You are here:
A Method to Identify Estuarine Water Quality Exceedances Associated with Ocean Conditions
Citation:
Brown, C. AND Walt Nelson. A Method to Identify Estuarine Water Quality Exceedances Associated with Ocean Conditions. ENVIRONMENTAL MONITORING AND ASSESSMENT. Springer, New York, NY, 187(133):14, (2015).
Impact/Purpose:
WED scientists have developed a method to help distinguish whether failures to meet water quality criteria are associated with natural coastal upwelling by using the statistical approach of logistic regression. Estuaries along the west coast of the United States periodically have high nutrient, high chlorophyll a, and low dissolved oxygen levels due to the intrusion of oceanic water into the estuaries. These chemical and biological conditions are generated by the physical process of upwelling which occurs near the coast. As a result, this oceanic water during upwelling periods often has water quality conditions which fail to meet water quality standards. In order to be able to correctly identify water bodies experiencing impairments due to human actions, these natural events need to be distinguished. Temperature and salinity characteristics of the water sampled in the estuary during flood tide can be used to help identify the likelihood that upwelling sources have contributed to the values of water quality parameters that fail to meet water quality criteria.
Description:
Wind driven coastal upwelling along the Pacific Northwest Coast of the US results in oceanic water that may be periodically entrained into adjacent estuaries and which possess high nutrients and low dissolved oxygen (DO). Measurement of water quality indicators during these upwelling water entrainment events would represent extreme values for water quality thresholds derived from typical estuarine conditions. Tools are therefore needed to distinguish upwelled waters from other causes of exceedances of water quality thresholds within estuaries of the region. We present an example application of logistic regression models to predict the probability of exceedance of a water quality threshold, using DO data from the Yaquina estuary, Oregon, USA. Models including water temperature and salinity correctly classified exceedances of DO about 80% of the time. Inclusion of in situ fluorescence in the logistic regression model for DO improved the model performance and reduced the rate of false positives.
