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首页> 外文期刊>Hydrology and Earth System Sciences >Calibration of channel depth and friction parameters in the LISFLOOD-FP hydraulic model using medium-resolution SAR data and identifiability techniques
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Calibration of channel depth and friction parameters in the LISFLOOD-FP hydraulic model using medium-resolution SAR data and identifiability techniques

机译:使用中分辨率SAR数据和可识别性技术校准LISFLOOD-FP水力模型中通道深度和摩擦参数

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Single satellite synthetic aperture radar (SAR) data are now regularly used to estimate hydraulic model parameters such as channel roughness, depth and water slope. However, despite channel geometry being critical to the application of hydraulic models and poorly known a priori, it is not frequently the object of calibration. This paper presents a unique method to simultaneously calibrate the bankfull channel depth and channel roughness parameters within a 2-D LISFLOOD-FP hydraulic model using an archive of moderate-resolution (150?m) ENVISAT satellite SAR-derived flood extent maps and a binary performance measure for a 30 × 50?km domain covering the confluence of the rivers Severn and Avon in the UK. The unknown channel parameters are located by a novel technique utilising the information content and dynamic identifiability analysis (DYNIA) (Wagener et al., 2003) of single and combinations of SAR flood extent maps to find the optimum satellite images for model calibration. Highest information content is found in those SAR flood maps acquired near the peak of the flood hydrograph, and improves when more images are combined. We found that model sensitivity to variation in channel depth is greater than for channel roughness and a successful calibration for depth could only be obtained when channel roughness values were confined to a plausible range. The calibrated reach-average channel depth was within 0.9?m (16?% error) of the equivalent value determined from river cross-section survey data, demonstrating that a series of moderate-resolution SAR data can be used to successfully calibrate the depth parameters of a 2-D hydraulic model.
机译:现在定期使用单卫星合成孔径雷达(SAR)数据来估算水力模型参数,例如通道粗糙度,深度和水坡度。然而,尽管通道几何形状对于水力模型的应用是至关重要的,并且先验知之甚少,但这并不是校准的经常对象。本文提出了一种独特的方法,该方法使用中分辨率(150?m)ENVISAT卫星SAR洪水范围图和二值二进制文件存档,同时校准二维LISFLOOD-FP水力模型中的河岸满槽深度和通道粗糙度参数覆盖英国塞文河和雅芳河交汇处的30乘50公里范围内的性能指标。未知信道参数通过一种新颖的技术进行定位,该技术利用信息内容和SAR洪水范围图的单个和组合来进行动态可识别性分析(DYNIA)(Wagener et al。,2003),以找到用于模型校准的最佳卫星图像。在洪水水位图的峰值附近获取的那些SAR洪水地图中发现了最高的信息含量,并且在组合更多图像时会有所改善。我们发现,模型对通道深度变化的敏感度大于通道粗糙度,并且只有在通道粗糙度值限制在合理范围内时,才能成功进行深度校准。校准后的平均河道深度在根据河流横截面调查数据确定的等效值的0.9?m(16 %%误差)以内,表明一系列中等分辨率的SAR数据可用于成功校准深度参数二维水力模型

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