Water Table Depth Estimates over the Contiguous United States Using a Random Forest Model
Water Table Depth Estimates over the Contiguous United States Using a Random Forest Model
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DOI：
10.1111/gwat.13362
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发表时间：
2023-10
期刊：
影响因子：
2.6
通讯作者：
Yueling Ma;E. Leonarduzzi;Amy Defnet;Peter Melchior;L. Condon;Reed M. Maxwell
中科院分区：
文献类型：
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作者：
Yueling Ma;E. Leonarduzzi;Amy Defnet;Peter Melchior;L. Condon;Reed M. Maxwell
地下水埋深（WTD）对地下水动力学和陆面过程之间的联系有着重要的影响。由于WTD观测的稀缺性，基于物理的地下水模型在大尺度上绘制WTD的能力正在增长;然而，与井观测相比，它们仍然面临着代表模拟WTD的挑战。在这项研究中，我们开发了一种纯数据驱动的方法来估计大陆尺度的WTD。我们应用随机森林（RF）模型估计WTD在大多数的连续美国（CONUS）的基础上提供WTD的观测。估计的WTD与油井观测结果非常一致，Pearson相关系数（r）为0.96（测试期间为0.81），Nash-Sutcliffe效率（NSE）为0.93（测试期间为0.65），均方根误差（RMSE）为6.87 m（测试期间为15.31 m）。每个网格单元的位置被评为最重要的功能，在估计WTD在大多数的CONUS，这可能是一个替代的空间信息。此外，RF模型的不确定性量化使用分位数回归森林。高不确定性通常与具有浅WTD的位置相关联。我们的研究表明，RF模型可以在大部分CONUS上产生合理的WTD估计，为基于物理的建模提供了一种替代方案，用于模拟大规模淡水资源。由于CONUS涵盖许多不同的水文制度，为CONUS训练的RF模型可以转移到具有类似水文制度和有限观测的其他地区。
Water table depth (WTD) has a substantial impact on the connection between groundwater dynamics and land surface processes. Due to the scarcity of WTD observations, physically‐based groundwater models are growing in their ability to map WTD at large scales; however, they are still challenged to represent simulated WTD compared to well observations. In this study, we develop a purely data‐driven approach to estimating WTD at continental scale. We apply a random forest (RF) model to estimate WTD over most of the contiguous United States (CONUS) based on available WTD observations. The estimated WTD are in good agreement with well observations, with a Pearson correlation coefficient (r) of 0.96 (0.81 during testing), a Nash‐Sutcliffe efficiency (NSE) of 0.93 (0.65 during testing), and a root mean square error (RMSE) of 6.87 m (15.31 m during testing). The location of each grid cell is rated as the most important feature in estimating WTD over most of the CONUS, which might be a surrogate for spatial information. In addition, the uncertainty of the RF model is quantified using quantile regression forests. High uncertainties are generally associated with locations having a shallow WTD. Our study demonstrates that the RF model can produce reasonable WTD estimates over most of the CONUS, providing an alternative to physics‐based modeling for modeling large‐scale freshwater resources. Since the CONUS covers many different hydrologic regimes, the RF model trained for the CONUS may be transferrable to other regions with a similar hydrologic regime and limited observations.