In the field of hydrological modeling, many alternative representations of natural processes exist. Choosing specific process formulations when building a hydrological model is therefore associated with a high degree of ambiguity and subjectivity. In addition, the numerical integration of the underlying differential equations and parametrization of model structures influence model performance. Identifiability analysis may provide guidance by constraining the a priori range of alternatives based on observations. In this work, a flexible simulation environment is used to build an ensemble of semidistributed, process‐based hydrological model configurations with alternative process representations, numerical integration schemes, and model parametrizations in an integrated manner. The flexible simulation environment is coupled with an approach for dynamic identifiability analysis. The objective is to investigate the applicability of the framework to identify the most adequate model. While an optimal model configuration could not be clearly distinguished, interesting results were obtained when relating model identifiability with hydro‐meteorological boundary conditions. For instance, we tested the Penman‐Monteith and Shuttleworth & Wallace evapotranspiration models and found that the former performs better under wet and the latter under dry conditions. Parametrization of model structures plays a dominant role as it can compensate for inadequate process representations and poor numerical solvers. Therefore, it was found that numerical solvers of high order of accuracy do often, though not necessarily, lead to better model performance. The proposed coupled framework proved to be a straightforward diagnostic tool for model building and hypotheses testing and shows potential for more in‐depth analysis of process implementations and catchment functioning.

中文翻译：

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如何定制基于过程的水文模型？柔性模型结构的动态可识别性分析

在水文建模领域，存在许多自然过程的替代表示。因此，在建立水文模型时选择特定的过程公式会带来高度的歧义和主观性。此外，基础微分方程的数值积分和模型结构的参数化也会影响模型性能。可识别性分析可以通过根据观察结果约束替代方法的先验范围来提供指导。在这项工作中，灵活的仿真环境用于以集成方式构建具有半过程，基于过程的水文模型配置的整体，具有替代过程表示，数值积分方案和模型参数化。灵活的仿真环境与动态可识别性分析方法结合在一起。目的是调查框架的适用性，以识别最适当的模型。虽然无法清晰地区分最佳模型配置，但将模型可识别性与水文气象边界条件相关时，会获得有趣的结果。例如，我们测试了Penman-Monteith和Shuttleworth＆Wallace蒸散模型，发现前者在潮湿条件下表现更好，而后者在干燥条件下表现更好。模型结构的参数化起着主导作用，因为它可以补偿不足的过程表示和不良的数值求解器。因此，人们发现，尽管不是必须的，但高精确度的数值求解器经常会执行，导致更好的模型性能。所提出的耦合框架被证明是用于模型构建和假设测试的直接诊断工具，并显示了对过程实现和集水区功能进行更深入分析的潜力。