Modification of exploration of long‐term nutrient trajectories for nitrogen (ELEMeNT-N) model to quantify legacy nitrogen dynamics in a typical watershed of eastern China
Jia Zhou, Yinghuai Wei, Kaibin Wu, Hao Wu, Xinyi Jiao, Minpeng Hu, Dingjiang Chen
Abstract
Abstract Legacy nitrogen (N) is recognized as a primary cause for the apparent failure of watershed N management strategies to achieve desired water quality goals. The ELEMeNT-N (exploration of long‐term nutrient trajectories for nitrogen) model, a parsimonious and process-based model, has the potential to effectively distinguish biogeochemical and hydrological legacy effects. However, ELEMeNT-N is limited in its ability to address long-term legacy N dynamics as it ignores temporal changes in soil organic N (SON) mineralization rates. This work represents the first use and modification of ELEMeNT-N to quantify legacy effects and capture spatial heterogeneity of legacy N accumulation in China. An exponential function based on mean annual temperature was employed to estimate yearly changes in SON mineralization rate. Based on a 31 year water quality record (1980–2010), the modified model achieved higher efficiency metrics for riverine N flux in the Yongan watershed in eastern China than the original model (Nash–Sutcliff coefficient: 0.87 vs. 0.72 and R 2 : 0.80 vs. 0.71). The modified ELEMeNT-N results suggested that the riverine N flux mainly originated from the legacy N pool (88.2%). The mean overall N lag time was 11.9 years (95% confidence intervals (CIs): 8.3–21.3), of which biogeochemical lag time was 9.7 years (6.3–18.4) and hydrological lag time was 2.2 years (2.0–3.0). Legacy N accumulation showed considerable spatial heterogeneity, with 219–239 kg N ha −1 accumulated in soil and 143–188 kg N ha −1 accumulated in groundwater. The ELEMeNT-N model was an effective tool for addressing legacy N dynamics, and the modified form proposed here enhanced its ability to capture SON mineralization dynamics, thereby providing managers with critical information to optimize watershed N pollution control strategies.