Land surface water is a key component of the global water cycle. Compared to remote sensing by satellites, both temporal extension and spatial continuity are superior in modeling of water surface area. However, overall evaluation of models representing different kinds of surface waters at the global scale is lacking. We estimated land surface water area (LSWA) using the Catchment‐based Macro‐scale Floodplain model (CaMa‐Flood), a global hydrodynamic model, and compared the estimates with Landsat at 3″ resolution (∼90 m at the equator) globally. Results show that the two methodologies show agreement in the general spatial patterns of LSWA (e.g., major rivers and lakes, open‐to‐sky floodplains), but globally consistent mismatches are found under several land surface conditions. CaMa‐Flood underestimates LSWA in high northern latitudes and coastal areas, as the presence of isolated lakes in local depressions or small coastal rivers is not considered by the model's physical assumptions. In contrast, model‐estimated LSWA is larger than Landsat estimates in forest‐covered areas (e.g., Amazon basin) due to the opacity of vegetation for optical satellite sensing, and in cropland areas due to the lack of dynamic water processes (e.g., re‐infiltration, evaporation, and water consumption) and constraints of water infrastructure (e.g., canals, levees). These globally consistent differences can be reasonably explained by the model's physical assumptions or optical satellite sensing characteristics. Applying filters (e.g., floodplain topography mask, forest and cropland mask) to the two datasets improves the reliability of comparison and allows the remaining local‐scale discrepancies to be attributed to locally varying factors (e.g., channel parameters, atmospheric forcing).

中文翻译：

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寻求改进的全球河流模型与卫星观测之间的陆-地-水-面积估计之间的比较

地表水是全球水循环的关键组成部分。与通过卫星进行的遥感相比，时间扩展和空间连续性在水表面积建模方面都更为出色。但是，缺乏在全球范围内代表不同种类地表水的模型的总体评价。我们使用基于集水区的宏观洪泛区模型（CaMa-Flood）（一种全球水动力模型）估算了地表水面积（LSWA），并将估算值与3s分辨率（赤道约90 m）的Landsat进行了比较。结果表明，这两种方法在LSWA的总体空间格局（例如主要河流和湖泊，开阔的洪泛区）中显示出一致性，但是在多个陆地表面条件下发现了全球一致的不匹配。CaMa-Flood低估了北部高纬度地区和沿海地区的LSWA，因为模型的物理假设并未考虑到局部洼地或沿海小河中孤立湖的存在。相反，由于光学卫星感测的植被不透明性，模型覆盖的LSWA在森林覆盖地区（例如，亚马逊河流域）比Landsat估计要大，而在农田地区，由于缺乏动态的水过程（例如，干旱和干旱），其LSWA大于Landsat的估计-渗透，蒸发和耗水）以及水基础设施的限制（例如，运河，堤坝）。这些全局一致的差异可以通过模型的物理假设或光学卫星感测特性来合理解释。应用过滤器（例如洪泛区地形遮罩，