Interannual hydroclimatic variability and its influence on winter nutrient loadings over the Southeast United States
- Department of Civil, Construction and Environmental Engineering, North Carolina State University, Raleigh, NC 27695-5908, USA
Abstract. It is well established in the hydroclimatic literature that the interannual variability in seasonal streamflow could be partially explained using climatic precursors such as tropical sea surface temperature (SST) conditions. Similarly, it is widely known that streamflow is the most important predictor in estimating nutrient loadings and the associated concentration. The intent of this study is to bridge these two findings so that nutrient loadings could be predicted using season-ahead climate forecasts forced with forecasted SSTs. By selecting 18 relatively undeveloped basins in the Southeast US (SEUS), we relate winter (January-February-March, JFM) precipitation forecasts that influence the JFM streamflow over the basin to develop winter forecasts of nutrient loadings. For this purpose, we consider two different types of low-dimensional statistical models to predict 3-month ahead nutrient loadings based on retrospective climate forecasts. Split sample validation of the predictive models shows that 18–45% of interannual variability in observed winter nutrient loadings could be predicted even before the beginning of the season for at least 8 stations. Stations that have very high coefficient of determination (> 0.8) in predicting the observed water quality network (WQN) loadings during JFM exhibit significant skill in predicting seasonal total nitrogen (TN) loadings using climate forecasts. Incorporating antecedent flow conditions (December flow) as an additional predictor did not increase the explained variance in these stations, but substantially reduced the root-mean-square error (RMSE) in the predicted loadings. Relating the dominant mode of winter nutrient loadings over 18 stations clearly illustrates the association with El Niño Southern Oscillation (ENSO) conditions. Potential utility of these season-ahead nutrient predictions in developing proactive and adaptive nutrient management strategies is also discussed.