GEOSPATIAL FRAMEWORK FOR CATALOGING AND ANALYZING FOR GROUNDWATER WITHDRAW INFORMATION

Understanding the spatial variability of groundwater withdrawals across a region is vital information for water managers and decision makers at local and regional scales. However, available groundwater demand data are only available in the State of Louisiana at the Parish scale. In this investigation, we attempt to overcome this obstacle by using Geographic Information System (GIS) tools to disaggregate Parish-level groundwater demand data into smaller enumeration units.

We used water well location data from the Louisiana Department of Natural Resources well registration database to pinpoint withdrawal locations. Water use data for groundwater on the Parish scale is available from the US Geological Survey and LA Department of Transportation and Development Water Use Reports. Withdrawal data at the Parish scale was disaggregated to individual well locations and weighted using the casing diameter of each well. We assumed that wells with larger casings consumed more water. Estimates of the withdrawal rate for each well are calculated by multiplying the withdrawal rates within each Parish by the casing diameter weight of a respective well. This was done for each water use sector, including agriculture (irrigation and aquaculture), industry, livestock, power generation, public supply and domestic rural use, and for each year of available data (1960-2010).

The results from data visualization exercises show that the largest consumers, and largest clusters of users, of groundwater are easy to identify when the water use data are spatially distributed. Calcasieu Parish, for example, in 2010 used 86.99 million gallons per day (MGD) of groundwater. Approximately 84.5% of this water withdrawal within the Parish was used by only 12.2% of the total wells. Furthermore, overlay analyses demonstrate spatial correlations with ancillary land cover, land use, and depth to groundwater datasets. By identifying effective methodologies to downscale water use data into smaller spatial units, we can better visualize those areas that need our attention, manage large spatially relevant datasets, and provide meaningful spatially explicit analyses to support water management decisions.