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Joint estimation of transmissivities and recharges—application: stochastic characterization of well capture zones

Journal of Hydrology
Publication Date
DOI: 10.1016/j.jhydrol.2003.10.021
  • Stochastic
  • Inverse Modelling
  • Conditioning
  • Sequential Self-Calibrated Algorithm
  • Recharge Estimation
  • Well Capture Zones
  • Design
  • Mathematics


Abstract This paper is concerned about aquifer models that are affected by the uncertainty on hydraulic conductivity and recharge. In particular, we study the impact of these sources of uncertainty on the stochastic characterization of drinking water well capture zones. The estimation of a drinking water well capture zone is important for the design of a protection zone around the well. The estimation is associated with uncertainty due to imperfect knowledge of the input parameters of the groundwater flow and mass transport equations. A first synthetic study shows that the uncertainty of the mean recharge has a larger impact on the well catchment uncertainty than the uncertainty of the spatial variability of recharge. Therefore, it is concluded that in practice the recharge can be estimated for one zone or a limited number of zones, without the need to estimate the recharge on the grid cell scale. The sequential self-calibrated method for the Monte Carlo type inverse modelling of groundwater flow and mass transport was extended for the estimation of recharge rates. Recharge rates can be estimated together with hydraulic conductivities, and possibly other parameters, using both head and concentration data. In a second synthetic study the joint estimation of transmissivities and recharge rates is illustrated. It is found that the transmissivity, hydraulic head and recharge fields are better characterised after the inverse conditioning. In order to stabilize the inverse estimation, it is essential that a regularization term is included in the objective function in case parameters are estimated zone-wise.

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