Bulletin of Chinese Academy of Sciences (Chinese Version)
Keywords
groundwater table; agroecosystem; ecosystem processes; experimental research; scientific infrastructure
Abstract
Irrigation zones located in the lower reaches of Yellow River is a vital food security base in China. Currently, this area is facing the problem of rapid decline of groundwater level due to serious water shortage and insufficient water resources allocation. Therefore, there are many unknown and uncertain impacts on the future agriculture and ecological environment, and it is the current hotspot of agroecosystem research. However, under the dynamic scenarios, for innovatively carrying out the studies on large groundwater table from surface, and conducting the critical process researches such as sectional structure of farmland soil, crop root growth dynamics, interaction between surface water and groundwater, groundwater-soil-plant-atmosphere continuum response, dynamic variation and influence mechanism of water-salt-nutrient of agroecosystem, it is urgently needed to build a comprehensive experiment simulation infrastructure. Funded by the "Key Scientific and Technological Infrastructure Construction Project for the Field Station Network of Chinese Academy of Sciences (CAS)", Yucheng Comprehensive Experiment Station of Chinese Academy of Sciences (Shandong Yucheng Agro-ecosystem National Observation and Research Station) completed the platform construction of GroundwaterAgroecosystem Experimental Simulator (Yucheng GAS), an infrastructure with three floors (10 m underground) constituted by 16 bottom sealed sample plots with controllable groundwater levels after five-year strenuous efforts (from 2016 to 2020). Yucheng GAS integrates nine subsystems including the large scale bottom sealed sample plots system, underground water level automatic controlling; soil temperature, water content, and salinity monitoring; soil water sampling; root growth monitoring; groundwater quantity and quality monitoring; critical zone observation of agroecosystem; water-carbon-nitrogen nexus in agroecosystem; and multispectral crop condition monitoring by unmanned aerial vehicle (UAV). Yucheng GAS, a comprehensive monitoring methodology with multiple methods of stereo-position monitoring, multi-factors chromatograph, and multi-scales three-dimensional observation, is expected to support the basic experimental research and international frontier explore in water-carbon-nitrogen cycles in agroecosystem, food security, ecological security, and environmental security under the scenario of dynamically changed groundwater in future.
First page
852
Last Page
861
Language
Chinese
Publisher
Bulletin of Chinese Academy of Sciences
References
1 欧阳竹, 邓祥征, 孙志刚, 等. 面向国民经济主战场的区域农业研究. 地理学报, 2020, 75(12):2636-2654.
2 国家统计局. 中国统计年鉴. 北京:中国统计出版社, 2020.
3 吴凯, 唐登银, 谢贤群. 黄淮海平原水量变化对农业生产力的影响及对策. 中国生态农业学报, 2001, 9(1):40-42.
4 吴凯, 唐登银, 谢贤群. 黄淮海平原典型区域的水问题和水管理. 地理科学进展, 2000, 19(2):136-141.
5 Chen J, Fukushima Y, Taniguchi M. Groundwater and its association with sustainability of agriculture in the North China Plain Changes in Water Resources Systems:Methodologies to maintain water security and ensure integrated management//Proceedings of Symposium HS3006 at IUGG2007. Perugia:IUGG, 2007:258-265.
6 Piao S, Ciais P, Huang Y, et al. The impacts of climate change on water resources and agriculture in China. Nature, 2010, 467:43-51.
7 Tilman D, Fargione J, Wolff B, et al. Forecasting agriculturally driven global environmental change. Science, 2001, 292:281-284.
8 Zhang Y, Shi P, Li F D, et al. Quantification of nitrate sources and fates in rivers in an irrigated agricultural area using environmental isotopes and a Bayesian isotope mixing model. Chemosphere, 2018, 208:493-501.
9 王宏乾. 黄河下游引黄供水规模变化及影响因素分析. 西安:西安理工大学, 2007.
10 Mougin C, Azam D, Caquet T, et al. A coordinated set of ecosystem research platforms open to international research in ecotoxicology, AnaEE-France. Environmental Science and Pollution Research International, 2015, 22(20):16215-16228.
11 Clobert J, Chanzy A, Le Galliard J-F, et al. How to integrate experimental research approaches in ecological and environmental studies:AnaEE France as an example. Frontiers in Ecology and Evolution, 2018, 6:43.
12 Norby R J, Zak D R. Ecological lessons from free-air CO2 enrichment (FACE) experiments. Annual Review of Ecology, Evolution, and Systematics, 2011, 42(1):181-203.
13 牛书丽, 王松, 汪金松, 等. 大数据时代的整合生态学研究——从观测到预测. 中国科学:地球科学, 2020, 50(10):1323-1338.
14 于贵瑞, 牛书丽, 李发东, 等. 陆地生态系统环境控制实验的研究方法及技术体系. 应用生态学报, 2021, 32(7):2275-2289.
15 Medlyn B E, Zaehle S, de Kauwe M G, et al. Using ecosystem experiments to improve vegetation models. Nature Climate Change, 2015, 5(6):528-534.
16 朱教君, 高添, 于立忠, 等. 温带次生林生态系统塔群监测研究平台(清原科尔塔群). 中国科学院院刊, 2021, 36(3):351-361.
17 沈彦俊, 闵雷雷, 吴林, 等. 华北山前平原农田关键带观测研究平台(栾城关键带观测平台). 中国科学院院刊, 2021, 36(4):502-511.
18 张志山, 赵洋, 张亚峰, 等. 中国北方沙区水量平衡自动模拟监测系统(沙坡头蒸渗仪群). 中国科学院院刊, 2021, 36(6):733-743.
19 杨萍. 中国科学院野外科学观测研究网络未来发展的思考. 中国科学院院刊, 2021, 36(1):104-112.
20 杨萍, 白永飞, 宋长春, 等. 野外站科研样地建设的思考、探索与展望. 中国科学院院刊, 2020, 35(1):125-135.
21 杨萍, 于秀波, 庄绪亮, 等. 中国科学院中国生态系统研究网络(CERN) 的现状及未来发展思路. 中国科学院院刊, 2008, 23(6):555-561.
22 杨萍. 需求导向优势互补合作共赢推进野外站联盟建设——野外站联盟建设的进展与展望. 中国科学院院刊, 2014, 29(5):636-639.
23 Du K, Qiao Y, Zhang Q, et al. Modeling soil water content and crop-growth metrics in a wheat field in the North China plain using RZWQM2. Agronomy, 2021, 11(6):1245.
24 李琦, 李发东, 张秋英, 等. 基于HYDRUS模型的华北平原小麦种植区水盐运移模拟. 中国生态农业学报(中英文), 2021, 29(6):1085-1094.
Recommended Citation
OUYANG, Zhu; QIAO, Yunfeng; ZHAN, Chesheng; SUN, Zhigang; YU, Yanchun; CAI, Xiaoguang; and LI, Fadong
(2021)
"Groundwater-Agroecosystem Experimental Simulator (Yucheng GAS),"
Bulletin of Chinese Academy of Sciences (Chinese Version): Vol. 36
:
Iss.
7
, Article 19.
DOI: https://doi.org/10.16418/j.issn.1000-3045.20210705006
Available at:
https://bulletinofcas.researchcommons.org/journal/vol36/iss7/19