Geostatistical interpolation model selection based on ArcGIS and spatio-temporal variability analysis of groundwater level in piedmont plains, northwest China.
Based on the geo-statistical theory and ArcGIS geo-statistical module, datas of 30 groundwater level observation wells were used to estimate the decline of groundwater level in Beijing piedmont. Seven different interpolation methods (inverse distance weighted interpolation, global polynomial interpolation, local polynomial interpolation, tension spline interpolation, ordinary Kriging interpolation, simple Kriging interpolation and universal Kriging interpolation) were used for interpolating groundwater level between 2001 and 2013. Cross-validation, absolute error and coefficient of determination (R(2)) was applied to evaluate the accuracy of different methods. The result shows that simple Kriging method gave the best fit. The analysis of spatial and temporal variability suggest that the nugget effects from 2001 to 2013 were increasing, which means the spatial correlation weakened gradually under the influence of human activities. The spatial variability in the middle areas of the alluvial-proluvial fan is relatively higher than area in top and bottom. Since the changes of the land use, groundwater level also has a temporal variation, the average decline rate of groundwater level between 2007 and 2013 increases compared with 2001-2006. Urban development and population growth cause over-exploitation of residential and industrial areas. The decline rate of the groundwater level in residential, industrial and river areas is relatively high, while the decreasing of farmland area and development of water-saving irrigation reduce the quantity of water using by agriculture and decline rate of groundwater level in agricultural area is not significant.
School of Water Resources and Environment, China University of Geosciences (Beijing), Beijing, 100083 PR China.
Yong
Y
Xiao
School of Water Resources and Environment, China University of Geosciences (Beijing), Beijing, 100083 PR China.
Xiaomin
X
Gu
School of Water Resources and Environment, China University of Geosciences (Beijing), Beijing, 100083 PR China ; Beijing Water Science and Technology Institute, Beijing, 100044 PR China.
Shiyang
S
Yin
School of Water Resources and Environment, China University of Geosciences (Beijing), Beijing, 100083 PR China.
Jingli
J
Shao
School of Water Resources and Environment, China University of Geosciences (Beijing), Beijing, 100083 PR China.
Yali
Y
Cui
School of Water Resources and Environment, China University of Geosciences (Beijing), Beijing, 100083 PR China.
Qiulan
Q
Zhang
Beijing Water Science and Technology Institute, Beijing, 100044 PR China ; Shandong Agricultural University, Taian, 271018 PR China.
Yong
Y
Niu
China; Groundwater level; Interpolation model; Piedmont plain; Spatio-temporal variability