testing alternative uses of electromagnetic data to reduce the prediction error of groundwater models
Clicks: 95
ID: 166181
2016
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This
article has not been analysed, so there is no overall score —
reader engagement is measured and shown alongside.
Reader Engagement
Steady Performance
28.2
/100
95 views
20 readers
AI Quality Assessment
Not analyzed
Readership in this journal
SteadyRanked #255 of 303 articles by views in materials research bulletin
Most read
Least read
Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 303 in total.
Mint this article as an NFT
Not yet mintedCreate a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.
5
SUSD
one-off · no wallet required
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.
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.
| Reference Key |
christensen2016hydrologytesting
Use this key to autocite in the manuscript while using
SciMatic Manuscript Manager or Thesis Manager
|
|---|---|
| Authors | ;N. K. Christensen;S. Christensen;T. P. A. Ferre |
| Journal | materials research bulletin |
| Year | 2016 |
| DOI |
10.5194/hess-20-1925-2016
|
| URL | |
| Keywords |
Citations
No citations found. To add a citation, contact the admin at info@scimatic.org
Comments
No comments yet. Be the first to comment on this article.