Flood simulations demand mathematical models, which are rigorously calibrated and validated against benchmarking datasets. For this purpose, experiments are conducted in a river-network-floodplain set-up. Hypothetical stepped hydrographs are passed through the channel-network, and fluvial flooding situations are created. Flood depths are recorded at various locations and evolving flood extents are extracted by image processing. TELEMAC 2D is tested against the observed data. The most accurate scheme for flood prediction is identified through sensitivity analysis. Inclusion of the turbulence model is found to improve the accuracy in predicting dynamic flood extents. The model seems to slightly overpredict inundation extents during the rising limb of the hydrographs and underpredict during the falling limb. In addition, certain aspects of a flood such as river–floodplain interaction and junction hydraulics cannot be reproduced with high precision by the 2D model. The experimental datasets can be a valuable resource to mathematical modellers and are freely downloadable.

洪水模拟需要数学模型，这些模型必须根据基准数据集进行严格的校准和验证。为此，在河网-洪泛区设置中进行实验。假设的阶梯状水文图通过河道网络，从而形成河流泛滥的情况。在各个位置记录洪水深度，并通过图像处理提取不断演变的洪水范围。针对观察到的数据对TELEMAC 2D进行了测试。通过敏感性分析确定最准确的洪水预报方案。发现包含湍流模型可以提高预测动态洪水范围的准确性。该模型似乎在水文测量仪的上升肢中略微高估了淹没程度，而在下降肢体中则低估了淹没程度。此外，二维模型无法高精度地再现洪水的某些方面，例如河流与平原之间的相互作用以及路口水力学。实验数据集可能是数学建模者的宝贵资源，可以免费下载。