Data for Variable streamflow response to forest disturbance in the western US: A large-sample hydrology approach

This resource contains the data and scripts used for:
Goeking, S. A. and D. G. Tarboton, (2022). Variable streamflow response to forest disturbance in the western US: A large-sample hydrology approach. Water Resources Research, 58, e2021WR031575. https://doi.org/10.1029/2021WR031575.

Abstract from the paper:
Forest cover and streamflow are generally expected to vary inversely because reduced forest cover typically leads to less transpiration and interception. However, recent studies in the western US have found no change or even decreased streamflow following forest disturbance due to drought and insect epidemics. We investigated streamflow response to forest cover change using hydrologic, climatic, and forest data for 159 watersheds in the western US from the CAMELS dataset for the period 2000-2019. Forest change and disturbance were quantified in terms of net tree growth (total growth volume minus mortality volume) and mean annual mortality rates, respectively, from the US Forest Service’s Forest Inventory and Analysis database. Annual streamflow was analyzed using multiple methods: Mann-Kendall trend analysis, time trend analysis to quantify change not attributable to annual precipitation and temperature, and multiple regression to quantify contributions of climate, mortality, and aridity. Many watersheds exhibited decreased annual streamflow even as forest cover decreased. Time trend analysis identified decreased streamflow not attributable to precipitation and temperature changes in many disturbed watersheds, yet streamflow change was not consistently related to disturbance, suggesting drivers other than disturbance, precipitation, and temperature. Multiple regression analysis indicated that although change in streamflow is significantly related to tree mortality, the direction of this effect depends on aridity. Specifically, forest disturbances in wet, energy-limited watersheds (i.e., where annual potential evapotranspiration is less than annual precipitation) tended to increase streamflow, while post-disturbance streamflow more frequently decreased in dry water-limited watersheds (where the potential evapotranspiration to precipitation ratio exceeds 2.35).

The following scripts (R language and environment for statistical computing) produce the results, figures, and tables in this paper (in the order in which they appear in the paper; requires either running data compilation/aggregation scripts first OR using provided data files watersheds.csv and wb_filtered.csv):
1. Map_watersheds.r
2. Analysis_M-K_trend_test.r
3. analysis_M-K_quadrant_figure.r
4. analysis_timetrend_linear.r
5. analysis_regressn_w-veg.r

The following scripts (R) compile the data, aggregated from other sources prior to the analyses in the scripts listed above:
1. compilation_CAMELS.r
2. compilation_Daymet.r
3. compilation_USGS.r
4. compilation_FIA.r
5. compilation_CAMELS_Daymet_USGS.r (must run scripts #1-3 first)
6. watershed_compilation.r (must run scripts #1-5 first)

本资源包含了用于以下研究的数据和脚本：Goeking, S. A. 和 D. G. Tarboton (2022). 美国西部森林干扰对径流变化的响应：基于大样本水文方法。水文学刊，58，e2021WR031575。https://doi.org/10.1029/2021WR031575。

论文摘要：森林覆盖和径流通常预期呈负相关，因为森林覆盖减少通常会导致蒸腾作用和截留作用减少。然而，最近美国西部的研究发现，由于干旱和虫害疫情，森林干扰后的径流并未发生变化，甚至有所下降。本研究利用来自美国西部159个流域的CAMELS数据集（2000-2019年）的水文、气候和森林数据，调查了森林覆盖变化对径流的响应。森林变化和干扰分别以净树木生长量（总生长量减去死亡率）和平均年死亡率来量化，数据来源于美国森林局森林调查与分析数据库。年度径流采用多种方法进行分析：Mann-Kendall趋势分析、时间趋势分析以量化不可归因于年降水和温度的变化，以及多元回归以量化气候、死亡率和干旱的贡献。许多流域在森林覆盖减少的同时，年度径流也出现下降。时间趋势分析发现，在许多受干扰的流域中，径流的变化并非由降水和温度变化引起，但径流变化与干扰之间的关系并不一致，表明除了干扰、降水和温度之外还有其他驱动因素。多元回归分析表明，尽管径流变化与树木死亡率显著相关，但这种效应的方向取决于干旱程度。具体而言，在湿润、能量限制型流域（即年潜在蒸散量小于年降水量的流域）中，森林干扰往往会增加径流，而在干旱、水资源限制型流域（即潜在蒸散量与降水量之比超过2.35的流域）中，干扰后的径流更频繁地减少。

以下脚本（R语言和统计计算环境）生成了论文中的结果、图表和表格（按论文中的顺序出现；需要先运行数据编译/聚合脚本，或使用提供的watersheds.csv和wb_filtered.csv数据文件）：
1. Map_watersheds.r
2. Analysis_M-K_trend_test.r
3. analysis_M-K_quadrant_figure.r
4. analysis_timetrend_linear.r
5. analysis_regressn_w-veg.r

以下脚本（R）在上述脚本的分析之前，从其他来源编译数据：
1. compilation_CAMELS.r
2. compilation_Daymet.r
3. compilation_USGS.r
4. compilation_FIA.r
5. compilation_CAMELS_Daymet_USGS.r（必须先运行脚本#1-3）
6. watershed_compilation.r（必须先运行脚本#1-5）