全尺寸现场绿色屋顶水文模型的性能比较

IF 3.1 Q2 GEOSCIENCES, MULTIDISCIPLINARY
Ico Broekhuizen , Santiago Sandoval , Hanxue Gao , Felipe Mendez-Rios , Günther Leonhardt , Jean-Luc Bertrand-Krajewski , Maria Viklander
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引用次数: 4

摘要

绿色屋顶可以成为可持续城市排水系统的重要组成部分,水文模型可以为规划目的提供关于绿色屋顶径流的有用信息。文献中已经提出了各种模型,但到目前为止还没有论文比较多个模型在多个全尺寸绿色屋顶上的性能。本文比较了4种模型:概念模型Urbis和SWMM,物理模型Hydrus-1D和Mike SHE,跨越两个现场站点(法国里昂和瑞典ume),每个站点有两个校准周期。模型预测的不确定性和准确性取决于所选择的校准地点和周期。从简单的概念模型Urbis得出的整体模型预测是最不准确和最不确定的;从流量预测区间覆盖的原始观测百分比来看,SWMM和Mike SHE的预测结果是最好的,但SWMM预测的不确定性较小。然而,从观察结果与概率流预测的一致程度来看,Hydrus的预测更为准确。Mike SHE在总径流量方面表现最好。在Urbis、SWMM和Hydrus中,模型预测中的不确定性几乎完全由随机不确定性驱动,而在Mike SHE中,参数不确定性起着重要作用。使用DREAM贝叶斯算法确定的参数可识别性和最有可能的参数值在所有模型的校准期间都不一致,这引发了对模型应用的泛化性的质疑。在所有模型中,降雨保持在事件之间变化很大的校准期对参数值的信息更丰富。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Performance comparison of green roof hydrological models for full-scale field sites

Green roofs can be valuable components in sustainable urban drainage systems, and hydrological models may provide useful information about the runoff from green roofs for planning purposes. Various models have been proposed in the literature, but so far no papers have compared the performance of multiple models across multiple full-size green roofs. This paper compared 4 models: the conceptual models Urbis and SWMM and the physically-based models Hydrus-1D and Mike SHE, across two field sites (Lyon, France and Umeå, Sweden) and two calibration periods for each site. The uncertainty and accuracy of model predictions were dependent on the selected calibration site and period. Overall model predictions from the simple conceptual model Urbis were least accurate and most uncertain; predictions from SWMM and Mike SHE were jointly the best in terms of raw percentage observations covered by their flow prediction intervals, but the uncertainty in the predictions in SWMM was smaller. However, predictions from Hydrus were more accurate in terms of how well the observations conformed to probabilistic flow predictions. Mike SHE performed best in terms of total runoff volume. In Urbis, SWMM and Hydrus uncertainty in model predictions was almost completely driven by random uncertainty, while parametric uncertainty played a significant role in Mike SHE. Parameter identifiability and most likely parameter values determined with the DREAM Bayesian algorithm were found to be inconsistent across calibration periods in all models, raising questions about the generalizability of model applications. Calibration periods where rainfall retention was highly variable between events were more informative for parameter values in all models.

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来源期刊
Journal of Hydrology X
Journal of Hydrology X Environmental Science-Water Science and Technology
CiteScore
7.00
自引率
2.50%
发文量
20
审稿时长
25 weeks
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