Distributed hydrological water quality models are increasingly being used to manage natural resources at the catchment scale but there are no calibration guidelines for selecting the most useful gauging stations. In this study, we investigated the influence of calibration schemes on the spatiotemporal performance of a fully distributed process-based hydrological water quality model (mHM-Nitrate) for discharge and nitrate simulations at Bode catchment in central Germany. We used a single- and two multi-site calibration schemes where the two multi-site schemes varied in number of gauging stations but each subcatchment represented different dominant land uses of the catchment. To extract a set of behavioral parameters for each calibration scheme, we chose a sequential multi-criteria method with 300.000 iterations. For discharge (Q), model performance was similar among the three schemes (NSE varied from 0.88 to 0.92). However, for nitrate concentration, the multi-site schemes performed better than the single site scheme. This improvement may be attributed to that multi-site schemes incorporated a broader range of data, including low Q and NO3- values, thus provided a better representation of within-catchment diversity. Conversely, adding more gauging stations in the multi-site approaches did not lead to further improvements in catchment representation but showed wider 95% uncertainty boundaries. Thus, adding observations that contained similar information on catchment characteristics did not seem to improve model performance and increased uncertainty. These results highlight the importance of strategically selecting gauging stations that reflect the full range of catchment heterogeneity rather than seeking to maximize station number, to optimize parameter calibration.

Excessive dissolved inorganic nitrogen (DIN) added to the urban river systems by point-source inputs, such as untreated wastewater and wastewater treatment plant (WWTP) effluent, constitutes a water-quality problem of growing concern in China. However, very little is known about their impacts on DIN retention capacity and pathways in receiving waters. In this study, a spatially-intensive water quality monitoring campaign was conducted to support the application of the river water quality model WASP7.5 to the PS-impacted Nanfei River, China. The DIN retention capacities and pathway of a reference upstream Reach A, a wastewater-impacted Reach B and an effluent-dominated Reach C were quantified using the model results after a Bayesian approach for parameter estimation and uncertainty analysis. The results showed that the untreated wastewater discharge elevated the assimilatory uptake rate but lowered its efficiency in Reach B; while the WWTP effluent discharge elevated both denitrification rate and efficiency and made Reach C a denitrification hotspot with increased nitrate concentration and hypoxic environment. The effects of the point-source inputs on the DIN retention pathways (assimilatory uptake vs. denitrification) were regulated by their impacts on river metabolism. Despite different pathways, the total DIN retention ratios of Reaches A, B and C under low-flow conditions were 30.3% km-1, 14.3% km-1 and 6.5% km-1, respectively, which indicated the instream DIN retention capacities were significantly impaired by the point-source inputs. This result suggests that the DIN discharged from point-source inputs to urban rivers will be transported downstream with the potential to create long-term ecological implications not only locally but also regionally.