Multiple regression models for hindcasting and forecasting midsummer hypoxia in the Gulf of Mexico

A new suite of multiple regression models was developed that describes relationships between the area of bottom water hypoxia along the northern Gulf of Mexico and Mississippi-Atchafalaya River nitrate concentration, total phosphorus (TP) concentration, and discharge. Model input variables were derived from two load estimation methods, the adjusted maximum likelihood estimation (AMLE) and the composite (COMP) method, developed by the U. S. Geological Survey. Variability in midsummer hypoxic area was described by models that incorporated May discharge, May nitrate, and February TP concentrations or their spring (discharge and nitrate) and winter (TP) averages. The regression models predicted the observed hypoxic area within +/-30%, yet model residuals showed an increasing trend with time. An additional model variable, Epoch, which allowed post-1993 observations to have a different intercept than earlier observations, suggested that hypoxic area has been 6450 km(2) greater per unit discharge and nutrients since 1993. Model forecasts predicted that a dual 45% reduction in nitrate and TP concentration would likely reduce hypoxic area to approximately 5000 km(2), the coastal goal established by the Mississippi River/Gulf of Mexico Watershed Nutrient Task Force. However, the COMP load estimation method, which is more accurate than the AMLE method, resulted in a smaller predicted hypoxia response to any given nutrient reduction than models based on the AMLE method. Monte Carlo simulations predicted that five years after an instantaneous 50% nitrate reduction or dual 45% nitrate and TP reduction it would be possible to resolve a significant reduction in hypoxic area. However, if nutrient reduction targets were achieved gradually (e. g., over 10 years), much more than a decade would be required before a significant downward trend in both nutrient concentrations and hypoxic area could be resolved against the large background of interannual variability. The multiple regression models and statistical approaches applied provide improved capabilities for evaluating dual nutrient management strategies to address Gulf hypoxia and a clearer perspective on the strengths and limitations of approaching the problem using regression models.