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 (R2) 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.
Highlights
Groundwater supply is compulsory for the subsistence of agriculture in Pakistan
Some dynamically spatiotemporally variables like groundwater recharge and groundwater pumping along with lateral inflow/outflow were estimated through separate approaches with fair reliability [34], and they were not considered as calibration parameters
Hydraulic conductivity and effective porosity were only selected as primary calibration parameters with equal weights for all the points as according to [9], most models that are used to understand the past, understand the present, or to forecast the future are calibrated by matching observed groundwater heads
Summary
Groundwater supply is compulsory for the subsistence of agriculture in Pakistan. It is believed that about 40% of irrigation needs in Punjab, the main food-producing province in the country, are met from groundwater [1,2]. Sensitivity analysis without utilization of automated techniques (i.e., the manual variation of different input parameters one by one while keeping all other parameters unchanged) cannot truly reflect the real system behaviour This approach exhibits model responses under non-calibrated conditions because changes in only one parameter at a time while keeping other parameters fixed can bring the model to non-calibrated state. The inverse parameter estimation tool PEST [32] was used for the automated calibration and sensitivity analysis of a regional groundwater flow modelling in the irrigated agricultural region of the Lower Chenab Canal (LCC), Rechna Doab, Punjab, Pakistan. The arrangement of this manuscript is that the general information of the model region is described first; secondly, it presents the conceptual model and the theory of modelling approaches and, the presentation of results and necessary discussion is done
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