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 effective 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 the 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 (${\mathit{NO}}_3^{\mathit{-}}$), 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 ${\mathit{NO}}_3^{\mathit{-}}$ 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; however, it 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.

中文翻译：

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对完全分布式流域水质模型进行数据有效的校准


分布式水文水质模型越来越多地用于管理流域规模的自然资源，但没有用于选择最有效的测量站的校准指南。在这项研究中，我们研究了校准方案对德国中部博德流域流量和硝酸盐模拟的完全分布式基于过程的水文水质模型（mHM-硝酸盐）时空性能的影响。我们使用了单站点和两个多站点校准方案，其中两个多站点方案的测量站数量不同，但每个子流域代表了流域不同的主要土地利用。为了为每个校准方案提取一组行为参数，我们选择了具有 300,000 次迭代的顺序多标准方法。对于放电 ( Q )，三​​个方案的模型性能相似（NSE 从 0.88 变化到 0.92）。然而，对于硝酸盐浓度（${\mathit{不}}_3^{\mathit{-}}\$\{\backslash mathit\{NO\}\}\_\{3\}^\{\backslash mathit\{-\}\}\$$ ），多站点方案的性能优于单站点方案。这种改进可能归因于多站点方案包含了更广泛的数据，包括低Q值和${\mathit{不}}_3^{\mathit{-}}\$\{\backslash mathit\{NO\}\}\_\{3\}^\{\backslash mathit\{-\}\}\$$值，从而更好地代表流域内的多样性。相反，在多站点方法中添加更多测量站并没有导致流域代表性的进一步改善，但显示出更宽的 95% 不确定性边界。 因此，添加包含流域特征类似信息的观测值似乎并没有提高模型性能；然而，这增加了不确定性。这些结果凸显了战略性选择反映流域异质性全方位的测量站的重要性，而不是寻求最大化站数量，以优化参数校准。