Transient Recharge Estimability Through Field-Scale Groundwater Model Calibration

Ground Water ◽  
2017 ◽  
Vol 55 (6) ◽  
pp. 827-840 ◽  
Author(s):  
Matthew J. Knowling ◽  
Adrian D. Werner
Keyword(s):  
Model Calibration ◽  
Groundwater Model ◽  
Field Scale ◽  
Transient Recharge

Effective Groundwater Model Calibration

2007 ◽  
Author(s):  
Mary C. Hill ◽  
Claire R. Tiedeman
Keyword(s):  
Model Calibration ◽  
Groundwater Model

scholarly journals Sequential and joint hydrogeophysical inversion using a field-scale groundwater model with ERT and TDEM data

2013 ◽  
Vol 17 (10) ◽  
pp. 4043-4060 ◽  
Author(s):  
D. Herckenrath ◽  
G. Fiandaca ◽  
E. Auken ◽  
P. Bauer-Gottwein
Keyword(s):  
Geophysical Data ◽  
Parameter Estimates ◽  
Model Parameters ◽  
Data Sets ◽  
Groundwater Model ◽  
Field Scale ◽  
Geophysical Model ◽  
Time Domain Electromagnetic ◽  
Synthetic Groundwater ◽  
Airborne Geophysical Data

Abstract. Increasingly, ground-based and airborne geophysical data sets are used to inform groundwater models. Recent research focuses on establishing coupling relationships between geophysical and groundwater parameters. To fully exploit such information, this paper presents and compares different hydrogeophysical inversion approaches to inform a field-scale groundwater model with time domain electromagnetic (TDEM) and electrical resistivity tomography (ERT) data. In a sequential hydrogeophysical inversion (SHI) a groundwater model is calibrated with geophysical data by coupling groundwater model parameters with the inverted geophysical models. We subsequently compare the SHI with a joint hydrogeophysical inversion (JHI). In the JHI, a geophysical model is simultaneously inverted with a groundwater model by coupling the groundwater and geophysical parameters to explicitly account for an established petrophysical relationship and its accuracy. Simulations for a synthetic groundwater model and TDEM data showed improved estimates for groundwater model parameters that were coupled to relatively well-resolved geophysical parameters when employing a high-quality petrophysical relationship. Compared to a SHI these improvements were insignificant and geophysical parameter estimates became slightly worse. When employing a low-quality petrophysical relationship, groundwater model parameters improved less for both the SHI and JHI, where the SHI performed relatively better. When comparing a SHI and JHI for a real-world groundwater model and ERT data, differences in parameter estimates were small. For both cases investigated in this paper, the SHI seems favorable, taking into account parameter error, data fit and the complexity of implementing a JHI in combination with its larger computational burden.

Interpolation-Aided Evaluation of Groundwater Model Calibration

2018 ◽  
Author(s):  
Wissam Al-Taliby ◽  
Ashok Pandit ◽  
Howell Heck ◽  
Antonio Berber ◽  
Kamal Mamoua
Keyword(s):  
Model Calibration ◽  
Groundwater Model

Groundwater Model Calibration by Meta-Heuristic Algorithms

2013 ◽  
Vol 27 (7) ◽  
pp. 2515-2529 ◽  
Author(s):  
O. Bozorg Haddad ◽  
M. Mohammad Rezapour Tabari ◽  
E. Fallah-Mehdipour ◽  
M. A. Mariño
Keyword(s):  
Model Calibration ◽  
Heuristic Algorithms ◽  
Groundwater Model

scholarly journals Testing alternative uses of electromagnetic data to reduce the prediction error of groundwater models

2016 ◽  
Vol 20 (5) ◽  
pp. 1925-1946 ◽  
Author(s):  
Nikolaj Kruse Christensen ◽  
Steen Christensen ◽  
Ty Paul A. Ferre
Keyword(s):  
Electrical Resistivity ◽  
Hydraulic Conductivity ◽  
Prediction Error ◽  
Model Calibration ◽  
Hydrologic Model ◽  
Geophysical Data ◽  
Model Parameters ◽  
Calibration Data ◽  
Groundwater Model ◽  
And Inversion

Abstract. In spite of geophysics being used increasingly, it is often unclear how and when the integration of geophysical data and models can best improve the construction and predictive capability of groundwater models. This paper uses a newly developed HYdrogeophysical TEst-Bench (HYTEB) that is a collection of geological, groundwater and geophysical modeling and inversion software to demonstrate alternative uses of electromagnetic (EM) data for groundwater modeling in a hydrogeological environment consisting of various types of glacial deposits with typical hydraulic conductivities and electrical resistivities covering impermeable bedrock with low resistivity (clay). The synthetic 3-D reference system is designed so that there is a perfect relationship between hydraulic conductivity and electrical resistivity. For this system it is investigated to what extent groundwater model calibration and, often more importantly, model predictions can be improved by including in the calibration process electrical resistivity estimates obtained from TEM data. In all calibration cases, the hydraulic conductivity field is highly parameterized and the estimation is stabilized by (in most cases) geophysics-based regularization. For the studied system and inversion approaches it is found that resistivities estimated by sequential hydrogeophysical inversion (SHI) or joint hydrogeophysical inversion (JHI) should be used with caution as estimators of hydraulic conductivity or as regularization means for subsequent hydrological inversion. The limited groundwater model improvement obtained by using the geophysical data probably mainly arises from the way these data are used here: the alternative inversion approaches propagate geophysical estimation errors into the hydrologic model parameters. It was expected that JHI would compensate for this, but the hydrologic data were apparently insufficient to secure such compensation. With respect to reducing model prediction error, it depends on the type of prediction whether it has value to include geophysics in a joint or sequential hydrogeophysical model calibration. It is found that all calibrated models are good predictors of hydraulic head. When the stress situation is changed from that of the hydrologic calibration data, then all models make biased predictions of head change. All calibrated models turn out to be very poor predictors of the pumping well's recharge area and groundwater age. The reason for this is that distributed recharge is parameterized as depending on estimated hydraulic conductivity of the upper model layer, which tends to be underestimated. Another important insight from our analysis is thus that either recharge should be parameterized and estimated in a different way, or other types of data should be added to better constrain the recharge estimates.

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