Constraining Remote River Discharge Estimation Using Reach-Scale Geomorphology
Constraining Remote River Discharge Estimation Using Reach-Scale Geomorphology
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DOI：
10.1029/2020wr027949
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发表时间：
2020-11-01
影响因子：
5.4
通讯作者：
Lin, P.
中科院分区：
文献类型：
--
作者：
Brinkerhoff, C. B.;Gleason, C. J.;Lin, P.
遥感领域的最新进展以及即将发射的 NASA/CNES/CSA/UKSA 联合地表水和海洋地形 (SWOT) 卫星将有助于改进未测量流域的河流流量估算。现有的排放方法依赖于“先前的河流知识”来推断并非直接从太空测量的参数。在这里，我们表明，通过根据河流独特的地貌和水力学对河流进行分类和参数化，可以改进流量估算。我们使用超过 370,000 个现场水力观测数据作为训练数据，测试无监督学习和“专家”方法，通过遥感将这些水力和地貌分配给河流。这种干预以及模型物理的更新构成了一种我们称之为“geoBAM”的新方法，这是贝叶斯多站水力几何曼宁 (BAM) 算法的更新。我们使用加拿大麦肯齐河流域 7,500 多条河流（其中 108 条已测量）的 Landsat 图像以及 19 条河流的模拟水力数据（模拟 SWOT 观测结果而没有测量误差）测试了 geoBAM。 geoBAM 比 BAM 有了相当大的改进，将 Mackenzie 河的 Nash-Sutcliffe 效率 (NSE) 中值从 -0.05 提高到 0.26，将 SWOT 河流从 0.16 提高到 0.46。此外，在 78/108 测量的 Mackenzie 河流和 8/19 SWOT 河流中，NSE 至少提高了 0.10。我们将 geoBAM 的改进归因于按类型而不是全局参数化河流，但如果参数分配错误，预测准确性会下降。这种方法很容易映射到全球范围内的河流，并为改进未来的流量估计铺平道路，特别是与水文模型相结合时。
Recent advances in remote sensing and the upcoming launch of the joint NASA/CNES/CSA/UKSA Surface Water and Ocean Topography (SWOT) satellite point toward improved river discharge estimates in ungauged basins. Existing discharge methods rely on "prior river knowledge" to infer parameters not directly measured from space. Here, we show that discharge estimation is improved by classifying and parameterizing rivers based on their unique geomorphology and hydraulics. Using over 370,000 in situ hydraulic observations as training data, we test unsupervised learning and an "expert" method to assign these hydraulics and geomorphology to rivers via remote sensing. This intervention, along with updates to model physics, constitutes a new method we term "geoBAM," an update of the Bayesian At-many-stations hydraulic geometry-Manning's (BAM) algorithm. We tested geoBAM on Landsat imagery over more than 7,500 rivers (108 are gauged) in Canada's Mackenzie River basin and on simulated hydraulic data for 19 rivers that mimic SWOT observations without measurement error. geoBAM yielded considerable improvement over BAM, improving the median Nash-Sutcliffe efficiency (NSE) for the Mackenzie River from -0.05 to 0.26 and from 0.16 to 0.46 for the SWOT rivers. Further, NSE improved by at least 0.10 in 78/108 gauged Mackenzie rivers and 8/19 SWOT rivers. We attribute geoBAM improvement to parameterizing rivers by type rather than globally, but prediction accuracy worsens if parameters are misassigned. This method is easily mapped to rivers at the global scale and paves the way for improving future discharge estimates, especially when coupled with hydrologic models.