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Groundwater in the E arth's critical zone: Relevance to large‐scale patterns and processes
Author(s) -
Fan Ying
Publication year - 2015
Publication title -
water resources research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.863
H-Index - 217
eISSN - 1944-7973
pISSN - 0043-1397
DOI - 10.1002/2015wr017037
Subject(s) - groundwater , water table , hydrology (agriculture) , bedrock , environmental science , land cover , drainage basin , streams , earth science , wetland , scale (ratio) , geology , physical geography , land use , geography , ecology , geomorphology , cartography , geotechnical engineering , biology , computer network , computer science
Abstract Although we have an intuitive understanding of the behavior and functions of groundwater in the Earth's critical zone at the scales of a column (atmosphere‐plant‐soil‐bedrock), along a toposequence (ridge to valley), and across a small catchment (up to third‐order streams), this paper attempts to assess the relevance of groundwater to understanding large‐scale patterns and processes such as represented in global climate and Earth system models. Through observation syntheses and conceptual models, evidence are presented that groundwater influence is globally prevalent, it forms an environmental gradient not fully captured by the climate, and it can profoundly shape critical zone evolution at continental to global scales. Four examples are used to illustrate these ideas: (1) groundwater as a water source for plants in rainless periods, (2) water table depth as a driver of plant rooting depth, (3) the accessibility of groundwater as an ecological niche separator, and (4) groundwater as the lower boundary of land drainage and a global driver of wetlands. The implications to understanding past and future global environmental change are briefly discussed, as well as critical discipline, scale, and data gaps that must be bridged in order for us to translate what we learn in the field at column, hillslope and catchment scales, to what we must predict at regional, continental, and global scales.