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首页> 外文期刊>Journal of Hydroinformatics >Joint application of event-based calibration and dynamic identifiability analysis in rainfall-runoff modelling: implications for model parametrisation
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Joint application of event-based calibration and dynamic identifiability analysis in rainfall-runoff modelling: implications for model parametrisation

机译：基于事件的校准和动态可识别性分析在降雨径流建模中的联合应用：对模型参数化的启示

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摘要

The goal of this work is to comparatively evaluate the potential of both event-based automatic calibration and Dynamic Identifiability Analysis (DYNIA) as proposed by wagener et al. This is combined with an investigation on a potential relation of a priori knowledge on event characteristics with optimal model parameters. A joint application of DYNIA and automatic parameter estimation leads to implications considering the informational content of both methods. Optimal model parameters, identified on an event basis, are tested for statistical relations with physical characteristics of the rainstorm events (e.g. intensity). In this paper, we present results of a modelling study in the Rietholzbach catchment (Switzerland), we employed the hydrological model WaSiM-ETH, using a combined DYNIA (Dynamic Identifiability Analysis) and automatic parameter estimation (PEST) approach to investigate best parameter sets as well as parameter variability along the time series of the hydrograph. The results of the study indicate that the "drainage" parameter identifiability for the long-term simulation is linked to the event-based calibrated parameter for the flood events. However, the parameter sets obtained with single-event calibration could not be fully linked to the chosen characteristic features derived from the precipitation forecast.
机译：这项工作的目的是比较评估基于事件的自动校准和动态确定性分析（DYNIA）的潜力，如payner等人提出的。这与对事件特征的先验知识与最佳模型参数的潜在关系的研究相结合。 DYNIA和自动参数估计的联合应用会导致考虑两种方法的信息内容。测试基于事件确定的最佳模型参数，以了解与暴雨事件的物理特征（例如强度）之间的统计关系。在本文中，我们介绍了在Rietholzbach流域（瑞士）进行的建模研究的结果，我们使用了水文模型WaSiM-ETH，并结合了DYNIA（动态可识别性分析）和自动参数估计（PEST）方法来研究最佳参数集以及沿水文曲线的时间序列的参数可变性。研究结果表明，长期模拟的“排水”参数可识别性与洪水事件的基于事件的校准参数有关。但是，通过单事件校准获得的参数集不能完全与从降水预报中得出的所选特征相联系。

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