Simulation of nitrate biogeochemistry and reactive transport in a California groundwater basin

Abstract

Nitrate is the number one drinking water contaminant in the United States. It is pervasive in surface and groundwater systems, and its principal anthropogenic sources have increased dramatically in the last 50 years. In California alone, one third of the public drinking-water wells has been lost since 1988 and nitrate contamination is the most common reason for abandonment. Effective nitrate management in groundwater is complicated by uncertainties related to multiple point and non-point sources, hydrogeologic complexity, geochemical reactivity, and quantification of dentrification processes. In this paper, we review an integrated experimental and simulation-based framework being developed to study the fate of nitrate in a 25 km-long groundwater subbasin south of San Jose, California, a historically agricultural area now undergoing rapid urbanization with increasing demands for groundwater. The modeling approach is driven by a need to integrate new and archival data that support the hypothesis that nitrate fate and transport at the basin scale is intricately related to hydrostratigraphic complexity, variability of flow paths and groundwater residence times, microbial activity, and multiple geochemical reaction mechanisms. This study synthesizes these disparate and multi-scale data into a three-dimensional and highly resolved reactive transport modeling framework.

Accuracy of travel time distribution (TTD) models as affected by TTD complexity, observation errors, and model and tracer selection

“…More recent studies of analytical models for estimating tracer dynamics have added structural complexity to account for 2‐D configurations with dispersion [e.g., Stauffer et al ., ], free‐form distributions based on histograms [ Visser et al ., ; Massoudieh et al ., ], and heavy tails of distributions [e.g., Zhang et al ., ]. While the analytical models have the advantage of ease of implementation, small number of parameters, and rapid computation, it remains unclear whether they are structurally adequate to represent the complex TTDs with multiple peaks and heavy tails that can result from issues of well construction [ Landon et al ., ] and spatially and temporally variable hydrology [ Weissmann et al ., ; Carle et al ., ; Kollet and Maxwell , ; Green et al ., ; Darracq et al ., ; van der Velde et al ., ]. In the present study, we test the accuracy of a novel heavy‐tailed model and multipeak model along with standard TTD models.…”

Integration of Soft Data Into Geostatistical Simulation of Categorical Variables

“…As discussed in the introduction, the use of driller's logs in categorical geostatistical simulation is of interest in hydrogeology, yet presents some uncertainty in how to treat driller's logs as conditioning data. As a demonstration of the use of driller's logs and tsim-s to large-scale simulation, Figure 8A shows an interpreted version of a driller's log dataset, and Figure 8B shows a 3-D simulation of hydrostratigraphic architecture for the Llagas groundwater subbasin south of San Jose, California (Carle et al, 2004;Carle et al, 2006).…”

“…This example confirms that large-scale 3-D stochastic simulation using tsim-s is feasible. The stochastic analysis was further applied to investigate permeability heterogeneity effects on nitrate transport from agricultural sources toward municipal wells (Carle et al, 2004, Carle et al, 2006.…”

“…The tsim code and its associated transition probability-based categorical geostatistical methodologies have subsequently been found to be useful to characterization and modeling of a variety of heterogeneous hydrogeological systems (Carle, 2000;Ritzi, 2000;Lu and Zhang, 2002;Bohling and Dubois, 2003;Zhang and Fogg, 2003;Carle et al, 2004;James, 2004;Troldborg, 2004;Weissmann et al, 2004;McDonald et al, 2005;Ye and Khaleel, 2008;Janza, 2009;Sakaki et al, 2009;Alberto, 2010;Engdahl et al, 2010b;Janza, 2009;Doherty and Christensen, 2011;Papapetrou and Theodossiou, 2012;Purkis et al, 2012;Guastaldi et al, 2014;He et al, 2015;Song et al, 2015;Weissmann et al, 2015;Krage et al, 2016;Zhu et al, 2016a;Meirovitz et al, 2017;Muskus and Falta, 2018;Erdal et al, 2019;Sun et al, 2019;Wu et al, 2019;Arshadi et al, 2020). Furthermore, tsim has been applied to the study of a range of problems and processes involving subsurface heterogeneity including groundwater recharge or river flow loss (Tompson et al, 1999;Izbicki, 2002;Fleckenstein et al, 2006;Frei et al, 2009;Engdahl et al, 2010a, Pryshlak et al, 2015Ganot et al, 2018;…”