Disentangling the Impacts of Microtopography and Shrub Distribution on Snow Depth in a Subarctic Watershed: Toward a Predictive Understanding of Snow Spatial Variability: Supporting Data and Code

This repository contains R code and associated datasets for reproducing the analysis described in the manuscript titled “Disentangling the Impacts of Microtopography and Shrub Distribution on Snow Depth in a Subarctic Watershed: Toward a Predictive Understanding of Snow Spatial Variability” (DOI: 10.1029/2024JG008604). The provided scripts facilitate a comprehensive analysis of snow depth variability influenced by microtopography and vegetation distribution in a subarctic watershed. Included datasets are high-resolution spatial maps of snow depth, terrain elevation, vegetation height, and distance from shrubs taller than 1 meter, all formatted as text files (.txt). These data are fully describe in doi:10.15485/2316038. Users can adapt the provided R scripts to accommodate different data formats or larger spatial domains, noting that some output files may require modification due to their size.The code includes implementations for boosted regression tree analysis adapted from methods outlined in Elith et al. (2008). Users interested in understanding or modeling landscape-scale snow distribution patterns, particularly in Arctic or subarctic ecosystems, will find this package useful. The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research.The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska.Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

本代码仓库包含用于复现题为《解析亚寒带流域微地形与灌丛分布对雪深的影响：实现雪空间变异的预测性认知》（DOI: 10.1029/2024JG008604）的学术论文中所述分析的R代码与配套数据集。所附脚本可全面分析亚寒带流域内受微地形与植被分布调控的雪深空间变异。本次配套提供的数据集包含雪深、地形高程、植被高度以及距1米以上灌丛的距离的高分辨率空间分布图，所有文件均采用纯文本格式（.txt）存储。所有数据的详细说明可参见DOI: 10.15485/2316038。用户可对所附R脚本进行适配调整，以兼容不同的数据格式或更大空间范围的研究场景，需注意部分输出文件可能因体积过大而需要进行修改。本代码包含基于Elith等人（2008）提出的方法改编的提升回归树（boosted regression tree）分析实现。对于希望理解或模拟景观尺度雪分布格局（尤其是北极或亚寒带生态系统）的研究者而言，本工具包极具参考价值。下一代生态系统实验：北极（Next-Generation Ecosystem Experiments: Arctic，简称NGEE Arctic）是一项旨在通过构建碳储量丰富的北极生态系统的预测性认知及其对气候的反馈机制，以降低地球系统模型不确定性的研究计划。该项目由美国能源部生物与环境研究办公室资助。NGEE Arctic项目设有两处野外研究站点：其一为位于阿拉斯加州乌特恰维克（原称巴罗）附近北坡的巴罗环境观测站（Barrow Environmental Observatory，简称BEO）内的北极多边形苔原沿海区域；其二为阿拉斯加州诺姆以北苏华德半岛的不连续永久冻土区内的多处区域。通过实地观测、控制实验与现有数据集的综合分析，NGEE Arctic为多尺度建模提供了更为完善的知识库，并推动了美国能源部地球系统模型——能源极端尺度地球系统模型（Energy Exascale Earth System Model，简称E3SM），尤其是其陆面模型组件（ELM）——在泛北极全球尺度下的过程表征精度提升。