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.

中文翻译：

---

达到规模地貌约束远程河流量估算

遥感方面的最新进展以及即将启动的NASA / CNES / CSA / UKSA联合地表水和海洋地形（SWOT）卫星的发布表明，可以改善未开垦盆地的河流流量估算。现有的排放方法依靠“先验河知识”来推断不是直接从空间测量的参数。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，可以改善流量估算。我们使用超过370,000个原位水力观测数据作为训练数据，测试了无监督学习和“专家”方法，以通过遥感将这些水力学和地貌学分配给河流。这种干预以及对模型物理的更新，构成了一种我们称为“ geoBAM”的新方法，它是对贝叶斯多工位液压几何曼宁（BAM）算法的更新。我们在Landsat影像上测试了geoBAM，该影像超过了加拿大Mackenzie流域的7,500多条河流（已测量108条），并针对模拟SWOT观测而没有测量误差的19条河流的模拟水力数据进行了测试。geoBAM与BAM相比取得了显着改善，麦肯齐河的纳什-萨特克利夫效率中值（NSE）从-0.05提高到0.26，SWOT河的Nash-Sutcliffe效率中值从0.16提高到0.46。此外，在78/108处麦肯齐河和8/19 SWOT河中，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对河流进行参数化，但是如果参数分配不正确，则预测准确性会下降。这种方法很容易映射到全球范围内的河流，并为改善未来的流量估算铺平了道路，尤其是与水文模型结合使用时。