Abstract
The aquifer heterogeneity is often simplified while conceptualizing numerical model due to lack of field data. Conducting field measurements to estimate all the parameters at the aquifer scale may not be feasible. Therefore, it is essential to determine the most significant parameters which require field characterization. For this purpose, the sensitivity analysis is performed on aquifer parameters, viz., anisotropic hydraulic conductivity, effective porosity and longitudinal dispersivity. The results of the sensitivity index and root mean square deviation indicated, that the longitudinal dispersivity and anisotropic hydraulic conductivity are the sensitive aquifer parameters to evaluate seawater intrusion in the study area. The sensitive parameters are further characterized at discrete points or at local scale by using regression analysis. The longitudinal dispersivity is estimated at discrete well points based on Xu and Eckstein regression formula. The anisotropic hydraulic conductivity is estimated based on established regression relationship between hydraulic conductivity and electrical resistivity with R2 of 0.924. The estimated hydraulic conductivity in x and y-direction are upscaled by considering the heterogeneous medium as statistically homogeneous at each layer. The upscaled model output is compared with the transversely isotropic model output. The bias error and root mean square error indicated that the upscaled model performed better than the transversely isotropic model. Thus, this investigation demonstrates the necessity of considering spatial heterogeneous parameters for effective modelling of the seawater intrusion in a layered coastal aquifer.
Highlights
The coastal aquifers provide fresh groundwater for more than 2 billion people worldwide [1].Groundwater stored in the coastal aquifers is susceptible to degradation due to its proximity to seawater, in combination with the intensive water demands
K field and longitudinal dispersivity are usedare as input conceptual
The performance of both the models are evaluated by bias error (b) and model numerical simulation results. The performance of both the models are evaluated by bias error root mean square error (RMSE)
Summary
The coastal aquifers provide fresh groundwater for more than 2 billion people worldwide [1]. Groundwater stored in the coastal aquifers is susceptible to degradation due to its proximity to seawater, in combination with the intensive water demands. The groundwater models provide a scientific and predictive tool for determining the appropriate solutions for SWI problems. Substantial research effort spanning a period of about 50 years has been devoted in understanding the groundwater flow and SWI at aquifer scale [2,3,4,5,6,7,8,9,10]. The recent studies pointed out the heterogeneity, anisotropy and layering are often neglected or simplified while conceptualizing numerical model at both aquifer and global scales [11,12]
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