Geographically weighted spatial modelling of sediment quality in Lake Okeechobee, Florida

Abstract

Lake Okeechobee is a large shallow lake with over 44% (as of 2006) being underlain with phosphorus-enriched mud sediments. Wind-induced sediment re-suspension may play a significant role in the nutrient cycling of this lake. It is critical to develop optimal models with low uncertainty to map the distribution and changes of total phosphorus (TP) over time. Geographically Weighted Regression (GWR) is a powerful spatial regression method that examines the details of relationship between the target variable and independent variables and their changes over space. The calibrated GWR models were applied to data sets of organic mud sediment from Lake Okeechobee collected in 1988, 1998 and 2006. Three sets of ancillary data were also used in the models: total iron (Fe), which was strongly and positively correlated to TP (0.69–0.72); mud thickness, which was moderately and positively correlated to TP (0.57–0.72); and site elevation, which was weakly but negatively correlated to TP (−0.46- −0.55). The GWR models were compared with Ordinary Kriging (OK), Ordinary Least-squares Regression (OLS) and Regression-Kriging (RK) to determine their accuracy. GWR models use both spatial auto-correlation and correlation between TP and independent variables to improve the model performance. The GWR models were superior to OK and RK models previously applied to these data sets. The GWR (total Fe) and GWR (mud thickness and site elevation) models were the most accurate based on lower root mean square errors (RMSE). GWR (Fe) and GWR (Thickness and Elevation) models were used to map TP concentrations and TP mass. Using TP concentrations and mud volumes, TP mass in the lake sediments was estimated for each sample set. The TP mass increased about 38–44% from 1988 to 1998 and decreased about 30–34% from 1998 to 2006. TP mass was similar in 1988 and 2006.