Uhlemann Sebastian, Dafflon Baptiste, Wainwright Haruko Murakami, Williams Kenneth Hurst, Minsley Burke, Zamudio Katrina, Carr Bradley, Falco Nicola, Ulrich Craig, Hubbard Susan
Earth and Environmental Sciences Area, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA.
Rocky Mountain Biological Laboratory, Gothic, CO 81224, USA.
Sci Adv. 2022 Mar 25;8(12):eabj2479. doi: 10.1126/sciadv.abj2479. Epub 2022 Mar 23.
Bedrock property quantification is critical for predicting the hydrological response of watersheds to climate disturbances. Estimating bedrock hydraulic properties over watershed scales is inherently difficult, particularly in fracture-dominated regions. Our analysis tests the covariability of above- and belowground features on a watershed scale, by linking borehole geophysical data, near-surface geophysics, and remote sensing data. We use machine learning to quantify the relationships between bedrock geophysical/hydrological properties and geomorphological/vegetation indices and show that machine learning relationships can estimate most of their covariability. Although we can predict the electrical resistivity variation across the watershed, regions of lower variability in the input parameters are shown to provide better estimates, indicating a limitation of commonly applied geomorphological models. Our results emphasize that such an integrated approach can be used to derive detailed bedrock characteristics, allowing for identification of small-scale variations across an entire watershed that may be critical to assess the impact of disturbances on hydrological systems.
基岩属性量化对于预测流域对气候扰动的水文响应至关重要。在流域尺度上估算基岩水力属性本质上具有挑战性，尤其是在以裂隙为主的区域。我们的分析通过将钻孔地球物理数据、近地表地球物理和遥感数据相联系，测试了流域尺度上地上和地下特征的协变性。我们使用机器学习来量化基岩地球物理/水文属性与地貌/植被指数之间的关系，并表明机器学习关系能够估算出它们的大部分协变性。尽管我们可以预测整个流域的电阻率变化，但输入参数变异性较低的区域能提供更好的估算结果，这表明了常用地貌模型存在局限性。我们的结果强调，这种综合方法可用于推导详细的基岩特征，从而能够识别整个流域内的小尺度变化，而这些变化对于评估扰动对水文系统的影响可能至关重要。
