In water management, source areas need to be identified and seasonal variability of nutrient flows assessed to facilitate design of cost-efficient mitigation programs. This study aimed at investigating to what degree sub-catchment spatial and temporal nutrient concentration variability could be captured by their agro-geographical characteristics and water quality modelling.
An agricultural catchment (160 km2) in Southeast Sweden was investigated with respect to source areas for phosphorus (P), nitrogen and particle losses. The specific aims were to 1) investigate the spatial variability of nutrient and particle concentrations and transport from different sub-catchments, 2) analyze if sub-catchment characteristics could explain differences in nutrient and particle concentration dynamics and overall nutrient losses, and 3) evaluate how well monitored temporal and spatial variability in nutrient concentrations could be simulated by a catchment model (HYPE). The purpose with the latter was to find recommendations for further model development and identify limitations for the use of catchment models in local water management.
Water flow was measured in two stations during 2009-2011. Grab samples were collected in synoptic sampling campaigns covering 10 sampling points during periods that represented various water flow regimes. Water samples were analyzed for total P (TP), dissolved phosphate (PO4-P), nitrate (NO3-N) and suspended matter (SUSP). The HYPE model was setup with the same detailed agro-geographical data as used for the statistical analyses of spatial and temporal correlations. The results showed that the sub-catchment variability of all measured nutrient concentrations were correlated with agro-geographical characteristics. All fractions of P concentrations were strongly correlated with soil type, whereas NO3-N concentrations were more related to crop factors. With regard to temporal dynamics of monitored concentrations, links to seasonality and water flow were more significant for NO3-N than for TP. Concentrations generated from the water quality model (HYPE) did not capture the subcatchment or temporal variability indicated from monitoring, particularly not for P concentrations. Neither did the modelled correlation between agro-geographical factors and concentrations correspond to that found for monitored concentrations. Some suggestions for model improvement were identified. Although water quality models are useful for local water management when it comes to modelling the impact of e.g. measures or climate change, our results suggest that their value might still be more limited when assessing variability on the subcatchment scale.