The role of retrospective weather forecasts in developing daily forecasts of nutrient loadings over the southeast US
- 1Department of Geological Sciences, University of North Carolina, Chapel Hill, NC 27599-3315, USA
- 2Department of Civil, Construction and Environmental Engineering, North Carolina State University, Raleigh, NC 27695-5908, USA
Abstract. It is well known in the hydrometeorology literature that developing real-time daily streamflow forecasts in a given season significantly depends on the skill of daily precipitation forecasts over the watershed. 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 daily nutrient loadings and the associated concentration could be predicted using daily precipitation forecasts and previously observed streamflow as surrogates of antecedent land surface conditions. By selecting 18 relatively undeveloped basins in the southeast US (SEUS), we evaluate the skill in predicting observed total nitrogen (TN) loadings in the Water Quality Network (WQN) by first developing the daily streamflow forecasts using the retrospective weather forecasts based on K-nearest neighbor (K-NN) resampling approach and then forcing the forecasted streamflow with a nutrient load estimation (LOADEST) model to obtain daily TN forecasts. Skill in developing forecasts of streamflow, TN loadings and the associated concentration were computed using rank correlation and RMSE (root mean square error), by comparing the respective forecast values with the WQN observations for the selected 18 Hydro-Climatic Data Network (HCDN) stations. The forecasted daily streamflow and TN loadings and their concentration have statistically significant skill in predicting the respective daily observations in the WQN database at all 18 stations over the SEUS. Only two stations showed statistically insignificant relationships in predicting the observed nitrogen concentration. We also found that the skill in predicting the observed TN loadings increases with the increase in drainage area, which indicates that the large-scale precipitation reforecasts correlate better with precipitation and streamflow over large watersheds. To overcome the limited samplings of TN in the WQN data, we extended the analyses by developing retrospective daily streamflow forecasts over the period 1979–2012 using reforecasts based on the K-NN resampling approach. Based on the coefficient of determination (R2Q-daily) of the daily streamflow forecasts, we computed the potential skill (R2TN-daily) in developing daily nutrient forecasts based on the R2 of the LOADEST model for each station. The analyses showed that the forecasting skills of TN loadings are relatively better in the winter and spring months, while skills are inferior during summer months. Despite these limitations, there is potential in utilizing the daily streamflow forecasts derived from real-time weather forecasts for developing daily nutrient forecasts, which could be employed for various adaptive nutrient management strategies for ensuring better water quality.