Quantifying the uncertainty linked to the degree to which the spatio-temporal variability of the catchment descriptors (CDs), and consequently calibration parameters (CPs), represented in the distributed hydrology models and its impacts on the simulation of flooding events is the main objective of this paper. Here, we introduce a methodology based on ensemble approach principles to characterize the uncertainties of spatio-temporal variations. We use two distributed hydrological models (water balance simulation model and Hydrotel) and six catchments with different sizes and characteristics, located in southern Quebec, to address this objective. We calibrate the models across four spatial (100, 250, 500 and 1000 m²) and two temporal (3 and 24 h) resolutions. Afterwards, all combinations of CDs-CPs pairs are fed to the hydrological models to create an ensemble of simulations for characterizing the uncertainty related to the spatial resolution of the modelling, for each catchment. The catchments are further grouped into large (>1000 km²), medium (between 500 and 1000 km²) and small (<500 km²) to examine multiple hypotheses. The ensemble approach shows a significant degree of uncertainty (over 100% error for estimation of extreme streamflow) linked to the spatial discretization of the modelling. Regarding the role of CDs, results show that first, there is no meaningful link between the uncertainty of the spatial discretization and catchment size, as spatio-temporal discretization uncertainty can be seen across different catchment sizes. Second, the temporal scale plays only a minor role in determining the uncertainty related to spatial discretization. Third, the more physically representative a model is, the more sensitive it is to changes in spatial resolution. Finally, the uncertainty related to model parameters is larger than that of CDs for most of the catchments. Yet, there are exceptions for which a change in spatio-temporal resolution can alter the distribution of state and flux variables, change the hydrologic response of the catchments and cause large uncertainties.

中文翻译：

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分布式水文模型的时空离散不确定性

量化与流域描述符 (CD) 的时空变化程度相关的不确定性，从而量化分布水文模型中表示的校准参数 (CP) 及其对洪水事件模拟的影响是这张纸。在这里，我们介绍了一种基于集成方法原理的方法来表征时空变化的不确定性。我们使用两个分布式水文模型（水平衡模拟模型和 Hydrotel）和位于魁北克南部的六个不同大小和特征的集水区来实现这一目标。我们在四个空间（100、250、500 和 1000 m ²) 和两个时间（3 和 24 小时）分辨率。之后，CDs-CPs 对的所有组合都被输入到水文模型中，以创建一个模拟集合，用于表征与每个流域的建模空间分辨率相关的不确定性。流域进一步分为大型（>1000 km ²）、中型（500 到 1000 km ²之间）和小型（<500 km ²) 来检验多个假设。集成方法显示了与建模的空间离散化相关的很大程度的不确定性（极端流量估计误差超过 100%）。关于 CD 的作用，结果表明，首先，空间离散化的不确定性与流域大小之间没有有意义的联系，因为可以在不同的流域大小中看到时空离散化不确定性。其次，时间尺度在确定与空间离散化相关的不确定性方面仅起次要作用。第三，模型在物理上的代表性越强，它对空间分辨率的变化就越敏感。最后，对于大多数流域，与模型参数相关的不确定性大于 CD 的不确定性。然而，