Abstract The relevance of hydrological processes varies in space and time resulting in typical temporal patterns for catchments. Contrasting catchments moreover differ in their catchment metrics. Hydrological models claim to be able to reproduce typical temporal patterns of dominant processes using site-specific model parameters. Thus, patterns of temporal dynamics in dominant modelled processes and their corresponding dominant parameters are a fingerprint of how a model represents the hydrological behaviour of a catchment and how these process patterns vary between contrasting catchments. In this study, we demonstrate how catchment metrics, modelled processes and parameter dominances can be jointly used to characterise catchments. We assess how catchment characteristics are represented in spatio-temporal process dynamics in models and how to understand the reasons for hydrological (dis)similarity among catchments along a landscape gradient. For this purpose, catchment metrics which characterise contrasting landscapes (lowland, mid-range mountain and alpine catchments) are related to dominant processes and parameters which were provided by a temporally resolved sensitivity analysis (TEDPAS) and simulations of a hydrological model. Our study shows that the applied model is able to represent the different processes and their seasonal variability according to the specific hydrological conditions of the study catchments. By analysing catchment metrics, modelled processes and model parameters jointly, we show that the largest differences are identified for the alpine catchment, whilst similarities are found among the other catchments. Following a landscape gradient, high flow phases are dominated by different flow components. In contrast, the model shows groundwater dominance in low flow phases in non-alpine catchments while in the alpine catchment low flows in winter are mainly controlled by snow processes. The joint analysis of catchment metrics, temporal dynamics of dominant processes and parameters can therefore be used to better disentangle similarities and differences among catchments from different landscapes.

中文翻译：

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分析模型中的时空过程和参数动态以表征对比流域

摘要 水文过程的相关性在空间和时间上各不相同，导致流域的典型时间模式。此外，对比流域的流域指标也有所不同。水文模型声称能够使用特定地点的模型参数再现主要过程的典型时间模式。因此，主要建模过程中的时间动态模式及其相应的主要参数是模型如何表示流域的水文行为以及这些过程模式如何在对比流域之间变化的指纹。在这项研究中，我们展示了如何联合使用流域指标、建模过程和参数优势来表征流域。我们评估流域特征如何在模型中的时空过程动力学中表示，以及如何理解沿景观梯度流域之间水文（不）相似的原因。为此，表征对比景观（低地、中山和高山流域）的流域指标与时间分辨敏感性分析 (TEDPAS) 和水文模型模拟提供的主要过程和参数相关。我们的研究表明，应用模型能够根据研究流域的特定水文条件来表示不同的过程及其季节性变化。通过联合分析流域指标、建模过程和模型参数，我们发现高山流域的差异最大，而在其他流域中也发现了相似之处。在景观梯度之后，高流量阶段由不同的流量分量主导。相比之下，该模型显示非高山流域低流量阶段地下水占主导地位，而高山流域冬季低流量主要受雪过程控制。因此，流域指标、主要过程和参数的时间动态的联合分析可用于更好地解决来自不同景观的流域之间的异同。该模型显示非高山集水区低流量阶段地下水占主导地位，而高山集水区冬季低流量主要受降雪过程控制。因此，流域指标、主要过程和参数的时间动态的联合分析可用于更好地解决不同景观流域之间的异同。该模型显示非高山集水区低流量阶段地下水占主导地位，而高山集水区冬季低流量主要受降雪过程控制。因此，流域指标、主要过程和参数的时间动态的联合分析可用于更好地解决来自不同景观的流域之间的异同。