Toward Improved Comparisons Between Land‐Surface‐Water‐Area Estimates From a Global River Model and Satellite Observations
Toward Improved Comparisons Between Land‐Surface‐Water‐Area Estimates From a Global River Model and Satellite Observations
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DOI：
10.1029/2020wr029256
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发表时间：
2021-04
影响因子：
5.4
通讯作者：
Xudong Zhou;C. Prigent;Dai Yamazaki
中科院分区：
文献类型：
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作者：
Xudong Zhou;C. Prigent;Dai Yamazaki
地表水是全球水循环的重要组成部分。与卫星遥感相比，在水面面积的模拟中，时间延展性和空间连续性都具有上级优势。然而，在全球范围内代表不同种类的地表沃茨的模型缺乏全面的评价。我们使用基于流域的宏观尺度洪泛区模型（CaMa-Flood）（一种全球水动力学模型）估算了陆地表面水域面积（LSWA），并将估算值与全球陆地卫星3 ″分辨率（赤道处为1090 m）进行了比较。结果表明，这两种方法在LSWA的一般空间模式（例如，主要河流和湖泊、露天洪泛区），但在几种地表条件下发现了全球一致的不匹配。CaMa ‐ Flood低估了北方高纬度地区和沿海地区的LSWA，因为模型的物理假设没有考虑局部洼地或小型沿海河流中孤立湖泊的存在。相比之下，在森林覆盖地区，模型估计的LSWA大于Landsat估计值（例如，亚马逊盆地）由于植被对于光学卫星感测的不透明性，以及在农田地区由于缺乏动态水过程（例如，再渗透、蒸发和水消耗）和水基础设施的限制（例如，运河、堤坝）。这些全球一致的差异可以合理地解释模型的物理假设或光学卫星传感特性。应用过滤器（例如，洪泛区地形掩模，森林和农田掩模）与两个数据集的比较提高了比较的可靠性，并允许将剩余的局部尺度差异归因于局部变化的因素（例如，通道参数、大气强迫）。
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).