Spatial-Temporal Patterns of Shallow Groundwater Levels in the Yellow River Delta, China

被引:0
|
作者
Fan, Xiaomei [1 ]
Johnson, Gwynn R. [2 ]
Xu, Jun [3 ]
Liu, Gaohuan [4 ]
机构
[1] Nanjing Univ Informat Sci & Technol, Sch Geog Sci, Nanjing 210044, Peoples R China
[2] Portland State Univ, Dept Civil & Environm Engn, 1930 SW 4th Ave, Portland, OR 97201 USA
[3] Fuzhou Univ, Spatial Informat Res Ctr Fujian Prov, Fuzhou 350002, Peoples R China
[4] Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China
基金
中国国家自然科学基金;
关键词
Yellow River delta; Groundwater hydrodynamics; Principal component; Expansion coefficients (EC); Water resource management; BAYESIAN MAXIMUM-ENTROPY; REGIONAL KENDALL TEST; RAINFALL; SYSTEM; ZONES; WATER; GIS;
D O I
10.1061/JHYEFF.HEENG-5701
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
Analysis of the various hydrologic features responsible for the spatial and temporal variability in large scale subsurface systems is a critical component of water resources engineering and management. Traditionally, modeling large-scale groundwater flow patterns includes simulations with underlying parameter estimations and associated overarching assumptions. More recently, subsurface hydrologists are using empirical orthogonal function analysis to separate and quantify the primary hydrologic components contributing to observed regional and local-scale groundwater hydrodynamics. In this case study, modern spatiotemporal geostatistics plus primary hydrologic component analysis were conducted on spatially and temporally distributed groundwater levels measured in the Yellow River Delta. The results demonstrate the significant interplay between surface water hydrodynamics, overall groundwater quality, and groundwater utilization at the local scale, plus the significant impact of regional-scale aquifer recharge on groundwater level seasonal variation, largely driven by precipitation events. With increased accessibility to remote sensing data, additional research using these or similar statistical approaches to interpolate, compress, and decompose those features driving subsurface fluid-flow variability will prove to be highly beneficial to water resource engineers and managers.
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页数:10
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