Dataset of ecosystem elements in Chaohu Lake Basin from 1861 to 2010

As one of the five major lakes in China, Chaohu Lake has a long history of human activities' print and produces significant impacts on the ecological environment and lake water bodies of the basin. Especially with global warming in the past century, the development of industry and agriculture, plus the urbanizing process in Chaohu Lake basin have intensified to shrink the area of vegetation, that's where serious environmental problems such as eutrophication and ecological environment damage were brought up. With the help of measured and simulated multi-source data, this research establishes a high-resolution, long-term dataset of ecosystem climate and vegetation growth. Thin plate smoothing spline spatial interpolation method is applied, based on STASH software, a bioclimate software was developed to establish climate data, including high-resolution data of temperature, precipitation, percentage of sunshine, and four climate elements from 1861 to 2010. This research combines machine learning methods with the NPP algorithm module, vegetation data for the same period are calculated, including NPP, NEP, and LAI data, quality control analysis is also conducted. You are able to find model data for quantitative research on potential vegetation reconstruction and vegetation change in the region, as well as basic data for accurately assessing the impact of climate change on the ecological environment of the Chaohu Lake basin in this dataset. It can also serve as a reference case for the construction of a typical watershed ecosystem dataset in China since the Industrial Revolution.

巢湖作为中国五大淡水湖之一，留存着悠久的人类活动印迹，对其流域的生态环境与湖体水环境具有显著影响。尤其在近百年间，伴随全球变暖、工农业发展与巢湖流域城市化进程的持续推进，流域植被覆盖面积不断缩减，进而引发了富营养化（eutrophication）、生态环境破坏等一系列严峻环境问题。本研究依托多源实测与模拟数据，构建了一套面向巢湖流域生态气候与植被生长的高分辨率长期数据集。本研究基于生物气候学专用软件STASH，采用薄板平滑样条空间插值法（Thin plate smoothing spline spatial interpolation method），构建了1861年至2010年的气温、降水、日照百分率等4项气候要素的高分辨率数据集。本研究将机器学习方法与净初级生产力（Net Primary Productivity, NPP）算法模块相结合，计算得到同期植被相关数据，涵盖净初级生产力（NPP）、净生态系统生产力（Net Ecosystem Productivity, NEP）与叶面积指数（Leaf Area Index, LAI），并完成了质量控制分析。本数据集可为该区域潜在植被修复与植被变化的定量研究提供模型支撑数据，同时可为精准评估气候变化对巢湖流域生态环境的影响提供基础数据支持。此外，本数据集还可为中国工业革命以来典型流域生态系统数据集的构建提供参考范例。