Abstract This paper emphasizes the importance of integrating outlet discharge and observed internal variables in the evaluation of distributed hydrological models outputs. It proposes a general methodology for a diagnostic evaluation of a complex distributed hydrological model, based on discharge data at the outlet and additional distributed information such as water level and surface soil moisture data. The proposed methodology is illustrated using the PUMMA model in the Mercier sub-catchment (6.6 km2). Model parameters are specified according to field data and a previous study performed in a neighbouring catchment (Jankowfsky et al., 2014), without calibration. The distributed water level and soil moisture network of sensors were useful in the model evaluation process. Thus, model parameters are specified either using in situ information or results from previous studies. A stepwise approach is used for model evaluation. It includes standard water balance assessment as well as comparison of observed and simulated outlet discharge, whether on annual or event timescales. Soil moisture sensors are used to assess the ability of the model to simulate seasonal water storage dynamics based on a normalized index. The water level sensors network is used on two timescales: on a seasonal timescale, sensors network is used to assess the model’s ability to simulate intermittency; whereas on event timescales, sensors network is used in determining the model’s ability to reproduce observed reaction as well as response times. Event timescales do also focus on the correlation between hydrological response and either rainfall event or antecedent soil moisture variables. Results show that the non-calibrated model is quite effective at capturing water flow and soil water-storage dynamics, but it fails to reproduce observed runoff volume during events. There is strong indication of a deficiency in the characterization of catchment storage and upstream flowpath description. The soil water content and a network of water level sensors provide interesting information about soil moisture and river flow dynamics. They however fail to provide quantitative information about catchment storage. This study opens interesting perspectives for the evaluation of distributed hydrological models using hydrological signatures. Furthermore, it highlights the requirement of quantitative as well as qualitative signatures for improving such models.

中文翻译：

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分布式水位和土壤水分数据在分布式水文模型评估中的价值：在法国 Mercier 流域（6.6 平方公里）的 PUMMA 模型中的应用

摘要 本文强调了整合出水口流量和观测内部变量在分布式水文模型输出评估中的重要性。它基于出口处的排放数据和额外的分布式信息（如水位和表层土壤水分数据），提出了一种诊断评估复杂分布式水文模型的通用方法。在 Mercier 子流域（6.6 平方公里）中使用 PUMMA 模型说明了所提议的方法。模型参数是根据现场数据和先前在邻近集水区进行的研究（Jankowfsky 等人，2014 年）指定的，无需校准。传感器的分布式水位和土壤水分网络在模型评估过程中很有用。因此，使用原位信息或先前研究的结果指定模型参数。逐步方法用于模型评估。它包括标准的水平衡评估以及观察和模拟出口排放的比较，无论是在年度还是事件时间尺度上。土壤湿度传感器用于评估模型基于标准化指数模拟季节性蓄水动态的能力。水位传感器网络用于两个时间尺度：在季节性时间尺度上，传感器网络用于评估模型模拟间歇性的能力；而在事件时间尺度上，传感器网络用于确定模型重现观察到的反应以及响应时间的能力。事件时间尺度也关注水文响应与降雨事件或前期土壤水分变量之间的相关性。结果表明，非校准模型在捕捉水流和土壤蓄水动态方面非常有效，但它无法重现事件期间观测到的径流体积。有强烈的迹象表明在流域蓄水和上游流道描述方面存在缺陷。土壤含水量和水位传感器网络提供有关土壤湿度和河流流动动态的有趣信息。然而，他们未能提供有关流域蓄水的定量信息。这项研究为使用水文特征评估分布式水文模型开辟了有趣的视角。此外，