Traditionally, groundwater and surface water flow models have been calibrated against two observation types: hydraulic heads and surface water discharge. It has repeatedly been demonstrated, however, that these classical observations do not contain sufficient information to calibrate flow models. To reduce the predictive uncertainty of flow models, the consideration of other observation types constitutes a promising way forward. Despite the ever‐increasing availability of other observation types, however, they are still unconventional when it comes to flow model calibration. By reviewing studies that included nonclassical observations in flow model calibration, benefits and challenges associated with their integration in flow model calibration were identified, and their information content was analyzed. While explicit simulation of mass transport processes in flow models poses challenges, even simplified approaches to integrate tracer concentrations yield significantly better calibration results than using only classical observations. For a majority of calibrated flow models, observations of tracer concentrations and of exchange fluxes were beneficial. Temperature observations improved the simulation of heat transport but often worsened all other model outcomes. Only when temperature observations were made within 2 m of the surface water‐groundwater interface did they have the potential to also improve flow and mass transport simulations. Surprisingly, many models were calibrated manually rather than with the widely available, mathematically robust and automated tools. There is a clear need for more systematic implementation of unconventional observations and automated flow model calibration as well as for more systematic quantification of the information content of unconventional observations.

中文翻译：

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超越水文地质学的经典观测：在地下水模型校准中包括交换通量，温度，示踪剂浓度，停留时间和土壤水分观测的优势

传统上，已经针对两种观测类型对地下水和地表水流动模型进行了校准：液压头和地表水排放。但是，已经反复证明，这些经典的观察结果没有包含足够的信息来校准流量模型。为了减少流动模型的预测不确定性，考虑其他观测类型构成了有希望的前进之路。尽管其他观测类型的可用性不断提高，但是，在进行流模型校准时，它们仍然是非常规的。通过审查包括非经典观测值在内的流量模型校准研究，确定了与将其集成到流量模型校准中相关的优势和挑战，并对其信息内容进行了分析。尽管在流动模型中对传质过程进行显式模拟带来了挑战，但即使是简化的示踪剂浓度积分方法，也比仅使用经典观测方法产生的校准结果要好得多。对于大多数校准流量模型，观察示踪剂浓度和交换通量是有益的。温度观测改善了热传递的模拟，但通常会使所有其他模型的结果恶化。只有在地表水与地下水界面的2 m以内进行温度观测时，它们才有可能改善流量和质量输运模拟。令人惊讶的是，许多模型是手动校准的，而不是使用广泛可用的，数学上强大的自动化工具进行的。