Ensemble-based stochastic permeability and flow simulation of a sparsely sampled hard-rock aquifer supported by high performance computing

Abstract

Calibrating the heterogeneous permeability distribution of hard-rock aquifers based on sparse data is challenging but crucial for obtaining meaningful groundwater flow models. This study demonstrates the applicability of stochastic sampling of the prior permeability distribution and Metropolis sampling of the posterior permeability distribution using typical production data and measurements available in the context of groundwater abstraction. The case study is the Hastenrather Graben groundwater abstraction site near Aachen, Germany. A three-dimensional numerical flow model for the Carboniferous hard-rock aquifer is presented. Monte Carlo simulations are performed, for generating 1,000 realizations of the heterogeneous hard-rock permeability field, applying Sequential Gaussian Simulation based on nine log-permeability values for the geostatistical simulation. Forward simulation of flow during a production test for each realization results in the prior ensemble of model states verified by observation data in four wells. The computationally expensive ensemble simulations were performed in parallel with the simulation code SHEMAT-Suite on the high-performance computer JURECA. Applying a Metropolis sampler based on the misfit between drawdown simulations and observations results in a posterior ensemble comprising 251 realizations. The posterior mean log-permeability is −11.67 with an uncertainty of 0.83. The corresponding average posterior uncertainty of the drawdown simulation is 1.1 m. Even though some sources of uncertainty (e.g. scenario uncertainty) remain unquantified, this study is an important step towards an entire uncertainty quantification for a sparsely sampled hard-rock aquifer. Further, it provides a real-case application of stochastic hydrogeological approaches demonstrating how to accomplish uncertainty quantification of subsurface flow models in practice.

Simulation of Spring Discharge Using Deep Learning, Considering the Spatiotemporal Variability of Precipitation

“…Varouchakis et al (2013) and Smith et al (2021) proposed interpolation techniques to enhance model performance with sparse data. Bruckmann et al (2020) employed sequential Gaussian simulation for the statistical modeling of groundwater flow, enabling the quantification of overall uncertainty in sparsely sampled hard rock aquifers. Sun et al (2020) also applied it to meteorological observations or regions with limited or no data on the Qinghai-Tibet Plateau by inversely evaluating the reconstructed precipitation with a glacier-hydrology model.…”

A Detailed Assessment of Groundwater Quality in the Kabul Basin, Afghanistan, and Suitability for Future Development

“…The study of geochemical properties of groundwater is one of the most important means for evaluation of groundwater quality, because the quality of groundwater can be changed by land use development, mining, climate change, or from natural chemical weathering as it flows through the subsurface [1,2], interacts with soil layers and rocks [3], and generally leads to increasing levels of dissolved solids [4,5]. In addition, groundwater [1] often provides that main source of freshwater [3] used for drinking, domestic, agricultural, industrial, and ecological supplies worldwide. Thus, preservation of groundwater quality is a growing concern globally [6,7].…”

“…The purposes of the current paper are two-fold: (1) to assess the general situation of groundwater quality in the Kabul Basin using recently collected data; and (2) to provide interpretation of the suitability of quality for drinking and for irrigation. Hence, physicochemical features of groundwater in the Kabul Basin, WQI, nitrate, bacteria, and hardness were used to reach the mentioned aims.…”