Identification of Groundwater Parameters Using an Adaptative Multiscale Method

Abstract

The identification of groundwater parameters in heterogeneous systems is a major challenge in groundwater modeling. Flexible parameterization methods are needed to assess the complexity of the spatial distributions of these parameters in real aquifers. In this article, we introduce an adaptative parameterization to identify the distribution of hydraulic conductivity within the large-scale (4400 km(2) ) Upper Rhine aquifer. The method is based on adaptative multiscale triangulation (AMT) coupled with an inverse problem procedure that identifies the parameters' distributions by reducing the error between measured and simulated heads. The AMT method has the advantage of combining both zonation and interpolation approaches. The AMT method uses area-based interpolation rather than an interpolation based on stochastic features. The method is applied to a standard 2D groundwater model that takes into account the interactions between the aquifer and surface water bodies, groundwater recharge, and pumping wells. The simulation period covers 204 months, from January 1986 to December 2002. Recordings at 109 piezometers are used for model calibration. The simulated heads are globally quite accurate and reproduce the main dynamics of the system. The local hydraulic conductivities resulting from the AMT method agree qualitatively with existing local experimental observations across the Rhine aquifer.

Assessing the water balance of the Upper Rhine Graben hydrosystem

“…334 335 1/ Transmissivity: Several distributions were obtained with HPP INV, by using various initial 336 conditions or criteria. These distributions are in good agreement with experimental data from 337 pumping tests (Majdalani and Ackerer, 2010). Four contrasted distributions were tested in the 338 present study to assess the sensitivity of the model to this parameter ( Figure 5 and Table 1 …”

“…The specific storage distribution used is the same as in Majdalani and Ackerer (2010), with 363 values of 0.05 everywhere, except along a band a few kilometres wide around the southern 364 part of the Rhine River, where the value is 0.12. 365 A reference simulation, based on mean range parameters (Table 1), is considered in order to 366 assess the impact of different parameters variation independently: eleven other simulations 367…”

“…It is based 238 on a classical hydrogeological model, and includes an adaptative multiscale triangulation for 239 the inversion algorithm (Majdalani and Ackerer, 2010). 240 Piezometric levels simulation is based on a finite elements scheme and the river flows are 241 simulated with a Muskingum scheme (Majdalani and Ackerer, 2010). Rivers-aquifer 242 interactions are computed from the difference between river and aquifer heads, with transfer 243 coefficients values calibrated during the inversion process (LUBW, 2006).…”

“…The HPP-INV model (Chardigny 1999, Majdalani andAckerer, 2010) is a modelling tool 237 using an inversion algorithm in order to assess aquifer hydrodynamic parameters. It is based 238 on a classical hydrogeological model, and includes an adaptative multiscale triangulation for 239 the inversion algorithm (Majdalani and Ackerer, 2010).…”

Identification of Groundwater Pollution Sources by a SCE-UA Algorithm-Based Simulation/Optimization Model

“…Foddis and Ackerer [11] investigated an artificial neural networks (ANNs)-based optimization model for determining pollutant characteristics in a two-dimensional aquifer. Other proposed methods also include the stochastic differential equations backward in time method [12], an adaptive simulated annealing (ASA)-based solution [13], the adaptive multi-scale method [14], the normal-score ensemble kalman filter method [15], the global multi-quadric collocation method [16], and the monte carlo type inverse modeling method [17].…”