Channel types predictions for the Sacramento River basin

Hydrologic and geomorphic classifications have gained traction in response to the increasing need for basin-wide water resources management. Regardless of the selected classification scheme, an open scientific challenge is how to extend information from limited field sites to classify tens of thousands to millions of channel reaches across a basin. To address this spatial scaling challenge, we leveraged machine learning to predict reach-scale geomorphic channel types using publicly available geospatial data.随着全流域水资源管理需求的持续增长，水文与地貌分类（Hydrologic and Geomorphic Classifications）日益受到学界重视。无论选用何种分类方案，当前均存在一项开放性科学挑战：如何将有限野外测点的信息推广应用，以对全流域内数万乃至数百万计的河道河段完成分类。为解决这一空间尺度拓展难题，本研究借助机器学习方法，利用公开可得的地理空间数据，对河段尺度的地貌河道类型进行预测。