Sources of Errors in Developing Monthly to Seasonal Nutrient Forecasts

Monday, 15 December 2014
Dominic Libera and Sankarasubramanian Arumugam, North Carolina State University at Raleigh, Raleigh, NC, United States
Excess nitrogen in a river system can cause an overabundance of aquatic plant growth that can cause negative effects on larger water bodies downstream. This can result in eutrophication resulting in large algae blooms that hurt local recreation and fish populations. Recent studies have focused on developing seasonal nutrient forecasts that can be used to control nonpoint reduction strategies. Given that the seasonal nutrients are developed using large-scale climate forecasts, it needs to be pre-processed for ingesting into a water quality model. By considering the LOADEST model, a USGS constituent load estimator, and the Soil &Water Assessment Tool (SWAT) this study quantifies the sources of errors in developing monthly to seasonal nutrient forecasts using climate information. For this purpose, we consider the observed streamflow and nutrient loadings at the Tar River at Tarboro, NC station for developing and testing the water quality models. This streamgage was chosen since it is part of the Hydro-Climatic Data Network (HCDN) which naturally considers basins that are relatively undeveloped with limited storage and pumping. The study also proposes two bias-correction procedures, a bivariate copula-based model and a canonical correlation model, for preserving the cross-correlation structure between the observed nutrients and streamflows. Climate forecasts from the ECHAM4.5 model and NOAA NCEP Climate Forecast System (CFS) will be downscaled and disaggregated for developing nutrient forecasts from the SWAT model and the LOADEST model. Using both the canonical correlation model and the bi-variate copula based bias-correction procedures, the forecasted streamflow and TN loadings will be bias-corrected to preserve the correlation structure. The study will also quantify and compare different sources of errors that propagate in developing monthly to seasonal nutrient forecasts using climate information.