Hydrogeological modeling for improving groundwater monitoring network and strategies

Abstract

The research aimed to investigate a new approach for spatiotemporal groundwater monitoring network optimization using hydrogeological modeling to improve monitoring strategies. Unmonitored concentrations were incorporated at different potential monitoring locations into the groundwater monitoring optimization method. The proposed method was applied in the contaminated megasite, Bitterfeld/Wolfen, Germany. Based on an existing 3-D geological model, 3-D groundwater flow was obtained from flow velocity simulation using initial and boundary conditions. The 3-D groundwater transport model was used to simulate transport of a-HCH with an initial ideal concentration of 100 mg/L injected at various hydrogeological layers in the model. Particle tracking for contaminant and groundwater flow velocity realizations were made. The spatial optimization result suggested that 30 out of 462 wells in the Quaternary aquifer (6.49 %) and 14 out of 357 wells in the Tertiary aquifer (3.92 %) were redundant. With a gradual increase in the width of the particle track path line, from 0 to 100 m, the number of redundant wells remarkably increased, in both aquifers. The results of temporal optimization showed different sampling frequencies for monitoring wells. The groundwater and contaminant flow direction resulting from particle tracks obtained from hydrogeological modeling was verified by the variogram modeling through a-HCH data from 2003 to 2009. Groundwater monitoring strategies can be substantially improved by removing the existing spatiotemporal redundancy as well as incorporating unmonitored network along with sampling at recommended interval of time. However, the use of this model-based method is only recommended in the areas along with site-specific experts' knowledge.

Impacts of High-Concentration Turbid Water on the Groundwater Environment of the Tedori River Alluvial Fan in Japan

“…To analyze these complex factors, it is considered effective to conduct a scenario analysis using a hydrological model that represents the water circulation within the alluvial fan. It is generally believed that the spatial distribution of geology and soil properties has a significant influence on hydrological characteristics [27], and 3D groundwater models are often employed for impact assessment (e.g., Thakur [28]; Siena and Riva [29]). However, the Tedori River alluvial fan is composed of relatively uniform sandy gravel deposits.…”

Geological and Hydrogeological Characterizations Matter for Submarine Groundwater Discharge (SGD) Estimations

“…This implies that the geologic and hydrogeologic characterization approaches can be used for qualitative evaluations of SGD. It also indicates that geologic and hydrogeologic understanding is necessary as a pre-actual SGD estimation study using any of the available methods, as it gives a comprehensive understanding of how the aquifer system operates (Thakur, 2017). In a study evaluating the legacy and drivers of groundwater nutrients and pesticides in an agriculturally impacted coastal Quaternary aquifer system in Southeastern Queensland, Shishaye et al (2021) identified possible preferential flow paths and perched systems by developing 3D geological models (Fig.…”

Spatiotemporal optimization of groundwater monitoring networks using data-driven sparse sensing methods

“…In general, the design of a monitoring network is considered a nonlinear and non-convex optimization problem whose optimal criterion measures the useful information contained in the information matrix of the design (Ushijima et al, 2021). GMN optimization approaches are commonly divided into the following three categories based on the techniques applied: (a) those based on hydrogeological conceptual models and hydrogeological expert knowledge, (b) those based on numerical groundwater flow models (Kim and Lee, 2007;Singh and Datta, 2016;Thakur, 2017;Sreekanth et al, 2017), and (c) those based on data analysis with (geo-)statistical techniques. Many studies have focused on the geostatistical ability of kriging frameworks to determine new monitoring wells based on the reduction of estimation variance as the optimization criterion (Nunes et al, 2004;Li et al, 2011;Varouchakis and Hristopulos, 2013;Bhat et al, 2015;Thakur, 2015;Ohmer et al, 2019).…”