Spatial data are playing an increasingly important role in watershed science and management. Large investments have been made by government agencies to provide nationally‐available spatial databases; however, their relevance and suitability for local watershed applications is largely unscrutinized. We investigated how goodness of fit and predictive accuracy of total phosphorus (TP) concentration models developed from nationally‐available spatial data could be improved by including local watershed‐specific data in the East Fork of the Little Miami River, Ohio, a 1,290 km² watershed. We also determined whether a spatial stream network (SSN) modeling approach improved on multiple linear regression (nonspatial) models. Goodness of fit and predictive accuracy were highest for the SSN model that included local covariates, and lowest for the nonspatial model developed from national data. Septic systems and point source TP loads were significant covariates in the local models. These local data not only improved the models but enabled a more explicit interpretation of the processes affecting TP concentrations than more generic national covariates. The results suggest SSN modeling greatly improves prediction and should be applied when using national covariates. Including local covariates further increases the accuracy of TP predictions throughout the studied watershed; such variables should be included in future national databases, particularly the locations of septic systems.

中文翻译：

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基于全国可用的空间数据覆盖范围的改进流域内磷浓度的预测模型。

空间数据在分水岭科学和管理中发挥着越来越重要的作用。政府机构已进行了大笔投资来提供全国可用的空间数据库；但是，它们在当地流域应用中的相关性和适用性在很大程度上未被审查。我们研究了如何通过在俄亥俄州小迈阿密河东叉（1,290 km ^2）中纳入局部流域特定数据，来改善根据全国可用空间数据开发的总磷（TP）浓度模型的拟合优度和预测准确性。分水岭。我们还确定了空间流网络（SSN）建模方法是否在多重线性回归（非空间）模型上得到了改善。包含局部协变量的SSN模型的拟合优度和预测准确性最高，而根据国家数据开发的非空间模型的拟合优度和预测准确性最低。化粪池系统和点源TP负荷是局部模型中的重要协变量。这些本地数据不仅改进了模型，而且比更通用的国家协变量，能够更清楚地解释影响TP浓度的过程。结果表明，SSN建模大大改善了预测，应在使用国家协变量时应用。包括局部协变量，进一步提高了整个研究流域中TP预测的准确性；