Modelling of Seasonal Variation in Nitrogen Retention and In-Lake Concentration: A Four-Year Mass Balance Study in 16 Shallow Danish Lakes

The mass balance for total nitrogen (N) was studied over a four-year period in 16 shallow mainly eutrophic 1st order Danish lakes. Water was sampled in the main inlet of each lake 18-26 times annually, and from the outlets and the lake 19 times annually. Water was also sampled from minor inlets, although less frequently. N input and output were calculated using daily data on discharge (Q), the latter being obtained either from the Q/H relationship based on automatic recordings of water level (H) for the main in- and outlet, or by means of Q/Q relationships for the minor inlets. Annual mean N retention in the lakes ranged from 47 to 234 mg N m-2d-1, and was particularly high in lakes with high N loading. Annual percentage retention (Nret-y%) ranged from 11 to 72%. Non-linear regression analysis revealed that hydraulic retention time and mean depth accounted for 75% of the variation in annual mean Nret-y% and, in combination with inlet N concentration, accounted for 84% of the variation in the in-lake N concentration. Nret% varied according to season, being higher in the second and third quarter than in the first and fourth quarter (median 18-19%). A simple model was developed for predicting monthly nitrogen retention (Nret-m) on the basis of external N loading, the lake water pool of nitrogen Npool, hydraulic loading and lake water temperature. Calibration of only two parameters on data from the randomly selected 8 out of 16 lakes rendered the model capable of accurately simulating seasonal dynamics of the in-lake N concentration and Nret-m in all 16 lakes. We conclude that with regard to shallow, eutrophic lakes with a relatively low hydraulic retention time, it is now possible to determine not only annual mean nitrogen retention, but also the seasonal variation in Nret-m. Prediction of seasonal variation in N loading of downstream N-limited coastal areas is thereby rendered much more reliable.