Parameterising hydrological models – Comparing optimisation and robust parameter estimation
Parameterising hydrological models – Comparing optimisation and robust parameter estimation
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DOI：
10.1016/j.jhydrol.2011.05.003
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发表时间：
2011-07
影响因子：
6.4
通讯作者：
J. Cullmann;T. Krausse;P. Saile
中科院分区：
文献类型：
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作者：
J. Cullmann;T. Krausse;P. Saile
在大多数情况下，校准是成功应用概念性和基于物理的降雨-径流模型的先决条件。本文的目的是比较分析Skahill和Doherty(2006)中描述的基于事件的自动校准(PEST)和Bárdossy和Singh(2008)提出的稳健参数估计(ROPE)的潜力。我们在瑞士Rietholzbach流域的建模研究结果表明，ROPE在验证中小型事件方面表现得更好。这表明，当建模者的意图集中于最大化模型的泛化能力时，绳索可能更适合于参数化模型，例如用于评估暂态过程特性。我们的研究以WASIM-ETH水文模型为基础，使用ROPE和自动参数估计相结合的方法来研究最优参数集。就计算所需的时间而言，本研究中使用的PEST算法的性能大约是ROPE应用程序的100倍。
In most conditions, calibration is a prerequisite for successfully applying conceptual and physically based rainfall–runoff models. The goal of this paper is to comparatively analyse the potential of both event-based automatic calibration (PEST) as described in Skahill and Doherty (2006) and robust parameter estimation (ROPE) as proposed by Bárdossy and Singh (2008). The results of our modelling study in the Rietholzbach catchment (Switzerland) show that ROPE performs better in validation of small to medium sized events. This indicates that ROPE might be better suited to parameterise models when the modellers intention is focussed on a maximising the generalisation capacity of the model, e.g. for evaluating transient process characteristics. We base our study on the hydrological model WaSiM-ETH, using a combined ROPE and automatic parameter estimation approach to investigate optimal parameter sets. The PEST algorithm used in this study outperforms the ROPE application by a factor of roughly 100 in terms of time required for computation.