Ecological aspects of the soil-water-plant-atmosphere system

环境科学 大气(单位) 含水量 水循环 水流 生态系统 土壤水分 水文学(农业) 大气科学 生态学 土壤科学 地理 气象学 地质学 生物 岩土工程
作者
Ravindra Kumar Rekwar,Abhik Patra,Hanuman Singh Jatav,Satish Kumar Singh,Kiran Kumar Mohapatra,Arnab Kundu,Asik Dutta,Ankita Trivedi,L. Devarishi Sharma,Mohsina Anjum,Ajin S. Anil,Sanjib Kumar Sahoo
出处
期刊:Elsevier eBooks [Elsevier BV]
卷期号:: 279-302 被引量:4
标识
DOI:10.1016/b978-0-323-85665-2.00009-1
摘要

The soil-water-plant-atmosphere system (SWPAS) is a “physically integrated, dynamic system in which interacting processes of mass and energy are performed.” The SWPAS system is comprised of four different components with varying physical and chemical properties that ultimately poses a complex mechanism. Water stress is primarily caused due to nonuniform precipitation. The exhaustion of this reservoir by a crop requires its artificial reloading, which is the case of irrigation. Soil moisture has been shown to have major implications for carbon storage and related climatic feedbacks. Therefore, it is more important than ever to understand how the flow of water interacts with ecosystem health and the mechanisms controlling water fluxes at the land-atmosphere interface. Atmosphere acts as an upper buffer that takes up, transforms, and protects water, as a substance, in the climatic system. The soil-plant-atmosphere continuum (SPAC) is the pathway for water moving from soil through plants to the atmosphere. Movement of water occurs in response to differences in the potential energy of water. The flow path of water through SPAC is complex with a series of resistances offered by different components of the system. Different atmospheric, plant canopy, and soil factors affect the water flow through SPAC. With increasing water scarcity, improvement in crop water productivity will be vital in terms of food security for the future generation. As a result, the effect of soil-plant-atmosphere interactions on how ecosystems respond to and exert influence on, the global environment remains difficult to predict.
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