Paper publication - Frontiers

Hydrodynamic models in coastal and estuarine systems are typically constrained by sparse bathymetry, boundary, and validation data, especially in regions where field campaigns are costly or impractical. Here we develop and test a fully satellite-driven framework for hydrodynamic modeling in South Africa’s Langebaan Lagoon without using any local in situ measurements. Bathymetry is derived by training multispectral Sentinel-2 reflectance against ICESat-2 ATL24 photon-derived depths using an XGBoost model optimized with Bayesian search. The final satellite derived bathymetry reproduces independent ATL24 points with RMSE = 0.45 m and R² = 0.97. This bathymetry was used in a depth-averaged Delft3D Flexible Mesh model driven at the open boundary by TPXO tidal harmonics and by ERA5 winds. We validate modeled water surface elevation against 16 SWOT low-rate (250 m, unsmoothed) passes in 2023. SWOT–model comparisons yield an overall RMSE of 0.11 m and R² = 0.61, with typical point differences \textless 0.10 m (~5% of the 2 m tidal range), and showed consistent spatial gradients in water level from the offshore boundary, through Saldanha Bay, and into the lagoon. At the offshore boundary, TPXO and SWOT sea surface heights agree closely (R² = 0.86). A 26 min phase lag, determined using a lag-correlation analysis, reduces the TPXO–SWOT RMSE from 0.18 m to 0.11 m, indicating that phase differences explain some of the mismatch, with remaining differences likely linked to non-tidal signals. Our results demonstrate that combining passive optical, photon-counting LiDAR, radar interferometry, and global tidal/atmospheric models enables robust, transferrable hydrodynamic modeling in data-scarce coastal systems, offering a cost-effective pathway for monitoring.

海岸与河口系统中的水动力模型（Hydrodynamic models）通常受限于稀疏的水深地形、边界条件与验证数据，尤其在野外实地观测作业成本高昂或不可行的区域。本研究针对南非兰格巴恩潟湖（Langebaan Lagoon）开发并测试了一套完全基于卫星的水动力建模方案，全程未引入任何本地原位实测数据。研究通过经贝叶斯搜索优化的XGBoost模型，将多光谱Sentinel-2影像反射率与ICESat-2卫星ATL24产品的光子反演水深进行训练，反演得到目标区域的水深地形数据。最终卫星反演的水深地形数据对独立ATL24测点的拟合效果极佳，均方根误差（RMSE）为0.45米，决定系数（R²）为0.97。该水深地形数据被应用于深度平均的Delft3D柔性网格模型（Delft3D Flexible Mesh），该模型的开边界强迫由TPXO潮汐调和常数与ERA5再分析风场驱动。研究采用2023年获取的16轨SWOT（Surface Water and Ocean Topography）低速（250米分辨率，未做平滑处理）过境观测数据，对模拟得到的水面高程进行验证。SWOT观测与模型模拟结果的整体均方根误差为0.11米，决定系数为0.61；典型测点的水位差值小于0.10米（约为2米潮差的5%），且从外海边界经萨达尼亚湾（Saldanha Bay）至潟湖内部的水位空间梯度分布特征一致。在外海边界处，TPXO潮汐模型与SWOT观测的海面高度吻合度极高（R²=0.86）。通过滞后相关分析得到的26分钟相位滞后，将TPXO潮汐模型与SWOT观测的均方根误差从0.18米降至0.11米，表明相位差异是造成部分匹配偏差的原因，剩余偏差或与非潮汐信号相关。本研究结果表明，结合被动光学遥感、光子计数激光雷达、雷达干涉测量以及全球潮汐/大气模型，可在数据匮乏的海岸系统中实现可靠且可迁移的水动力建模，为海岸监测提供了一条高性价比的技术路径。