全球森林生态系统蓄水能力及其影响因素文献数据集（1964-2017）

本研究团队通过提取森林蓄水能力相关同行评议文献中的观测数据，构建了一套涵盖林冠层、凋落物层和土壤层关键数据的全球森林生态系统蓄水能力及其影响因素文献数据库。数据主要包括：（1）文献背景信息（作者、发表时间、标题）；（2）研究区（洲、国家、纬度）；（3）地形因素（高程、坡度）；（4）气候因素（年均温、年蒸散发）；（5）森林属性因素（林龄、树高、胸径、林分密度、林冠密度、植被覆盖度、叶面积指数、截留率）；（6）凋落物特性因素（凋落物厚度、凋落物质量）；（7）土壤物理性质（毛管孔隙度、非毛管孔隙度）。数据提取步骤如下：在中国知网和Web of Science数据库检索以上数据关键词，经筛查得到1967-2019年发表的同行评议文献259篇，然后汇总文献信息、研究区信息以及不同林层的蓄水能力观测数据，建立起文献数据库。共提取1288次全球观测数据，观测时间为1964-2017年，其中90.94%集中在1990-2017年。本数据集为全球尺度的水文模型校准和森林蓄水影响因素分析提供了基础数据，有助于生成修正模型的全球基准产品，有望降低大尺度模型误差，从而更精确地实现由点到面的森林生态系统蓄水能力模拟和预测。

Our research team constructed a literature database of global forest ecosystem water storage capacity and its influencing factors, covering key data from the canopy layer, litter layer and soil layer, by extracting observation data from peer-reviewed literature related to forest water storage capacity. The dataset mainly includes: (1) Literature background information (authors, publication year, title); (2) Study area (continent, country, latitude); (3) Topographic factors (elevation, slope); (4) Climatic factors (mean annual temperature, annual evapotranspiration); (5) Forest attribute factors (stand age, tree height, diameter at breast height (DBH), stand density, canopy density, vegetation coverage, leaf area index (LAI), interception rate); (6) Litter characteristic factors (litter thickness, litter mass); (7) Soil physical properties (capillary porosity, non-capillary porosity). The data extraction process was as follows: We searched for relevant keywords of this dataset in the China National Knowledge Infrastructure (CNKI) and Web of Science databases, and screened out 259 peer-reviewed articles published between 1967 and 2019. Then we compiled the literature information, study area information and water storage capacity observation data of different forest layers to establish this literature database. A total of 1288 global observation data points were extracted, with the observation period ranging from 1964 to 2017, and 90.94% of which were concentrated between 1990 and 2017. This dataset provides fundamental data for global-scale hydrological model calibration and analysis of factors affecting forest water storage. It facilitates the development of global benchmark products for model refinement, is expected to reduce errors in large-scale models, and enables more accurate point-to-area simulation and prediction of forest ecosystem water storage capacity.

搜集汇总

背景与挑战

背景概述

该数据集是一个全球森林生态系统蓄水能力及其影响因素的文献数据库，涵盖了1964-2017年间1288次全球观测数据，包括地形、气候、森林属性等多维度信息。数据集旨在为水文模型校准和森林蓄水能力分析提供基础数据支持，有助于降低大尺度模型误差。