|
|
Shyu*, G., Cheng, B., Chiang, C., Yao, P., & Chang, T. (2011). Applying factor analysis combined with kriging and information entropy theory for mapping and evaluating the stability of groundwater quality variation in Taiwan. Int. J. Environ. Res. Public Health, 8, 1084–1109.
|
|
|
|
Hooshmand, A., Delghandi, M., Izadi, A., & Aali, K. A. (2011). Application of kriging and cokriging in spatial estimation of groundwater quality parameters. Afr. J. Agric. Res., 6 (14), 3402–3408.
|
|
|
|
Dhar, A., & Patil, R. S. (2011). Fuzzy uncertainty based design of groundwater quality monitoring networks. J. Environ. Res. Develop., 5(3a), 683–688.
|
|
|
|
Herckenrath*, D., Langevin, C. D., & Doherty, J. (2011). Predictive uncertainty analysis of a saltwater intrusion modelusing null‐space Monte Carlo. Water Resour. Res., 47, 05504.
|
|
|
|
Jesús Carrera, J. J. H., Luit J. Slooten, Enric Vázquez-Suñé. (2010). Computational and conceptual issues in the calibration of seawater intrusion models. Hydrogeology Journal, 18. Retrieved June 26, 2024, from http://dx.doi.org/10.1007/s10040-009-0524-1
Abstract: The inverse problem of seawater intrusion(SWI) is reviewed. It represents a challenge because of both conceptual and computational difficulties and because coastal aquifer models display many singularities:(1) head measurements need to be complemented with density information; (2) salinity concentration data are
very sensitive to flow within the borehole. Data problems can be reduced by incorporating the measurement process within model calibration; (3) SWI models are extremely sensitive to aquifer bottom topography; (4) the initial conditions may be far from steady state and depend on the location and type of sea-aquifer connection. Problems with aquifer geometry and initial conditions can be addressed by parameterization, which allows for modification during inversion. The four sets of difficulties can be partly overcome by using tidal response and electrical conductivity data, which are highly informative and
provide extensive coverage. Still, SWI inversion is extremely demanding from a computation point of view. Computational improvements are discussed.
|
|
