Spatial-Temporal Geostatistical Prediction of Water Quality Indicators in the Chesapeake Bay

Authors: Michael R Desjardins*, Johns Hopkins Bloomberg School of Public Health, Frank C Curriero, Johns Hopkins Bloomberg School of Public Health
Topics: Quantitative Methods, Geographic Information Science and Systems, Water Resources and Hydrology
Keywords: Geostatistics, Kriging, Space-time, Vibrio, Water Quality
Session Type: Paper
Presentation File: No File Uploaded


The Chesapeake Bay is one of the most widely studied bodies of water in the United States and around the world. Routine monitoring of water quality indicators (e.g. temperature and salinity) relies on fixed sampling stations throughout the Bay. Utilizing this rich monitoring data, various methods develop continuous surfaces of water quality indicators to further characterize the health of the Bay as well as to support wildlife and human health research studies. Statistical methods have successfully been applied in this arena mostly focused on development of spatial prediction approaches for generating continuous water quality surfaces. However, most studies often just focus on spatial prediction, not fulling utilizing the rich and temporally connected aspect of the water quality monitoring data. Considering both the space and time dimensions together allows models to be more flexible to account for longer term temporal trends as well as shorter term space-time interactions for improved predictions. For this study, we utilized a space-time geostatistical approach to produce continuous prediction surfaces of water temperature, salinity, dissolved oxygen, turbidity, and phosphorous in the Chesapeake Bay between 2007 and 2010. Our data includes 148 monitoring sites with monthly observations of the water quality indicators. Our space-time kriging models considered a combination of both large-scale space and time trends as well as small scale space-time dependence. Cross-validation was conducted to evaluate model performance based on prediction accuracy and precision. The prediction surfaces will be utilized as predictor variables in subsequent forecasting models of Vibrio parahaemolyticus in the Chesapeake Bay.

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