TY - GEN
T1 - Kriging method for estimation of groundwater resources in a basin with scarce monitoring data
AU - Lu, Chengpeng
AU - Shu, Longcang
AU - Chen, Xunhong
AU - Tao, Yuezan
AU - Zhang, Ying
PY - 2009
Y1 - 2009
N2 - Construction of the water table map is a key step in the assessment of water resotjrces. However, the scarcity of groundwater monitoring data in some basins remains a problem for determination of a reliable vario^ram model, which is the starting point for kriging interpolation. Researchers have used the secondary variable, the sampling number of which is usually much greater than that of the primary variable, in assisting the spatial interpolation of the primary variable, e.g. by the regression kriging and co-kriging methods. These methods still require a variogram model to characterize the spatial structure of the primaiy variable. In this study, the authors proposed an approach that derives the variogram model of the groundwater level based on the elevation of the land surface data sets. The measurements of land surface elevation are widely available to researchers, and the density of the data locations is much larger than that of groundwater monitoring records. The land surface elevation was assumed to have a linear relationship with the groundwater level. A relationship between the variogram model for the groundwater level and the variogram model for the land surface elevation were estabhshed; the variogram model for the former can be directly inferred from the variogram model of the latter. In the derivation of the groundwater level variogram, the precipitation data can also be taken into account. This approach was implemented for the Nanjing watershed, China. A variogram model of the groundwater level was obtained from the DEM data set of 1000 m × 1000 m grid spacing.
AB - Construction of the water table map is a key step in the assessment of water resotjrces. However, the scarcity of groundwater monitoring data in some basins remains a problem for determination of a reliable vario^ram model, which is the starting point for kriging interpolation. Researchers have used the secondary variable, the sampling number of which is usually much greater than that of the primary variable, in assisting the spatial interpolation of the primary variable, e.g. by the regression kriging and co-kriging methods. These methods still require a variogram model to characterize the spatial structure of the primaiy variable. In this study, the authors proposed an approach that derives the variogram model of the groundwater level based on the elevation of the land surface data sets. The measurements of land surface elevation are widely available to researchers, and the density of the data locations is much larger than that of groundwater monitoring records. The land surface elevation was assumed to have a linear relationship with the groundwater level. A relationship between the variogram model for the groundwater level and the variogram model for the land surface elevation were estabhshed; the variogram model for the former can be directly inferred from the variogram model of the latter. In the derivation of the groundwater level variogram, the precipitation data can also be taken into account. This approach was implemented for the Nanjing watershed, China. A variogram model of the groundwater level was obtained from the DEM data set of 1000 m × 1000 m grid spacing.
KW - Geostatistics
KW - Groundwater resources
KW - Kriging
KW - Regression
UR - http://www.scopus.com/inward/record.url?scp=78751651181&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=78751651181&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:78751651181
SN - 9781907161049
T3 - IAHS-AISH Publication
SP - 136
EP - 144
BT - New Approaches to Hydrological Prediction in Data-sparse Regions
T2 - Symposium HS.2 at the Joint Convention of the International Association of Hydrological Sciences, IAHS and the International Association of Hydrogeologists, IAH
Y2 - 6 September 2009 through 12 September 2009
ER -