CALCULATING ERRORS OF INTERPOLATION METHODS FOR BATHYMETRIC SURVEYS

Water storage in reservoirs is the common practice for handling water supply and demand. Producing current high-resolution sedimentation studies will help in managing reservoirs. A common approach to understanding sedimentation is to use bathymetric maps conducted over periods of time. However, bathymetric mapping is created using point-data, and various methods can be used to interpolate those data over space. We explored the error associated with different methods of interpolation. We conducted bathymetric surveys in two reservoirs (Lake Bloomington and Evergreen Lake) in Central Illinois in 2014. Water from these reservoirs is used as the municipal water source for over 80,000 residents of the City of Bloomington and surrounding area. We used a HydroLite-TM set up to collect 2014 sounding and GPS coordinate data. The survey data were stored using TerraSync software installed on a Trimble GeoXT and exported as shapefiles. The point shapefiles were interpolated in a geographic information system (GIS) to evaluate and compare root mean square (RMS) error values for different interpolation methods, such as inverse distance weighting, kriging, natural neighbor, and spline. Preliminary investigations suggested that for the 1999 data, spline with tension was the interpolation method that produced the lowest RMS error. We expect a different interpolation method to give us the lowest RMS error for the 2014 data because the much higher point density. The interpolated lakebed elevations will be compared to data collected in 1999 to calculate sedimentation accumulation.