal and spatial interpolation of the observed water elevations. The process for performing this dual interpolation was automated in a geoprocessing script and placed on a server to form a cloud-based water level mapping system. The user-interface, accessible via the web, uses a Google Earth plug-in to visualize the results. The system is based on the Texas Water Development Board (TWDB) groundwater database, but can be adapted to use other regional databases. As part of this system we created a temporal interpolation geoprocessing tool to estimate the piezometric heads for all wells in a given region at a specific date using a regression analysis. This interpolation tool is coupled with other geoprocessing tools to filter data and interpolate point elevations spatially to produce water level, and depth to groundwater maps. The web interface allows users to generate these maps at locations and times of interest. A sequence of maps can be animated to visualize how water levels are changing in time. Short-term predictions of future water levels can be made using the same time series regression analysis.
An automated, web-based groundwater mapping and visualization system
Room 303/305
In response to the record one-year drought in 2011-2012, a Texas Drought Technology Steering Committee (TDTSC) consisting of academics and water managers was formed to develop new tools and strategies to assist the state in monitoring, predicting, and responding to drought events. Conjoining with this effort, as part of the CI-WATER initiative to advance cyber infrastructure in water resources we developed a cloud-based water level mapping system for generating and visualizing changes in groundwater levels over time. To accurately assess impacts and trends in groundwater levels requires the development of detailed water level maps at various scales. Creating such dynamic maps can be challenging due to the massive amounts of data and processing required. Furthermore, wells are not typically sampled at the same points in time, and so developing a spatial water table map for a particular date requires both temporal and spatial interpolation of the observed water elevations. The process for performing this dual interpolation was automated in a geoprocessing script and placed on a server to form a cloud-based water level mapping system. The user-interface, accessible via the web, uses a Google Earth plug-in to visualize the results. The system is based on the Texas Water Development Board (TWDB) groundwater database, but can be adapted to use other regional databases. As part of this system we created a temporal interpolation geoprocessing tool to estimate the piezometric heads for all wells in a given region at a specific date using a regression analysis. This interpolation tool is coupled with other geoprocessing tools to filter data and interpolate point elevations spatially to produce water level, and depth to groundwater maps. The web interface allows users to generate these maps at locations and times of interest. A sequence of maps can be animated to visualize how water levels are changing in time. Short-term predictions of future water levels can be made using the same time series regression analysis.
https://digitalcommons.usu.edu/runoff/2013/AllAbstracts/30