Large scale nutrient modelling using globally available datasets: A test for the Rhine basin

Nutrient discharge to coastal waters from rivers drainingpopulated areas can cause vast algal blooms. Changing conditions in thedrainage basin, like land use change, or climate induced changes inhydrology, may alter riverine nitrogen (N) and phosphorus (P) fluxes andfurther increase the pressure on coastal water quality. Several largescale models have been employed to quantify riverine nutrient fluxes ona yearly to decadal timescale. Seasonal variation of these fluxes,governed by internal nutrient transformations and attenuation, is oftenlarger than the inter-annual variation and may contain crucialinformation on nutrient transfer through river basins and shouldtherefore not be overlooked. In the last decade the increasingavailability of global datasets at fine resolutions has enabled themodelling of multiple basins using a coherent dataset. Furthermore, theuse of global datasets will aid to global change impact assessment. Wedeveloped a new model, RiNUX, to adequately simulate present and futureriver nutrient loads in large river basins. The RiNUX model captures theintra-annual variation at the basin scale in order to provide moreaccurate estimates of future nutrient loads in response to globalchange. With an incorporated dynamic sediment flux model, theparticulate nutrient loads can be assessed. It is concluded that theRiNUX model provides a powerful, spatial and temporal explicit tool toestimate intra-annual variations in riverine nutrient loads in largeriver basins. The model was calibrated using the detailed RHIN datasetand its overall efficiency was tested using a coarser dataset GLOB forthe Rhine basin. Using the RHIN dataset seasonal variable nutrient loadat the river outlet can be satisfactorily modelled for both total N ( E= 0.50) and total P ( E = 0.47). The largest prediction errors occur inestimating high TN loads. When using the GLOB dataset, the modelefficiency is lower for TN ( E = 0.12), due to overestimated nutrientemissions. For TP, the model efficiency is only slightly lower ( E =0.36) in comparison to the RHIN dataset. Despite the lower modelefficiencies for the GLOB dataset, we conclude that this datasetprovided reasonably good estimates of seasonal nutrient loads in theRhine basin and is considered promising for application to other, lessdocumented, large river basins.