Inverse Parametrization of a Regional Groundwater Flow Model with the Aid of Modelling and GIS: Test and Application of Different Approaches

Abstract

Abstract:The use of inverse methods allow efficient model calibration. This study employs PEST to calibrate a large catchment scale transient flow model. Results are demonstrated by comparing manually calibrated approaches with the automated approach. An advanced Tikhonov regularization algorithm was employed for carrying out the automated pilot point (PP) method. The results indicate that automated PP is more flexible and robust as compared to other approaches. Different statistical indicators show that this method yields reliable calibration as values of coefficient of determination (R 2 ) range from 0.98 to 0.99, Nash Sutcliffe efficiency (ME) range from 0.964 to 0.976, and root mean square errors (RMSE) range from 1.68 m to 1.23 m, for manual and automated approaches, respectively. Validation results of automated PP show ME as 0.969 and RMSE as 1.31 m. The results of output sensitivity suggest that hydraulic conductivity is a more influential parameter. Considering the limitations of the current study, it is recommended to perform global sensitivity and linear uncertainty analysis for the better estimation of the modelling results.

Spatiotemporal Analysis of Extracted Groundwater Volumes Estimated from Electricity Consumption

“…In addition, groundwater pumping and recharge rates are estimated differently. In general, these rates can be evaluated using numerical inverse methods, water‐table fluctuation method, water budget method, and various electric consumption methods (Arnold et al 2000; Healy and Cook 2002; Usman et al 2018). These methods are often used to estimate model parameters.…”

Hydrogeological Modeling in Tropical Regions via FeFlow

“…A good calibration of a model ensures that the vestigial data between the observed and the simulated data are minimized and the parameters uncertainties are lower (Gan et al, 2018;Simmons et al, 2017). This procedure was developed using the inverse method (M. Usman et al, 2018;X. Wang, Jardani, & Jourde, 2017).…”

“…This given the swiftness that this technique shows to determine the best adjustment parameters, applying low subjectivity in the calibration procedure and also the availability of hydrogeological modeling software to easily integrate the hydrological results (N.-Z. Sun & Sun, 2015; M. Usman et al, 2018;Zhou, Gómez-Hernández, & Li, 2014). Despite this advantage, the method is limited to the satisfactory sampling of field data (Llopis-Albert, Merigó, & Palacios-Marqués, 2015;Pool, Carrera, Alcolea, & Bocanegra, 2015;H.…”

“…The use of those CZ for the values assignation to hydraulic parameters can lead to unnecessary uncertainties in the modeling and, therefore, to produce erroneous parameter values and current geological homogeneity (Linde, Ginsbourger, Irving, Nobile, & Doucet, 2017;Tiedeman & Green, 2013;Woodward, Wöhling, & Stenger, 2016). Having this into consideration, the PP technique has been proposed to help improve the spatial variability interpretation of parameters, in cases of groundwater flow modeling that cannot be obtained by working with CZ (Christensen & Doherty, 2008;Jiménez, Mariethoz, Brauchler, & Bayer, 2016;Le Ravalec-Dupin, 2010;M. Usman et al, 2018).…”