Probability inundation map for a scenario combining storm surge and sea-level rise

Coastal managers require reliable spatial data on the extent and timing of potential coastal inundation, particularly in a changing climate. Most sea level rise (SLR) vulnerability assessments are undertaken using the easily implemented bathtub approach, where areas adjacent to the sea and below a given elevation are mapped using a deterministic line dividing potentially inundated from dry areas. This method only requires elevation data usually in the form of a digital elevation model (DEM). However, inherent errors in the DEM and spatial analysis of the bathtub model propagate into the inundation mapping. The aim of this study was to assess the impacts of spatially variable and spatially correlated elevation errors in high-spatial resolution DEMs for mapping coastal inundation. Elevation errors were best modelled using regression-kriging. This geostatistical model takes the spatial correlation in elevation errors into account, which has a significant impact on analyses that include spatial interactions, such as inundation modelling. The spatial variability of elevation errors was partially explained by land cover and terrain variables. Elevation errors were simulated using sequential Gaussian simulation, a Monte Carlo probabilistic approach. 1,000 error simulations were added to the original DEM and reclassified using a hydrologically correct bathtub method. The probability of inundation to a scenario combining a 1 in 100 year storm event over a 1 m SLR was calculated by counting the proportion of times from the 1,000 simulations that a location was inundated. This probabilistic approach can be used in a risk-aversive decision making process by planning for scenarios with different probabilities of occurrence. For example, results showed that when considering a 1% probability exceedance, the inundated area was approximately 11% larger than mapped using the deterministic bathtub approach. The probabilistic approach provides visually intuitive maps that convey uncertainties inherent to spatial data and analysis.海岸管理人员亟需可靠的空间数据，以明确潜在海岸淹没的范围与发生时序，在气候变化背景下这一需求尤为迫切。绝大多数海平面上升（Sea Level Rise, SLR）脆弱性评估均采用操作简便的浴缸模型法：以确定性阈值线划分潜在淹没区与干燥区域，据此绘制毗邻海洋且海拔低于给定阈值的区域分布图。该方法仅需以数字高程模型（Digital Elevation Model, DEM）形式呈现的高程数据。然而，数字高程模型固有的误差与浴缸模型的空间分析过程会将误差传递至淹没制图环节。本研究旨在评估高空间分辨率数字高程模型中空间变异且空间相关的高程误差对海岸淹没制图的影响。研究采用回归克里金法对高程误差进行最优建模。该地质统计模型考虑了高程误差的空间相关性，这对包含空间交互作用的分析（如淹没模拟）具有显著影响。高程误差的空间变异可部分通过土地覆盖与地形变量得到解释。研究采用序贯高斯模拟法——一种蒙特卡洛概率模拟方法——对高程误差进行模拟。将1000组误差模拟结果叠加至原始数字高程模型，并采用水文校正后的浴缸模型法进行重分类。针对“1米海平面上升叠加百年一遇风暴”的组合情景，通过统计1000组模拟中某一位置被淹没的次数占比，计算该位置的淹没概率。该概率化方法可用于风险规避型决策流程，即针对不同发生概率的情景开展规划工作。例如，研究结果显示，当考虑1%超越概率时，淹没面积较确定性浴缸模型法绘制的结果约大11%。该概率化方法可生成视觉直观的地图，用以传递空间数据与分析过程中固有的不确定性。