Abstract
We describe a field application of a new data assimilation method recently proposed by Panzeri et al. (2013, 2014). The method couples a modified Ensemble Kalman Filter (EnKF) algorithm with stochastic moment equations (MEs) governing space-time variations of (theoretical ensemble) mean and covariance values of groundwater flow state variables (hydraulic heads and fluxes). Whereas traditional EnKF entails Monte Carlo (MC) simulations and suffers from inbreeding, our approach obviates both. Synthetic case studies have shown the ME-based approach to be computationally efficient and accurate when compared to MC-based results. Here we use our ME-based method to assimilate drawdown data recorded during cross-hole pumping tests in the heterogeneous alluvial Lauswiesen aquifer near Tübingen, Germany. Our results include an estimate of log transmissivity distribution throughout the aquifer and corresponding measures of estimation error. We validate our calibrated model by using it to predict drawdowns recorded during another pumping test at the site and compare its performance with that of standard MC-based EnKF.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 205-216 |
| Number of pages | 12 |
| Journal | Journal of Hydrology |
| Volume | 521 |
| DOIs | |
| State | Published - Feb 1 2015 |
Fingerprint
Keywords
- Aquifer characterization
- Cross-hole pumping tests
- Data assimilation
- Ensemble kalman filter
- Field-scale application
- Stochastic moment equations
ASJC Scopus subject areas
- Water Science and Technology
Cite this
EnKF coupled with groundwater flow moment equations applied to Lauswiesen aquifer, Germany. / Panzeri, M.; Riva, M.; Guadagnini, A.; Neuman, Shlomo P.
In: Journal of Hydrology, Vol. 521, 01.02.2015, p. 205-216.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - EnKF coupled with groundwater flow moment equations applied to Lauswiesen aquifer, Germany
AU - Panzeri, M.
AU - Riva, M.
AU - Guadagnini, A.
AU - Neuman, Shlomo P
PY - 2015/2/1
Y1 - 2015/2/1
N2 - We describe a field application of a new data assimilation method recently proposed by Panzeri et al. (2013, 2014). The method couples a modified Ensemble Kalman Filter (EnKF) algorithm with stochastic moment equations (MEs) governing space-time variations of (theoretical ensemble) mean and covariance values of groundwater flow state variables (hydraulic heads and fluxes). Whereas traditional EnKF entails Monte Carlo (MC) simulations and suffers from inbreeding, our approach obviates both. Synthetic case studies have shown the ME-based approach to be computationally efficient and accurate when compared to MC-based results. Here we use our ME-based method to assimilate drawdown data recorded during cross-hole pumping tests in the heterogeneous alluvial Lauswiesen aquifer near Tübingen, Germany. Our results include an estimate of log transmissivity distribution throughout the aquifer and corresponding measures of estimation error. We validate our calibrated model by using it to predict drawdowns recorded during another pumping test at the site and compare its performance with that of standard MC-based EnKF.
AB - We describe a field application of a new data assimilation method recently proposed by Panzeri et al. (2013, 2014). The method couples a modified Ensemble Kalman Filter (EnKF) algorithm with stochastic moment equations (MEs) governing space-time variations of (theoretical ensemble) mean and covariance values of groundwater flow state variables (hydraulic heads and fluxes). Whereas traditional EnKF entails Monte Carlo (MC) simulations and suffers from inbreeding, our approach obviates both. Synthetic case studies have shown the ME-based approach to be computationally efficient and accurate when compared to MC-based results. Here we use our ME-based method to assimilate drawdown data recorded during cross-hole pumping tests in the heterogeneous alluvial Lauswiesen aquifer near Tübingen, Germany. Our results include an estimate of log transmissivity distribution throughout the aquifer and corresponding measures of estimation error. We validate our calibrated model by using it to predict drawdowns recorded during another pumping test at the site and compare its performance with that of standard MC-based EnKF.
KW - Aquifer characterization
KW - Cross-hole pumping tests
KW - Data assimilation
KW - Ensemble kalman filter
KW - Field-scale application
KW - Stochastic moment equations
UR - http://www.scopus.com/inward/record.url?scp=84919459241&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84919459241&partnerID=8YFLogxK
U2 - 10.1016/j.jhydrol.2014.11.057
DO - 10.1016/j.jhydrol.2014.11.057
M3 - Article
VL - 521
SP - 205
EP - 216
JO - Journal of Hydrology
JF - Journal of Hydrology
SN - 0022-1694
ER -