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^–2 d^–1, and was particularly high in lakes with high N loading. Annual percentage retention (N _ret –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 N _ret –y% and, in combination with inlet N concentration, accounted for 84% of the variation in the in-lake N concentration. N _ret % 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 (N _ret –m) on the basis of external N loading, the lake water pool of nitrogen N _pool , 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 N _ret –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 N _ret–m . Prediction of seasonal variation in N loading of downstream N-limited coastal areas is thereby rendered much more reliable.