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首页> 外文期刊>Hydrology and Earth System Sciences Discussions >Benchmarking the predictive capability of hydrological models for river flow and flood peak predictions across over 1000?catchments in Great Britain
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Benchmarking the predictive capability of hydrological models for river flow and flood peak predictions across over 1000?catchments in Great Britain

机译:基于1000多次河流流动和洪峰预测的水文模型的预测能力?英国集水区

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摘要

Benchmarking model performance across large samples of catchments is useful to guide model selection and future model development. Given uncertainties in the observational data we use to drive and evaluate hydrological models, and uncertainties in the structure and parameterisation of models we use to produce hydrological simulations and predictions, it is essential that model evaluation is undertaken within an uncertainty analysis framework. Here, we benchmark the capability of several lumped hydrological models across Great Britain by focusing on daily flow and peak flow simulation. Four hydrological model structures from the Framework for Understanding Structural Errors?(FUSE) were applied to over 1000?catchments in England, Wales and Scotland. Model performance was then evaluated using standard performance metrics for daily flows and novel performance metrics for peak flows considering parameter uncertainty. Our results show that lumped hydrological models were able to produce adequate simulations across most of Great Britain, with each model producing simulations exceeding a?0.5 Nash–Sutcliffe efficiency for at least 80% of catchments. All four models showed a similar spatial pattern of performance, producing better simulations in the wetter catchments to the west and poor model performance in central Scotland and south-eastern England. Poor model performance was often linked to the catchment water balance, with models unable to capture the catchment hydrology where the water balance did not close. Overall, performance was similar between model structures, but different models performed better for different catchment characteristics and metrics, as well as for assessing daily or peak flows, leading to the ensemble of model structures outperforming any single structure, thus demonstrating the value of using multi-model structures across a large sample of different catchment behaviours. This research evaluates what conceptual lumped models can achieve as a performance benchmark and provides interesting insights into where and why these simple models may fail. The large number of river catchments included in this study makes it an appropriate benchmark for any future developments of a national model of Great Britain.
机译:在大型集流体上的基准模型性能对于指导模型选择和未来的模型开发是有用的。在我们用来推动和评估水文模型的观察数据中的不确定性,以及我们用于产生水文模拟和预测的模型结构和参数的不确定性,必须在不确定性分析框架内进行模型评估。在这里,我们通过专注于日常流动和峰值流模拟来基准在英国跨英国几种流动水文模型的能力。来自框架的四种水文模型结构,用于了解结构误差?(保险丝)应用于超过1000?英格兰,威尔士和苏格兰的集水区。然后,使用标准性能指标评估模型性能,用于考虑参数不确定性的峰值流动的日常流程和新颖性能度量。我们的研究结果表明,集体水文模型能够在大多数英国产生足够的模拟,每个模型都会产生仿真超过a?0.5纳什 - Sutcliffe效率至少为80%的流域。所有四种模型都表现出类似的空间模式,在苏格兰中部和英格兰东南部的潮湿地区产生更好的模拟。较差的模型性能通常与集水平衡有关,模型无法捕获水平没有关闭的流域水文。总体而言,模型结构之间的性能相似,但不同模型对于不同的集水区特征和度量,以及评估日常或峰值流动,导致模型结构的集合优于任何单一结构,从而展示了使用多个结构的值-Model结构跨越不同的集水行为样本。这项研究评估了什么概念集集模型可以实现作为性能基准,并在这些简单模型可能失败的位置和为什么提供有趣的洞察力。本研究中包含的大量河流集水区使其成为任何未来英国国家模式的未来发展的适当基准。

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