Digital elevation data are essential to estimate coastal vulnerability to flooding due to sea-level rise. Shuttle Radar Topography Mission (SRTM) 1 arc-second global is considered the best free global digital elevation data set available. Inundation estimates from SRTM, however, are subject to uncertainty due to inaccuracies in the elevation data. Small systematic errors in low, flat areas can generate large errors in inundation models, and SRTM is subject to positive bias in the presence of vegetation canopy, such as along channels and within marshes. In this study, we conducted an error assessment and developed a statistical error model for SRTM to improve the quality of elevation data in the Mississippi River Delta (MRD) region. Vegetation cover, SRTM elevation, and slope were found to be closely associated with SRTM error for a random sample of 10,000 small sites across the MRD region, with an ordinary least squares regression model using these variables explaining over 80 percent of the variation in error. Residuals from this model were spatially autocorrelated, and a variogram model was readily fit to them. We conclude by speculating on the utility of application of this model, developed for the MRD region, to similar near-coastal riverine regions around the world.

数字高程数据对于估计沿海因海平面上升而遭受洪水的脆弱性至关重要。航天飞机雷达地形任务 (SRTM) 1 弧秒全球被认为是可用的最佳免费全球数字高程数据集。然而，由于高程数据的不准确，来自 SRTM 的洪水估计会受到不确定性的影响。低洼平坦区域的小系统误差会在淹没模型中产生大误差，并且 SRTM 在植被冠层存在的情况下会受到正偏差的影响，例如沿河道和沼泽内。在本研究中，我们对 SRTM 进行了误差评估并开发了统计误差模型，以提高密西西比河三角洲 (MRD) 地区高程数据的质量。植被覆盖、SRTM 海拔、发现 MRD 区域内 10,000 个小站点的随机样本的 SRTM 误差和斜率密切相关，使用这些变量的普通最小二乘回归模型解释了 80% 以上的误差变化。该模型的残差在空间上是自相关的，并且变异函数模型很容易拟合它们。最后，我们推测了这种为 MRD 地区开发的模型在世界各地类似的近沿海河流地区的应用效用。